Remote Sensing Estimation Of Forest Stand Age Based On GWR Model And Forest Fire Data

In this study,the age of non-disturbed forest was estimated by the geographically weighted regression(GWR)model.The information of forest fire severity was obtained by using remote sensing data and forest fire occurrence history data.The interaction between forest fire severity and forest type was discussed,and the age of disturbed forest was estimated.Finally,the spatial distribution of stand age in Heilongjiang province was obtained.Heilongjiang forest was taken as the study area,based on the multi-spectral data of the study area and the forest resources inventory data,stepwise regression method was used to extract three types of response variables,including the reI1mote sensing factors(Greeness,Wetness),stand factors(ADBH,ASH)and the topographic factor(Altitude).The GWR model was used to establish the stand age estimation model of non-disturbed forest.The global Moran I index was used to characterize the spatial autocorrelation of the model residuals.Mapping the age distribution of non-disturbed forest and exploring the spatial distribution of stand age.Visual interpretation of multi-spectral data was used to extracted the burmed area.Fire severity was divided into four classes according to the dNBR.The ArcGIS software was used to do an overlaying analysis on the fire severity map with vegetation type map.The fire severity map and vegetation type map were superimposed to discuss the replacement of different forest types under different fire severities.When the stand age of disturbed forest was defined,the stand age of the forest which did not change of tree species was unchanged,and the stand age of the forest in the year of forest fire was 0,and accumulated from 1 when the new dominant species germinated.The results show that the average age of non-disturbed forests in Heilongjiang is 48 years,with a standard deviation of 16 years.The R2adj of the GWR model is 0.68,and the RMSE is 16.1717.Using Moran I to test the residual of the model,it is found that the GWR model can eliminate the spatial autocorrelation of residuals well.The overall spatial distribution of forest age in the study area was uneven,and the forest age in Daxing^n Mountains was generally higher than the average level of Heilongjiang forest area.Forest fires occurred mainly in Daxing'an Mountains and Xiaoxing'an Mountains areas in Heilongjiang Province in 2000?2010.According to dNBR,fire severity was divided into four classes:unburned,low,moderate and high,high severity burned area was 29157 hm2,moderate severity burned area was 180268 hm2,low severity burned area was 318507 hm2 forest and Quercus mongolica forest had the largest burned area in the whole study area,accounting for 28.63%and 47.23%respectively.According to the replacement of different forest types under different fire severities in the burned area,the age of disturbed forest was determined,and the spatial distributionI map of disturbed forest age was plotted.This study shows that GWR model can effectively estimate the age of non-disturbed forest in Heilongjiang Province,and successfully reduce the spatial autocorrelation of residual.In the process of estimating forest age,forest fire disturbance factors were added to obtain more realistic spatial distribution data of forest age,which provided data support for NPP,NEP,forest carbon storage,forest biomass and other related research in Heilongjiang area.