AUTHOR=Chen Zhichao , Zhang Xufei , Jiao Yiheng , Cheng Yiqiang , Zhu Zhenyao , Wang Shidong , Zhang Hebing TITLE=Investigating the spatio-temporal pattern evolution characteristics of vegetation change in Shendong coal mining area based on kNDVI and intensity analysis JOURNAL=Frontiers in Ecology and Evolution VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2023.1344664 DOI=10.3389/fevo.2023.1344664 ISSN=2296-701X ABSTRACT=

Alterations in vegetation cover serve as a significant indicator of land ecology. The Shendong Coal Mining Area, being the largest coal base globally, holds significant importance for national energy security. Moreover, it has gained recognition for its environmentally conscious approach to coal mining, characterized by the simultaneous implementation of mining activities and effective governance measures. In order to assess the ongoing vegetation recovery and the temporal changes in vegetation within the Shendong Coal Mining Area, we initially utilized Landsat TM/ETM+/OLI remote sensing data. Using the Google Earth Engine (GEE), we developed a novel kernel-normalized vegetation index (kNDVI) and subsequently generated a comprehensive kNDVI dataset spanning the years 2000 to 2020. In addition, the Sen (Theil-Sen median) trend analysis method and MK (Mann-Kendall) test were utilized to examine the temporal trends over a span of 21 years. Furthermore, the Hurst exponent model was employed to forecast the persistent changing patterns of kNDVI. The utilization of the intensity analysis model was ultimately employed to unveil the magnitude of vegetation dynamics. The findings indicated a notable positive trend in the overall kNDVI of vegetation within the study area. In relation to the analysis of changing trends, the vegetation in the region underwent a slight improvement from 2000 to 2010, followed by a significant improvement from 2010 to 2020. During this transition period, a total of 289.07 km2, which represents 32.36% of the overall transition area, experienced a shift in vegetation. The predictive findings from the Hurst model indicate that while the majority of areas within the mining region will exhibit an upward trend in vegetation growth, there will be certain areas that will demonstrate a decline. These declining areas account for 39.08% of the total transition area. Furthermore, the intensity analysis results reveal notable disparities in the characteristics of vegetation growth and evolution between the periods of 2000-2010 and 2010-2020. Throughout the entirety of the transformation process, the transition from slight improvement to significant improvement prevails in terms of both relative intensity and absolute intensity, surpassing alternative transformation processes. Various trend transitions display diverse intensity characteristics that adhere to the overarching principles governing shifts in vegetation growth. Furthermore, the utilization of the intensity analysis framework and intensity spectrum employed in this study demonstrates their efficacy in elucidating the temporal dynamics of vegetation changes. Furthermore, this study plays a pivotal role in the surveillance and assessment of the efficacy of ecological restoration in mining regions. It carries substantial implications for comparable land ecological restoration efforts in mining and reclamation, thereby furnishing a theoretical foundation.