Evaluating the impact of classification algorithms and spatial resolution on the accuracy of land cover mapping in a mountain environment in Pakistan

Satellite images of various spatial resolutions and different image classification techniques have been utilized for land cover (LC) mapping at local and regional scale studies. Mapping capabilities and achievable accuracies of LC classification in a mountain environment are, however, influenced by the spatial resolution of the utilized images and applied classification techniques. Hence, developing and characterizing regionally optimized methods are essential for the planning and monitoring of natural resources. In this study, the potential of four non-parametric image classification techniques, i.e., k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and neural network (NN) on the accuracy of LC classification was evaluated in the Hindu Kush mountains ranges of northern Pakistan. Moreover, we have assessed the impact of the spatial resolution of the utilized satellite imagery, i.e., SPOT-5 with 2.5 m and Landsat-8 with 30 m on the accuracy of the derived LC classification. For the classification of LC based on SPOT-5, we have achieved the highest overall classification accuracy (OCA) = 89% with kappa coefficient (KC = 0.86) using SVM followed by k-NN, RF, and NN. However, for LC classification derived from Landsat-8 imagery, we achieved the highest OCA = 71% with KC = 0.59 using RF and SVM followed by k-NN and NN. The higher accuracy derived from SPOT-5 versus Landsat-8 indicated that the results of LC classification based on SPOT-5 are more accurate and reliable than Landsat-8. The findings of the present study will be useful for the classification and mapping task of LC in a mountain environment using SPOT-5 and Landsat-8 at local and regional scale studies.