Urban tree species classification using multisource satellite remote sensing data and street view imagery

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ABSTRACT Urban tree species distribution plays an important role in supporting both the urban forest carbon stocks estimation and sustainable urban ecological planning. Practical constraints, such as high labor costs and limited data coverage, yet present a major challenge in rapid census and classification of urban tree species at city scale. This paper aims to address this requirement by developing a large-scale urban tree species classification model through the combination of Global Ecosystem Dynamics Investigation (GEDI) data, GF-5 images, GF-6 images and Street View Imagery (SVI). The model employs Random Forest (RF) algorithm and incorporates reference data from both conventional forest inventory, comprising 866 samples and a large collection of virtual tree inventory plot automatically generated from SVI, totaling 8681 samples. From the results, the model achieves an overall accuracy of 77.89% and Kappa coefficient of 0.75. The model shows better recognition for tree species with unique crown morphology and clustered distributions. The finding indicates that satellite datasets can provide statistically significant contributions to urban tree species classification. Visible light spectrum, leaf-area and tree height metrics, as well as climate and geographic location metrics, exhibit notable value in the classification. Overall, this study can provide a viable research pathway for extending urban tree species classification to a larger scale.