Spatial-Temporal Evolution and Driving Forces of NDVI in China’s Giant Panda National Park

Mengxin Pu, Yinbing Zhao, Zhongyun Ni, Zhongliang Huang, Wanlan Peng, Yi Zhou, Jingjing Liu and Yingru Gong
Additional contact information
Mengxin Pu: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Yinbing Zhao: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Zhongyun Ni: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Zhongliang Huang: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Wanlan Peng: College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China
Yi Zhou: College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China
Jingjing Liu: College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China
Yingru Gong: College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China

IJERPH, 2022, vol. 19, issue 11, 1-31

Abstract: Identifying the ecological evolution trends and vegetation driving mechanisms of giant panda national parks can help to improve the protection of giant panda habitats. Based on the research background of different geomorphological zoning, we selected the MODIS NDVI data from 2000 to 2020 to analyze the NDVI trends using a univariate linear model. A partial correlation analysis and multiple correlation analysis were used to reveal the influence of temperature and precipitation on NDVI trends. Fourteen factors related to meteorological factors, topographic factors, geological activities, and human activities were selected, and the Geographically Weighted Regression model was used to study the mechanisms driving NDVI change. The results were as follows: (1) The NDVI value of Giant Panda National Park has fluctuated and increased in the past 21 years, with an annual growth rate of 4.7%/yr. Affected by the Wenchuan earthquake in 2008, the NDVI value fluctuated greatly from 2008 to 2012, and reached its peak in 2018. (2) The NDVI in 94% of the study area improved, and the most significant improvement areas were mainly distributed in the northern and southern regions of Southwest Subalpine and Middle Mountain and the Xiaoxiangling area. Affected by the distribution of fault zones and their local activities, vegetation degradation was concentrated in the Dujiangyan–Anzhou area of Hengduan Mountain Alpine Canyon. (3) The Geographically Weighted Regression analysis showed that natural factors were dominant, with climate and elevation having a double-factor enhancement effect, the peak acceleration of ground motion and fault zone having a superimposed effect, and river density and slope having a double effect, all of which had a significant impact on the NDVI value of the surrounding area. To optimize the ecological security pattern of the Giant Panda National Park, we recommended strengthening the construction of ecological security projects through monitoring meteorological changes, preventing, and controlling geo-hazards, and optimizing the layout and intensity of human activities.

Keywords: vegetation; geographically weighted regression; climate change; Wenchuan earthquake; ecological security (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/11/6722/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/11/6722/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:11:p:6722-:d:828792

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().