Geoai to Implement Individual Tree Inventory : Framework and Application of Heat Mitigation
Individual tree inventory (ITI) is critical for urban planning, including urban heat mitigation. However, individual tree inventory is usually incomplete and costly due to the challenges of data collection in the dynamic urban landscape. This research developed a methodical GeoAI framework to build a comprehensive ITI and quantify tree species cooling on rising urban heat. A faster RCNN object detection model was implemented to detect individual tree canopy and seven tree species (Callery pear, Chinese elm, English elm, Mugga ironbark, Plane tree, Spotted gum and White cedar). The land-surface temperature (LST) was derived from Landsat 8 surface reflectance imagery. Two models for ITI were further developed for spatial and statistical analysis. Firstly, an ‘Individual tree-based model’ stores the attributes of tree species and its vertical configuration obtained from LiDAR along with its tree canopy area and surface temperature. Secondly, ‘LST zone-based model’ stores tree canopy cover and buildings area in each zone unit. Pearson correlation, global linear regression, and Geographically weighted regression (GWR) was applied to establish the relationship between tree characteristics and buildings area (explanatory variables) in determining local temperature (dependent variable). Results showed that English elm has the highest cooling and least by Mugga ironbark in the study area. GWR results demonstrate that 94 % of the LST was explained by tree height and tree canopy area. The LST zone-based model showed that 85% of the LST was explained by the percentage of tree species and buildings. Maps of the local R2 and coefficients of the independent variables provide spatially explicit information on the cooling of different tree species compared to building areas. The implemented GeoAI approach provides important insights to urban planners and government to monitor urban trees with the enhanced individual tree inventory and strategies mitigation plan to reduce the impact of climate change and global warming
Year of publication: |
[2022]
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Authors: | Das, Shinjita ; Sun, Qian Chayn ; Zhou, Heng |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
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