Skip to main content
Advanced Search

Filters: Tags: Landsat (X)

389 results (35ms)   

Filters
Date Range
Extensions
Types
Contacts
Categories
Tag Types
Tag Schemes
View Results as: JSON ATOM CSV
thumbnail
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...
thumbnail
Regional, high-resolution mapping of vegetation cover and biomass is central to understanding changes to the terrestrial carbon (C) cycle, especially in the context of C management. The third most extensive vegetation type in the United States is pinyon-juniper (P-J) woodland, yet the spatial patterns of tree cover and aboveground biomass (AGB) of P-J systems are poorly quantified. We developed a synoptic remote-sensing approach to scale up pinyon and juniper projected cover (hereafter "cover") and AGB field observations from plot to regional levels using fractional photosynthetic vegetation (PV) cover derived from airborne imaging spectroscopy and Landsat satellite data. Our results demonstrated strong correlations...
thumbnail
The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2011. 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 scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer...
thumbnail
The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2011. 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 scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer...
thumbnail
The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2010. 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 scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This...
thumbnail
These data comprise 505 unique geospatial raster datasets which describe persistent river ice (aufeis) occurrence within floodplains of select rivers and creeks in the 1002 Area of the Arctic National Wildlife Refuge in northern Alaska. Each raster is derived from a historical observation made by one of five Landsat satellites. Surface reflectance in the green and shortwave infrared wavelengths are used to classify the aufeis occurrence within individual pixels. Pixel values of "1" correspond to aufeis presence while "0" corresponds to its absence. Rasters are generated from cloud and snow-filtered images captured between May and September for the years 1985-2022.
Spring‐fed wetlands are ecologically important habitats in arid and semi‐arid regions. Springs have been suggested as possible hydrologic refugia from droughts and climate change; however, springs that depend on recent precipitation or snowmelt for recharge may be vulnerable to warming and drought intensification. Springs that are expected to maintain their ecohydrologic function in a warmer, drier climate may be priorities for conservation and restoration. Identifying such springs is difficult because many springs lack hydrologic records of adequate temporal extent and resolution to assess their resilience to water cycle changes. This study demonstrates proof‐of‐concept for the assessment of certain spring types...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder within the LC Map "Sampled Fires" folder represents an individual fire. 2. Within the folder attached zipped folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR)...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
thumbnail
These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch...
thumbnail
These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch...
thumbnail
These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch...
thumbnail
Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. In this study, we developed the Landsat Burned Area (BA) algorithm, an update from the Landsat Burned Area Essential Climate Variable (BAECV) algorithm. We present the BA algorithm and products, changes relative to the BAECV algorithm and products, and updated validation metrics. We also present spatial and temporal patterns of burned area across the conterminous U.S. and a comparison with other burned area datasets. The BA algorithm identifies burned areas in analysis ready data (ARD) time-series of Landsat imagery from 1984...
thumbnail
The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally-dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm makes use of predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Scene-level products include pixel-level burn probability (BP) and burn classification (BC) images, corresponding to each Landsat image in the ARD time series. Annual composite products are also available by summarizing the scene level products. Prior to generating annual composites, individual scenes that had > 0.010...
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...
We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: 1) Google Earth Engine (GEE) and 2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (ETa) estimates with AmeriFlux Eddy Covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States for five water-years (Oct-Sep; 2009, 2011, 2013, 2016, 2018). USGS Evapotranspiration...
map background search result map search result map Multiscale analysis of tree cover and aboveground carbon stocks in pinyon-juniper woodlands Data Release for the validation of the USGS Landsat Burned Area Product across the conterminous U.S. (ver. 2.0, May 2020) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2003) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2009) BLM REA NWP 2011 FI C 2002 MTBS Landsat Burned Area Products Data Release - combined sensor products National Park Service Thematic Burn Severity Mosaic in 2011 (ver. 6.0, January 2024) Upper Klamath Basin Landsat Image for July 2, 2006: Path 44 Row 31 Upper Klamath Basin Landsat Image for September 20, 2006: Path 44 Row 31 Upper Klamath Basin Landsat Image for October 7, 2004: Path 45 Rows 30 and 31 Rasters of Observed Aufeis Deposits Within Rivers of the 1002 Area Based on Historical Landsat Imagery, 1985-2022 Rasters of Observed Aufeis Deposits Within Rivers of the 1002 Area Based on Historical Landsat Imagery, 1985-2022 Upper Klamath Basin Landsat Image for July 2, 2006: Path 44 Row 31 Upper Klamath Basin Landsat Image for September 20, 2006: Path 44 Row 31 Upper Klamath Basin Landsat Image for October 7, 2004: Path 45 Rows 30 and 31 Multiscale analysis of tree cover and aboveground carbon stocks in pinyon-juniper woodlands BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2003) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2009) BLM REA NWP 2011 FI C 2002 MTBS Data Release for the validation of the USGS Landsat Burned Area Product across the conterminous U.S. (ver. 2.0, May 2020) Landsat Burned Area Products Data Release - combined sensor products National Park Service Thematic Burn Severity Mosaic in 2011 (ver. 6.0, January 2024)