This data describes the City of Asheville jurisdictional boundaries.This data shows the active jurisdictional boundaries for Asheville, NC. The data is available as a REST Service API, shapefile, KML, or spreadsheet

This collection comprises geospatial datasets used to create the Beaverdam Valley Neighborhood Association community map and the resulting map in pdf and jpeg formats. This scope of the map covers the borders of Buncombe County, North Carolina, the city limits of Asheville, NC, and the three registered neighborhoods of the Beaverdam Valley (Beaverdam Valley, Hills of Beaverdam, and Beaverdam Run). The geospatial data includes the following layers and associated files:

"AVL City Limits.geojson": City of Asheville GIS municipal boundary data

"AVL City Limits.qmd": QGIS metadata file for the above

"AVL Neighborhoods.geojson": City of Asheville GIS registered neighborhood data

"AVL Neighborhoods.qmd": QGIS metadata file for the above

"Buncombe_County_Parcels.geojson": Buncombe County GIS parcel data.

"Buncombe_County_Parcels.qmd": QGIS metadata file for the above

"BV Boundaries.geojson": Beaverdam Valley Neighborhood boundaries.

"BV Boundaries.qmd": QGIS metadata file for the above

"BV Parcel Intersection.geojson": Intersection of the Beverdam Valley Neighborhood boundaries with the Buncombe County Parcel data.

"BV Parcel Intersection.qmd": QGIS metadata file for the above

"BVNA_Map_2022_v2.pdf": BVNA CIP Community Map

"BVNA_Map_2022_v2_825.jpg": BVNA CIP Community Map

"City Limits.geojson": Buncombe county boundaries and city limits boundaries witin the county.

"QGIS BVNA CIP.zip": Zip file containing the above layers in a QGIS project folder and file.

About the Project: The Beaverdam Valley Neighborhood Association (BVNA) Community Informatics Project aims to gain deeper understanding of the Beaverdam Valley community and to work towards gathering and sharing information about the community and its history. This collection represents a deliverable produced under the 2022-2023 City of Asheville Neighborhood Matching Grant program.

This dataset is the 20ft Digital Elevation Model (DEM) for all of Buncombe County, NC. The DEMs were developed from Light Detection and Ranging (LIDAR) data acquired January though February through April 2003, with partial re-flights for gap data in December 2003. Cell values in the DEMs were derived from a Triangulated Irregular Network (TIN) produced from the bare earth mass points and breaklines. The dataset was provided to the Buncombe County by the NC Floodplain Mapping Project as pre-release data in July and Sept 2006 .Specific information about individual data tiles can be obtained at www.ncfloodmaps.com

From the site: “The Geologic Atlas of the United States is a set of 227 folios published by the U.S. Geological Survey between 1894 and 1945. Each folio includes both topographic and geologic maps for each quad represented in that folio, as well as description of the basic and economic geology of the area. The Geologic Atlas collection is maintained by the Map & GIS Library. The repository interface with integrated Yahoo! Maps was developed by the Digital Initiatives -- Research & Technology group within the TAMU Libraries using the Manakin interface framework on top of the DSpace digital repository software. Additional files of each map are available for download for use in GIS or Google Earth. A tutorial is provided which describes how to download theses files.”

NEMAC Climate Resilience/Risk Raw DataLandslide, Flood and Wildfire Risk, 7 layers in total.Assessment Report: https://drive.google.com/file/d/1X_Gr4eUCmkXPOzAcvyxCe-uZPkX84Byz/viewThe assessment report give field names, data source information and metadata.For Asheville's Climate Resource guide please visit: https://www.ashevillenc.gov/news/asheville-climate-change-guide-release-and-renewable-energy-initiative-draft-plan/Open Data - to download, use the Download Filtered Dataset option to download the individual layers.

Duplicate street names in the City of Asheville, NC.

Reason for SelectionMany Southeastern ecosystems rely on regular, low-intensity fires to maintain habitat, encourage native plant growth, and reduce wildfire risk. Historically in the South, “fires burned as often as once a year or more in Coastal Plain pine systems or as infrequently as every 50 years or more on north-facing or cove sites in the mountains”, typically started by lightning or by Indigenous Americans using fire to manage open savannas. As a result, the forests and grasslands of the South contain many species that not only tolerate fire but require it. Fire suppression during the 20th century led to the loss and deterioration of many fire-adapted ecosystems and their associated wildlife and plant species. Today, “prescribed burning is an important tool throughout Southern forests, grasslands, and croplands” (Waldrop and Goodrick 2012).Input DataBase Blueprint 2022 extent2019 National Land Cover Database (NLCD): Land coverFloodplain Inundation Frequency Southeast version, available on request (email yvonne_allen@fws.gov)Landsat 8 Burned Area Products (ver. 2.0, Oct 2021)Monitoring Trends in Burn Severity (2020 data release, released 4-22-2022): National Burned Areas Boundaries Dataset Base Blueprint 2022 subregionsSoutheast Blueprint 2023 extentMapping StepsIdentify burns using the annual burn frequency rasters in the Landsat 8 Burned Area (LBA) Products. Note: This LBA data source differs from the burned area probability raster used by Southeast FireMap 1.0 (Beta). The burn probability data was found to greatly overestimate the extent of burned area across the full region of the Southeast. Currently Southeast FireMap is limited to the historic range of longleaf pine. Sum the annual LBA rasters to calculate the number of times a pixel has been burned from 2013-2021 using the ArcPy Spatial Analyst Cell Statistics “SUM” function. Reclassify to a value of 0 the burned areas that are most likely to be false positives. Assign a value of 0 to pixels classified in the 2019 NLCD as one of the following land cover types: Cultivated crops, barren (31), all urban (21, 22, 23, 24), woody wetlands (90) and open water (11). Fire in these pixels was often either not natural or likely misclassified. Clusters of pixels in barren landcover were often industrial sites and quarries. Assign a value of 0 to areas with inundation frequency values from 5 to 100. Inundated vegetation is often misclassified as burned area since they have similar spectral signatures in remote sensing.Identify burns using the annual Monitoring Trends in Burn Severity (MTBS) data. These data are very robust, but only capture large fires on a subset of public lands. Therefore, we use them in conjunction with the LBA data. Sum the annual MTBS rasters to calculate the number of times a pixel has been burned from 2013-2021 using the ArcPy Spatial Analyst Cell Statistics “SUM” function. Combine LBA and MTBS results using ArcPy Spatial Analyst Cell Statistics to calculate the maximum number of times a pixel was classified as burned using the LBA and MTBS datasets. Reclassify the resulting raster so that all values of 3+ receive the maximum value of 3 in the final indicator, as shown below.Clip to the spatial extent of Base Blueprint 2022.Use the Base Blueprint 2022 subregions to mask out the “Marine Shelf and Extension” and “Marine Gulf Stream” subregions from the indicator. These two subregions were not evaluated for fire frequency because they are outside the scope of this terrestrial indicator.As a final step, clip to the spatial extent of Southeast Blueprint 2023. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:3 = Burned 3+ times from 2013-20212 = Burned 2 times from 2013-20211 = Burned 1 time from 2013-20210 = Not burned from 2013-2021 or row cropKnown IssuesThe LBA data layers overestimate fire frequency in open areas with wet soils. Wet soils can be much darker than dry soils and may be misclassified as burned areas. This misclassification was improved by removing areas classified as cultivated crops. A mask built upon the combination of Floodplain Inundation Frequency and NLCD woody wetlands was also used to reduce misclassifications.This indicator overestimates fire frequency in places with major impediments to burned area detection/mapping. Impediments include rapid green-up following a burn, cloud cover and shadows obscuring burn signatures, difficulty detecting or differentiating a low intensity burn signature beneath tree canopies, and the satellite product resolution often being too coarse to capture fine-scale differences or small burns.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedAllen, Y. 2016. Landscape Scale Assessment of Floodplain Inundation Frequency Using Landsat Imagery. River Research and Applications 32:1609–1620. [https://doi.org/10.1002/rra.2987]. Hawbaker, T.J., Vanderhoof, M.K., Schmidt, G.L., Beal, Y., Picotte, J.J., Takacs, J.D., Falgout, J.T., and Dwyer, J.L, 2020, The Landsat Burned Area products for the conterminous United States (ver. 2.0, October 2021): U.S. Geological Survey data release, [https://doi.org/10.5066/P9QKHKTQ]. Monitoring Trends in Burn Severity. Burned Areas Boundaries Dataset. 2020 Data Release. Published April 28, 2022. [https://www.mtbs.gov/]. Teske, Casey, Melanie K. Vanderhoof, Todd J. Hawbaker, Joe Noble, and John K. Hiers. 2021. Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA, Fire, 4, no. 2: 26. [https://doi.org/10.3390/fire4020026]. U.S. Geological Survey (USGS). Published June 2021. National Land Cover Database (NLCD) 2019 Land Cover Conterminous United States. Sioux Falls, SD. [https://doi.org/10.5066/P9KZCM54]. Waldrop, Thomas A.; Goodrick, Scott L. 2012. (Slightly revised 2018). Introduction to prescribed fires in Southern ecosystems. Science Update SRS-054. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 80 p. [https://www.srs.fs.usda.gov/pubs/su/su_srs054.pdf]. Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123. [https://doi.org/10.1016/j.isprsjprs.2018.09.006].