Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm

• Published in: International journal of applied earth observation and geoinformation Vol. 102; p. 102414
• Main Authors: Cao, Jingjing, Liu, Kai, Zhuo, Li, Liu, Lin, Zhu, Yuanhui, Peng, Liheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2021; Elsevier
• Subjects: algorithms; canopy; canopy height; forest restoration; hyperspectral imagery; Hyperspectral imaging; lidar; Light detection and ranging (LiDAR); logit analysis; mangrove forests; Mangrove species classification; Rotation forest (RoF); spatial data; Unmanned aerial vehicle (UAV); Rotation forest (RoF); Light detection and ranging (LiDAR); Unmanned aerial vehicle (UAV); Mangrove species classification; Hyperspectral imaging
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2021.102414

[Display omitted] •An effective approach for mangrove monitoring with UAV hyperspectral and LiDAR data.•LiDAR-derived CHM helps better recognition of spectrally similar mangrove species.•RoF outperforms RF and LMT for accurate mangrove species classification.•Understory mangroves benefit most from combining spectral and structural information. Accurate and timely monitoring of mangrove species information is crucial for precise management and practical conservation. Conventional hyperspectral techniques employed in mangrove monitoring are often limited to achieve the fine classification of mangrove species, due to the low spatial resolution of space-borne images and the high cost of airborne images. Moreover, using the spectral information alone is not adequate for fine-scale classification of mangrove species in complex ecosystems, because the spectral discriminability of mangrove species is generally restricted by complex canopy structures. To address these limitations, this study proposes a novel mangrove species classification method that integratively uses unmanned aerial vehicle (UAV)-based Nano-hyperspec hyperspectral imagery, light detection and ranging (LiDAR) data, and the rotation forest (RoF) ensemble learning algorithm. The proposed method was tested in China’s largest artificially planted mangroves, Qi'ao Island. First, we extracted spectral features from UAV-based hyperspectral data and structural information from LiDAR data; then we utilized the RoF algorithm to classify mangrove species based on the spectral and structural features and compared with two other popular ensemble learning algorithms, namely random forest (RF) and logistic model tree (LMT). Results showed that the combined hyperspectral and LiDAR data produced satisfactory results for all three classifiers with overall accuracy (OA) higher than 95%, and the proposed method achieved the highest OA of 97.22% and Kappa coefficient of 0.9686. Our study proved that incorporating the canopy height information can improve the classification accuracy, with the OA and Kappa coefficient being 2.43% and 0.0274 higher than using the original spectral bands alone, respectively. It is also found that the RoF algorithm is more accurate and stable in classifying mangrove species than those of RF and LMT. These findings indicated that the proposed approach could achieve fine-scale mangrove monitoring and further facilitate mangrove forest restoration and management.