Individual tree segmentation and tree species classification in subtropical broadleaf forests using UAV-based LiDAR, hyperspectral, and ultrahigh-resolution RGB data

Accurate classification of individual tree species is essential for inventorying, managing, and protecting forest resources. Individual tree species classification in subtropical forests remains challenging as existing individual tree segmentation algorithms typically result in over-segmentation in subtropical broadleaf forests, in which tree crowns often have multiple peaks. In this study, we proposed a watershed-spectral-texture-controlled normalized cut (WST-Ncut) algorithm, and applied it to delineate individual trees in a subtropical broadleaf forest situated in Shenzhen City of southern China (114 degrees 23 ' 28 '' E, 22 degrees 43 ' 50 '' N). Using this algorithm, we first obtained accurate crown boundary of individual broadleaf trees. We then extracted different suites of vertical structural, spectral, and textural features from UAV-based LiDAR, hyperspectral, and ultrahigh-resolution RGB data, and used these features as inputs to a random forest classifier to classify 18 tree species. The results showed that the proposed WST-Ncut algorithm could reduce the over-segmentation of the watershed segmentation algorithm, and thereby was effective for delineating individual trees in subtropical broadleaf forests (Recall = 0.95, Precision = 0.86, and F-score = 0.91). Combining the structural, spectral, and textural features of individual trees provided the best tree species classification results, with overall accuracy reaching 91.8%, which was 10.2%, 13.6%, and 19.0% higher than that of using spectral, structural, and textural features alone, respectively. In addition, results showed that better individual tree segmentation would lead to higher accuracy of tree species classification, but the increase of the number of tree species would result in the decline of classification accuracy.