Estimating stand volume of Xylosma racemosum forest based on texture parameters and derivative texture indices of ALOS imagery

The Xylosma racemosum forest located in Huairou District of Beijing was chosen as research objects, texture parameters as well as derivative texture indices of different window sizes from ALOS fusion imagery with resolution of 2.5 m were measured. Stepwise multiple regression models were developed to describe the relationship between textures (including texture parameters and derivative texture indices) and field measurements of stand volume. The main objective was to compare estimation accuracy between model established by texture parameters and that by derivative texture indices, select the most effective Xylosma racemosum stand volume estimate model and select the most effective window size. Results indicate that the value of adjusted R2 of fitting models established by derivative texture indices were better than those of texture parameters at the same window size, the value of adjusted R2 of stand volume model could be improved significantly by combination of texture parameters and derivative texture indices at the same window size, the optimal estimation model of Xylosma racemosum stand volume was obtained when all of the texture parameters and derivative texture indices of all window sizes were introduced into stepwise multiple regression, 11×11 was the optimal window size with the largest adjusted R2 for fitting Xylosma racemosum stand volume by texture parameters and derivative texture indices generated at one single window size.