GeoAI to implement an individual tree inventory: Framework and application of heat mitigation |
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| Institution: | 1. Geospatial Sciences, School of Science, STEM College, RMIT University, GPO Box 2476V, Victoria 3001, Australia;2. School of Tourism and Geography Science, Qingdao University, Qingdao 266071, China |
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| Abstract: | Individual Tree Inventory (ITI) is critical for urban planning, including urban heat mitigation. However, an ITI is usually incomplete and costly due to data collection challenges 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.The object detection Faster R-CNN model with Inception ResNet V2 was implemented to detect individual trees 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, the ‘LST zone-based model’ stores tree canopy cover and building areas in each zone unit. Pearson correlation, global linear regression, and geographically weighted regression (GWR) were applied to establish the relationship between tree attributes, building areas (explanatory variables) with 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. |
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| Keywords: | Urban tree species High-resolution aerial imagery Object detection Faster R-CNN Land Surface Temperature (LST) Geographically Weighted Regression modelling (GWR) |
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