AndreaBozzo/ceres-open-data-index huggingface dataset:
Remotely sensed and in-situ chlorophyll a and temperature data from Blue Mesa Reservoir, Colorado

This data set includes estimates of aquatic chlorophyll a concentration and reservoir temperature for Blue Mesa Reservoir, CO.  A Random Forest modeling approach was trained to model near-surface aquatic chlorophyll a using near-coincident Sentinel-2 satellite imagery and water samples analyzed for chlorophyll a concentration. The trained chlorophyll a model was applied to Sentinel-2 imagery to produce maps of modeled chlorophyll a concentrations at 10 m spatial resolution for May through October for 2016 through 2023.  Chlorophyll a concentrations for three sections (basins) of Blue Mesa Reservoir were extracted from the raster data to produce time-series of modeled chlorophyll a concentration summary statistics (e.g., median, standard deviation, 90th percentile, etc).
Water temperatures were approximated using the provisional Landsat surface temperature (PST) product collected with sensors on board Landsat 5, 7, 8, and 9 for May through October between 2000 and 2023. PST values for Landsat 8 and Landsat 9 were scaled to match in-situ water temperature observations in the top 1 m of the water column using a multivariate linear regression model. A harmonized water temperature record was produced by adjusting Landsat 7 PST values to align with the  adjusted Landsat 8 values for near-coincident image dates.  Similarly, Landsat 5 PST values were adjusted to match the adjusted Landsat 7 values.
The modeled chlorophyll a and temperatures had root mean square errors of 1.9 micrograms per liter and 0.7 degrees Celsius, respectively.
This data release includes three components with tabular and raster data:
1) Tabular .csv format in-situ and remotely sensed chlorophyll a data from Blue Mesa Reservoir, Colorado May through October 2016 - 2023: Data used to train the chlorophyll a model (chl_model_training.csv), ) and modeled chlorophyll a time series (chl_rs_values.csv)
2) Raster format remotely sensed aquatic chlorophyll a for Blue Mesa Reservoir, Colorado May through October 2016 - 2023: Raster data include 167 geotiffs of modeled chlorophyll a concentrations in zipped directory (chl_conc_ug_L.zip)
3) Tabular in-situ and remotely sensed temperature data from Blue Mesa Reservoir, Colorado May through October 2000 - 2023: Data used to train the temperature model (temp_model_training.csv) and modeled temperature timeseries (temp_rs_values.csv)

Tags: algae, aquatic-chlorophyll, biaota, blue-mesa-reservoir, colorado, curecanti-national-recreation-area, inlandwaters, remote-sensing, usgs-64f26460d34ed30c2053d8c3, water-temperature
Organization: Department of the Interior
LANDFIRE 2022 Fuel Disturbance (FDist) CONUS

LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. LF 2022 Fuel Disturbance (FDist) uses the latest Annual Disturbance products from the effective disturbance years of 2013 to 2022. FDist is created from LF 2022 Historical Disturbance (HDist) which in turn aggregates the Annual Disturbance products. FDist groups similar disturbance types, severities and time since disturbance categories which represent disturbance scenarios within the fuel environment. FDist is used in conjunction with Fuel Vegetation Type (FVT), Cover (FVC), and Height (FVH) to calculate Canopy Cover (CC), Canopy Height (CH), Canopy Bulk Density (CBD), Canopy Base Height (CBH), Fire Behavior Fuel Models 13 Anderson (FBFM13) and 40 Scott and Burgan (FBFM40). 
FDist is developed using the aggregated Annual Disturbance products from 2013 to 2022. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

Tags: biota, conterminous-united-states, continental-u-s, conus, disturbance, fdist, fires, fuel-disturbance, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f250fdd34e095955171ead
Organization: Department of the Interior
LANDFIRE Annual Disturbance CONUS 2021

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed "event" perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery.  
To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. 
Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.

Tags: annual-disturbance, annual-disturbance-2021, biota, conterminous-united-states, continental-u-s, conus, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f24ef9d34e095955171ea4, yearly-disturbance
Organization: Department of the Interior
Geochemical sediment source and target analysis in White Clay Creek, Pennsylvania (ver. 2.0, March 2024)

This data release includes one comma delimited table that represents a summary of source and target samples collected for White Clay Creek between 2020 and 2023, in support of sediment fingerprinting modelling. This table contains sediment sample information and results of particle size, elemental composition, and fallout radionuclide analyses. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
First posted - October 18, 2023 (available from author).
Revised - March 28, 2024 (version 2.0).
This data release has been revised to include data from samples collected in 2023 that were not published in version 1.0.

Tags: environment, geochemistry, geomorphology, hydrology, pennsylvania, piedmont, united-states, usgs-6509cf0cd34eb6693be8f46d, water-quality
Organization: Department of the Interior
LANDFIRE 2022 Forest Canopy Cover (CC) AK

LANDFIRE's (LF) 2022 Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. CC supplies information for fire behavior models to determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. To create this product, plot level CC values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance CC and Canopy Height (CH) are used as predictors of disturbed CC using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and time since disturbance. CC is used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). In LF 2022, fuel products are created with LF 2016 Remap vegetation in areas that were un-disturbed in the last ten years. 
To designate disturbed areas where CC is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

Tags: ak, alaska, biota, canopy-cover, cc, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f24e16d34e095955171e92
Organization: Department of the Interior
LANDFIRE 2022 Forest Canopy Bulk Density (CBD) AK

LANDFIRE's (LF) 2022 Canopy Bulk Density (CBD) describes the mass of available canopy fuel per unit canopy volume that would burn in a crown fire. A spatially explicit map of CBD supplies information used in fire behavior models to determine the spread characteristics of active crown fires across the landscape. CBD for disturbed and non-disturbed areas is determined via a general linear model (GLM) relating Canopy Height (CH) and Canopy Cover (CC) to CBD (Reeves et al 2009). In LF 2022, fuel products are created with LF 2016 Remap vegetation in areas that were un-disturbed in the last ten years.  
To designate disturbed areas where CBD is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

Tags: ak, alaska, biota, canopy-bulk-density, cbd, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f24dbbd34e095955171e8d
Organization: Department of the Interior
LANDFIRE 2022 Fuel Vegetation Type (FVT) AK

LANDFIRE (LF) 2022 Fuel Vegetation Type (FVT) represents the LF Existing Vegetation Type Ecological Systems (EVT) product, modified to represent pre-disturbance EVT in areas where disturbances have occurred over the past 10 years. Due to shifting EVT codes and labels throughout the years, the FVT codes are based on an early version of EVT codes translated from the current version. FVT is an input for fuel transitions related to disturbance. Fuel products in LF 2022 were created with LF 2016 Remap vegetation in non-disturbed areas. 
To designate disturbed areas where FVT is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

Tags: ak, alaska, biota, evt, existing-vegetation-type, fires, fuel-vegetation-type, fvt, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f24d91d34e095955171e8b
Organization: Department of the Interior
LANDFIRE 2022 Fuel Vegetation Height (FVH) AK

LANDFIRE (LF) 2022 Fuel Vegetation Height (FVH) represents the LF Existing Vegetation Height (EVH) product, modified to represent pre-disturbance EVH in areas where disturbances have occurred over the past 10 years. EVH is mapped as continuous estimates of canopy height for tree, shrub, and herbaceous lifeforms with a potential range of 0-100m. Continuous EVH values are binned to align with fuel model assignments when creating FVH. FVH is an input for fuel transitions related to disturbance. Fuel products in LF 2022 were created with LF 2016 Remap vegetation in non-disturbed areas. 
To designate disturbed areas where FVH is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

Tags: ak, alaska, biota, evh, existing-vegetation-height, fires, fuel-vegetation-height, fvh, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f24d5bd34e095955171e88
Organization: Department of the Interior
LANDFIRE 2022 Fuel Vegetation Cover (FVC) AK

LANDFIRE (LF) 2022 Fuel Vegetation Cover (FVC) represents the LF Existing Vegetation Cover (EVC) product, modified to represent pre-disturbance EVC in areas where disturbances have occurred over the past 10 years. EVC is mapped as continuous estimates of canopy cover for tree, shrub, and herbaceous lifeforms with a potential range from 10% to 100%. Continuous EVC values are binned to align with fuel model assignments when creating FVC. FVC is an input for fuel transitions related to disturbance. Fuel products in LF 2022 were created with LF 2016 Remap vegetation in non-disturbed areas. 
To designate disturbed areas where FVC is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

Tags: ak, alaska, biota, evc, existing-vegetation-cover, fires, fuel-vegetation-cover, fvc, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f24d07d34e095955171e85
Organization: Department of the Interior
LANDFIRE 2022 Existing Vegetation Type (EVT) AK

LANDFIRE's (LF) 2022 update (LF 2022) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western hemisphere. In this context, a terrestrial ecological system is defined as a group of plant community types that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients. EVT also includes ruderal or semi-natural vegetation types within the U.S. National Vegetation Classification [(NVC) https://usnvc.org/]. See the EVT product page (https://www.landfire.gov/evt.php) for more information about ecological systems and NVC classifications.  
EVT is mapped using decision tree models, field data, Landsat imagery, topography, and biophysical gradient data. Decision tree models are developed separately for tree, shrub, and herbaceous lifeforms which are then used to produce a lifeform specific EVT product. These models are generated for each Environmental Protection Agency (EPA) Level III Ecoregion (https://www.epa.gov/eco-research/ecoregions). Riparian, alpine, sparse, and other site-specific EVTs are constrained by predetermined masks. Urban and developed areas are derived from the National Land Cover Database (NLCD), and the latest Microsoft Building Footprint dataset. Agricultural lands originate from the 2022 Cropland Data Layer (CDL) and the 2019 California Statewide Crop Mapping layer. Burnable developed classes are identified from building footprint dataset thresholds. LF 2022 retains circa 2016 EVT labels except where shifts in urban, agriculture, and developed classes occur. While Existing Vegetation Cover (EVC) and Height (EVH) are updated using transition rulesets with ST-Sim to account for disturbances, EVT remains unchanged, therefore EVT lifeform is not synchronized to the EVC/EVH lifeform as in some previous versions. LF uses EVT as an input for LF 2022 Fuel Vegetation Type (FVT).

Tags: ak, alaska, biota, evt, existing-vegetation-type, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f24ca3d34e095955171e82
Organization: Department of the Interior
Density and salinity data to validate an equation of state for hypersaline water in Great Salt Lake, Utah, 2021–2022

This U.S. Geological Survey (USGS) Data Release provides salinity and density data collected at Great Salt Lake (GSL), Utah monitoring locations during 2021–2022. Temperature, specific conductance, and dissolved oxygen were collected using an In-Situ AquaTROLL 600. Sample density was measured using an Anton Paar DMA 35Ex at 5° increments between 5° and 40° C. Practical salinity was measured at the University of Miami’s Rosenstiel School of Marine and Atmospheric Science using a Guideline Salinometer. These data were used to validate the equation of state for Great Salt Lake at salinities higher than the established range, 23–182 g/L (Naftz, et al, 2011). Data supports increasing the upper density range from 1.145 g/cm3 to 1.214 g/cm3. This increase allows for salinity monitoring up to 270 g/L and encompasses all (north and south arm) GSL waters.

Tags: density, equation-of-state, great-salt-lake, hypersaline, salinity, terminal-lakes, usgs-64f24c0fd34e095955171e76, utah, water-quality
Organization: Department of the Interior
LANDFIRE 2022 Historical Disturbance (HDist) CONUS

LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. Historical Disturbance (HDist) is developed from the base annual LF disturbance products, and attribute code system, to represent the history of disturbance for a 10-year span. Each year's disturbance scenarios are checked against time relevant LF vegetation products to check for logical inconsistencies. Errant codes are flagged and updated to a discard code with the remaining disturbance types cross-walked/aggregated to Fuel Disturbance (FDist) types. HDist includes the year of disturbance that is recorded for that pixel. In LF 2022, the time since disturbance code is the same for both HDist and FDist. Starting with LF 2016 Remap, HDist replaced Vegetation Disturbance (VDist) from previous LF versions.

Tags: biota, conterminous-united-states, continental-u-s, conus, disturbance, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, hdist, historical-disturbance, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-64f24796d34e095955171e56
Organization: Department of the Interior