Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery

被引:109
|
作者
Wang, Dezhi [1 ,2 ]
Wan, Bo [1 ,2 ]
Liu, Jing [3 ,4 ]
Su, Yanjun [5 ]
Guo, Qinghua [5 ]
Qiu, Penghua [6 ]
Wu, Xincai [1 ,2 ]
机构
[1] China Univ Geosci Wuhan, Fac Informat Engn, Lumo Rd 388, Wuhan 430074, Hubei, Peoples R China
[2] Natl Engn Res Ctr Geog Informat Syst, Lumo Rd 388, Wuhan 430074, Hubei, Peoples R China
[3] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing, Jiangsu, Peoples R China
[4] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China
[5] Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
[6] Hainan Normal Univ, Coll Geog & Environm Sci, Longkun South St 99, Haikou 571158, Hainan, Peoples R China
基金
美国国家科学基金会;
关键词
Mangroves; Aboveground biomass; UAV-LiDAR; Sentinel-2; Random forest; OBJECT-BASED APPROACH; GROWING STOCK VOLUME; AIRBORNE LIDAR; ALLOMETRIC MODELS; SPECTRAL INDEXES; CANOPY COVER; CARBON STOCK; RED-EDGE; HEIGHT; LEAF;
D O I
10.1016/j.jag.2019.101986
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G similar to LiDAR similar to S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha(-1). The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G similar to LiDAR similar to S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G similar to S2 model) (R-2 = 0.62 > 0.52, RMSE = 50.36 Mg ha(-1) < 56.63 Mg ha(-1)). Through a trend extrapolation method, this study inferred that the G similar to LiDAR similar to S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G similar to 52 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R-2 = 0.62, RMSE = 51.03 Mg ha(-1)). Consequently, this study presents the first investigation of AGE for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.
引用
收藏
页数:16
相关论文
共 10 条
  • [1] Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling
    Wang, Dezhi
    Wan, Bo
    Qiu, Penghua
    Zuo, Zejun
    Wang, Run
    Wu, Xincai
    REMOTE SENSING, 2019, 11 (18)
  • [2] Improved estimation of aboveground biomass of regional coniferous forests integrating UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries
    Yueting Wang
    Xiang Jia
    Guoqi Chai
    Lingting Lei
    Xiaoli Zhang
    Plant Methods, 19
  • [3] Improved estimation of aboveground biomass of regional coniferous forests integrating UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries
    Wang, Yueting
    Jia, Xiang
    Chai, Guoqi
    Lei, Lingting
    Zhang, Xiaoli
    PLANT METHODS, 2023, 19 (01)
  • [4] Mapping mangrove species using combined UAV-LiDAR and Sentinel-2 data: Feature selection and point density effects
    Wang, Dezhi
    Wan, Bo
    Qiu, Penghua
    Tan, Xiang
    Zhang, Quanfa
    ADVANCES IN SPACE RESEARCH, 2022, 69 (03) : 1494 - 1512
  • [5] Quantifying Mangrove aboveground biomass changes: Analysis of conservation impact in blue forests projects using sentinel-2 satellite imagery
    Farzanmanesh, Raheleh
    Khoshelham, Kourosh
    Volkova, Liubov
    Thomas, Sebastian
    Ravelonjatovo, Jaona
    Weston, Christopher J.
    FOREST ECOLOGY AND MANAGEMENT, 2024, 561
  • [6] Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China
    Zhang, Linjing
    Zhang, Xiaoxue
    Shao, Zhenfeng
    Jiang, Wenhao
    Gao, Huimin
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2023, 16 (01) : 158 - 182
  • [7] Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China
    Li, Chunhua
    Zhou, Lizhi
    Xu, Wenbin
    REMOTE SENSING, 2021, 13 (08)
  • [8] Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm
    Simarmata, Nirmawana
    Wikantika, Ketut
    Darmawan, Soni
    Harto, Agung Budi
    Sakti, Anjar Dimara
    Santo, Aki Asmoro
    JOURNAL OF APPLIED REMOTE SENSING, 2024, 18 (01)
  • [9] Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data
    Wang, Yueting
    Zhang, Xiaoli
    Guo, Zhengqi
    ECOLOGICAL INDICATORS, 2021, 126
  • [10] Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data
    Huang, Zhuomei
    Tian, Yichao
    Zhang, Qiang
    Huang, Youju
    Liu, Rundong
    Huang, Hu
    Zhou, Guoqing
    Wang, Jingzhen
    Tao, Jin
    Yang, Yongwei
    Zhang, Yali
    Lin, Junliang
    Tan, Yuxin
    Deng, Jingwen
    Liu, Hongxiu
    GEOCARTO INTERNATIONAL, 2022, 37 (27) : 15778 - 15805