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The U. S. Fish and Wildlife Service (FWS) requests burn severity assessments through an agreement with the U.S. Geological Survey (USGS) to be completed by analysts with the Monitoring Trends in Burn Severity (MTBS) Program. These data products are burned area boundary shapefiles derived from post-fire sensor data (including Landsat TM, Landsat ETM+, Landsat OLI). The pre-fire and post-fire subsets included were used to create Normalized Burn Ratio (NBR) and then a differenced Normalized Burn Ratio (dNBR) image. The objective of this assessment was to generate burned area boundaries for each fire. Data bundles also include post-fire subset, pre-fire subset, NBR, and dNBR images. This map layer is a thematic raster...
The U. S. Fish and Wildlife Service (FWS) requests burn severity assessments through an agreement with the U.S. Geological Survey (USGS) to be completed by analysts with the Monitoring Trends in Burn Severity (MTBS) Program. These data products are burned area boundary shapefiles derived from post-fire sensor data (including Landsat TM, Landsat ETM+, Landsat OLI). The pre-fire and post-fire subsets included were used to create Normalized Burn Ratio (NBR) and then a differenced Normalized Burn Ratio (dNBR) image. The objective of this assessment was to generate burned area boundaries for each fire. Data bundles also include post-fire subset, pre-fire subset, NBR, and dNBR images. This map layer is a thematic raster...
This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS during calendar year 2021 that do not meet standard MTBS size criteria. These data are published to augment the data that are available from the MTBS program. This product was produced using the methods of the Monitoring Trends in Burn Severity Program (MTBS), however these fires do not meet the size criteria for a standard MTBS assessment. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. MTBS typically...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: AK,
Alaska,
Burn severity,
Burned area,
CONUS,
This product is published on a provisional basis to provide necessary information to individuals assessing burn severity impacts on a time sensitive basis. This product was produced using the methods of the Monitoring Trends in Burn Severity (MTBS) Program; however, this fire may not meet the criteria for an MTBS initial assessment. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. MTBS typically maps fires using an initial assessment (immediately after the fire) or an extended assessment (peak of green the season...
The National Park Service (NPS) requests burn severity assessments through an agreement with the U.S. Geological Survey (USGS) to be completed by analysts with the Monitoring Trends in Burn Severity (MTBS) Program. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: AK,
Alaska,
Burn severity,
Burned area,
CONUS,
This dataset provides a near-real-time estimate of 2017 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatrass 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 19, 2017. This is the second iteration of an early estimate of herbaceous annual cover for 2017 over the same geographic area. The previous dataset used eMODIS NDVI data gathered through May 1 (https://doi.org/10.5066/F7445JZ9). The pixel values for this most recent estimate ranged from 0 to100% with...
The dataset provides an estimate of 2017 herbaceous annual percent cover predicted on May 1st with an emphasis on annual grasses. The pixel values range from 0 to100 with an overall mean value of 7.1 and a standard deviation of +/-10.5. The model's test mean error rate (n = 1670), based on nine different randomizations, equals 4.9% with a standard deviation of +/- 0.15. This dataset was generated by integrating ground-truth measurements of annual herbaceous percent cover with 250-m spatial resolution eMODIS NDVI satellite derived data and geophysical variables into regression-tree software. The geographic coverage includes the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied...
A new version of USGS’s FORE-SCE model was used to produce unprecedented landscape projections for four ecoregions in the Prairie Potholes region of Great Plains. The scenarios are consistent with the same scenarios modeled for the Great Plains Landscape Conservation Cooperative region. The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (29 land use and land cover classes), 3) broad spatial extent (covering approximately 350,000 square kilometers), 4) use of real land ownership boundaries to ensure realistic representation of landscape patterns, and 5) representation of both anthropogenic land use and natural vegetation change. A variety of scenarios were...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Climate Research and Development,
FORE-SCE,
Great Plains,
Great Plains,
Iowa,
Three ET datasets were generated to evaluate the potential integration of Landsat and Sentinel-2 data for improved ET mapping. The first ET dataset was generated by linear interpolation (Lint) of Landsat-based ET fraction (ETf) images of before and after the selected image dates. The second ET dataset was generated using the regular SSEBop approach using the Landsat image only (Lonly). The third ET dataset was generated from the proposed Landsat-Sentinel data fusion (L-S) approach by applying ETf images from Landsat and Sentinel. The scripts (two) used to generate these three ET datasets are included – one script for processing SSEBop model to generate ET maps from Lonly and another script for generating ET maps...
This dataset is the first (circa 2000) of two 500-meter land use land cover (LULC) time-periods datasets (2000, and 2013) aids in monitoring change in West Africa’s land resources. To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification” (Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation using Esri’s ArcGIS Desktop...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Cape Verde,
Capo Verde,
biota,
land cover,
land use,
Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This dataset include land cover attributes for the year 2000 assigned by 7 image analysts, working independently of each other, to a set of 300 sample locations from a region of the Pacific Northwest of the United States. This data was used in an evaluation of the impact of interpreter variability on variance estimation.
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Earth Resources Observation and Science (EROS) Center,
LCMAP,
Land Use Change,
Land Use/ Land Cover,
Landcover,
A validation assessment of Land Cover Monitoring, Assessment, and Projection annual land cover products (2000–2019) for Hawaii 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 (2000–2019) to a reference sample of 600 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, 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...
Airborne light detection and ranging (lidar) can provide high-quality topographic information over large areas. Lidar is an active remote sensing technology that employs laser ranging in near-infrared and green spectral wavelengths to provide three-dimensional (3D) point information for objects, including Earth’s surface, vegetation, and infrastructure. The U.S. Geological Survey (USGS) National Geospatial Program (NGP) 3D Elevation Program (3DEP) seeks to systematically acquire airborne topographic lidar for the conterminous U.S. (conus), Hawaii, and the U.S. territories. A series of field accuracy assessment surveys, using conventional surveying methods (i.e. total station and Global Navigation Satellite System...
High interannual variability of forage production in semi-arid grasslands leads to uncertainties when livestock producers make decisions such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibly with higher confidence. We use a recently developed forage production model, ForageAhead, that uses environmental and seasonal climate variables to estimate the annual forage production approximated by remotely sensed vegetation data. The model uses observed seasonal climate data from winter and spring as an input together...
Information on the spatio-temporal distribution of rainfall is critical for addressing water-related disasters, especially in the Middle East and North Africa's (MENA) arid to semi-arid regions. However, the availability of reliable rainfall datasets for most river basins is limited. In this study, we utilized observations from satellite-based rainfall data, in situ rain gauge observations, and rainfall climatology to determine the most suitable precipitation dataset in the MENA region. First, we evaluated seven different rainfall products (CPC, GPCC, TRMM, PERSIANN, RFE, CHIRPS, MSWEP) using rain gauge observations obtained from Jordan (139 stations), Palestine (9 stations), and Lebanon (16 stations). The validation...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Jordan,
Lebanon,
MENA,
USGS Science Data Catalog (SDC),
bias estimation,
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...
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. The NLCD 2021 release is update based, so the Land Cover and Impervious Surface products released in...
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