Driving force analysis and ecological assessment of spatiotemporal changes in vegetation cover in the Kunlun Mountains from 2000 to 2020

YUAN Jianglong; ZHAO Honghui; LIU Xiaohuang; LI Hongyu; JIANG Dong; ZHAO Chuanyan; XING Liyuan; LUO Xinping; WANG Ran; WANG Chao

doi:10.12029/gc20240102003

GEOLOGY IN CHINA > 2024 > 51(6): 1822-1838. > DOI: 10.12029/gc20240102003

Yuan Jianglong, Zhao Honghui, Liu Xiaohuang, Li Hongyu, Jiang Dong, Zhao Chuanyan, Xing Liyuan, Luo Xinping, Wang Ran, Wang Chao. 2024. Driving force analysis and ecological assessment of spatiotemporal changes in vegetation cover in the Kunlun Mountains from 2000 to 2020[J]. Geology in China, 51(6): 1822−1838. DOI: 10.12029/gc20240102003

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Driving force analysis and ecological assessment of spatiotemporal changes in vegetation cover in the Kunlun Mountains from 2000 to 2020

YUAN Jianglong^{1, 2,},
ZHAO Honghui^{1, 3},
LIU Xiaohuang^{1, 3, ,},
LI Hongyu^{1, 3},
JIANG Dong^{1, 4},
ZHAO Chuanyan^{1, 5},
XING Liyuan^{1, 3},
LUO Xinping^{1, 3},
WANG Ran^{1, 3},
WANG Chao^{1, 3}

1.
Key Laboratory of Natural Resource Coupling Process and Effects, Beijing 100055, China
2.
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830017, Xinjiang, China
3.
Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing 100055, China
4.
Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
5.
College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, Gansu, China

Funds: Supported by the Third Comprehensive Scientific Expedition to Xinjiang of the Ministry of Science and Technology (No.2022xjkk090405, No.2021xjkk140104) and the project of China Geological Survey (No.DD20230514).

More Information

Author Bio:
YUAN Jianglong, male, born in 1999, master candidate, mainly engaged in research on the application of geo−referenced remote sensing to the environment; E−mail: 792807914@qq.com
Corresponding author:
LIU Xiaohuang, male, born in 1972, professor level senior engineer, mainly engaged in natural resources observation research; E-mail: 15313256806@163.com.
Received Date: January 01, 2024
Revised Date: May 06, 2024

Graphical Abstract

Abstract

Abstract

This paper is the result of ecological geological survey engineering.
Objective
Vegetation cover is a critical indicator of ecosystem stability. Researches about the factors influencing vegetation cover changes in the ecologically sensitive Kunlun Mountains region are limited. This study aims to analyze the spatiotemporal variations of vegetation cover and their driving factors in the Kunlun Mountains and to perform a regional ecological assessment.
Methods
The Normalized Difference Vegetation Index (NDVI) was used to examine the spatiotemporal patterns of vegetation cover from 2000 to 2020, and the Geodetector (GD) and Random Forest (RF) models were applied to identify the primary drivers of NDVI changes. Meanwhile, RF and Long Short−Term Memory Network (LSTM) models were used for predict the NDVI variations, and the Remote Sensing Ecological Index (RSEI) was used to evaluate environmental quality.
Results
NDVI showed a generally temporal increasing−decreasing−increasing trend, characterized by a spatial distribution that higher values in the west and north and lower values in the east and south. The RF model identified the digital elevation model (DEM), precipitation (Pre), distance to towns (Town), and gross domestic product (GDP) as the most influential factors, whereas the GD model showed evapotranspiration (ET), DEM, GDP, and temperature (Temp) had the greatest explanatory power. The RF model was more effective for NDVI regression analysis than the LSTM model in the study area. The RSEI value showed an increase in 80.52% of the area, but the overall variation was considerable, with 78.86% of the area showing significant variation.
Conclusions
DEM, GDP, Pre, and ET are the primary factors affecting vegetation cover in the region and the significant improvement in the regional ecosystem over the past two decades. It also reveals a significant enhancement in regional ecological quality, indicating that ecological conservation measures have been effective.
Highlights
(1) A comprehensive spatiotemporal analysis of NDVI was conducted in the Kunlun Mountains using a variety of methods, including the Hurst index, coefficient of variation, and trend analysis; (2) RF and LSTM methods for regression analysis of NDVI was employed in the Kunlun Mountains. RF importance analysis and GD explanatory power analysis was utilized to investigate the dominant factors driving NDVI in the region; (3) The RSEI was calculated to evaluate the ecological environmental quality of the Kunlun Mountains and provide reasonable recommendations.

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References (86)

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Driving force analysis and ecological assessment of spatiotemporal changes in vegetation cover in the Kunlun Mountains from 2000 to 2020