Aboveground biomass of typical invasive mangroves and its distribution patterns using UAV-LiDAR data in a subtropical estuary: Maoling River estuary, Guangxi, China. (March 2022)

• Record Type: Journal Article
• Authors: Tian, Yichao; Zhang, Qiang; Huang, Hu; Huang, Youju; Tao, Jin; Zhou, Guoqing; Zhang, Yali; Yang, Yongwei; Lin, Junliang
• Is Part Of: Ecological indicators. Volume 136(2022)
• Publication Date: 2022-03
• Subjects: Machine learning algorithms -- UAV–LiDAR -- Aboveground biomass -- Invasive mangroves -- Hydrology and geomorphology -- Beibu Gulf -- Maoling river estuary; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.108694
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: The accuracy of UAV–LiDAR point cloud height and intensity variables in AGB of invasive mangrove is compared for the first time. The AGB of mangroves invaded by typical estuaries in subtropical China was estimated by UAV–LiDAR. We developed a LiDAR mangrove aboveground biomass retrieval system. CBR ML algorithm shows good effect in estimating AGB of invasive mangrove; AGB of the invasive mangrove is related to elevation of the beach surface and main tide. Abstract: Quantitative assessment of aboveground biomass (AGB) and spatial distribution pattern of exotic mangrove plants ( Sonneratia apetala ) is of great significance for blue carbon management and ecological restoration in typical subtropical estuaries in China. Although unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) has certain advantages in the investigation of the vertical three-dimensional structure of mangroves, the existing mangrove investigation results are mainly based on plot investigation method. Few scholars use machine learning (ML) method to estimate AGB of invasive Sonneratia apetala by combining plot investigation and LiDAR data. Therefore, on the basis of the height and intensity variables of UAV-LiDAR data, this study used four different ML algorithms, namely, xgboost regressor (XGBR), catboost regressor (CBR), light gradient boosting regressor (LGBR) and AdaBoost regressor (ABR), to estimate AGB of invasive mangrove. Then, the quantitative relationship between invasive mangrove biomass and hydrological unit was analysed. We found that CBR model had the highest accuracy in estimation of mangrove AGB (R 2 = 0.7644, RMSE = 11.1725 Mg/ha), followed by XGBR model (R 2 = 0.6759, 13.1053 Mg/ha). However, LGBR model (R 2 = 0.3506, RMSE = 18.5510 Mg/ha) had poor fitting effect. The AGB of invasive mangroves showed a spatial distribution pattern of high in northwest and low in southeast, and its value ranged from 7.31 Mg/ha to 114.04 Mg/ha, with an average of 25.57 Mg/ha. The AGB of invasive mangroves was independent of the area size of the hydrological response unit but depended on the elevation of the beach surface and the distance from the main tidal ditch. This study demonstrates the feasibility of UAV-LiDAR remote sensing and CBR model in estimating AGB of invasive mangrove species, which can provide scientific basis and technical support for the assessment of invasive mangrove ecosystem and the protection of local mangrove tree species.