Spectral Band Design for Urban Forest Remote Sensing Based on Low-Altitude UAV

Abstract

Urban forests are a type of forest ecosystem characterized by a strong interconnection between human activities and the natural environment. They play a crucial role in storing above-ground carbon and regulating urban climates. Customizing low-altitude unmanned aerial vehicle (UAV)-borne multispectral remote sensing (RS) systems with finely tuned sensors to accurately identify individual tree species can assist in mapping the spatial distribution of carbon stocks. The objective of this study is to investigate the robust correlation between specific spectral bands and the characteristics of urban forest species by quantifying the information encapsulated within those particular spectral bands, in order to facilitate the implementation of a low-altitude UAV-borne multispectral remote sensing system. The proposed approach was compared to four major methods for reducing band dimensionality, namely Analysis of Variance (ANOVA), Random Forest (RF), Step Discriminant Analysis (SDA), and Principal Component Analysis (PCA). The results of three classification models, namely support vector machine (SVM), multi-layer perceptron (MLP), and Boosting Tree, demonstrate that the feature band combination extracted by our method consistently achieves the highest overall classification accuracy (ranging from 83.33% to 98.48%) across different types of urban forests, indicating its potential for fine identification of tree species. This research provides a fundamental basis for the dissemination and implementation of low-altitude UAV-borne multispectral remote sensing technology in urban forestry management. Index Terms-Urban forest, low-altitude unmanned aerial vehicle (UAV)-borne remote sensing (RS), accurate identification of tree species, band design.