Future Climate Impact on the Desertification in the Dry Land Asia Using AVHRR GIMMS NDVI3g Data

Abstract

Dry Land Asia is the largest arid and semi-arid region in the northern hemisphere that suffers from land desertification. Over the period 1982-2011, there were both overall improvement and regional degeneration in the vegetation NDVI. We analyze future climate changes in these area using two ensemble-average methods from CMIP5 data. Bayesian Model Averaging shows a better capability to represent the future climate and less uncertainty represented by the 22-model ensemble than does the Simple Model Average. From 2006 to 2100, the average growing season temperature value will increase by 2.9 °C, from 14.4 °C to 17.3 °C under three climate scenarios (RCP 26, RCP 45 and RCP 85). We then conduct multiple regression analysis between climate changes compiled from the Climate Research Unit database and vegetation greenness from the GIMMS NDVI3g dataset. There is a general acceleration in the desertification trend under the RCP 85 scenario in middle and northern part of Middle Asia, northwestern China except Xinjiang and the Mongolian Plateau (except the middle part). The RCP 85 scenario shows a more severe desertification trend than does RCP 26. Desertification in dry land Asia, OPEN ACCESSRemote Sens. 2015, 7 3864 particularly in the regions highlighted in this study, calls for further investigation into climate change impacts and adaptations.

Spatio-Temporal Changes in Vegetation Activity and Its Driving Factors during the Growing Season in China from 1982 to 2011

“…In Inner Mongolia (A2), P1 (Ce = 4.78) was the strongest explanatory factor for vegetation variations, consistent with previous studies [28,29,34]. Similarly, in Northwest China (A3), P1 (Ce = 1.92) and P3 (Ce = 2.17) also benefited vegetation growth by improving the insufficient water supply.…”

“…Although such correlations assist in understanding the impacts of climate change on vegetation dynamics and shed light on the mechanisms controlling changes in vegetation activities [22], it is difficult to distinguish the effects of individual climatic parameters and identify how vegetation responds to the combined effects of multiple factors [25]. For example, in arid regions, increased precipitation is beneficial to plant growth, but higher temperature leads to stronger photosynthetic activity as well as increased evaporation [21,22,[26][27][28][29]. As a result, some confusion will arise from using a simple correlation analysis to compare different climatic factors, especially those from previous periods.…”

The Response of Vegetation Phenology and Productivity to Drought in Semi-Arid Regions of Northern China

“…The Global Inventory Modeling and Mapping Studies (GIMMS) 15-day composite NDVI3g dataset with an 8 km spatial resolution applied here has been shown to be more accurate than the GIMMS NDVI for monitoring vegetation activity and phenological change [33]. Data were derived from the AVHRR instrument on board the NOAA satellite series (7,9,11,14,(16)(17)(18)(19) for the time period of July 1981 to December 2013.…”

Dynamic Response of Satellite-Derived Vegetation Growth to Climate Change in the Three North Shelter Forest Region in China

“…Compared to the old version, the AVHRR NDVI3g dataset has a finer spatial resolution, longer time interval, and higher vegetation activity detection accuracy [23]. The reliability of this dataset has been assessed in many studies [23,24], and it has been widely used in research on vegetation in China [19,25]. The maximum value composite (MVC) method was used to reduce noise in the data and generate the monthly NDVI series [26].…”