Filters: Tags: USGS Science Data Catalog (SDC) (X) > partyWithName: Earth Resources Observation and Science (EROS) Center (X)
160 results (13ms)|
Filters
Date Range
Extensions Types
Contacts
Categories Tag Types
|
Data are cross-listed on https://rangelands.app/cheatgrass/ Cheatgrass (Bromus tectorum) and other invasive annual grasses represent one of the single largest threats to the health and resilience of western rangelands. To address this challenge, the Western Governors Association (WGA)-appointed Western Invasive Species Council convened a cheatgrass working group to develop a new regional vision for invasive annual grass management across the West. Foundational to implementing this new vision is the creation of a common spatial map to guide strategic actions. The WGA cheatgrass working group sought to develop a 30-m base map of annual herbaceous cover to support a common spatial strategy for tackling invasive annual...
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...
The various post-fire data products available on the Burn Severity Portal are produced using satellite imagery. The timing of the satellite imagery used, relative to the fire event, typically depends on the vegetation type and structure where the fire occurred. Each mapping program produces a suite of data products based on user intended user needs. You can find additional details in each of the available areas. First posted - September 6, 2022 Revised - January 6, 2023 (version 2.0) Revised - March 31, 2023 (version 3.0) Revised - August 8, 2023 (version 4.0) Revised - October 26, 2023 (version 5.0) Revised - January 26, 2024 (version 6.0) Revised - April 29, 2024 (version 7.0)
These datasets provide early estimates of 2024 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from April to late June. Typically, the EAG estimates are publicly released within 7-13 days of the latest satellite observation used for that version. Each weekly release contains five fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) Field Brome (Bromus arvensis); 4) medusahead (Taeniatherum caput-medusae); and 5) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory,...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Field Brome,
Great Basin,
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
Categories: Data,
Data Release - Revised;
Tags: GIS,
Image processing,
Land Use Land Cover Theme,
Land cover,
NGDA,
Surface Urban Heat Island (SUHI) extent, intensity, and hotspots data of land surface temperature (LST) are provided across 50 regions throughout the Continental U.S. The annual land surface temperature (LST) were derived from Landsat U.S. Analysis Ready Data (ARD). The time series land surface Temperature (LST) and land cover change products were used to produce SUHI intensity and hotspots products. The data ranges from 1985-present, and covers data within 5 km of each city. SUHI Intensity data is intended to quantify the difference between urban surface temperatures and the surrounding non-urban environment. The calculation takes the difference between a specific urban pixel’s land surface temperature (LST) and...
Categories: Data Release - Revised;
Tags: 5 km buffer zones,
CONUS,
CONUS,
Landsat ARD,
Landsat Analysis Ready Data (ARD),
To support Hurricane Florence impact modeling of storm-induced flooding and sediment transport, the U.S. Geological Survey (USGS) Coastal National Elevation Database (CoNED) Applications Project has created an integrated 1-meter topobathymetric digital elevation model (TBDEM) for coastal North Carolina, and South Carolina. High-resolution coastal topobathymetric data are required to characterize flooding, storms, and sea-level rise inundation hazard zones and other earth science applications, such as the development of sediment transport and storm surge models. This TBDEM consists of the best available multi-source topographic and bathymetric elevation data for the Coastal Carolinas including neighboring bays, estuaries,...
Monitoring rangelands by identifying the departure of contemporary conditions from long-term ecological potential allows for the disentanglement of natural biophysical gradients driving change from changes due to land uses and other disturbance types. We developed maps of ecological potential (EP) for shrub, sagebrush (Artemisia spp.), perennial herbaceous, litter, and bare ground fractional cover in Wyoming, USA. EP maps correspond to the potential natural vegetation cover expected by environmental conditions in the absence of anthropogenic and natural disturbance as represented by the best growing conditions and least disturbed period of the Landsat archive. EP was predicted using regression tree models with inputs...
This dataset provides a near-real-time estimate of 2019 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data gathered through June 24, 2019. This is the second iteration of an early estimate of herbaceous annual cover for 2019 over the same geographic area. The previous dataset used eMODIS NDVI data gathered through April 28, 2019 (https://doi.org/10.5066/P9ZEK5M1). The pixel values for this most recent estimate ranged from 0 to100%...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Bromus rubens,
Bromus tectorum,
California,
Colorado,
Desert,
Global alfalfa-reference potential evapotranspiration (ETr) is a key model parameter in actual evapotranspiration (ETa) modeling for worldwide applications. This dataset was constructed for use with the Operational Simplified Surface Energy Balance (SSEBop) model as a key driver of the final ETa magnitude. SSEBop is a parametric energy balance-based model that determines actual ET as the product of two independent estimates: 1) the SSEBop modeled ET fraction (ETf), an index nominally varying between 0 and 1 and derived from observed Landsat surface temperature using satellite psychrometry, and 2) the potential ET (maximum) under environmental conditions for an alfalfa crop (in millimeters). As SSEBop ETf can now...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Climatology,
ET modeling,
Global,
Hydrology,
Reference Evapotranspiration,
The landscape of the conterminous United States has changed dramatically over the last 200 years, with agricultural land use, urban expansion, forestry, and other anthropogenic activities altering land cover across vast swaths of the country. While land use and land cover (LULC) models have been developed to model potential future LULC change, few efforts have focused on recreating historical landscapes. Researchers at the US Geological Survey have used a wide range of historical data sources and a spatially explicit modeling framework to model spatially explicit historical LULC change in the conterminous United States from 1992 back to 1938. Annual LULC maps were produced at 250-m resolution, with 14 LULC classes....
We employed decision-tree mapping models in two formats to establish a time series (2001 - 2015) of sagebrush condition class in the western United States. The formats were predictive and descriptive, and each model produced distinct spatially explicit datasets. The predictive model mapped the probability of sagebrush recovery, tipping point (environmental degradation), or stable classes. The descriptive model mapped rules that were defined by environmental thresholds. The thresholds were defined by the interaction between the independent variables and the dependent variable. Mapping areas of stability and areas of change using machine-learning algorithms allows both the identification of dominant abiotic variables...
We integrated 250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) with land cover, biogeophysical (e.g., soils, topography) and climate data into regression-tree software (Cubist®). We integrated this data to create a time series of spatially explicit predictions of herbaceous annual vegetation cover in sagebrush ecosystems, with an emphasis on annual grasses. Annual grass cover in sagebrush ecosystems is highly variable year-to-year because it is strongly dependent on highly variable weather patterns, particularly precipitation timing and totals. Annual grass cover also reflects past disturbances and management decisions. We produced 17 consecutive...
Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, enhancing a more complete understanding of broad-scale ecosystem processes. This data release presents maps of estimates of annual gross primary production (GPP) and annual ecosystem respiration (RE) that were derived from weekly summaries of gross photosynthesis (Pg) and ecosytem respiration (Re). To conduct this study we used carbon data from flux towers that are scattered strategically across the conterminous United States (CONUS). We also calculate and present a map of average annual net ecosystem production (NEP). We present and analyze...
We developed a 30-m spatial resolution forest regrowth time map for CONUS over 1985–2017. This map is the first attempt, as far as we know, to quantify tree regrowth rate at a national extent in the United States. The method used all available Landsat images to detect disturbances over forest lands and classify grass/shrub to tree class transitions on an annual basis. The average regrowth time was then calculated for each pixel that experienced tree regrowth process. The regrowth map was validated with independent reference data, showing average one-year of difference and 6-year standard deviation difference in the national tree regrowth time. The southeast is a major tree regrowth region where our map also showed...
U.S. Geological Survey (USGS) scientists conducted field data collection efforts during the weeks of September 9-13 and November 18-22, 2019, using a combination of technologies to map and validate topography, vegetation, and features in two areas of interest (AOI's) in north central Colorado. The western AOI included land managed by the Bureau of Land Management and the U.S. Forest Service. The eastern AOI included agricultural and urban areas. The work was initiated as an effort to test and evaluate the Leica Geosystems CountryMapper* sensor. The CountryMapper is a hybrid sensor that collects imagery and light detection and ranging (lidar) data simultaneously. The CountryMapper has the potential to collect data...
The USGS’s FORE-SCE model was used to produce a long-term landscape dataset for the Delaware River Basin (DRB). Using historical landscape reconstruction and scenario-based future projections, the data provided land-use and land-cover (LULC) data for the DRB from year 1680 through 2100, with future projections from 2020-2100 modeled for 7 different socioeconomic-based scenarios, and 3 climate realizations for each socioeconomic scenario (21 scenario combinations in total). The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (20 land use and land cover classes), 3) broad spatial extent (covering the entirety of the Delaware River basin, corresponding to USGS...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Delaware River,
Delaware River Basin,
Delaware River Basin,
FORE-SCE,
Future,
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, SD developed a cloud validation dataset from 48 unique Landsat 9 Collection 2 images. These images were selected at random from the Landsat 9 archive from various locations around the world. While these validation images were subjectively designed by a single analyst, they provide useful information for quantifying the accuracy of clouds flagged by various cloud masking algorithms. Each mask is provided in GeoTIFF format, and includes all bands from the original Landsat 9 Collection 2 Level-1 data product (COG GeoTIFF), and its associated Level-1 metadata (MTL.txt file).
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. 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 (1984–2021)...
Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and...
Categories: Data;
Tags: USGS Science Data Catalog (SDC),
United States,
conus,
crop cover mapping,
modelling,
|
|
