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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,
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...
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...
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-7 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-7 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch...
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-7 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-7 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch...
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...
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 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 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...
Abstract: Time series Landsat data have been used to track ecosystem disturbances using an algorithm such as the vegetation change tracker. However, efficiently identifying and separating types of disturbances (e.g., wildfires and harvests) still remain a technical challenge. In this letter, we tested the support vector machine algorithm in separating forest disturbance types, including wildfires, harvests, and other disturbance types (a generalized disturbance class, including insect disease outbreak, tornado, snow damage, and drought-induced mortality) in the Greater Yellowstone Ecosystem (GYE) using annual Landsat images from 1984 to 2010. The algorithm has been proven to be highly reliable, with overall accuracy...
Categories: Publication;
Types: Journal Citation;
Tags: Disturbance,
Disturbance type mapping,
Landsat,
SVM,
Support vector machine,
This data release comprises the raster data files and code necessary to perform all analyses presented in the associated publication. The 16 TIF raster data files are classified surface water maps created using the Dynamic Surface Water Extent (DSWE) model implemented in Google Earth Engine using published technical documents. The 16 tiles cover the country of Cambodia, a flood-prone country in Southeast Asia lacking a comprehensive stream gauging network. Each file includes 372 bands. Bands represent surface water for each month from 1988 to 2018, and are stacked from oldest (Band 1 - January 1988) to newest (Band 372 - December 2018). DSWE classifies pixels unobscured by cloud, cloud shadow, or snow into five...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Cambodia,
Dynamic Surface Water Extent,
Geography,
Hydrology,
Inundation,
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 stratified random reference sample of 600 Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are recorded for each year, 2000–2019.
The sagebrush rangelands of the Great Basin provide crucial habitat for a diversity of wildlife, including the pronghorn and the greater sage-grouse. These water-limited, highly-managed ecosystems have already been degraded by wildfires, the expansion of invasive grasses, and livestock grazing, and are expected to experience additional stress as climate and land use conditions change. Effective management of sagebrush ecosystems in the future will require the ability to understand and predict these future changes. To address this need, researchers will identify historical rates and causes of vegetation change in shrubland ecosystems, then use this information to develop potential future climate and land use scenarios...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2018,
CASC,
Completed,
Landsat,
Landscapes,
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 uses 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 burned proportion...
Categories: Data,
Data Release - Revised;
Tags: Burned area,
Landsat,
Prescribed fire,
USGS Science Data Catalog (SDC),
United States,
This dataset represents a summary of potential cropland inundation for the state of California applying high-frequency surface water map composites derived from two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with high-quality cropland maps generated by the California Department of Water Resources (DWR). Using Google Earth Engine, we examined inundation dynamics in California croplands from 2003 –2020 by intersecting monthly surface water maps (n=216 months) with mapped locations of precipitation amounts, rice, field, truck (which comprises truck, nursery, and berry crops), deciduous (deciduous fruits and nuts), citrus (citrus and subtropical), vineyards,...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service,
Shapefile;
Tags: California,
Geography,
Hydrology,
Landsat,
MODIS,
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