First Experience with Zhuhai-1 Hyperspectral Data for Urban Dominant Tree Species Classification in Shenzhen, China

Abstract

An accurate spatial distribution map of the urban dominant tree species is crucial for evaluating the ecosystem service value of urban forests and formulating urban sustainable development strategies. Spaceborne hyperspectral remote sensing has been utilized to distinguish tree species, but these hyperspectral data have a low spatial resolution (pixel size ≥ 30 m), which limits their ability to differentiate tree species in urban areas characterized by fragmented patches and robust spatial heterogeneity. Zhuhai-1 is a new hyperspectral satellite sensor with a higher spatial resolution of 10 m. This study aimed to evaluate the potential of Zhuhai-1 hyperspectral imagery for classifying the urban dominant tree species. We first extracted 32 reflectance bands and 18 vegetation indices from Zhuhai-1 hyperspectral data. We then used the random forest classifier to differentiate 28 dominant tree species in Shenzhen based on these hyperspectral features. Finally, we analyzed the effects of the classification paradigm, classifier, and species number on the classification accuracy. We found that combining the hyperspectral reflectance bands and vegetation indices could effectively distinguish the 28 dominant tree species in Shenzhen, obtaining an overall accuracy of 76.8%. Sensitivity analysis results indicated that the pixel-based classification paradigm was slightly superior to the object-based paradigm. The random forest classifier proved to be the optimal classifier for distinguishing tree species using Zhuhai-1 hyperspectral imagery. Moreover, reducing the species number could slowly improve the classification accuracy. These findings suggest that Zhuhai-1 hyperspectral data can identify the urban dominant tree species with accuracy and holds potential for application in other cities.