A Novel Scheme about Skeleton Optimization Designed for ISTTWN Algorithm

Abstract

The ISTTWN algorithm overcame the defect of separating the production process of skeleton points and skeleton lines in tree branch point cloud skeleton extraction and improved the accuracy of the extracted initial skeletons, but the skeletons need further optimization. In the existing skeleton optimization, it is difficult to see the stump adjustment, and most of the bifurcation optimization and skeleton smoothness adopt fitting. Based on the characteristics of the initial skeletons extracted by the ISTTWN algorithm, this research optimizes the skeleton from four aspects. An algorithm for the stump adjustment for reconstructing the stump based on the layer and hierarchical relationship and an algorithm for the bifurcation optimization based on the local branch point cloud and cosine correlation are proposed, and an existing pruning method and a skeleton smoothing method are used. The results show that the skeleton optimization method proposed or used in this research has a high computational efficiency in general and can ultimately retain the necessary skeleton lines. In a visual analysis, the optimized skeleton is obviously much more natural and more in line with the actual topology of trees. In the quantitative analysis, the completeness, accuracy and effectiveness reached 97.82%, 95.72% and 89.47%, respectively. In this study, in addition to the existing tree parameters extracted by the skeleton or generalized cylinder model, the generated skeleton is used to extract the branch attributes. The R2 of the deflection angle of the branch tip, distance from branch tip and branch length are about 0.897, 0.986 and 0.988, respectively, which illustrates that their models are very good. This research can further expand the application of the skeleton.

Projection of the potential distribution of suitable habitats for Siberian crane (Grus leucogeranus) in the middle and lower reaches of the Yangtze River basin

“…Currently, the acquisition of NDVI data is mainly based on satellite data. If it is possible to use LIDAR data, more detailed data can be obtained, which may lead to better results (Yang and Wen et al, 2022a;Yang and Wen et al, 2022b). It was noted that although the Siberian crane is a migratory bird that chooses to winter in the MLYRB, its distribution is not limited to the wetlands around Poyang and Dongting Lakes, but also extends to surrounding farmland and coastal areas.…”