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A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1 annual land cover products (1985–2017) for the Conterminous United States (CONUS) was conducted with an independently collected reference data set. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) to a reference sample of 24,971 randomly-selected Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are recorded for each year, 1985–2017.
Multispectral remote sensing data acquired by the Landsat 8 Operational Land Imager (OLI) sensor were analyzed using a new, automated technique to generate a map of exposed mineral and vegetation groups in the western San Juan Mountains, Colorado and the Four Corners Region of the United States (Rockwell and others, 2021). Spectral index (e.g. band-ratio) results were combined into displayed mineral and vegetation groups using Boolean algebra. New analysis logic has been implemented to exploit the coastal aerosol band in Landsat 8 OLI data and identify concentrations of iron sulfate minerals. These results may indicate the presence of near-surface pyrite, which can be a potential non-point source of acid rock drainage....
Categories: Data;
Types: ArcGIS REST Map Service,
ArcGIS Service Definition,
Downloadable,
Map Service;
Tags: Arizona,
Colorado,
Four Corners region,
Lake City caldera,
New Mexico,
This part of the data release contains the water-level measurement data compiled and synthesized from various sources. This collection includes two tables that contain all the water-level measurements that were considered to develop the water-level altitude maps (Input_VisGWDB), and a table of median water-level data that were used to develop the water-level altitude maps (MedianWaterLevelData). These digital data accompany Houston, N.A., Thomas, J.V., Foster, L.K., Pedraza, D.E., and Welborn, T.L., 2020, Hydrogeologic framework, groundwater-level Altitudes, groundwater-level changes, and groundwater-storage changes in selected alluvial basins of the upper Rio Grande Focus Area Study, Colorado, New Mexico, and...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Abiquiu Reservoir,
Ahumada,
Alamosa,
Alamosa County,
Alamosa Creek,
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point Cloud Coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Point clouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.
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) to a reference sample of 26,971 Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are recorded for each year, 1985-2018. LCMAP Collection 1.0 annual land cover products covered years 1985–2017 and the validation of the Collection 1.0 products were reported in the LCMAP Version 1.0 Annual Land Cover and Land Cover...
A validation assessment of Land Cover Monitoring, Assessment, and Projection Version 1 annual land cover products (1985–2017) for the Conterminous United States was conducted with an independently collected reference data set. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) to a reference sample of 24,971 randomly-selected Landsat resolution (30m x 30m) pixels. The interpreted land cover attributes were crosswalked to the LCMAP annual land cover classes: Developed, Cropland, Grass/Shrub, Tree Cover, Wetland, Water, Ice/Snow and Barren. Validation analysis directly compared reference labels with annual LCMAP land cover map attributes by cross...
This data release contains structure-from-motion (SfM) point cloud data from aerial surveys conducted over the Mud Creek landslide on Big Sur from 1967-2018. Data sources include scanned aerial photographs, digital images collected from fixed wing aircraft, and digital images collected from multirotor UAS.
Over the summer of 2020, the Illinois Waterway (IWW) was closed to complete maintenance on lock chambers along the Illinois River. This closure restricted vessel traffic along the river and potentially changed habitat characteristics for aquatic vegetation establishment and growth. To assess if patterns of vegetation establishment and growth changed during the closure, peak biomass imagery from 2019 (pre closure) and 2021 (post closure) were compared for a vegetation response. This assessment found locations where aquatic vegetation increased and locations where aquatic vegetation decreased.
A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.3 annual land cover products (1985–2021) 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 (1984–2021) to a reference sample of 26,971 Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are recorded for each year, 1985-2021. LCMAP Collection 1.0 annual land cover products covered years 1985–2017 and the validation of the Collection 1.0 products were reported in the LCMAP Version 1.0 Annual Land Cover and Land Cover...
This raster dataset depicts phase 1 pinyon-juniper expansion , where shrubs and herbs are the dominant vegetation and conifers occupy greater than zero percent to ten percent, intersecting documented sage-grouse habitat management categories (Coates et al., 2016a, Coates et al., 2016b). These data support the following publication: K. Benjamin Gustafson, Peter S. Coates, Cali L. Roth, Michael P. Chenaille, Mark A. Ricca, Erika Sanchez-Chopitea, Michael L. Casazza, Using object-based image analysis to conduct high- resolution conifer extraction at regional spatial scales, International Journal of Applied Earth Observation and Geoinformation, Volume 73, December 2018, Pages 148-155, ISSN 0303-2434, https://doi.org/10.1016/j.jag.2018.06.002....
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Great Basin,
Nevada,
Northeastern California,
United States of America,
biota,
A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.1 annual land cover products (1985–2019) for the Conterminous United States was conducted with an independently collected reference data set. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) to a reference sample of 24,971 randomly-selected Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are recorded for each year, 1985–2018. LCMAP Version 1.0 annual land cover products covered years 1985–2017 and the validation of the Version 1.0 products were reported in the LCMAP Version 1.0 Annual Land Cover and...
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Atlantic Ocean,
Barrier Island,
CMGP,
Coastal Habitat,
Coastal and Marine Geology Program,
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Atlantic Ocean,
Barrier Island,
CMGP,
Coastal Habitat,
Coastal and Marine Geology Program,
Values for predicted probabilities of avian species occupancy were determined using colonization-extinction models (MacKenzie and others, 2003) as implemented in R (Version 3.4.4; https://www.r-project.org/) via the ‘colext’ function of the Unmarked package (Version 0.12-0; Fiske and Chandler 2011). Performance of a null model (without covariates) and 153 additional models that assessed the effects of geographic coordinates and habitat context covariates were evaluated using Akaike information criteria (AIC; Burnham and Anderson, 2002). When more than one model had substantial support, their respective model weights were used to spatially predict occupancy relative to covariate influence. Predictive model covariates...
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Atlantic Ocean,
Barrier Island,
CMHRP,
Coastal Habitat,
Coastal and Marine Hazards and Resources Program,
This data describes the environmental covariates that are associated with nineteen Mule deer (Odocoileus hemionus) locations taken from 2012-2014 within the Desert National Wildlife Refuge of Nevada. These data support the following publication: Lowrey, C., Longshore, K.M., Choate, D.M., Nagol, J.R., Sexton, J. and Thompson, D., 2019. Ecological effects of fear: How spatiotemporal heterogeneity in predation risk influences mule deer access to forage in a sky‐island system. Ecology and Evolution.
Categories: Data;
Tags: Desert National Wildlife Refuge,
Mammals,
USGS Science Data Catalog (SDC),
Wildlife Biology,
biota,
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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