AndreaBozzo/ceres-open-data-index huggingface dataset:
Species Distribution Models for Pectis imberbis, a Rare Plant Species in Southeastern Arizona

Species distribution models (SDMs) can be an important tool in rare species conservation. Specifically, SDMs have been used to location previously unknown populations and identify sites for reintroduction or translocation. With these goals in mind, we applied SDM to a recently listed plant species, Pectis imberbis, which is found in the Madrean Archipelago region of southern Arizona, USA, and northern Mexico. We used presence-pseudoabsence data and applied 10 replicates of 5 modeling algorithms, generalized linear model (GLM), generalized additive model (GAM, generalized boosted model (GBM, aka boosted regression trees), random forests (RF), and classification tree analysis (CTA) to 4 different predictor datasets which were developed with correlation analysis, feature selection, and variable importance analysis. The resulting models were evaluated based on k-fold cross validation using 4 different metrics: Cohen’s kappa statistic (K), the area under the curve of the receiver operating characteristic curve (ROC), the True Skill Statistic (TSS), and the Boyce Index (BI). High performing models were then included in ensemble model building using the ensemble mean, ensemble median, and committee averaging methods. We applied optimized threshold values to transform continuous species presence probability rasters into binary presence/absence rasters. We also computed the coefficient of variation for the model components of each predictor dataset.
Based on calibration metrics, coefficient of variation between component models, consistency across known populations, and data coverage, the best model from these analyses for this application is the ensemble committee averaging based on the ROC metric using the 5-predictor dataset with USGS geologic data and a threshold based on optimizing the TSS (USGS-5v_EMcaByROC_binTSS.tif).
However, we present all 100 modeling outputs rasters (24 continuous rasters, 72 binary rasters, and 4 coefficient of variation rasters) in this data release.

Tags: arizona, biota, botany, ecology, endangered-populations, endangered-species, environment, madrean-archipelago, native-species, rare-species, sonora, species-distribution-model, translocation-threatened-and-endangered-species, united-states, usgs-6675cc03d34e6f159fd10f00
Organization: Department of the Interior
Geospatial Data for Delineating Drainage Basins in the Nevada StreamStats Study Area

This data release contains vector and raster geospatial data for delineating drainage basins in the Nevada StreamStats study area. Included are vector streamline, waterbody, and inner wall data used to hydrologically condition the digital elevation model. The hydrologically-conditioned digital elevation model, flow direction, flow accumulation, and stream delineation raster data are also included.

Tags: basins, digital-elevation-model, drainage-basins, elevation, geospatial-analysis, geospatial-datasets, hydrology, inlandwaters, lakes, modeling, playas, reservoirs, river-systems, rivers, streamflow, streams, surface-water-non-marine, usgs-6675c55ad34e6f159fd10eb5
Organization: Department of the Interior
Upper Colorado River Basin: Monitoring sites and water chemistry data used to develop a specific conductance - salinity proxy model

Salinity levels in streams and tributaries of the Colorado River Basin have been a major concern for years. Recently, the United States Geological Survey’s (USGS) Next Generation Water Observing System (NGWOS) program expanded stream monitoring networks including the number of sites where continuous (15-minute) specific conductance is measured in the Colorado River Headwaters and Gunnison River subbasins located east of the Colorado-Utah state line (hereafter, UCOL). Salinity and total dissolved solids (TDS) can be estimated using specific conductance and water type as a proxy (McCleskey et al., 2023); thus, the UCOL is an ideal basin to apply the proxy. The data presented in this data release, including monitoring site information and water chemistry data, were used to develop a specific conductance and water type proxy model for salinity and TDS for sixty-six USGS monitoring sites in the UCOL. The monitoring site information and water-quality data for the sample sites in the UCOL were retrieved from the Water Quality Portal (Read et al., 2017) using the USGS dataRetrieval R package (De Cicco et al., 2018). The dataset contains 80,206 discrete water analyses collected between 1990 and 2023. The water chemistry data includes the concentrations of calcium, magnesium, sodium, potassium, chloride, sulfate, fluoride, nitrate, iron, boron, aluminum, alkalinity, and silica. A subset of this data includes 4,588 samples all of which have at least the concentrations of calcium, magnesium, sodium, potassium, chloride, and sulfate reported, a charge balance &lt; ±10%, and a specific conductance imbalance &lt; ±15%, unless the specific conductance was less than 100 µS/cm in which case the specific conductance difference was &lt; ± 10 µS/cm. Finally, salinity and TDS were calculated for the discrete samples in the subset (McCleskey et al., 2023).
References
De Cicco, L.A., Hirsch, R.M., Lorenz, D. and Watkins, W.D., 2018. dataRetrieval: R packages for discovering and retrieving water data available from Federal hydrologic web services, doi:10.5066/P9X4L3GE.
McCleskey, R.B., Cravotta, C.A., Miller, M.P., Tillman, F.D., Stackelberg, P., Knierim, K.J. and Wise, D., 2023. Salinity and total dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type. Applied Geochemistry, 154.
Read, E.K., Carr, L., De Cicco, L., Dugan, H.A., Hanson, P.C., Hart, J.A., Kreft, J., Read, J.S. and Winslow, L.A., 2017. Water quality data for national-scale aquatic research: The Water Quality Portal. Water Resources Research, 53(2): 1735-1745

Tags: colorado, colorado-river, dissolved-solids, environment, gunnison-river, inlandwaters, location, salinity, specific-conductance, usgs-6675ab95d34e6f159fd10b5e, water-chemistry
Organization: Department of the Interior
Corresponding dataset for effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly

This study experimentally tested whether utilizing trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. This dataset includes the data from spotted lanternflys (SLF) surveys in 20 vineyards in Pennsylvania and New Jersey, USA using both human and trained detection dogs as observers. We used a multi-scale occupancy model to estimate detection probability of human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. 
The dataset includes six csv files with the data from the project, including the human surveys, dog surveys, detection data for each, and associated covariate data. It also includes the scripts used to process the data for analysis and the modeling scripts including:
1) Build occupancy dataset.R: formats vine and forest detection data along with transect covariates for occupancy analysis. 
2) Run Lanternfly Occupancy Landsape Covs FiniteMix with detection.R: runs the multiscale occupancy analysis. 
3) Run search times.R: does the search time analysis. 
4) Process Posterior.R: takes the output of (2) and (3) and produces posterior point and interval estimates along with plots.

Tags: biota, detection-dog, invasive-species, lycorma-delicatula, multi-scale-occupancy-model, new-jersey, occupancy, pennsylvania, spotted-lanternfly, usgs-66758bcdd34ecf5e4746b4d0
Organization: Department of the Interior
Autonomous Underwater Vehicle Water-Quality Measurements in the Cape Fear River, multiple locations, North Carolina, 2022

This U.S. Geological Survey (USGS) data release presents water-quality and bathymetric data measured with an Autonomous Underwater Vehicle (AUV) during spatial surveys in the Cape Fear River near Lock and Dam 1 near Riegelwood, NC and at the confluence of the Haw and Deep Rivers near Moncure, NC in 2022.  The Cape Fear River Basin is contained entirely within North Carolina and has a drainage area of 3,464 square miles.  Two data collection trips were conducted that included 15 surveys.  Data from each survey are presented in a CSV file, named with the survey date, time, and location.  Data were measured by the AUV and logged at 0.25-second intervals.  The AUV was equipped with a YSI EXO1 multiparameter sonde with sensors to measure the following water-quality parameters: water temperature (degrees Celsius) and specific conductivity (microSiemens per centimeter at 25 degrees Celsius) (YSI sensor 599827), pH (standard units; YSI sensor 577602), chlorophyll and phycocyanin fluorescence (relative fluorescence units; YSI sensor 599102-01), and dissolved oxygen (milligrams per liter; YSI sensor 599100-01).  Sensors were calibrated before and checked after each data collection trip per USGS water-quality sampling protocols (YSI, 2020; Wagner and others, 2006; Wilde, variously dated).  See Data Quality section for additional information related to calibration and checks.  Bathymetric data were measured with an on-board depth sounder and pressure transducer.  Parameters include depth from surface, depth to bottom, and total water column.  See Data Quality section for additional information related to positional accuracy and corrections.

Tags: autonomous-underwater-vehicle-auv, biota, bladen-county-nc, brunswick-county-nc, cape-fear, chatham-county-nc, chlorophyll, columbus-county-nc, cumberland-county-nc, dissolved-oxygen, harnett-county-nc, lee-county-nc, lower-cape-fear, north-america, north-carolina, phycocyanin-blue-green-algae, river-systems, specific-conductance, surface-water-quality, temperature, united-states, upper-cape-fear, usgs-66758947d34ecf5e4746b495, water-quality
Organization: Department of the Interior
Field spectra acquired during a tracer experiment on the Missouri River near Lexington, MO, on May 11, 2024

This data release includes field spectra collected from a boat during a tracer experiment performed on the Missouri River near Lexington, Missouri, on May 11, 2024.  One of the primary goals of this study was to assess the feasibility of inferring concentrations of a visible dye (Rhodamine WT) in a large, highly turbid natural river channel using data from a Uncrewed Aircraft Systems (UAS)-based hyperspectral imaging system. Previous research on remote sensing of tracer dye concentrations demonstrated the ability to obtain moderately precise concentration estimates from standard red-green-blue (RGB) video and orthophotos and this experiment allowed us to evaluate the hypothesis that more detailed spectral information could enable concentrations to be inferred with greater accuracy and precision. The broader objective motivating tracer studies along the Missouri River is to gain insight regarding the dispersion processes that influence the movement and survival of endangered sturgeon larvae.
This data release provides access to a data set obtained to support this experiment: Field spectra recorded along a channel cross section as the plume of Rhodamine WT dye passed by.  The data consist of reflectance measurements obtained above the water surface over the wavelength range from 400-900 nm.
Please refer to the metadata file for further detail about this data set.  Overall, these data were used to assess the potential for estimating tracer dye concentrations in turbid rivers from UAS-based hyperspectral image data.

Tags: concentration, dispersion, field-spectra, lexington, missouri, missouri-river, pallid-sturgeon, rhodamine-dye, river, tracer-study, usgs-66c482a7d34e03388288bc00
Organization: Department of the Interior
LANDFIRE Limited Annual Disturbance Puerto Rico US Virgin Islands 2023

The LANDFIRE Limited Disturbance (LDist)23 product is a new product introduced with the LF 2023 Update (LF 2023). LDist23 is an early "draft" of the LANDFIRE Annual Disturbance product and includes disturbance events captured through October 31, 2023. LDist23 is the first of three Annual Disturbance products releasing in the LF 2023 Update. LDist23 releases in January 2024, then Preliminary Disturbance (PDist)23 will release mid-year 2024. PDist23 will be the second "draft" of Annual Disturbance for the LF 2023 update. Finally, in the fall of 2024 the final "draft" of Annual Disturbance (Dist23) will be released.

Tags: annual-disturbance, biota, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, ia, image-collections, imagerybasemapsearthcover, insular-areas, landfire-2023, ldist, lf-2023, limited-annual-disturbance-2023, oconus, puerto-rico, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, us-virgin-islands, usgs-6674631ed34e68d163086b77, yearly-disturbance
Organization: Department of the Interior
LANDFIRE Limited Annual Disturbance CONUS 2023

The LANDFIRE Limited Disturbance (LDist)23 product is a new product introduced with the LF 2023 Update (LF 2023). LDist23 is an early "draft" of the LANDFIRE Annual Disturbance product and includes disturbance events captured through October 31, 2023. LDist23 is the first of three Annual Disturbance products releasing in the LF 2023 Update. LDist23 releases in January 2024, then Preliminary Disturbance (PDist)23 will release mid-year 2024. PDist23 will be the second "draft" of Annual Disturbance for the LF 2023 update. Finally, in the fall of 2024 the final "draft" of Annual Disturbance (Dist23) will be released.

Tags: annual-disturbance, biota, conterminous-united-states, continental-u-s, conus, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2023, ldist, lf-2023, limited-annual-disturbance-2023, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-667462f7d34e68d163086b75, yearly-disturbance
Organization: Department of the Interior
LANDFIRE 2023 Fuel Disturbance (FDist) AK

LANDFIRE disturbance products are developed to provide temporal and spatial information related to landscape change. In the LF 2023 Update (LF 2023) Fuel Disturbance (FDist) uses the latest Annual Disturbance products from the effective disturbance years of 2014 to 2023. FDist is created from LF 2023 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 Forest Canopy Cover (CC), Height (CH), Bulk Density (CBD), Base Height (CBH), 13 Anderson Fire Behavior Fuel Models (FBFM13) and 40 Scott and Burgan (FBFM40). 
FDist is developed using the most current aggregated Annual Disturbance products from 2014 to 2023. All existing disturbances between 2014-2023 are represented in LF 2023, and the products are intended to be used in 2024 (the year of release).  
When using any product from the LF 2023 fuel product suite, users should consider adjusting fuel layers for disturbances that occurred after the end of the 2023 fiscal year (after October 1st, 2023). Disturbances that occurred after the end of the 2023 fiscal year are not accounted for within LF 2023 fuel products. Learn more about LF 2023 at https://landfire.gov/data/lf2023.

Tags: ak, alaska, biota, disturbance, fdist, fires, fuel-disturbance, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2023, lf-2023, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-667461c0d34e68d163086b50
Organization: Department of the Interior
LANDFIRE 2023 Scott and Burgan Fire Behavior Fuel Model (FBFM40) AK

LANDFIRE's 2023 Update (LF 2023) 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, grass/shrub, 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 the FBFM40 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 (LFTFC) Tool and Database for both disturbed and non-disturbed areas. In LF 2023 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 2014 to 2023 in the LF Fuel Disturbance (FDist) product are used. All existing disturbances between 2014-2023 are represented in LF 2023, and the products are intended to be used in 2024 (the year of release).  
When using any product from the LF 2023 fuel product suite, users should consider adjusting fuel layers for disturbances that occurred after the end of the 2023 fiscal year (after October 1st, 2023). Disturbances that occurred after the end of the 2023 fiscal year are not accounted for within LF 2023 fuel products. Learn more about LF 2023 at https://landfire.gov/data/lf2023.

Tags: ak, alaska, biota, fbfm, fbfm40, fire-behavior-fuel-model-40, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2023, lf-2023, 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-6674618fd34e68d163086b4e
Organization: Department of the Interior
LANDFIRE 2023 Anderson Fire Behavior Fuel Model (FBFM13) AK

LANDFIRE's 2023 Update (LF 2023) 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 the FBFM13 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 (LFTFC) Tool and Database. In LF 2023, 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 2014 to 2023 in the LF Fuel Disturbance (FDist) product are used. All existing disturbances between 2014-2023 are represented in LF 2023, and the products are intended to be used in 2024 (the year of release).  
When using any product from the LF 2023 fuel product suite, users should consider adjusting fuel layers for disturbances that occurred after the end of the 2023 fiscal year (after October 1st, 2023). Disturbances that occurred after the end of the 2023 fiscal year are not accounted for within LF 2023 fuel products. Learn more about LF 2023 at https://landfire.gov/data/lf2023

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-2023, lf-2023, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-66746169d34e68d163086b4c
Organization: Department of the Interior
Attributes for NHDPlus Version 2.1 Catchments and Modified Routing of Upstream Watersheds for Selected Regions of the Conterminous United States

This metadata record describes a series of data sets of climate and land-use/land-cover features (provided in parquet file format) linked to NHDPlus Version 2.1’s (NHDPlusV2) stream segments, their associated catchments and their upstream watersheds within selected regions in the conterminous United States. All data can be linked to NHDPlusV2 using the COMID field in these tables and the ComID in the flowline shapefiles or FEATUREID in the catchment shapefiles in the NHDPlusV2 data suite.

Tags: climate, great-lakes, inlandwaters, land-use, mid-atlantic, nhdplusv2, pacific-northwest, south-atlantic-west, upper-colorado, usgs-66c4dee3d34e033882892161
Organization: Department of the Interior
LANDFIRE 2023 Existing Vegetation Height (EVH) AK

LANDFIRE's (LF) 2023 update (LF 2023) Existing Vegetation Height (EVH) represents the average height of the dominant vegetation for a 30-m cell. EVH is produced separately for tree, shrub, and herbaceous lifeforms using training data depicting the weighted average height by species cover and Existing Vegetation Type (EVT) lifeform. Decision tree models using field reference data, lidar, and Landsat are developed separately for each lifeform, then lifeform specific height class layers are merged along with land cover into a single EVH product based on the dominant lifeform of each pixel. EVH ranges are continuous for the herbaceous lifeform category ranging from 0.1 to 1 meter with decimeter increments, 0.1 to 3 meters for shrub lifeform, and 1 to 99 meters for tree lifeform. If the height values of each lifeform exceed the continuous value range, they are binned into the appropriate maximum height class. EVH is then reconciled through QA/QC measures to ensure lifeform is synchronized with Existing Vegetation Cover (EVC).  
In Alaska, developed and agricultural lands were derived from the National Land Cover Database (NLCD) 2016 Land Cover. Roads were derived from the NLCD 2016 Developed Urban Descriptor for south-central Alaska and a state-wide dataset from HERE Technologies. In the Alaska 90km buffer area developed and agricultural lands were derived from the North American Land Change Monitoring System ((NALCMS) https://www.mrlc.gov/data/north-american-land-change-monitoring-system) 2015 Land Cover and new developed lands were added from the NALCMS 2020 Land Cover for LF 2023. 
Disturbance events after 2016 are accounted for by incorporating transition rulesets using LF 2023 Fuel Disturbance (FDist). LF uses EVH as an input for LF 2023 Fuel Vegetation Height (FVH).

Tags: ak, alaska, biota, evh, existing-vegetation-height, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, image-collections, imagerybasemapsearthcover, landfire-2023, lf-2023, oconus, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-66743ba1d34e80199d7543a9
Organization: Department of the Interior