ABSTRACT
Accurate spatial modelling of forest characteristics is one of the most important challenges in remote sensing applications. In this study, we compared the ability of Multiple Linear Regression (MLR), Geographically weighted regression (GWR), and Random Forest (RF) to estimate different forest attributes based on field sample data and Landsat 8 image. CA was modelled with the highest accuracy compared to other variables using GWR. GWR outperformed other methods. The highest and the lowest values of RMSE were for BA using RF (31.0%) and CA using GWR (12.0%), respectively.
Disclosure statement
No potential conflict of interest was reported by the author(s).