Effect of natural conditions and mining activities on vegetation variations in arid and semiarid mining regions
2019
Liu, Shiliang | Li, Wenping | Qiao, Wei | Wang, Qiqing | Hu, Yanbo | Wang, Zhenkang
Variation in vegetation can provide fundamental information about environment and resources management, especially in mining areas with vulnerable ecosystems. Research has demonstrated that vegetation patterns across river basins, plateaus, and mountains have been affected by climate change. However, variation in vegetation caused by natural conditions and mining activities in arid and semiarid mining areas is rarely examined. Using the Yushenfu mining area as a case study, based on the Sen + Mann–Kendall method, spatiotemporal characteristics of variation in vegetation were first analyzed using normal difference vegetation index (NDVI) time series data from 2006 to 2015. For comparison, a traditional linear regression method was applied to characterize NDVI trends, indicating the feasibility of the Sen + Mann–Kendall method. Subsequently, the effect of geomorphology, groundwater depth, and drought index on NDVI was assessed. The same geomorphology or groundwater depth did not correspond to a specific NDVI value, and a relationship between the drought index and NDVI was obtained. Then, unexploited areas and mined areas were compared, revealing that mining activities have a very obvious negative impact on vegetation ecology. Furthermore, the buried depth of the eco-safety groundwater table was determined to be 1.5–5.0 m based on the curve describing the relationship between groundwater depth and NDVI. This analysis produced an ecological sensitivity map with four key zones: primary, secondary, and tertiary sensitive areas and non-sensitive areas. These results provide a theoretical basis for developing fair and equitable outcomes for mineral resource development and environmental protection.
Show more [+] Less [-]AGROVOC Keywords
Bibliographic information
This bibliographic record has been provided by National Agricultural Library