Observation of coal mining activities in Ha Long and Cam Pha cities, Quang Ninh province, Vietnam using Sentinel-2 data

Khac Dang Vu; Hieu Ta Duc; Yen Ngô Thị Hải Yến; Anh Nguyen Khac; Hai Pham Thanh; Christiane Weber

doi:10.15625/2615-9783/23010

Author affiliations

Authors

Dang Vu Khac Faculty of Geography, Hanoi National University of Education, Vietnam
Ta Duc Hieu Faculty of Geography, Ho Chi Minh City University of Education, Vietnam
Ngô Thị Hải Yến Faculty of Geography, Hanoi National University of Education, Vietnam
Nguyen Khac Anh Faculty of Geography, Hanoi National University of Education, Vietnam
Pham Thanh Hai Institute of Earth Sciences, Vietnam Academy of Sciences and Technology, Vietnam
Christiane Weber UMR TETIS, CNRS, Université de Montpellier, France

DOI:

https://doi.org/10.15625/2615-9783/23010

Keywords:

Carbon index, exposed coal site, Sentinel-2 image, visual interpretation, thresholding classification

Abstract

Mapping the spatial distribution of exposed coal sites over a large territory and ensuring temporal continuity is essential for environmental assessment. It helps identify relationships between coal mining activities and environmental issues such as water pollution from coal contamination or air pollution from particulate matter in the coastal area of Ha Long - Cam Pha. The carbon index has proven to be an effortless yet effective technique. It is calculated using the spectral ratio between two shortwave infrared bands (Band 11 - SWIR1 and Band 12 - SWIR2) of Sentinel-2 imagery. The selection of an appropriate carbon threshold value - used to distinguish exposed coal from other land cover types, particularly urban areas - was validated through visual interpretation of high-resolution satellite images from the Google Earth dataset. This approach, leveraging Sentinel-2 imagery with higher spatial resolution, differs from previous studies that applied complex algorithms or multiple spectral indicators to lower-resolution images for exposed coal site detection. The results, with an overall accuracy of 87.35% and a Kappa coefficient of 0.73, provide valuable support for monitoring and managing natural resource exploitation. Additionally, this method identifies illegal coal mines and seawater contamination, aiding in coal industry management, etc. These findings lay a crucial foundation for assessing the environmental impact of coal mining activities and proposing solutions for ecological restoration in coastal areas where diverse and complex socio-economic activities occur.

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