Xu Yong, Huang Wen-Ting, Zheng Zhi-Wei, Dai Qiang-Yu, Li Xin-Yi
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
Huan Jing Ke Xue. 2023 Feb 8;44(2):900-911. doi: 10.13227/j.hjkx.202203254.
Vegetation net primary productivity (NPP) is an important parameter for evaluating the quality of terrestrial ecosystems. It is of great importance to study the spatio-temporal evolution of vegetation NPP and its driving force for regional ecological environment protection and sustainable development. On the basis of MODIS NPP data, meteorological data, DEM data, population density data, GDP data, and land use type data, this study used linear regression analysis, R/S analysis, and a Geodetector model to analyze the spatio-temporal variation in vegetation NPP and its future changing trend on both regional and landform scales and to detect the influencing factors that affect the spatial differentiation of vegetation NPP. The results showed that the vegetation NPP exhibited an extremely significant upward trend in southwest China from 2000 to 2020. On the landform scale, the vegetation NPP had showed an upward trend in all landforms, except for the southern Tibet Plateau; among them, the vegetation NPP in the Sichuan Basin showed the most obvious upward trend. The variation in vegetation NPP exhibited obvious spatial heterogeneity in southwest China, with the changing rate of "high in the east and low in the west." The areas with an upward trend of vegetation NPP were greater than the areas with a downward trend, but the changing trend was dominated by a decreasing trend in the future, both in southwest China and each landform unit. The Geodetector results showed that elevation was the dominant factor controlling the spatial differentiation of vegetation NPP in southwest China and all landform units, except for the Yunan-Guizhou Plateau, in which the spatial differentiation of vegetation NPP was mostly dominated by temperature. The interaction detection results showed that the interaction between the influencing factors was manifested as two-factor enhancement or nonlinear enhancement. The interaction between elevation and temperature showed the highest impact on vegetation NPP distribution. On the landform scale, the spatial differential of vegetation NPP was dominated by the interaction between elevation and climate factors or elevation and GDP in the Guangxi Hills, Sichuan Basin, Zoige Plateau, Hengduan Mountains, and southern Tibet Plateau and between climate factors in the Yunan-Guizhou Plateau. The above results indicated that vegetation NPP variation and the influencing factors that dominate its spatial differential in southwest China showed obvious scale effects. Therefore, exploring the dynamic variation in vegetation NPP and its influencing factors at different spatial scales has practical significance for a comprehensive understanding of the vegetation cover situation and formulating regional ecological restoration plans in southwest China.
植被净初级生产力(NPP)是评估陆地生态系统质量的重要参数。研究植被NPP的时空演变及其驱动力对区域生态环境保护和可持续发展具有重要意义。本研究基于MODIS NPP数据、气象数据、DEM数据、人口密度数据、GDP数据和土地利用类型数据,采用线性回归分析、R/S分析和地理探测器模型,分析了区域和地形尺度上植被NPP的时空变化及其未来变化趋势,并探测了影响植被NPP空间分异的影响因素。结果表明,2000—2020年中国西南地区植被NPP呈极显著上升趋势。在地形尺度上,除藏南高原外,各地形植被NPP均呈上升趋势;其中,四川盆地植被NPP上升趋势最为明显。中国西南地区植被NPP变化呈现明显的空间异质性,呈“东高西低”的变化率。植被NPP上升趋势的面积大于下降趋势的面积,但未来无论是中国西南地区还是各地形单元,变化趋势均以下降趋势为主。地理探测器结果表明,海拔是控制中国西南地区及除云贵高原外各地形单元植被NPP空间分异的主导因素,云贵高原植被NPP的空间分异主要受温度控制。交互探测结果表明,影响因素之间的交互作用表现为双因素增强或非线性增强。海拔与温度的交互作用对植被NPP分布的影响最大。在地形尺度上,广西丘陵、四川盆地、若尔盖高原、横断山脉、藏南高原植被NPP的空间分异主要受海拔与气候因子或海拔与GDP的交互作用影响,云贵高原受气候因子之间的交互作用影响。上述结果表明,中国西南地区植被NPP变化及其主导其空间分异的影响因素具有明显的尺度效应。因此,探索不同空间尺度下植被NPP的动态变化及其影响因素,对于全面了解中国西南地区植被覆盖状况和制定区域生态修复计划具有现实意义。