Long-term land cover changes assessment in the Jiului Valley mining basin in Romania

Abstract

Introduction: Highlighting and assessing land cover changes in a heterogeneous landscape, such as those with surface mining activities, allows for understanding the dynamics and status of the analyzed area. This paper focuses on the long-term land cover changes in the Jiului Valley, the largest mining basin in Romania, using Landsat temporal image series from 1988 to 2017.Methods: The images were classified using the supervised Support Vector Machine (SVM) algorithm incorporating four kernel functions and two common algorithms (Maximum Likelihood Classification - MLC) and (Minimum Distance - MD). Seven major land cover classes have been identified: forest, pasture, agricultural land, built-up areas, mined areas, dump sites, and water bodies. The accuracy of every classification algorithm was evaluated through independent validation, and the differences in accuracy were subsequently analyzed. Using the best-performing SVM-RBF algorithm, classified maps of the study area were developed and used for assessing land cover changes by post-classification comparison (PCC).Results and discussions: All three algorithms displayed an overall accuracy, ranging from 76.56% to 90.68%. The SVM algorithms outperformed MLC by 4.87%–8.80% and MD by 6.82%–10.67%. During the studied period, changes occurred within analyzed classes, both directly and indirectly: forest, built-up areas, mined areas, and water bodies experienced increases, whereas pasture, agricultural land, and dump areas saw declines. The most notable changes between 1988 and 2017 were observed in built-up and dump areas: the built-up areas increased by 110.7%, while the dump sites decreased by 53.0%. The mined class showed an average growth of 6.5%. By highlighting and mapping long-term land cover changes in this area, along with their underlying causes, it became possible to analyze the impact of land management and usage on sustainable development and conservation effort over time.