Sensitivity Testing Analysis of Airborne Hyperspectral Lidar Signals for Monitoring Insects and Diseases Based on 3d Radiative Transfer Model

Monitoring insect and disease disturbances in the lower parts of the forest has always been a hot research topic. Hyperspectral LiDAR (HSL), a new sensor, makes it possible to monitor these pest disturbances. In this study, we used the 3D radiative transfer model LESS to simulate AHSL point cloud data and conducted a sensitivity analysis of the point cloud data for monitoring forest insect and disease. A virtual forest scene was first reconstructed with explicit geometric structures using terrain laser scanning (TLS) data and ground measurement data. Based on measured optical properties of damaged foliage, some different damage scenarios with different stress levels were defined. AHLS and hyperspectral imagery (HI) were simulated for different forest pest disturbance scenarios. For the AHSL point cloud data, we select a commonly used stress index, RENDVI, as the evaluation vegetation index for assessing the sensitivity of the hyperspectral LiDAR signal in monitoring insect and disease disturbances. According to different damaged locations, different damage scenarios were rasterized into images. The corresponding hyperspectral images were also compared with the AHSL data. The result show that AHSL has great potential for monitoring forest insect and disease disturbances compared to HI. This study demonstrates that AHSL has great potential in responding to spectral signal changes in the lower and middle parts of the forest, and which may also be a powerful tool for early detection of forest pest disturbances.