Low-cost UAV aerial photogrammetry and airborne lidar scanning have been widely used in forest biomass survey and mapping. However, the feature dimension after multisource remote sensing fusion is too high and screening key features to achieve feature dimension reduction is of great significance for improving the accuracy and efficiency of biomass estimation. In this study, UAV image and point cloud data were combined to estimate and map the biomass of subtropical forests. Firstly, a total of 173 dimensions of visible light vegetation index, texture, point cloud height, intensity, density, canopy, and topographic features were extracted as variables. Secondly, the Kendall Rank correlation coefficient and permutation importance (PI) index were used to identify the key features of biomass estimation among different tree species. The random forest (RF) model and XGBoost model finally were used to compare the accuracy of biomass estimation with different variable sets. The experimental results showed that the point cloud height, canopy features, and topographic factors were identified as the key parameters of the biomass estimate, which had a significant influence on the biomass estimation of the three dominant tree species in the study area. In addition, the differences in the importance of characteristics among the tree species were discussed. The fusion features combined with the PI index screening and RF model achieved the best estimation accuracy, the R2 of 0.7356, 0.8578, and 0.6823 were obtained for the three tree species, respectively.

In order to reduce the impact of global warming, forestry carbon sink trading is an effective approach to achieving carbon neutrality, while carbon storage estimation plays an important role as the basis of the whole carbon sink trading. Therefore, an accurate estimation of carbon storage is conducive to the sustainable development of carbon sink trading. In this paper, we use laser SLAM technology to model an artificial forest in three dimensions, extract the tree parameters by the point cloud processing software, and calculate the carbon storage according to the allometric growth equation of the tree species. The experimental results show that the loop path is the best among the three-path planning of ZEB-HORIZON scanner data acquisition. For large-scale plantations, the fusion data acquisition of linear and loop paths by Livox Mid-40 and ZEB-HORIZON LIDAR can be adopted with a highly precise and a complete 3D point cloud obtained. The Lidar360 software is used for single wood segmentation and parameter extraction, and the manual measurement is taken as the quasi-true value. After the measurement accuracy analysis, the carbon storage estimation is met. Using the volume source biomass method in the sample plot inventory method, the carbon storages of camphor and cypress in the experimental area were estimated through the allometric growth equation of camphor and cypress and the international conversion rate.

It is fundamental to acquire accurate point cloud information on rock discontinuities efficiently and comprehensively when evaluating the stability of rock masses. Taking a high and steep cliff as an example, we combined 3D laser scanning and UAV photogrammetry technology to collect rock data, and proposed an intelligent identification method for rock discontinuities based on the multi-source fusion of point clouds. First, the 3D-laser-collected point cloud data were used as the basis to fuse with the UAV-photogrammetry-collected data, and the unified coordinate system and improved ICP algorithm were used to obtain the complete 3D point cloud in the study area. Secondly, we used neighborhood information entropy to achieve adaptive neighborhood-scale selection and to obtain the optimal neighborhood radius for the KNN search, to effectively calculate the point cloud normal vector and rock mass orientation information. Finally, the KDE algorithm and DBSCAN algorithm were combined for rock discontinuity clustering to achieve intelligent identification and information extraction of the rock structural plane. The clustering results were imported into the DSE program developed based on Matlab to calculate the discontinuity spacing and continuity of the rock mass structure, and to efficiently obtain the parameters of rock mass occurrence. The research results showed that this method can effectively solve the problem of incomplete-data-acquisition ground 3D laser scanning in complex geological conditions, and UAV photogrammetry prone to blurred images in depressed areas. When the extraction results were compared with the field-measured rock occurrence, the average dip angle error was about 2°, the average dip direction error was 1°, and the recognition results met the accuracy requirements. The research results provide a feasible scheme for the identification and extraction of discontinuities of high and steep rock masses.

With the rapid development of the geographic information service industry, point cloud data are widely used in various fields, such as architecture, planning, cultural relics protection, mining engineering, etc. Despite that there are many approaches to collecting point clouds, we are facing the problem of point cloud holes caused by the inability of a 3D laser scanner to collect data completely in the narrow space of the mine access shaft. Thus, this paper uses RGB-D cameras to collect data and reconstruct the hole in the point cloud. We used a 3D laser scanner and RGB-D depth camera to collect the 3D point cloud data of the access shaft roadway. The maximum error was 2.617 cm and the minimum error was 0.031 cm by measuring the distance between the feature points, which satisfied the visualization repair of the missing parts of the 3D laser scanner data collection. We used the FPTH + ICP algorithm, ISS + ICP algorithm, SVD + ICP algorithm, and 3D-NDT algorithm to perform registration and fusion of the processed 3D point cloud and the original point cloud and finally repaired the hole. The study results show that the ISS + ICP registration algorithm had the most matching points and the lowest RMSE value of 13.8524 mm. In addition, in the closed and narrow roadway, the RGB-D camera was light and easy to operate and the point data acquired by it had relatively high precision. The three-dimensional point cloud of the repaired access shaft roadway has a good fit and can meet the repair requirements.

It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R²) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources.