Enhancing Plant Ecological Unit Mapping Accuracy with Auxiliary Data from Landsat-8 in a Heterogeneous Rangeland

Mapping Plant Ecological Units (PEUs) supports sustainable rangeland management, yet distinguishing them from multispectral imagery remains challenging due to high intra-class variability and spectral overlap. This study evaluates the contribution of auxiliary data layers to improve PEU classification from Landsat-8 OLI imagery in semi-arid rangelands of northeastern Iran. A Random Forest (RF) classifier was trained using field samples and multiple feature combinations, including spectral indices, topographic variables (DEM, slope, aspect), and Principal Component Analysis (PCA) components. Classification performance was assessed using Overall Accuracy (OA), Kappa coefficient, and non-parametric Friedman and post-hoc tests to determine significant differences among scenarios. The results show that auxiliary features consistently enhanced classification performance compared with spectral bands alone. In particular, integrating DEM and PCA layers yielded the highest accuracy (OA = 79.3%, κ = 0.71), with statistically significant improvement (p < 0.05). The findings demonstrate that incorporating topographic and transformed spectral information can effectively reduce class confusion and improve the separability of PEUs in complex rangeland environments. The proposed workflow provides a transferable approach for ecological unit mapping in other semi-arid regions facing similar environmental and management challenges.