AndreaBozzo/ceres-open-data-index huggingface dataset:
LANDFIRE 2022 Scott and Burgan Fire Behavior Fuel Model (FBFM40) HI

LANDFIRE's (LF) 2022 40 Scott and Burgan Fire Behavior Fuel Models (FBFM40) product represents distinct distributions of fuel loadings found among surface fuel components (live and dead), size classes, and fuel types (Scott &amp; Burgan 2005). The fuel models are described by the most common fire carrying fuel type (grass, brush, timber, or slash), loading and surface area-to-volume ratio by size class and component, fuel bed depth, and moisture of extinction. FBFM40 contains more fuel models for every fuel type than 13 Anderson Fire Behavior Fuel Models (FBFM13), and the number of fuel models representing relatively high dead fuel moisture content is increased. In FBFM40 fuel models with an herbaceous component are dynamic, to simulate curing, rather than remaining constant. To create this product, expert rulesets were developed to understand how different types of disturbance would change pre-disturbance fuel models to post disturbance fuels, based on the severity and time since disturbance. These rulesets are represented in the LF Total Fuel Change Tool and Database for both disturbed and non-disturbed areas. In the LF 2022 update, non-disturbed fuels are the same as LF 2016 Remap for natural vegetation. 
To designate disturbed areas where FBFM40 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: biota, fbfm, fbfm40, fire-behavior-fuel-model-40, fires, geographic-information-systems, geospatial-datasets, hawaii, hazard-preparedness, hi, image-collections, imagerybasemapsearthcover, landfire-2022, lf-2022, oconus, raster-digital-data, remote-sensing, scott-and-burgan-fire-behavior-fuel-model, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-6566628ed34e3aa43a43fac1
Organization: Department of the Interior
LANDFIRE 2022 Forest Canopy Height (CH) HI

LANDFIRE's (LF) 2022 Forest Canopy Height (CH) describes the average height of the top of the canopy for a stand. CH is used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CH supplies information for fire behavior models, such as FARSITE (Finney 1998), that can determine the starting point of embers in the spotting model, wind reductions, and the volume of crown fuels. To create this product, plot level CH values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance Canopy Cover and CH are used as predictors of disturbed CH using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and time since disturbance. CH 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 CH 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: biota, canopy-height, ch, fires, geographic-information-systems, geospatial-datasets, hawaii, hazard-preparedness, hi, 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-65665fead34e3aa43a43faae
Organization: Department of the Interior
LANDFIRE Annual Disturbance HI 2022

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-2022, biota, fires, geographic-information-systems, geospatial-datasets, hawaii, hazard-preparedness, hi, 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-65665c93d34e3aa43a43fa87, yearly-disturbance
Organization: Department of the Interior
LANDFIRE Annual Disturbance HI 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, fires, geographic-information-systems, geospatial-datasets, hawaii, hazard-preparedness, hi, 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-65665b18d34e3aa43a43fa80, yearly-disturbance
Organization: Department of the Interior
Unoccupied Aerial System-mounted image velocimetry and Doppler velocity radar data for computation of river velocity and discharge collected at seven locations in Colorado in 2023: Videos

This child item contains the raw videos captured by small Unoccupied Aircraft Systems (sUAS) at each field site. It also includes a summary file with metadata for each video. 
&lt;br&gt;
Each field site is abbreviated in various files in this data release. File and folder names quickly identify which site a particular file or dataset represents. The following abbreviations are used:
&lt;ul&gt;
&lt;li&gt;ACS: Anthracite Creek at Somerset, Colorado, USA&lt;/li&gt;
&lt;li&gt;BRA: Blue River below Dillon, Colorado, USA (collected in August 2023)&lt;/li&gt;
&lt;li&gt;BRJ: Blue River below Dillon, Colorado, USA (collected in June 2023)&lt;/li&gt;
&lt;li&gt;CRG: Colorado River below Glenwood Springs, Colorado, USA&lt;/li&gt;
&lt;li&gt;CRR: Colorado River above Roaring Fork River at Glenwood Springs, Colorado, USA&lt;/li&gt;
&lt;li&gt;MCA: Maroon Creek near Aspen, Colorado, USA&lt;/li&gt;
&lt;/ul&gt;

Tags: acoustic-doppler-current-profiling, aerial-video, doppler-radar-velocimetry, drone, hydrographic-datasets, image-velocimetry, inlandwaters, large-scale-particle-image-velocimetry, probability-concept, remote-sensing, space-time-image-velocimetry, stream-discharge, stream-gage-measurement, streamflow, suas, unmanned-aircraft-system, unoccupied-aircraft-system, usgs-65660bd6d34e3aa43a43f6e3, usgs-science-data-catalog-sdc, videography, water-resources
Organization: Department of the Interior
Unoccupied Aerial System-mounted image velocimetry and Doppler velocity radar data for computation of river velocity and discharge collected at seven locations in Colorado in 2023: Ancillary Scripts

This child item contains Microsoft Windows batch and Python script files that reproduce the processing steps for specific files associated with image calibration, collating video metadata, and extracting velocity data from associated hydroacoustic discharge measurements. The following scripts are included:
&lt;br&gt;
&lt;ul&gt;
&lt;li&gt;create_calibration_image.bat&lt;/li&gt;
&lt;li&gt;extract_metadata.bat&lt;/li&gt;
&lt;li&gt;parse_qrev_map_files.py&lt;/li&gt;
&lt;li&gt;parse_qrevms_files.py&lt;/li&gt;
&lt;/ul&gt;
See the FGDC metadata Data Quality Section Process Steps for details on these scripts.
&lt;br&gt;
Each field site is abbreviated in various files in this data release. File and folder names quickly identify which site a particular file or dataset represents. The following abbreviations are used:
&lt;ul&gt;
&lt;li&gt;ACS: Anthracite Creek at Somerset, Colorado, USA&lt;/li&gt;
&lt;li&gt;BRA: Blue River below Dillon, Colorado, USA (collected in August 2023)&lt;/li&gt;
&lt;li&gt;BRJ: Blue River below Dillon, Colorado, USA (collected in June 2023)&lt;/li&gt;
&lt;li&gt;CRG: Colorado River below Glenwood Springs, Colorado, USA&lt;/li&gt;
&lt;li&gt;CRR: Colorado R above Roaring Fork R at Glenwood Springs, Colorado, USA&lt;/li&gt;
&lt;li&gt;ERW: Eagle River below Milk Creek near Wolcott, Colorado, USA&lt;/li&gt;
&lt;li&gt;MCA: Maroon Creek near Aspen, Colorado, USA&lt;/li&gt;
&lt;li&gt;RFG: Roaring Fork at Glenwood Springs, Colorado, USA&lt;/li&gt;
&lt;/ul&gt;
&lt;strong&gt;Software Requirements&lt;/strong&gt;
To execute the scripts included in this child item, users will need to have the following software:
&lt;ol&gt;
&lt;li&gt;Python Software Foundation. Python Language Reference, version 3.9. Available at http://www.python.org &lt;/li&gt;
&lt;li&gt;Microsoft Windows batch files can be executed in a Windows Command Prompt.&lt;/li&gt;
&lt;/ol&gt;

Tags: acoustic-doppler-current-profiling, aerial-video, doppler-radar-velocimetry, drone, hydrographic-datasets, image-velocimetry, inlandwaters, large-scale-particle-image-velocimetry, probability-concept, remote-sensing, space-time-image-velocimetry, stream-discharge, stream-gage-measurement, streamflow, suas, unmanned-aircraft-system, unoccupied-aircraft-system, usgs-65660aa3d34e3aa43a43f6d3, usgs-science-data-catalog-sdc, videography, water-resources
Organization: Department of the Interior
Upper Skagit River intrinsic potential results

We assessed habitat suitability for salmonids across selected tributaries upstream from three hydroelectric dams on the upper Skagit River in Whatcom County, northern Washington. We used NetMap, a commercial toolset within the ArcMap geographic information system (GIS), to analyze stream attributes based upon a synthetic stream channel network derived from digital elevation models. The GIS-derived stream attributes—including gradient, bankfull width, valley width index, elevation, and stream flow—allowed us to examine the spatial distribution and relative quality of spawning and rearing habitat for salmonids based on existing intrinsic potential (IP) models. As a first step, we created maps of potential anadromous fish distribution by identifying potential migration barriers within the synthetic stream network. Next, we applied a suite of existing IP models for steelhead, Coho, and Chinook Salmon (Oncorhynchus mykiss, O. kisutch, and O. tshawytscha, respectively) to estimate low, medium, and high IP habitat for each species. Three different IP models were used for each species, based on species preference curves from populations from coastal Oregon, northern California, Alaska, and western Washington.

Tags: biota, habitat-suitability-indices, usgs-65651951d34e3aa43a43d03c
Organization: Department of the Interior
Geochemical analyses of rock samples collected from the Judith Mountains and Crazy Mountains alkalic complexes, Montana

Geochemical data include major, minor and trace element results for 42 alkaline silicate igneous rock samples. Samples were collected from natural exposures/outcrops during field work in the Central Montana Alkaline Province (CMAP) during the summer of 2022. All samples were analyzed by the U.S. Geological Survey contract laboratory, SGS Labs. Major, minor, and trace elements were determined via wavelength dispersive X-ray fluorescence (WDXRF), inductively coupled plasma-optical emission spectrometry (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS). Additional analytes determined were carbonate carbon by combustion and infrared detection (IR); gold, platinum, and palladium by lead fusion fire assay and ICP-MS; fluoride by ion-selective electrode; ferrous iron by titration; non-essential and essential water by gravimetric methods, and total sulfur and total carbon by IR.
Data are reported in comma-separated values (CSV) files. All column headers are described in the accompanying data dictionary. Geologic reference material preferred and mean values are also provided in the accompanying QAQC_Values.csv.

Tags: alkaline-complexes, central-montana-alkalic-province, chemical-analysis, cmap, crazy-mountains, crazy-peak, critical-elements, critical-minerals, exploration-geochemistry, geochemical-data, geochemistry, geology-geophysics-and-geochemistry-science-center, geoscientificinformation, gggsc, igneous-rocks, judith-mountains, judith-peak, mineral-resources, mineral-resources-program, montana, mrp, natural-resource-exploration, petrology, rock-chemistry, united-states, united-states-geological-survey, usgs, usgs-6564fd52d34e3aa43a43cb2f, whole-rock
Organization: Department of the Interior
Datasets for the U.S. Geological Survey - Idaho National Laboratory groundwater and surface-water monitoring networks, v1.1

A collection of analysis-ready datasets for the U.S. Geological Survey - Idaho National Laboratory (USGS-INL) groundwater and surface-water monitoring networks, administered by the USGS-INL Project Office in cooperation with the U.S. Department of Energy. The data collected from wells and surface-water stations at the Idaho National Laboratory and surrounding areas have been used to describe the effects of waste disposal on water contained in the eastern Snake River Plain aquifer, located in the southeastern part of Idaho, and the availability of water for long-term consumptive and industrial use. The datasets include long-term monitoring records dating back to measurements from 1922. Geospatial data describing the areas from which samples were collected or observations were made are also included.

Tags: boundaries, butte, contamination-and-pollution, data-release, dissolved-metals, dissolved-organic-compounds, geoscientificinformation, geospatial-datasets, groundwater-quality, idaho, idaho-national-engineering-laboratory, snake-river-plain-basaltic-rock-aquifers, study-areas, time-series-datasets, toxic-radionuclide-contamination, usgs-6564e862d34e3aa43a43c4ca, waste-treatment-and-disposal, water-quality, water-quality-data, water-sampling
Organization: Department of the Interior
Multibeam Bathymetry Data Collected in 2018 from Grand Bay and Point Aux Chenes Bay Alabama/Mississippi: Processed elevation point data (x,y,z)

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi (AL/MS) October 22-23, 2018. This dataset, Grand_Bay_2018_MBB_xyz.zip, includes the processed point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

Tags: alabama, applanix-pos-mv-wavemaster-ii, bathymetry, elevation, geophysics, geoscientificinformation, grand-bay, grand-bay-national-wildlife-refuge, hydrography, imagerybasemapsearthcover, location, marine-geology, mississippi, multibeam-sonar, ocean-characteristics, ocean-processes, oceans, point-aux-chenes-bay, seafloor-mapping, spcmsc, st-petersburg-coastal-and-marine-science-center, teledyne-reson-t50-p, u-s-geological-survey, usgs, usgs-6561e7d5-cd72-4d28-b459-b8720f050fc1
Organization: Department of the Interior
ECCOE 2022 Surface Reflectance Validation Dataset

Scientists and engineers from the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) Cal/Val Center of Excellence (ECCOE) collected in situ measurements using field spectrometers to support the validation of surface reflectance products derived from Earth observing remote sensing imagery. Data provided in this data release were collected during select Earth observing satellite overpasses and tests during the months of May through October 2022.
Data was collected at three field sites:  the ground viewing radiometer (GVR) site on the USGS EROS facility in Minnehaha County, South Dakota, a private land holding near the City of Arlington in Brookings County, South Dakota, and a private land holding in Sanborn County, South Dakota.
Each field collection file includes the calculated surface reflectance of each wavelength collected using a dual field spectrometer methodology.  The dual field spectrometer methodology allows for the calculated surface reflectance of each wavelength to be computed using one or both of the spectrometers.  The use of the dual field spectrometers system reduces uncertainty in the field measurements by accounting for changes in solar irradiance.    Both single and dual spectrometer calculated surface reflectance are included with this dataset.  The differing methodologies of the calculated surface reflectance data are denoted as "Single Spectrometer" and "Dual Spectrometer".  Field spectrometer data are provided as Comma Separated Values (CSV) files and GeoPackage files.  The 09 May 2022 and the 16 June 2022 collection data are calculated using single spectrometer only, due to a technical issue with a field spectrometer.

Tags: arlington, brookings-county, enmap, environmental-mapping-and-analysis-program, field-spectrometer, imagerybasemapsearthcover, landsat, landsat-8, landsat-9, landsat-level-2, minnehaha-county, sanborn-county, sentinel-2, south-dakota, surface-reflectance, surface-reflectance-validation, usgs-655e9120d34e3aa43a43b477, usgs-eros-center
Organization: Department of the Interior
EPA Impaired Waters presence and causes for HUC12 watersheds across the Conterminous U.S.

This dataset summarizes impairment status for HUC12 watersheds at the CONUS scale using the EPA publicly available Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System (ATTAINS) geospatial package and the USGS Watershed Boundary Dataset (WBD). ATTAINS is an online system maintained by the EPA containing information about the condition of the Nation’s surface waters, as reported by individual states. These data were downloaded in October 2023 and primarily reflect 2022 stream conditions. Because of the varying data types that exist in the database, an independent methodology was developed for summarizing impairment status for HUC12 watersheds using the USGS WBD as detailed in this metadata record. These process steps ensured consistency, standardization, and transparency of various spatial attributes.  HUC12 watersheds flagged  as “Impaired” indicate there are impaired waters within the HUC12 watershed, but does not imply all waters within the HUC12 are listed as impaired. A supplemental table indicating the cause of impairment at the HUC12 scale, where known, is also provided.

Tags: contaminants, contamination-and-pollution, conterminous-u-s, conus, ecosystem, environment, ground-water, impaired-waters, management, natural-resources, streams, surface-water-quality, usgs-655d3cb2d34ee4b6e05cca85, water, water-quality, watershed-management
Organization: Department of the Interior