The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

Abstract

Vegetation dynamics in relation to climatic changes and anthropogenic activities is critical for terrestrial ecosystem management. The objective of this study was to investigate spatiotemporal change of vegetation and their driving forces during growing seasons (between April and October and including the spring, summer and autumn) in the Tumen River Basin (TRB) using Normalized Difference Vegetation Index (NDVI) and climate data spanning from 2000 to 2015. A linear regression, Pearson correlation coefficients and the residual trend (RESTREND) was applied for this study. Our results demonstrate that vegetation increased during different periods of the growing season in most of the areas of the TRB over 16 years. Our results demonstrate that vegetation increased during different periods of the growing season in most of the areas of the TRB over 16 years; those in growing season (spring, summer, and autumn) were characterized by the increase in rates by 0.0012/year, 0.0022/year, 0.0011/year, and 0.0019/year, respectively. Forested regions are characterized by the largest increase (0.0021/year) in NDVI compared with other vegetation types across the entire study area. The trends in NDVI across the study area were influenced by both climatic variations and human disturbances. The human activities such as reforestation and agricultural practices are the primary driver, greater than climatic factors, during growing season, including summer and autumn. Temperature and precipitation has had a significant influence on NDVI in a limited area (temp = 0.86%, p < 0.05 and precipitation = 1.93%, p < 0.05) during growing season. The significant role of precipitation on NDVI change throughout growing season and the summer is larger than that of temperature across the TRB, although the influence of the latter becomes most significant during the spring and autumn. The RESTREND method shows that human activity during the growing season, including the spring, summer, and autumn, have led to enhancements in NDVI across more than 70% of the TRB over the last 16 years, with the most significant improvements seen in forested land and farmland. At the same time, a significant reduction in residual (i.e., degraded areas) NDVI values for different growing seasons had characterized farmland and urban land at low altitudes. This study provides important background information regarding the influence of human activities on land degradation and provides a scientific foundation for the development of ecological restoration policies within the TRB. We found that the RESTREND method can be used to detect human drivers of vegetation in the regions with semi-humid and humid monsoon, where the significant correlation between NDVI and climatic factors exists.

Temporal-Spatial Variations and Influencing Factor of Land Use Change in Xinjiang, Central Asia, from 1995 to 2015

“…Human activities are important factors that may induced regional land use changes [32], mainly including the economy, while population size plays a dominant role in land use changes in Xinjiang [45][46][47][48], especially cropland changes [49]. The social and economic development has driven the unused land transfer into cropland in the middle and lower reaches of the river basin.…”

Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea

“…The slope was calculated from mean values of NDVI as the dependent variable and the time as an independent variable. In order to analyze MODIS NDVI change through time, a pixel-based smallest power-function linear regression formula, as proposed and used by previous authors, was applied, as given in Equation 1 [25][26][27]:…”

Greening Implication Inferred from Vegetation Dynamics Interacted with Climate Change and Human Activities over the Southeast Qinghai–Tibet Plateau

“…The Pearson correlation coefficient was used to determine the effects of precipitation and surface air temperature on the vegetation cover from 2000 to 2016 in this paper. The method has been widely applied to analyze the correlation between climate factors and NDVI [62,63]. The correlation coefficient (r xy ) is calculated as…”

“…Residual trend analysis (RESTREND) can differentiate vegetation dynamic variations (denoted by NDVI) caused by human activities from those resulting from climate change [67][68][69]. It is clear that there are two reasonable assumptions involved in RESTREND [62,70,71]. First, vegetation dynamic variations are controlled by human activities and climate change.…”