首页|基于地理探测器和PLS-SEM的藏东南植被变化及驱动因子分析

基于地理探测器和PLS-SEM的藏东南植被变化及驱动因子分析

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青藏高原作为全球海拔最高的生态脆弱区域,是研究植被变化及其对环境驱动因子响应的理想场所。以往的研究多集中于分析各驱动因子对植被变化产生的直接影响,对于因子之间的相互作用及其对植被变化的间接影响的研究相对较少。该文选取藏东南地区作为研究区,在Google Earth Engine平台上计算了核归一化差异植被指数,并分析了 2000-2020年间该区域植被覆盖的时空变化。通过地理探测器方法分析了气候、地形、人类活动等因子对植被的直接影响,在此基础上引入偏最小二乘结构方程模型(PLS-SEM)探讨了各因子间相互作用及对植被直接效应和潜在效应。研究结果显示:(1)2000-2020年间,藏东南地区植被覆盖整体较好,其中64。04%的区域显示出植被覆盖度上升的趋势;(2)该地区植被覆盖变化的主要影响因子包括高程、气温和潜在蒸散发,这些因子的解释力均超过50%;(3)地形因子通过其直接效应(-0。298)和潜在效应(-0。437)对植被变化产生了显著的负面影响(总效应为-0。735),成为最主要的影响因素。
Analysis of Vegetation Change and Driving Factors in Southeastern Tibet Based on Geographical Detector and PLS-SEM
The Tibetan Plateau,the highest ecologically fragile region in the world,provides an ideal setting for studying vegetation change and its responses to environmental drivers;however,previous studies have focused on the direct effects of these drivers on vegetation change,with relatively few examining the interactions between the drivers and their indirect ef-fects.In this study,we selected southeast Tibet as the study area,calculated the kernel normalised difference vegetation index(kNDVI)on the Google Earth Engine platform,and analyzed the spatial and temporal changes of vegetation cover from 2000 and 2020,and the geo-detector method was used to analyze the direct effects of factors such as climate,topography,and hu-man activities on vegetation cover;and basing on the above studies,the partial least squares structural equation model(PLS-SEM)was introduced to explore the interaction between various factors and their direct and potential effects on vegetation.The results of the study showed on the whole,the vegetation cover of southeast Tibet was improved in 2000-2020,with 64.04%of the area showing an increasing trend.The main factors influencing the changes in vegetation cover involved elevation,air temperature and potential evapo-transpiration,with these factors explanatory power being over 50%of the variance;and the topographic factor had exerted a significant negative impact on vegetation change through its direct(-0.298)effect and potential(-0.437)effect resulting in a total effect of-0.735,becoming the most dominant influencing factor.

kNDVIvegetation cover changedriving factorsgeo-detectorPLS-SEMsoutheast Tibet

高翻翻、向洋、王诗媛、赵龙飞、侯曼、边思源、骆鑫

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西安科技大学测绘科学与技术学院,陕西 西安 710054

kNDVI 植被覆盖变化 驱动因子 地理探测器 PLS-SEM 藏东南地区

2024

环境科学与技术
湖北省环境科学研究院

环境科学与技术

CSTPCD北大核心
影响因子:0.943
ISSN:1003-6504
年,卷(期):2024.47(12)