Abstract
Accurate classification of tree-species is essential for sustainably managing forest resources and effectively monitoring species diversity. In this study, we used simultaneously acquired hyperspectral and LiDAR data from LiCHy (Hyperspectral, LiDAR and CCD) airborne system to classify tree-species in subtropical forests of southeast China. First, each individual tree crown was extracted using the LiDAR data by a point cloud segmentation algorithm (PCS) and the sunlit portion of each crown was selected using the hyperspectral data. Second, different suites of hyperspectral and LiDAR metrics were extracted and selected by the indices of Principal Component Analysis (PCA) and the mean decrease in Gini index (MDG) from Random Forest (RF). Finally, both hyperspectral metrics (based on whole crown and sunlit crown) and LiDAR metrics were assessed and used as inputs to Random Forest classifier to discriminate five tree-species at two levels of classification. The results showed that the tree delineation approach (point cloud segmentation algorithm) was suitable for detecting individual tree in this study (overall accuracy = 82.9%). The classification approach provided a relatively high accuracy (overall accuracy > 85.4%) for classifying five tree-species in the study site. The classification using both hyperspectral and LiDAR metrics resulted in higher accuracies than only hyperspectral metrics (the improvement of overall accuracies = 0.4–5.6%). In addition, compared with the classification using whole crown metrics (overall accuracies = 85.4–89.3%), using sunlit crown metrics (overall accuracies = 87.1–91.5%) improved the overall accuracies of 2.3%. The results also suggested that fewer of the most important metrics can be used to classify tree-species effectively (overall accuracies = 85.8–91.0%).
Highlights
Forests cover approximately 30% of total land area [1] and account for the majority of tree-species on land [2]
Each individual tree crown was extracted using point cloud segmentation algorithm (PCS) by the Light Detection and Ranging (LiDAR) data after de-noising and filtering, and sunlit portions in each crown were selected from hyperspectral data
The LiDAR metrics computed from discrete LiDAR data within crowns and the hyperspectral metrics in individual tree crown and in sunlit portions were utilized to Random Forest classifier to discriminate five tree-species at two levels of classification
Summary
Forests cover approximately 30% of total land area [1] and account for the majority of tree-species on land [2]. The tree-species classifications using airborne hyperspectral data were mainly operated at the pixel and crown scale. Clark et al [15] found that the overall accuracy of tree-species classification at crown scale was 7% higher than accuracy achieved at pixel scale. Clark et al [15] classified seven tree-species in a tropical forest and found that the overall accuracy of classifier using sunlit spectra was 4.2% higher than using whole crown spectra. Jensen et al [29] used hyperspectral metrics such as band means, band ratios and vegetation indices to classify 500 tree-species in urban forest, which achieved the overall accuracy of 91.4%. Clark et al [20] used suits of hyperspectral metrics including derivative, absorption and vegetation indices to classify seven tree-species in tropical rainforest, and the overall accuracies were 70.6% at crown scale. The hyperspectral data are restricted to the horizontal information and generally limited in quantifying vertical structure of forest [23]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
