Spatiotemporal vegetation dynamics and their influence factors at a large coal-fired power plant in Xilinhot, Inner Mongolia

ABSTRACT

We monitored the vegetation dynamics in a large coal-fired power plant and investigated the factors influencing these dynamics. The findings improve the understanding of the impact of climate change and human activities on vulnerable ecosystems and contribute to the sustainable development of these regions. We used normalized difference vegetation index (NDVI) images derived from a 16-day maximum-value composite of the Moderate Resolution Imaging Spectroradiometer data and employed the rate of change in greenness and coefficient of variation as indicators of the vegetation dynamics in Xilinhot, Inner Mongolia, from 2001 to 2013. We investigated the driving factors of vegetation dynamics in regions with different vegetation variations based on correlation and stepwise regression analyses. The results show that the vegetation dynamics of the study area have improved over the past 13 years. The vegetation dynamics were highly correlated with the average relative humidity of the current month. That is, the vegetation dynamics in the study area were mainly influenced by meteorological factors. Some regions suffered from vegetation degradation, accounting for 15.27% of the total area. The NDVI values of regions displaying vegetation degradation were jointly influenced by meteorological and human factors. The degree of vegetation degradation was negatively correlated with the population engaged in secondary industry. This indicates that industrial and mining development was the primary cause of vegetation degradation. However, the development of animal husbandry (characterized by the number of livestock on hand at the end of the year) had no significant influence on the vegetation dynamics.

KEYWORDS:

• Vegetation degradation
• normalized difference vegetation index (NDVI)
• large coal-fired power plant
• dynamic monitoring

Acknowledgment

This study was supported by the State Key Research Development Program of China (Grant No. 2016YFC0501101).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the State Key Research Development Program of China [Grant No. 2016YFC0501101].