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Estimation of aboveground biomass of mangrove forest using UAV-LiDAR
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Jinxuan Luo1, 2, Yichao Tian1, 2, 3, *, Qiang Zhang1, Jin Tao1, Youju Huang4, Jingzhen Wang2, Yali Zhang1, Zhuomei Huang1, Jingwen Deng1, Yuxin Tan1
Haiyang Xuebao | 2023, 45(8) : 108 - 119
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Haiyang Xuebao | 2023, 45(8): 108-119
• Article •
Estimation of aboveground biomass of mangrove forest using UAV-LiDAR
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Jinxuan Luo1, 2, Yichao Tian1, 2, 3, *, Qiang Zhang1, Jin Tao1, Youju Huang4, Jingzhen Wang2, Yali Zhang1, Zhuomei Huang1, Jingwen Deng1, Yuxin Tan1
Affiliations
  • 1Key Laboratory of Marine Geographic Information Resources Exploitation and Utilization, College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China
  • 2Guangxi Key Laboratory of Marine Environmental Change and Disaster Research of Beibu Gulf, Beibu Gulf Marine Development Research Center, Beibu Gulf University, Qinzhou 535011, China
  • 3Guangxi Key Laboratory for Geospatial Informatics and Geomatics Engineering, Guilin University of Technology, Guilin 541004, China
  • 4Guangxi Zhuang Autonomous Region Institute of Natural Resources Remote Sensing, Nanning 530028, China
Published: 2023-08-31 doi: 10.12284/hyxb2023088
Outline
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As one of the vegetation types with the highest carbon storage in tropical regions, the area of mangrove forest shows a trend of fragmentation and reduction. The spatial distribution and dynamic information of mangrove biomass are crucial to the estimation of greenhouse gas flux and carbon storage, as well as policy formulation and implementation. However, both optical data and SAR data commonly used for biomass estimation have signal saturation phenomenon, and traditional estimation algorithms for mangrove biomass estimation have high data requirements and relatively low estimation accuracy. In order to solve this problem, this study compared the accuracy of four gradient enhanced decision tree algorithms for estimating aboveground biomass (AGB) of invasive mangrove species Sonneria apetala used UAV-LiDAR data, and discussed the importance of variables in the modeling process. The results indicate that: (1) XGBR had a high fitting ability for the estimation of mangrove AGB, reaching R² = 0.833 8, RMSE = 1.55 Mg/hm2. (2) The predicted AGB in the study area ranged from 73.10 Mg/hm2 to 190.00 Mg/hm2, with an average of 109.10 Mg/hm2. (3) LiDAR index describing canopy height characteristics is an important variable for estimating mangrove AGB. Conclusion: This study proved the feasibility of UAV-LiDAR data and XGBR model for estimating the AGB of mangrove forests, in order to provide data support for the blue carbon research of mangrove ecosystems.

mangrove  /  UAV LiDAR data  /  aboveground biomass  /  gradient enhanced decision tree  /  inversion; Beibu Gulf
Jinxuan Luo, Yichao Tian, Qiang Zhang, Jin Tao, Youju Huang, Jingzhen Wang, Yali Zhang, Zhuomei Huang, Jingwen Deng, Yuxin Tan. Estimation of aboveground biomass of mangrove forest using UAV-LiDAR[J]. Haiyang Xuebao, 2023 , 45 (8) : 108 -119 . DOI: 10.12284/hyxb2023088
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Article Info
doi: 10.12284/hyxb2023088
  • Receive Date：2022-10-23
  • Online Date：2025-12-28
  • Published：2023-08-31
Article Data
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History
  • Received：2022-10-23
  • Revised：2023-03-14
Funding
Affiliations
    1Key Laboratory of Marine Geographic Information Resources Exploitation and Utilization, College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China
    2Guangxi Key Laboratory of Marine Environmental Change and Disaster Research of Beibu Gulf, Beibu Gulf Marine Development Research Center, Beibu Gulf University, Qinzhou 535011, China
    3Guangxi Key Laboratory for Geospatial Informatics and Geomatics Engineering, Guilin University of Technology, Guilin 541004, China
    4Guangxi Zhuang Autonomous Region Institute of Natural Resources Remote Sensing, Nanning 530028, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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