气候变化和人类活动对祁连山植被NPP影响的定量分析

Anle Yang, Han Zhang, X. Yang, Xiaoping Zhang
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引用次数: 3

摘要

摘要祁连山作为中国重要的生态安全屏障和水内涵区,研究祁连山植被NPP驱动机制可为西北地区生态安全保护提供参考。基于2000-2019年祁连山MOD17A3遥感数据,采用一维线性回归、偏相关分析、多元线性回归、残差分析和土地流转矩阵分析。分析了祁连山植被NPP的时空动态及其与降水、温度和人类活动的相关性,量化了气候变化和人类活动对植被NPP的影响。结果表明:1)2000—2019年祁连山植被NPP总体呈波动上升趋势,自东向西递减,多年平均值为138.54 g C·m−2·a−1,年均增加1.67 g C·m−2·a−1;2)植被NPP与降水、温度呈显著正相关,其中降水对植被NPP的影响更为显著;3)草地面积增加最多,对植被NPP的贡献为183.12%,未利用地面积减少最多,对植被NPP的贡献为- 119.6%;④在植被恢复区,气候变化和人类活动对植被恢复的贡献率分别为89%和11%。而在植被退化区,它们分别解释了22%和78%的植被退化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative analysis of the impact of climate change and human activities on vegetation NPP in the Qilian Mountain
Abstract Qilian Mountain as an important ecological security barrier and water connotation area in China, Research on its vegetation NPP driving mechanism can provide reference for ecological security protection in northwest China. Based on Qilian Mountain MOD17A3 remote sensing data from 2000-2019, using one-dimensional linear regression, partial correlation analysis, multiple linear regression, residual analysis and land transfer matrix. The spatial and temporal dynamics of vegetation NPP in the Qilian Mountain and its correlation with precipitation, temperature and human activities were analyzed, and quantified the effects of climate change and human activities on vegetation NPP. The results show that: 1) from 2000 to 2019 vegetation NPP in Qilian Mountains had an overall fluctuating upward trend and decreased from east to west, with a multi-year average of 138.54 g C·m−2·a−1 and an average annual increase of 1.67 g C·m−2·a−1; 2) vegetation NPP was positively correlated with precipitation and temperature, with precipitation having a more significant effect on it; 3) the area of grassland in LUCC increased the most and contributed 183.12% to vegetation NPP, while the area of unused land decreased the most and its contribution to vegetation NPP was −119.6%; 4) In the vegetation recovery area, climate change and human activities explained 89% and 11% of the vegetation recovery, respectively. While in the vegetation degradation area, they explained 22% and 78% of the vegetation degradation, respectively.
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