Changes of SOC Content in China’s Shendong Coal Mining Area during 1990–2020 Investigated Using Remote Sensing Techniques

Xuting Yang, Wanqiang Yao, Pengfei Li, Jinfei Hu, Hooman Latifi, Li Kang, Ningjing Wang and Dingming Zhang
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Xuting Yang: College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Wanqiang Yao: College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Pengfei Li: College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Jinfei Hu: College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Hooman Latifi: Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
Li Kang: Ministry of Nature Resources, Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Xi’an 710021, China
Ningjing Wang: College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Dingming Zhang: College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China

Sustainability, 2022, vol. 14, issue 12, 1-21

Abstract: Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R 2 > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg −1 in 1990 to 9.23 gC·kg −1 in 2010 and then declined to 5.31 gC·Kg −1 in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.

Keywords: loess plateau; coal mining area; SOC content prediction; human disturbance; vegetation restoration; climate change (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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