Abstract: [Objective] The non-disturbed forest age was estimated by the geographically weighted regression (GWR) model. The information of forest fire severity was obtained by using remote sensing data and history data of forest fire occurrence. Then the forest fire intensity was graded. 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.[Method] In this study, 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 five significant factors as the independent variables, including the remote sensing factors of Greeness, Wetness, stand average breast diameter (ADBH), stand average tree height (ASH) and 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. The spatial distribution map of non-disturbed forest age in the study area was drawn and the spatial distribution status of stand age was explored. Combining the location and area records of forest fires, visual interpretation of multi-spectral data was used to extract the burned 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 succession of different forest types under different fire severities. When the stand age of disturbed forest was defined, the age of forest without tree species replacement remained unchanged. The stand with tree species replacement was classified its age as 0 in the year of forest fire occurrence, and accumulated from 1 at the beginning of germination of new dominant species, so as to deduce the age of forest after disturbance.[Result] The average age of non-disturbed forests in Heilongjiang was 48 years, with a standard deviation of 16 years. The R_adj² of the GWR model was 0.68, and the RMSE was 16.171 7. Moran I was used to test the residual of the model, and it was found that the GWR model was able to 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'an 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 29 157 hm², moderate severity burned area was 180 268 hm², and low severity burned area was 318 507 hm². Larix gmelinii 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 distribution map of disturbed forest age was plotted.[Conclusion] 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 forest NPP, NEP, forest carbon storage, forest biomass and other related research in Heilongjiang area.

Key words: stand age, multi-spectral remote sensing, geographically weighted regression(GWR) model, fire severity, disturbance