Vegetation dynamics based on NDVI in Yangtze River Basin (China) during 1982-2015

Abstract

Knowledge of vegetation dynamics is important for the sustainability of natural resources and understanding the changes in ecosystems and its impact to earth’s environment. We have carried out analysis of GIMMS (Global inventory modelling and mapping studies) NDVI3g (third generation normalized difference vegetation data for the period 1982-2015. Based on the Linear Regression (LR) analysis, the Mann-Kendall (MK) test with Sen’s slope estimator and Kriging interpolation method, we have investigated the spatiotemporal variations of vegetation NDVI in the Yangtze River Basin (YRB). The results show pronounced increase in the annual mean NDVI at the rate of 0.01/10yr during 1982-2015, with significant turning point (TP) around 1994. The spatial distribution of the annual mean NDVI reasonably increased in the northern, eastern and south-western YRB, while decreased in the Yangtze River Delta (YRD) and parts of the southern YRB during 1982-2015. Pronounced change in NDVI trend is found in different seasons, for example, the increasing trend during spring (0.02/10yr) and in autumn (0.02/10yr) was higher compared to winter (0.006/10yr) and summer (0.002/10yr) seasons.

Hydrologic regime alteration and influence factors in the Jialing River of the Yangtze River, China

“…Figure 8 show the results of spatial and temporal alterations of NDVI in the Jialing River basin, from which it can be seen that: the alterations of vegetation cover in the basin are mainly concentrated in the northern area of Jialing River, while the southern site does not show large fluctuations due to the gentle topography and urban concentration. According to the trend of NDVI values from 1998 to 2020, it can be seen that the NDVI values in the Jialing River basin show an increasing trend, indicating a gradual increase in vegetation cover from the 1990s, which is consistent with the results of NDVI increase in the Yangtze River basin simulated by Cui et al 33 . Zhou et al showed that due to the large-scale soil and water conservation measures implemented in southwest China, the increase in vegetation cover in the Jialing River basin has significantly increased, while the effect of increased vegetation cover on rivers in the basin is mainly focused on sediment changes, while the effect of runoff changes is not obvious 22 .…”

Methods for estimating agricultural cropland yield based on the comparison of NDVI images analyzed by means of Image segmentation algorithms: A tool for spatial planning decisions

“…and needs of farmers would increase success of end-users and other stakeholders to ensure effective co-production [6]. Regarding management decisions from analysis and monitoring of processed NDVI images, methods to systematize and improve efficiency in using NDVI image-based resources seeking to increase agricultural productivity have been recently tackled [3], [5], [7], [8]- [14]. NDVI images can correlate spectral and spatial digital information with the practical interest variables of usefulness in Precision Agriculture.…”