The main objective of this study is to classify landforms within a watershed using advanced spatial statistics and image processing algorithms to identify and extract local geomorphometric properties of Digital Elevation Models (DEMs) with 30 m resolution. This study presents a customized GIS application for semi-automated landform classification based on Topographic Position Index (TPI). By using TPI, the, landscape was classified into both slope position and landform category. Landform categories were determined by classifying the landscape using 2 TPI grids at different scales (neighborhoods: a 50 m radius and 450 m radius). Four slope position categories and 10 landform, categories were generated. Important environmental gradients obtained from the DEM in this study are slope direction (Aspect), slope position, slope shape (planform curvature), topographic moisture index and stream power index. These gradients were then used to identify thresholds for classification of crests, flats, depressions and slopes. This study shows that DEMs offer many more potential habitat descriptors than simply a set of elevation values. Terraces, river captures and karstic closed or open depressions, which are frequently found in the landscape of the study area, were represented with TPI. The classification results can be used in applications related to precision agriculture, land degradation studies and spatial modeling applications where landform is identified as an influential factor in the processes under study.