20170308 raster digital data Tree canopy was derived from 2015 high-resolution remotely sensed data. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected. Tree canopy raster dataset was developed for land cover mapping in support with the City of Durham, North Carolina and SavaTree Consulting Group. The goal of this project is to increase municipal decision makers' understanding of their green infrastructure with the amount of tree canopy that currently exists and the amount of land that could theoretically support tree canopy. Complete None planned -79.007525 -78.759131 36.137124 35.866213 None Tree Canopy Tree Canopy Assessment None None University of Vermont Spatial Analysis Laboratory Jarlath O'Neil-Dunne Director mailing and physical

81 Carrigan Drive

Aiken Room 205

Burlington VT 05405 US 802-656-3324 802-656-8683 joneildu@uvm.edu University of Vermont Spatial Analysis Laboratory in collaboration with City of Durham, North Carolina. Esri ArcGIS 10.4.0.5524 This dataset was mapped to the extent of the 2015 LiDAR provided by the City of Durham, North Carolina. Therefore some areas may not be mapped. unknown 0 None unknown ground condition Pleiades Imagery None 20150101 ground condition LiDAR 20150101 12000 None 20150101 ground condition NAIP Raster Pixel 49266 36675 treecanopy_2015_durhamnc.img.vat OID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Value Value given to cell as to whether TC is present of not. University of Vermont Spatial Analysis Laboratory 0 TC Present University of Vermont Spatial Analysis Laboratory Count Number of cells within for each Value Esri Number of cells per Value 0 20170308 University of Vermont Spatial Analysis Laboratory Jarlath O'Neil-Dunne Director mailing and physical

Aiken Room 205

81 Carrigan Drive

Burlington VT 05405 US 802-656-3324 802-656-8683 joneildu@uvm.edu FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time