Research On Remote Sensing Biomass Estimate Of Eucalyptus Plantation

Using Landsat TM images, digital elevation models and the2009Forest Resource Inventory datas to study the object, Eucalyptus forest of Gaofeng forest farm, which aimed at Inves tigating the remote sensing estimation methods of Eucalyptus Plantation biomass. Selecting346small patches randomly from the Eucalyptus study area as the sampling classes to do the correlation analysis with the Eucalyptus measured biomass. Then selecting242samples to build regression model and as training samples of BP artificial neural network model as the principles of random sampling. Last,71samples could be used for the simulation of samples of artificial neural network model and the remaining33samples from the sample in small classes could be analyzed the accuracy of the model testing and errors.The results shown:①elating to the Eucalyptus forest biomass significantly about10within the19variable factors which derived form the remote sensing images and digital elevation model.They can be sortse in order like: PVI>GVI>TM4>DVI>NDVI>TM3> MSAVI>TM7>SAVI> RVI. There are8factors isplaying significant relation at0.01level such and The remaining dowell at0.05level sush as SAV、RVI within the10variable actors> the bighest correlation coefficient is0.577.②Respectively building linear regression equation, nonlinear regression and multiple linear egression models by using the the Eucalyptus biomass and10variables which filtered through the correlation analysis. By comparison, the best Eucalyptus forest biomass regression model is like this: Y=246.808+16.899TM3-13.729TM4+1.767TM7+29.735RVI+488.234 NDVI+13.617GVI+184.261SAVI-514.677MSAVI, which R is0.571, significantly at0.05level.③the relative errors between non-linear model of Eucalyptus biomass established by using BP artificial neural networks and measured biomass is10.1%, estimation about89.9%. However, accuracy of the optimal regression model of Eucalyptus biomass estimation was only84.7%, indicating that the nonlinear theory of artificial neural network could reflect Eucalyptus forest biomass about time turthly.④Using BP artificial neural networks to estimate Eucalyptus forest biomass in the eara, there are totally159066.511t.