Change Detection for Surface Mining Boundary Based on Multi-source Remote Sensing Data Techniques
Abstract
The aim of this study is to evaluate the optimization and potential suitability by applying satellite technology to detect changes in horizontal and vertical surface mining in Surint Omya Chemicals (Thailand) Co.,Ltd., and Silasanon Co., Ltd.,. This study is focusing on open-source satellite earth observation and free software (QGIS and two plugins of SCP and Orfeo toolbox). Firstly, Sentinel-1, Sentinel-2 and Landsat 8 data were selected as a satellite-based data source in this study. The Mean-Shift segmentation and Random Forest (RF) algorithm were used for extracting horizontal mining boundaries from Sentinel-2 and Landsat 8 data. In addition, The InSAR technique was used for extract vertical mining with a DEM from Sentinel-1 data. Finally, the horizontal and vertical mining change detection were validated using the high-resolution data obtained by UAV and statistic approach by calculation of R² and RMSE. The result reveals that Sentinel-2 is shown a medium suitability for horizontal boundary mining change detection because the result of the change is accordingly related to the reference and can be accepted. While Landsat-8 is not a suitable choice for horizontal change detection in small area mining because the change pattern has not relation to reference, and Sentinel-1 is not suitable for detecting the change in vertical mining in the small mining areas. The results from this study can be applied to investigate changes in mining initially to find suspicious areas that are likely to mine outside the permissible limits. Reduces operating time Reduce costs and prevent damage to mineral resources in a timely manner. Keywords : Remote sensing ; InSAR ; Landsat 8 ; Sentinel 1 ; Sentinel 2References
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gov/landsat-8/landsat-8-overview
Agency, E. S. SNAP. Retrieved October 20, 2021, from https://earth.esa.int/eogateway/tools/snap
Agency, E. S. (2021). Level-1 SLC Products. Retrieved December 10, 2021, from https://sentinels.copernicus.eu/
web/sentinel/technical-guides/sentinel-1-sar/products-algorithms/level-1-algorithms/single-look-complex
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Braun, A. (2021). Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations. Open Geosciences, 13(1), 532-569. doi:doi:10.1515/geo-2020-0246
Braun, A. (2020). TOPS Interferometry Tutorial for DEM generation.
Congedo, L. (2021). Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS. Journal of Open Source Software, 6(64).
Dalto, J. (2017). What Is Surface Mining. Retrieved August 13, 2021, from https://www.vectorsolutions.com/
resources/blogs/what-is-surface-mining/
Department of Primary Industries and Mines. (2014). Mission. Retrieved November 5, 2021, from http://www.dpim.go.th/en/mission.php
Ibrahim, E., Lema, L., Barnabé, P., Lacroix, P., & Pirard, E. (2020). Small-scale surface mining of gold placers: Detection, mapping, and temporal analysis through the use of free satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 93, 102194. doi:https://doi.org/10.1016/j.jag.2020.
102194
Jangid, A. (2017). Introduction to QGIS.
Kotaridis, I. & Lazaridou, M. (2020). Delineation of Open-Pit Mining Boundaries on Multispectral Imagery. Kotaridis, I. and Lazaridou, M.(2020).‘Delineation of Open-Pit Mining Boundaries on Multispectral Imagery’. in Remote Sensing. IntechOpen. doi, 10.
Organization, G.-I. a. S. T. D. A. P. (2013). Fundamental Geographic Data Set: FGDS.
Ponguta, S. Global mapper. Retrieved September 25, 2021, from https://www.geoilenergy.com/en/software/
geosoluciones/global-mapper
Toolbox, O. (2019). Open Source processing of remote sensing images Retrieved July 22, 2021, from https://www.orfeo-toolbox.org/
Wang, S. et al. (2020). Evaluating the Feasibility of Illegal Open-Pit Mining Identification Using Insar Coherence. Remote Sensing, 12(3). doi:10.3390/rs12030367
Wu, Q., Song, C., Liu, K., & Ke, L. (2020). Integration of TanDEM-X and SRTM DEMs and Spectral Imagery to Improve the Large-Scale Detection of Opencast Mining Areas. Remote Sensing, 12(9), 1451. [Online]. Retrieved June 18, 2021, from https://www.mdpi.com/2072-4292/12/9/1451
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Published
2023-01-04
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