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Journal of Arid Land  2020, Vol. 12 Issue (4): 594-608    DOI: 10.1007/s40333-020-0057-y
Research article     
Land degradation sensitivity assessment and convergence analysis in Korla of Xinjiang, China
Jinchen DING, Yunzhi CHEN*(), Xiaoqin WANG, Meiqin CAO
Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China
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Land degradation has a major impact on environmental and socio-economic sustainability. Scientific methods are necessary to monitor the risk of land degradation. In this study, the environmental sensitive area index (ESAI) was utilized to assess land degradation sensitivity and convergence analysis in Korla, a typical oasis city in Xinjiang of China, which is located on the northeast border of the Tarim Basin. A total of 18 indicators depicting soil, climate, vegetation, and management qualities were used to illustrate spatial-temporal patterns of land degradation sensitivity from 1994 to 2018. We investigated the causes of spatial convergence and divergence based on the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results show that the branch of the Tianshan Mountains and oasis plain had a low sensitivity to land degradation, while the Tarim Basin had a high risk of land degradation. More than two-thirds of the study area can be categorized as "critical" sensitivity classes. The largest percentage (32.6%) of fragile classes was observed for 2006. There was no significant change in insensitive or low-sensitivity areas, which accounted for less than 0.4% of the entire observation period. The ESAI of the four time periods (1994-1998, 1998-2006, 2006-2010, and 2010-2018) formed a series of convergence patterns. The convergence patterns of 1994-1998 and 1998-2006 can be explained by the government's efforts to "Returning Farmland to Forests" and other governance projects. In 2006-2010, the construction of afforested work intensified, but industrial development and human activities affected the convergence pattern. The pattern of convergence in most regions between 2010 and 2018 can be attributed to the government's implementation of a series of key ecological protection projects, which led to a decrease in sensitivity to land degradation. The results of this study altogether suggest that the ESAI convergence analysis is an effective early warning method for land degradation sensitivity.

Key wordsland degradation      quality index      convergence analysis      remote sensing      environmental sensitive area index      Korla     
Received: 08 August 2019      Published: 10 July 2020
Corresponding Authors: Yunzhi CHEN     E-mail:
About author: *Corresponding author: CHEN Yunzhi (E-mail:
Cite this article:

DING Jinchen, CHEN Yunzhi, WANG Xiaoqin, CAO Meiqin. Land degradation sensitivity assessment and convergence analysis in Korla of Xinjiang, China. Journal of Arid Land, 2020, 12(4): 594-608.

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Fig. 1 Landsat TM imagery of the study area (Bands 5-4-3)
Indicator Parameter Class Description Score
SQI Texture 1 Loamy, sandy clay loam, and clay loam 1.0
2 Sandy clay and silt loam 1.2
3 Silt, clay, and silt clay 1.6
4 Sand 2.0
Slope (%) 1 <6 1.0
2 6-18 1.2
3 18-35 1.5
4 >35 2.0
Surface albedo 1 Somber 1.0
2 Moderately bright 1.5
3 Bright 2.0
Soil dryness 1 Low 1.0
2 Moderate 1.3
3 High 1.6
4 Very high 2.0
Soil salinity 1 None or slight 1.0
2 Moderate 1.4
3 Severe 1.7
4 Very severe 2.0
Soil moisture 1 Very high 1.0
2 High 1.3
3 Moderate 1.7
4 Low 2.0
Table 1 Calculation parameters of soil quality index (SQI)
Indicator Parameter Class Description Score
CQI Precipitation (mm) 1 >1000 1.0
2 650-1000 1.3
3 280-650 1.6
4 <280 2.0
Aridity index (mm/mm) 1 Humid (>0.65) 1.0
2 Dry sub-humid (0.50-0.65) 1.2
3 Semi-arid (0.20-0.50) 1.5
4 Arid (0.03-0.20) 1.7
5 Hyper-arid (<0.03) 2.0
Aspect 1 N, NE, NW, E, and flat areas 1.0
2 S, SE, SW, and W 2.0
Wind speed (m/s) 1 <2.0 1.0
2 2.0-3.5 1.3
3 3.5-4.5 1.6
4 >4.5 2.0
Table 2 Calculation parameters of climate quality index (CQI)
Indicator Parameter Class Description Score
VQI Fire risk 1 Agricultural crops, water, wetland, bare land, and urban areas 1.0
2 Sparse vegetation 1.3
3 Shrubland and grassland 1.6
4 Evergreen forest and conifer forest 2.0
Erosion protection 1 Evergreen forest, conifer forest, and urban areas 1.0
2 Shrubland and grassland 1.3
3 Sparse vegetation 1.6
4 Agricultural crops 1.8
5 Water, wetland, and bare land 2.0
Drought resistance 1 Water, wetland, and urban areas 1.0
2 Evergreen forest and conifer forest 1.2
3 Shrubland and grassland 1.4
4 Agricultural crops 1.7
5 Sparse vegetation and bare land 2.0
Vegetation coverage (%) 1 >75 1.0
2 50-75 1.3
3 25-50 1.5
4 10-25 1.8
5 <10 2.0
Table 3 Calculation parameters of vegetation quality index (VQI)
Indicator Parameter Class Description Score
MQI GDP (gross output value/km2) 1 <500 1.0
2 500-1000 1.2
3 1000-1500 1.4
4 1500-2000 1.6
5 2000-4000 1.8
6 >4000 2.0
Population density (people/km2) 1 <20 1.0
2 20-60 1.2
3 60-100 1.4
4 100-150 1.6
5 150-200 1.8
6 >200 2.0
Agricultural intensity 1 Evergreen forest, conifer forest, urban areas, water, wetland, bare land, and sparse vegetation 1.0
2 Grassland and shrubland 1.5
3 Agricultural crops 2.0
Policy enforcement 1 Urban areas 1.0
2 Agricultural crops, water, wetland, grassland, and shrubland 1.5
3 Evergreen forest, conifer forest, sparse vegetation, and bare land 2.0
Table 4 Calculation parameters of management quality index (MQI)
Score range Class Description
<1.17 N (non-affected) Insensitive to land degradation
1.17-1.22 P (potential) Low sensitivity to land degradation
1.23-1.26 F1 (fragile 1) Moderately sensitive to land degradation
1.27-1.32 F2 (fragile 2) Moderately sensitive to land degradation
1.33-1.37 F3 (fragile 3) Moderately sensitive to land degradation
1.38-1.41 C1 (critical 1) Highly or very highly sensitive to land degradation
1.42-1.53 C2 (critical 2) Highly or very highly sensitive to land degradation
>1.53 C3 (critical 3) Highly or very highly sensitive to land degradation
Table 5 Classes and corresponding ranges of environmental sensitive area index (ESAI)
Fig. 2 Spatial distributions of ESAI (environmental sensitive area index) in 1994 (a), 1998 (b), 2006 (c), 2010 (d), and 2018 (e), and spatial distribution of mean ESAI from 1994 to 2018 (f), as represented by different sensitivity classes of ESAI. The description of sensitivity classes is shown in Table 5.
Class Area percentage (%)
1994 1998 2006 2010 2018
Non-affected 0.04 0.04 0.03 0.00 0.02
Potential 0.15 0.33 0.39 0.03 0.31
Fragile 1 0.41 1.50 2.35 0.40 1.22
Fragile 2 4.91 9.02 13.89 6.97 7.38
Fragile 3 9.07 14.98 16.36 13.83 14.74
Critical 1 12.77 15.42 15.14 12.00 13.25
Critical 2 53.75 52.64 49.96 47.75 45.49
Critical 3 18.90 6.07 1.88 19.02 17.59
Table 6 Area percentages of ESAI sensitivity classes in different years
Fig. 3 Spatial distribution of ESAI in 2018 (represented by different sensitivity classes; a) and the field survey photos of land degradation sensitivity areas (b, Tamarix ramosissima; c, cotton plantations; d, extensive desert areas; and e, saline-alkali soil)
Fig. 4 Spatial trend surface analysis of land degradation sensitivity in 1994 (a), 1998 (b), 2006 (c), 2010 (d), and 2018 (e)
Fig. 5 Spatial distributions of coefficients and local R2 values of the ESAI spatial convergence analysis from the geographically weighted regression (GWR) model over the time intervals of 1994-1998 (a1 and a2), 1998-2006 (b1 and b2), 2006-2010 (c1 and c2), and 2010-2018 (d1 and d2)
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