DOI: 10.5061/dryad.12bd9
title: Data from: Exacerbated grassland degradation and desertification in Central Asia during 2000-2014

description: Grassland degradation and desertification is a complex process, including
 both state conversion (e.g., grasslands to deserts) and gradual
 within-state change (e.g., greenness dynamics). Existing studies hardly
 separated the two components and analyzed it as a whole based on time
 series vegetation index data, which however cannot provide a clear and
 comprehensive picture for grassland degradation and desertification. Here
 we proposed an integrated assessment strategy, by considering both state
 conversion and within-state change of grasslands, to investigate grassland
 degradation and desertification process in Central Asia. First, annual
 maps of grasslands and sparsely vegetated land were generated to track the
 state conversions between them. The results showed increasing grasslands
 were converted to sparsely vegetated lands from 2000 to 2014, with
 desertification region concentrating in the latitude range of 43-48°N. A
 frequency analysis of grassland versus sparsely vegetated land
 classification in last 15 years allowed a recognition of persistent desert
 zone (PDZ), persistent grassland zone (PGZ), and transitional zone (TZ).
 The TZ was identified in southern Kazakhstan as one hotspot which was
 vulnerable and unstable for desertification. Furthermore, the trend
 analysis of Enhanced Vegetation Index during thermal growing season
 (EVITGS) was investigated in individual zones using Linear Regression and
 Mann-Kendall approaches. An overall degradation across the area was found;
 moreover, the second desertification hotspot was identified in the
 northern Kazakhstan with significant decreasing in EVITGS, which was
 located in PGZ. Finally, attribution analyses of grassland degradation and
 desertification were conducted by considering precipitation, temperature,
 and three different drought indices. We found persistent droughts were the
 main factor for grassland degradation and desertification in Central Asia.
 Considering both state conversion and gradual within-state change
 processes, this study provided reference information for identification of
 desertification hotspots to support further grassland degradation and
 desertification treatment, and the method could be useful to be extended
 to other regions.
description: Spatial distribution of
 grassland and sparsely vegetated land in Central Asia from 2000 to
 2014Raster data for annual
 spatial distribution of grassland and sparsely vegetated land in targeted
 area of Central Asia over the period 2000-2014. The data was generated by
 using standard deviation of Enhanced Vegetation Index (EVI) during thermal
 growing season (EVITGS). The resolution is 463 m, and the coordinate
 system is Sinusoidal. 0 and 1 mean grassland and sparsely vegetated land,
 respectively.Data1_AnnualClassification.rarSpatial pattern of the frequency of sparsely vegetated land in Central AsiaRaster data for spatial distribution of the frequency of sparsely vegetated land in Central Asia. The resolution is 463 m, and the coordinate system is Sinusoidal. The values of raster layer show the frequency of sparsely vegetated land during 2000-2014.Data2_SparselyVegetatedLand_Frequency.tifSpatial distribution of persistent grassland zone (PGZ), transition zone (TZ), and persistent desert zone (PDZ) in Central AsiaRaster data for spatial distribution of persistent grassland zone (PGZ), transition zone (TZ), and persistent desert zone (PDZ) in Central Asia. The resolution is 463 m, and the coordinate system is Sinusoidal. 1, 2, and 3 mean persistent grassland zone (PGZ), transition zone (TZ), and persistent desert zone (PDZ), respectively.Data3_cenAsia_ThreeZones.tifSpatial patterns of Linear Regression trend and Mann-Kendall trend of Enhanced Vegetation Index (EVI) during thermal growing season (EVITGS) in Central AsiaThe raster layers include spatial distribution of Linear Regression trend, Mann-Kendall trend of Enhanced Vegetation Index (EVI), Linear Regression-based P value, and Mann-Kendall-based P value during thermal growing season (EVITGS) in Central Asia over the period 2000-2014. The values of raster layers are the slope of EVITGS change. The scaling factor is 0.0001. The resolution is 463 m, and the coordinate system is Sinusoidal.Data4_EVItrend.rar