This reference contains raster datasets for Habitat - Land Cover Mapping and Change Detection (PRIMR ID: FF01RMLH00-127). These geodatabases contain raster landcover maps of the Double O, Buena Vista and P-Ranch units of Malheur National Wildlife Refuge, and point layers of photo locations.

This reference contains raster datasets for Habitat - Land Cover Mapping and Change Detection (PRIMR ID: FF01RMLH00-127). These geodatabases contain raster landcover maps of the Double O, Buena Vista and P-Ranch units of Malheur National Wildlife Refuge, and point layers of photo locations.

This data set consists of current 2019 and historic ~1940 vegetative cover classification of Chassahowitzka NWR based on the Florida Natural Area Inventory classification. Also vegetation plot locations and associated vegetation variables to track changes in the vegetative community collected in 2019 ae provided to support the larger project. Exotic plant and rare animal ocations are provided. Data are presented as point and polygon shapefiles in WGS 1984 lat/long. A pdf metafile is included.

This data set consists of current 2019 and historic ~1940 vegetative cover classification of Chassahowitzka NWR based on the Florida Natural Area Inventory classification. Also vegetation plot locations and associated vegetation variables to track changes in the vegetative community collected in 2019 ae provided to support the larger project. Exotic plant and rare animal ocations are provided. Data are presented as point and polygon shapefiles in WGS 1984 lat/long. A pdf metafile is included.

We expanded on previously developed methodology to incorporate information on habitat selection and survival during reproductive life stages and specific seasons with updated sage-grouse location and known fate datasets, while also including brood-rearing areas that are understood to be threatened and important for population persistence. We combined predictive habitat map surfaces for each life stage and season with updated information on current occupancy patterns to classify habitat based on its suitability and probability of occupancy. We carried out additional steps to delineate specific example habitat management areas, specifically (1) incorporated corridors connecting key nesting and brood-rearing habitat, (2) corrected outputs for pre-wildfire habitat conditions within areas burned in the last 16 years, and (3) masked out areas of anthropogenic development. Our methodological example of deriving habitat management areas was intended to help inform decisions by BLM and other land managers regarding conservation and management of sage-grouse. Associated data products in the form of habitat maps provide updated, detailed, and comprehensive information about the status of habitats and can be useful to partner agencies in their efforts to designate and rank habitats for this species of high conservation concern in Nevada and California, with full recognition that on-the-ground field data and local sources of information and expertise should be used in conjunction with inferences from these models.

We expanded on previously developed methodology to incorporate information on habitat selection and survival during reproductive life stages and specific seasons with updated sage-grouse location and known fate datasets, while also including brood-rearing areas that are understood to be threatened and important for population persistence. We combined predictive habitat map surfaces for each life stage and season with updated information on current occupancy patterns to classify habitat based on its suitability and probability of occupancy. We carried out additional steps to delineate specific example habitat management areas, specifically (1) incorporated corridors connecting key nesting and brood-rearing habitat, (2) corrected outputs for pre-wildfire habitat conditions within areas burned in the last 16 years, and (3) masked out areas of anthropogenic development. Our methodological example of deriving habitat management areas was intended to help inform decisions by BLM and other land managers regarding conservation and management of sage-grouse. Associated data products in the form of habitat maps provide updated, detailed, and comprehensive information about the status of habitats and can be useful to partner agencies in their efforts to designate and rank habitats for this species of high conservation concern in Nevada and California, with full recognition that on-the-ground field data and local sources of information and expertise should be used in conjunction with inferences from these models.

All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and meaningful sagebrush restoration. Inputs into the tool included occupancy data layers for six focal species (Brewer’s sparrow (Spizella breweri), sagebrush sparrow (Artemisiospiza nevadensis), sage thrasher (Oreoscoptes montanus), greater sage-grouse (Centrocercus urophasianus), pronghorn (Antilocapra americana) and greater short-horned lizard (Phrynosoma hernandesi) generated within the Sagebrush Ecosystem Conservation and Management: Ecoregional Assessment Tools and Models for the Wyoming Basins (Hanser et al. 2011). The layer to assess restoration suitability was predicted time to sagebrush recovery (Monroe et al. 2021; https://doi.org/10.5066/P9XV8GH7). Raster layers (360x360m cell size) associated with figure 4 in Duchardt et al. 2021 include a comparison of selected cells with no connectivity requirements (PReSETnoconn_nolock.tif), with a penalty for unconnected features (PReSEThighconn_nolock.tif), and with a penalty for unconnected features and including “locked-in” protected lands (PReSEThighconn_lock.tif). PReSET tool is currently housed at USGS FORT.

This geodatabase contains three (3) rasters, two (2) of which represent landcover for Selawik National Wildlife Refuge and surrounding areas. The third raster contains plot-based ground characteristics for pixels classified with high confidence. The two landcover rasters contain attribute information for soils, vegetation, and ecotypes; and differ slightly in their classifications because one encompasses a broader geographic area (lc_arcn), and therefore some classes are more generalized than in the other (lc_nokose). The classification of local-scale ecosystems (ecotypes) combines physiography (e.g., riverine, coastal), topography (DEM), geology and vegetation from the landcover spectral database derived from the satellite image processing. These layers are used to model ecotypes in a way that best partitions geomorphic, hydrologic, pedologic, and vegetative characteristics. Map projection: Albers Alaska, NAD 83, meters. ***NOTE*** The lc_nokose raster was used for the landcover classifications in the final report, as it is more specific to Selawik National Wildlife Refuge than the other landcover raster (lc_arcn), which includes some surrounding National Park Service lands and differs slightly in its classifications at the pixel level. PDF maps are provided here for reference to help visualize what the data look like before downloading. Full resolution maps can be viewed in the final report (ServCat #49603).

This raster dataset depicts phase 1 pinyon-juniper expansion , where shrubs and herbs are the dominant vegetation and conifers occupy greater than zero percent to ten percent, intersecting documented sage-grouse habitat management categories (Coates et al., 2016a, Coates et al., 2016b). These data support the following publication: K. Benjamin Gustafson, Peter S. Coates, Cali L. Roth, Michael P. Chenaille, Mark A. Ricca, Erika Sanchez-Chopitea, Michael L. Casazza, Using object-based image analysis to conduct high- resolution conifer extraction at regional spatial scales, International Journal of Applied Earth Observation and Geoinformation, Volume 73, December 2018, Pages 148-155, ISSN 0303-2434, https://doi.org/10.1016/j.jag.2018.06.002. Cali L. Roth, Peter S. Coates, K. Benjamin Gustafson, Michael P. Chenaille, Mark A. Ricca, Erika Sanchez-Chopitea, and Michael L. Casazza, 2018. A customized framework for regional classification of conifers using automated feature extraction. Journal of Agricultural and Biological Science, in review. References: Coates, P.S., Casazza, M.L., Brussee B.E., Ricca, M.A., Gustafson, K.B., Sanchez-Chopitea, E., Mauch, K., Niell, L., Gardner, S., Espinosa, S., Delehanty, D.J. 2016a, Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—an updated decision-support tool for management: U.S. Geological Survey Open-File Report 2016-1080, 160 p., http://doi.org/10.3133/ofr20161080. ISSN: 2331-1258 (online) Coates, P.S., Casazza, M.L., Brussee B.E., Ricca, M.A., Gustafson, K.B., Sanchez-Chopitea, E., Mauch, K., Niell, L., Gardner, S., Espinosa, S., and Delehanty, D.J., 2016b, Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—an updated decision-support tool for management: U.S. Geological Survey data release, http://doi.org/10.5066/F7CC0XRV.

LiDAR geospatial data were collected on hatchie National Wildlife Refuge in late fall-winter 2011-2012. The associated deliverables from the project are provided as geo-referenced in zipped folders. These datasets include 1-foot counter, bare earth DEM, Bare earth Hillshade, bare earth Terrain, control points, DSM, GT_report, Intensity, LAS, and Shapefiles. Associated Metadata may not be fully compliant.