Remote Sensing Detection of Vegetation and Landform Damages by Coal Mining on the Tibetan Plateau

Qianhan Wu, Kai Liu, Chunqiao Song, Jida Wang, Linghong Ke, Ronghua Ma, Wensong Zhang, Hang Pan and Xinyuan Deng
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Qianhan Wu: Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Kai Liu: Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Chunqiao Song: Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Jida Wang: Department of Geography, Kansas State University, Manhattan, KS 66506, USA
Linghong Ke: School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
Ronghua Ma: Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Wensong Zhang: Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Hang Pan: Department of Geographic Information Science, Nanjing University, Nanjing 210023, China
Xinyuan Deng: Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

Sustainability, 2018, vol. 10, issue 11, 1-17

Abstract: In order to satisfy the needs of constant economic growth, the pressure to exploit natural resources has been increasing rapidly in China. Particularly with the implementation of the National Western Development Strategies since 1999, more and more mining activities and related infrastructure constructions have been conducted on the Tibetan Plateau (TP). Mining activities are known to have substantial impacts on plant dynamics and hence the water and energy cycles. Identifying mining activities and quantifying their effects on vegetation cover are critical to the monitoring and protection of the pristine TP environment. Thus, this study aims to develop an automated approach that detects the timing of initial mining development and assess the spatial distribution of mining-ruined vegetation. The Breaks for Additive Seasonal and Trend (BFAST) algorithm was used to decompose the signal in the normalized difference vegetation index (NDVI) time series derived from high-frequency MODIS images, and to detect abrupt changes of surface vegetation. Results show that the BFAST algorithm is able to effectively identify abrupt changes in vegetation cover as a result of open-mining development on the studied alpine grassland. The testing study in Muli Town of Qinghai Province shows that the mining development began in 2003 and massive destructions of vegetation cover followed between 2008 and 2012. The integrated use of Landsat imagery and multi-temporal DEMs further reveals detailed areal and volumetric changes in the mining site. This study demonstrates the potential of applying multi-mission satellite datasets to assess large-scale environmental influences from mining development, and will be beneficial to environmental conservation and sustainable use of natural resources in remote regions.

Keywords: remote sensing; vegetation; mining; Tibetan Plateau; BFAST; time series (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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