AndreaBozzo/ceres-open-data-index huggingface dataset:
Geospatial Dataset for the Geologic Framework and Hydrostratigraphy of the Edwards and Trinity Aquifers Within Northern Medina County, Texas at 1:24,000

The karstic Edwards and Trinity aquifers are classified as major sources of water in south-central Texas by the Texas Water Development Board, and both are classified as major aquifers by the State of Texas. The Edwards and Trinity aquifers developed because of the original depositional history of the carbonate limestone and dolomite rocks that contain them, and the primary and secondary porosity, diagenesis, fracturing, and faulting that modified the porosity, permeability, and transmissivity of each aquifer and of the geologic units separating the aquifers. Previous studies such as those by the U.S. Geological Survey (USGS) and the Edwards Aquifer Authority (EAA) have mapped the geology, hydrostratigraphy, and structure in these areas at various scales.  The purpose of this data release is to present the data that were collected and compiled to describe the geologic framework and hydrostratigraphy of northern Medina county, Texas in order to help water managers, water purveyors, and local residents better understand and manage water resources. The scope of the larger work and this accompanying data release is focused on the geologic framework and hydrostratigraphy of the outcrops and hydrostratigraphy of the rocks that contain the Edwards and Trinity aquifers within northern Medina county, Texas. These digital data accompany Clark and others (2024), which supersedes Scientific Investigations Map 3461.

Tags: austin-group, buda-limestone, del-rio-clay, devils-river-limestone, devils-river-trend, digitization, diversion-lake, eagle-ford-group, edwards-aquifer, edwards-group, fort-terrett-formation, geography, geologic-contacts, geologic-maps, geology, georgetown-formation, geoscientificinformation, geospatial-datasets, glen-rose-limestone, groundwater, haby-crossing-fault, hondo, hondo-creek, hydrogeology, hydrostratigraphy, kainer-formation, karst, maverick-basin, medina-county, medina-lake, medina-lake-fault, medina-river, parker-creek-fault, pecan-gap-chalk, person-formation, porosity, san-marcos-platform, seco-creek, stratigraphic-thickness, stratigraphy, surficial-geologic-units, taylor-group, texas, trinity-aquifer, trinity-group, usgs-64cc1812d34e2d58db2fa981, verde-creek, washita-group, woodard-cave-fault
Organization: Department of the Interior
Mercury in bird blood and benthic aquatic invertebrates in Plumas National Forest, 2015-2016

The dataset includes specifics on bird blood and aquatic invertebrate tissue mercury concentrations measured using direct mercury analysis.

Tags: aquatic-biology, benthic-ecosystems, biota, birds, ecotoxicology, environment, field-sampling, inlandwaters, macroinvertebrates, mercury-contamination, ornithology, usgs-64f89bc3d34ed30c205464da, wildlife-biology
Organization: Department of the Interior
Remotely sensed data from a reach of the Sacramento River near Glenn, California, used to perform Particle Image Velocimetry (PIV) within the Robot Operating System (ROS)

This data release provides an example data set to accompany the manuscript titled "A Robot Operating System (ROS) package for mapping flow fields in rivers via Particle Image Velocimetry (PIV)", submitted to the journal Software X. This ROS *.bag file contains remotely sensed data acquired during an Uncrewed Aircraft System (UAS) flight along a reach of the Sacramento River near Glenn, California, USA, on September 16, 2022. Cooperators on this project include the Intelligent Robotics Group from the National Aeronautics and Space Administration (NASA) Ames Research Center and the National Oceanographic and Atmospheric Administration (NOAA) Southwest Fisheries Science Center.
The file bag6xs600.bag contains a single topic named "/camera/image_raw" of the standard ROS message type sensor_msgs/Image. The individual messages published on this topic are frames acquired by an Allied Vision Mako G-503C color (RGB) camera at a rate of approximately 4 Hz. This camera was one of several included in an experimental payload designed for measuring streamflow. For this test flight, the payload was deployed from a DJI Matrice M600 hovering approximately 137 m above the river surface. The data are not geo-referenced but the pixel size of the images is approximately 0.078 m.
To simply view the data, the Foxglove Studio program linked below (https://foxglove.dev/download) provides a convenient platform. To actually read the bag file and perform PIV, please refer to the USGS GitLab repository for the ROSPIV network (https://code.usgs.gov/wma/osd/trivia/-/tree/ROSPIV/ROSPIV?ref_type=heads), which includes a document outlining how to set up ROS on a Windows computer, a user's guide for the ROSPIV network, and a demo script for illustrating its application using the bag file provided in this data release.

Tags: california, glenn, particle-image-velocimetry-piv, remote-sensing, river, robot-operating-system-ros, sacramento-river, uncrewed-aircraft-system-uas, usgs-64f8926ed34ed30c205464b7, velocity
Organization: Department of the Interior
LANDFIRE Annual Disturbance AK 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: ak, alaska, annual-disturbance, annual-disturbance-2021, biota, 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-64f73e2ed34ed30c20544661, yearly-disturbance
Organization: Department of the Interior
LANDFIRE 2022 Anderson Fire Behavior Fuel Model (FBFM13) AK

LANDFIRE's (LF) 2022 13 Anderson Fire Behavior Fuel Models (FBFM13) product represents distinct distributions of fuel loadings found among surface fuel components (live and dead), size classes, and fuel types (Anderson 1982). 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. LF FBFM13 can be used for fire spread related characteristic models. 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. In the LF 2022 update, non-disturbed fuels are the same as LF 2016 Remap for natural vegetation. 
To designate disturbed areas where FBFM13 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, anderson-fire-behavior-fuel-model, biota, fbfm, fbfm13, fire-behavior-fuel-model-13, 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-64f73df0d34ed30c20544655
Organization: Department of the Interior
LANDFIRE 2022 Scott and Burgan Fire Behavior Fuel Model (FBFM40) AK

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 and 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: ak, alaska, biota, fbfm, fbfm40, fire-behavior-fuel-model-40, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, 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-64f73dacd34ed30c20544653
Organization: Department of the Interior
Burned Area Reflectance Classification Thematic Burn Severity Mosaic for CONUS in 2023

These data products are preliminary burn severity assessments derived from data obtained from suitable imagery (including Landsat TM, Landsat ETM+, Landsat OLI, Sentinel 2A, and Sentinel 2B). The pre-fire and post-fire subsets included were used to create a differenced Normalized Burn Ratio (dNBR) image. The dNBR image attempts to portray the variation of burn severity within a fire. The severity ratings are influenced by the effects to the canopy.  The severity rating is based upon a composite of the severity to the understory (grass, shrub layers), midstory trees and overstory trees. Because there is often a strong correlation between canopy consumption and soil effects, this algorithm works in many cases for Burned Area Emergency Response (BAER) teams whose objective is a soil burn severity assessment. It is not, however, appropriate in all ecosystems or fires. It is expected that BAER teams will adjust the thresholds to match field observations to produce a soil burn severity. This map layer is a thematic raster image of burn severity classes for all inventoried fires occurring in CONUS during calendar year 2023. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires which were not discernable from available imagery.

Tags: baer, burn-severity, burned-area, burned-area-emergency-response, differenced-normalized-burn-ratio, fire-location, imagerybasemapsearthcover, landsat, location, normalized-burn-ratio, sentinel, united-states, us, usgs-64cbff6bd34e2d58db2fa916, wildfire, wildland-fire
Organization: Department of the Interior
LANDFIRE 2022 Canadian Forest Fire Danger Rating System (CFFDRS) AK

The LANDFIRE (LF) Canadian Forest Fire Danger Rating System (CFFDRS) product depicts fuel types as an identifiable association of fuel elements of distinctive species, form, size, arrangement, and continuity. CFFDRS exhibits characteristic fire behavior under the specified burn conditions. In LF 2022 Canadian fuel models are derived from the Fuel Model Guide to Alaska Vegetation (Alaska Fuel Model Guide Task Group, 2018) and subsequent updates. The LF CFFDRS product contains the fuel models used for the Fire Behavior Prediction (FBP) system fuel type inputs. Default values assigned to the Canadian Fuel Models required to run the Prometheus fire behavior software (Prometheus, 2021) are added as attributes to the LF CFFDRS product. 
To designate disturbed areas where CFFDRS is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance layer are used. All existing disturbances between 2013-2022 are represented in LF 2022 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 given the reduction in latency from when a disturbance occurs to the release date of fuel layers 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 layers, as those changes would not be accounted for.  Learn more about LF 2022 at https://landfire.gov/lf_230.php

Tags: ak, alaska, biota, canadian-forest-fire-danger-rating-system, cffdrs, 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-64f73b09d34ed30c2054464f
Organization: Department of the Interior
LANDFIRE 2022 Fuel Disturbance (FDist) AK

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: ak, alaska, biota, disturbance, fdist, fires, fuel-disturbance, 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-64f73a4dd34ed30c2054464d
Organization: Department of the Interior
Pre-mining environmental baseline characterization of the Nuqra Khana iron deposit: 2020 field season

These data include geochemical analyses of rock, soil, sediment, and water samples collected from the Nuqra Khana iron deposit, Panjshir Province, Afghanistan in 2020.

Tags: environmental-chemistry, geochemistry, usgs-64f73453d34ed30c2054462e
Organization: Department of the Interior
Groundwater wells from Minnesota, Wisconsin, and Michigan state databases and USGS NWIS database with static water level data within 10km of the Lake Superior watershed

This dataset includes over 49,000 well records from the state well drillers databases in Minnesota, Wisconsin, and Michigan.  Each state well has at a minimum the well depth and a static water level.  Static water levels were mostly determined when the well was constructed.  Data included in this shapefile include the well construction date, well depth, well elevation (if determined), type of well, the methods used for determining the well location and elevation (if determined), casing and screen depths (where reported), the static water level and a date, and the year the well was constructed.  The field names from each of the state databases were harmonized to merge the data, and a table of the original field names is included.  Tabular files are included with codes describing the original field names mapped to the combined field names, and descriptions of codes used to describe the well location method, well depth method, and well type for each state.  The USGS wells in this dataset were pulled from the National Water Information System (NWIS) and have at least one water level measurement.  Additional data for the NWIS wells can be retrieved from the USGS National Water Dashboard https://dashboard.waterdata.usgs.gov/app/nwd/en/?region=lower48&amp;aoi=default.

Tags: biota, groundwater, groundwater-level, michigan, minnesota, upper-peninsula, usgs-64f64438d34ed30c20542f79, wells, wisconsin
Organization: Department of the Interior
Digital Elevation Models (DEMs) Derived from Lidar Point Clouds (LPCs) Collected by Small, Uncrewed Aircraft Systems (sUAS) at Three Study Areas in Colorado, 2020-22

These digital elevation models (DEMs) were generated from light detection and ranging (lidar) point clouds (LPCs) derived from lidar data collected during multiple field campaigns at three study areas near Winter Park, Colorado. Small, uncrewed aircraft systems (sUAS) were used to collect lidar datasets to represent snow-covered and snow-free periods.

Tags: bare-earth, berthoud-pass, colorado, colorado-river, digital-elevation-model, fraser, lidar, snow, snow-depth, snow-water-equivalent, suas, usgs-64f40e91d34ed30c2054062a, winter-park
Organization: Department of the Interior
Model subareas and moisture zones used in a sensitivity analysis of a water-budget model completed in 2022 for the island of Maui, Hawaii

A sensitivity analysis of groundwater-recharge estimates from a water-budget model was completed for the islands of Oahu and Maui, Hawaii (Johnson and others, 2023). Results of the sensitivity analysis were used to quantify the relative importance of selected model parameters to recharge estimates for three moisture zones (dry, mesic, and wet) on Oahu and Maui. This shapefile contains the boundaries of the moisture zones and boundaries of the model subareas that were used in the model simulations for Maui. The shapefile attributes include the names of the land-cover types assigned to model subareas and the mean annual recharge values determined for the model subareas for the baseline scenario of the sensitivity analysis.

Tags: biota, boundaries, climatologymeteorologyatmosphere, environment, geoscientificinformation, groundwater, hawaii, inlandwaters, land-use-and-land-cover, maui, pacific-islands, usgs-64f27edfd34ed30c2053de6c, water-budget, water-cycle
Organization: Department of the Interior