1.0 DCPLUS 20221003 14443500 20230203 14443500 https://www.arcgis.com/sharing/rest/content/items/4e629a07ff9f4a43b52693ef77b4cdec/info/thumbnail/thumbnail1665079800109.png https://www.arcgis.com/home/item.html?id=4e629a07ff9f4a43b52693ef77b4cdec 4e629a07ff9f4a43b52693ef77b4cdec Feature Service https://services1.arcgis.com/gGHDlz6USftL5Pau/arcgis/rest/services/Baltimore_Patch_Statistics_update/FeatureServer Baltimore_Patch_Statistics 2022-10-03T14:44:35 2023-02-03T18:06:34 Forest Service northern research station urban forest patch Baltimore Forest patches of Baltimore City created from 2015 urban tree canopy map using high-resolution LiDAR and NAIP imagery <div style='font-family:Arial, Helvetica, sans-serif; color:rgb(34, 34, 34); font-size:small;'>This forest patch layer was created using a 2015 high-resolution urban tree canopy map of Baltimore City derived from LiDAR and NAIP imagery (https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-bes.3210.110). To identify forest area, tree canopy over impervious surfaces was first subtracted from the canopy layer, including buildings and roads identified from planimetric data available from Baltimore City (https://data.baltimorecity.gov/). Morphological spatial pattern analysis (MSPA; Vogt et al. 2007) was then used to distinguish forest patches from remaining tree canopy using an edge parameter of 15 m based on observed changes in vegetation composition and structure (Baker, unpublished data). MSPA applies the edge parameter to distinguish interiors (i.e. ‘cores’) from surrounding edges, as well as five other morphometric primitives (i.e. branches, bridges, loops, and islets) that reflect how canopy is or is not connected to cores. Patches always include core areas and their surrounding edges, as well as any perforations. Patches are separated into two size classes: Forested Natural Areas are patches with greater core area and thickness than Groves.</div><div style='font-family:Arial, Helvetica, sans-serif; color:rgb(34, 34, 34); font-size:small;'>Patches are classified by land ownership categories using local parcel data available from Baltimore City (https://data.baltimorecity.gov/). Ownership categories include: Federal, State, Municipal, Commercial/Industrial, Institutional, and Private Residential owners. Parcels with unknown ownership were assigned Municipal ownership.</div><div style='font-family:Arial, Helvetica, sans-serif; color:rgb(34, 34, 34); font-size:small;'>Vogt P, Riitters K H, Estreguil C, Kozak J, Wade T G and Wickham J D 2007 Mapping spatial patterns with morphological image processing Landsc. Ecol. 22 171–7.</div> <span style='color:rgb(34, 34, 34); font-family:Calibri, sans-serif; font-size:14.6667px;'>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, and assumes no legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.</span> -76.71201830054387 -76.52944713877139 39.37256725881379 39.19714921700427 1 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 4e629a07ff9f4a43b52693ef77b4cdec https://services1.arcgis.com/gGHDlz6USftL5Pau/arcgis/rest/services/Baltimore_Patch_Statistics_update/FeatureServer Feature Service EPSG