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Riparian and wetland systems were determined from NHD waterbodies, SWReGAP riparian landcover types, and LANDFIRE riparian existing vegetation types. Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ and DEV_N_FZ) represent human development intensity values modeled...
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Riparian and wetland systems were determined from NHD waterbodies, SWReGAP riparian landcover types, and LANDFIRE riparian existing vegetation types. Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ and DEV_N_FZ) represent human development intensity values modeled...
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This dataset presents current and future change agent models and combined future potential for change (PFC). Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ and DEV_N_FZ) represent human development intensity values modeled from the landscape condition model. Current...
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This dataset presents current and future change agent models and combined future potential for change (PFC). Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ and DEV_N_FZ) represent human development intensity values modeled from the landscape condition model. Current...
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The Visual Resource Management Classes data were provided by BLM.This dataset presents current and future change agent models and combined future potential for climate change (PFC) within Visual Resource Management Classes. Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ...
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This dataset provides an estimate of near-term future (i.e., 2025-2030) human development intensity in the San Luis Valley - Taos Plateau study area. It is the result of a fuzzy model that integrates numerous human land use datasets along an intensity index. Input datatsets include roads, urban areas, agriculture, grazing, NASA city lights, and NLCD impervious surfaces. The attribute DEV_N_FZ is used to symbolize near-term future human development intensity. This model is identical to the near-term future ecological landscape condition model. Please refer to landscape condition model documentation for details on model development.
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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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The Visual Resource Inventory Classes data were provided by BLM. This dataset presents current and future change agent models and combined future potential for climate change (PFC) within Visual Resource Inventory Classes. Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ...
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This dataset presents current and future change agent models and combined future potential for change (PFC). Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ and DEV_N_FZ) represent human development intensity values modeled from the landscape condition model. Current...
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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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Riparian and wetland systems were determined from NHD waterbodies, SWReGAP riparian landcover types, and LANDFIRE riparian existing vegetation types. Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ and DEV_N_FZ) represent human development intensity values modeled...
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The Visual Resource Management Classes data were provided by BLM.This dataset presents current and future change agent models and combined future potential for climate change (PFC) within Visual Resource Management Classes. Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ...
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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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The Visual Resource Inventory Classes data were provided by BLM. This dataset presents current and future change agent models and combined future potential for climate change (PFC) within Visual Resource Inventory Classes. Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ...
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This dataset presents current and future change agent models and combined future potential for change (PFC). Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ and DEV_N_FZ) represent human development intensity values modeled from the landscape condition model. Current...
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The Visual Resource Management Classes data were provided by BLM.This dataset presents current and future change agent models and combined future potential for climate change (PFC) within Visual Resource Management Classes. Potential for change (PFC) was determined by calculating the maximum potential for change among all change agents within each 1 km reporting unit. Current and future landscape intactness (LCM_C_FZ and LCM_N_FZ) are based on measures of landscape development and invasive species. Current vegetation departure (VDEP) is based on LANDFIRE vegetation departure and characterizes the departure of current vegetation from historic reference vegetation conditions. Current and future human development (DEV_C_FZ...


map background search result map search result map Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2013–2014 Development: Development delineation: Fire Island, NY, 2012 Development: Development delineation: Rockaway Peninsula, NY, 2012 Development: Development delineation: Rockaway Peninsula, NY, 2013–2014 BLM REA SLV 2013 Riparian Wetland PFC Landscape Intactness BLM REA SLV 2013 N DEV 1km Poly BLM REA SLV 2013 Riparian Wetland PFC Near Term Invasives BLM REA SLV 2013 VRI PFC 1km Poly N IID BLM REA SLV 2013 GUSG Proposed CH PonchaPass PFC 1km Poly Near Term Human Development BLM REA SLV 2013 GUSG Proposed CH PonchaPass PFC 1km Poly Fire BLM REA SLV 2013 GUSG Proposed CH PonchaPass PFC 1km Poly Near Term Potential for Change BLM REA SLV 2013 VRI PFC 1km Poly N Potential for Change BLM REA SLV 2013 VRM PFC 1km Poly N IID BLM REA SLV 2013 VRM PFC 1km Poly C Fire BLM REA SLV 2013 Riparian Wetland PFC Near Term Climate BLM REA SLV 2013 GUSG Proposed CH PonchaPass PFC 1km Poly Vegetation Departure BLM REA SLV 2013 VRM PFC 1km Poly VDEP Development: Development delineation: Coast Guard Beach, MA, 2013-2014 Development: Development delineation: Cape Hatteras, NC, 2014 Development: Development delineation: Rhode Island National Wildlife Refuge, RI, 2014 Development: Development delineation: Coast Guard Beach, MA, 2013-2014 Development: Development delineation: Rockaway Peninsula, NY, 2012 Development: Development delineation: Rockaway Peninsula, NY, 2013–2014 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2013–2014 BLM REA SLV 2013 GUSG Proposed CH PonchaPass PFC 1km Poly Near Term Human Development BLM REA SLV 2013 GUSG Proposed CH PonchaPass PFC 1km Poly Fire BLM REA SLV 2013 GUSG Proposed CH PonchaPass PFC 1km Poly Near Term Potential for Change BLM REA SLV 2013 GUSG Proposed CH PonchaPass PFC 1km Poly Vegetation Departure Development: Development delineation: Fire Island, NY, 2012 Development: Development delineation: Rhode Island National Wildlife Refuge, RI, 2014 Development: Development delineation: Cape Hatteras, NC, 2014 BLM REA SLV 2013 VRI PFC 1km Poly N IID BLM REA SLV 2013 VRI PFC 1km Poly N Potential for Change BLM REA SLV 2013 VRM PFC 1km Poly N IID BLM REA SLV 2013 VRM PFC 1km Poly C Fire BLM REA SLV 2013 VRM PFC 1km Poly VDEP BLM REA SLV 2013 Riparian Wetland PFC Landscape Intactness BLM REA SLV 2013 Riparian Wetland PFC Near Term Invasives BLM REA SLV 2013 Riparian Wetland PFC Near Term Climate BLM REA SLV 2013 N DEV 1km Poly