Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of the important tree species in plantation in southern China. Rapid and accurate acquisition of individual tree above-ground biomass (IT-AGB) information is of vital importance for precise monitoring and scientific management of Chinese fir forest resources. Unmanned Aerial Vehicle (UAV) oblique photogrammetry technology can simultaneously obtain high-density point cloud data and high spatial resolution spectral information, which has been a main remote sensing source for obtaining forest fine three-dimensional structure information and provided possibility for estimating IT-AGB. In this study, we proposed a novel approach to estimate IT-AGB by introducing the color space intensity information into a regression-based model that incorporates three-dimensional point cloud and two-dimensional spectrum feature variables, and the accuracy was evaluated using a leave-one-out cross-validation approach. The results demonstrated that the intensity variables derived from the color space were strongly correlated with the IT-AGB and obviously improved the estimation accuracy. The model constructed by the combination of point cloud variables, vegetation index and RGB spatial intensity variables had high accuracy (R2 = 0.79; RMSECV = 44.77 kg; and rRMSECV = 0.25). Comparing the performance of estimating IT-AGB models with different spatial resolution images (0.05, 0.1, 0.2, 0.5 and 1 m), the model was the best at the spatial resolution of 0.2 m, which was significantly better than that of the other four. Moreover, we also divided the individual tree canopy into four directions (East, West, South and North) to develop estimation models respectively. The result showed that the IT-AGB estimation capacity varied significantly in different directions, and the West-model had better performance, with the estimation accuracy of 67%. This study indicates the potential of using oblique photogrammetry technology to estimate AGB at an individual tree scale, which can support carbon stock estimation as well as precision forestry application.
Chinese fir (Cunninghamia lanceolate (Lamb.) Hook) individual tree parameters extraction is important for scientific forest management. However, high-precision parameters extraction by field investigation or spaceborne optical remote sensing is difficult when the forest is dense and the terrain is complex. This study proposes a framework for extracting individual tree parameters by combining low-cost and high-efficiency unmanned aerial vehicle (UAV)-based oblique photogrammetry with manned airborne light detection and ranging (LiDAR) data and explores the influence of spatial resolution on the accuracy of parameter extraction. The variable window filtering (VWF) was used to detect individual tree. The marker-controlled watershed segmentation (MCWS) and seed region growing (SRG) algorithms were used to delineate the crown. The individual tree detection using VWF based on 1 m resolution achieves precision of 80%. For the crown delineation, it is more accurate based on the 0.25 m resolution using MCWS algorithm with the detection accuracy (DA) of 65%. The results show that the proposed framework can effectively detect the tree and delineate the crown under complex terrain conditions and the optimal resolution for different parameter extraction is determined, which has important guiding significance to determine the flight parameters and reduce unnecessary data processing.
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