Filters: Tags: Wildfire (X)
556 results (112ms)|
Filters
Date Range
Extensions Types
Contacts
Categories Tag Types
|
Geospatial data were developed to characterize pre-fire biomass, burn severity, and biomass consumed for the Black Dragon Fire that burned in northern China in 1987. Pre-fire aboveground tree biomass (Mh/ha) raster data were derived by relating plot-level forest inventory data with pre-fire Landsat imagery from 1986 and 1987. Biomass data were generated for individual species: Dahurian larch (Larix gmelinii Rupr. Kuzen), white birch (Betula platyphylla Suk), aspen (Populus davidiana Dode and Populus suaveolens Fischer), and Mongolian Scots pine (Pinus sylvestris var. mongolica Litvinov). A raster layer of total aboveground tree biomass was also generated. Burned area was manually delineated using the normalized...
Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can produce dangerous flash floods and debris flows. The USGS conducts post-fire debris-flow hazard assessments for select fires in the Western U.S. We use geospatial data related to basin morphometry, burn severity, soil properties, and rainfall characteristics to estimate the probability and volume of debris flows that may occur in response to a design storm.
Polygon locations of fire perimeters in the Sky Island mountain ranges in the Madrean Archipelago Ecoregion of the southwestern United States and northern Mexico. These fires occurred from 1985 to 2011 and were mapped using Landsat satellite imagery.
Mountain streams provide important habitats for many species, but their faunas are especially vulnerable to climate change because of ectothermic physiologies and movements that are constrained to linear networks that are easily fragmented. Effectively conserving biodiversity in these systems requires accurate downscaling of climatic trends to local habitat conditions, but downscaling is difficult in complex terrains given diverse microclimates and mediation of stream heat budgets by local conditions. We compiled a stream temperature database (n = 780) for a 2500-km river network in central Idaho to assess possible trends in summer temperatures and thermal habitat for two native salmonid species from 1993 to 2006....
Categories: Publication;
Types: Citation,
Journal Citation;
Tags: Boise River basin,
Ecological Applications,
Ecological Society of America,
Idaho,
Oncorhynchus mykiss,
This dataset represents 25 parallel longitudinal profiles that were extracted from terrestrial lidar point clouds taken during six survey periods. The six lidar surveys were conducted between 7 October 2010 and 8 October 2013. Over that time a colluvial hollow eroded into a fluvial channel. The longitudinal profiles show the topography of the colluvial hollow for each survey period. The width of the original colluvial hollow was approximately 1.25 m, and a longitudinal profile was extracted every 5 cm for the entire length of the hollow, resulting in 25 parallel longitudinal profiles. These data can be used to observe the transition of the colluvial hollow to a fluvial channel and furthermore they show the development...
Types: Citation;
Tags: Fourmile Canyon, Colorado,
GHSC,
Geologic Hazards Science Center,
LHP,
LIDAR,
Polygon locations of fire perimeters in the Sky Island mountain ranges in the Madrean Archipelago Ecoregion of the southwestern United States and northern Mexico. These fires occurred from 1985 to 2017 and were mapped using Landsat satellite imagery.
The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in dense time series of Landsat image stacks to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm makes use of predictors derived from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Outputs of the BAECV algorithm consist of pixel-level burn probabilities for each Landsat scene, and annual burn probability, burn classification, and burn date composites. These products were generated for the conterminous United States for 1984 through 2015. These data are also available for download at https://rmgsc.cr.usgs.gov/outgoing/baecv/BAECV_CONUS_v1.1_2017/...
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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...
Types: Downloadable;
Tags: BLM,
Bureau of Land Management,
Burned Area Extent,
CBR 2010,
Central Basin and Range,
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...
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...
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...
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...
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...
The FSim burn probability was used to determine the burn probability of the white sturgeon range in the ecoregion. This layer was used to examine wildfire risk to areas within the white sturgeon range.
|
|
