准确调查红树林生物量有利于评估红树林生态系统碳汇潜力。基于实地调查数据和Landsat 8遥感影像及DEM数据提取22个特征变量,利用随机森林(RF)、支持向量机模型(SVM)和极端梯度提升(XGBoost)3种机器学习方法,对西门岛红树林进行生物量遥感反演。结果表明:1)与RF算法和SVM算法相比,XGBoost算法构建的模型具有更好的估测效果(R²=0.932,E_RMS=1.514 t/hm²,E_MA=1.313 t/hm²),能更准确地估测红树林生物量;2)在递归特征消除法(RFE)筛选出的10个重要特征因子中,植被指数对红树林生物量估测的相对重要性较高;3)10个重要特征因子构成的XGBoost模型生物量反演得出,红树林生物量估测值范围为9.138~29.229 t/hm²,这与实地调查结果非常相近。XGBoost机器学习算法在红树林生物量反演中表现出较好的效果,该结果能为中国人工红树林碳储量的核算提供技术参考。

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,E_RMS=0.514 t/hm²,E_MA=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.