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Subdominant Tree Detection using High density Airborne LiDAR data
Accurate crown detection and delineation of dominant and subdominant trees is crucial for accurate inventorying of forests at the individual tree level. State-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exibits a reduced accuracy when dealing with subdominant trees. In this paper, we propose a novel approach to accurately detect and delineate both dominant and subdominant tree crowns in conifer dominated multistoried forests using small footprint high density airborne LiDAR data. Here, dominant tree crowns are 3D delineated using a state-of-the-art crown delineation technique. The 3D point data corresponding to each 3D dominant tree segment are projected onto a novel 3D space, where subdominant trees can be detected and delineated. Subdominant tree crowns are detected and their approximate boundaries are extracted using a 2D analysis on the projected data. The projection induces a local variation in the 3D texture for the dominant tree data while minimally affects the subdominant tree data. This variation is modeled by using 3D Grey Level Co-occurrence Matrix (GLCM) texture features. The texture information from GLCM analysis combined with the 2D boundary information is used for delineating the dominant and subdominant crowns. The evaluation was performed using both manually and automatically segmented 3D dominant tree segments. The high detetion and delineation accuracies obtained prove the performance of the proposed method.