Document Type : Original Research Paper
Authors
- Amirmahmood Razavian 1
- Alireza Arab Amiri 1
- Abolghasem Kamkar Rouhani 1
- Meysam Davoodabdi Farahani 2
1 Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
2 Department of Remote sensing, Hekmat institute of higher education, Qom, Iran
Abstract
Mining activities cause environmental pollution. Satellite remote sensing is considered an effective strategy for monitoring pollution, as other direct methods of testing soil pollution levels are often costly and face accessibility challenges in certain areas. Unlike optical sensors, radar systems can capture data in all weather conditions and operate around the clock. However, radar systems do not display details and borders of zones and lack multispectral data collection capability. Consequently, combining various characteristics of optical images and radar data offers a comprehensive approach to monitoring pollution. Given these pros and cons, a combination of optical and radar images from the Sentinel satellite was employed in this study to identify surface and physical pollution areas caused by mining activities. The proposed method is a combination of Curvelet Transform, Simple Linear Iterative Clustering, Principle Components Analysis, and integration of radar and optical results using a statistical based clustering scheme, which allows the detection of contaminated zones. This research benefits from several innovative strategies, such as the separate processing and integration of optical and radar images, the simultaneous application of the curvelet transform and principle component analysis, and the utilization of two distinct clustering methods. Finally, the results obtained from radar and optical images of the Damghan region in Semnan province, Iran, on a 1 to 100.000 scale showed the proposed methodology can segment the contaminated zone caused by the eastern Alborz coal preparation plant through soil pollution modelling.
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