Remote Sensing Analysis Of Forest Site Quality In Greater Khingan Range Of Heilongjiang Province Based On GWR

Based on the Landsat-5 TM images of 2010 and the inventory data of national forest resources in 2010,a remote sensing information model was established in this paper.The forest site class index was estimated successfully.Considering the terrain factors,the spatial distribution of forest site quality was analyzed systematically and scientifically,which provided certain data support and theoretical basis for forest ecosystem management and afforestation.In this study,The Greater Khingan Range area in Heilongjiang Province was taken as the study area,two types of response variables,including the remote sensing factors(MSAVI,DVI)and the stand factors(ABD,FCD)were considered in the modeling processes.Both global and local(geographically weighted regression,GWR)modeling techniques were utilized to fit the models to evaluate and analyze the site quality of the study area.What's more,the spatial distribution of forest site class index was explored along with the changing topography.Comparing the two methods,The GWR model was chosen as the model used to map the site class index space distribution.The global Moran Index was used to characterize the spatial autocorrelation of the model residuals at different spatial scales(from 8km to 80 km,with an interval of 8km).The results shows that: the spatial distribution of the site class index in Greater Khingan Range region tend to be a clustered distribution,and a high site quality index appeared in the northeastern part of the study area,while the southwestern portion with a low site quality index,also the maximum value was observed in the northern region.Both remote sensing factors and stand factors affect the distribution of forests site class index.The GWR model outperformed the Global model in both model fitting and validation performance.The R2 of the Globe model was 0.48,the AIC was 1816 with a RMSE of 1.74,while the R2 of the GWR model was 0.53,the AIC was 1784 and the RMSE was 1.29.Global model and local geographical weighted regression model can effectively estimate forest site class index,the geographically weighted regression model can solve the spatial autocorrelation of the model residuals,and generate more ideal prediction results,which is more feasible to estimate the site class index.