Estimation of aboveground biomass of mangrove forest using UAV-LiDAR

Luo Jinxuan; Tian Yichao; Zhang Qiang; Tao Jin; Huang Youju; Wang Jingzhen; Zhang Yali; Huang Zhuomei; Deng Jingwen; Tan Yuxin

doi:10.12284/hyxb2023088

Volume 45 Issue 8

Aug. 2023

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Luo Jinxuan，Tian Yichao，Zhang Qiang, et al. 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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Luo Jinxuan，Tian Yichao，Zhang Qiang, et al. Estimation of aboveground biomass of mangrove forest using UAV-LiDAR[J]. Haiyang Xuebao，2023, 45(8)：108–119 doi: 10.12284/hyxb2023088

Citation:

Luo Jinxuan，Tian Yichao，Zhang Qiang, et al. 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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Estimation of aboveground biomass of mangrove forest using UAV-LiDAR

doi: 10.12284/hyxb2023088

1.
Key Laboratory of Marine Geographic Information Resources Exploitation and Utilization, College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China
2.
Guangxi Key Laboratory of Marine Environmental Change and Disaster Research of Beibu Gulf, Beibu Gulf Marine Development Research Center, Beibu Gulf University, Qinzhou 535011, China
3.
Guangxi Key Laboratory for Geospatial Informatics and Geomatics Engineering, Guilin University of Technology, Guilin 541004, China
4.
Guangxi Zhuang Autonomous Region Institute of Natural Resources Remote Sensing, Nanning 530028, China

Received Date: 2022-10-23
Rev Recd Date: 2023-03-14

Available Online: 2023-08-18

Publish Date: 2023-08-31

Abstract

Abstract

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

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References(62)

References

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