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This data set contains soil water infiltration measurements using Beerkan infiltration rings in four Oregon agricultural soils amended with biochar. Gasified biochars made from wheat straw (AgEnergy, Spokane, WA) and conifer wood (BioLogical, Philomath, OR) were tilled into soils at experimental stations in Madras (loam), Pendleton (silt loam), Aurora (sandy loam), and Klamath Falls (loamy sand). The biochars were incorporated by tillage in fall 2016 to a depth of 12 cm at rates equating to 0, 9, 18, and 36 Mg/ha (about 0.5, 1, 2, and 4% by mass in the tillage zone), with three replicate plots per treatment. In April and May 2017 infiltration was measured by inserting small rings into the soil surface and determining the time required for repeated 10 0mL volumes of water to infiltrate. From each infiltration experiment we estimated steady-state infiltration rate, and where possible we also estimated field saturated soil hydraulic conductivity (Kfs). Diagnostic plots demonstrated that in about half of the measurements sets, Kfs could be estimated by modeling infiltration as a two-term function of sorptivity and Kfs. In the remaining plots, additional unknown factors that influenced infiltration prevented estimation of Kfs, possibly due to air entrapment, soil layering, or ring insertion effects in the remaining experiments. Increasing biochar amendment rates led to an increase in infiltration rate only for the CW biochar at the silt loam site. 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Diagnostic plots demonstrated that in about half of the measurements sets, Kfs could be estimated by modeling infiltration as a two-term function of sorptivity and Kfs. In the remaining plots, additional unknown factors that influenced infiltration prevented estimation of Kfs, possibly due to air entrapment, soil layering, or ring insertion effects in the remaining experiments. Increasing biochar amendment rates led to an increase in infiltration rate only for the CW biochar at the silt loam site. 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Data location: GeoPlatform (\"https://www.geoplatform.gov/\") and EPA Environmental Dataset Gateway (https://edg.epa.gov/). Abstract: We apply the hydrologic landscapes (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We map climate vulnerability by integrating the HL approach into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. 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For NMFS species not found in either location, a request was made directly to the NMFS scientists. The last download of the species locations occurred in November 2020.","distribution_titles":["EPA GeoPlatform Hosted Feature Service","EPA Geoplatform Item page"],"harvest_record":"https://catalog.data.gov/harvest_record/e365ae1f-03ef-437b-b2c1-3dd32656eba9","harvest_record_raw":"https://catalog.data.gov/harvest_record/e365ae1f-03ef-437b-b2c1-3dd32656eba9/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/e365ae1f-03ef-437b-b2c1-3dd32656eba9/transformed","has_download":false,"has_spatial":true,"identifier":"https://edg.epa.gov/WAFer_harvest/ISO/ocspp-geo-records_Endangered_Species_Critical_Habitat_Areas.xml","keyword":["United States","Agriculture","Biology","Conservation","Ecology","Ecosystem","Environment","Exposure","Hazards","Land","Modeling","Pesticides","Regulatory","Risk","Toxics","Water","020:083","Downloadable Data"],"last_harvested_date":"2026-09-27T20:37:25.038474","organization":{"aliases":["EPA"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"82b85475-f85d-404a-b95b-89d1a42e9f6b","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/epa.png","name":"U.S. Environmental Protection Agency","organization_type":"Federal Government","slug":"epa"},"parent_identifier":null,"popularity":7,"publisher":"U.S. Environmental Protection Agency, Office of Pesticide Programs","slug":"critical-habitat-for-endangered-species","spatial_centroid":{"lat":29.738139,"lon":0.10364840000000583},"spatial_shape":{"coordinates":[[[[179.776438,-14.548618],[180.0,-14.548618],[180.0,74.024896],[179.776438,74.024896],[179.776438,-14.548618]]],[[[-180.0,74.024896],[-180.0,-14.548618],[-179.146415,-14.548618],[-179.146415,74.024896],[-180.0,74.024896]]]],"type":"MultiPolygon"},"theme":["geospatial"],"title":"Critical Habitat for Endangered Species","type":"dataset"},{"_score":10.0486,"_sort":[1790472756481,10.0486,0,"d2a41847-8a2e-4d32-b875-f8b738c3d850"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jacob A Fleck","hasEmail":"mailto:jafleck@usgs.gov"},"description":"Little is known about mercury concentrations at bay margins, and how climate change, including changes in temperature and precipitation, will affect mercury exposure and methylmercury (MeHg) production. In addition, the San Francisco South Bay Salt Ponds Restoration Program (SBSPRP) needs more efficient and effective mercury monitoring approaches at larger spatial scales to understand changes in mercury at a regional level. The goal of this new project, a collaboration between the USGS Western Geographic Science Center, Water Mission Area, and the California Water Science Center (CAWSC), is to develop the capacity to map total mercury (THg) and MeHg in South San Francisco Bay (SFB) through the satellite remote sensing (Sentinel-2) of two known mercury indicators: total suspended solids (TSS) and colored dissolved organic matter (CDOM). In addition to quantifying the accuracy of this approach, this research aims in South SFB using the Sentinel-2 satellite in order to 1) improve understanding of region-wide mercury spatial and temporal trends associated with weather events and wetland restoration management activities, and 2) improve mercury monitoring efficiencies and capabilities moving forward. One main project objective is to assess the capacity to detect anomalies in remotely sensed TSS, CDOM, and mercury species associated with recent weather events or restoration activities through time series analysis.  \nThe optical measurements reported here were collected to aid in the characterization of water sources and mixtures and establish proxies (surrogates) for mercury and methylmercury concentrations and to provide ground-truthing for remotely sensed models of dissolved organic carbon (DOC) concentrations. Data are compiled into five tables: 1) full fluorescence spectra in vectorized format (LSB_Hg_RS_EEMs_vectors.csv), 2) full absorbance spectra for 1 centimeter (cm) path measurements (LSB_Hg_RS_1cm_ABS_scans.csv), 3) full absorbance spectra for 10 cm path measurements (SSFB_Hg_RS_10cm_ABS_scans.csv), 4) absorption coefficients derived from the 10cm path measurements (SSFB_Hg_RS_10cm_ag_scans.csv), and 5) summary file of commonly extracted optical indicators and calculated wavelength-array values derived from the optical data that correspond to arrays measured by field-based sensors for use in statistical analyses and model development (LSB_Hg_RS_OMRL_Sample_Summary.csv).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13JOXXA","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a1f4a72b66b018da518f7ee.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a1f4a72b66b018da518f7ee","keyword":["Absorbance","Aqualog","Dissolved Organic Matter","Fluorescence","Hydrology","USGS:6a1f4a72b66b018da518f7ee","Water Quality","environment","geoscientificInformation"],"modified":"2026-09-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.4200, 37.4000, -121.9000, 37.6400","theme":["geospatial"],"title":"Optical measurements for surface water samples collected within South San Francisco Bay in support of mercury modeling"},"description":"Little is known about mercury concentrations at bay margins, and how climate change, including changes in temperature and precipitation, will affect mercury exposure and methylmercury (MeHg) production. In addition, the San Francisco South Bay Salt Ponds Restoration Program (SBSPRP) needs more efficient and effective mercury monitoring approaches at larger spatial scales to understand changes in mercury at a regional level. The goal of this new project, a collaboration between the USGS Western Geographic Science Center, Water Mission Area, and the California Water Science Center (CAWSC), is to develop the capacity to map total mercury (THg) and MeHg in South San Francisco Bay (SFB) through the satellite remote sensing (Sentinel-2) of two known mercury indicators: total suspended solids (TSS) and colored dissolved organic matter (CDOM). In addition to quantifying the accuracy of this approach, this research aims in South SFB using the Sentinel-2 satellite in order to 1) improve understanding of region-wide mercury spatial and temporal trends associated with weather events and wetland restoration management activities, and 2) improve mercury monitoring efficiencies and capabilities moving forward. One main project objective is to assess the capacity to detect anomalies in remotely sensed TSS, CDOM, and mercury species associated with recent weather events or restoration activities through time series analysis.  \nThe optical measurements reported here were collected to aid in the characterization of water sources and mixtures and establish proxies (surrogates) for mercury and methylmercury concentrations and to provide ground-truthing for remotely sensed models of dissolved organic carbon (DOC) concentrations. Data are compiled into five tables: 1) full fluorescence spectra in vectorized format (LSB_Hg_RS_EEMs_vectors.csv), 2) full absorbance spectra for 1 centimeter (cm) path measurements (LSB_Hg_RS_1cm_ABS_scans.csv), 3) full absorbance spectra for 10 cm path measurements (SSFB_Hg_RS_10cm_ABS_scans.csv), 4) absorption coefficients derived from the 10cm path measurements (SSFB_Hg_RS_10cm_ag_scans.csv), and 5) summary file of commonly extracted optical indicators and calculated wavelength-array values derived from the optical data that correspond to arrays measured by field-based sensors for use in statistical analyses and model development (LSB_Hg_RS_OMRL_Sample_Summary.csv).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/15ca54a1-0bba-449c-add3-c77050e14527","harvest_record_raw":"https://catalog.data.gov/harvest_record/15ca54a1-0bba-449c-add3-c77050e14527/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a1f4a72b66b018da518f7ee","keyword":["Absorbance","Aqualog","Dissolved Organic Matter","Fluorescence","Hydrology","USGS:6a1f4a72b66b018da518f7ee","Water Quality","environment","geoscientificInformation"],"last_harvested_date":"2026-09-27T01:32:36.481698","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"optical-measurements-for-surface-water-samples-collected-within-south-san-francisco-bay-in","spatial_centroid":{"lat":37.495999999999995,"lon":-122.21200000000002},"spatial_shape":{"coordinates":[[[-122.42,37.4],[-122.42,37.64],[-121.9,37.64],[-121.9,37.4],[-122.42,37.4]]],"type":"Polygon"},"theme":["geospatial"],"title":"Optical measurements for surface water samples collected within South San Francisco Bay in support of mercury modeling","type":"dataset"},{"_score":8.811924,"_sort":[1790472742953,8.811924,0,"2de4c217-bd1c-4956-a485-727d64cb6f41"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael Osland","hasEmail":"mailto:mosland@usgs.gov"},"description":"Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. This file includes the subplot-level porewater physicochemical data.\nThis work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1GDTXUR","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.545cfd67e4b0ba8303f713b3.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_545cfd67e4b0ba8303f713b3","keyword":["Cedar Key","Gulf of Mexico","Port Aransas","Port Fourchon","State of Florida","State of Louisiana","State of Texas","USGS:545cfd67e4b0ba8303f713b3","United States of America","conductivity","environment","pH","porewater","salinity","soil","temperature"],"modified":"2026-09-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-97.14012, 27.829952, -83.029848, 29.2","theme":["geospatial"],"title":"Dataset 10: New porewater data collection: subplot-level physicochemical"},"description":"Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. This file includes the subplot-level porewater physicochemical data.\nThis work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/dd14165e-9e54-4e6c-9b86-0632416be629","harvest_record_raw":"https://catalog.data.gov/harvest_record/dd14165e-9e54-4e6c-9b86-0632416be629/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_545cfd67e4b0ba8303f713b3","keyword":["Cedar Key","Gulf of Mexico","Port Aransas","Port Fourchon","State of Florida","State of Louisiana","State of Texas","USGS:545cfd67e4b0ba8303f713b3","United States of America","conductivity","environment","pH","porewater","salinity","soil","temperature"],"last_harvested_date":"2026-09-27T01:32:22.953705","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"dataset-10-new-porewater-data-collection-subplot-level-physicochemical","spatial_centroid":{"lat":28.3779712,"lon":-91.4960112},"spatial_shape":{"coordinates":[[[-97.14012,27.829952],[-97.14012,29.2],[-83.029848,29.2],[-83.029848,27.829952],[-97.14012,27.829952]]],"type":"Polygon"},"theme":["geospatial"],"title":"Dataset 10: New porewater data collection: subplot-level physicochemical","type":"dataset"},{"_score":9.510168,"_sort":[1790472673160,9.510168,6,"d28e89d2-032c-4857-86d6-a72a1e709f85"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"David Moeser","hasEmail":"mailto:casc-data@usgs.gov"},"description":"These data include snow depth and snow water equivalent (SWE) for a field campaign on April 9, 2024.  The field area is comprised of 311 surveyed points in, on the perimeter of, and surrounding six forest openings next to Coal Creek off Coal Bank Pass in the San Juan Mountains in southwestern Colorado, USA.  These measurements were taken to look at the relationship between snow accumulation and snow melt patterns between forest gaps of various sizes, and forest edges of various sizes (edge of forest gaps).  Canopy metrics, including canopy height, total gap area, mean distance to canopy, canopy closure, leaf area index, non-directional edginess, canopy edginess with a southern aspect, and canopy edginess with a northern aspect were defined using aerial lidar data for the San Juan Mountains and can be found in an affiliated data release titled, \u2018High Resolution Canopy Structure and Density Metrics for Southwest Colorado Derived from 2019 Aerial Lidar.\u2019  These metrics are also included herein for the 311 surveyed points.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1EAGT6Y","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.66b164fed34e5d7d928b1190.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66b164fed34e5d7d928b1190","keyword":["San Juan Mountains","Southwest Colorado","USGS:66b164fed34e5d7d928b1190","canopy density","canopy structure","environment","precipitation (atmospheric)","snow depth","snow water equivalent"],"modified":"2024-09-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-107.765923, 37.697812, -107.756824, 37.704060","theme":["geospatial"],"title":"Snow Measurements in Specific Canopy Structure Regimes for April 9, 2024, North of Coal Creek, San Juan Mountains, Colorado, USA"},"description":"These data include snow depth and snow water equivalent (SWE) for a field campaign on April 9, 2024.  The field area is comprised of 311 surveyed points in, on the perimeter of, and surrounding six forest openings next to Coal Creek off Coal Bank Pass in the San Juan Mountains in southwestern Colorado, USA.  These measurements were taken to look at the relationship between snow accumulation and snow melt patterns between forest gaps of various sizes, and forest edges of various sizes (edge of forest gaps).  Canopy metrics, including canopy height, total gap area, mean distance to canopy, canopy closure, leaf area index, non-directional edginess, canopy edginess with a southern aspect, and canopy edginess with a northern aspect were defined using aerial lidar data for the San Juan Mountains and can be found in an affiliated data release titled, \u2018High Resolution Canopy Structure and Density Metrics for Southwest Colorado Derived from 2019 Aerial Lidar.\u2019  These metrics are also included herein for the 311 surveyed points.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c1fd65c8-d5ae-4aef-bc94-fd1d2b23cd3a","harvest_record_raw":"https://catalog.data.gov/harvest_record/c1fd65c8-d5ae-4aef-bc94-fd1d2b23cd3a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66b164fed34e5d7d928b1190","keyword":["San Juan Mountains","Southwest Colorado","USGS:66b164fed34e5d7d928b1190","canopy density","canopy structure","environment","precipitation (atmospheric)","snow depth","snow water equivalent"],"last_harvested_date":"2026-09-27T01:31:13.160073","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":6,"publisher":"U.S. Geological Survey","slug":"snow-measurements-in-specific-canopy-structure-regimes-for-april-9-2024-north-of-coal-cree","spatial_centroid":{"lat":37.7003112,"lon":-107.7622834},"spatial_shape":{"coordinates":[[[-107.765923,37.697812],[-107.765923,37.70406],[-107.756824,37.70406],[-107.756824,37.697812],[-107.765923,37.697812]]],"type":"Polygon"},"theme":["geospatial"],"title":"Snow Measurements in Specific Canopy Structure Regimes for April 9, 2024, North of Coal Creek, San Juan Mountains, Colorado, USA","type":"dataset"},{"_score":9.871696,"_sort":[1790472541809,9.871696,0,"e7655769-500a-4b07-b330-f284c30c74b1"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Steven Frank","hasEmail":"mailto:casc-data@usgs.gov"},"description":"In this study, we investigated how the interaction of urbanization, latitudinal warming, and scale insect abundance affected urban tree health. We predicted that trees in warmer, lower latitude cities would be in poorer health at lower levels of urbanization than trees at cooler, higher latitudes due to the interaction of urbanization, latitudinal temperature, and herbivory. To evaluate our predictions, we surveyed the abundance of scale insect herbivores on a single, common tree species (Acer rubrum) in eight US cities spanning 10\u00b0 of latitude. We estimated urbanization at two extents, a local one that accounted for the direct effects on an individual tree, and a larger one that captured the surrounding urban landscape.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1YDVJN2","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.62b9e2d0d34e8f4977cc9f14.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62b9e2d0d34e8f4977cc9f14","keyword":["USGS:62b9e2d0d34e8f4977cc9f14","acer rubrum","biota","climate","environment","external research support","latitude","pests","urban heat island"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"theme":["geospatial"],"title":"Scale insect abundance, impervious surface proportions, and temperature data for Acer rubrum study trees"},"description":"In this study, we investigated how the interaction of urbanization, latitudinal warming, and scale insect abundance affected urban tree health. We predicted that trees in warmer, lower latitude cities would be in poorer health at lower levels of urbanization than trees at cooler, higher latitudes due to the interaction of urbanization, latitudinal temperature, and herbivory. To evaluate our predictions, we surveyed the abundance of scale insect herbivores on a single, common tree species (Acer rubrum) in eight US cities spanning 10\u00b0 of latitude. We estimated urbanization at two extents, a local one that accounted for the direct effects on an individual tree, and a larger one that captured the surrounding urban landscape.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6248918f-efa7-4af0-bc9e-12ce2bfedad5","harvest_record_raw":"https://catalog.data.gov/harvest_record/6248918f-efa7-4af0-bc9e-12ce2bfedad5/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62b9e2d0d34e8f4977cc9f14","keyword":["USGS:62b9e2d0d34e8f4977cc9f14","acer rubrum","biota","climate","environment","external research support","latitude","pests","urban heat island"],"last_harvested_date":"2026-09-27T01:29:01.809754","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"scale-insect-abundance-impervious-surface-proportions-and-temperature-data-for-acer-rubrum","spatial_centroid":null,"spatial_shape":null,"theme":["geospatial"],"title":"Scale insect abundance, impervious surface proportions, and temperature data for Acer rubrum study trees","type":"dataset"},{"_score":8.950693,"_sort":[1790472142788,8.950693,1,"a7cdcf8c-59d2-4ff5-af81-9d956a7fbd3a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Katherine Warnell","hasEmail":"mailto:katie.warnell@duke.edu"},"description":"Wild insect pollination has significant positive effects on pollinator-dependent crop production.  While managed honeybees are often used to provide pollination to pollinator-dependent crops, visits by wild insect pollinators have been shown to be more effective in increasing fruit set than managed pollinators, and wild insect pollination increases fruit set even when managed pollinator visitation is high (Garibaldi et al. 2013). The total value of the pollination services provided by wild, native insects has been estimated at $3.07 billion annually (2003 dollars) in the United States (Losey & Vaughan 2006).\nTo assess the spatial distribution of potential wild insect pollination, we mapped the supply of potential wild pollinator habitat (forest, grassland, wetland, and shrubland land cover types) and the demand for pollination (directly pollinator-dependent crops).  A foraging travel distance for temperate native bees (1308 meters) was used to estimate relative pollinator activity on cropland based on distance from habitat.  We also calculated the proportion of pollinator habitat within pollinator travel distance of crops.  Methods for this analysis were adapted from the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative mapping project (Olander et al. 2017).\nThis information was summarized by county and subwatershed (HUC 12) to identify regional priority areas for conservation and restoration of pollinator habitat. Datasets provided include county and subwatershed shapefiles identifying priority areas and including attributes used to select priorities, and raster datasets used to calculate those attributes.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/69zz-7f78","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5d288fcee4b0941bde65132a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d288fcee4b0941bde65132a","keyword":["Alabama","Arkansas","Florida","Georgia","Louisiana","Mississippi","Missouri","North Carolina","South Carolina","Tennessee","USGS:5d288fcee4b0941bde65132a","environment","farming","geospatial datasets","habitats","natural resource management","pollination","pollinators","remediation"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-95.8139, 23.8934, -74.5003, 40.6853","theme":["geospatial"],"title":"Conservation and Restoration Priorities for Wild Pollinator Habitat"},"description":"Wild insect pollination has significant positive effects on pollinator-dependent crop production.  While managed honeybees are often used to provide pollination to pollinator-dependent crops, visits by wild insect pollinators have been shown to be more effective in increasing fruit set than managed pollinators, and wild insect pollination increases fruit set even when managed pollinator visitation is high (Garibaldi et al. 2013). The total value of the pollination services provided by wild, native insects has been estimated at $3.07 billion annually (2003 dollars) in the United States (Losey & Vaughan 2006).\nTo assess the spatial distribution of potential wild insect pollination, we mapped the supply of potential wild pollinator habitat (forest, grassland, wetland, and shrubland land cover types) and the demand for pollination (directly pollinator-dependent crops).  A foraging travel distance for temperate native bees (1308 meters) was used to estimate relative pollinator activity on cropland based on distance from habitat.  We also calculated the proportion of pollinator habitat within pollinator travel distance of crops.  Methods for this analysis were adapted from the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative mapping project (Olander et al. 2017).\nThis information was summarized by county and subwatershed (HUC 12) to identify regional priority areas for conservation and restoration of pollinator habitat. Datasets provided include county and subwatershed shapefiles identifying priority areas and including attributes used to select priorities, and raster datasets used to calculate those attributes.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c90115b2-0518-4659-a4ed-115b9cf87cef","harvest_record_raw":"https://catalog.data.gov/harvest_record/c90115b2-0518-4659-a4ed-115b9cf87cef/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d288fcee4b0941bde65132a","keyword":["Alabama","Arkansas","Florida","Georgia","Louisiana","Mississippi","Missouri","North Carolina","South Carolina","Tennessee","USGS:5d288fcee4b0941bde65132a","environment","farming","geospatial datasets","habitats","natural resource management","pollination","pollinators","remediation"],"last_harvested_date":"2026-09-27T01:22:22.788187","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"conservation-and-restoration-priorities-for-wild-pollinator-habitat","spatial_centroid":{"lat":30.610159999999997,"lon":-87.28846},"spatial_shape":{"coordinates":[[[-95.8139,23.8934],[-95.8139,40.6853],[-74.5003,40.6853],[-74.5003,23.8934],[-95.8139,23.8934]]],"type":"Polygon"},"theme":["geospatial"],"title":"Conservation and Restoration Priorities for Wild Pollinator Habitat","type":"dataset"},{"_score":8.028393,"_sort":[1790472042900,8.028393,2,"0b964c64-6d1d-4654-95d8-444e1f119251"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael J Osland","hasEmail":"mailto:mosland@usgs.gov"},"description":"Coastal wetland ecosystems are expected to migrate landward in response to accelerated sea-level rise. However, due to differences in topography and coastal urbanization extent, estuaries vary in their ability to accommodate wetland migration. The landward movement of wetlands requires suitable conditions, such as a gradual slope and land free of urban development. Urban barriers can constrain migration and result in wetland loss (coastal squeeze). For future-focused conservation planning purposes, there is a pressing need to quantify and compare the potential for wetland landward movement and coastal squeeze. For 41 estuaries in the northern Gulf of Mexico (i.e., the USA gulf coast), we quantified and compared the area available for the landward migration of tidal saline wetlands and the area where urban development is expected to prevent migration (coastal squeeze), under three alternative future sea-level rise scenarios (0.5-, 1.0-, and 1.5-m by 2100).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7S75F7K","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5989fd82e4b09fa1cb0cc903.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5989fd82e4b09fa1cb0cc903","keyword":["Alabama","Florida","Gulf of Mexico","Louisiana","Mississippi","North America","Texas","USGS:5989fd82e4b09fa1cb0cc903","United States","biota","climate change","effects of climate change","environment","estuaries","estuary","geospatial datasets","natural resource management","planning Cadastre","sea level change","sea-level change","wetlands"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-97.9958, 24.1683, -80.3992, 32.0006","theme":["geospatial"],"title":"Landward migration of tidal saline wetlands with sea-level rise and urbanization: a comparison of northern Gulf of Mexico estuaries"},"description":"Coastal wetland ecosystems are expected to migrate landward in response to accelerated sea-level rise. However, due to differences in topography and coastal urbanization extent, estuaries vary in their ability to accommodate wetland migration. The landward movement of wetlands requires suitable conditions, such as a gradual slope and land free of urban development. Urban barriers can constrain migration and result in wetland loss (coastal squeeze). For future-focused conservation planning purposes, there is a pressing need to quantify and compare the potential for wetland landward movement and coastal squeeze. For 41 estuaries in the northern Gulf of Mexico (i.e., the USA gulf coast), we quantified and compared the area available for the landward migration of tidal saline wetlands and the area where urban development is expected to prevent migration (coastal squeeze), under three alternative future sea-level rise scenarios (0.5-, 1.0-, and 1.5-m by 2100).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f6e7a650-da7b-43ee-96a5-885d91133433","harvest_record_raw":"https://catalog.data.gov/harvest_record/f6e7a650-da7b-43ee-96a5-885d91133433/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5989fd82e4b09fa1cb0cc903","keyword":["Alabama","Florida","Gulf of Mexico","Louisiana","Mississippi","North America","Texas","USGS:5989fd82e4b09fa1cb0cc903","United States","biota","climate change","effects of climate change","environment","estuaries","estuary","geospatial datasets","natural resource management","planning Cadastre","sea level change","sea-level change","wetlands"],"last_harvested_date":"2026-09-27T01:20:42.900703","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"landward-migration-of-tidal-saline-wetlands-with-sea-level-rise-and-urbanization-a-compari","spatial_centroid":{"lat":27.30122,"lon":-90.95716},"spatial_shape":{"coordinates":[[[-97.9958,24.1683],[-97.9958,32.0006],[-80.3992,32.0006],[-80.3992,24.1683],[-97.9958,24.1683]]],"type":"Polygon"},"theme":["geospatial"],"title":"Landward migration of tidal saline wetlands with sea-level rise and urbanization: a comparison of northern Gulf of Mexico estuaries","type":"dataset"},{"_score":9.714993,"_sort":[1790471763723,9.714993,0,"375be7dd-ae76-4e19-8805-0f717a2a1aa8"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Anthony D'Amato","hasEmail":"mailto:casc-data@usgs.gov"},"description":"This raster map represents imputed forest community classes from forest inventory data summarized from the FVS national forest inventory data. Grid cells with the value of zero or lower are non-forest or unsimulated/inactive areas in the map. The LANDIS-II simulation model requires starting information on forest species composition that is both mapped and summarized in a text file. Forest plot data is summarized by species and estimated cohort age. Each unique plot makes up an initial community that populates the forest landscape. These plot data are tied to grid cells in geographic space based on their similarity and group membership within a processing stream that hybridizes clustering and canonical discriminate analysis. The goal of this analysis is to realistically represent current forest conditions both locally and across space. Our simulations of the Green Mountain National Forest (GMNF) study area start in the year 2010. Forest conditions were populated by FVS inventory and Mountain Birdwatch plots measured between 2000 and 2013. We analyzed forest inventory data and multi-temporal Landsat data from different seasons to create this map. Each value in the map represents a plot ID for an FVS inventory plot that was imputed to a location based on class membership in a class map created with Landsat data. When used with the LANDIS-II model and associated input files, this map will populate each cell with species cohorts of observed ages from the imputed plot inventory information.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DGEMFW","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.59b1528ce4b020cdf7d90282.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_59b1528ce4b020cdf7d90282","keyword":["Green Mountains National Forest","USGS:59b1528ce4b020cdf7d90282","Vermont","biota","ecological processes","environment","geospatial datasets","natural resource management"],"modified":"2026-09-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-73.3324, 42.6975, -72.6485, 44.2107","theme":["geospatial"],"title":"Initial Forest Community Map of Green Mountains National Forest for LANDIS-II Model, Derived from Landsat and FVS data"},"description":"This raster map represents imputed forest community classes from forest inventory data summarized from the FVS national forest inventory data. Grid cells with the value of zero or lower are non-forest or unsimulated/inactive areas in the map. The LANDIS-II simulation model requires starting information on forest species composition that is both mapped and summarized in a text file. Forest plot data is summarized by species and estimated cohort age. Each unique plot makes up an initial community that populates the forest landscape. These plot data are tied to grid cells in geographic space based on their similarity and group membership within a processing stream that hybridizes clustering and canonical discriminate analysis. The goal of this analysis is to realistically represent current forest conditions both locally and across space. Our simulations of the Green Mountain National Forest (GMNF) study area start in the year 2010. Forest conditions were populated by FVS inventory and Mountain Birdwatch plots measured between 2000 and 2013. We analyzed forest inventory data and multi-temporal Landsat data from different seasons to create this map. Each value in the map represents a plot ID for an FVS inventory plot that was imputed to a location based on class membership in a class map created with Landsat data. When used with the LANDIS-II model and associated input files, this map will populate each cell with species cohorts of observed ages from the imputed plot inventory information.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6daff607-8174-4342-91bb-c35d8d77bec9","harvest_record_raw":"https://catalog.data.gov/harvest_record/6daff607-8174-4342-91bb-c35d8d77bec9/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_59b1528ce4b020cdf7d90282","keyword":["Green Mountains National Forest","USGS:59b1528ce4b020cdf7d90282","Vermont","biota","ecological processes","environment","geospatial datasets","natural resource management"],"last_harvested_date":"2026-09-27T01:16:03.723049","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"initial-forest-community-map-of-green-mountains-national-forest-for-landis-ii-model-derive","spatial_centroid":{"lat":43.30278,"lon":-73.05884},"spatial_shape":{"coordinates":[[[-73.3324,42.6975],[-73.3324,44.2107],[-72.6485,44.2107],[-72.6485,42.6975],[-73.3324,42.6975]]],"type":"Polygon"},"theme":["geospatial"],"title":"Initial Forest Community Map of Green Mountains National Forest for LANDIS-II Model, Derived from Landsat and FVS data","type":"dataset"},{"_score":9.316064,"_sort":[1790471512226,9.316064,0,"3af17cbb-85ec-48f2-abc0-e2c48a50123f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael Osland","hasEmail":"mailto:mosland@usgs.gov"},"description":"Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. This file includes the individual-level tall tree stratum vegetation data.\nThis work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1GDTXUR","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.545bfd63e4b009f8aec9b6ce.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_545bfd63e4b009f8aec9b6ce","keyword":["Cedar Key","Gulf of Mexico","Port Aransas","Port Fourchon","State of Florida","State of Louisiana","State of Texas","USGS:545bfd63e4b009f8aec9b6ce","United States of America","environment","vegetation"],"modified":"2026-09-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"theme":["geospatial"],"title":"Dataset 3: New vegetation data collection: individual-level tall tree stratum"},"description":"Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. This file includes the individual-level tall tree stratum vegetation data.\nThis work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3699dd4e-da08-47ae-98b9-dbb6086b945d","harvest_record_raw":"https://catalog.data.gov/harvest_record/3699dd4e-da08-47ae-98b9-dbb6086b945d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_545bfd63e4b009f8aec9b6ce","keyword":["Cedar Key","Gulf of Mexico","Port Aransas","Port Fourchon","State of Florida","State of Louisiana","State of Texas","USGS:545bfd63e4b009f8aec9b6ce","United States of America","environment","vegetation"],"last_harvested_date":"2026-09-27T01:11:52.226818","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"dataset-3-new-vegetation-data-collection-individual-level-tall-tree-stratum","spatial_centroid":null,"spatial_shape":null,"theme":["geospatial"],"title":"Dataset 3: New vegetation data collection: individual-level tall tree stratum","type":"dataset"},{"_score":8.778549,"_sort":[1790471398268,8.778549,1,"0147c0d3-3c9d-41ef-a9fc-e698db43f52f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Nicholas M Enwright","hasEmail":"mailto:enwrightn@usgs.gov"},"description":"This data release includes digital elevation models (DEMs) that were created using Monte Carlo simulations with the aim to reduce elevation overestimation in coastal wetlands at Plum Island, MA. We evaluated the performance of coastal wetland elevation refinement by using bare earth DEMs and DEMs created from the last return from the light detection and ranging (lidar) point cloud, hereafter referred to as the minimum bin DEM. We additionally created DEMs for a range of resolutions \u2013 3 m, 5 m, and 10 m. The elevation source data were from 2021. Using high-accuracy elevation data collected from 2014 to 2023 by the Plum Island Ecosystems Long-Term Ecological Research program and others, we assessed initial error of the DEMs. For each DEM, we used Monte Carlo simulation with 1,000 iterations to create new DEM realizations. Next, we summarized the 1,000 DEM realizations by calculating the 5th \u2013 95th percentiles (that is, median and every 5th percentile) of elevation by pixel across all iterations. This data release includes all percentiles from each DEM type and resolution. We used the previously mentioned in-situ elevation observations to assess the performance of the DEM error reduction. These DEMs are recommended for use in wetland modeling over the full range of percentiles.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1ED6XKF","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.685aa9b1d4be024a9b2868d4.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_685aa9b1d4be024a9b2868d4","keyword":["Massachusetts","Plum Island Estuary","USGS:685aa9b1d4be024a9b2868d4","digital elevation models","ecology","elevation","environment","geography","land use change","mathematical simulation","remote sensing","sea-level change","spatial analysis","wetland ecosystems"],"modified":"2026-09-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-70.9453, 42.6339, -70.7315, 42.8400","theme":["geospatial"],"title":"Digital elevation models with elevation uncertainty treatment, Plum Island Estuary, Massachusetts, 2021 (ver. 2.0, May 2026)"},"description":"This data release includes digital elevation models (DEMs) that were created using Monte Carlo simulations with the aim to reduce elevation overestimation in coastal wetlands at Plum Island, MA. We evaluated the performance of coastal wetland elevation refinement by using bare earth DEMs and DEMs created from the last return from the light detection and ranging (lidar) point cloud, hereafter referred to as the minimum bin DEM. We additionally created DEMs for a range of resolutions \u2013 3 m, 5 m, and 10 m. The elevation source data were from 2021. Using high-accuracy elevation data collected from 2014 to 2023 by the Plum Island Ecosystems Long-Term Ecological Research program and others, we assessed initial error of the DEMs. For each DEM, we used Monte Carlo simulation with 1,000 iterations to create new DEM realizations. Next, we summarized the 1,000 DEM realizations by calculating the 5th \u2013 95th percentiles (that is, median and every 5th percentile) of elevation by pixel across all iterations. This data release includes all percentiles from each DEM type and resolution. 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Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. 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The program uses both historical data and contemporary data collections to assess and monitor changes in the aerial and subaqueous extent of islands, habitat types, sediment texture and geotechnical properties, environmental processes, and vegetation composition. Examples of BICM datasets include still and video aerial photography for documenting shoreline changes, shoreline positions, habitat mapping, land change analyses, light detection and ranging (lidar) surveys for topographic elevations, single-beam and swath bathymetry, and sediment grab samples. For more information about the BICM program, see Kindinger and others (2013). \nThe U.S. Geological Survey, Wetland and Aquatic Research Center provides support to the BICM program through the development of habitat map products using aerial imagery and lidar elevation data and assessing change in habitats over time. These data provide a snapshot of barrier island habitats and can be combined with other past and/or future maps to monitor these valuable natural resources over time. Previous efforts of this habitat mapping program included developing habitat maps for 2008 and 2015\u20132016 for the following BICM regions (Enwright and others, 2020): 1) West Chenier; 2) East Chenier; 3) Acadiana Bays (only Marsh Island); 4) Early Lafourche Delta; 5) Late Lafourche Delta; 6) Modern Delta (only Chaland Headland and Shell Island); and 7) Chandeleur Islands. Additionally, a habitat change analysis was conducted comparing reaches mapped in 2008 and 2015\u20132016. The current effort of this habitat mapping program includes developing habitat maps for 2021 for the previously mentioned regions. A habitat change analysis will be conducted comparing reaches mapped 2015\u20132016 and 2021. The BICM program has developed two habitat classification schemes which include a detailed 15-class habitat scheme and a general eight-class habitat scheme. The detailed scheme was developed specifically for this habitat mapping effort and builds off the general scheme used in previous BICM habitat mapping efforts (Fearnley and others, 2009). The additional classes developed in the detailed scheme are primarily used to further delineate various dune habitats, separate marsh and mangrove, and distinguish between beach and unvegetated barrier flat habitats. To ensure comparability between this effort and previous BICM map products, we have crosswalked the detailed classes to general habitat classes previously used by Fearnley and others (2009). \nIn other words, the general habitat classes included in these products were not directly interpreted using aerial imagery and lidar elevation data. Thus, we recommend only using these general habitat classes for analyses that include previous BICM habitat maps (1996\u20132005). For more information about the BICM program, see Kindinger and others (2013). For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (i.e., dataset and recommended symbology). For more information about the BICM program, see Kindinger and others (2013). \nReferences:\nKindinger, J.L., Buster, N.A., Flocks, J.G., Bernier, J.C., and Kulp, M.A., 2013, Louisiana Barrier Island Comprehensive Monitoring (BICM) program summary report\u2014Data and analyses 2006 through 2010: U.S. Geological Survey Open-File Report 2013\u20131083, 86 p., at https://pubs.usgs.gov/of/2013/1083/.\nEnwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program\u2014Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015\u201316: U.S. Geological Survey Open-File Report 2020\u20131030, 57 p., https://doi.org/10.3133/ofr20201030.\nFearnley, S., Brien, L., Martinez, L., Miner, M., Kulp, M., and Penland, S., 2009, Chenier Plain, South-Central Louisiana, and Chandeleur Islands, Habitat mapping and change analysis 1996 to 2005, Part 1\u2014Methods for habitat mapping and change analysis 1996 to 2005\u2014Louisiana Barrier Island Comprehensive Monitoring Program (BICM) 5: New Orleans, University of New Orleans, Pontchartrain Institute for Environmental Sciences, 11 p.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1ZQP7SN","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.68505a2cd4be025b9e8c6b6a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_68505a2cd4be025b9e8c6b6a","keyword":["Gulf of America","Louisiana","Louisiana Coastal","Modern Delta Region","Plaquemines Parish","USGS:68505a2cd4be025b9e8c6b6a","barrier islands","barrier vegetation","beach","coastal wetlands","dune","environment","farming","habitat map","mangrove","marsh","planningCadastre","remote sensing","scrub/shrub"],"modified":"2026-09-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-89.8837, 29.1798, -89.4280, 29.3839","theme":["geospatial"],"title":"Louisiana Barrier Island Comprehensive Monitoring Program \u2013 2021 habitat map, Modern Delta Region (ver. 2.0, September 2026)"},"description":"The Barrier Island Comprehensive Monitoring (BICM) program was developed by Louisiana\u2019s Coastal Protection and Restoration Authority (CPRA) and is implemented as a component of the System Wide Assessment and Monitoring Program (SWAMP). The program uses both historical data and contemporary data collections to assess and monitor changes in the aerial and subaqueous extent of islands, habitat types, sediment texture and geotechnical properties, environmental processes, and vegetation composition. Examples of BICM datasets include still and video aerial photography for documenting shoreline changes, shoreline positions, habitat mapping, land change analyses, light detection and ranging (lidar) surveys for topographic elevations, single-beam and swath bathymetry, and sediment grab samples. For more information about the BICM program, see Kindinger and others (2013). \nThe U.S. Geological Survey, Wetland and Aquatic Research Center provides support to the BICM program through the development of habitat map products using aerial imagery and lidar elevation data and assessing change in habitats over time. These data provide a snapshot of barrier island habitats and can be combined with other past and/or future maps to monitor these valuable natural resources over time. Previous efforts of this habitat mapping program included developing habitat maps for 2008 and 2015\u20132016 for the following BICM regions (Enwright and others, 2020): 1) West Chenier; 2) East Chenier; 3) Acadiana Bays (only Marsh Island); 4) Early Lafourche Delta; 5) Late Lafourche Delta; 6) Modern Delta (only Chaland Headland and Shell Island); and 7) Chandeleur Islands. Additionally, a habitat change analysis was conducted comparing reaches mapped in 2008 and 2015\u20132016. The current effort of this habitat mapping program includes developing habitat maps for 2021 for the previously mentioned regions. A habitat change analysis will be conducted comparing reaches mapped 2015\u20132016 and 2021. The BICM program has developed two habitat classification schemes which include a detailed 15-class habitat scheme and a general eight-class habitat scheme. The detailed scheme was developed specifically for this habitat mapping effort and builds off the general scheme used in previous BICM habitat mapping efforts (Fearnley and others, 2009). The additional classes developed in the detailed scheme are primarily used to further delineate various dune habitats, separate marsh and mangrove, and distinguish between beach and unvegetated barrier flat habitats. To ensure comparability between this effort and previous BICM map products, we have crosswalked the detailed classes to general habitat classes previously used by Fearnley and others (2009). \nIn other words, the general habitat classes included in these products were not directly interpreted using aerial imagery and lidar elevation data. Thus, we recommend only using these general habitat classes for analyses that include previous BICM habitat maps (1996\u20132005). For more information about the BICM program, see Kindinger and others (2013). For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (i.e., dataset and recommended symbology). For more information about the BICM program, see Kindinger and others (2013). \nReferences:\nKindinger, J.L., Buster, N.A., Flocks, J.G., Bernier, J.C., and Kulp, M.A., 2013, Louisiana Barrier Island Comprehensive Monitoring (BICM) program summary report\u2014Data and analyses 2006 through 2010: U.S. Geological Survey Open-File Report 2013\u20131083, 86 p., at https://pubs.usgs.gov/of/2013/1083/.\nEnwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program\u2014Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015\u201316: U.S. Geological Survey Open-File Report 2020\u20131030, 57 p., https://doi.org/10.3133/ofr20201030.\nFearnley, S., Brien, L., Martinez, L., Miner, M., Kulp, M., and Penland, S., 2009, Chenier Plain, South-Central Louisiana, and Chandeleur Islands, Habitat mapping and change analysis 1996 to 2005, Part 1\u2014Methods for habitat mapping and change analysis 1996 to 2005\u2014Louisiana Barrier Island Comprehensive Monitoring Program (BICM) 5: New Orleans, University of New Orleans, Pontchartrain Institute for Environmental Sciences, 11 p.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/91f4fa35-a0c9-4c30-8895-1246a701f24a","harvest_record_raw":"https://catalog.data.gov/harvest_record/91f4fa35-a0c9-4c30-8895-1246a701f24a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_68505a2cd4be025b9e8c6b6a","keyword":["Gulf of America","Louisiana","Louisiana Coastal","Modern Delta Region","Plaquemines Parish","USGS:68505a2cd4be025b9e8c6b6a","barrier islands","barrier vegetation","beach","coastal wetlands","dune","environment","farming","habitat map","mangrove","marsh","planningCadastre","remote sensing","scrub/shrub"],"last_harvested_date":"2026-09-27T00:53:47.274481","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"louisiana-barrier-island-comprehensive-monitoring-program-2021-habitat-map-modern-delta-re","spatial_centroid":{"lat":29.26144,"lon":-89.70142000000001},"spatial_shape":{"coordinates":[[[-89.8837,29.1798],[-89.8837,29.3839],[-89.428,29.3839],[-89.428,29.1798],[-89.8837,29.1798]]],"type":"Polygon"},"theme":["geospatial"],"title":"Louisiana Barrier Island Comprehensive Monitoring Program \u2013 2021 habitat map, Modern Delta Region (ver. 2.0, September 2026)","type":"dataset"},{"_score":9.077706,"_sort":[1790470404474,9.077706,0,"be0ef096-7975-4ffc-8d91-459c3339e3ce"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Lindsay E. F. Hunt","hasEmail":"mailto:lhunt@usgs.gov"},"description":"This dataset is the sixth installment of a yearly connectivity update for forested habitat within the Great Lakes Restoration Initiative's (GLRI) Terrestrial Habitats & Connectivity (TH&C) work group's Pilot Area. The Pilot Area is a region of the northern Great Lakes Basin between Ashland, WI and the Keweenaw Peninsula and is bounded by Lake Superior in the north and the basin boundary in the south, including a 70 km buffer. Each year the TH&C selects project proposals for funding within the pilot area. These proposals involve either restoration, research, or land acquisition aimed at improving or increasing forest connectivity in this area. The intended purpose of each year\u2019s update is to understand the effects of funded projects on the groundwork and help inform the location and purpose of future project proposals. The post-fiscal year 2025 forest connectivity maps serve as an additional time step for comparison from each previous fiscal year into the future.  \nTo create the post FY25 installment of GLRI's Terrestrial Habitats & Connectivity work group's pilot area yearly connectivity maps, we identified the geospatial locations where restoration work benefitting forested habitats was completed throughout FY25. These locations were then assigned the lowest resistance value. Then using the forest habitat resistance layer from Great Lakes Restoration Initiative's Terrestrial Habitats and Connectivity Work group's Pilot Area's post-fiscal year 2025 we overwrote the post FY24 resistance values with the resistance values assigned to the FY25 locations where work was done. This analysis produced two connectivity maps: a cumulative current map and a normalized current map. The cumulative current map shows where potential movement pathways between forests are located within the pilot area, while the normalized current map shows where obstructed movement, diffuse movement, and channelized movement occurs within the pilot area. These maps provide important information on how restoration efforts from FY21, FY22, FY23, FY24, and FY25 in the pilot area are affecting forest habitat connectivity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14MMKBI","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a42901e1ba49b09ad17da03.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a42901e1ba49b09ad17da03","keyword":["Connectivity","Forest Habitat","Great Lakes","Northern Wisconsin/Michigan","USGS:6a42901e1ba49b09ad17da03","biota","dispersal (organisms)","environment","habitat fragmentation","imageryBaseMapsEarthCover","land use change","remote sensing","structure"],"modified":"2026-09-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-92.7750, 45.2500, -86.3710, 48.2097","theme":["geospatial"],"title":"Great Lakes Restoration Initiative's Terrestrial Habitats & Connectivity Work Group's Pilot Area's Post-Fiscal Year 2025 Forest Habitat Connectivity"},"description":"This dataset is the sixth installment of a yearly connectivity update for forested habitat within the Great Lakes Restoration Initiative's (GLRI) Terrestrial Habitats & Connectivity (TH&C) work group's Pilot Area. The Pilot Area is a region of the northern Great Lakes Basin between Ashland, WI and the Keweenaw Peninsula and is bounded by Lake Superior in the north and the basin boundary in the south, including a 70 km buffer. Each year the TH&C selects project proposals for funding within the pilot area. These proposals involve either restoration, research, or land acquisition aimed at improving or increasing forest connectivity in this area. The intended purpose of each year\u2019s update is to understand the effects of funded projects on the groundwork and help inform the location and purpose of future project proposals. The post-fiscal year 2025 forest connectivity maps serve as an additional time step for comparison from each previous fiscal year into the future.  \nTo create the post FY25 installment of GLRI's Terrestrial Habitats & Connectivity work group's pilot area yearly connectivity maps, we identified the geospatial locations where restoration work benefitting forested habitats was completed throughout FY25. These locations were then assigned the lowest resistance value. Then using the forest habitat resistance layer from Great Lakes Restoration Initiative's Terrestrial Habitats and Connectivity Work group's Pilot Area's post-fiscal year 2025 we overwrote the post FY24 resistance values with the resistance values assigned to the FY25 locations where work was done. This analysis produced two connectivity maps: a cumulative current map and a normalized current map. The cumulative current map shows where potential movement pathways between forests are located within the pilot area, while the normalized current map shows where obstructed movement, diffuse movement, and channelized movement occurs within the pilot area. These maps provide important information on how restoration efforts from FY21, FY22, FY23, FY24, and FY25 in the pilot area are affecting forest habitat connectivity.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5422c2a9-4fa8-430c-a53d-81bdd06b1016","harvest_record_raw":"https://catalog.data.gov/harvest_record/5422c2a9-4fa8-430c-a53d-81bdd06b1016/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a42901e1ba49b09ad17da03","keyword":["Connectivity","Forest Habitat","Great Lakes","Northern Wisconsin/Michigan","USGS:6a42901e1ba49b09ad17da03","biota","dispersal (organisms)","environment","habitat fragmentation","imageryBaseMapsEarthCover","land use change","remote sensing","structure"],"last_harvested_date":"2026-09-27T00:53:24.474463","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"great-lakes-restoration-initiatives-terrestrial-habitats-amp-connectivity-work-groups-pilo-e7121","spatial_centroid":{"lat":46.43388,"lon":-90.21340000000001},"spatial_shape":{"coordinates":[[[-92.775,45.25],[-92.775,48.2097],[-86.371,48.2097],[-86.371,45.25],[-92.775,45.25]]],"type":"Polygon"},"theme":["geospatial"],"title":"Great Lakes Restoration Initiative's Terrestrial Habitats & Connectivity Work Group's Pilot Area's Post-Fiscal Year 2025 Forest Habitat Connectivity","type":"dataset"},{"_score":9.61414,"_sort":[1790470384979,9.61414,0,"c4170fa3-939e-496d-a857-9731e41d87d4"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Andy Bock","hasEmail":"mailto:abock@usgs.gov"},"description":"The National Hydrologic Geospatial Fabric Reference and Derived Hydrofabrics is a geospatial dataset of connected rivers, streams, lakes, catchments, hydrologic locations, and relevant attributes to support multi-scale and integrative hydrologic modeling and analysis.\nThis child dataset contains features and attributes representing the National Hydrologic Geospatial Fabric Refactored Hydrofabric. The Refactored Hydrofabric is a processed version of the Reference Hydrofabric with a more uniform distribution of catchment size. The complexity of different models requires flexibility to create different spatial levels of resolution and detail. Catchments with flowpath-lengths shorter than the user-specified minimum length threshold that increase data volume and compute cost but don't add hydrologic fidelity have been consolidated into larger ones. Catchments whose flowpath lengths are longer than the user-specified maximum flowpath-length threshold are split apart to provide uniform fidelity and better represent network outlets such as streamgages. When refactoring, key hydrologic locations like stream gages are preserved precisely, unless they are specified specifically as split events.\nThis item contains an Open Geospatial Consortium geopackage (gpkg) containing refactored flowlines, catchments, and other layers for all of Conterminous United States CONUS (refactor_CONUS.gpkg). See the processing steps for specific refactoring details.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9NFPB5S","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.61fbfdced34e622189cb1b0a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61fbfdced34e622189cb1b0a","keyword":["Geographic Information Systems","Hydrologic Response Units","Hydrologic modeling","Points of Interest","Routing Network","USGS:61fbfdced34e622189cb1b0a","environment","geoscientificInformation","inlandWaters"],"modified":"2026-09-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.7337, 24.6304, -66.9496, 52.8800","theme":["geospatial"],"title":"National Hydrologic Geospatial Fabric Refactored Hydrofabric"},"description":"The National Hydrologic Geospatial Fabric Reference and Derived Hydrofabrics is a geospatial dataset of connected rivers, streams, lakes, catchments, hydrologic locations, and relevant attributes to support multi-scale and integrative hydrologic modeling and analysis.\nThis child dataset contains features and attributes representing the National Hydrologic Geospatial Fabric Refactored Hydrofabric. The Refactored Hydrofabric is a processed version of the Reference Hydrofabric with a more uniform distribution of catchment size. The complexity of different models requires flexibility to create different spatial levels of resolution and detail. Catchments with flowpath-lengths shorter than the user-specified minimum length threshold that increase data volume and compute cost but don't add hydrologic fidelity have been consolidated into larger ones. Catchments whose flowpath lengths are longer than the user-specified maximum flowpath-length threshold are split apart to provide uniform fidelity and better represent network outlets such as streamgages. When refactoring, key hydrologic locations like stream gages are preserved precisely, unless they are specified specifically as split events.\nThis item contains an Open Geospatial Consortium geopackage (gpkg) containing refactored flowlines, catchments, and other layers for all of Conterminous United States CONUS (refactor_CONUS.gpkg). See the processing steps for specific refactoring details.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5ce2a524-b4e4-4a7c-a960-065aa548f0fc","harvest_record_raw":"https://catalog.data.gov/harvest_record/5ce2a524-b4e4-4a7c-a960-065aa548f0fc/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61fbfdced34e622189cb1b0a","keyword":["Geographic Information Systems","Hydrologic Response Units","Hydrologic modeling","Points of Interest","Routing Network","USGS:61fbfdced34e622189cb1b0a","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-09-27T00:53:04.979454","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"national-hydrologic-geospatial-fabric-refactored-hydrofabric","spatial_centroid":{"lat":35.930240000000005,"lon":-101.62006},"spatial_shape":{"coordinates":[[[-124.7337,24.6304],[-124.7337,52.88],[-66.9496,52.88],[-66.9496,24.6304],[-124.7337,24.6304]]],"type":"Polygon"},"theme":["geospatial"],"title":"National Hydrologic Geospatial Fabric Refactored Hydrofabric","type":"dataset"},{"_score":10.323158,"_sort":[1790470292050,10.323158,0,"81e5e55c-faeb-46d2-859e-0475af612e5b"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Cody Hudson","hasEmail":"mailto:chudson@intera.com"},"description":"Daily streamflow and reservoir water elevation data for modeled locations in the Red River Basin. Values reported are for 18 different GCM (Global Climate Model) / RCP (Representative Concentration Pathway) / GDM Downscaling scenarios. Climate data from each scenario was input into a Variable Infiltration Capacity (VIC) model, that output flow values. These values were then input into RiverWare, to determine the impacts on regulated flows, lake levels and water availability. RiverWare was used for this project, because of its ability to simulate water use, reservoir operations, and local/interstate regulations.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/C91599","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.57d84d1ae4b090824ff9ac7b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57d84d1ae4b090824ff9ac7b","keyword":["Climate Change","Oklahoma","Red River","RiverWare","USGS:57d84d1ae4b090824ff9ac7b","Water Availability","environment"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-99.9612, 33.7317, -94.6297, 35.6655","theme":["geospatial"],"title":"RiverWare Daily Simulated values of Streamflow from 2006-2099: Oklahoma"},"description":"Daily streamflow and reservoir water elevation data for modeled locations in the Red River Basin. Values reported are for 18 different GCM (Global Climate Model) / RCP (Representative Concentration Pathway) / GDM Downscaling scenarios. Climate data from each scenario was input into a Variable Infiltration Capacity (VIC) model, that output flow values. These values were then input into RiverWare, to determine the impacts on regulated flows, lake levels and water availability. RiverWare was used for this project, because of its ability to simulate water use, reservoir operations, and local/interstate regulations.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/59b7ae13-b980-452b-ba0e-ba20a3f31439","harvest_record_raw":"https://catalog.data.gov/harvest_record/59b7ae13-b980-452b-ba0e-ba20a3f31439/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57d84d1ae4b090824ff9ac7b","keyword":["Climate Change","Oklahoma","Red River","RiverWare","USGS:57d84d1ae4b090824ff9ac7b","Water Availability","environment"],"last_harvested_date":"2026-09-27T00:51:32.050796","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"riverware-daily-simulated-values-of-streamflow-from-2006-2099-oklahoma","spatial_centroid":{"lat":34.505219999999994,"lon":-97.82860000000001},"spatial_shape":{"coordinates":[[[-99.9612,33.7317],[-99.9612,35.6655],[-94.6297,35.6655],[-94.6297,33.7317],[-99.9612,33.7317]]],"type":"Polygon"},"theme":["geospatial"],"title":"RiverWare Daily Simulated values of Streamflow from 2006-2099: Oklahoma","type":"dataset"},{"_score":4.790865,"_sort":[1790470273412,4.790865,0,"2b991e1f-2b8f-45e0-99c2-aec7a26b77e7"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Tabitha A Graves","hasEmail":"mailto:tgraves@usgs.gov"},"description":"This data release contains the citation details, abstracts, and expert annotations of 235 publications on the topic of the potential effects of oil and gas development on pollinating insects in the western United States.\nPublications were collected through a structured literature search and a semi-automated content analysis. We developed a search string designed to pull relevant papers from Scopus and Web of Science. We used a set of benchmark publications identified by experts that were known to be relevant to the topic to refine the search string. The search returned 1,435 unique publications. Text mining of the title, abstract, and keywords quantified the relevancy of each paper to the topic and was used to discard the least relevant papers. We divided the remaining 731 publications into ten disturbance-based categories. The publications went through a three-step review process to remove irrelevant papers and to annotate relevant papers with key information related to the disturbance category. Lastly, text mining of the full manuscripts using the KWICer R code (Bailey and others 2026) was used to count the number of mentions of key words and phrases related to disturbance type, pollinator taxa, effect on pollinators, geographic location, ecosystem type, and study methodology.\nThe annotated bibliography contains 235 unique publications across the 10 disturbances categories: Air and chemical pollution (87 publications), air turbulence (4), dust (6), habitat loss and fragmentation (23), invasive plants (52), light pollution (37), noise pollution (6), oil and gas (7), roads (38), and soil compaction and trampling (10). The publications date from 1992 to 2026 and are mostly scientific articles (220) with some review papers (13), one editorial material, and one brief report. Full-manuscript text mining results are summarized in a set of figures that show the publication year, ecosystem type, and geographic area, as well as matrices that show the frequency and co-occurrence of pollinator taxa, disturbance type, and effect on pollinators in the body of literature. Together with the annotated bibliography, these elements provide key information that can help streamline regulatory processes related to oil and gas development on public lands.\nBailey, L.N., Varner, D.M., Whipple, S.E., 2026, KWICer: Producing an annotated bibliography from a set of PDFs by quantifying keywords, (Version 1.0.0): U.S. Geological Survey software release, https://doi.org/10.5066/P1476GUY.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14A5GRG","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a8619cc1ba49b21c50f4f5e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a8619cc1ba49b21c50f4f5e","keyword":["Argentina","Austria","Belarus","Belgium","Bolivia","Brazil","Canada","Chad","Chile","China","Costa Rica","Czech Republic","Denmark","Estonia","Finland","France","Georgia","Germany","Ghana","Greece","Guinea","Hungary","India","Indonesia","Iran","Ireland","Israel","Italy","Japan","Jordan","Kenya","Latvia","Lesotho","Libya","Lithuania","Luxembourg","Madagascar","Maldives","Mexico","Monaco","Mongolia","Nepal","Netherlands","Niger","Norway","Pakistan","Panama","Poland","Portugal","Romania","Russia","Seychelles","Singapore","Slovakia","Slovenia","South Africa","Spain","Sri Lanka","Sweden","Switzerland","Taiwan","Thailand","Turkey","USGS:6a8619cc1ba49b21c50f4f5e","Ukraine","United Kingdom","United States","Venezuela","Zimbabwe","air pollution","air turbulence","biota","dust pollution","energy resources","environment","habitat alteration and disturbance","habitat fragmentation","invasive species","light pollution","natural gas resources","natural resource extraction","natural resource management","noise pollution","oil resources","pollination","pollinators","roads","soil compaction","soil resources"],"modified":"2026-09-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, -90.0000, 180.0000, 90.0000","theme":["geospatial"],"title":"Potential effects of oil and gas development on pollinating insects in the western United States: An annotated bibliography produced through a structured literature search and semi-automated content analysis"},"description":"This data release contains the citation details, abstracts, and expert annotations of 235 publications on the topic of the potential effects of oil and gas development on pollinating insects in the western United States.\nPublications were collected through a structured literature search and a semi-automated content analysis. We developed a search string designed to pull relevant papers from Scopus and Web of Science. We used a set of benchmark publications identified by experts that were known to be relevant to the topic to refine the search string. The search returned 1,435 unique publications. Text mining of the title, abstract, and keywords quantified the relevancy of each paper to the topic and was used to discard the least relevant papers. We divided the remaining 731 publications into ten disturbance-based categories. The publications went through a three-step review process to remove irrelevant papers and to annotate relevant papers with key information related to the disturbance category. Lastly, text mining of the full manuscripts using the KWICer R code (Bailey and others 2026) was used to count the number of mentions of key words and phrases related to disturbance type, pollinator taxa, effect on pollinators, geographic location, ecosystem type, and study methodology.\nThe annotated bibliography contains 235 unique publications across the 10 disturbances categories: Air and chemical pollution (87 publications), air turbulence (4), dust (6), habitat loss and fragmentation (23), invasive plants (52), light pollution (37), noise pollution (6), oil and gas (7), roads (38), and soil compaction and trampling (10). The publications date from 1992 to 2026 and are mostly scientific articles (220) with some review papers (13), one editorial material, and one brief report. Full-manuscript text mining results are summarized in a set of figures that show the publication year, ecosystem type, and geographic area, as well as matrices that show the frequency and co-occurrence of pollinator taxa, disturbance type, and effect on pollinators in the body of literature. Together with the annotated bibliography, these elements provide key information that can help streamline regulatory processes related to oil and gas development on public lands.\nBailey, L.N., Varner, D.M., Whipple, S.E., 2026, KWICer: Producing an annotated bibliography from a set of PDFs by quantifying keywords, (Version 1.0.0): U.S. Geological Survey software release, https://doi.org/10.5066/P1476GUY.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3308038b-b606-4ed7-a1c6-15b0b2a24ac7","harvest_record_raw":"https://catalog.data.gov/harvest_record/3308038b-b606-4ed7-a1c6-15b0b2a24ac7/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a8619cc1ba49b21c50f4f5e","keyword":["Argentina","Austria","Belarus","Belgium","Bolivia","Brazil","Canada","Chad","Chile","China","Costa Rica","Czech Republic","Denmark","Estonia","Finland","France","Georgia","Germany","Ghana","Greece","Guinea","Hungary","India","Indonesia","Iran","Ireland","Israel","Italy","Japan","Jordan","Kenya","Latvia","Lesotho","Libya","Lithuania","Luxembourg","Madagascar","Maldives","Mexico","Monaco","Mongolia","Nepal","Netherlands","Niger","Norway","Pakistan","Panama","Poland","Portugal","Romania","Russia","Seychelles","Singapore","Slovakia","Slovenia","South Africa","Spain","Sri Lanka","Sweden","Switzerland","Taiwan","Thailand","Turkey","USGS:6a8619cc1ba49b21c50f4f5e","Ukraine","United Kingdom","United States","Venezuela","Zimbabwe","air pollution","air turbulence","biota","dust pollution","energy resources","environment","habitat alteration and disturbance","habitat fragmentation","invasive species","light pollution","natural gas resources","natural resource extraction","natural resource management","noise pollution","oil resources","pollination","pollinators","roads","soil compaction","soil resources"],"last_harvested_date":"2026-09-27T00:51:13.412675","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"potential-effects-of-oil-and-gas-development-on-pollinating-insects-in-the-western-united-","spatial_centroid":{"lat":-18.0,"lon":-36.0},"spatial_shape":{"coordinates":[[[-180.0,-90.0],[-180.0,90.0],[180.0,90.0],[180.0,-90.0],[-180.0,-90.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Potential effects of oil and gas development on pollinating insects in the western United States: An annotated bibliography produced through a structured literature search and semi-automated content analysis","type":"dataset"},{"_score":8.532398,"_sort":[1790470243210,8.532398,2,"e199d8a3-fdc7-4d48-8206-406eb0ebabbe"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Graziella DRenzo","hasEmail":"mailto:gdirenzo@umass.edu"},"description":"This data release includes the data associated with the manuscript \"Freshwater mussel distribution and catchment prioritization for mussel conservation in the Northeastern United States.\"  It describes native freshwater mussel distribution data for Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, and Connecticut as well as catchment priority scores for different conservation activities.  The data release also includes some data to enable better understanding of the models used in the associated manuscript such as model standard deviations and model parameter permutation importance values.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13HZTMZ","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.677728fed34ee88ba0b15b25.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_677728fed34ee88ba0b15b25","keyword":["Aquatic","Connecticut","Lentic","Lotic","Maine","Massachusetts","MaxEnt","New Hampshire","Northeast","Rhode Island","USGS:677728fed34ee88ba0b15b25","United States","Vermont","biota","elevation","environment","farming","freshwater","geospatial datasets","inlandWaters","modeling","mussels","river","stream","unionidae"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-73.6857, 41.0274, -67.0094, 47.3254","theme":["geospatial"],"title":"Mussel occurrence and catchment priority for mussel conservation in the Northeastern U.S."},"description":"This data release includes the data associated with the manuscript \"Freshwater mussel distribution and catchment prioritization for mussel conservation in the Northeastern United States.\"  It describes native freshwater mussel distribution data for Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, and Connecticut as well as catchment priority scores for different conservation activities.  The data release also includes some data to enable better understanding of the models used in the associated manuscript such as model standard deviations and model parameter permutation importance values.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/cb829d41-b60b-468d-a1db-9556611659ba","harvest_record_raw":"https://catalog.data.gov/harvest_record/cb829d41-b60b-468d-a1db-9556611659ba/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_677728fed34ee88ba0b15b25","keyword":["Aquatic","Connecticut","Lentic","Lotic","Maine","Massachusetts","MaxEnt","New Hampshire","Northeast","Rhode Island","USGS:677728fed34ee88ba0b15b25","United States","Vermont","biota","elevation","environment","farming","freshwater","geospatial datasets","inlandWaters","modeling","mussels","river","stream","unionidae"],"last_harvested_date":"2026-09-27T00:50:43.210190","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"mussel-occurrence-and-catchment-priority-for-mussel-conservation-in-the-northeastern-u-s","spatial_centroid":{"lat":43.5466,"lon":-71.01518},"spatial_shape":{"coordinates":[[[-73.6857,41.0274],[-73.6857,47.3254],[-67.0094,47.3254],[-67.0094,41.0274],[-73.6857,41.0274]]],"type":"Polygon"},"theme":["geospatial"],"title":"Mussel occurrence and catchment priority for mussel conservation in the Northeastern U.S.","type":"dataset"},{"_score":8.7617855,"_sort":[1790470223694,8.7617855,0,"0e4b1b8b-5e9b-428f-81cf-9a11eb16240f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael Osland","hasEmail":"mailto:mosland@usgs.gov"},"description":"Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. \nThis file includes the sub-plot-level small plot vegetation cover data.\nThis work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1GDTXUR","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.545cfa50e4b0ba8303f711e2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_545cfa50e4b0ba8303f711e2","keyword":["Avicennia germinans","Cedar Key","Gulf of Mexico","Port Aransas","Port Fourchon","Spartina alterniflora","State of Florida","State of Louisiana","State of Texas","USGS:545cfa50e4b0ba8303f711e2","United States of America","environment","habitat","vegetation"],"modified":"2026-09-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"theme":["geospatial"],"title":"Dataset 6: New vegetation data collection: sub-plot-level small plot cover"},"description":"Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. \nThis file includes the sub-plot-level small plot vegetation cover data.\nThis work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/502933c3-d436-43d8-b2bd-631b8518d1ad","harvest_record_raw":"https://catalog.data.gov/harvest_record/502933c3-d436-43d8-b2bd-631b8518d1ad/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_545cfa50e4b0ba8303f711e2","keyword":["Avicennia germinans","Cedar Key","Gulf of Mexico","Port Aransas","Port Fourchon","Spartina alterniflora","State of Florida","State of Louisiana","State of Texas","USGS:545cfa50e4b0ba8303f711e2","United States of America","environment","habitat","vegetation"],"last_harvested_date":"2026-09-27T00:50:23.694133","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"dataset-6-new-vegetation-data-collection-sub-plot-level-small-plot-cover","spatial_centroid":null,"spatial_shape":null,"theme":["geospatial"],"title":"Dataset 6: New vegetation data collection: sub-plot-level small plot cover","type":"dataset"},{"_score":10.524185,"_sort":[1790470177533,10.524185,0,"88a02eff-5b51-4463-898d-71612ec3fce1"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"This shapefile contains polygons representing the current range of twelve ecosystems in the southeastern U.S. and the Caribbean. These were the focal ecosystems for the research project. The polygons were created by digitizing a representative area that contained each ecosystem's extent, as defined from GAP land cover or NatureServe's National Map land cover data.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P146ZFNK","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5488c510e4b02acb4f0c945d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5488c510e4b02acb4f0c945d","keyword":["USGS:5488c510e4b02acb4f0c945d","climate change","ecosystems","environment","geospatial datasets","vulnerability"],"modified":"2026-09-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-102.45498189, 17.194070518, -56.324313299, 43.809030881","theme":["geospatial"],"title":"Current Range Polygons for Twelve Focal Ecosystems of the Southeastern U.S. and Caribbean"},"description":"This shapefile contains polygons representing the current range of twelve ecosystems in the southeastern U.S. and the Caribbean. These were the focal ecosystems for the research project. The polygons were created by digitizing a representative area that contained each ecosystem's extent, as defined from GAP land cover or NatureServe's National Map land cover data.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5a50de55-19aa-452a-91bc-ce11905cf52c","harvest_record_raw":"https://catalog.data.gov/harvest_record/5a50de55-19aa-452a-91bc-ce11905cf52c/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5488c510e4b02acb4f0c945d","keyword":["USGS:5488c510e4b02acb4f0c945d","climate change","ecosystems","environment","geospatial datasets","vulnerability"],"last_harvested_date":"2026-09-27T00:49:37.533290","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"current-range-polygons-for-twelve-focal-ecosystems-of-the-southeastern-u-s-and-caribbean","spatial_centroid":{"lat":27.8400546632,"lon":-84.0027144536},"spatial_shape":{"coordinates":[[[-102.45498189,17.194070518],[-102.45498189,43.809030881],[-56.324313299,43.809030881],[-56.324313299,17.194070518],[-102.45498189,17.194070518]]],"type":"Polygon"},"theme":["geospatial"],"title":"Current Range Polygons for Twelve Focal Ecosystems of the Southeastern U.S. and Caribbean","type":"dataset"},{"_score":8.950693,"_sort":[1790470140732,8.950693,0,"aada3a90-26fb-4ff8-bebd-79b44c4a1ffc"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Katherine Warnell","hasEmail":"mailto:katie.warnell@duke.edu"},"description":"Wild insect pollination has significant positive effects on pollinator-dependent crop production.  To assess the spatial distribution of potential wild insect pollination, we mapped the supply of potential wild pollinator habitat (forest, grassland, wetland, and shrubland land cover types) and the demand for pollination (directly pollinator-dependent crops).  A foraging travel distance for temperate native bees (1308 meters) was used to estimate relative pollinator activity on cropland based on distance from habitat.  We also calculated the proportion of pollinator habitat within pollinator travel distance of crops.  This information was summarized by subwatershed (HUC12) to identify regional priority areas for conservation and restoration of pollinator habitat.  This shapefile identifies regional conservation priority and restoration priority subwatersheds and includes information on the extent of pollinator-dependent crops, the mean relative pollinator activity on pollinator-dependent crops, and the proportion of pollinator habitat within pollinator flight distance of pollinator-dependent crops by subwatershed for the southeastern United States.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/69zz-7f78","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5d0a619fe4b0e3d3115fba57.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0a619fe4b0e3d3115fba57","keyword":["Alabama","Arkansas","Florida","Georgia","Louisiana","Mississippi","Missouri","North Carolina","South Carolina","Tennessee","USGS:5d0a619fe4b0e3d3115fba57","agricultural sites","environment","farming","habitats","natural resource management","pollination","pollinators","remediation"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-95.8607, 23.8938, -74.4004, 40.8044","theme":["geospatial"],"title":"Conservation and Restoration Priorities for Wild Pollinator Habitat in the Southeast United States, by Subwatershed (2011)"},"description":"Wild insect pollination has significant positive effects on pollinator-dependent crop production.  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This shapefile identifies regional conservation priority and restoration priority subwatersheds and includes information on the extent of pollinator-dependent crops, the mean relative pollinator activity on pollinator-dependent crops, and the proportion of pollinator habitat within pollinator flight distance of pollinator-dependent crops by subwatershed for the southeastern United States.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/27b8516b-1895-47e5-9b6f-9ed33955c60d","harvest_record_raw":"https://catalog.data.gov/harvest_record/27b8516b-1895-47e5-9b6f-9ed33955c60d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0a619fe4b0e3d3115fba57","keyword":["Alabama","Arkansas","Florida","Georgia","Louisiana","Mississippi","Missouri","North Carolina","South Carolina","Tennessee","USGS:5d0a619fe4b0e3d3115fba57","agricultural sites","environment","farming","habitats","natural resource management","pollination","pollinators","remediation"],"last_harvested_date":"2026-09-27T00:49:00.732244","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"conservation-and-restoration-priorities-for-wild-pollinator-habitat-in-the-southeast--2011-1330d","spatial_centroid":{"lat":30.65804,"lon":-87.27658},"spatial_shape":{"coordinates":[[[-95.8607,23.8938],[-95.8607,40.8044],[-74.4004,40.8044],[-74.4004,23.8938],[-95.8607,23.8938]]],"type":"Polygon"},"theme":["geospatial"],"title":"Conservation and Restoration Priorities for Wild Pollinator Habitat in the Southeast United States, by Subwatershed (2011)","type":"dataset"},{"_score":9.215691,"_sort":[1790470074217,9.215691,1,"801011d8-3cb2-4d6f-b4bc-f837e77a2d33"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Ronald B. 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Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. 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The field area is comprised of 311 surveyed points in, on the perimeter of, and surrounding six forest openings next to Coal Creek off Coal Bank Pass in the San Juan Mountains in Southwest Colorado, USA.  These measurements were taken to look at the relationship between snow accumulation and snow melt patterns between forest gaps of various sizes, and forest edges of various sizes (edge of forest gaps).  Canopy metrics, including canopy height, total gap area, mean distance to canopy, canopy closure, leaf area index, non-directional edginess, canopy edginess with a southern aspect, and canopy edginess with a northern aspect were defined using aerial lidar data for the San Juan Mountains and can be found in an affiliated data release titled, \u2018High Resolution Canopy Structure and Density Metrics for Southwest Colorado Derived from 2019 Aerial Lidar.\u2019  These metrics are also included herein for the 311 surveyed points.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9E943GE","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.64496275d34ee8d4aded92f9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64496275d34ee8d4aded92f9","keyword":["San Juan Mountains","Southwest Colorado","USGS:64496275d34ee8d4aded92f9","canopy density","canopy structure","environment","snow depth","snow water equivalence"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-107.765923, 37.697812, -107.756824, 37.704060","theme":["geospatial"],"title":"Snow Measurements in Specific Canopy Structure Regimes for the 2022-2023 Water Years, North of Coal Creek, San Juan Mountains, Colorado, USA"},"description":"These data include snow depth and snow water equivalence (SWE) for the 2022 and 2023 water years during 16 separate field campaigns.  The field area is comprised of 311 surveyed points in, on the perimeter of, and surrounding six forest openings next to Coal Creek off Coal Bank Pass in the San Juan Mountains in Southwest Colorado, USA.  These measurements were taken to look at the relationship between snow accumulation and snow melt patterns between forest gaps of various sizes, and forest edges of various sizes (edge of forest gaps).  Canopy metrics, including canopy height, total gap area, mean distance to canopy, canopy closure, leaf area index, non-directional edginess, canopy edginess with a southern aspect, and canopy edginess with a northern aspect were defined using aerial lidar data for the San Juan Mountains and can be found in an affiliated data release titled, \u2018High Resolution Canopy Structure and Density Metrics for Southwest Colorado Derived from 2019 Aerial Lidar.\u2019  These metrics are also included herein for the 311 surveyed points.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/13d47bff-8ced-4287-aa52-57e65de10119","harvest_record_raw":"https://catalog.data.gov/harvest_record/13d47bff-8ced-4287-aa52-57e65de10119/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64496275d34ee8d4aded92f9","keyword":["San Juan Mountains","Southwest Colorado","USGS:64496275d34ee8d4aded92f9","canopy density","canopy structure","environment","snow depth","snow water equivalence"],"last_harvested_date":"2026-09-27T00:38:55.669016","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"snow-measurements-in-specific-canopy-structure-regimes-for-the-2022-2023-water-years-north","spatial_centroid":{"lat":37.7003112,"lon":-107.7622834},"spatial_shape":{"coordinates":[[[-107.765923,37.697812],[-107.765923,37.70406],[-107.756824,37.70406],[-107.756824,37.697812],[-107.765923,37.697812]]],"type":"Polygon"},"theme":["geospatial"],"title":"Snow Measurements in Specific Canopy Structure Regimes for the 2022-2023 Water Years, North of Coal Creek, San Juan Mountains, Colorado, USA","type":"dataset"},{"_score":9.1297245,"_sort":[1790469459872,9.1297245,0,"fd996629-9e2b-4330-a54d-0533aee8c24c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael Osland","hasEmail":"mailto:mosland@usgs.gov"},"description":"Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. 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Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. This file includes the subplot-level shear strength data.\nThis work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0dbb243c-6054-443a-8ef7-e39275a6b702","harvest_record_raw":"https://catalog.data.gov/harvest_record/0dbb243c-6054-443a-8ef7-e39275a6b702/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_545cfce8e4b0ba8303f71365","keyword":["Cedar Key","Gulf of Mexico","Port Aransas","Port Fourchon","State of Florida","State of Louisiana","State of Texas","USGS:545cfce8e4b0ba8303f71365","United States of America","environment","shear strength","soil"],"last_harvested_date":"2026-09-27T00:37:39.872732","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"dataset-9-new-soil-data-collection-subplot-level-shear-strength","spatial_centroid":{"lat":28.3779712,"lon":-91.4960112},"spatial_shape":{"coordinates":[[[-97.14012,27.829952],[-97.14012,29.2],[-83.029848,29.2],[-83.029848,27.829952],[-97.14012,27.829952]]],"type":"Polygon"},"theme":["geospatial"],"title":"Dataset 9: New soil data collection: subplot-level shear strength","type":"dataset"},{"_score":10.6502,"_sort":[1790469337021,10.6502,8,"8cd021d3-2ddd-423b-a774-a007bfc76322"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Joe Heffron","hasEmail":"mailto:joseph.heffron@usda.gov"},"description":"This repository contains data supporting the publication, \"Association of runoff risk with private well contamination in a fractured dolostone aquifer.\" In this study, a cohort of private wells in the Silurian dolomite aquifer of northeast Wisconsin, USA, was repeatedly sampled to determine an association between modeled runoff risk using the Runoff Risk Advisory Forecast (RRAF, v. 2.1; https://runoffriskadvisory.wi.gov/) and well water contamination. The dataset comprises water quality data for private well samples (nitrate concentration, total coliforms, and bovine microbial source tracking [MST] markers), modeled surface runoff risk for each well over the duration of the study, and well-specific risk factors related to well construction and location.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.15482/USDA.ADC/31114333","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.dcde8656-023e-4381-b5a5-bae9a3aefdd1.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_dcde8656-023e-4381-b5a5-bae9a3aefdd1","keyword":["USGS:dcde8656-023e-4381-b5a5-bae9a3aefdd1","environment","health","inlandWaters"],"modified":"2026-08-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.4619, 43.4689, -87.4292, 45.0270","theme":["geospatial"],"title":"Data from: Association of runoff risk with private well contamination in a fractured dolostone aquifer"},"description":"This repository contains data supporting the publication, \"Association of runoff risk with private well contamination in a fractured dolostone aquifer.\" In this study, a cohort of private wells in the Silurian dolomite aquifer of northeast Wisconsin, USA, was repeatedly sampled to determine an association between modeled runoff risk using the Runoff Risk Advisory Forecast (RRAF, v. 2.1; https://runoffriskadvisory.wi.gov/) and well water contamination. The dataset comprises water quality data for private well samples (nitrate concentration, total coliforms, and bovine microbial source tracking [MST] markers), modeled surface runoff risk for each well over the duration of the study, and well-specific risk factors related to well construction and location.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/638013b2-0595-4c8a-8303-65e5fc8d1c8c","harvest_record_raw":"https://catalog.data.gov/harvest_record/638013b2-0595-4c8a-8303-65e5fc8d1c8c/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_dcde8656-023e-4381-b5a5-bae9a3aefdd1","keyword":["USGS:dcde8656-023e-4381-b5a5-bae9a3aefdd1","environment","health","inlandWaters"],"last_harvested_date":"2026-09-27T00:35:37.021880","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":8,"publisher":"U.S. Geological Survey","slug":"data-from-association-of-runoff-risk-with-private-well-contamination-in-a-fractured-dolost","spatial_centroid":{"lat":44.09214,"lon":-88.04882},"spatial_shape":{"coordinates":[[[-88.4619,43.4689],[-88.4619,45.027],[-87.4292,45.027],[-87.4292,43.4689],[-88.4619,43.4689]]],"type":"Polygon"},"theme":["geospatial"],"title":"Data from: Association of runoff risk with private well contamination in a fractured dolostone aquifer","type":"dataset"},{"_score":10.31085,"_sort":[1790469164735,10.31085,0,"56ae10b4-e656-4c9b-9367-eca41e883bd3"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Cody Hudson","hasEmail":"mailto:chudson@intera.com"},"description":"Daily streamflow and reservoir water elevation data for modeled locations in the Red River Basin. Values reported are for 18 different GCM (Global Climate Model) / RCP (Representative Concentration Pathway) / GDM Downscaling scenarios. Climate data from each scenario was input into a Variable Infiltration Capacity (VIC) model, that output flow values. These values were then input into RiverWare, to determine the impacts on regulated flows, lake levels and water availability. RiverWare was used for this project, because of its ability to simulate water use, reservoir operations, and local/interstate regulations.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/C91599","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.57d84c15e4b090824ff9ac75.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57d84c15e4b090824ff9ac75","keyword":["Climate Change","Louisiana","Red River","RiverWare","USGS:57d84c15e4b090824ff9ac75","Water Availability","environment"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-94.0221, 31.2497, -91.2644, 32.9882","theme":["geospatial"],"title":"RiverWare Daily Simulated values of Streamflow from 2006-2099: Louisiana"},"description":"Daily streamflow and reservoir water elevation data for modeled locations in the Red River Basin. Values reported are for 18 different GCM (Global Climate Model) / RCP (Representative Concentration Pathway) / GDM Downscaling scenarios. Climate data from each scenario was input into a Variable Infiltration Capacity (VIC) model, that output flow values. These values were then input into RiverWare, to determine the impacts on regulated flows, lake levels and water availability. RiverWare was used for this project, because of its ability to simulate water use, reservoir operations, and local/interstate regulations.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/66636a8e-dfb9-442a-9323-f82ccee8f2f0","harvest_record_raw":"https://catalog.data.gov/harvest_record/66636a8e-dfb9-442a-9323-f82ccee8f2f0/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57d84c15e4b090824ff9ac75","keyword":["Climate Change","Louisiana","Red River","RiverWare","USGS:57d84c15e4b090824ff9ac75","Water Availability","environment"],"last_harvested_date":"2026-09-27T00:32:44.735891","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"riverware-daily-simulated-values-of-streamflow-from-2006-2099-louisiana","spatial_centroid":{"lat":31.945100000000004,"lon":-92.91902},"spatial_shape":{"coordinates":[[[-94.0221,31.2497],[-94.0221,32.9882],[-91.2644,32.9882],[-91.2644,31.2497],[-94.0221,31.2497]]],"type":"Polygon"},"theme":["geospatial"],"title":"RiverWare Daily Simulated values of Streamflow from 2006-2099: Louisiana","type":"dataset"},{"_score":8.898771,"_sort":[1790469073996,8.898771,0,"877a9f1c-f576-4f80-abbc-67b5e25986c8"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Katherine Warnell","hasEmail":"mailto:katie.warnell@duke.edu"},"description":"Wild insect pollination has significant positive effects on pollinator-dependent crop production.  To assess the spatial distribution of potential wild insect pollination, we mapped the supply of potential wild pollinator habitat (forest, grassland, wetland, and shrubland land cover types) and the demand for pollination (directly pollinator-dependent crops).  A foraging travel distance for temperate native bees (1308 meters) was used to estimate relative pollinator activity on cropland based on distance from habitat.  We also calculated the proportion of pollinator habitat within pollinator travel distance of crops.  This information was summarized by county to identify regional priority areas for conservation and restoration of pollinator habitat.  This shapefile identifies regional conservation priority and restoration priority counties and includes information on the extent of pollinator-dependent crops, the mean relative pollinator activity on pollinator-dependent crops, and the proportion of pollinator habitat within pollinator flight distance of pollinator-dependent crops by county for the southeastern United States.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/69zz-7f78","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5d0a5f35e4b0e3d3115fba16.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0a5f35e4b0e3d3115fba16","keyword":["Alabama","Arkansas","Florida","Georgia","Louisiana","Mississippi","Missouri","North Carolina","South Carolina","Tennessee","USGS:5d0a5f35e4b0e3d3115fba16","agricultural sites","environment","farming","habitats","natural resource management","pollination","pollinators","remediation"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-95.8139, 23.8934, -74.5003, 40.6853","theme":["geospatial"],"title":"Conservation and Restoration Priorities for Wild Pollinator Habitat in the Southeast United States, by County (2011)"},"description":"Wild insect pollination has significant positive effects on pollinator-dependent crop production.  To assess the spatial distribution of potential wild insect pollination, we mapped the supply of potential wild pollinator habitat (forest, grassland, wetland, and shrubland land cover types) and the demand for pollination (directly pollinator-dependent crops).  A foraging travel distance for temperate native bees (1308 meters) was used to estimate relative pollinator activity on cropland based on distance from habitat.  We also calculated the proportion of pollinator habitat within pollinator travel distance of crops.  This information was summarized by county to identify regional priority areas for conservation and restoration of pollinator habitat.  This shapefile identifies regional conservation priority and restoration priority counties and includes information on the extent of pollinator-dependent crops, the mean relative pollinator activity on pollinator-dependent crops, and the proportion of pollinator habitat within pollinator flight distance of pollinator-dependent crops by county for the southeastern United States.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b9819478-2d30-4b33-9053-78fa2e1623b5","harvest_record_raw":"https://catalog.data.gov/harvest_record/b9819478-2d30-4b33-9053-78fa2e1623b5/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0a5f35e4b0e3d3115fba16","keyword":["Alabama","Arkansas","Florida","Georgia","Louisiana","Mississippi","Missouri","North Carolina","South Carolina","Tennessee","USGS:5d0a5f35e4b0e3d3115fba16","agricultural sites","environment","farming","habitats","natural resource management","pollination","pollinators","remediation"],"last_harvested_date":"2026-09-27T00:31:13.996338","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"conservation-and-restoration-priorities-for-wild-pollinator-habitat-in-the-southeast--2011","spatial_centroid":{"lat":30.610159999999997,"lon":-87.28846},"spatial_shape":{"coordinates":[[[-95.8139,23.8934],[-95.8139,40.6853],[-74.5003,40.6853],[-74.5003,23.8934],[-95.8139,23.8934]]],"type":"Polygon"},"theme":["geospatial"],"title":"Conservation and Restoration Priorities for Wild Pollinator Habitat in the Southeast United States, by County (2011)","type":"dataset"},{"_score":12.448154,"_sort":[1790468941104,12.448154,1,"ada40940-ab68-4634-bf7d-f710eea12ade"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Daniel M Wagner","hasEmail":"mailto:dwagner@usgs.gov"},"description":"This dataset contains site information and results of flood frequency analysis for 64 urban streamflow gaging stations (streamgages) operated by the U.S. Geological Survey (USGS) in Alabama and Mississippi. Site information and annual peak-flow data from the 1885 - 2024 water years were obtained from the USGS National Water Information System (NWIS) database (U.S. Geological Survey, 2025). Flood frequency analysis was conducted in version 8.0.0 of USGS PeakFQ software (Siefken and others, 2024) in the R environment (R Core Team, 2024) following the guidelines set forth in Bulletin 17C (England and others, 2018) and using the approaches described in the most recent flood frequency reports for Alabama and Mississippi (Anderson, 2020 and Anderson, 2018, respectively). Site information is provided in .csv format (\"AL_MS_UrbanFFreq_SiteInfo.csv\"). Peak-flow (\"AL_MS_UrbanFFreq_nwis_peak.txt\"), specification (\"AL_MS_UrbanFFreq.psf\"), and output files from PeakFQ (\"AL_MS_UrbanFFreq_emp.csv\", \"AL_MS_UrbanFFreq_lp3.csv\", \"AL_MS_UrbanFFreq_mgb.csv\", \"AL_MS_UrbanFFreq_qnt.csv\", and \"AL_MS_UrbanFFreq_trd.csv\") are provided.  Estimates of the 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 annual exceedance probabilities (the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively) are in file \"AL_MS_UrbanFFreq_qnt.csv\".\nThe data release was revised August 4, 2026.  Column \"index_no\" was added to file \"AL_MS_UrbanFFreq_SiteInfo.csv\" and a description of the column was added to the metadata.  The values in column \"index_no\" are used as the streamgage labels in Figure 1 in the related primary publication.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1JSTYKU","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.67ae1895d34e3f09c0e0f00c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_67ae1895d34e3f09c0e0f00c","keyword":["Alabama","Lower Mississippi Region - 2-digit Hydrologic Unit Code - 08","Mississippi","South-Atlantic-Gulf Region - 2-digit Hydrologic Unit Code - 03","Tennessee Region - 2-digit Hydrologic Unit Code - 06","USGS:67ae1895d34e3f09c0e0f00c","flood frequency","inlandWaters"],"modified":"2026-09-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-91.6550, 30.1741, -84.8882, 35.0080","theme":["geospatial"],"title":"At-site flood frequency estimates for 64 urban streamgages in Alabama and Mississippi using data through water year 2024 (ver. 2.0, August 2026)"},"description":"This dataset contains site information and results of flood frequency analysis for 64 urban streamflow gaging stations (streamgages) operated by the U.S. Geological Survey (USGS) in Alabama and Mississippi. Site information and annual peak-flow data from the 1885 - 2024 water years were obtained from the USGS National Water Information System (NWIS) database (U.S. Geological Survey, 2025). Flood frequency analysis was conducted in version 8.0.0 of USGS PeakFQ software (Siefken and others, 2024) in the R environment (R Core Team, 2024) following the guidelines set forth in Bulletin 17C (England and others, 2018) and using the approaches described in the most recent flood frequency reports for Alabama and Mississippi (Anderson, 2020 and Anderson, 2018, respectively). Site information is provided in .csv format (\"AL_MS_UrbanFFreq_SiteInfo.csv\"). Peak-flow (\"AL_MS_UrbanFFreq_nwis_peak.txt\"), specification (\"AL_MS_UrbanFFreq.psf\"), and output files from PeakFQ (\"AL_MS_UrbanFFreq_emp.csv\", \"AL_MS_UrbanFFreq_lp3.csv\", \"AL_MS_UrbanFFreq_mgb.csv\", \"AL_MS_UrbanFFreq_qnt.csv\", and \"AL_MS_UrbanFFreq_trd.csv\") are provided.  Estimates of the 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 annual exceedance probabilities (the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively) are in file \"AL_MS_UrbanFFreq_qnt.csv\".\nThe data release was revised August 4, 2026.  Column \"index_no\" was added to file \"AL_MS_UrbanFFreq_SiteInfo.csv\" and a description of the column was added to the metadata.  The values in column \"index_no\" are used as the streamgage labels in Figure 1 in the related primary publication.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6938ef00-96ac-4c1d-8399-61da90c5928e","harvest_record_raw":"https://catalog.data.gov/harvest_record/6938ef00-96ac-4c1d-8399-61da90c5928e/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_67ae1895d34e3f09c0e0f00c","keyword":["Alabama","Lower Mississippi Region - 2-digit Hydrologic Unit Code - 08","Mississippi","South-Atlantic-Gulf Region - 2-digit Hydrologic Unit Code - 03","Tennessee Region - 2-digit Hydrologic Unit Code - 06","USGS:67ae1895d34e3f09c0e0f00c","flood frequency","inlandWaters"],"last_harvested_date":"2026-09-27T00:29:01.104008","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"at-site-flood-frequency-estimates-for-64-urban-streamgages-in-alabama-and-mississippi-2024","spatial_centroid":{"lat":32.107659999999996,"lon":-88.94828},"spatial_shape":{"coordinates":[[[-91.655,30.1741],[-91.655,35.008],[-84.8882,35.008],[-84.8882,30.1741],[-91.655,30.1741]]],"type":"Polygon"},"theme":["geospatial"],"title":"At-site flood frequency estimates for 64 urban streamgages in Alabama and Mississippi using data through water year 2024 (ver. 2.0, August 2026)","type":"dataset"},{"_score":10.442551,"_sort":[1790468859579,10.442551,3,"6e8269e2-6c0a-4011-b743-7d854649c37a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jesslyn Brown","hasEmail":"mailto:casc-data@usgs.gov"},"description":"NASS USDA estimates the irrigated croplands at county level every five years. But this estimation does not provide the geospatial information of the irrigated croplands. To provide a comprehensive, consistent, and timely geospatially detailed information about irrigated cropland conterminous U.S. (CONUS), the \u201cModerate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US)\u201d product was produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center with funding from several USGS programs (National Land Imaging and National Water-Quality Assessment). A primary objective was to identify, and map irrigated agricultural areas to factor into water quality studies and drought monitoring investigations. This product uses three primary data inputs, (a) USDA county-level irrigation area statistics for 2002, (b) annual peak eMODIS Normalized Difference Vegetation Index (NDVI), and (c) a land cover mask for agricultural lands derived from NLCD to map the spatial distribution of irrigated lands across the conterminous United States. The MIrAD Version 4 offers the datasets for the years 2002, 2007, 2012, and 2017 at 250-m and 1-km spatial resolutions. The validation of MIrAD-US is a challenge because no other single-source current datasets are available at a national scale for comparison. Thus, this dataset should be considered provisional until a formal accuracy assessment can be completed. The product update is planned for every 5 years, synchronized with the update of the Census of Agriculture by the U.S Department of Agriculture (USDA) but contingent upon availability of Collection 6 (C6) Aqua eMODIS data and funding.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9NA3EO8","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5db08e84e4b0b0c58b56e04f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5db08e84e4b0b0c58b56e04f","keyword":["USGS:5db08e84e4b0b0c58b56e04f","agriculture","environment","farming","irrigation","multispectral imaging","remote sensing"],"modified":"2020-08-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-128.2624, 22.6867, -65.6851, 51.5890","theme":["geospatial"],"title":"Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Datasets for the Conterminous United States (MIrAD-US)"},"description":"NASS USDA estimates the irrigated croplands at county level every five years. But this estimation does not provide the geospatial information of the irrigated croplands. To provide a comprehensive, consistent, and timely geospatially detailed information about irrigated cropland conterminous U.S. (CONUS), the \u201cModerate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US)\u201d product was produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center with funding from several USGS programs (National Land Imaging and National Water-Quality Assessment). A primary objective was to identify, and map irrigated agricultural areas to factor into water quality studies and drought monitoring investigations. This product uses three primary data inputs, (a) USDA county-level irrigation area statistics for 2002, (b) annual peak eMODIS Normalized Difference Vegetation Index (NDVI), and (c) a land cover mask for agricultural lands derived from NLCD to map the spatial distribution of irrigated lands across the conterminous United States. The MIrAD Version 4 offers the datasets for the years 2002, 2007, 2012, and 2017 at 250-m and 1-km spatial resolutions. The validation of MIrAD-US is a challenge because no other single-source current datasets are available at a national scale for comparison. Thus, this dataset should be considered provisional until a formal accuracy assessment can be completed. 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Although ample literature exists on the climatological aspects of drought, little is known on whether existing drought indices can predict the damages and how different human communities respond and adapt to the hazard. This project examines (1) whether existing drought indices can predict the occurrence of drought events and their actual damages; (2) how the adaptive capacity (i.e., resilience) varies across space; and (3) what public outreach and engagement effort would be most effective for mitigation of risk and impacts. The study region includes all 503 counties in Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. This data set was created to examine the first objective of the project. The Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI) data, available only at the climate-division level, were downscaled into county-level indices over the 1975-2010 period. The drought damage data, acquired from the Spatial Hazards Events and Losses Database for the United States (SHELDUSTM), were tabulated for the same time period. Statistical correlations were conducted between drought indices and drought damages to test whether these indices accurately represent the drought damage in the study region. This data set contains the two county-level drought indices and drought damage for the period 1975-2010, which should be useful to future related studies.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/C9X59C","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.582a1085e4b01fad8725485c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_582a1085e4b01fad8725485c","keyword":["Arkansas","Drought damage","Louisiana","New Mexico","Oklahoma","Palmer Drought Severity Index","Palmer Hydrologic Drought Index","SHELDUS","Statistical Downscaling","Texas","USGS:582a1085e4b01fad8725485c","droughts","external research support","modeling"],"modified":"2026-09-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.0513, 37.0015, -89.0218, 25.8456","theme":["geospatial"],"title":"Downscaled Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI) for 503 counties in South Central United States"},"description":"Drought is a natural hazard that inflicts costly damage to the environment and human communities. Although ample literature exists on the climatological aspects of drought, little is known on whether existing drought indices can predict the damages and how different human communities respond and adapt to the hazard. This project examines (1) whether existing drought indices can predict the occurrence of drought events and their actual damages; (2) how the adaptive capacity (i.e., resilience) varies across space; and (3) what public outreach and engagement effort would be most effective for mitigation of risk and impacts. The study region includes all 503 counties in Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. This data set was created to examine the first objective of the project. The Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI) data, available only at the climate-division level, were downscaled into county-level indices over the 1975-2010 period. The drought damage data, acquired from the Spatial Hazards Events and Losses Database for the United States (SHELDUSTM), were tabulated for the same time period. Statistical correlations were conducted between drought indices and drought damages to test whether these indices accurately represent the drought damage in the study region. 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Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. 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The cumulative current map shows where potential movement pathways between open habitat are located within the pilot area, while the normalized current map shows where obstructed movement, diffuse movement, and channelized movement occurs within the pilot area. These maps provide important information on how restoration efforts in the pilot area from both FY21, FY22, FY23, and FY24 are affecting open habitat connectivity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1V7S2J9","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a3e97431ba49ba6dca26eb6.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a3e97431ba49ba6dca26eb6","keyword":["Connectivity","Great Lakes","Keweenaw Peninsula","Lake Superior","Open Habitat","USGS:6a3e97431ba49ba6dca26eb6","biota","dispersal (organisms)","ecology","environment","habitat fragmentation","imageryBaseMapsEarthCover","land use change","remote sensing","structure"],"modified":"2026-09-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-93.4094, 44.6675, -85.8861, 48.7713","theme":["geospatial"],"title":"Great Lakes Restoration Initiative's Terrestrial Habitats & Connectivity Work Group's Pilot Area's Post-Fiscal Year 2025 Open Habitat Connectivity"},"description":"This dataset is the sixth installment of a yearly connectivity update for open habitat within the Great Lakes Restoration Initiative's (GLRI) Terrestrial Habitats & Connectivity (TH&C) work group's Pilot Area. 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The 2025 open habitat connectivity maps serves as an additional time step for comparison for each fiscal year from pre 2021 into the future.  \nTo create the post FY25 installment of GLRI's Terrestrial Habitats & Connectivity work group's pilot area yearly connectivity maps, we identified the geospatial locations where restoration work benefitting open habitats was completed throughout FY25. These locations were then assigned the lowest resistance value. Then using the open habitat resistance layer from Great Lakes Restoration Initiative's Terrestrial Habitats and Connectivity Work group's Pilot Area's post-fiscal year 2024 we overwrote the post FY24 resistance values with the resistance values assigned to the FY25 locations where work was done. This analysis produced two connectivity maps: a cumulative current map and a normalized current map. The cumulative current map shows where potential movement pathways between open habitat are located within the pilot area, while the normalized current map shows where obstructed movement, diffuse movement, and channelized movement occurs within the pilot area. These maps provide important information on how restoration efforts in the pilot area from both FY21, FY22, FY23, and FY24 are affecting open habitat connectivity.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d54c5ed6-1359-45cb-82fa-3e1380bfb988","harvest_record_raw":"https://catalog.data.gov/harvest_record/d54c5ed6-1359-45cb-82fa-3e1380bfb988/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a3e97431ba49ba6dca26eb6","keyword":["Connectivity","Great Lakes","Keweenaw Peninsula","Lake Superior","Open Habitat","USGS:6a3e97431ba49ba6dca26eb6","biota","dispersal (organisms)","ecology","environment","habitat fragmentation","imageryBaseMapsEarthCover","land use change","remote sensing","structure"],"last_harvested_date":"2026-09-27T00:16:52.146733","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"great-lakes-restoration-initiatives-terrestrial-habitats-amp-connectivity-work-groups-pilo-4ab10","spatial_centroid":{"lat":46.30902,"lon":-90.40008},"spatial_shape":{"coordinates":[[[-93.4094,44.6675],[-93.4094,48.7713],[-85.8861,48.7713],[-85.8861,44.6675],[-93.4094,44.6675]]],"type":"Polygon"},"theme":["geospatial"],"title":"Great Lakes Restoration Initiative's Terrestrial Habitats & Connectivity Work Group's Pilot Area's Post-Fiscal Year 2025 Open Habitat Connectivity","type":"dataset"},{"_score":12.437417,"_sort":[1790468012637,12.437417,6,"46f7c7a7-7371-48b4-afd7-c9b6214c425e"],"dcat":{"@type":"dcat:Dataset","accessLevel":"non-public","contactPoint":{"@type":"vcard:Contact","fn":"NOAA National Centers for Environmental Information","hasEmail":"mailto:ncei.info@noaa.gov"},"describedByType":"application/octet-stream","description":"This dataset includes physical, chemical, meteorological, and biological data, including zooplankton abundance, primary production, carotenoids, phaeophytin, fluorescence, phaeopigments, fatty acids, chlorophylls, aggregates and particulate matter, \"marine snow\", thymidine and leucine uptake, nitrate and saturated ammonium uptake rates, oxygen production, irradiance, Lu683, PAR, Thorium-234 activity, chemical concentrations, light attenuation and transmission, water temperature, currents, salinity, and other data.\n\nThese data were collected during the Joint Global Ocean Flux Study (JGOFS) Arabian Sea Process Study between 1992 and 1997, the Equatorial Pacific (EqPac) Process Study along 140\u00b0W during 1992, the Antarctic Environment and Southern Ocean Process Study (AESOPS), 1996 to 1998, and the North Atlantic Bloom Experiment (NABE) April through July 1989, as well as the Bermuda Atlantic Time Series (BATS) study which began in 1989. This dataset also includes data from other related JGOFS Antarctic and Southern Ocean expeditions, including the Antarctic Polar Frontal Zone (APFZ) cruises in 1997 and 1998, the ANT X/6 expedition and Oregon State University cruises on POLAR DUKE between 1990 and 1995. As an extra added bonus, this dataset also includes the North Pacific Process Study (NPPS) CD-ROM that was produced by the Japan Oceanographic Data Center (JODC).\n\nThe U.S. Joint Global Ocean Flux Study (JGOFS) Equatorial Pacific (EqPac) process study was conducted along 140\u00b0W during the calendar year 1992. 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GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). 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THOMPSON","R/V WECOMA","ROVER","RV Alpha Helix","RV Nathaniel B. Palmer","RV Polarstern","SAGAR KANYA","WEATHERBIRD II","Bermuda Biological Station for Research","Bermuda Institute of Ocean Sciences","Bigelow Laboratory for Ocean Sciences","Duke University","Fish Research Institute","Gulf Coast Research Laboratory","Harvard University","Horn Point Laboratory","Lamont-Doherty Earth Observatory","Louisiana Universities Marine Consortium","Moss Landing Marine Laboratories","NASA Goddard Space Flight Center","National Institute of Oceanography - India","North Carolina State University","Old Dominion University","Oregon State University","Oregon State University, College of Earth, Ocean, and Atmospheric Sciences","Royal Netherlands Institute for Sea Research","San Diego State University","San Francisco State University","Scripps Institution of Oceanography","Skidaway Institute of Oceanography","State University of New York at Stony Brook","Texas A&M University","The College of William and Mary","The University of North Carolina at Chapel Hill","University of Bergen","University of California - Santa Cruz","University of Connecticut, Marine Research Laboratory","University of Delaware, College of Earth, Ocean, and Environment, School of Marine Science and Policy","University of Georgia, School of Marine Programs","University of Hawai\u02bbi at M\u0101noa","University of Miami Rosenstiel School of Marine and Atmospheric Science","University of Minnesota - Duluth, Large Lakes Observatory","University of Rhode Island, Graduate School of Oceanography","University of Southern California","University of Tennessee","University of Washington","US NASA/Jet Propulsion Laboratory","US National Aeronautic and Space Administration","Virginia Institute of Marine Science","Woods Hole Oceanographic Institution","Bermuda Institute of Ocean Sciences","Bigelow Laboratory for Ocean Sciences","Duke University","Gulf Coast Research Laboratory","Harvard University","Horn Point Laboratory","Japan Oceanographic Data Center","Lamont-Doherty Earth Observatory","Louisiana Universities Marine Consortium","Moss Landing Marine Laboratories","NASA Goddard Space Flight Center","National Institute of Oceanography - India","North Carolina State University","Old Dominion University","Oregon State University","Oregon State University, College of Earth, Ocean, and Atmospheric Sciences","Royal Netherlands Institute for Sea Research","San Diego State University","San Francisco State University","Scripps Institution of Oceanography","Skidaway Institute of Oceanography","State University of New York at Stony Brook","Texas A&M University","University of Bergen","University of California - Santa Cruz","University of Connecticut, Marine Research Laboratory","University of Delaware, College of Earth, Ocean, and Environment, School of Marine Science and Policy","University of Georgia, School of Marine Programs","University of Hawai\u02bbi at M\u0101noa","University of Miami Rosenstiel School of Marine and Atmospheric Science","University of Rhode Island, Graduate School of Oceanography","University of Southern California","University of Washington","US NASA/Jet Propulsion Laboratory","US National Aeronautic and Space Administration","Virginia Institute of Marine Science","Woods Hole Oceanographic Institution","BERMUDA ATLANTIC TIME SERIES (BATS)","Hydrostation S","JOINT GLOBAL OCEAN FLUX STUDY (JGOFS)","Joint Global Ocean Flux Study - North Atlantic Bloom Experiment (JGOFS/NABE)","Joint Global Ocean Flux Study / Arabian Sea Process Study (JGOFS/Arabian Sea)","JOINT GLOBAL OCEAN FLUX STUDY/EQUATORIAL PACIFIC BASIN STUDY (JGOFS/EQPAC)","Joint Global Ocean Flux Study: Hawaii Ocean Time-series (JGOFS HOT)","US JGOFS ANTARCTIC ENVIRONMENT AND SOUTHERN OCEAN PROCESS STUDY (JGOFS/AESOPS)","Arabian Sea","Equatorial Pacific Ocean","Gulf of Oman","Indian Ocean","Laccadive Sea","North Atlantic Ocean","North Pacific Ocean","Northwest Atlantic Ocean (limit-40 W)","Ross Sea","South Atlantic Ocean","South Pacific Ocean","Southeast Atlantic Ocean (limit-20 W)","Southern Ocean","TOGA Area - Pacific (30 N to 30 S)","oceanography","BBSR > Bermuda Biological Station for Research","BIGELOW > Bigelow Laboratory of Ocean Sciences","COLUMBIA/LDEO > Lamont-Doherty Earth Observatory, Columbia University","CWM/VIMS > Virginia Institute of Marine Science, College of William and Mary","DOC/NOAA/NESDIS/NODC > National Oceanographic Data Center, NESDIS, NOAA, U.S. Department of Commerce","IN/NIO > National Institute of Oceanography, India","JP/JODC > Japan Oceanographic Data Center","Jet Propulsion Laboratory","MLML > Moss Landing Marine Laboratories","NL/NWO/NIOZ > Royal Netherlands Institute for Sea Research (NIOZ)","OR-STATE/CEOAS > College of Earth, Ocean, and Atmospheric Sciences, Oregon State University","U-MIAMI/RSMAS > Rosenstiel School of Marine and Atmospheric Science, University of Miami","UGA/SkIO > Skidaway Institute of Oceanography, University of Georgia","UMCES/HPL > Horns Point Laboratory, University of Maryland Center for Environmental Science","URI/GSO > Graduate School of Oceanography, University of Rhode Island","WHOI > WOODS HOLE OCEANOGRAPHIC INSTITUTION","NBP96-4A","NBP9604","NBP965","NBP97-1","NBP97-3","NBP97-8","NBP98-2","RR_KIWI07","RR_KIWI08","RR_KIWI09","RR_KIWI6","TTN007","TTN008","TTN011","TTN012","TTN013","TTN043","TTN045","TTN049","TTN050","TTN053","TTN054","AESOPS > U.S. JGOFS Antarctic Environment and Southern Ocean Process Study","HOT > Hawaiian Ocean Time Series Project","JGOFS > Joint Global Ocean Flux Study, IGBP","EARTH SCIENCE > AGRICULTURE > AGRICULTURAL AQUATIC SCIENCES > FISHERIES","EARTH SCIENCE > ATMOSPHERE > AEROSOLS > AEROSOL OPTICAL DEPTH/THICKNESS","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY > CARBON AND HYDROCARBON COMPOUNDS > ATMOSPHERIC CARBON DIOXIDE > PARTIAL PRESSURE OF CARBON DIOXIDE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC PRESSURE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION > INCOMING SOLAR RADIATION","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > AIR TEMPERATURE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND DIRECTION","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND SPEED","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > BACTERIA/ARCHAEA","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > BACTERIA/ARCHAEA > CYANOBACTERIA (BLUE-GREEN ALGAE)","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PLANTS > MICROALGAE > DIATOMS","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PROTISTS > PLANKTON","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PROTISTS > PLANKTON > PHYTOPLANKTON","EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > PHOTOSYNTHESIS","EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > PRIMARY PRODUCTION","EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > RESPIRATION RATE","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > AQUATIC ECOSYSTEMS > PLANKTON","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > AQUATIC ECOSYSTEMS > PLANKTON > ZOOPLANKTON","EARTH SCIENCE > BIOSPHERE > VEGETATION > BIOMASS","EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > WATER DEPTH","EARTH SCIENCE > OCEANS > MARINE SEDIMENTS","EARTH SCIENCE > OCEANS > OCEAN ACOUSTICS > ACOUSTIC VELOCITY","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ALKALINITY","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > AMMONIA","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CARBON","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CARBON DIOXIDE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CHLOROPHYLL","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > HYDROCARBONS","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > INORGANIC CARBON","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > NITRATE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > NITRITE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > NITROGEN","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > NUTRIENTS","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ORGANIC CARBON","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ORGANIC MATTER","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > OXYGEN","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PH","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PHOSPHATE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PIGMENTS","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PIGMENTS > CHLOROPHYLL","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > SILICATE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > SUSPENDED SOLIDS","EARTH SCIENCE > OCEANS > OCEAN CIRCULATION > OCEAN CURRENTS","EARTH SCIENCE > OCEANS > OCEAN OPTICS","EARTH SCIENCE > OCEANS > OCEAN OPTICS > ATTENUATION/TRANSMISSION","EARTH SCIENCE > OCEANS > OCEAN OPTICS > FLUORESCENCE","EARTH SCIENCE > OCEANS > OCEAN OPTICS > IRRADIANCE","EARTH SCIENCE > OCEANS > OCEAN OPTICS > PHOTOSYNTHETICALLY ACTIVE RADIATION","EARTH SCIENCE > OCEANS > OCEAN OPTICS > SECCHI DEPTH","EARTH SCIENCE > OCEANS > OCEAN OPTICS > TURBIDITY","EARTH SCIENCE > OCEANS > OCEAN PRESSURE > WATER PRESSURE","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > POTENTIAL TEMPERATURE","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > SEA SURFACE TEMPERATURE","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE","EARTH SCIENCE > OCEANS > OCEAN WAVES > SEA STATE","EARTH SCIENCE > OCEANS > OCEAN WAVES > WAVE HEIGHT","EARTH SCIENCE > OCEANS > OCEAN WAVES > WAVE PERIOD","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > CONDUCTIVITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > DENSITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY","EARTH SCIENCE > OCEANS > TIDES","EARTH SCIENCE > OCEANS > TIDES > TIDAL HEIGHT","19'-butanoyloxyfucoxanthin","19'-hexanoyloxyfucoxanthin","alloxanthin","alpha-carotene","beta-carotene","chlorophyll a","chlorophyll b","chlorophyll c3","chlorophyllide a","cis-19'-hexanoyloxyfucoxanthin","cis-fucoxanthin","diadinoxanthin","diatoxanthin","divinyl chlorophyll a","divinyl chlorophyll b","fucoxanthin","lutein","monovinyl chlorophyll a","monovinyl chlorophyll b","peridinin","phaeopigments","prasinoxanthin","violaxanthin","zeaxanthin","BATHYTHERMOGRAPHS","CAMERA > CAMERA","CTD > Conductivity, Temperature, Depth","CURRENT METERS","DRIFTING BUOYS > DRIFTING BUOYS","FLOW CYTOMETRY","FLUORESCENCE MICROSCOPY > FLUORESCENCE MICROSCOPY","FLUOROMETERS > FLUOROMETERS","GAS CHROMATOGRAPHS > GAS CHROMATOGRAPHS","GRAB SAMPLERS","MASS SPECTROMETERS > MASS SPECTROMETERS","MICROSCOPES > MICROSCOPES","MOCNESS > MOCNESS Plankton Net","NETS > NETS","NISKIN BOTTLES","OPTSPEC > Optical Spectrometer","OXYGEN METERS > OXYGEN METERS","PAR SENSORS > Photosynthetically Active Radiation Sensors","PH METERS > PH METERS","PLANKTON NETS","RADIOMETERS","SALINOMETERS","SECCHI DISKS > SECCHI DISKS","SEDIMENT CORERS > SEDIMENT CORERS","THERMOMETERS > THERMOMETERS","TIDE GAUGES > TIDE GAUGES","TRANSMISSOMETERS","TRAPS > TRAPS","VIDEO CAMERA > VIDEO CAMERA","XBT > Expendable Bathythermographs","FIXED OBSERVATION STATIONS > FIXED OBSERVATION STATIONS","Ships","ALPHA HELIX (call sign: WSD7078, ICES code: 31HX, 1966-)","ATLANTIS II (call sign: KADC, ICES code: 31AN, 1963-1996)","CAPE HATTERAS (call sign: WRZ8934, ICES code: 32KZ, 1981-)","CAPE HENLOPEN (ICES code: 32CW)","CROMWELL (ICES code: 31WM)","ENDEAVOR (call sign: WAUW, ICES code: 33EN)","ENDEAVOR (call sign: WCE5063, ICES code: 32EV, 1976-)","MOANA WAVE (call sign: WUS9293, ICES code: 32MW, 1974-2011)","NA'INA (ICES code: 33NA)","NATHANIEL B. PALMER (call sign: WPB3210, ICES code: 3206, 1992-)","NEW HORIZON (call sign: WKWB, ICES code: 32NM, 1978-2015)","POLAR DUKE (call sign: WCX7445, ICES code: 33PD, 1984-1988)","Polarstern (call sign: DBLK, ICES code: 06AQ, 1982-)","ROGER REVELLE (call sign: KAOU, ICES code: 33RR, 1996-)","ROVER (ICES code: 32YS)","SAGAR KANYA (call sign: VTJR, ICES code: 41SG)","THOMAS G. THOMPSON (call sign: KTDQ, ICES code: 3250, 1991-)","WEATHERBIRD (call sign: AGOR, ICES code: 320G)","WECOMA (call sign: WSD7079, ICES code: 32WC, 1976-)","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN","OCEAN > ATLANTIC OCEAN > SOUTH ATLANTIC OCEAN","OCEAN > INDIAN OCEAN","OCEAN > INDIAN OCEAN > ARABIAN SEA","OCEAN > PACIFIC OCEAN","OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN","OCEAN > PACIFIC OCEAN > SOUTH PACIFIC OCEAN","OCEAN > SOUTHERN OCEAN","OCEAN > SOUTHERN OCEAN > ROSS SEA"],"landingPage":"https://www.ncei.noaa.gov/contact","language":[],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2026-03-23T00:00:00.000+00:00","publisher":{"@type":"org:Organization","name":"NOAA National Centers for Environmental Information"},"references":["https://usjgofs.whoi.edu/research/index.html","https://doi.org/10.1016/s0967-0645(00)00059-x","https://doi.org/10.1016/0967-0645(96)00004-5","https://discover.library.noaa.gov/permalink/01NOAA_INST/1qbesct/alma991001196289707381","https://discover.library.noaa.gov/permalink/01NOAA_INST/1qbesct/alma991000435089707381","https://doi.org/10.5670/oceanog.1992.01","https://www.ncei.noaa.gov/archive/accession/Station-ALOHA-HOT","https://www.ncei.noaa.gov/archive/accession/BIOS-Hydrostation_S","https://www.ncei.noaa.gov/archive/accession/0291424","https://www.ncei.noaa.gov/archive/accession/0299432","https://www.ncei.noaa.gov/archive/accession/0299443","https://www.ncei.noaa.gov/archive/accession/0299541","https://www.ncei.noaa.gov/archive/accession/0299425","https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/Coastal/BATS.html"],"rights":"otherRestrictions","spatial":"87.97,-78.05,-179.684,66.0","temporal":"1968-01-01T00:00:00+00:00/2003-01-01T00:00:00+00:00","theme":["geospatial"],"title":"Data from the Joint Global Ocean Flux Study (JGOFS) Time Series and Process Studies, 1992 to 1997"},"description":"This dataset includes physical, chemical, meteorological, and biological data, including zooplankton abundance, primary production, carotenoids, phaeophytin, fluorescence, phaeopigments, fatty acids, chlorophylls, aggregates and particulate matter, \"marine snow\", thymidine and leucine uptake, nitrate and saturated ammonium uptake rates, oxygen production, irradiance, Lu683, PAR, Thorium-234 activity, chemical concentrations, light attenuation and transmission, water temperature, currents, salinity, and other data.\n\nThese data were collected during the Joint Global Ocean Flux Study (JGOFS) Arabian Sea Process Study between 1992 and 1997, the Equatorial Pacific (EqPac) Process Study along 140\u00b0W during 1992, the Antarctic Environment and Southern Ocean Process Study (AESOPS), 1996 to 1998, and the North Atlantic Bloom Experiment (NABE) April through July 1989, as well as the Bermuda Atlantic Time Series (BATS) study which began in 1989. This dataset also includes data from other related JGOFS Antarctic and Southern Ocean expeditions, including the Antarctic Polar Frontal Zone (APFZ) cruises in 1997 and 1998, the ANT X/6 expedition and Oregon State University cruises on POLAR DUKE between 1990 and 1995. As an extra added bonus, this dataset also includes the North Pacific Process Study (NPPS) CD-ROM that was produced by the Japan Oceanographic Data Center (JODC).\n\nThe U.S. Joint Global Ocean Flux Study (JGOFS) Equatorial Pacific (EqPac) process study was conducted along 140\u00b0W during the calendar year 1992. Four process cruises took place, with a fifth benthic cruise and sediment trap legs adding to the overall study.\n\nMost Hawaii Ocean Times Series (HOT) data are available separately, although some HOT data are included in this dataset.","distribution_titles":["NCEI Dataset Landing Page","Browse granules","Search for granules","https://usjgofs.whoi.edu/research/index.html","https://doi.org/10.1016/s0967-0645(00)00059-x","https://doi.org/10.1016/0967-0645(96)00004-5","https://discover.library.noaa.gov/permalink/01NOAA_INST/1qbesct/alma991001196289707381","https://discover.library.noaa.gov/permalink/01NOAA_INST/1qbesct/alma991000435089707381","https://doi.org/10.5670/oceanog.1992.01","https://www.ncei.noaa.gov/archive/accession/Station-ALOHA-HOT","https://www.ncei.noaa.gov/archive/accession/BIOS-Hydrostation_S","https://www.ncei.noaa.gov/archive/accession/0291424","https://www.ncei.noaa.gov/archive/accession/0299432","https://www.ncei.noaa.gov/archive/accession/0299443","https://www.ncei.noaa.gov/archive/accession/0299541","https://www.ncei.noaa.gov/archive/accession/0299425","https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/Coastal/BATS.html","GCMD Keyword Forum Page","NCEI Contact Information"],"harvest_record":"https://catalog.data.gov/harvest_record/8b65680f-6215-4e01-bce0-3746645bf8b5","harvest_record_raw":"https://catalog.data.gov/harvest_record/8b65680f-6215-4e01-bce0-3746645bf8b5/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/8b65680f-6215-4e01-bce0-3746645bf8b5/transformed","has_download":true,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/NESDIS/ncei/accessions/iso/xml/US-JGOFS-Program.xml","keyword":["0000145","0000285","0000407","0000463","0000498","0000499","0000504","0000519","0000523","0000562","0000887","0000888","0000889","0000890","0000894","0000895","0000896","0000897","0000898","0000899","0000947","0000990","0001155","0001873","0002369","9100172","9200023","9200088","9200089","9200160","9500082","9500091","9600065","9600091","9600115","9600118","9700034","9700039","9700048","9700049","9700050","9700052","9700053","9700054","9700055","9700056","9700058","9700059","9700062","9700068","9700083","9700085","9700086","9700087","9700088","9700106","9700107","9700108","9700109","9700110","9700112","9700115","9700116","9700177","9700178","9700180","9700194","9700195","9700196","9700197","9700205","9700206","9700207","9700208","9700209","9700210","9700221","9800020","9800030","9800032","9800033","9800037","9800042","9800055","9800061","9800071","9800072","9800073","9800077","9800079","9800085","9800086","9800088","9800092","9800093","9800095","9800109","9800116","9800120","9800127","9800128","9800131","9800132","9800154","9800155","9800158","9800160","9800161","9800166","9800183","9800186","9800188","9800200","9900010","9900014","9900051","9900063","9900066","9900067","9900090","9900097","9900106","9900113","9900135","9900143","9900146","9900154","9900162","9900163","9900164","9900167","9900168","9900177","9900183","9900185","9900192","9900196","9900217","9900218","9900219","9900239","AEROSOL OPTICAL THICKNESS","AIR TEMPERATURE","AIR TEMPERATURE - WET BULB","alloxan","Aluminum","AMMONIA (NH3)","AMMONIUM (NH4)","Attenuation/Transmission","bacteria","BACTERIA - BACTERIAL DENSITY","bacteria - cyanobacteria","BACTERIA - HETEROTROPHIC","BACTERIA - PLATE COUNTS","BAROMETRIC PRESSURE","BEAM ATTENUATION COEFFICIENT","biological data","BIOMASS","BIOMASS - PLANKTON","CARBON","CARBON - ORGANIC - SEDIMENT","CARBON - TOTAL ORGANIC","CARBON ASSIMILATION","CAROTENOIDS - PLANT","CAS (CHEMICAL ABSTRACTS SERVICE) PARAMETER CODES","CELL COUNTS","CHLOROFLUOROCARBON-11 (CFC-11)","CHLOROFLUOROCARBON-12 (CFC-12)","CHLOROPHYLL","CHLOROPHYLL A","CHLOROPHYLL A CONCENTRATION","CHLOROPHYLL B","CHLOROPHYLL C","CONDUCTIVITY","CURRENT DIRECTION","CURRENT METER - EAST-WEST COMPONENT (U)","CURRENT METER - NORTH-SOUTH COMPONENT (V)","CURRENT SPEED","CURRENT SPEED - EAST/WEST COMPONENT (U)","CURRENT SPEED - NORTH/SOUTH COMPONENT (V)","DEPTH - OBSERVATION","DEPTH - SENSOR","Diadinoxanthin","diatom","DISSOLVED INORGANIC CARBON (DIC)","DISSOLVED ORGANIC CARBON","DISSOLVED ORGANIC MATTER","DISSOLVED OXYGEN","DOWNWELLING RADIANCE","DYNAMIC DEPTH ANOMALY","Eukaryote","FISHERY SURVEY","FLUORESCENCE","fucoxanthin","HELIUM","HYDROCARBONS - TOTAL RESOLVED","HYDROSTATIC PRESSURE","Iron","irradiance","leucine","LIGHT - PERCENT","LIGHT ATTENUATION","LIGHT TRANSMISSION","MANGANESE","METALS","MICROPLANKTON","NANOPLANKTON","NITRATE","nitrate + nitrite content (concentration)","NITRITE","NITROGEN","NITROGEN - PARTICULATE","NUTRIENTS","OPTICAL BACKSCATTER","OXYGEN","Partial pressure (or fugacity) of carbon dioxide - atmosphere","partial pressure of carbon dioxide - water","PARTICULATE CARBON","Particulate Inorganic Carbon","PARTICULATE MATTER - DRY WEIGHT","PARTICULATE ORGANIC CARBON","PARTICULATE ORGANIC NITROGEN (PON)","pH","PHAEOPHYTIN","PHAEOPIGMENT CONCENTRATION","PHAEOPIGMENTS","phosphate","PHOSPHATE - TOTAL","PHOTOSYNTHETIC ACTIVE RADIATION (PAR)","PHOTOSYNTHETIC CAPACITY - EXPERIMENT ELAPSED TIME","PHYTOPLANKTON","PHYTOPLANKTON - DRY WEIGHT","PHYTOPLANKTON - WET WEIGHT","PHYTOPLANKTON BIOMASS","PIGMENTS","POLYCHLORINATED BIPHENYLS","POLYNUCLEAR AROMATIC HYDROCARBONS (PAH)","Potential temperature (theta)","PRIMARY PRODUCTIVITY","PRIMARY PRODUCTIVITY - PHYTOPLANKTON","Prochlorococcus","RADIATION - TOTAL INCIDENT","RADIUM ISOTOPES","respiration rates","SALINITY","SEA STATE","SEA SURFACE TEMPERATURE","Secchi depth","SEDIMENT PROPERTIES","SEDIMENTS","SEDIMENTS - DRY WEIGHT","SEDIMENTS - PARTICLE SIZE FRACTIONS","SIGMA-T","SIGMA-THETA","silicate","Silicon","SOLAR RADIATION - ATMOSPHERIC","SOUND VELOCITY","species abundance","SPECIES IDENTIFICATION - WET WEIGHT","surface irradiance","suspended solids","Synechococcus","thorium isotope","thymidine","TIDE HEIGHT","TIDE STAGE","total alkalinity","TOTAL CARBON","Total nitrogen","TOTAL PHOSPHATE","TOTAL PHOSPHORUS","TRACE METALS","Tritium (Hydrogen isotope)","turbidity","UPWELLING IRRADIANCE","UPWELLING RADIANCE","UREA","Violaxanthin","WATER DENSITY","water depth","WATER TEMPERATURE","WAVE HEIGHT","WAVE PERIOD","WEATHER","WIND DIRECTION","WIND SPEED","zinc","ZOOPLANKTON","ZOOPLANKTON ABUNDANCE","ZOOPLANKTON BIOMASS","ZOOPLANKTON IDENTITIES","ZOOPLANKTON SPECIES IDENTITIES","ZOOPLANKTON SPECIES NUMBER PER SAMPLE","bathythermograph - AXBT","bathythermograph - BT","bathythermograph - XBT","bottle","buoy - drifting buoy","camera","chromatograph","CTD","CTD - moored CTD","current meter","Flow Cytometer","fluorescence microscope","fluorometer","irradiance detector","laboratory analysis","mass spectrometer","meteorological sensor","microscope","Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS)","net","net - plankton net","net - zooplankton net","Niskin bottle","optical backscatter sensor","oxygen sensor","PAR Sensor","pH sensor","pump casts","radiometer","salinometer","SeaSoar","Secchi disk","sediment sampler - corer","sediment sampler - grab","spectrophotometer","surface seawater intake","thermometer","tide gauge","transmissometer","trap","trap - sediment","video - LAPS - autonomous video camera","visual analysis","benthic","biological","chemical","continuous","current measurements","geological","laboratory analyses","meteorological","optical","physical","plankton","profile","pump cast","surface measurements","time series","tows","tows - plankton tows","tows - undulating tow","underway","water chemistry","AIRCRAFT","ATLANTIS II","CAPE HATTERAS","CAPE HENLOPEN","CROMWELL","ENDEAVOR","ENDEAVOR","FIXED PLATFORM OF UNITED STATES","MOANA WAVE","MULTIPLE SHIPS OF NORWAY","NA'INA","NEW HORIZON","R/V Polar Duke","R/V Roger Revelle","R/V THOMAS G. THOMPSON","R/V WECOMA","ROVER","RV Alpha Helix","RV Nathaniel B. Palmer","RV Polarstern","SAGAR KANYA","WEATHERBIRD II","Bermuda Biological Station for Research","Bermuda Institute of Ocean Sciences","Bigelow Laboratory for Ocean Sciences","Duke University","Fish Research Institute","Gulf Coast Research Laboratory","Harvard University","Horn Point Laboratory","Lamont-Doherty Earth Observatory","Louisiana Universities Marine Consortium","Moss Landing Marine Laboratories","NASA Goddard Space Flight Center","National Institute of Oceanography - India","North Carolina State University","Old Dominion University","Oregon State University","Oregon State University, College of Earth, Ocean, and Atmospheric Sciences","Royal Netherlands Institute for Sea Research","San Diego State University","San Francisco State University","Scripps Institution of Oceanography","Skidaway Institute of Oceanography","State University of New York at Stony Brook","Texas A&M University","The College of William and Mary","The University of North Carolina at Chapel Hill","University of Bergen","University of California - Santa Cruz","University of Connecticut, Marine Research Laboratory","University of Delaware, College of Earth, Ocean, and Environment, School of Marine Science and Policy","University of Georgia, School of Marine Programs","University of Hawai\u02bbi at M\u0101noa","University of Miami Rosenstiel School of Marine and Atmospheric Science","University of Minnesota - Duluth, Large Lakes Observatory","University of Rhode Island, Graduate School of Oceanography","University of Southern California","University of Tennessee","University of Washington","US NASA/Jet Propulsion Laboratory","US National Aeronautic and Space Administration","Virginia Institute of Marine Science","Woods Hole Oceanographic Institution","Bermuda Institute of Ocean Sciences","Bigelow Laboratory for Ocean Sciences","Duke University","Gulf Coast Research Laboratory","Harvard University","Horn Point Laboratory","Japan Oceanographic Data Center","Lamont-Doherty Earth Observatory","Louisiana Universities Marine Consortium","Moss Landing Marine Laboratories","NASA Goddard Space Flight Center","National Institute of Oceanography - India","North Carolina State University","Old Dominion University","Oregon State University","Oregon State University, College of Earth, Ocean, and Atmospheric Sciences","Royal Netherlands Institute for Sea Research","San Diego State University","San Francisco State University","Scripps Institution of Oceanography","Skidaway Institute of Oceanography","State University of New York at Stony Brook","Texas A&M University","University of Bergen","University of California - Santa Cruz","University of Connecticut, Marine Research Laboratory","University of Delaware, College of Earth, Ocean, and Environment, School of Marine Science and Policy","University of Georgia, School of Marine Programs","University of Hawai\u02bbi at M\u0101noa","University of Miami Rosenstiel School of Marine and Atmospheric Science","University of Rhode Island, Graduate School of Oceanography","University of Southern California","University of Washington","US NASA/Jet Propulsion Laboratory","US National Aeronautic and Space Administration","Virginia Institute of Marine Science","Woods Hole Oceanographic Institution","BERMUDA ATLANTIC TIME SERIES (BATS)","Hydrostation S","JOINT GLOBAL OCEAN FLUX STUDY (JGOFS)","Joint Global Ocean Flux Study - North Atlantic Bloom Experiment (JGOFS/NABE)","Joint Global Ocean Flux Study / Arabian Sea Process Study (JGOFS/Arabian Sea)","JOINT GLOBAL OCEAN FLUX STUDY/EQUATORIAL PACIFIC BASIN STUDY (JGOFS/EQPAC)","Joint Global Ocean Flux Study: Hawaii Ocean Time-series (JGOFS HOT)","US JGOFS ANTARCTIC ENVIRONMENT AND SOUTHERN OCEAN PROCESS STUDY (JGOFS/AESOPS)","Arabian Sea","Equatorial Pacific Ocean","Gulf of Oman","Indian Ocean","Laccadive Sea","North Atlantic Ocean","North Pacific Ocean","Northwest Atlantic Ocean (limit-40 W)","Ross Sea","South Atlantic Ocean","South Pacific Ocean","Southeast Atlantic Ocean (limit-20 W)","Southern Ocean","TOGA Area - Pacific (30 N to 30 S)","oceanography","BBSR > Bermuda Biological Station for Research","BIGELOW > Bigelow Laboratory of Ocean Sciences","COLUMBIA/LDEO > Lamont-Doherty Earth Observatory, Columbia University","CWM/VIMS > Virginia Institute of Marine Science, College of William and Mary","DOC/NOAA/NESDIS/NODC > National Oceanographic Data Center, NESDIS, NOAA, U.S. Department of Commerce","IN/NIO > National Institute of Oceanography, India","JP/JODC > Japan Oceanographic Data Center","Jet Propulsion Laboratory","MLML > Moss Landing Marine Laboratories","NL/NWO/NIOZ > Royal Netherlands Institute for Sea Research (NIOZ)","OR-STATE/CEOAS > College of Earth, Ocean, and Atmospheric Sciences, Oregon State University","U-MIAMI/RSMAS > Rosenstiel School of Marine and Atmospheric Science, University of Miami","UGA/SkIO > Skidaway Institute of Oceanography, University of Georgia","UMCES/HPL > Horns Point Laboratory, University of Maryland Center for Environmental Science","URI/GSO > Graduate School of Oceanography, University of Rhode Island","WHOI > WOODS HOLE OCEANOGRAPHIC INSTITUTION","NBP96-4A","NBP9604","NBP965","NBP97-1","NBP97-3","NBP97-8","NBP98-2","RR_KIWI07","RR_KIWI08","RR_KIWI09","RR_KIWI6","TTN007","TTN008","TTN011","TTN012","TTN013","TTN043","TTN045","TTN049","TTN050","TTN053","TTN054","AESOPS > U.S. JGOFS Antarctic Environment and Southern Ocean Process Study","HOT > Hawaiian Ocean Time Series Project","JGOFS > Joint Global Ocean Flux Study, IGBP","EARTH SCIENCE > AGRICULTURE > AGRICULTURAL AQUATIC SCIENCES > FISHERIES","EARTH SCIENCE > ATMOSPHERE > AEROSOLS > AEROSOL OPTICAL DEPTH/THICKNESS","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY > CARBON AND HYDROCARBON COMPOUNDS > ATMOSPHERIC CARBON DIOXIDE > PARTIAL PRESSURE OF CARBON DIOXIDE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC PRESSURE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION > INCOMING SOLAR RADIATION","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > AIR TEMPERATURE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND DIRECTION","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND SPEED","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > BACTERIA/ARCHAEA","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > BACTERIA/ARCHAEA > CYANOBACTERIA (BLUE-GREEN ALGAE)","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PLANTS > MICROALGAE > DIATOMS","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PROTISTS > PLANKTON","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PROTISTS > PLANKTON > PHYTOPLANKTON","EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > PHOTOSYNTHESIS","EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > PRIMARY PRODUCTION","EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > RESPIRATION RATE","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > AQUATIC ECOSYSTEMS > PLANKTON","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > AQUATIC ECOSYSTEMS > PLANKTON > ZOOPLANKTON","EARTH SCIENCE > BIOSPHERE > VEGETATION > BIOMASS","EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > WATER DEPTH","EARTH SCIENCE > OCEANS > MARINE SEDIMENTS","EARTH SCIENCE > OCEANS > OCEAN ACOUSTICS > ACOUSTIC VELOCITY","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ALKALINITY","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > AMMONIA","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CARBON","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CARBON DIOXIDE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CHLOROPHYLL","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > HYDROCARBONS","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > INORGANIC CARBON","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > NITRATE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > NITRITE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > NITROGEN","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > NUTRIENTS","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ORGANIC CARBON","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ORGANIC MATTER","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > OXYGEN","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PH","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PHOSPHATE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PIGMENTS","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PIGMENTS > CHLOROPHYLL","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > SILICATE","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > SUSPENDED SOLIDS","EARTH SCIENCE > OCEANS > OCEAN CIRCULATION > OCEAN CURRENTS","EARTH SCIENCE > OCEANS > OCEAN OPTICS","EARTH SCIENCE > OCEANS > OCEAN OPTICS > ATTENUATION/TRANSMISSION","EARTH SCIENCE > OCEANS > OCEAN OPTICS > FLUORESCENCE","EARTH SCIENCE > OCEANS > OCEAN OPTICS > IRRADIANCE","EARTH SCIENCE > OCEANS > OCEAN OPTICS > PHOTOSYNTHETICALLY ACTIVE RADIATION","EARTH SCIENCE > OCEANS > OCEAN OPTICS > SECCHI DEPTH","EARTH SCIENCE > OCEANS > OCEAN OPTICS > TURBIDITY","EARTH SCIENCE > OCEANS > OCEAN PRESSURE > WATER PRESSURE","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > POTENTIAL TEMPERATURE","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > SEA SURFACE TEMPERATURE","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE","EARTH SCIENCE > OCEANS > OCEAN WAVES > SEA STATE","EARTH SCIENCE > OCEANS > OCEAN WAVES > WAVE HEIGHT","EARTH SCIENCE > OCEANS > OCEAN WAVES > WAVE PERIOD","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > CONDUCTIVITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > DENSITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY","EARTH SCIENCE > OCEANS > TIDES","EARTH SCIENCE > OCEANS > TIDES > TIDAL HEIGHT","19'-butanoyloxyfucoxanthin","19'-hexanoyloxyfucoxanthin","alloxanthin","alpha-carotene","beta-carotene","chlorophyll a","chlorophyll b","chlorophyll c3","chlorophyllide a","cis-19'-hexanoyloxyfucoxanthin","cis-fucoxanthin","diadinoxanthin","diatoxanthin","divinyl chlorophyll a","divinyl chlorophyll b","fucoxanthin","lutein","monovinyl chlorophyll a","monovinyl chlorophyll b","peridinin","phaeopigments","prasinoxanthin","violaxanthin","zeaxanthin","BATHYTHERMOGRAPHS","CAMERA > CAMERA","CTD > Conductivity, Temperature, Depth","CURRENT METERS","DRIFTING BUOYS > DRIFTING BUOYS","FLOW CYTOMETRY","FLUORESCENCE MICROSCOPY > FLUORESCENCE MICROSCOPY","FLUOROMETERS > FLUOROMETERS","GAS CHROMATOGRAPHS > GAS CHROMATOGRAPHS","GRAB SAMPLERS","MASS SPECTROMETERS > MASS SPECTROMETERS","MICROSCOPES > MICROSCOPES","MOCNESS > MOCNESS Plankton Net","NETS > NETS","NISKIN BOTTLES","OPTSPEC > Optical Spectrometer","OXYGEN METERS > OXYGEN METERS","PAR SENSORS > Photosynthetically Active Radiation Sensors","PH METERS > PH METERS","PLANKTON NETS","RADIOMETERS","SALINOMETERS","SECCHI DISKS > SECCHI DISKS","SEDIMENT CORERS > SEDIMENT CORERS","THERMOMETERS > THERMOMETERS","TIDE GAUGES > TIDE GAUGES","TRANSMISSOMETERS","TRAPS > TRAPS","VIDEO CAMERA > VIDEO CAMERA","XBT > Expendable Bathythermographs","FIXED OBSERVATION STATIONS > FIXED OBSERVATION STATIONS","Ships","ALPHA HELIX (call sign: WSD7078, ICES code: 31HX, 1966-)","ATLANTIS II (call sign: KADC, ICES code: 31AN, 1963-1996)","CAPE HATTERAS (call sign: WRZ8934, ICES code: 32KZ, 1981-)","CAPE HENLOPEN (ICES code: 32CW)","CROMWELL (ICES code: 31WM)","ENDEAVOR (call sign: WAUW, ICES code: 33EN)","ENDEAVOR (call sign: WCE5063, ICES code: 32EV, 1976-)","MOANA WAVE (call sign: WUS9293, ICES code: 32MW, 1974-2011)","NA'INA (ICES code: 33NA)","NATHANIEL B. PALMER (call sign: WPB3210, ICES code: 3206, 1992-)","NEW HORIZON (call sign: WKWB, ICES code: 32NM, 1978-2015)","POLAR DUKE (call sign: WCX7445, ICES code: 33PD, 1984-1988)","Polarstern (call sign: DBLK, ICES code: 06AQ, 1982-)","ROGER REVELLE (call sign: KAOU, ICES code: 33RR, 1996-)","ROVER (ICES code: 32YS)","SAGAR KANYA (call sign: VTJR, ICES code: 41SG)","THOMAS G. THOMPSON (call sign: KTDQ, ICES code: 3250, 1991-)","WEATHERBIRD (call sign: AGOR, ICES code: 320G)","WECOMA (call sign: WSD7079, ICES code: 32WC, 1976-)","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN","OCEAN > ATLANTIC OCEAN > SOUTH ATLANTIC OCEAN","OCEAN > INDIAN OCEAN","OCEAN > INDIAN OCEAN > ARABIAN SEA","OCEAN > PACIFIC OCEAN","OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN","OCEAN > PACIFIC OCEAN > SOUTH PACIFIC OCEAN","OCEAN > SOUTHERN OCEAN","OCEAN > SOUTHERN OCEAN > ROSS SEA"],"last_harvested_date":"2026-09-27T00:13:32.637657","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"5f4f1195-e770-4a2a-8f75-195cd98860ce","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/noaa.png","name":"National Oceanic and Atmospheric Administration, Department of Commerce","organization_type":"Federal Government","slug":"noaa"},"parent_identifier":null,"popularity":6,"publisher":"NOAA National Centers for Environmental Information","slug":"data-from-the-joint-global-ocean-flux-study-jgofs-time-series-and-process-studies-199-1997","spatial_centroid":{"lat":-6.024999999999999,"lon":-27.545799999999996},"spatial_shape":{"coordinates":[[[[87.97,-78.05],[180.0,-78.05],[180.0,66.0],[87.97,66.0],[87.97,-78.05]]],[[[-180.0,66.0],[-180.0,-78.05],[-179.684,-78.05],[-179.684,66.0],[-180.0,66.0]]]],"type":"MultiPolygon"},"theme":["geospatial"],"title":"Data from the Joint Global Ocean Flux Study (JGOFS) Time Series and Process Studies, 1992 to 1997","type":"dataset"},{"_score":0.51536465,"_sort":[1790467987344,0.51536465,2,"5fa739dd-ba93-4c00-9fa7-20b0432aed5f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"non-public","contactPoint":{"@type":"vcard:Contact","fn":"NOAA National Centers for Environmental Information","hasEmail":"mailto:ncei.info@noaa.gov"},"describedByType":"application/octet-stream","description":"This collection includes various coral reef data that was extracted by analyses of Structure-from-Motion (SfM) imagery of coral reef ecosystems across the Pacific Islands Regions by the Ecosystem Sciences Division (ESD) of the Pacific Islands Fisheries Science Center (PIFSC) of NOAA.  This collection includes coral reef benthic cover, vital rates (growth, mortality, recruitment), structural complexity, and coral demographic data.  Coral reef benthic cover, vital rates, and demographic data were produced by annotations of orthoprojections generated from SfM imagery, whereas structural complexity was derived from the digital elevation models, which were produced from orthomosaics, also generated from SfM imagery. Source SfM imagery is archived separately due to filesize.  Refer to specific accession pages for funding agencies, dates, and regions.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://www.ncei.noaa.gov/archive/accession/PIFSC-SfM-DerivedData","describedByType":"application/octet-stream","description":"Navigate directly to the URL for a descriptive web page with download links.","mediaType":"text/html","title":"NCEI Dataset Landing Page"},{"@type":"dcat:Distribution","accessURL":"https://data.noaa.gov/onestop/collections/granules/ff6e702e-f2d8-45b4-8dfd-db8ea0ab527a","describedByType":"application/octet-stream","description":"Browse data granules belonging to this collection (a granule is the smallest aggregation of data that can be independently described and retrieved).","mediaType":"text/html","title":"Browse granules"},{"@type":"dcat:Distribution","accessURL":"https://www.ncei.noaa.gov/metadata/granule/geoportal/?from=0&size=10&esdsl=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22query_string%22%3A%7B%22analyze_wildcard%22%3Atrue%2C%22query%22%3A%22fileid%3APIFSC-SfM-DerivedData.*%22%7D%7D%5D%7D%7D%7D#searchPanel","describedByType":"application/octet-stream","description":"Search for data granules belonging to this collection (a granule is the smallest aggregation of data that can be independently described and retrieved).","mediaType":"text/html","title":"Search for granules"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/63093","describedByType":"application/octet-stream","description":"InPort Metadata Record","mediaType":"text/html","title":"https://www.fisheries.noaa.gov/inport/item/63093"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/67851","describedByType":"application/octet-stream","description":"InPort metadata record","mediaType":"text/html","title":"https://www.fisheries.noaa.gov/inport/item/67851"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/59172","describedByType":"application/octet-stream","description":"InPort metadata record","mediaType":"text/html","title":"https://www.fisheries.noaa.gov/inport/item/59172"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/65764","describedByType":"application/octet-stream","description":"InPort metadata record","mediaType":"text/html","title":"https://www.fisheries.noaa.gov/inport/item/65764"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/63097","describedByType":"application/octet-stream","description":"InPort metadata record","mediaType":"text/html","title":"https://www.fisheries.noaa.gov/inport/item/63097"},{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.25923/h2q8-jv47","describedByType":"application/octet-stream","description":"Structure-from-Motion SOP for processing photomosaic imagery","mediaType":"text/html","title":"https://doi.org/10.25923/h2q8-jv47"},{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.25923/a9se-k649","describedByType":"application/octet-stream","description":"NOAA Central Library: Technical Memorandum","mediaType":"text/html","title":"https://doi.org/10.25923/a9se-k649"},{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.25923/w1k2-0y84","describedByType":"application/octet-stream","description":"NOAA Central Library: Technical Memorandum","mediaType":"text/html","title":"https://doi.org/10.25923/w1k2-0y84"},{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.25923/f19b-je14","describedByType":"application/octet-stream","description":"PIFSC special publication","mediaType":"text/html","title":"https://doi.org/10.25923/f19b-je14"},{"@type":"dcat:Distribution","accessURL":"https://repository.library.noaa.gov/view/noaa/48782","describedByType":"application/octet-stream","description":"NOAA Central Library: Dataset","mediaType":"text/html","title":"https://repository.library.noaa.gov/view/noaa/48782"},{"@type":"dcat:Distribution","accessURL":"https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords","describedByType":"application/octet-stream","description":"Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords","mediaType":"text/html","title":"GCMD Keyword Forum Page"},{"@type":"dcat:Distribution","accessURL":"https://www.ncei.noaa.gov/contact","describedByType":"application/octet-stream","description":"Information for contacts at NCEI.","mediaType":"text/html","title":"NCEI Contact Information"},{"@type":"dcat:Distribution","accessURL":"https://library.noaa.gov/","describedByType":"application/octet-stream","description":"Institution web page","mediaType":"text/html","title":"NOAA Library website"}],"identifier":"https://data.noaa.gov/waf/NOAA/NESDIS/ncei/accessions/iso/xml/PIFSC-SfM-DerivedData.xml","issued":"2020-03-09T00:00:00.000+00:00","keyword":["0209247","0244392","0253462","0257945","0268310","0282457","0283598","0286084","0307802","0308745","BENTHIC SPECIES","biological data","CORAL","CORAL - BLEACHING","CORAL - CENSUS","CORAL - COLONY SIZE","CORAL - FISH BITES","CORAL - SPECIES IDENTIFICATION","DEBRIS","DEPTH - BOTTOM","HABITAT - BENTHIC","REEF AND/OR BOTTOM REGIME - PERCENT COVER","photograph","scale","swimmer/diver","visual observation","biological","derived products","GIS product","imagery","in situ","laboratory analyses","survey - biological","survey - coral reef","NOAA Ship Hi'ialakai","NOAA Ship Oscar Elton Sette","NOAA Ship Rainier","NOAA Pacific Islands Fisheries Science Center","NOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division","NOAA Pacific Islands Fisheries Science Center","Coral Reef Conservation Program (CRCP)","CORAL REEF STUDIES","National Coral Reef Monitoring Program (NCRMP)","Pacific Reef and Assessment Monitoring Program (Pacific RAMP)","US DOC; NOAA; Office of Oceanic and Atmospheric Research; Ocean Acidification Program (OAP)","Coastal Waters of Hawaii","Hawaiian Islands Humpback Whale National Marine Sanctuary","Marianas Trench Marine National Monument","National Marine Sanctuary of American Samoa","North Pacific Ocean","Northwest Pacific Ocean (limit-180)","Pacific Remote Islands Marine National Monument","Papah\u0101naumoku\u0101kea Marine National Monument","Philippine Sea","Rose Atoll Marine National Monument","South Pacific Ocean","oceanography","CRED","CREP","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","NCRMP","National Coral Reef Monitoring Proram","PIFSC","Pacific Island Fisheries Science Center","RAMP","Reef Assessment and Monitoring Program","Scripps Institution of Oceanography","CRED","CREP","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","DAR","DLNR","Division of Aquatic Resources","ESD","Ecosystem Sciences Division","Hawai'i Department of Land and Natural Resources","NCRMP","National Coral Reef Monitoring Proram","PIFSC","Pacific Island Fisheries Science Center","Pacific Islands Fisheries Science Center","RAMP","Reef Assessment and Monitoring Program","Scripps Institution of Oceanography","DOC/NOAA/NMFS > National Marine Fisheries Service, NOAA, U.S. Department of Commerce","DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","31283","31349","31360","31361","31430","31525","743","Assessing impacts of Hawaii's 2019 coral bleaching event on coral recovery and coral communities","Determining whether herbivore-focused management strategies enhance critical ecosystem functions provided by fishes","Hawai'i coral bleaching assessment and analysis","Mapping Reef Fish Patterns Across the Marianas Archipelago","Maximizing information and resilience indicators extracted from monitoring efforts to inform fisheries management","National Coral Reef Monitoring Program (NCRMP)","Scaling up coral reef science through Photogrammetry and Artificial Intelligence","Hawaii Coral Bleaching Collaborative (HCBC)","Papahanaumokuakea Marine Debris Project","Numeric Data Sets > Benthic","Map Images > Coral Mapping","Numeric Data Sets > Benthic","Numeric Data Sets > Biology","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > COTS","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > Fish","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > Gastropod","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Damage Assessment > Biological","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Quadrat Monitoring > In Situ","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Quadrat Monitoring > Photograph Analysis","EARTH SCIENCE > Biosphere > Aquatic Habitat > Benthic Habitat","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Ecological Dynamics > Predation > Coral Predation","EARTH SCIENCE > Biosphere > Vegetation > Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Cover","EARTH SCIENCE > Biosphere > Vegetation > Algae > Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Crustose Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Fleshy Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Turf Algae","EARTH SCIENCE > Biosphere > Zoology > Corals","EARTH SCIENCE > Biosphere > Zoology > Corals > Coral Condition","EARTH SCIENCE > Biosphere > Zoology > Corals > Coral Diseases > Bleaching","EARTH SCIENCE > Biosphere > Zoology > Corals > Coral Growth","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > COTS","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > Fish","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > Gastropod","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Damage Assessment > Biological","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Damage Assessment > Marine Debris","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Quadrat Monitoring > Photograph Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Coral Colony Size and Condition","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Damage Assessment > Visual","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > GIS","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Biological","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Photographic Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Scleractinia (stony corals)","EARTH SCIENCE > Biosphere > Zoology > Sponges","EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Hard Seafloor Substrate","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Benthic biology","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Coral Cover","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Hard Coral Cover","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Rugosity","EARTH SCIENCE > Oceans > Marine Biology > Coral","EARTH SCIENCE > Oceans > Marine Biology > Coral Communities","EARTH SCIENCE > Oceans > Marine Biology > Marine Invertebrates","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > WATER DEPTH","EARTH SCIENCE > OCEANS > COASTAL PROCESSES > CORAL REEFS > CORAL BLEACHING","EARTH SCIENCE > OCEANS > MARINE ENVIRONMENT MONITORING > MARINE SUBMERGED DEBRIS","EARTH SCIENCE > SOLID EARTH > GEOMORPHIC LANDFORMS/PROCESSES > COASTAL LANDFORMS > CORAL REEFS","CORAL - CONDITION","CORAL - FISH BITES","CORAL - FISH BITES (count and category)","CORAL - PATCH SIZE","CORAL - SPECIES IDENTIFICATION","Complexity metrics","Corallivory","Fractal dimension","Height range","Reef habitat complexity","Rugosity","SfM","Structure from Motion","Structure from Motion (SfM)","digital elevation model","VISUAL OBSERVATIONS > VISUAL OBSERVATIONS","Agisoft Metashape","Agisoft Metashape software","ArcGIS","ArcGIS Pro","Computer","Dive Computer","GIS","R software","TagLab","Viscore","Ships","Hi'ialakai (call sign: WTEY, ICES code: 33HL, 2004-)","OSCAR ELTON SETTE (call sign: WTEE, ICES code: 33OC, 2003-)","RAINIER (call sign: WTEF, ICES code: 315R, 1968-)","M/V IMUA","Various Small Vessels","COUNTRY/TERRITORY > Northern Mariana Islands > Asuncion Island > Asuncion Island (19N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Maug > Maug Island (20N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Northern Mariana Islands > Northern Mariana Islands ( CNMI ) (18N146E0000)","COUNTRY/TERRITORY > Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Oahu (21N157W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Oahu Island > Oahu (21N157W0003)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Asuncion Island > Asuncion Island (19N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Mariana Archipelago > Northern Mariana Islands ( CNMI ) (18N146E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Aguihan > Aguihan Island (Aguijan) (14N145E0006)","COUNTRY/TERRITORY > Northern Mariana Islands > Asuncion Island > Asuncion Island (19N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Maug > Maug Island (20N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Northern Mariana Islands > Northern Mariana Islands ( CNMI ) (18N146E0000)","COUNTRY/TERRITORY > Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Rota > Rota Island ( Luta ) (14N145E0007)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Sarigan Island > Sarigan Island (16N145E0003)","COUNTRY/TERRITORY > Northern Mariana Islands > Tinian > Tinian Island (14N145E0005)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > American Samoa (14S170W0000)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Ofu Island (14S169W0013)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Olosega Island (14S169W0014)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Rose Atoll (14S168W0001)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Ta'u Island (14S169W0012)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Tutuila Island (14S170W0016)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Hawaii (21N160W0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Hawaii Island (19N155W0003)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Kauai Island (22N159W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Molokai Island (21N157W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Kure Atoll (28N178W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Northwestern Hawaiian Islands (28N178W0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Oahu (21N157W0003)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Pearl and Hermes Reef (27N176W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Kauai > Niihau Island (21N160W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Lanai Island (20N156W0002)","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Maui Island (20N156W0004)","COUNTRY/TERRITORY > United States of America > Hawaiian Islands (21N157W0027)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Baker Island (00N176W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Howland Island (00S176W0001)","OCEAN BASIN > Pacific Ocean > American Samoa > American Samoa (14S170W0000)","OCEAN BASIN > Pacific Ocean > American Samoa > Rose Atoll (14S168W0001)","OCEAN BASIN > Pacific Ocean > American Samoa > Tutuila Island (14S170W0016)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Baker Island > Baker Island (00N176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands (21N157W0027)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii > Hawaii (21N160W0000)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii Island > Hawaii Island (19N155W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Kauai Island > Kauai Island (22N159W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Lanai Island > Lanai Island (20N156W0002)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Maui Island > Maui Island (20N156W0004)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Molokai Island > Molokai Island (21N157W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Niihau Island > Niihau Island (21N160W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Oahu Island > Oahu (21N157W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Howland Island > Howland Island (00S176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands (28N178W0000)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Kure Atoll (28N178W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Pearl and Hermes Reef (27N176W0001)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ofu Island (14S169W0013)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Olosega Island (14S169W0014)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ta'u Island (14S169W0012)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Aguihan Island Reefs > Aguihan Island (Aguijan) (14N145E0006)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Asuncion Island > Asuncion Island (19N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Mariana Archipelago > Northern Mariana Islands ( CNMI ) (18N146E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Rota Island > Rota Island ( Luta ) (14N145E0007)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Sarigan Island > Sarigan Island (16N145E0003)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Tinian Island Reefs > Tinian Island (14N145E0005)","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > HAWAII","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > AMERICAN SAMOA","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > HAWAIIAN ISLANDS","OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN","OCEAN > PACIFIC OCEAN > SOUTH PACIFIC OCEAN","Mariana Archipelago","Papahanaumokuakea Marine National Monument","0T3EBB","0X8B6K","2J39XL","4KDA2E","6RT3B5","8UFY62","E00U92","HB8J3W","KXC5TK","MTG8N5"],"landingPage":"https://www.ncei.noaa.gov/contact","language":[],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2025-10-31T00:00:00.000+00:00","publisher":{"@type":"org:Organization","name":"NOAA National Centers for Environmental Information"},"references":["https://www.fisheries.noaa.gov/inport/item/63093","https://www.fisheries.noaa.gov/inport/item/67851","https://www.fisheries.noaa.gov/inport/item/59172","https://www.fisheries.noaa.gov/inport/item/65764","https://www.fisheries.noaa.gov/inport/item/63097","https://doi.org/10.25923/h2q8-jv47","https://doi.org/10.25923/a9se-k649","https://doi.org/10.25923/w1k2-0y84","https://doi.org/10.25923/f19b-je14","https://repository.library.noaa.gov/view/noaa/48782"],"rights":"otherRestrictions","spatial":"145.812292,-14.54871,-178.34,28.385","temporal":"2014-05-06T00:00:00+00:00/2023-08-05T00:00:00+00:00","theme":["geospatial"],"title":"Benthic cover and other related datasets derived from Structure-from-Motion (SfM) imagery of coral reef ecosystems in Pacific Islands Regions"},"description":"This collection includes various coral reef data that was extracted by analyses of Structure-from-Motion (SfM) imagery of coral reef ecosystems across the Pacific Islands Regions by the Ecosystem Sciences Division (ESD) of the Pacific Islands Fisheries Science Center (PIFSC) of NOAA.  This collection includes coral reef benthic cover, vital rates (growth, mortality, recruitment), structural complexity, and coral demographic data.  Coral reef benthic cover, vital rates, and demographic data were produced by annotations of orthoprojections generated from SfM imagery, whereas structural complexity was derived from the digital elevation models, which were produced from orthomosaics, also generated from SfM imagery. Source SfM imagery is archived separately due to filesize.  Refer to specific accession pages for funding agencies, dates, and regions.","distribution_titles":["NCEI Dataset Landing Page","Browse granules","Search for granules","https://www.fisheries.noaa.gov/inport/item/63093","https://www.fisheries.noaa.gov/inport/item/67851","https://www.fisheries.noaa.gov/inport/item/59172","https://www.fisheries.noaa.gov/inport/item/65764","https://www.fisheries.noaa.gov/inport/item/63097","https://doi.org/10.25923/h2q8-jv47","https://doi.org/10.25923/a9se-k649","https://doi.org/10.25923/w1k2-0y84","https://doi.org/10.25923/f19b-je14","https://repository.library.noaa.gov/view/noaa/48782","GCMD Keyword Forum Page","NCEI Contact Information","NOAA Library website"],"harvest_record":"https://catalog.data.gov/harvest_record/fbe13af9-7b61-4b5e-a109-54941d28e8d9","harvest_record_raw":"https://catalog.data.gov/harvest_record/fbe13af9-7b61-4b5e-a109-54941d28e8d9/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/fbe13af9-7b61-4b5e-a109-54941d28e8d9/transformed","has_download":false,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/NESDIS/ncei/accessions/iso/xml/PIFSC-SfM-DerivedData.xml","keyword":["0209247","0244392","0253462","0257945","0268310","0282457","0283598","0286084","0307802","0308745","BENTHIC SPECIES","biological data","CORAL","CORAL - BLEACHING","CORAL - CENSUS","CORAL - COLONY SIZE","CORAL - FISH BITES","CORAL - SPECIES IDENTIFICATION","DEBRIS","DEPTH - BOTTOM","HABITAT - BENTHIC","REEF AND/OR BOTTOM REGIME - PERCENT COVER","photograph","scale","swimmer/diver","visual observation","biological","derived products","GIS product","imagery","in situ","laboratory analyses","survey - biological","survey - coral reef","NOAA Ship Hi'ialakai","NOAA Ship Oscar Elton Sette","NOAA Ship Rainier","NOAA Pacific Islands Fisheries Science Center","NOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division","NOAA Pacific Islands Fisheries Science Center","Coral Reef Conservation Program (CRCP)","CORAL REEF STUDIES","National Coral Reef Monitoring Program (NCRMP)","Pacific Reef and Assessment Monitoring Program (Pacific RAMP)","US DOC; NOAA; Office of Oceanic and Atmospheric Research; Ocean Acidification Program (OAP)","Coastal Waters of Hawaii","Hawaiian Islands Humpback Whale National Marine Sanctuary","Marianas Trench Marine National Monument","National Marine Sanctuary of American Samoa","North Pacific Ocean","Northwest Pacific Ocean (limit-180)","Pacific Remote Islands Marine National Monument","Papah\u0101naumoku\u0101kea Marine National Monument","Philippine Sea","Rose Atoll Marine National Monument","South Pacific Ocean","oceanography","CRED","CREP","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","NCRMP","National Coral Reef Monitoring Proram","PIFSC","Pacific Island Fisheries Science Center","RAMP","Reef Assessment and Monitoring Program","Scripps Institution of Oceanography","CRED","CREP","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","DAR","DLNR","Division of Aquatic Resources","ESD","Ecosystem Sciences Division","Hawai'i Department of Land and Natural Resources","NCRMP","National Coral Reef Monitoring Proram","PIFSC","Pacific Island Fisheries Science Center","Pacific Islands Fisheries Science Center","RAMP","Reef Assessment and Monitoring Program","Scripps Institution of Oceanography","DOC/NOAA/NMFS > National Marine Fisheries Service, NOAA, U.S. Department of Commerce","DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","31283","31349","31360","31361","31430","31525","743","Assessing impacts of Hawaii's 2019 coral bleaching event on coral recovery and coral communities","Determining whether herbivore-focused management strategies enhance critical ecosystem functions provided by fishes","Hawai'i coral bleaching assessment and analysis","Mapping Reef Fish Patterns Across the Marianas Archipelago","Maximizing information and resilience indicators extracted from monitoring efforts to inform fisheries management","National Coral Reef Monitoring Program (NCRMP)","Scaling up coral reef science through Photogrammetry and Artificial Intelligence","Hawaii Coral Bleaching Collaborative (HCBC)","Papahanaumokuakea Marine Debris Project","Numeric Data Sets > Benthic","Map Images > Coral Mapping","Numeric Data Sets > Benthic","Numeric Data Sets > Biology","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > COTS","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > Fish","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > Gastropod","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Damage Assessment > Biological","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Quadrat Monitoring > In Situ","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Quadrat Monitoring > Photograph Analysis","EARTH SCIENCE > Biosphere > Aquatic Habitat > Benthic Habitat","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Ecological Dynamics > Predation > Coral Predation","EARTH SCIENCE > Biosphere > Vegetation > Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Cover","EARTH SCIENCE > Biosphere > Vegetation > Algae > Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Crustose Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Fleshy Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Turf Algae","EARTH SCIENCE > Biosphere > Zoology > Corals","EARTH SCIENCE > Biosphere > Zoology > Corals > Coral Condition","EARTH SCIENCE > Biosphere > Zoology > Corals > Coral Diseases > Bleaching","EARTH SCIENCE > Biosphere > Zoology > Corals > Coral Growth","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > COTS","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > Fish","EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars On Hard Coral > Gastropod","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Damage Assessment > Biological","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Damage Assessment > Marine Debris","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Quadrat Monitoring > Photograph Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Coral Colony Size and Condition","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Damage Assessment > Visual","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > GIS","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Biological","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Photographic Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Scleractinia (stony corals)","EARTH SCIENCE > Biosphere > Zoology > Sponges","EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Hard Seafloor Substrate","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Benthic biology","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Coral Cover","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Hard Coral Cover","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Rugosity","EARTH SCIENCE > Oceans > Marine Biology > Coral","EARTH SCIENCE > Oceans > Marine Biology > Coral Communities","EARTH SCIENCE > Oceans > Marine Biology > Marine Invertebrates","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > WATER DEPTH","EARTH SCIENCE > OCEANS > COASTAL PROCESSES > CORAL REEFS > CORAL BLEACHING","EARTH SCIENCE > OCEANS > MARINE ENVIRONMENT MONITORING > MARINE SUBMERGED DEBRIS","EARTH SCIENCE > SOLID EARTH > GEOMORPHIC LANDFORMS/PROCESSES > COASTAL LANDFORMS > CORAL REEFS","CORAL - CONDITION","CORAL - FISH BITES","CORAL - FISH BITES (count and category)","CORAL - PATCH SIZE","CORAL - SPECIES IDENTIFICATION","Complexity metrics","Corallivory","Fractal dimension","Height range","Reef habitat complexity","Rugosity","SfM","Structure from Motion","Structure from Motion (SfM)","digital elevation model","VISUAL OBSERVATIONS > VISUAL OBSERVATIONS","Agisoft Metashape","Agisoft Metashape software","ArcGIS","ArcGIS Pro","Computer","Dive Computer","GIS","R software","TagLab","Viscore","Ships","Hi'ialakai (call sign: WTEY, ICES code: 33HL, 2004-)","OSCAR ELTON SETTE (call sign: WTEE, ICES code: 33OC, 2003-)","RAINIER (call sign: WTEF, ICES code: 315R, 1968-)","M/V IMUA","Various Small Vessels","COUNTRY/TERRITORY > Northern Mariana Islands > Asuncion Island > Asuncion Island (19N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Maug > Maug Island (20N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Northern Mariana Islands > Northern Mariana Islands ( CNMI ) (18N146E0000)","COUNTRY/TERRITORY > Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Oahu (21N157W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Oahu Island > Oahu (21N157W0003)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Asuncion Island > Asuncion Island (19N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Mariana Archipelago > Northern Mariana Islands ( CNMI ) (18N146E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Aguihan > Aguihan Island (Aguijan) (14N145E0006)","COUNTRY/TERRITORY > Northern Mariana Islands > Asuncion Island > Asuncion Island (19N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Maug > Maug Island (20N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Northern Mariana Islands > Northern Mariana Islands ( CNMI ) (18N146E0000)","COUNTRY/TERRITORY > Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Rota > Rota Island ( Luta ) (14N145E0007)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Sarigan Island > Sarigan Island (16N145E0003)","COUNTRY/TERRITORY > Northern Mariana Islands > Tinian > Tinian Island (14N145E0005)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > American Samoa (14S170W0000)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Ofu Island (14S169W0013)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Olosega Island (14S169W0014)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Rose Atoll (14S168W0001)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Ta'u Island (14S169W0012)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Tutuila Island (14S170W0016)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Hawaii (21N160W0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Hawaii Island (19N155W0003)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Kauai Island (22N159W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Molokai Island (21N157W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Kure Atoll (28N178W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Northwestern Hawaiian Islands (28N178W0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Oahu (21N157W0003)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Pearl and Hermes Reef (27N176W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Kauai > Niihau Island (21N160W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Lanai Island (20N156W0002)","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Maui Island (20N156W0004)","COUNTRY/TERRITORY > United States of America > Hawaiian Islands (21N157W0027)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Baker Island (00N176W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Howland Island (00S176W0001)","OCEAN BASIN > Pacific Ocean > American Samoa > American Samoa (14S170W0000)","OCEAN BASIN > Pacific Ocean > American Samoa > Rose Atoll (14S168W0001)","OCEAN BASIN > Pacific Ocean > American Samoa > Tutuila Island (14S170W0016)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Baker Island > Baker Island (00N176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands (21N157W0027)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii > Hawaii (21N160W0000)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii Island > Hawaii Island (19N155W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Kauai Island > Kauai Island (22N159W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Lanai Island > Lanai Island (20N156W0002)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Maui Island > Maui Island (20N156W0004)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Molokai Island > Molokai Island (21N157W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Niihau Island > Niihau Island (21N160W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Oahu Island > Oahu (21N157W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Howland Island > Howland Island (00S176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands (28N178W0000)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Kure Atoll (28N178W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Pearl and Hermes Reef (27N176W0001)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ofu Island (14S169W0013)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Olosega Island (14S169W0014)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ta'u Island (14S169W0012)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Aguihan Island Reefs > Aguihan Island (Aguijan) (14N145E0006)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Asuncion Island > Asuncion Island (19N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Mariana Archipelago > Northern Mariana Islands ( CNMI ) (18N146E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Rota Island > Rota Island ( Luta ) (14N145E0007)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Sarigan Island > Sarigan Island (16N145E0003)","OCEAN BASIN > Pacific Ocean > Western Pacific 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These data were generated using a suite of landscape and thermal predictor variables and point data from walleye electrofishing surveys. Data are represented as probabilities (range 0-1) of recruitment during each time period (current, mid-century and late-century). Data were generated using a Random Forest approach. The final model contained three variables: lake area, growing degree days (base 5 \u00b0C), and the upstream probability of adults in the connected river system. Lake area and growing degree days were the most influential predictor variables (measured by variable importance).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9ZLM6UK","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.646ed3e0d34e4e58932cf826.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_646ed3e0d34e4e58932cf826","keyword":["Midwest","USGS:646ed3e0d34e4e58932cf826","Wisconsin","biota","climate change","environment","farming","fisheries"],"modified":"2026-09-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-92.8894, 42.4721, -86.7700, 46.7700","theme":["geospatial"],"title":"Current and projected mid- and late-century walleye recruitment in inland lakes of Wisconsin, USA"},"description":"These data represent current and projected mid- and late-century adult walleye recruitment in inland lakes of Wisconsin, USA. These data were generated using a suite of landscape and thermal predictor variables and point data from walleye electrofishing surveys. Data are represented as probabilities (range 0-1) of recruitment during each time period (current, mid-century and late-century). Data were generated using a Random Forest approach. The final model contained three variables: lake area, growing degree days (base 5 \u00b0C), and the upstream probability of adults in the connected river system. Lake area and growing degree days were the most influential predictor variables (measured by variable importance).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d07d1686-44c3-4fa1-8a6b-6b4c41da32fd","harvest_record_raw":"https://catalog.data.gov/harvest_record/d07d1686-44c3-4fa1-8a6b-6b4c41da32fd/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_646ed3e0d34e4e58932cf826","keyword":["Midwest","USGS:646ed3e0d34e4e58932cf826","Wisconsin","biota","climate change","environment","farming","fisheries"],"last_harvested_date":"2026-09-27T00:10:51.947928","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"current-and-projected-mid-and-late-century-walleye-recruitment-in-inland-lakes-of-wisconsi","spatial_centroid":{"lat":44.19126,"lon":-90.44163999999999},"spatial_shape":{"coordinates":[[[-92.8894,42.4721],[-92.8894,46.77],[-86.77,46.77],[-86.77,42.4721],[-92.8894,42.4721]]],"type":"Polygon"},"theme":["geospatial"],"title":"Current and projected mid- and late-century walleye recruitment in inland lakes of Wisconsin, USA","type":"dataset"},{"_score":12.5379925,"_sort":[1790467846092,12.5379925,1,"8ccf2101-d072-4bd1-8b5f-26717e00cb09"],"dcat":{"@type":"dcat:Dataset","accessLevel":"non-public","contactPoint":{"@type":"vcard:Contact","fn":"NOAA National Centers for Environmental Information","hasEmail":"mailto:ncei.info@noaa.gov"},"describedByType":"application/octet-stream","description":"Near-shore shallow water Conductivity-Temperature-Depth (CTD) surveys provided in this collection were conducted at selected sites around the Pacific Remote Island Areas as part of the ongoing National Coral Reef Monitoring Program (NCRMP). 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(20N144E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Guguan > Guguan Island (17N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Maug > Maug Island (20N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Northern Mariana Islands > Northern Mariana Islands ( CNMI ) (18N146E0000)","COUNTRY/TERRITORY > Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Rota > Rota Island ( Luta ) (14N145E0007)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Sarigan Island > Sarigan Island (16N145E0003)","COUNTRY/TERRITORY > Northern Mariana Islands > Tinian > Tinian Island (14N145E0005)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Agrihan Island > Agrihan Island (18N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Aguihan Island Reefs > Aguihan Island (Aguijan) (14N145E0006)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Alamagan Island > Alamagan Island (17N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Asuncion Island > Asuncion Island (19N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Farallon de Pajaros > Farallon de Pajaros (20N144E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guguan Island > Guguan Island (17N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Mariana Archipelago > Northern Mariana Islands ( CNMI ) (18N146E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Rota Island > Rota Island ( Luta ) (14N145E0007)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Sarigan Island > Sarigan Island (16N145E0003)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Tinian Island Reefs > Tinian Island (14N145E0005)","OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN","CNMI","Commonwealth of the Northern Mariana Islands","Mariana Archipelago","Mariana Islands","Marianas","Marianas Trench Marine National Monument","Triennial","98FNBT","A2B2JN","E5NM36","UJYYCH"],"landingPage":"https://www.ncei.noaa.gov/contact","language":[],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2026-04-15T00:00:00.000+00:00","publisher":{"@type":"org:Organization","name":"NOAA National Centers for Environmental Information"},"references":["https://www.ncei.noaa.gov/archive/accession/0157633","https://www.fisheries.noaa.gov/inport/item/36820","https://www.pifsc.noaa.gov/library/pubs/Hoeke_etal_JOO_2009.pdf"],"rights":"otherRestrictions","spatial":"145.833,13.2311,144.626,20.545","temporal":"2014-03-24T00:00:00+00:00/2014-03-24T00:00:00+00:00","theme":["geospatial"],"title":"National Coral Reef Monitoring Program: Shallow water conductivity-temperature-depth (CTD) profiles for selected locations across the Mariana Archipelago"},"description":"Near-shore shallow water Conductivity-Temperature-Depth (CTD) surveys provided in this collection were conducted at selected sites around the Mariana Archipelago as part of the ongoing National Coral Reef Monitoring Program (NCRMP). Shallow water CTD surveys provide vertical profiles of temperature, salinity, and turbidity providing indications for water masses and local sea water chemistry changes. These surveys are conducted to characterize the spatial structure of the physical and chemical properties of the ocean environment influencing the living coral reef resources observed during Rapid Ecological Assessments (REA) and towed-diver surveys, both of which are archived separately.\n\nShallow CTD casts are vertical profiles (max 30 meter depth, downcast only) of water column conductivity, temperature, and pressure (Sea-Bird Electronics, SBE19-plus; accuracy of 0.005 S m-1 in conductivity, 0.0002C in temperature, and 0.1% in pressure). A transmissometer (Wetlabs Inc; provides profiles of beam transmittance, related to turbidity) and a dissolved oxygen sensor (Sea-Bird Electronics, SBE43; accuracy of 2% of saturation) are also attached. Data are collected by lowering the CTD in a profiling mode from a small boat at a descent rate of approximately 0.5 to 0.75m s-1 to a maximum depth of 30m. Data processing is performed using Seabird Instrument's SeaSoft SBE Data Processing Software.","distribution_titles":["NCEI Dataset Landing Page","Browse granules","Search for granules","https://www.ncei.noaa.gov/archive/accession/0157633","https://www.fisheries.noaa.gov/inport/item/36820","Coral reef ecosystem integrated observing system: In-situ oceanographic observations at the US Pacific islands and atolls","GCMD Keyword Forum Page","NCEI Contact Information"],"harvest_record":"https://catalog.data.gov/harvest_record/b0e44e7d-830c-42c0-bd2d-f53e0dc72541","harvest_record_raw":"https://catalog.data.gov/harvest_record/b0e44e7d-830c-42c0-bd2d-f53e0dc72541/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/b0e44e7d-830c-42c0-bd2d-f53e0dc72541/transformed","has_download":true,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/NESDIS/ncei/accessions/iso/xml/NCRMP-CTD-Marianas.xml","keyword":["0161168","0226662","0283295","0314033","CONDUCTIVITY","DEPTH - SENSOR","DISSOLVED OXYGEN","HYDROSTATIC PRESSURE","SALINITY","SIGMA-T","WATER DENSITY","WATER TEMPERATURE","CTD","in situ","physical","profile","NOAA Ship Hi'ialakai","NOAA Ship Rainier","Coral Reef Ecosystem Program","NOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division","Coral Reef Ecosystem Program","NOAA Pacific Islands Fisheries Science Center","Coral Reef Conservation Program (CRCP)","CORAL REEF STUDIES","National Coral Reef Monitoring Program (NCRMP)","Pacific Reef and Assessment Monitoring Program (Pacific RAMP)","Marianas Trench Marine National Monument","North Pacific Ocean","Northwest Pacific Ocean (limit-180)","Philippine Sea","oceanography","CRED","CREP","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","NCRMP","National Coral Reef Monitoring Program","PIFSC","Pacific Islands Fisheries Science Center","RAMP","Reef Assessment Monitoring Program","CRED","CREP","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","NCRMP","National Coral Reef Monitoring Program","PIFSC","Pacific Islands Fisheries Science Center","DOC/NOAA/NMFS > National Marine Fisheries Service, NOAA, U.S. Department of Commerce","DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","743","National Coral Reef Monitoring Program","National Coral Reef Monitoring Program (NCRMP)","Numeric Data Sets > Oceanography","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Physical","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Ocean Pressure > Sea level Pressure","EARTH SCIENCE > Oceans > Ocean Pressure > Water Pressure","EARTH SCIENCE > Oceans > Ocean Temperature > Thermocline > Profile","EARTH SCIENCE > Oceans > Ocean Temperature > Water Temperature","EARTH SCIENCE > Oceans > Salinity/Density > Conductivity","EARTH SCIENCE > Oceans > Salinity/Density > Density","EARTH SCIENCE > Oceans > Salinity/Density > Salinity","Monitoring","EARTH SCIENCE > OCEANS > COASTAL PROCESSES > CORAL REEFS","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > OXYGEN","EARTH SCIENCE > OCEANS > OCEAN OPTICS > TURBIDITY","EARTH SCIENCE > OCEANS > OCEAN PRESSURE > WATER PRESSURE","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > CONDUCTIVITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > DENSITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY","Climate Change","CTD > Conductivity, Temperature, Depth","Ships","Hi'ialakai (call sign: WTEY, ICES code: 33HL, 2004-)","RAINIER (call sign: WTEF, ICES code: 315R, 1968-)","COUNTRY/TERRITORY > Northern Mariana Islands > Agrihan Island > Agrihan Island (18N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Aguihan > Aguihan Island (Aguijan) (14N145E0006)","COUNTRY/TERRITORY > Northern Mariana Islands > Alamagan Island > Alamagan Island (17N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Asuncion Island > Asuncion Island (19N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Farallon de Pajaros > Farallon de Pajaros (20N144E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Guguan > Guguan Island (17N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Maug > Maug Island (20N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Northern Mariana Islands > Northern Mariana Islands ( CNMI ) (18N146E0000)","COUNTRY/TERRITORY > Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Rota > Rota Island ( Luta ) (14N145E0007)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Sarigan Island > Sarigan Island (16N145E0003)","COUNTRY/TERRITORY > Northern Mariana Islands > Tinian > Tinian Island (14N145E0005)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Agrihan Island > Agrihan Island (18N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Aguihan Island Reefs > Aguihan Island (Aguijan) (14N145E0006)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Alamagan Island > Alamagan Island (17N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Asuncion Island > Asuncion Island (19N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Farallon de Pajaros > Farallon de Pajaros (20N144E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guguan Island > Guguan Island (17N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Mariana Archipelago > Northern Mariana Islands ( CNMI ) (18N146E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Rota Island > Rota Island ( Luta ) (14N145E0007)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Sarigan Island > Sarigan Island (16N145E0003)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Tinian Island Reefs > Tinian Island (14N145E0005)","OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN","CNMI","Commonwealth of the Northern Mariana Islands","Mariana Archipelago","Mariana Islands","Marianas","Marianas Trench Marine National Monument","Triennial","98FNBT","A2B2JN","E5NM36","UJYYCH"],"last_harvested_date":"2026-09-27T00:10:45.062713","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"5f4f1195-e770-4a2a-8f75-195cd98860ce","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/noaa.png","name":"National Oceanic and Atmospheric Administration, Department of Commerce","organization_type":"Federal Government","slug":"noaa"},"parent_identifier":null,"popularity":1,"publisher":"NOAA National Centers for Environmental Information","slug":"national-coral-reef-monitoring-program-shallow-water-conductivity-temperature-depth-ctd-pr-15fd0","spatial_centroid":{"lat":16.15666,"lon":145.3502},"spatial_shape":{"coordinates":[[[145.833,13.2311],[145.833,20.545],[144.626,20.545],[144.626,13.2311],[145.833,13.2311]]],"type":"Polygon"},"theme":["geospatial"],"title":"National Coral Reef Monitoring Program: Shallow water conductivity-temperature-depth (CTD) profiles for selected locations across the Mariana Archipelago","type":"dataset"}],"sort":"last_harvested_date"}