AndreaBozzo/ceres-open-data-index huggingface dataset:
Mangrove forest digital elevation model for Pohnpei, Federated States of Micronesia, 2019

Surface elevation of the mangrove forest is important for understanding current and future vulnerability to sea-level rise. Due to the lack of LiDAR data and insufficient accuracy of space-borne synthetic aperture radar-derived elevation models, we leveraged data from differential leveling surveys to generate a digital elevation model (DEM) for the mangrove forest of Pohnpei. We created a general slope model with a shore-normal transect of elevation and a general additive model (GAM) spine.  To create a continuous DEM, the GAM spline was then stretched across a grid representing the width of mangrove, created using digitized boundaries of the seaward and interior mangrove edge. This simplified approach assumes that the relationship between slope and width of the mangrove forest is constant. This DEM should be considered an intermediate product until a higher quality DEM is made available.

Tags: digital-elevation-model, environment, pacific-ocean, pohnpei, senyavin-islands, tidal-mangrove-forest, usgs-608b4077d34e3d6ea566f872
Organization: Department of the Interior
Hawai‘i Island locations of ‘Apapane and ‘I‘iwi from automated radio telemetry tracking system 2014 to 2016

We used an automated radio telemetry network to track the movement of two nectivorous Hawaiian honeycreepers, the ʻapapane (Himatione sanguinea) and ʻiʻiwi (Drepanis coccinea), collecting high temporal and spatial resolution data across the annual cycle. We identify movement syndromes using a multivariate analysis of multiple movement metrics and assessed seasonal changes in movement behavior.  Birds made long-distance flights, including multi-day forays outside the tracking array, but exhibited a high degree of fidelity to a core use area, even in the non-breeding period. Both species visited forests at elevations where avian malaria potentially occurs, although overall exhibited very little seasonal change in elevation (&lt; 150 m) and regularly returned to high-elevation roost sites at night. Birds were tracked from January 2014 to July 2016.

Tags: biota, birds, hakalau-forest-national-wildlife-refuge, hawaii-island, movement-ecology, telemetry, usgs-608f9fd5d34e93746a70d20d
Organization: Department of the Interior
Land Change Monitoring, Assessment, and Projection (LCMAP) CONUS Collection 1.2 Annual Land Cover and Land Cover Change Validation Tables (1985–2018) for the Conterminous United States

A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.2 annual land cover products (1985–2018) for the Conterminous United States was conducted with an independently collected reference dataset. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) at to a reference sample of 26,971 Landsat resolution (30m x 30m) pixels. These pixels were selected from a sample frame of all pixels in the ARD grid system which fell within the map area (Dwyer et al., 2018). Interpretation used the TimeSync reference data collection tool which visualizes Landsat images and Landsat data values for all usable images in the time series (1984–2018) (Cohen et al., 2010). Interpreters also referred to air photos and high resolution images available in Google Earth as well as several ancillary data layers. The interpreted land cover attributes were crosswalked to the LCMAP annual land cover classes: Developed, Cropland, Grass/Shrub, Tree Cover, Wetland, Water, Snow/Ice and Barren.
Validation analysis directly compared reference labels with annual LCMAP land cover map attributes by cross tabulation.  The results of that assessment are reported here as confusion matrices for land cover agreement and land cover change agreement. Accuracy and standard errors have been calculated using stratified estimation (Stehman, 2014). Land cover class proportions were also estimated from the reference data for each year, 1985–2018. A cluster sampling formulation was used to calculate standard sampling error for summary tables reporting results for multiple years of data comparison.

Tags: accuracy-assessment, environment, land-change-monitoring-assessment-and-projection, land-use-and-land-cover, land-use-change, landsat, lcmap, time-series-datasets, usgs-609020a6d34e93746a7103e4, validation
Organization: Department of the Interior
Collection, analysis, and age-dating of sediment cores from mangrove wetlands in San Juan Bay Estuary, Puerto Rico, 2016

The San Juan Bay Estuary, Puerto Rico, contains mangrove forests that store significant amounts of organic carbon in soils and biomass. There is a strong urbanization gradient across the estuary, from the highly urbanized and clogged Caño Martin Peña in the western part of the estuary, a series of lagoons in the center of the estuary, and a tropical forest reserve (Piñones) in the easternmost part with limited urbanization. We collected sediment cores to determine carbon burial rates and vertical sediment accretion from five sites in the San Juan Bay Estuary. Cores were radiometrically-dated using lead-210 and the Plum age model. Sites had soil C burial rates ranging from 50 grams per meter squared per year (g m-2 y-1) in the San José lagoon to 632 g m-2 y-1 in the Caño Martin Peña in recent decades. Soil accretion and carbon burial rates were greater in recent decades (1970-2016) compared to historic decades (1930-1970) at some of the forest mangrove sites (i.e. Caño Martin Peña). Apparently, not only urbanization, but site-specific flushing patterns, landscape setting, and soil characteristics affected soil C burial rates. This dataset can help evaluate how differences in urbanization (low in the forest preserve to high in the clogged canal), flushing, and landscape setting influence soil accretion and carbon burial in urban, tropical mangrove forests.

Tags: 137-cesium, 210-lead, accretion-rate, age-model, cano-de-martin-pena-1613013, carbon, carbon-burial, carbon-isotope-analysis, commonwealth-of-puerto-rico-1779808, ecological-restoration, environment, geoscientificinformation, health, laguna-de-pinones-1611336, laguna-la-torrecilla-1611340, laguna-san-jose-1611343, mangrove-sediments, nitrogen, oceans, plum-age-model, radiometric-dating, san-juan-1612697, sea-level-change, sedimentation, soil-chemistry, united-states-of-america-1890467, usgs-60902e3fd34e93746a710491, utilitiescommunication, wetland-ecosystems, wetland-soils, wetlands
Organization: Department of the Interior
Environmental Conditions during Growth of Bouteloua curtipendula in Mining Wastes

A 45-day mesocosom experiment was conducted to determine how well a perennial grass species (Bouteloua curtipendula) grew in mining wastes from southeastern Arizona. The experiment tested whether the addition of an endophyte seed coat combined with a top dressing of compost improved grass growth, organic matter accumulation, and metal stabilization in the mining wastes (i.e., phytostabilization).   The mining wastes were sourced from two formerly active hard rock mines: waste rock from a porphyry copper open pit mine and tailings from a tunnel and shaft polymetallic mine.  The plants were grown for 45 days in an indoor growing complex.  These data give the environmental conditions of the indoor growing complex (light, temperature, and water conditions) during the experiment.

Tags: bioremediation, biota, laboratory-experiments, usgs-609045a1d34e791692de5189, vegetation
Organization: Department of the Interior
Historical video footage of Mount St. Helens volcano - 1981

This data release contains digital video files from the USGS of Mount St. Helens volcano in the months leading up to the cataclysmic eruption on May 18, 1980 as well as the first two years of the eruption. Original videos were collected on an assortment of cameras and film types, including 16 mm, 8 mm and Super 8 mm during USGS field campaigns on the ground as well as from the air and on time-lapse camera stations setup around the volcano. Videos capture USGS crews collecting data as well as an assortment of volcanic processes, including dome growth, ash emissions, rockslides, and pyroclastic flows. These film records represent a tremendous resource to the volcanology community as they represent visual recordings of a truly remarkable event in history. The analog film reels were digitized through funding from the USGS National Geological and Geophysical Data Preservation Program (NGGDPP) to reduce the risk of data loss and to make these visual recordings accessible and available in a modern digital format.
Video clips are organized into 4 sections: March 28-May 17, 1980; May 18-Dec 31, 1980; 1981; 1982. Each video is labeled by YYYY_MM_DD_USGS and categorized by either FW = fieldwork, TL = timelapse, or MISC = miscellaneous.

Tags: biota, debris-flow, lahar, lava-dome, mount-st-helens, pyroclastic-flow, usa, usgs-609077dbd34e791692de543f, volcanic-ash, volcanic-eruption, volcano, washington-state
Organization: Department of the Interior
Historical video footage of Mount St. Helens volcano - 1982

This data release contains digital video files from the USGS of Mount St. Helens volcano in the months leading up to the cataclysmic eruption on May 18, 1980 as well as the first two years of the eruption. Original videos were collected on an assortment of cameras and film types, including 16 mm, 8 mm and Super 8 mm during USGS field campaigns on the ground as well as from the air and on time-lapse camera stations setup around the volcano. Videos capture USGS crews collecting data as well as an assortment of volcanic processes, including dome growth, ash emissions, rockslides, and pyroclastic flows. These film records represent a tremendous resource to the volcanology community as they represent visual recordings of a truly remarkable event in history. The analog film reels were digitized through funding from the USGS National Geological and Geophysical Data Preservation Program (NGGDPP) to reduce the risk of data loss and to make these visual recordings accessible and available in a modern digital format.
Video clips are organized into 4 sections: March 28-May 17, 1980; May 18-Dec 31, 1980; 1981; 1982. Each video is labeled by YYYY_MM_DD_USGS and categorized by either FW = fieldwork, TL = timelapse, or MISC = miscellaneous.

Tags: biota, debris-flow, lahar, lava-dome, mount-st-helens, pyroclastic-flow, usa, usgs-609077eed34e791692de5441, volcanic-ash, volcanic-eruption, volcano, washington-state
Organization: Department of the Interior
Input data, model output, and R scripts for a machine learning streamflow model on the Wyoming Range, Wyoming, 2012–17

A machine learning streamflow (MLFLOW) model was developed in R (model is in the Rscripts folder) for modeling monthly streamflow from 2012 to 2017 in three watersheds on the Wyoming Range in the upper Green River basin. Geospatial information for 125 site features (vector data are in the Sites.shp file) and discrete streamflow observation data and environmental predictor data were used in fitting the MLFLOW model and predicting with the fitted model. Tabular calibration and validation data are in the Model_Fitting_Site_Data.csv file, totaling 971 discrete observations and predictions of monthly streamflow. Geospatial information for 17,518 stream grid cells (raster data are in the Streams.tif file) and environmental predictor data were used for continuous streamflow predictions with the MLFLOW model. Tabular prediction data for all the study area (17,518 stream grid cells) and study period (72 months; 2012–17) are in the Model_Prediction_Stream_Data.csv file, totaling 1,261,296 predictions of spatially and temporally continuous monthly streamflow. Additional information about the datasets is in the metadata included in the four zipped dataset files and about the MLFLOW model is in the readme included in the zipped model archive folder.

Tags: environment, geoscientificinformation, geospatial-analysis, geospatial-datasets, hydrology, machine-learning, mathematical-modeling, multivariate-statistical-analysis, river-reaches, river-systems, streamflow, upper-green, usgs-60914970d34e791692e13638, wyoming, wyoming-landscape-conservation-initiative
Organization: Department of the Interior
Lake Michigan Sea Duck Survey 2009-2014

These data contain observation and null polygons for waterfowl aerial surveys of Lake Michigan collected through the years 2009-2014. Polygons were created adjacent to either side of the flight lines (transects). The right and left offset of the polygons from the flight path, was determined using the average altitude of the plane along the transect and the observation angle through the plane’s window. Observed birds were counted and identified by species. This count data was attributed to the polygon closest to the point along the transect where the observation occurred, and on the side of the plane in which the observation took place. The point data represent counts where each point represents a single species. If multiple species were observed, then the points will spatial coincident. Physical and biological attributes that may be influential to the distribution of waterfowl have been joined to the observation and null polygons.

Tags: biota, birds, usgs-609158add34e791692e140e6
Organization: Department of the Interior
Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada

This U.S. Geological Survey data release consists multiple datasets used to simulate the extents of flood inundation along the Muddy River, near Moapa, Nevada. Flood-inundation extents equal the maximum area of flood inundation and were estimated using a coupled one-dimensional (1D) and two-dimensional (2D) hydraulic model. The modeled extents represent six annual exceedance probabilities simulated for the current (2019) levee location adjacent to the Muddy River and a new levee location associated with a proposed restoration of a selected reach along the Muddy River. The data release includes:
1) a polygon dataset of the flood-inundation extents (MuddyRiver_Flood_Inundation_p.shp); 
2) a zip file containing all relevant files to document and run the PeakFQ flood-frequency analysis used as input into the hydraulic model (0941600_Flood_Frequency_Archive.zip); 
3) a zip file containing all relevant files to document and run the coupled 1D and 2D Hydrological Engineering Center-River Analysis System (HEC-RAS) hydraulic model used to generate a polygon dataset of flood-inundation extents (SWmodel_Archive.zip); 
4) a polygon dataset of the current and proposed levee locations (MuddyRiver_Levee_p.shp); 
5) a point dataset of survey points (RTK-TS_survey_x.shp) collected from April 1 to August 9, 2019, using real-time kinematic global navigation satellite system (GNSS) and total station (TS); 
and 
6) a point dataset of seven static reference locations (Static_GNSS_x.shp) collected from March 29 to August 9, 2019, using a single-baseline online positioning user service – static (OPUS-S) GNSS survey.

Tags: freshwater-ecosystems, inlandwaters, mathematical-modeling, moapa, moapa-valley, muddy-river, nevada, river-reaches, streamflow, surface-water-non-marine, usgs-609190c9d34e791692e15c6d
Organization: Department of the Interior
LCMAP CONUS Intensification Reference Data Product 1984–2019 land cover, land use and change process attributes

This product contains LCMAP CONUS Intensification Reference plot location data in a .shp format as well as annual land cover, land use, and change process variables for each reference data plot in a separate .csv table.  The same information available in the.csv file is also provided in a .xlsx format. 
The LCMAP Reference Data Product was utilized for evaluation and validation of the Land Change Monitoring, Assessment, and Projection (LCMAP) land cover and land cover change products. The LCMAP Reference Data Product includes the collection of an independent dataset of 25,000 randomly-distributed 30-meter by 30-meter plots across the conterminous United States (CONUS). The LCMAP Reference Data Products collected variables related to primary and secondary land use, primary and secondary land cover(s), change processes, and other ancillary variables annually across CONUS from 1984–2018. 
This dataset includes 2,000 additional samples collected annually for 1984-2019 via manual image interpretation to increase the sample size of rare change classes at the CONUS level.  This increased sample size will improve standard errors of the user's accuracy estimates for rare classes and improve estimates of area amounts of land cover change.  These 2,000 samples were selected using a stratified random sampling process with 4 strata based off the LCMAP Collection 1.0 Science Products. These intensification samples were selected to assist in reducing the standard error focused on change classes.

Tags: environment, image-analysis, land-change-monitoring-assessment-and-projection, land-use-and-land-cover, land-use-change, landsat, lcmap, location, time-series-datasets, usgs-6091ad37d34e791692e1683f
Organization: Department of the Interior
Orthoimagery for basin A1

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091c9f6d34e791692e1695a, vegetation
Organization: Department of the Interior
Final surface model (SRF) for basin A1

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091ca43d34e791692e16961, vegetation
Organization: Department of the Interior
Vegetation classification model (Veg) for basin A1

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091ca60d34e791692e16963, vegetation
Organization: Department of the Interior
Digital Elevation Model (DEM) for basin A2

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cabfd34e791692e1696b, vegetation
Organization: Department of the Interior