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The Forest and Rangeland Ecosystem Science Center 's mission is to provide scientific understanding and the technology needed to support sound management and conservation of our nation's natural resources, with emphasis on western ecosystems. The scientists from FRESC capitalize on their diverse expertise to answer critically important scientific questions shaped by the equally diverse environments of the western United States. FRESC scientists collaborate with each other and with partners to provide rigorous, objective, and timely information and guidance for the management and conservation of biological systems in the West and worldwide. Research activities are concentrated in Washington, Oregon, Idaho, Nevada,...
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The Louisiana State Legislature created Coastal Wetlands Planning, Protection, and Restoration Act (CWPPRA) in order to conserve, restore, create and enhance Louisiana's coastal wetlands. The wetland restoration plans developed persuant to these acts specifically require an evaluation of the effectiveness of each coastal wetlands restoration project in achieving long-term solutions to arresting coastal wetlands loss. This data set includes mosaicked aerial photographs for the Channel Armor Gap Crevasse (MR-06) project for 2016. This data set is used as a basemap land/water classification. It also serves as a visual tool for project managers to help them identify any obvious problems or land loss within their project...
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Problem - The purpose of this project is to create a watershed GIS (Geographic Information System) to support the comprehensive cleanup and restoration of Onondaga Lake that is underway. A GIS is a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location. Given the broad scope of the Onondaga Lake Partnership's (OLP) mission, a GIS is a powerful tool that can organize, store, and share information pertinent to the management of the natural resources of the Onondaga Lake watershed. The OLP GIS will be used for land use planning, resource management, scientific monitoring, and data presentation. The project has...
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This inventory was originally created by Gorum and others (2014) describing the landslides triggered by a sequence of earthquakes, with the largest being the M 6.2 17 km N of Puerto Aisen, Chile earthquake that occurred on 21 April 2007 at 23:45:56 UTC. Care should be taken when comparing with other inventories because different authors use different mapping techniques. This inventory includes landslides triggered by a sequence of earthquakes rather than a single mainshock. Please check the author methods summary and the original data source for more information on these details and to confirm the viability of this inventory for your specific use. With the exception of the data from USGS sources, the inventory...
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This inventory was originally created by Xu and others (2014) describing the landslides triggered by the M 5.9 Gansu, China earthquake, also known as the Minxian - Zhangxian earthquake, that occurred on 21 July 2013 at 23:45:56 UTC. Care should be taken when comparing with other inventories because different authors use different mapping techniques. This inventory also could be associated with other earthquakes such as aftershocks or triggered events. Please check the author methods summary and the original data source for more information on these details and to confirm the viability of this inventory for your specific use. With the exception of the data from USGS sources, the inventory data and associated metadata...
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This inventory was originally created by the Ministerio de Medio Ambiente y Recursos Naturales, El Salvador (2001) describing the landslides triggered by the M 7.7 San Miguel, El Salvador earthquake that occurred on 13 January 2001 at 17:33:32 UTC. Care should be taken when comparing with other inventories because different authors use different mapping techniques. This inventory also could be associated with other earthquakes such as aftershocks or triggered events. Please check the author methods summary and the original data source for more information on these details and to confirm the viability of this inventory for your specific use. With the exception of the data from USGS sources, the inventory data and...
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This inventory was originally created by Zhao (2021) describing the landslides triggered by the M 7.5 Palu, Indonesia earthquake that occurred on 28 September 2018 at 10:02:45 UTC. Care should be taken when comparing with other inventories because different authors use different mapping techniques. This inventory also could be associated with other earthquakes such as aftershocks or triggered events. Please check the author methods summary and the original data source for more information on these details and to confirm the viability of this inventory for your specific use. With the exception of the data from USGS sources, the inventory data and associated metadata were not acquired by the U.S. Geological Survey...
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Burn probability (BP) for Fireline Intensity Class 5 (FIL5) with flame lengths in the range of 2.4-3.7 m predicted for the 2080-2100 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the...
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Burn probability (BP) for Fireline Intensity Class 4 (FIL4) with flame lengths in the range of 1.8-2.4 m predicted for the 2050-2070 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the...
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Burn probability (BP) for Fireline Intensity Class 2 (FIL2) with flame lengths in the range of 0.6-1.2 m predicted for the 2050-2070 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the...
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The SRLCC provided funds to the states of Arizona and New Mexico to support development of the states Crucial Habitat Assessment Tools (CHATs) which provide a decision support system to better incorporate wildlife values, sensitive animals and plants, and important ecosystem features into land use decision-making to reduce conflicts and surprises.Several states have released wildlife mapping tools that are the foundation for displaying crucial wildlife and corridor information. The state and regional CHATs are non-regulatory, and give project planners and the general public access to credible scientific data on a broad scale for use in project analysis, siting and planning. This includes large-scale development...
Categories: Data, Project; Types: ArcGIS REST Map Service, ArcGIS Service Definition, Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: AZ-01, AZ-02, AZ-03, AZ-04, AZ-05, All tags...
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Burn probability (BP) for Fireline Intensity Class 6 (FIL6) with flame lengths in the range of 3.7-15 m predicted for the 2080-2100 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5...
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Burn probability (BP) raster dataset predicted for the 2080-2100 period in the Rio Grande area was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5 Representative Concentration Pathway.
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given the magnitude of the threat posed by sea-level rise, and the urgency to better understand it, there is an increasing need to forecast sea-level rise effects on barrier islands. To address this problem, scientists in the U.S. Geological Survey (USGS) Coastal and Marine Geology program are developing Bayesian networks as a tool to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as the piping plover (Charadrius melodus)...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assateague Island, Assateague Island, Assateague Island National Seashore, Assateague Island National Seashore, Atlantic Ocean, All tags...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Atlantic Ocean, Barrier Island, Bayesian Network, CMGP, Coastal Erosion, All tags...


map background search result map search result map Support to Western States Crucial Habitat Assessment Tools Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 4, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 5, predicted for 2080 to 2100 for Rio Grande study area Burn Probability for Fireline Intensity Class 6, predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Onondaga Lake Watershed Geographic Information System Gorum and others (2014) Ministerio de Medio Ambiente y Recursos Naturales, El Salvador (2001) Xu and others (2014) Channel Armor Gap Crevasse (MR-06): 2016 Land-Water Classification SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2010 Zhao (2021) Channel Armor Gap Crevasse (MR-06): 2016 Land-Water Classification SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 Xu and others (2014) DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010 Ministerio de Medio Ambiente y Recursos Naturales, El Salvador (2001) Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 4, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 5, predicted for 2080 to 2100 for Rio Grande study area Burn Probability for Fireline Intensity Class 6, predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Support to Western States Crucial Habitat Assessment Tools