Ordinary least squares (OLS) regression is the primary statistical method in previous studies for vegetation distribution patterns in relation to landform. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between NDVI (Normalized Difference Vegetation Index) patterns and changing trends of landform (elevation, slope) and solar intensity (insolation and duration of sunshine) in Mt Geum-gang of North-Korea. Results denoted that GWR was more powerful than OLS in interpreting relationships between NDVI patterns and landform/solar intensity, since GWR was characterized by higher adjusted R2, and reduced spatial autocorrelations in model residuals. Unlike OLS regression, GWR allowed the coefficients of explanatory variables to differ by locality by giving relatively more weight to NDVI patterns which are affected by local landform and solar factors. The strength of the regression relationships in the GWR increased significantly, by showing regression coefficient of higher than 70% (0.744) in the southern ridge of the experimental area. It is anticipated that this research output will serve to increase the scientific and objective vegetation monitoring in relation to landform and solar intensity by overcoming serious constraints suffered from the past non-GWR-based approach.