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Journal of Geographical Sciences    2018, Vol. 28 Issue (12) : 1933-1952     DOI: 10.1007/s11442-018-1572-z
Research Articles |
Visualization and quantification of significant anthropogenic drivers influencing rangeland degradation trends using Landsat imagery and GIS spatial dependence models: A case study in Northeast Iran
OMID Abdi(),ZEINAB Shirvani,MANFRED F. Buchroithner
Institute for Cartography, Geosciences Department, TU Dresden, Germany
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Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main steps: (1) conducting a trend analysis of rangeland degradation and anthropogenic dynamics in 1986-2000 and 2000-2015, (2) visualizing the effects of anthropogenic drivers on rangeland degradation using bivariate local spatial autocorrelation (BiLISA), and (3) quantifying spatial dependence between anthropogenic driving forces and rangeland degradation using spatial regression approaches. The results show that 0.77% and 0.56% of rangelands are degraded annually during the first and second periods. The BiLISA results indicate that dry-farming, irrigated farming and construction areas were significant drivers in both periods and grazing intensity was a significant driver in the second period. The spatial lag (SL) model (wi=0.3943, Ei=1.4139) with two drivers of dry-farming and irrigated farming in the first period and the spatial error (SE) model (wi=0.4853, Ei=1.515) with livestock density, dry-farming and irrigated farming in the second period showed robust performance in quantifying the driving forces of rangeland degradation. To conclude, the BiLISA maps and spatial models indicate a serious intensification of the anthropogenic impacts of ongoing conditions on the rangelands of northeast Iran in the future.

Keywords rangeland degradation      Landsat      GIS      anthropogenic driving forces      BiLISA      spatial regression     
Issue Date: 27 December 2018
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ZEINAB Shirvani
MANFRED F. Buchroithner
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OMID Abdi,ZEINAB Shirvani,MANFRED F. Buchroithner. Visualization and quantification of significant anthropogenic drivers influencing rangeland degradation trends using Landsat imagery and GIS spatial dependence models: A case study in Northeast Iran[J]. Journal of Geographical Sciences, 2018, 28(12): 1933-1952.
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Figure 1  The procedure of GCPs collecting for (a) 1986 on the color composite image of Landsat 5, (b) 2000 on the merged image of Landsat 7, (c) 2015 on the merged image of Landsat 8, and (d) location of the study area and an example view
Figure 2  The procedures of visualization and quantification of degraded rangeland: (A) data preprocessing and image enhancement, (B) GCPs and training set selection, (C) image classification and land-cover change detection, (D) creation of a database for Rangeland Management Plans (RMPs), and (E) spatial autocorrelation approaches.
Figure 3  The expansion of anthropogenic land-cover and livestock density from 1986 to 2000 (a, c) and from 2000 to 2015 (b, d) in the RMPs
Figure 4  LISA clustering during the period of 1986-2000 (a) and the period of 2000-2015 (b) with p < 0.05
Figure 5  BiLISA clustering showing driving forces (dry-farming, irrigated farming, construction areas, orchards, water bodies and livestock density) influencing degradation of rangelands with four categories of High-High (a, b), High-Low (c, d), Low-High (e, f) and Low-Low (h, I) in the periods of 1986-2000 and 2000-2015 (b) with p < 0.05
1986-2000 2000-2015
Drivers Local
Moran’s I
Number of RMPs Degradation rate (%) Local Moran’s I Cluster/ Outlier Number
of RMPs
Degradation rate (%)
Dry-farming (DF) 0.533*** High-High 16 22

High-High 22 17.31
Low-Low 40 1.30 Low-Low 30 0.89
Low-High 4 8.10 Low-High 7 0.54
High-Low 0 0 High-Low 1 3.27
Not Significant 118 9 Not Significant 118 4.04
Irrigated Farming (IF) 0.200*** High-High 9 16.92

High-High 7 20.77
Low-Low 17 0.89 Low-Low 24 0.97
Low-High 7 3.46 Low-High 5 0.93
High-Low 1 14.38 High-Low 2 11.64
Not Significant 144 10.67 Not Significant 140 7.33
Orchards (OR) -0.00987ns High-High 3 11.96

High-High 1 13.69
Low-Low 0 0 Low-Low 0 0
Low-High 7 1.40 Low-High 8 0.58
High-Low 43 19.36 High-Low 25 17.38
Not Significant 125 3.86 Not Significant 144 2.73
Construction Area (CA) 0.0754* High-High 4 14.33

High-High 7 17.58
Low-Low 1 2.03 Low-Low 5 0.54
Low-High 10 1.65 Low-High 10 2.25
High-Low 10 17.97 High-Low 0 0
Not Significant 153 9.86 Not Significant 156 6.14
Water Body (WB) - High-High - -

High-High 3 7.29
Low-Low - - Low-Low 1 0.048
Low-High - - Low-High 9 0.82
High-Low - - High-Low 13 13.57
Not Significant - - Not Significant 152 6.34

Livestock Density (LD)

High-High 7 14.47

High-High 6 29.28
Low-Low 8 2.01 Low-Low 15 0.58
Low-High 3 15.98 Low-High 4 0.67
High-Low 1 17.83 High-Low 3 8.38
Not Significant 159 9.71 Not Significant 150 7.30
Table 1  Bivariate local Moran’s I statistics: spatial autocorrelation between degradation of rangelands and expansion of anthropogenic drivers (empirical pseudo significance based on 999 random permutations)
Ordinary Least Squares Spatial Lag Spatial Error
Model estimation Coefficient (β) t-
Standard Error Coefficient (β) z-Statistic Standard Error Coefficient (β) z-Statistic Standard Error
Constant (β0) 24.40ns 1.27 19.15 14.63ns 0.751 19.49 23.353ns 1.183 19.736
Livestock density (x1) -4.43ns -0.578 7.663 -6.266ns 7.599 -0.825 -3.579ns -0.468 7.642
Dry-farming (x2) 0.908*** 39.008 .0233 0.877*** 29.543 0.029 0.905*** 37.824 0.024
Irrigated farming (x3) 1.004*** 24.234 0.041 0.991*** 24.013 0.041 1.002*** 24.121 0.041
Orchards (x4) 0.695ns 0.598 1.162 0.606ns 0.534 1.135 0.633ns 0.553 1.144
Construction areas (x5) 1.917ns 0.837 2.291 2.083ns 0.931 2.236 2.092ns 0.923 2.266
?wy - - - 0.0502* 1.611 0.031 - - -
Lambda (?wv) - - - 0.078ns 0.775 0.100
The model fit criteria Coefficient Coefficient Coefficient
AICc 2339.371 2338.643 2338.955
Log likelihood -1163.685 -1163.321 -1163.477
wi 0.2789 0.3943 0.3266
Ei**** 1.000 1.4139 1.1710
Table 2  Regression analyses of spatial dependencies between degradation rates of rangelands with expansion rates of anthropogenic drivers and livestock density in 1986-2000
Ordinary Least Squares Spatial Lag Spatial Error
Model estimation Coefficient (β) t-
Standard Error Coefficient (β) z-
Standard Error Coefficient (β) z-
Standard Error
Constant (β0) -2.004ns -0.0811 24.692 -8.783ns -0.355 24.755 -20.536ns -0.625 32.868
Livestock density
8.474* 2.318 3.655 8.182* 2.262 3.617 12.236*** 3.445 3.552
Dry-farming (x2) 0.879*** 21.917 0.0401 0.853*** 18.298 0.047 0.878*** 20.836 0.042
Irrigated farming
-0.162ns -0.134 1.212 -0.213ns -0.176 1.202 1.997* 1.729 1.155
Orchards (x4) -12.993ns -0.415 31.285 -12.287ns -0.402 30.571 -2.805ns -0.094 29.799
Construction areas (x5) 5.283ns 0.259 20.373 6.011ns 0.302 19.908 5.674ns 0.295 19.225
Water bodies (x6) 0.656ns 0.935 0.702 0.640ns 0.933 0.687 -0.220ns -0.341 0.647
ρwy - - - 0.050 ns 0.977 0.051 - - -
Lambda (λwv) - - - - - - 0.369*** 4.304 0.086
The model
fit criteria
Coefficient Coefficient Coefficient
AICc 2503.395 2502.560 2502.545
Log likelihood -1244.281 -1243.780 -1243.272
wi 0.3202 0.4833 0.4853
Ei**** 1.000 1.509 1.515
Table 3  Regression analyses of spatial dependencies between degradation rates of rangelands with expansion rates of anthropogenic drivers and livestock density in 2000-2015
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