Due to the lower canopy height at the maximum crown width at the bottom of young Picea crassifolia trees, they are mixed with undergrowth. This makes it challenging to accurately determine crown size using CHM data or point cloud data. UAV imagery, on the other hand, incorporates rich color information and, after processing, can effectively distinguish between spruce trees and ground vegetation. In this study, the experimental site was an artificial young forest of Picea crassifolia in Shangshan Village, Qinghai Province, China. UAV images were used to obtain normalized saturation data for the sample plots. A marker-controlled watershed segmentation algorithm was employed to extract tree parameters, and the results were compared with those obtained via point cloud clustering segmentation and the marker-controlled watershed segmentation algorithm based on Canopy Height Model (CHM) images. The research results showed that the single tree recognition capabilities of the three types of data were similar, with F-measures of 0.96, 0.95, and 0.987 for the CHM image, UAV imagery, and point cloud data, respectively. The mean square errors of crown width information extracted from the UAV imagery using the marker-controlled watershed segmentation algorithm were 0.043, 0.125, and 0.046 for the three sample plots, which were better than the values of 0.103, 0.182, and 0.074 obtained from CHM data, as well as the values of 0.36, 0.461, and 0.4 obtained from the point cloud data. The point cloud data exhibited better fitting results for tree height extraction compared to the CHM images. This result indicates that UAV-acquired optical imagery has applicability in extracting individual tree feature parameters and can compensate for the deficiencies of CHM and point cloud data.

While the use of large tropical trees to predict aboveground biomass (AGB) in forests has previously been studied, the applicability of this approach in arid regions remains unquantified. In the natural forests of arid mountains of Northwestern China, this study collected individual tree data from 105 plots across 11 sites through field measurements. The objective was to assess the feasibility of using large trees for predicting plot AGB in these natural forests of arid mountains. This entailed determining the contribution of large trees, based on which a plot AGB prediction model was constructed. This study also aimed to identify the optimal number of large trees needed for accurate AGB prediction. The findings indicate that within the natural forests of arid mountains, only seven large trees (approximately 12% of the trees in a plot) are necessary to account for over 50% of the plot AGB. By measuring 18 large trees within a plot, this study achieved a precise plot AGB estimation, resulting in a model rRMSE of 0.27. The regression fit R² for the predicted AGB and the estimated AGB was 0.79, effectively aligning the predicted and measured AGB. In the Tianshan Mountains’ natural forests, the prediction model yielded further improvements with an rRMSE of 0.13 and a remarkable regression R² of 0.92 between predicted and estimated AGB. However, due to variances in tree size distribution and tree species biomass, the Altai Mountains’ natural forest was found to be unsuitable for predicting plot AGB using large trees. This study establishes that large trees can effectively represent plot AGB in the natural forests of arid mountains. Employing forest surveys or remote sensing to collect data from a few large trees instead of the entire tree population enables accurate plot AGB prediction. This research serves as the initial quantification of large tree utilization for plot AGB prediction in the natural forests of arid mountains, carrying substantial implications for future arid forest inventories, carbon accounting, and the formulation of prudent conservation strategies.

Green development is an important path to breaking the “growth-pollution” paradox and realizing the United Nations 2030 Agenda for Sustainable Development. Most of the countries along the “Belt and Road” are developing countries with fragile ecological environments, and the pressure of economic activities on environmental resources exceeds the global average, so the countries have a strong will to promote green development. Accurately grasping the level of green development and the dynamic evolution trend of BRI countries can help to explore the implementation effect of policies at a certain stage, and on this basis, exploring regional correlations and differences can help to provide data support for future regional cooperation and policy formulation. Based on this, this study comprehensively evaluates the level of green development in the BRI countries by constructing a multi-dimensional evaluation index system and then explores the correlation and regional differences of green development in the countries along the route by utilizing the Moran index and the Thiel index. The results indicate that the green development levels of BRI countries have shown an upward trend; the spatial association exhibited a fluctuating upward trend, with significant changes in local clustering characteristics. Overall disparities in green development levels were relatively small, with intra-group differences being the primary contributor to these disparities, and West Asia and the Middle East displayed larger variations in green development levels. This study provides policy recommendations for promoting green development in BRI countries.