Land use land cover change analysis of Afşin Elbistan coal basin with two different classification methods

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2015
Arıcan, İlke
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Surface coal mining is one of the most widespread energy and economic source for the communities and if implemented inappropriately, causes negative short and long term environmental, social and economic effects (such as loss of vegetation, migration, decreasing water resources, etc.). In order to detect, minimize and avoid all of these impacts, affected areas should be monitored and mapped, constantly. Monitoring surface mining activities by using land use land cover (LULC) maps is one of the effective methods for large areas. However, finding the suitable method for constructing LULC maps for monitoring has been a challenge. Remote Sensing (RS) and Geographical Information Systems (GIS) have been helpful for generation of LULC maps. In this study, two change detection methods, namely, the post-classification change detection based on Support Vector Machine (SVM) and Change-Detection-Driven Transfer Learning Approach (CDTL) are used to monitor change detection in LULC classes in the Afşin-Elbistan Coal Basin, which is one of the largest surface coal mines in Turkey. The Landsat imageries of Afşin-Elbistan Coal Basin for the years of between 1984 and 2014 are utilized for the analyses. The progressive change in the LULC classes since the beginning of the mining activities are obtained quantitavely. The LULC change detection maps reveal that the vegetation class increases by 3.2%, forest class increases by 55.9%, agriculture class decreases by 5.9%, settlement class decreases by 96.2%, water class increases by 608.1% and soil class decreases by 8.1% between the years 1984 and 2014. It is found that the most and the least effected classes are water and vegetation, respectively. The comparison of post classification change detection based on SVM and CDTL shows that difference in the results are not significantly high. Nevertheless, post classification change detection based on SVM results in higher accuracies than CDTL for each year.
Coal mines and mining., Remote sensing., Land cover., Land use.
http://etd.lib.metu.edu.tr/upload/12619291/index.pdf
https://hdl.handle.net/11511/24894
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
İ. Arıcan, “Land use land cover change analysis of Afşin Elbistan coal basin with two different classification methods,” M.S. - Master of Science, Middle East Technical University, 2015.
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