Estimation of aboveground vegetation biomass in oasis-desert transition zone based on Sentinel-2
Monitoring the above-ground biomass of vegetation in the oasis-desert transition zone is an important means to evaluate vegetation growth and monitor the desertification.In this study,Sentinel-2 image data was used to construct an above-ground biomass estimation model.The performance of the statistical model and two machine learning algorithm models were compared,and the above-ground biomass of vegetation in the oasis-desert transition zone of the Weigan-Kuqa River oasis was estimated.The results showed that,among the statistical models,RTVI has the best fitting effect and the most significant correlation with the nonlinear model of above-ground biomass.In machine learning algorithms,the random forest model is superior to the support vector machine regression model.The results show that the RTVI nonlinear estimation model and the random forest model have better extrapolation abilities.In the inversion of above-ground biomass of the oasis-desert transition zone,the random forest model achieves higher accuracy,the verification set R2 is 0.65,RMSE and MAE are 255.08g·m-2 and 192.93g·m-2,respectively.Compared with other models,the random forest model can be more accurate in the case of small samples,and provide a basis for scientific monitoring of the aboveground biomass of vegetation in the oasis-desert transition zone and maintaining the stable development of the oasis.
estimation of aboveground biomassvegetation indexmachine learning algorithmSentinel-2oasis-desert transition zone
NETL
NSTL
万方数据
