基于空间尺度效应的西南地区植被NPP影响因子探测

植被净初级生产力(NPP)是评价陆地生态系统质量的重要参数,研究植被NPP时空演变特征及其驱动力对区域生态环境保护和可持续发展具有重大意义.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,采用一元线性回归分析、 R/S分析和地理探测器模型,分析西南地区及其六大地貌单元植被NPP时空演变特征及未来变化趋势,探究植被NPP空间分异的影响因子.结果表明,2000～2020年西南地区植被NPP整体呈极显著上升趋势.地貌单元中,除青藏高原南部外,其余地貌单元植被NPP均表现为改善态势,其中四川盆地和云贵高原表现为极显著改善.西南地区的植被NPP变化斜率整体呈现“东高西低”的分布格局.西南地区及各地貌单元植被NPP呈上升趋势的区域面积均大于呈下降趋势的区域面积,但未来植被NPP变化趋势均以下降为主.地理探测器结果表明,除云贵高原植被NPP空间分异主要受气温影响外,海拔是西南地区及各地貌单元植被NPP空间分异的主导因子.交互探测结果表明,影响因子之间的交互作用均表现为双因子增强或非线性增强,其中,海拔∩温度对西南地区植被NPP空间分异的解释力最大.地...

Detecting Influencing Factor of Vegetation NPP in Southwest China Based on Spatial Scale Effect

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.