| Issue |
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|
|
|---|---|---|
| Article Number | 06028 | |
| Number of page(s) | 6 | |
| Section | Artificial Recognition and Application | |
| DOI | https://doi.org/10.1051/matecconf/202133606028 | |
| Published online | 15 February 2021 | |
Vegetation variation trend and its influencing factors in Urad Grassland over remote sensing
1 Henan Key Lab Spatial Infor. Appl. Eco-environmental Protection, Zhengzhou, China
* Corresponding author: Zhangyueying001@126.com
In this work, the temporal-spatial dynamic variation of vegetation coverage from 2010 to 2019 in Urad Grassland has been explored by remote sensing technique. The change of precipitation and temperature has obvious effects on the vegetation, which affects the agriculture and the stability of the ecosystem. Pixel dichotomy approach and correlation analysis are introduced to analyse the temporal and spatial evolution trend of vegetation coverage and its response to climate change in the past 10 years. Specifically, the correlation between vegetation coverage and the critical climate impacting factors such as temperature and precipitation are fully investigated. The results show the vegetation coverage in the study area was influenced by effectors including climate, topography, human activities and government policies. The average annual vegetation coverage showed a downward trend in general from 2010 to 2019. Statistical correlation analysis indicates that the correlation between the vegetation coverage and the precipitation is positive in most parts of the area, while the correlation between the vegetation coverage and the precipitation shows negative with difference with various geographic features. As a result, precipitation was the major natural factor that affected dynamics of vegetation coverage in Urad grassland.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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