Estimation of mangrove heights and aboveground biomass using UAV-LiDAR, Sentinel-1 and ZY-3 stereo images
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 heigh...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125001694 |
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| Summary: | 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 R2 values of 0.875 and 0.685, respectively. The ZY-3 stereo images based on MDN obtained the optimal inversion results (R2 = 0.719), with an improvement ranging from 0.143 to 0.198 when compared to the PLSR and RF algorithms. (3) Avicennia marina was associated with the highest estimation accuracy (R2 = 0.897) compared to the other dominant mangrove species. Aegiceras corniculatum and Avicennia marina were associated with the highest inversion accuracy within the height range of 2–3 m (R2 = 0.925, R2 = 0.814, respectively), whereas Kandelia candel yielded the optimal inversion results at the height range of 1–2 m (R2 = 0.652). (4) The aboveground biomass of Aegiceras cornicatum and Kandelia candel ranged from 20.176 to 103.164 Mg/ha and 132.019 to 719.226 Mg/ha, respectively, and the aboveground biomass of Avicennia marina was mainly distributed within the range of 169.916 to 803.204 Mg/ha. Our study provides a reference for monitoring the heights and health of mangroves, as well as their protection and development. |
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| ISSN: | 1574-9541 |