From plot to region: canopy height-mediated mangrove aboveground biomass mapping by synergizing UAV-LiDAR and multi-source satellite data
6 affiliations
- 1Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Nanning Normal University
- 2School of Geography Science and Planning, Nanning Normal University
- 3Guangxi Beihai Wetland Ecosystem Observation and Research Station
- 4Department of Forestry and Natural Resources, TP Cooper Building, University of Kentucky
- 5Department of Earth System Science, Tsinghua University
- 6Guangxi Forest Resources and Environment Monitoring Center
Abstract
Introduction: Mangroves are critical coastal blue carbon ecosystems, yet regional-scale mapping of aboveground biomass (AGB) remains challenging because of the limited availability of field measurements.
Methods: To address this limitation, we developed a hierarchical structural-spectral upscaling framework in the Guangxi Shankou National Mangrove Nature Reserve, China. UAV-LiDAR-derived canopy height was first used as a structural reference to train three machine-learning models for regional canopy height retrieval using Sentinel-1, Sentinel-2, and SDGSAT-1 observations. XGBoost achieved the best performance on the held-out test set (R ² = 0.80, RMSE = 0.81 m) and was subsequently used to generate a continuous 10-m canopy height product. Satellite-retrieved canopy height was then integrated with multispectral vegetation indices to develop a structural-spectral log-linear AGB model based on 32 field plots.
Results: Leave-one-out cross-validation showed that canopy height provided the dominant predictive contribution (R ² = 0.63, RMSE = 20.23 Mg ha ⁻¹ ), while the addition of the mean enhanced vegetation index (EVI) further improved performance to R ² = 0.68 and RMSE = 18.72 Mg ha ⁻¹ , outperforming both strucutural-only and spectral-only models. Regional mapping estimated total mangrove AGB at 44,112 Mg, with a mean density of 49.20 Mg ha ⁻¹ . Higher biomass was concentrated in interior stands, whereas lower biomass occurred predominantly along seaward edges and marginal zones. Biomass trajectories also differed among geomorphic settings, with increasing, decreasing, and unimodal patterns observed along different coastal-distance gradients.
Discussion: Overall, the proposed framework provides a practical pathway for extending localized UAV-LiDAR structural information to regional mangrove AGB mapping while reducing dependence on wall-to-wall active sensing, thereby supporting regional blue carbon assessment and spatially targeted conservation and management of climate-vulnerable mangrove ecosystems.
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BibTeX
@article{xiang2026from,
title = {{From plot to region: canopy height-mediated mangrove aboveground biomass mapping by synergizing UAV-LiDAR and multi-source satellite data}},
author = {Yunyan Xiang and Wen H. Cai and Hongxin Su and Jian Yang and Le Yu and Jiali Wang and Anshu Huang and Dewen Tong},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1964480},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1964480/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - From plot to region: canopy height-mediated mangrove aboveground biomass mapping by synergizing UAV-LiDAR and multi-source satellite data
AU - Yunyan Xiang
AU - Wen H. Cai
AU - Hongxin Su
AU - Jian Yang
AU - Le Yu
AU - Jiali Wang
AU - Anshu Huang
AU - Dewen Tong
PY - 2026
DA - 2026/10/09
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1964480
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1964480/full
AB - Introduction: Mangroves are critical coastal blue carbon ecosystems, yet regional-scale mapping of aboveground biomass (AGB) remains challenging because of the limited availability of field measurements. Methods: To address this limitation, we developed a hierarchical structural-spectral upscaling framework in the Guangxi Shankou National Mangrove Nature Reserve, China. UAV-LiDAR-derived canopy height was first used as a structural reference to train three machine-learning models for regional canopy height retrieval using Sentinel-1, Sentinel-2, and SDGSAT-1 observations. XGBoost achieved the best performance on the held-out test set (R ² = 0.80, RMSE = 0.81 m) and was subsequently used to generate a continuous 10-m canopy height product. Satellite-retrieved canopy height was then integrated with multispectral vegetation indices to develop a structural-spectral log-linear AGB model based on 32 field plots. Results: Leave-one-out cross-validation showed that canopy height provided the dominant predictive contribution (R ² = 0.63, RMSE = 20.23 Mg ha ⁻¹ ), while the addition of the mean enhanced vegetation index (EVI) further improved performance to R ² = 0.68 and RMSE = 18.72 Mg ha ⁻¹ , outperforming both strucutural-only and spectral-only models. Regional mapping estimated total mangrove AGB at 44,112 Mg, with a mean density of 49.20 Mg ha ⁻¹ . Higher biomass was concentrated in interior stands, whereas lower biomass occurred predominantly along seaward edges and marginal zones. Biomass trajectories also differed among geomorphic settings, with increasing, decreasing, and unimodal patterns observed along different coastal-distance gradients. Discussion: Overall, the proposed framework provides a practical pathway for extending localized UAV-LiDAR structural information to regional mangrove AGB mapping while reducing dependence on wall-to-wall active sensing, thereby supporting regional blue carbon assessment and spatially targeted conservation and management of climate-vulnerable mangrove ecosystems.
SN - 1664-462X
ER -