Crown volume is an important tree factor used in forest surveys as a prerequisite for estimating biomass and carbon stocks. This study developed a method for accurately calculating the crown volume of individual trees from vehicle-borne laser scanning (VLS) data using a concave hull by slices method. CloudCompare, an open-source three-dimensional (3D) point cloud and mesh processing software package, was used with VLS data to segment individual trees from which single tree crowns were extracted by identifying the first branch point of the tree. The slice thickness and number to be fitted to the canopy point cloud were adaptively determined based on the change rate in area with height, with the area of each slice calculated using the concave hull algorithm with portions of the crown regarded as truncated cones. The overall volume was then calculated as the sum of all sub-volumes. The proposed method was experimentally validated on 30 urban trees by comparing the crown volumes calculated using the proposed method with those calculated using five existing methods (manual measurement, 3D convex hull, 3D alpha shape, convex hull by slices, and voxel-based). The proposed method produced the smallest average crown volume. Gaps and holes in the point cloud were regarded as part of the crown by the manual measurement, 3D convex hull, and convex hull by slices method, resulting in the calculated volume being higher than the true value; the proposed method reduced this effect. These results indicate that the concave hull by slices method can more effectively calculate the crown volume of a single tree from VLS data.
Background
Spatial allocation of medical resources is closely related to people’s health. Thus, it is important to evaluate the abundance of medical resources regionally and explore the spatial heterogeneity of medical resource allocation.
Methods
Using medical geographic big data, this study analyzed 369 Chinese cities and constructed a medical resource evaluation model based on the grading of medical institutions using the Delphi method. It evaluated China’s medical resources at three levels (economic sectors, economic zones, and provinces) and discussed their spatial clustering patterns. Geographically weighted regression was used to explore the correlations between the evaluation results and population and gross domestic product (GDP).
Results
The spatial heterogeneity of medical resource allocation in China was significant, and the following general regularities were observed: 1) The abundance and balance of medical resources were typically better in the east than in the west, and in coastal areas compared to inland ones. 2) The average primacy ratio of medical resources in Chinese cities by province was 2.30. The spatial distribution of medical resources in the provinces was unbalanced, showing high concentrations in the primate cities. 3) The allocation of medical resources at the provincial level in China was summarized as following a single-growth pole pattern supplemented by bipolar circular allocation and balanced allocation patterns. The agglomeration patterns of medical resources in typical cities were categorized into single-center and balanced development patterns. GDP was highly correlated to the medical evaluation results, while demographic factors showed, low correlations. Large cities and their surrounding areas exhibited obvious response characteristics.
Conclusions
These findings provide policy-relevant guidance for improving the spatial imbalance of medical resources, strengthening regional public health systems, and promoting government coordination efforts for medical resource allocation at different levels to improve the overall functioning of the medical and health service system and bolster its balanced and synergistic development.
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