Journal of Marine Sciences ›› 2023, Vol. 41 ›› Issue (4): 84-93.DOI: 10.3969/j.issn.1001-909X.2023.04.008

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Research of carbon storage assessment of island vegetation based on UAV multispectral remote sensing:A case study of Dazhuzhi Island in Dongtou

XIE Jiaqi1(), ZHANG Zhao1,*(), ZHOU Wen1, WANG Jinwang2, CHEN Yahui1   

  1. 1. Wenzhou Marine Center, MNR, Wenzhou 325013, China
    2. Zhejiang Province Subtropical Crops Institute, Wenzhou 325005, China
  • Received:2022-11-28 Revised:2023-09-13 Online:2023-12-15 Published:2024-01-30


Taking Dazhuzhi Island (Dongtou, Wenzhou) as the research area, UAV equipped with multispectral sensors was used to acquire high-resolution remote sensing images, the optimal spectral band combination was selected to classify the island vegetation, and the vegetation types was divided into arbors, shrubs and herbs by supervised classification. The accuracy of vegetation classification was 99.72%, and the Kappa coefficient was 0.995 4. The spatial distribution of dominant species of arbors and shrubs was obtained by using the deep convolutional neural network (the precision rate was 0.79), and combined with the biomass equations, the spatial distribution of the biomass of dominant species of arbors and shrubs was inversed (arbors’ R2=0.97, shrubs’ R2=0.99). The biomass inversion equations of 3 shrub dominant species (Ficus erecta, Mallotus japonicas, and Eurya emarginata) were constructed by field sampling, and the other dominant species biomass inversion equations were obtained from literature. Based on the biomass and spatial distribution of dominant species, the carbon storage of arbors and shrubbys was 300.36 t and 47.59 t, respectively. Using normalized difference vegetation index (NDVI) to invert the spatial distribution of herb biomass (R2=0.99), combined with the biomass equation of the dominant herb species (Zoysia sinica) constructed from the measured data, the carbon storage of herbs was 21.59 t on Dazhuzhi Island.

Key words: island, vegetation carbon storage, unmanned aerial vehicle (UAV), multispectral remotely sensed image

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