Mapping carbon stock and growth of individual street trees using LiDAR-camera fusion-based mobile mapping system

Urban street trees account for a significant fraction of trees in urban areas, yet the carbon stocks of street trees remain largely unexamined. This study introduces a framework utilizing a Light Detection and Ranging (LiDAR)-camera fusion-based Mobile Mapping System (MMS) to estimate carbon stocks in individual street trees. This system allows simultaneous collection of species information and structural parameters on a city-wide scale, enabling the estimation of carbon stock. The framework comprises two principal components: the detection of individual street trees and the estimation of their respective carbon stocks. To detect individual street trees, we initially employed image-based deep learning model to diminish the effort needed in constructing point cloud training data and designing a universal rule applicable to complex and diverse urban streetscapes. In the carbon stock estima…