Threshold Vegetation Greenness under Water Balance in Different Desert Areas over the Silk Road Economic Belt

Abstract

The sustainability of dryland vegetation growth over the Silk Road Economic Belt is under threat of water shortage, and the determination of water carrying capacity for vegetation is critically essential to balance water supply and water demand for the maintenance of existing ecosystems. To better understand how and why vegetation growth varies in different desert areas, this study first analyzed the spatiotemporal variation of the normalized difference vegetation index (NDVI). Then, we investigated the relationship between NDVI and climatic factors (precipitation, soil water content, air temperature, evapotranspiration), and estimated the threshold NDVI under water balance in different desert areas. Results showed that the higher NDVI was mainly distributed in Kazakhstan, Russia, and Azerbaijan, and it increased in approximately 53% of desert areas from 1982 to 2015 in the whole study region. The mean annual NDVI showed a simultaneous increasing trend in all desert areas from 1982 to 1994, and decreased significantly only in the cold arid desert area (p < 0.01, −0.0067 decade−1) or had no significant change in other desert areas after 1994 (p > 0.01). The climate condition generally appeared as a warming and drying trend in the past 34 years, with varied changing rates in different desert areas. NDVI presented a strong positive relationship with both precipitation and evapotranspiration in most desert areas. The threshold values of the mean annual NDVI under water balance between 1982 and 2015 were approximately 0.1041 (hot arid desert), 0.1337 (cold arid desert), 0.1346 (cold arid semi-desert), 0.0951 (hot arid desert semi-desert), 0.0776 (polar desert tundra), 0.1071 (hot arid desert shrub), 0.1377 (cold arid desert steppe), and 0.0701 (polar desert steppe), respectively. The responses of these threshold values to precipitation were all positive in different desert areas. These results provide an enhanced understanding of vegetation dynamics and ecological conservation, which are of great importance to implementing adaptation and mitigation measures for terrestrial ecosystems over the Silk Road Economic Belt.

Dissecting the Characteristics and Driver Factors on Global Water Use Efficiency Using GLASS Data Sets

“…The effect rate is calculated as the indirect effect coefficient divided by the sum of the direct and indirect effect coefficients, multiplied by 100%. Subsequently, the effects of drivers PRE, TEM, PDSI, VPD and SM (1–4) on WUE were analyzed using maximum likelihood estimation in SEM (Ma et al., 2020).…”

Status and Prospect of Ecological Environment in the Belt and Road Initiative Regions

“…Most of the countries in the Belt and Road region are developing countries in semiarid and arid areas with relatively lower income levels, high rural poverty and heavy environmental threats, making its vegetation highly sensitive to climate change and anthropogenic activities over multiple timescales [ 52 , 55 , 56 , 57 ]. Climate change dominates the overall vegetation coverage change in Belt and Road region, with warmer temperatures and droughts the likely main causes of the greening to browning shifts [ 52 , 58 ].…”

“…With its fragile ecosystem, poor infrastructure, relatively low income and high vulnerability to climate change, the Belt and Road region has experienced accelerated warming at roughly twice the rate of the global land [ 61 , 63 ]. In the context of climate change, rising temperatures have further exacerbated water stress in the Belt and Road region by increasing surface evapotranspiration and stimulating considerable glacial retreat, especially in Central and West Asian countries, which are currently facing water shortage problems [ 34 , 55 , 60 , 61 , 64 ]. Furthermore, climate change might make future water resources more unevenly distributed among the Belt and Road region ( Table 7 ) [ 34 ].…”

Assessing the Recent Trends of Land Degradation and Desertification in Romania Using Remote Sensing Indicators

“…Earth observation data play an essential role in mapping and assessing LD and DS by providing indicators like NDVI, NPP, Evapotranspiration (ET), Land Surface Temperature (LST), and albedo [29][30][31]. NDVI combined with land use information is frequently used for analyzing LD and DS due to its sensitivity to changes in vegetation cover [32] and is also associated with productivity [33][34][35].…”