Estimation of mangrove heights and aboveground biomass using UAV-LiDAR, Sentinel-1 and ZY-3 stereo images

作者：Bolin Fu, Yingying Wei, Linhang Jiang, Hang Yao, Xiaomin Li, Yanli Yang, Mingming Jia, Weiwei Sun · 发表于：Ecological Informatics · 年份：2025 · DOI：10.1016/j.ecoinf.2025.103160 · 被引用次数：18 · 研究领域：Remote Sensing and LiDAR Applications、Oil Palm Production and Sustainability、Coastal wetland ecosystem dynamics

Mangroves are crucial blue carbon ecosystems that are essential for promoting sustainable global development. Tree height is a key indicator of mangrove health; however, accurately estimating mangrove height in complex coastal environments is challenging. In this study, we constructed mangrove height inversion models using multiple types of remote sensing data and machine learning algorithms (partial least squares regression (PLSR), random forest (RF), and mixture density network (MDN)). We evaluated the performance of UAV-LiDAR point clouds, ZY-3 stereo images, and Sentinel-1 polarimetric and interferometric data in mangrove height inversion, and explored the accuracy differences among the dominant species. We also estimated the aboveground biomass of different dominant mangrove species to better understand their ecological functions and health conditions. The results showed the following: (1) The canopy height model and height variables of the LiDAR point clouds, DVI and near-infrared bands of the ZY-3 stereo images, and polarimetric decomposition parameters of the Sentinel-1 SAR images were more sensitive to mangrove heights. (2) The LiDAR point clouds and Sentinel-1 SAR images achieved the highest inversion accuracy when using the RF algorithm, with R 2 values of 0.875 and 0.685, respectively. The ZY-3 stereo images based on MDN obtained the optimal inversion results (R 2 = 0.719), with an improvement ranging from 0.143 to 0.198 when compared to the PLSR and RF algorithms...