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Forest and Grassland Resources Research >

2024 , Vol. 0 >Issue 1: 65 - 72

DOI: https://doi.org/10.13466/j.cnki.lczyyj.2024.01.009

Scientific Research

Remote Sensing Inversion of Mangrove Biomass Based on Machine Learning

  • Jun HAO ,
  • Kangting LYU ,
  • Tianqi HU ,
  • Yunge WANG ,
  • Gang XU
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  • 1. Zhejiang College of Security Technology,Wenzhou 325000,Zhejiang,China
    2. Wenzhou Collaborative Innovation Center for Space-borne,Airborne and Ground Monitoring Situational Awareness Technology,Wenzhou 325000,Zhejiang,China
    3. China University of Mining and Technology,Xuzhou 221116,Jiangsu,China,4.Wenzhou Future City Research Institute,Wenzhou 325000,Zhejiang,China
    4. Wenzhou Key Laboratory of Natural Disaster Remote Sensing Monitoring and Early Warning,Wenzhou 325000,Zhejiang,China
    5. School of Geosciences and Info-Physics,Central South University,Changsha 410083,China

Received date: 2023-10-16

  Revised date: 2024-01-12

  Online published: 2024-03-22

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Abstract

Accurately investigating mangrove biomass is beneficial for evaluating the carbon sink potential of mangrove ecosystems.Based on field survey data,Landsat 8 remote sensing images and DEM data,22 feature variables were extracted to carry out remote sensing inversion of mangrove biomass in the Ximen Island,which used three machine learning methods:Random Forest(RF),Support Vector Machine model(SVM)and eXtreme Gradient Boosting(XGBoost).The results showed:1)Compared to the RF model and SVM model,the XGBoost model had a better estimation performance(R2=0.932,ERMS=0.514 t/hm2,EMA=0.313 t/hm2),which could more accurately estimate the mangrove biomass.2)Among the 10 important characteristic factors selected by Recursive Feature Elimination(RFE),the vegetation index has a relatively high importance in estimating mangrove biomass.3)The biomass inversion map of the XGBoost model,which is composed of 10 important characteristic factors,showed that the estimated mangrove biomass ranges from 9.138 to 29.229 t/hm2,which was similar to the findings of the field survey.It can be seen that the XGBoost algorithm shows good application capabilities in mangrove biomass.This research will provide a technical reference for the accounting of carbon storage in the Chinese mangroves.

Key words： mangroves; Landsat 8; XGBoost; biomass; remote sensing inversion.

Cite this article

Jun HAO , Kangting LYU , Tianqi HU , Yunge WANG , Gang XU . Remote Sensing Inversion of Mangrove Biomass Based on Machine Learning[J]. Forest and Grassland Resources Research, 2024 , 0(1) : 65 -72 . DOI: 10.13466/j.cnki.lczyyj.2024.01.009

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