Remote Sensing Estimation of Pine Forest Biomass in Weining County
Based on the survey data of forest resource planning in 2017 and Landsat 8 OLI remote sensing images in Weining County,BP neural network and support vector machine(SVM)models were constructed for Pinus yunnanensis and Pinus armandii in Weining County,respectively,and the optimal models were selected to invert the above ground biomass of the two pine forests in Weining County.The results show that:Among the two mod-els,the support vector machine(SVM)model had the best estimation effect.The R2 coefficient of model was 0.409,and the root mean square error(RMSE)was 39.04.The unit biomass of the Pinus yunnanensis was mainly distributed in the range of 3～30 t/hm2,followed by the range of 30～120 t/hm2.It is concentrated in the south-west of Weining County.The R2 coefficient of the model was 0.35,and the root mean square error(RMSE)was 47.6.The unit biomass of Pinus armandii was mainly distributed in 2～30 t/hm2,followed by 30～150 t/hm2,and concentrated in the northern part of Weining County.
Pinus yunnanensisPinus armandiiforest biomassBP Neural Network,Support Vector Machine(SVM)Weining County
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NSTL
万方数据
