Abstract
The Hanjiang River Basin is the source area of the Middle Route Project of the South-to-North Water Diversion Project, and the vegetation coverage in this basin directly affects the quality of the ecological environment. This study is based on long time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data synthesized over 16 days from 2000 to 2016 in the Hanjiang River Basin. Major climatic data (temperature and rainfall) and topographic data (elevation, slope, and aspect) are employed to analyze the driving forces of NDVI changes. The results demonstrate the following: for the 2000–2016 period, the average annual NDVI is 0.823, with a change trend of 0.025 year−1. The overall NDVI upstream is higher than that downstream. The average annual value of NDVI upstream is 0.844, with a change trend of 0.036 year−1, and that of downstream is 0.799, with a change trend of 0.022 year−1. The spatial distribution of NDVI was significantly increased in the area around the upstream section of the river and near the Danjiangkou Reservoir, and the distribution of NDVI around the central city was significantly reduced. The NDVI was positively correlated with temperature and rainfall, and the impacts differed among different regions. At elevations below 2000 m, the NDVI shows an increasing trend with increasing elevation, and at elevations exceeding 2000 m, the NDVI is negatively correlated with elevation. Slope is positively correlated with the NDVI. The influence of aspect on the NDVI was small.
Highlights
Vegetation constitutes the foundation of the terrestrial ecosystem and represents a natural link among the atmosphere, water, and soil, thereby playing important roles in the energy 509 Page 2 of 16Arab J Geosci (2018) 11: 509 subjective and arbitrary results, and it is difficult to guarantee accuracy (Qin et al 2006)
Among the many remote sensing indices, the Normalized Difference Vegetation Index (NDVI), which was proposed by Rouse et al (1973) in 1973, is often used to characterize vegetation cover, growth, yield, and health status (Tucker 1979; Wiegand and Richardson 1987; Calvao and Palmeirim 2004)
The results demonstrate that the vegetation coverage in 87.0% of the study area showed an increasing trend, the vegetation coverage in 12.6% of the study area showed a decreasing trend, and the vegetation coverage was unchanged in 0.38% of the study area (Fig. 3)
Summary
Arab J Geosci (2018) 11: 509 subjective and arbitrary results, and it is difficult to guarantee accuracy (Qin et al 2006). These traditional methods are difficult to operate and require advanced equipment and permissible environments to acquire dynamic measurements of the vegetation coverage over a large area. Remote sensing technology represents the primary method for studying changes in the vegetation cover. The NDVI effectively reflects information regarding vegetation growth and cover and partially eliminates various atmospheric conditions, including the solar elevation angle, satellite observation angle, topography, and cloud shadows. The NDVI is the most commonly used remote sensing index for monitoring vegetation (Vermote et al 1997; Tang et al 2017; Wang et al 2013), and many scholars employ NDVI data to study changes in vegetation cover at both global and regional scales (de Jong et al 2013; Kong et al 2017; Julien et al 2006; Tourre et al 2008; Bi et al 2013; Huber et al 2011; Piao and Fang 2003)
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