Remote sensing estimation of the biomass of Pinus kesiya var.langbianensis forest based on BP neural networks
Taking the biomass of Simao pine,(Pinus kesiya var.langbianensis)in Puer county as the research target,Landsat TM 8 images,DEM (resolution:30 meters),the forest resources inventory data in 2006 and the ground sample data from 2012-2013 as the data source.The Simao Pine's distribution image in the study area was extracted by ENVI,and 14 factors (11 remote sensing factors,3 terrain factors) was selected as the alternative variables.By using BP neural networks module in MATLAB,the estimation model of Simao Pine's biomass of study area was established.The results showed that the best optimal training algorithm was Ploak-Ribiere and the hidden layer's nodes are 9,R2=0.85,RMSE=14 t/hm2,P=74.75％.％,and the predicted total biomass of Simao pine was 62 185 871.9 t,the perunit area's biomass was 51.06 t/hm2 in Puer county by taking the pixel as unit and extracting the independent variable factors.
NETL
NSTL
万方数据
维普
