Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation of individual tree crown and species classification using drone high-resolution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environments. In this study, we improve upon the state of the Mask region-based convolution neural network (Mask R-CNN) with our proposed attention complementary network (ACNet) and edge detection R-CNN (ACE R-CNN) for individual tree species identification in high-density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R-CNN. We demonstrate the performance of ACE R-CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1-score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50-FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accelerates the convergence speed of the model training. This work demonstrates the improved performance of ACE R-CNN for individual tree species identification and provides a new solution for tree-level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment.

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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.

Background Forest aboveground biomass (AGB) is not only the basis for estimating forest carbon storage, but also an important parameter for evaluating forest carbon cycle contribution and forest ecological function. Data saturation and fewer field plots limit the accuracy of AGB estimation. In response to these questions, we constructed a point-line-polygon framework for regional coniferous forests AGB mapping using field survey data, UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries in this study. Under this framework, we explored the feasibility of acquiring the LiDAR sampling plots using the LiDAR sampling strategy consistent with the field survey, and analyzed the potentials of multi-scale wavelet transform (WT) textures and tree species stratification for improving AGB estimation accuracy of coniferous forests in North China. Results The results showed that UAV-LiDAR strip data of high density point clouds could be used as a sampling tool to achieve sample amplification. Experimental comparison results showed that the Sentinel-based AGB estimation models incorporating the multi-scale WT textures and SAR data performed better, and the model based on coniferous forests tree species significantly improved the performance of AGB estimation. Additionally, the accuracy comparison using different validation sets indicated that the proposed LiDAR sampling strategy under the point-line-polygon framework was suitable for estimating coniferous forests AGB on a large area. The highest accuracy of AGB estimation of larch, Chinese pine and all coniferous forests was 74.55%, 78.96%, and 73.42%, respectively. Conclusions The proposed approach can successfully alleviate the data signal saturation issue and accurately produce a large-scale wall-to-wall high-resolution AGB map by integrating optical and SAR data with a relative small number of field plots.

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