Coupling UAV Hyperspectral and LiDAR Data for Mangrove Classification Using XGBoost in China’s Pinglu Canal Estuary

Abstract

The fine classification of mangroves plays a crucial role in enhancing our understanding of their structural and functional aspects which has significant implications for biodiversity conservation, carbon sequestration, water quality enhancement, and sustainable development. Accurate classification aids in effective mangrove management, protection, and preservation of coastal ecosystems. Previous studies predominantly relied on passive optical remote sensing images as data sources for mangrove classification, often overlooking the intricate vertical structural complexities of mangrove species. In this study, we address this limitation by incorporating unmanned aerial vehicle-LiDAR (UAV-LiDAR) point cloud 3D data with UAV hyperspectral imagery to perform multivariate classification of mangrove species. Five distinct variable scenarios were employed: band characteristics (S1), vegetation index (S2), texture measures (S3), fused hyperspectral characteristics (S4), and a canopy height model (CHM) combined with UAV hyperspectral characteristics and LiDAR point cloud data (S5). To execute this classification task, an extreme gradient boosting (XGBoost) machine learning algorithm was employed. Our investigation focused on the estuary of the Pinglu Canal, situated within the Maowei Sea of the Beibu Gulf in China. By comparing the classification outcomes of the five variable scenarios, we assessed the unique contributions of each variable to the accurate classification of mangrove species. The findings underscore several key points: (1) The fusion of multiple features in the image scenario led to a higher overall accuracy (OA) compared to models that employed individual features. Specifically, scenario S4 achieved an OA of 88.48% and scenario S5 exhibited an even more impressive OA of 96.78%. These figures surpassed those of the individual feature models where the results were S1 (83.35%), S2 (83.55%), and S3 (71.28%). (2) Combining UAV hyperspectral and LiDAR-derived CHM data yielded improved accuracy in mangrove species classification. This fusion ultimately resulted in an OA of 96.78% and kappa coefficient of 95.96%. (3) Notably, the incorporation of data from individual bands and vegetation indices into texture measures can enhance the accuracy of mangrove species classification. The approach employed in this study—a combination of the XGBoost algorithm and the integration of UAV hyperspectral and CHM features from LiDAR point cloud data—proved to be highly effective and exhibited strong performance in classifying mangrove species. These findings lay a robust foundation for future research efforts focused on mangrove ecosystem services and ecological restoration of mangrove forests.

Improving Land Use and Land Cover Information of Wunbaik Mangrove Area in Myanmar Using U-Net Model with Multisource Remote Sensing Datasets

“…Integration of topographic and canopy height information has been explored to show accuracy improvement in LULC classification [38][39][40][41]. This study exhibited Multi-Error-Removed-Improved-Terrain (MERIT) (https://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_ DEM/, accessed on 20 January 2020) and Global Canopy Height Model (GCHM) datasets (https://langnico.github.io/globalcanopyheight/, accessed on 17 July 2023) with the aim of leveraging classification accuracy in distinguishing mangrove and non-mangrove forest in a mixed ecosystem of the large study area.…”