Improving accuracy of long-term land-use change in coal mining areas using wavelets and Support Vector Machines

ABSTRACT

Old satellite sensors lack several quality features such as high spatial and spectral resolution. For accurate long-term change detection, improvement of the quality of old satellite images is required. In the present study, we used two wavelet-based image enhancement techniques [(discrete wavelet transform (DWT) and dual tree-complex wavelet transform (DT-CWT)] for improving the quality of Landsat 2 data of 1975 and Landsat 8 data of 2015 to study the impact of coal mining on land use change over a period of four decades. The enhanced images were subjected to land-use classification using Support Vector Machines. Land-use classification accuracy was measured using confusion matrix-based accuracy assessment. Accuracy assessment revealed that the overall classification accuracy of DWT enhanced images was 82.10% for the year 1975 and 88.46% for the year 2015. The overall classification accuracy of DT-CWT enhanced images was 85.71% for the year 1975 and 88.54% for the year 2015. The results of change detection revealed that the total areal coverage of dense vegetation increased by 65%, indicating that the rate of land degradation had slowed down due to the legislative and policy changes to promote sustainable development in coal mining after 1986.

Acknowledgements

The authors acknowledge the support provided by the Department of Environmental Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India, for carrying out the research work.

Disclosure statement

No potential conflict of interest was reported by the authors.