SGI Interactive Map. Tree Canopy Cover. This product provides a high- resolution estimate. Thematic raster data represents. Data are in 1m spatial resolution suitable for analysis in a GIS. The. canopy cover product is derived from 1 meter 4- band NAIP imagery from various. A spatial wavelet analysis (SWA) algorithm was implemented.
MATLAB for conifer detection. It is intended to target conifer canopy. SWA method used detects non- conifer tree species in some.
Therefore, users should ground truth results when making. SWA is particularly good at detecting. The SWA. algorithm requires a minimum of 2- 3 pixels in width for tree detection. Conifer tree detection accuracy decreases in.
This map provides an. County level data are in an Albers Equal Area coordinate system, 1 meter. Original analyses were performed in their respective UTM zone. NAIP imagery. As counties may span multiple UTM zones, data.
Reprojection may be. Funding support provided by: Western Association of Fish and Wildlife. Agencies, U. S. Fish and Wildlife Service Inter- Landscape Conservation. Cooperative, Bureau of Land Management, National Fish and Wildlife.
Foundation, Utah Department of Natural Resources Watershed Restoration. Initiative, Natural Resources Conservation Service Conservation Effects. Assessment Project, and Intermountain West Joint Venture SGI- SWAT. Partnership. State level data can be downloaded from the table below or the University. Montana FTP server. Falkowski et al. Mapping tree canopy cover in support of. North America. Rangeland.
Search for jobs or a new career on our advanced job finder. Find employment opportunities by location, industry, experience, salary, benefits, and more. Together, the Unizin Consortium is finding solutions for the biggest issues facing higher education institutions today: affordability, access, and learner success. We know the current state of digital education is changing. Now that your team has finally delivered its project, there is one more important step before the team disbands: the project post-mortem. The name might sound forbidding (some people prefer to call it a “project.
Helping Sitka and Its Visitors Maintain a Healthy and Safe Lifestyle. Sitka Medical Center Provides Professional Health Care for the Whole Family. Routine Physicals, Vaccinations, or Wellness. Notice: Due to security risks on web sites, Chrome no longer supports many plugins that are used throughout the web. Plugins including Silverlight, Java, and Unity will not work. In order to view the Webinar archives, you will.
Ecology & Management. Ecosystem Resilience & Resistance. This layer depicts a simplified index of relative ecosystem resilience to. Potential ecosystem R& R. Chambers et al. Soils data were derived from two primary sources: 1) completed and. Soil Survey Geographic Database. SSURGO), and 2) the State Soils Geographic Database (STATSGO2) to fill.
SSURGO data were not available (Fig. Using best available.
R& R: high, moderate, and low (Table 1; Chambers et al. Maestas et al. Soils with high water tables, wetlands, or frequent. This tool is most appropriate for regional and landscape level planning. When combined with other data layers, such as the. PACs and existing land cover, planners can use this tool to. At more local scales, depicting the actual soil. Application of this tool is just one step in the planning process.
R& R. Thematic raster data represent resilience and resistance in the following. Wetland/Riparian.
High. 2: Moderate. Low. Null: Not Available. Figure 1. Soil survey data sources assembled in October 2. R& R. Abrupt transitions in soil. SSURGO adjoins coarser.
STATSGO2 data. Table 1. Rating of relative resilience and resistance by soil taxonomic. Soil taxonomic name. Common name. R& R rating. Cryic/Udic- Typic. Cold/moist. High. Cryic/Ustic- Typic.
Cold/summer moist. High. Cryic/Xeric- Typic. Cold/moist. High. Cryic/Xeric bordering on Aridic. Cold/moist bordering on dry.
High. Cryic/Aridic bordering on Xeric. Cold/dry bordering on moist.
Moderate. Cryic/Aridic- Typic. Cold/dry. Moderate. Frigid/Ustic- Typic. Cool/summer moist. High. Frigid/Xeric- Typic. Cool/moist. High. Frigid/Ustic bordering on Aridic.
Cool/summer moist bordering on dry. Moderate. Frigid/Xeric bordering on Aridic. Cool/moist bordering on dry. Moderate. Frigid/Aridic bordering on Ustic. Cool/dry bordering on summer moist. Moderate. Frigid/Aridic- Typic. Cool/dry. Moderate.
Frigid/Aridic bordering on Xeric. Cool/dry bordering on moist. Moderate. Mesic/Ustic- Typic. Warm/summer moist. Moderate. Mesic/Xeric- Typic.
Warm/moist. Moderate. Mesic/Ustic bordering on Aridic. Warm/summer moist bordering on dry. Moderate (Prairies)Low (Cold Deserts)Mesic/Aridic bordering on Ustic. Warm/dry bordering on summer moist. Moderate (Prairies)Low (Cold Deserts)Mesic/Aridic bordering on Xeric. Warm/dry bordering on moist.
Low. Mesic/Aridic- Typic. Warm/dry. Low. Chambers et al. Using resistance and resilience concepts to reduce. Fort Collins. CO, USA: U. S. Department of Agriculture, Forest Service, RMRS- GTR- 3. Using resilience and resistance concepts to manage. Gunnison sage- grouse, and greater.
Fort Collins, CO: U. S. Department of Agriculture. Forest Service, Rocky Mountain Research Station.
Mapping Potential Ecosystem. Resilience and Resistance across Sage- Grouse Range using Soil Temperature. Moisture Regimes. Sage Grouse Initiative. Tapping Soil Survey Information for Rapid Assessment.
Sagebrush Ecosystem Resilience and Resistance. Rangelands 3. 8: 1. Various dataets were used, including. USDA NRCS SSURGO, and USDA NASS Cropland Data Layer.
CDL). Independent models were produced for each county; county level. Data are available by state: Produced by Jeffrey S. Evans, The Nature Conservancy. Lipsey, M. K., K.
E. One step ahead of the plow: Using cropland. Biological. Conservation. Reducing cultivation risk for at- risk.
Predicting outcomes of conservation easements for sage- grouse. It is based on models described in Stevens et al.
If visualization fails. After toggling the layer on, use the drawing tools in the upper left hand corner to. Points represent lek locations; fence. Polygons represent areas of bird concentration, e. Fence collision risk is calculated the same as points.
Polygons need to be drawn counter- clockwise for correct. Alternatively, a zipped shapefile of points or polygons can be uploaded. Commonly used coordinate. Attributes are ignored.
An. example of a zipped shapefile is. Once submitted, the points or polygons of the shapefile will be. Please note that there may be a lag while processing the.
If necessary. follow these instructions to convert 3. D shapefiles to 2. D. shapefiles using ESRI Arc. Map. Click 'Calculate layer' to visualize fence collision risk. Click 'Reset. layer' to reset point and polygon features. The visualization is scale. For more accurate results it is necessary to zoom to an.
When fence collision risk is visualized, the data may be. Geo. Tiff. This allows the data to be brought into GIS.
Enter your. email address in the box and click 'Download layer'. A confirmation will. Large areas will be exported as. GIS. software. Follow the symbology instructions to easily visualize the layer in. Arc. Map. Lek information is temporarily cached for processing; it is automatically.
Lek information is not archived or permanently. All lek information is transported securely. Stevens, B. Griffiths. K. Mapping sage- grouse fence- collision risk: Spatially. Wildlife Society. This is more detailed content on Sage Grouse MGMT ZONES.