Timeline / Data.gov — Health Datasets

changed Source changed

A new raw object was archived. Both versions are preserved. 795 line(s) added, 799 line(s) removed.

Evidence

What changed derived

This diff is not evidence. It was produced by civic-memory.diff_engine 1.1.0 at 2026-09-12T00:29:41+00:00 by normalizing the two archived objects above. The objects are authoritative; this reading of them can be regenerated or deleted without loss. 795 line(s) added, 799 line(s) removed.

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- "description": "We developed numerous datasets describing mining activity and landscape conditions for all known active, inactive, abandoned, and legacy surface mines in the Eastern United States Appalachian region to support monitoring and regulatory needs. These data include 1) a study area boundary, 2) a compiled set of spatial footprints for known mines from federal, state, academic research, and non-government organizations (extent of mining activity over time), 3) spatial subdivisions (subunits) of each mine footprint (based on the year of most probable vegetation loss since 1985), attributed with metrics to denote restoration status and recovery, 4) tabular attributes of subunits describing pre-European vegetation communities, annual surface conditions from remotely sensed vegetation indices (1985-2022), changes in land cover and land use types, and elevation changes, 5) tabular attributes of subunits describing the annual aggregated recovery metric and percent forest recovery, 6) areas mined within United States communities (Census tracts) and population demographics, and 7) eleven raster datasets (30-meter spatial resolution) describing annual and cumulative metrics for barren, grassland, grass/shrub, and planted forest year within mining footprints since 1985.\nSummary of data products: Refer to the supplemental section of this metadata file for a list, description, and potential use of the data (must download because not rendered on website). Descriptions also exist in other metadata files associated with the project.\nBackground:\nUntil the Surface Mining Control and Reclamation Act of 1977 (SMCRA) (refer to O’Donnell and others, 2024 for a summary of state/federal/tribal regulations), there were no federal regulations on the reclamation of coal mines. The U.S. Department of the Interior Office of Surface Mining Reclamation and Enforcement (OSMRE) was also established in 1977 to \"protect citizens and the environment during mining and assure that the land is restored to beneficial use following mining.\" Mining regulations are encouraged through a monetary bond, a financial incentive between two parties (for example, mine company and state) to ensure agreed-upon obligations are met. Such obligations might include how to reclaim a mine after a company has completed mineral extraction. The Appalachian Regional Reforestation Initiative (ARRI; established in 2004) cooperates with OSMRE, state agencies (Alabama, Kentucky, Maryland, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia), the coal industry, environmental organizations, academic institutions, and landowners to assist with restoring forests and returning mine lands to pre-mining conditions or environmental services on coal mines in the Eastern United States. These efforts have more recently leaned on the forestry reclamation approach (FRA; Adams, 2017), a document that establishes best practices for reforesting mines, and OSMRE advisory reports. Until the establishment of ARRI, most reclamation focused on soil stabilization and establishment of grasses, shrubs, and nonnative plants (from 1977 to 2004). These sites primarily remain of little to no economic value because reclamation/restoration methods have resulted in compacted soils, an abundance of invasive species, and an inability to support forest growth and ecological succession.\nImportant caveats/limitations: \nPlease review the metadata accuracy reporting sections of each metadata file (data product) and all process steps describing data inputs and methods used in our analysis. \nData on mining locations within the United States are incomplete, and no single dataset provides sufficient information on where and when mining occurred. Because we are using data provided by federal and state government agencies, as well as published data based on mapping mine footprints using remotely sensed data, there is a significant variety of information and accuracy in source data. We, therefore, rely on the redundancy of data sources to improve mine location and the information documented for each mine. The aspatial information collected from the source data used to attribute footprints was intended to provide evidence that the footprint captures documented mining activity. When footprints indicated no mining activity from available source data, we discarded these data from the mine footprints.\nDue to the lack of publicly available data on mining and reclamation activities in the United States, our understanding of restoration success is limited. For example, we do not have complete records of when mining began and ended, or of the methods used for reclamation. A lack of this information might affect the success of soil remediation (for example, topsoil replacement and soil preparation before restoration) and restoration of vegetation (for example, species types used, planting methods, and mitigation of invasive species). The accompanying mine footprints provide evidence of mining activity that relies on aspatial information from independent data, which we include in our data and documentation. All subsequent analyses of data within mine footprints are based on multiple data sources and are intended to assess landscape changes as reflected in the temporal portrayal of those data.\nGiven that we have only investigated multispectral Landsat data without accompanying field data, as opposed to hyperspectral remotely sensed data (such as the Airborne Visible Infrared Imaging Spectrometer [AVIRIS]), we are limited to summarizing vegetation conditions at broad community levels (for example, trees, shrubs, grass). If data were available on mining activity and reclamation methods, using hyperspectral remotely sensed data would make more sense. We could then improve our understanding of species composition and whether invasive species are present (for example, kudzu and Autumn olive; other: mimosa, multiflora rose, bush honeysuckle, Japanese grass, Japanese spirea, and garlic mustard). We could also investigate the abundance and diversity of species within and beyond mine footprints to determine restoration success. Hyperspectral data, accompanied with field data, could also help detect if there are toxins absorbed by plants that cause threats to flora, wildlife, and people. Understanding site conditions post-mining is essential for understanding restoration success. Terrain characteristics (for example, slope and slope position, aspect, and elevation) affect moisture conditions, types of vegetation suitable for planting at a site, and time for vegetation recovery. Methods of soil preparation are important and usually require single to triple-shank rippers mounted on heavy equipment to uncompact soils (generally, at least four feet in depth). Due to a lack of data on on-site preparation and planting, we are limited in understanding why some sites recover more successfully or at faster rates, which would otherwise be helpful to mine operators and land stewards. \nLandsat is a useful data product for regional assessments because it is free to the public and has a long history (1970s-present). Remotely sensed hyperspectral data is only available when acquired from aircraft and is therefore limited spatially and temporally. Hyperspectral data is also not freely available to the public and is more commonly used for local applications, with less frequent repeat collections. Our products are, therefore, useful for regional assessments of vegetation recovery but are limited to more general questions about vegetation cover and productivity. These products do not include information about site toxicity, alterations to soil pH (acidity versus alkaline/basic conditions that can be affected by mining), effects of heavy metals on vegetation, site preparation methods required for restoration, or similar characteristics that may affect restoration success. Such information was not publicly available but would be valuable for improving methods to measure future restoration success.\nTypes of mining activity:\nAbandoned mine lands: Mine lands where mining or processing activity is determined to have ceased. The Abandoned Mine Land (AML) Reclamation Program was established in 1981 to address the physical safety and environmental hazards posed by abandoned mines (both before and after SMCRA).\nLegacy mines: These are mines reclaimed under the SMCRA, where mine operators no longer have legal responsibilities (bonds released).\nInactive mines: These include mine lands where operators are not currently extracting resources, the lands have not been reclaimed, and bonds have not been released.\nActive mines: These include mine lands where operators are currently extracting resources and bonds have not been released.\nKeywords: vegetation, vegetation recovery, forest recovery, ecosystem condition, disturbance, land cover change, hydrology, watershed processes, mining, mine footprint, abandoned mine lands, legacy mines, inactive mines, active mines, surface mines, abandoned mines and quarries, reclamation, restoration, reforestation, revegetation, topography, elevation change, geomorphic processes, mountain top removal, valley infill, remote sensing, spectral indices, normalized difference vegetation index, normalized burn ratio, normalized difference moisture index, aggregated recovery metric, time series analysis, geography, land surface characteristics, land use change, land use and land cover, spatial analysis",
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- "description": "We developed numerous datasets describing mining activity and landscape conditions for all known active, inactive, abandoned, and legacy surface mines in the Eastern United States Appalachian region to support monitoring and regulatory needs. These data include 1) a study area boundary, 2) a compiled set of spatial footprints for known mines from federal, state, academic research, and non-government organizations (extent of mining activity over time), 3) spatial subdivisions (subunits) of each mine footprint (based on the year of most probable vegetation loss since 1985), attributed with metrics to denote restoration status and recovery, 4) tabular attributes of subunits describing pre-European vegetation communities, annual surface conditions from remotely sensed vegetation indices (1985-2022), changes in land cover and land use types, and elevation changes, 5) tabular attributes of subunits describing the annual aggregated recovery metric and percent forest recovery, 6) areas mined within United States communities (Census tracts) and population demographics, and 7) eleven raster datasets (30-meter spatial resolution) describing annual and cumulative metrics for barren, grassland, grass/shrub, and planted forest year within mining footprints since 1985.\nSummary of data products: Refer to the supplemental section of this metadata file for a list, description, and potential use of the data (must download because not rendered on website). Descriptions also exist in other metadata files associated with the project.\nBackground:\nUntil the Surface Mining Control and Reclamation Act of 1977 (SMCRA) (refer to O’Donnell and others, 2024 for a summary of state/federal/tribal regulations), there were no federal regulations on the reclamation of coal mines. The U.S. Department of the Interior Office of Surface Mining Reclamation and Enforcement (OSMRE) was also established in 1977 to \"protect citizens and the environment during mining and assure that the land is restored to beneficial use following mining.\" Mining regulations are encouraged through a monetary bond, a financial incentive between two parties (for example, mine company and state) to ensure agreed-upon obligations are met. Such obligations might include how to reclaim a mine after a company has completed mineral extraction. The Appalachian Regional Reforestation Initiative (ARRI; established in 2004) cooperates with OSMRE, state agencies (Alabama, Kentucky, Maryland, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia), the coal industry, environmental organizations, academic institutions, and landowners to assist with restoring forests and returning mine lands to pre-mining conditions or environmental services on coal mines in the Eastern United States. These efforts have more recently leaned on the forestry reclamation approach (FRA; Adams, 2017), a document that establishes best practices for reforesting mines, and OSMRE advisory reports. Until the establishment of ARRI, most reclamation focused on soil stabilization and establishment of grasses, shrubs, and nonnative plants (from 1977 to 2004). These sites primarily remain of little to no economic value because reclamation/restoration methods have resulted in compacted soils, an abundance of invasive species, and an inability to support forest growth and ecological succession.\nImportant caveats/limitations: \nPlease review the metadata accuracy reporting sections of each metadata file (data product) and all process steps describing data inputs and methods used in our analysis. \nData on mining locations within the United States are incomplete, and no single dataset provides sufficient information on where and when mining occurred. Because we are using data provided by federal and state government agencies, as well as published data based on mapping mine footprints using remotely sensed data, there is a significant variety of information and accuracy in source data. We, therefore, rely on the redundancy of data sources to improve mine location and the information documented for each mine. The aspatial information collected from the source data used to attribute footprints was intended to provide evidence that the footprint captures documented mining activity. When footprints indicated no mining activity from available source data, we discarded these data from the mine footprints.\nDue to the lack of publicly available data on mining and reclamation activities in the United States, our understanding of restoration success is limited. For example, we do not have complete records of when mining began and ended, or of the methods used for reclamation. A lack of this information might affect the success of soil remediation (for example, topsoil replacement and soil preparation before restoration) and restoration of vegetation (for example, species types used, planting methods, and mitigation of invasive species). The accompanying mine footprints provide evidence of mining activity that relies on aspatial information from independent data, which we include in our data and documentation. All subsequent analyses of data within mine footprints are based on multiple data sources and are intended to assess landscape changes as reflected in the temporal portrayal of those data.\nGiven that we have only investigated multispectral Landsat data without accompanying field data, as opposed to hyperspectral remotely sensed data (such as the Airborne Visible Infrared Imaging Spectrometer [AVIRIS]), we are limited to summarizing vegetation conditions at broad community levels (for example, trees, shrubs, grass). If data were available on mining activity and reclamation methods, using hyperspectral remotely sensed data would make more sense. We could then improve our understanding of species composition and whether invasive species are present (for example, kudzu and Autumn olive; other: mimosa, multiflora rose, bush honeysuckle, Japanese grass, Japanese spirea, and garlic mustard). We could also investigate the abundance and diversity of species within and beyond mine footprints to determine restoration success. Hyperspectral data, accompanied with field data, could also help detect if there are toxins absorbed by plants that cause threats to flora, wildlife, and people. Understanding site conditions post-mining is essential for understanding restoration success. Terrain characteristics (for example, slope and slope position, aspect, and elevation) affect moisture conditions, types of vegetation suitable for planting at a site, and time for vegetation recovery. Methods of soil preparation are important and usually require single to triple-shank rippers mounted on heavy equipment to uncompact soils (generally, at least four feet in depth). Due to a lack of data on on-site preparation and planting, we are limited in understanding why some sites recover more successfully or at faster rates, which would otherwise be helpful to mine operators and land stewards. \nLandsat is a useful data product for regional assessments because it is free to the public and has a long history (1970s-present). Remotely sensed hyperspectral data is only available when acquired from aircraft and is therefore limited spatially and temporally. Hyperspectral data is also not freely available to the public and is more commonly used for local applications, with less frequent repeat collections. Our products are, therefore, useful for regional assessments of vegetation recovery but are limited to more general questions about vegetation cover and productivity. These product
+ "description": "Multiple linear regression models were developed using data collected in 2016 and 2017 from three recurring bloom sites in Kabetogama Lake in northern Minnesota. These models were developed to predict concentrations of cyanotoxins (anatoxin-a, microcystin, and saxitoxin) that occur within the blooms. Virtual Beach software (version 3.0.6) was used to develop four models: two cyanotoxin mixture (MIX) models and two microcystin (MC) models. Models include those using readily available environmental variables (for example, wind speed and specific conductance) and those using additional comprehensive variables (based on laboratory analyses). Many of the independent variables were averages over a certain time period prior to a sample date, whereas other independent variables were lagged between 4 and 8 days. Funding for this work was provided by the U.S Geological Survey – National Park Service Partnership and the U.S. Geological Survey Environmental Health Program (Toxic Substance Hydrology and Contaminant Biology). The resulting model equations and final datasets are included in this data release while an associated child item model archive includes all the files needed to run an