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This data set is a combination of Urban Forestry Division managed trees and tree locations from 2023, and estimated trees modeled using an automated feature extraction process applied to 2022 LiDAR data. All trees were then processed to add geographic information including ownership information, arborist, political boundaries, census tracts, and neighborhood planning zones. |
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This data set is a combination of Urban Forestry Division managed trees and tree locations from 2023, and estimated trees modeled using an automated feature extraction process applied to 2022 LiDAR data. All trees were then processed to add geographic information including ownership information, arborist, political boundaries, census tracts, and neighborhood planning zones. |
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DDOT Urban Forestry |
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5000 |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>DC Urban Forestry managed trees (UFA Street Trees) from 2023 were combined with trees extracted from DC 2022 LiDAR source data processed using the “Extract Trees using Cluster Analysis” script which is included as part of Esri’s 3D Basemap solution. All LiDAR-derived trees within 2 meters of a Urban Forestry Division tree were removed as being duplicates.</SPAN></P><P><SPAN>Tree diameter (DBH, in inches) was estimated for the LiDAR-derived trees from calculated tree height (in feet) based on the equation: DBH = 0.4003*height - 1.9557. This equation was derived from a statistical analysis of a detailed park inventory tree data set and has an R^2 = 0.7418. Extreme outliers were also modified, with any DBH larger than 80 inches being converted to a DBH of 80 inches. Trees in forested areas were triplicated with small geographic off-sets to approximate the understory conditions found in forested landscapes as detailed by the US Forest Service Forest Inventory Analysis (FIA). All DBH and Species of estimated trees was then calibrated to roughly match the distribution observed in the DC Forest Inventory Analysis (FIA). </SPAN></P><P><SPAN>Trees were then joined to forest patch information derived from a 2019 urban tree canopy map derived from 2019 orthoimagery and LiDAR data by USFSNorthResearchStation using method from: Alonzo, M., Baker, M. E., Gao, Y., & Shandas, V. (2021). Spatial configuration and time of day impact the magnitude of urban tree canopy cooling. Environmental Research Letters, 16(8), 084028. 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. https://doi.org/10.1007/s10980-006-9013-2</SPAN></P><P><SPAN>Tree were also spatially joined to ownership data, political boundaries, arborist zones, census tract data, and neighborhood planning zones.</SPAN></P></DIV></DIV></DIV> |
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| title:
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DC Trees |
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["DC trees","estimated trees","extracted trees","urban forestry","DDOT","Street Trees","trees"] |
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en-US |
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50000 |
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