Vertical Structure Classification of a Forest Sample Plot Based on Point Cloud Data

Abstract

The vertical structure of forests affects energy transfer and material exchange within forest ecosystems and is of great significance to scientific forestry and ecology research. In this paper, the Shangri-La forest plot in northwestern Yunnan Province of China was the study area. The forest sample plot point cloud data were obtained by terrestrial laser scanning technology. A new method for classifying the vertical structure of forest sample plots based on point cloud data is proposed. The method comprehensively utilizes morphological filtering and comparative shortest-path (CSP) algorithm point cloud segmentation technology. Additionally, the method proposes the concept of secondary CSP segmentation that precisely classifies three types of vertical features in forest point cloud data: trees, shrubs and the ground. Finally, an accuracy analysis showed that the error rate of the tree results was 1.87%, and the error rate of the shrub results was 16.23%.