The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data.

Discrete point cloud data from unmanned aerial vehicle laser scanning (UAV-LS) can provide information on the three-dimensional structure of a forest, the leaf area index (LAI) at the landscape or sample plot scales, the distribution of the vertical forest structure at a fine resolution, and other information. The retrieved parameters, however, may be affected in a non-negligible way by the inclusion of scan angle information. In this study, we introduced a relational model that encompasses the angular effect, predicted the mechanism of this effect, and extracted the vegetation structure indices that the angular effect might influence. Second, we quantified the direct and indirect effects, particularly the magnitude of the angular effect in broadleaf forests, and used mediated effects to investigate the components and processes that influence the angular effect. The findings demonstrate that some of the differences between the LAIe extracted by UAV-LS and the Decagon LAIe considering the angular effect of UAV-LS can be explained by adjusting physical LiDAR parameters (aerial height, laser divergence fraction, and scanning angle) and vertical forest structure variables. Along continuous and closed forest vertical gradients, the indirect angle impact is negative for the upper canopy and positive for the understory. Three-dimensional vegetation measurements were created using multiangle LiDAR data. In conclusion, this article (1) addresses the angular effect in UAV-LS; and (2) discusses how the angular effect affects 3D vegetation parameters such as LAIe, demonstrates the nonlinear trend of the angular effect, and demonstrates how multiangle LiDAR data can be used to obtain 3D vegetation parameters. This study serves as a reference for reducing the uncertainty in simulations of the angular effect and vegetation light transmission, in addition to the uncertainty in analyses of the vegetation characteristics determined by UAV-LS (e.g., the uncertainty of LAIe).

The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.