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 data set provides two landcover and vegetation maps for the Seward Peninsula, Alaska. These maps were produced from existing maps, Landsat imagery, and color infrared aerial photography covering the period 1976-06-01 to 1999-09-01.

Relational database with data supporting an ecological land survey and land cover mapping of Selawik National Wildlife Refuge.

Relational database with data supporting an ecological land survey and land cover mapping of Selawik National Wildlife Refuge.

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.

The land cover/vegetation classification of the Kodiak Archipelago was produced by digital image analysis of a 3-date temporal composite of Landsat ETM+ scenes acquired between September 1999 and September 2000. Final report and classified raster published in 2007. See the associated User Guide for full details.

We compiled the Landsat Level-3 Fractional Snow Covered Area (fSCA) product (Painter et al. 2009, Selkowitz et al. 2017) for the tiles intersecting the study area for 2000–2017. The product estimates fractional snow cover for each 30-m pixel, with possible values of 0% and 15–100%. We masked all data that was not “Clear” (pixel value of 0) in the Revised Cloud Mask layer. Then we calculated the median snow cover for bimonthly periods from all the Landsat imagery for the period (e.g.,all images for 1–15 May, 2000–2017). We assigned the median snow cover fraction to the midpoint of the period (e.g., 8 May). Then we interpolated to a daily time step (e.g., a pixel that was 80% snow covered on 23 April and 20% snow covered on 8 May was estimated to 50% on 1 May). Finally, we stacked the daily observations, and extracted the last date with ≥15% snow cover as the “last snow day.”

This dataset consists of raster geotiff outputs of annual map projections of land use and land cover for the California Central Valley for the period 2011-2101 across 5 future scenarios. Four of the scenarios were developed as part of the Central Valley Landscape Conservation Project. The 4 original scenarios include a Bad-Business-As-Usual (BBAU; high water availability, poor management), California Dreamin’ (DREAM; high water availability, good management), Central Valley Dustbowl (DUST; low water availability, poor management), and Everyone Equally Miserable (EEM; low water availability, good management). These scenarios represent alternative plausible futures, capturing a range of climate variability, land management activities, and habitat restoration goals. We parameterized our models based on close interpretation of these four scenario narratives to best reflect stakeholder interests, adding a baseline Historical Business-As-Usual scenario (HBAU) for comparison. For these future map projections, the model was initialized in 2011 and run forward on an annual time step to 2101. Each filename has the associated scenario ID (scn418 = DUST, scn419 = DREAM, scn420 = HBAU, scn421 = BBAU, and scn426 = EEM), State Class identification as “sc”, model iteration (= it1 in all cases as only 1 Monte Carlo simulation was modeled), and timestep as “ts” information embedded in the file naming convention. For example, the filename scn418.sc.it1.ts2027.tif represents the DUST scenario (scn418), state class information (sc), iteration 1 (it1), for the 2027 model year (ts2027). The full methods and results of this research are described in detail in the parent manuscript "Integrated modeling of climate, land use, and water availability scenarios and their impacts on managed wetland habitat: A case study from California’s Central Valley" (2021).

developing effective habitat conservation and management strategies. The relationship between available habitat and waterfowl numbers obtained from aerial survey transects is not well studied. To determine these relationships, multispectral SPOT-5 satellite imagery acquired for Sequoyah National Wildlife Refuge close to the time of waterfowl surveys was used to map habitat conditions. Robust Random Forest classification trees were used to model 16 land cover types using 416 reference locations collected in the field or derived from aerial photos close to or during waterfowl survey dates. The normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and a simple ratio (SR) of red and near infrared bands were used to enhance classification accuracy for key habitat areas and abundance of water. Terrain variables such as slope, solar illumination and cosine transformed aspect derived from a digital elevation model (DEM) were also used to enhance habitat classification. Random Forest (RF) models were also compared to support vector machines (SVM) and cforest (CF) conditional inference trees. We used error matrices and the Kappa agreement statistic (K) to compare model results from each classifier. Results indicated that a tuned RF classifier showed better performance (K=0.73) than SVM (K=0.65) and unbiased cforest trees (K=0.63). Overall class agreement between similar RF and cforest models, designed to reduce predictor variable selection bias, was also relatively low (K=0.47). A final tuned RF model was selected resulting in 75% accuracy overall and was used to map habitat types for the refuge and surrounding landscape. We found that elevation and minimum noise fraction (MNF) bands were the most important predictor variables. MNF bands can help to reduce the number of correlated variables entering into a classification model when a larger number of correlated spectral bands are used. Similar forest types such as riverine, bottomland hardwood, and floodplain forest showed the greatest misclassification error. Overall, the RF model and SPOT-5 leaf-off imagery generated accurate land cover data for assessing habitat conditions during waterfowl surveys.

This raster dataset comes from the National Land Cover Database (NLCD), 2016 version. It represents land cover across the contiguous 48 states, circa 2016. Each 30-meter-square pixel has been classified using a standard land cover classification scheme, and some of these categories have been aggregated further according to procedures outlined in EPA's Report on the Environment (www.epa.gov/roe). Data were originally processed and compiled by the Multi-Resolution Land Characteristics Consortium (MRLC), a U.S. federal inter-agency group, based on Landsat satellite imagery.