Tree crown segmentation and species classification in a wet eucalypt forest from airborne hyperspectral and LiDAR data
Yadav, BKV and Lucieer, A and Baker, SC and Jordan, GJ, Tree crown segmentation and species classification in a wet eucalypt forest from airborne hyperspectral and LiDAR data, International Journal of Remote Sensing, 42, (20) pp. 7952-7977. ISSN 0143-1161 (2021) [Refereed Article]
To sustainably manage forest biodiversity and monitor changes in species patterning, mapping the spatial distribution of tree species is indispensable. Remote sensing can provide powerful tools for mapping species, but this task is complex in areas with high plant diversity and multi-layered canopies. This paper addresses the issue of classifying wet eucalypt forest plants by examining tree crown segmentation and species classification using different combinations of remote sensing datasets against mapped tree locations. This study explores optimal segmentation parameters for tree crown delineation compared to manually digitized tree crowns. The best segmentation accuracy of 88.71%, resulted from segmenting a combined Minimum Noise Fraction (MNF) dataset derived from hyperspectral imagery (HSI) and a LiDAR-derived Canopy Height Model (CHM). Object-based classification of tree species was performed using a random forest classifier. The fused dataset of MNF and CHM produced the highest overall accuracy of 78.26% for four vegetation classes, while the fused HSI, indices, and CHM performed best (66.67%) with five vegetation classes. However, both approaches had a high overall performance. The CHM contributed to tree crown segmentation and species classification accuracy, and fused datasets were more robust to spatially discriminate wet eucalypt forest species compared to a single dataset. Eucalyptus obliqua was classified with the highest accuracy of 90.86% for four classes using the fused MNF and CHM dataset, and 86.11% for five classes using the fused HSI, indices, and CHM dataset. An important understorey species – the tree fern (Dicksonia antarctica) – was classified with the highest accuracy of 83.54% for four classes using HSI. Therefore, fusing hyperspectral and LiDAR data could classify both the overstorey and dominant understorey species, and thus play a crucial role in identifying forest biological diversity. This approach will be useful for forest managers and ecologists to plan sustainable management of eucalypt forest biodiversity and produce maps for monitoring species of interest.
remote sensing, lidar, hyperspectral, tree, forest, segmentation, classification, species