Abstract: Forest spatial structure and dynamics pattern are crucial to forest management and ecological modelling. Unmanned aerial vehicle (UAV) based light detecting and ranging (LiDAR) and photogrammetry could provide comprehensive spatial structure and species of forest, and have unrivalled advantages in the long-time monitoring of forest environment at individual tree or stand scale. UAV-based LiDAR system usually carries multiple echoes/full wave laser scanner, and assembles high precision global navigation satellite system (GNSS) & inertial measurement unit (IMU) which is used to ensure the position accuracy of backscatter signals of transmitted laser pulses. UAV-based photogrammetry system mainly carries visual (RGB)/multiband camera, and assembles low precision GNSS & IMU. Automated 3D reconstruction algorithms can estimate the locations and orientations of cameras and camera internal parameters using highly overlapping aerial photographs, and generate initial rectified images and point cloud with relative coordinates, which can be georeferenced by ground control points (GCPs), reference images, etc. The accuracy of image matching can be improved using high precision GNSS, stabilized platform, etc. Individual tree segmentation algorithms were generally used to extract structure information of individual trees, such as tree tops, crown edges, locations of trees, etc., from point cloud of LiDAR or photogrammetry reconstruction. The structure features of individual trees can also be recognized from projected voxel space or canopy height model (CHM) generated from point cloud. Forest stand structure information were usually estimated by height profile algorithms from point cloud or synthetic waveform. The point cloud can be directly used to calculate features, such as height percentile, echo index, etc., or generate synthetic waveforms based frequency or intensity of echoes at specified bin of height. The waveform features, such as percentile, leading edge, trailing edge, etc., can be extracted from synthetic waveforms. The estimation values of forest structure parameters were obtained based on the relationship between field measurements and the features of point cloud or waveforms. The terrain under forest canopy can be detected from point cloud of LiDAR or photogrammetry reconstruction. The accuracy of terrain from photogrammetry reconstruction was similar to that from LiDAR in low canopy closure area, but lower than that from LiDAR in high canopy closure area. Multitemporal measurements of UAV-based LiDAR and photogrammetry can be used to monitor forest structure change caused by manual pruning, selective cutting, forest fire, disease and pest damage, etc., and phenological change, such as brunches and leaves growing, leaves falling, etc. The estimation accuracy of forest structure parameters extracted using UAV-based LiDAR and photogrammetry were affected by acquisition patterns, data processing algorithms, forest growing season, terrain, etc. The art of state repertoire hasn't been suitable to wide utilization in forestry. The UAV flying should follow the constrains of national/local laws and regulations, which has been managed according to some conditions, such as empty weight, max take-off weight, etc., in China. In the future, UAV data acquisition and processing system will be more intelligent, miniaturized, low-cost, and better serve the needs of forestry applications.

Key words: unmanned aerial vehicle(UAV), light detection and ranging(LiDAR), photogrammetry, point cloud, forest