Shanshan Wang

Dr. Shanshan Wang


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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QLQuansheng Li
Quansheng Li
FLFeiyue Li
Feiyue Li
JGJunting Guo
Junting Guo
LGLi Guo
Li Guo
SWShanshan Wang
Shanshan Wang
Chengye ZhangChengye Zhang
Chengye Zhang
Understanding the synergistic effect between topography and vegetation in the underground coal mine is of great significance for the ecological restoration and sustainable development of mining areas. This paper took advantage of unmanned aerial vehicle (UAV) remote sensing to obtain high-precision topographic factors (i.e., digital elevation model (DEM), slope, and aspect) in the Shangwan Coal Mine. Then, a normalized difference vegetation index (NDVI) was calculated utilizing Landsat images from 2017 to 2021, and the NDVI with the same spatial resolution as the slope and aspect was acquired by down-sampling. Finally, the synergistic effect of topography and vegetation in the underground mining area was revealed by dividing the topography obtained using high-precision data into 21 types. The results show that: (1) the vegetation cover was dominated by “slightly low-VC”, “medium-VC”, and “slightly high-VC” in the study area, and there was a positive correlation between the slope and NDVI when the slope was more than 5°. (2) When the slope was slight, the aspect had less influence on the vegetation growth. When the slope was larger, the influence of the aspect increased in the study area. (3) “Rapidly steep–semi-sunny slope” was the most suitable combination for the vegetation growth in the study area. This paper revealed the relationship between the topography and vegetation. In addition, it provided a scientific and effective foundation for decision-making of ecological restoration in the underground coal mine.

23 October 2024 in Global Ecology and Conservation
Verified
Journal Article
Open access
HLHaodong Liu
Haodong Liu
GZGaoqiang Zhang
Gaoqiang Zhang
TLTianqi Li
Tianqi Li
SRSiyuan Ren
Siyuan Ren
BCBaihua Chen
Baihua Chen
WLWei LI
Wei LI

27 June 2023 in Journal of Mountain Science
Verified
Journal Article
Closed access
PDF file
XWXueliang Wang
Xueliang Wang
JSjuanjuan sun
juanjuan sun
SWShanshan Wang
Shanshan Wang
SQShengwen Qi
Shengwen Qi
HZHaijun Zhao
Haijun Zhao
XWXin-Hui Wang
Xin-Hui Wang