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Journal of Tropical Oceanography

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Laser point cloud inversion of three-dimensional structure and biomass of mangroves

XIE Yutong1, HUANGYouju2, TIAN Yichao1,3,4,5*, HAN Guangping3, ZHANG Qiang1, TAO Jin1, DU Jinze1, PENG Zijie1   

  1. 1.College of Marine Sciences and College of Resources and Environment, Beibu Gulf University, Qinzhou, Guangxi 535011;

    2.Key Laboratory of Tropical Marine Ecosystems and Biological Resources, Ministry of Natural Resources, Fourth Institute of Oceanography, Ministry of Natural Resources, Beihai, Guangxi 536015;

    3.Guangxi Zhuang Autonomous Region Remote Sensing Institute of Natural Resources, Nanning, Guangxi 530023;

    4.Pinglu Canal and Coastal Ecosystem Observation and Research Station of the Beibu Gulf, Key Laboratory of Marine Environmental Changes and Disasters of the Beibu Gulf, Qinzhou, Guangxi 535011;

    5.Key Laboratory of Marine Geographic Information Resource Development and Utilization of the Beibu Gulf, Beibu Gulf University, Qinzhou, Guangxi 535011;

    6.Beibu Gulf University Beibu Gulf Marine Development Research Center, Qinzhou, Guangxi 535011)



  • Received:2025-09-01 Revised:2025-10-22 Accepted:2025-11-12
  • Supported by:
    the National Natural Science Foundation of China(Grant No.42261024); Guangxi Bagui Young Scholar.,Guangxi Forestry Science and Technology Promotion demonstration project(Guilin scientific research [2022] no. 4); Key Research Base of Humanities and Social Sciences in Guangxi Universities"Beibu Gulf Ocean Development Research Center"(Grant No. BHZKY2202); major projects of key research bases for humanities and social sciences in Guangxi universities(Grant JDZD202214); igh-level talent introduction project of Beibu Gulf University(Grant No.2019KYQD28); Innovation Project of Guangxi Graduate Education(YCSW2025623); Marine Science First-Class Subject, Beibu Gulf University(DRB003); Key Laboratory of Tropical Marine Ecosystems and Biological Resources, Ministry of Natural Resources(2023ZD06)

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Abstract: Rapid and accurate acquisition of three-dimensional structural parameters of mangroves is crucial for estimating their aboveground biomass (AGB). Although many studies have combined spectral data to estimate mangrove AGB, research using automatic machine learning (AutoML) for model selection and analysis of feature interpretability is still limited. Based on allometric growth equations, this study extracted three-dimensional structural information of mangroves from high-resolution unmanned aerial vehicle (UAV) LiDAR point cloud data and combined spectral features from domestic GF-2 satellite imagery. The study area was the mangrove forest at the estuary of the Qinzhou River in Beibu Gulf, Guangxi, China. The three-dimensional structure analysis based on point cloud revealed the canopy morphology characteristics of the mangrove forest in this area. On this basis, an AGB inversion model for mangroves was constructed using the AutoML Flaml framework. The results showed that the LightGBM algorithm model selected by the Flaml framework performed well (training set accuracy 0.98, test set accuracy 0.84, test set standard deviation 11.32). Structural parameters extracted from LiDAR point cloud (such as height statistics) and spectral features such as blue band and NPCI significantly contributed to AGB inversion. The overall mangrove area in the study area decreased by approximately 50.59%, and the biomass loss was about 49.36%, but the average density increased. This study verified the feasibility and advantages of combining LiDAR point cloud and AutoML in efficiently inverting the three-dimensional structure and biomass of mangroves, providing important data support and methodological references for the assessment of the mangrove ecosystem in the study area.

Key words: Mangrove AGB, LiDAR point cloud, domestic GF-2 satellite, automatic machine learning, Guangxi, China Mangrove AGB, LiDAR point cloud, domestic GF-2 satellite, automatic machine learning, Guangxi, China