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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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Abstract  

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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OMID Abdi
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.
URL:  
http://www.geogsci.com/EN/10.1007/s11442-018-1572-z     OR     http://www.geogsci.com/EN/Y2018/V28/I12/1933
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
Cluster/
Outlier
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

0.509***
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

0.157***
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

-0.0273ns
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

0.293***
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 - -

-0.0407ns
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)


0.005ns
High-High 7 14.47

0.0601ns
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-
Statistic
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-
Statistic
Standard Error Coefficient (β) z-
Statistic
Standard Error Coefficient (β) z-
Statistic
Standard Error
Constant (β0) -2.004ns -0.0811 24.692 -8.783ns -0.355 24.755 -20.536ns -0.625 32.868
Livestock density
(x1)
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
(x3)
-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
[1] Aguiar A P D, Camara G, Escada M I S, 2007. Spatial statistical analysis of land-use determinants in the Brazilian Amazonia: Exploring intra-regional heterogeneity.Ecological Modelling, 209(2-4): 169-188. doi: 10.1016/ j.ecolmodel.2007.06.019.http://linkinghub.elsevier.com/retrieve/pii/S0304380007003377
doi: 10.1016/j.ecolmodel.2007.06.019
[2] Anderson J R, 1976. A land use and land cover classification system for use with remote sensor data. Washington: U.S. US Government Printing Office.
[3] Angassa A, 2014. Effects of grazing intensity and bush encroachment on herbaceous species and rangeland condition in southern Ethiopia.Land Degradation & Development, 25: 438-451. doi: 10.1002/ldr.2160.http://onlinelibrary.wiley.com/doi/10.1002/ldr.2160/pdf
doi: 10.1002/ldr.2160
[4] Ansari N, Fayaz M, Ghasemi M H, 2009a. Estimate of Irano-Turanian zone rangelands degradation rate by measuring and suggestion index.Iranian journal of Range and Desert Research, 16(3): 293-304. (in Persian)http://www.cabdirect.org/abstracts/20103093372.html
doi: 10.1109/CEIDP.2002.1048843
[5] Ansari N, Seyed Akhlaghi Shal S, Ghasemi M, 2009b. Determination of socio-economic factors on natural resources degradation of Iran.Iranian Journal of Range and Desert Research, 15(4): 508-524. (in Persian)http://www.cabdirect.org/abstracts/20093216669.html
[6] Anselin L, Rey S J, 2014. GeoDa:Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL Paperback. Geoda Press LLC, USA, 394pp.
[7] Anselin L, Syabri I, Kho Y, 2006. GeoDa: An introduction to spatial data analysis.Geographical Analysis, 38: 5-22.http://www.blackwell-synergy.com/toc/gean/38/1
doi: 10.1111/gean.2006.38.issue-1
[8] Badripour H, Eskandari N, Rezaei S A, 2006. Rangelands of Iran, an overview. Ministry of Jihad-e-Agriculture, Forest Range and Watershed Management Organization, Technical Office of Rangeland. Tehran: Pooneh. (in Persian)
[9] Basnet B, Vodacek A, 2015. Tracking land use/land cover dynamics in cloud prone areas using moderate resolution satellite data: A case study in Central Africa.Remote Sensing, 7: 6683-6709. doi: 10.3390/rs70606683.http://www.mdpi.com/2072-4292/7/6/6683
doi: 10.3390/rs70606683
[10] Bedunah D J, Angerer J P, 2012. Rangeland degradation, poverty, and conflict: How can rangeland scientists contribute to effective responses and solutions?Rangeland Ecology & Management, 65(6): 606-612. doi: 10.2111/REM-D-11-00155.1.http://www.jstor.org/stable/23355250
doi: 10.2111/REM-D-11-00155.1
[11] Bobek H, 1962. Iran: Probleme eines unterentwickelten Landes alter Kultur. Themen zur Geographie und Gemeinschaftskunde, 74.
[12] Bouziani M, Go?ta K, He D C, 2010. Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge.ISPRS Journal of Photogrammetry and Remote Sensing, 65: 143-153. doi: 10.1016/j.isprsjprs.2009.10.002.https://linkinghub.elsevier.com/retrieve/pii/S092427160900121X
doi: 10.1016/j.isprsjprs.2009.10.002
[13] Burnham K P, Anderson D R, 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. Springer Science & Business Media.
[14] Cameron D R, Marty J, Holland R F, 2014. Whither the rangeland? Protection and conversion in California’s rangeland ecosystems.PLoS One, 9(8): e103468. doi: 10.1371/journal.pone.0103468.http://dx.plos.org/10.1371/journal.pone.0103468
doi: 10.1371/journal.pone.0103468 pmid: 25141171
[15] Canty M J, 2014. Image Analysis, Classification and Change Detection in Remote Sensing:With Algorithms for ENVI/IDL and Python. Boca Raton: CRC Press, 576pp.http://dl.acm.org/citation.cfm?id=1823465
doi: 10.1080/13658810802029470
[16] Chen Y, Chang K, Han Fet al., 2016. Investigating urbanization and its spatial determinants in the central districts of Guangzhou, China.Habitat International, 51: 59-69. doi: 10.1016/j.habitatint.2015.10.013.https://linkinghub.elsevier.com/retrieve/pii/S0197397515002192
doi: 10.1016/j.habitatint.2015.10.013
[17] Churches C E, Wampler P J, Sun Wet al., 2014. Evaluation of forest cover estimates for Haiti using supervised classi?cation of Landsat data.International Journal of Applied Earth Observation and Geoinformation, 30: 203-216. doi: 10.1016/j.jag.2014.01.020.https://linkinghub.elsevier.com/retrieve/pii/S0303243414000300
doi: 10.1016/j.jag.2014.01.020
[18] Cliff A D, Ord J K, 1973. Spatial Autocorrelation. London: Pion Limited.
[19] Cohen J, 1960. A coefficient of agreement for nominal scales.Educational and Psychological Measurement, 20: 37-46.http://journals.sagepub.com/doi/10.1177/001316446002000104
doi: 10.1177/001316446002000104
[20] Congalton R G, Green K, 2009. Assessing the Accuracy of Remotely Sensed Data:Principles and Practices. 2nd ed. Boca Raton: CRC Press, 200pp.
[21] Coppin P, Jonckheere I, Nackaerts Ket al., 2004. Review article digital change detection methods in ecosystem monitoring: A review.International Journal of Remote Sensing, 25: 1565-1596. doi: 10.1080/ 0143116031000101675.https://www.tandfonline.com/doi/full/10.1080/0143116031000101675
doi: 10.1080/0143116031000101675
[22] Cressie N A C, Wikle C K, 2011. Statistics for Spatio-Temporal Data. Hoboken, NJ: Wiley, 624pp.http://www.tandfonline.com/doi/abs/10.1080/09332480.2014.914769
doi: 10.1080/09332480.2014.914769
[23] Daffertshofer A, Lamoth C J C, Meijer O Get al., 2004. PCA in studying coordination and variability: A tutorial.Clinical Biomechanics, 19: 415-428. doi: 10.1016/j.clinbiomech.2004.01.005.http://linkinghub.elsevier.com/retrieve/pii/S0268003304000166
doi: 10.1016/j.clinbiomech.2004.01.005 pmid: 15109763
[24] Davudirad A K, Sadeghi S H, Sadoddin A, 2016. The impact of development plans on hydrological changes in the Shazand watershed, Iran.Land Degradation & Development, 27(4): 1236-1244. doi: 10.1002/ldr.2523.http://onlinelibrary.wiley.com/doi/10.1002/ldr.2523/pdf
doi: 10.1002/ldr.2523
[25] DEI, 2003. Iran’s Initial National Communication to UNFCCC. Department of Environment and United Nation Department Programme: Tehran, 206pp.
[26] DEI, 2010. Iran second national communication to UNFCCC. National Climate Change Office of Iran at the Department of Environment: Tehran, 205pp.
[27] Elias M, Hensel O, Richter Uet al., 2015. Land conversion dynamics in the Borana rangelands of Southern Ethiopia: an integrated assessment using remote sensing techniques and field survey data. Environments,7; 2(1):1-31. doi: 10.3390/environments2010001.
[28] Erener A, Düzgün H S, 2009. A methodology for land use change detection of high resolution pan images based on texture analysis.Italian Journal of Remote Sensing, 41: 47-59.
[29] Eskandari Z, Chavoshi S, 2002. Effects of livestock management on the erosion control: Case study of Isfahan Rangelands.Iranian Journal of Rang and Desert Research, 9: 943-957.
[30] Fortin M-J, Dale M R T, 2005. Spatial Analysis:A Guide for Ecologists. Cambridge: Cambridge University Press, 1-30.
[31] Gaitán J J, Bran D E, Geng Jet al., 2017. Aridity and overgrazing have convergent effects on ecosystem structure and functioning in Patagonian rangelands.Land Degradation & Development, 29(2): 210-218. doi: 10.1002/ldr.2694.http://onlinelibrary.wiley.com/doi/10.1002/ldr.2694/pdf
doi: 10.1002/ldr.2694
[32] Gangkofner U G, Pradhan P S, Holcomb D W, 2008. Optimizing the high-pass filter addition technique for image fusion.Photogrammetric Engineering and Remote Sensing, 74(9): 1107-1118. doi: https://doi.org/10.14358/ PERS.73.9.1107.http://openurl.ingenta.com/content/xref?genre=article&amp;issn=0099-1112&amp;volume=74&amp;issue=9&amp;spage=1107
doi: 10.14358/PERS.73.9.1107
[33] Giri C P, 2012. Remote Sensing of Land Use and Land Cover:Principles and Applications. Boca Raton: CRC Press, 477pp.
[34] Guo L, Chehata N, Mallet Cet al., 2011. Relevance of airborne lidar and multispectral image data for urban scene classification using random forests.ISPRS Journal of Photogrammetry and Remote Sensing, 66(1): 56-66. doi: 10.1016/j.isprsjprs. 2010.08.007.https://linkinghub.elsevier.com/retrieve/pii/S0924271610000705
doi: 10.1016/j.isprsjprs.2010.08.007
[35] Hansen M C, Loveland T R, 2012. A review of large area monitoring of land cover change using Landsat data.Remote Sensing of Environment, 122: 66-74. doi: 10.1016/j.rse.2011.08.024.https://linkinghub.elsevier.com/retrieve/pii/S0034425712000314
doi: 10.1016/j.rse.2011.08.024
[36] Hein L, De Ridder N, 2006. Desertification in the Sahel: A reinterpretation.Global Change Biology, 12: 751-758. doi: 10.1111/j.1365-2486.2006.01135.x.http://www.blackwell-synergy.com/toc/gcb/12/5
doi: 10.1111/j.1365-2486.2006.01135.x
[37] Hoekstra J M, Boucher T M, Ricketts T Het al., 2005. Confronting a biome crisis: Global disparities of habitat loss and protection.Ecology Letters, 8: 23-29. doi: 10.1111/j.1461-0248.2004.00686.x.http://onlinelibrary.wiley.com/doi/10.1111/j.1461-0248.2004.00686.x/full
doi: 10.1111/j.1461-0248.2004.00686.x
[38] Hu D, Yang G, Wu Qet al., 2008. Analyzing land use changes in the Metropolitan Jilin City of Northeastern China using remote sensing and GIS.Sensors, 8: 5449-5465. doi: 10.3390/s8095449.http://www.mdpi.com/1424-8220/8/9/5449
doi: 10.3390/s8095449 pmid: 3705513
[39] Huete A R, 1988. A soil-adjusted vegetation index (SAVI).Remote Sensing of Environment, 25: 295-309. doi: 10.1016/0034- 4257(88)90106-X.http://linkinghub.elsevier.com/retrieve/pii/003442578890106X
doi: 10.1016/0034-4257(88)90106-X
[40] Hurvich C, Tsai C L, 1989. Regression and time series model selection in small samples.Biometrika, 76: 297-307. doi: 10.1093/biomet/76.2.297.https://academic.oup.com/biomet/article-lookup/doi/10.1093/biomet/76.2.297
doi: 10.2307/2336663
[41] Hussain M, Chen D, Cheng Aet al., 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches.ISPRS Journal of Photogrammetry and Remote Sensing, 80: 91-106. doi: 10.1016/j.isprsjprs.2013.03.006.https://linkinghub.elsevier.com/retrieve/pii/S0924271613000804
doi: 10.1016/j.isprsjprs.2013.03.006
[42] Jamshidi A R, Amini A M, 2013. Evaluation of factors affecting on natural resource degradation from the viewpoint of expert management of natural resources in Ilam province.Journal of Conservation and Utilization of Natural Resources, 1(4): 91-105. (in Persian)
[43] Kalkhan M A, 2011. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping. CRC Pres, 184pp.http://www.crcpress.com/product/isbn/9781420069778
[44] Kardavani P, 2015. Pastures,Problems and Solutions in Iran. 7th ed. Tehran: University of Tehran Press (UTP), 540pp.
[45] Khorshidi M, Ansari N, 2003. Understanding rural and nomadic communities’ knowledge of natural resources degradation and its affecting factors in the Bazoft Dehestan of the Chaharmahal and Bakhtiari province,Iranian Journal of Range and Desert Research, 10(1): 95-109. (in Persian)
[46] Kiage L M, 2013. Perspectives on the assumed causes of land degradation in the rangelands of Sub-Saharan Africa.Progress in Physical Geography, 37(5): 664-684. doi: 10.1177/0309133313492543.http://journals.sagepub.com/doi/10.1177/0309133313492543
doi: 10.1177/0309133313492543
[47] Lesschen J P, Verburg P H, 2005. Statistical methods for analysing the spatial dimension of changes in land use and farming systems. LUCC Report Series No.7, LUCC Focus 3 Office and ILRI, 78pp.http://agris.fao.org/agris-search/search.do?recordID=NL2012024181
[48] Li C, Wang J, Wang Let al., 2014. Comparison of classification algorithms and training sample sizes in urban land classification with Landsat Thematic Mapper Imagery.Remote Sensing, 6(2): 964-983. doi: 10.3390/ rs6020964.http://www.mdpi.com/2072-4292/6/2/964
doi: 10.3390/rs6020964
[49] Li X L, Gao J, Brierley Get al., 2013. Rangeland degradation on the Qinghai-Tibet Plateau: Implications for rehabilitation.Land Degradation & Development, 24: 72-80. doi: 10.1002/ldr.1108.http://onlinelibrary.wiley.com/doi/10.1002/ldr.1108/full
doi: 10.1002/ldr.1108
[50] Lloyd C D, 2010. Local Models for Spatial Analysis. 2nd ed. Boca Raton: CRC Press, 352pp.
[51] Lu D, Mausel P, Brondízio Eet al., 2004. Change detection techniques.International Journal of Remote Sensing, 25(12): 2365-2401. doi: 10.1080/0143116031000139863.https://www.tandfonline.com/doi/full/10.1080/0143116031000139863
doi: 10.1080/0143116031000139863
[52] Lu D, Weng Q, Moran E et al., 2011. Remote Sensing Image Classification. Boca Raton: CRC Press, 219-240.
[53] Mahyou H, Tychon B, Balaghi Ret al., 2016. A knowledge-based approach for mapping land degradation in the arid rangelands of north Africa.Land Degradation & Development, 27: 1574-1585. doi: 10.1002/ldr.2470.http://onlinelibrary.wiley.com/doi/10.1002/ldr.2470/pdf
doi: 10.1002/ldr.2470
[54] Miehe S, Kluge J, von Wehrden Het al., 2010. Long-term degradation of Sahelian rangeland detected by 27 years of field study in Senegal.Journal of Applied Ecology, 47: 692-700. doi: 10.1111/j.1365-2664.2010.01815.x.http://blackwell-synergy.com/doi/abs/10.1111/jpe.2010.47.issue-3
doi: 10.1111/j.1365-2664.2010.01815.x
[55] Otukei J R, Blaschke T, 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms.International Journal of Applied Earth Observation and Geoinformation, 12S: S27-S31. doi: 10.1016/j.jag.2009.11.002.http://www.sciencedirect.com/science/article/pii/S0303243409001135
doi: 10.1016/j.jag.2009.11.002
[56] Overmars K P, de Koning G H J, Veldkamp A, 2003. Spatial autocorrelation in multi-scale land use models.Ecological Modelling, 164: 257-270. doi: 10.1016/S0304-3800(03)00070-X.http://linkinghub.elsevier.com/retrieve/pii/S030438000300070X
doi: 10.1016/S0304-3800(03)00070-X
[57] Oyana T J, Margai F, 2015. Spatial Analysis:Statistics, Visualization, and Computational Methods. Boca Raton: CRC Press, 323pp.
[58] Rao C R, 2002. Linear Statistical Inference and Its Applications. 2nd ed. New York: Wily, 656pp.http://www.ams.org/mathscinet-getitem?mr=221616
doi: 10.2307/2987205
[59] Richter R, 2001. Atmospheric and Topographic Correction: Model ATCOR3, DLR-IB 564-03/00, Wessling, Germany: DLR-German Aerospace Center.
[60] R?der A, Udelhoven T, Hill Jet al., 2008. Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece.Remote Sensing of Environment, 112: 2863-2875. doi: 10.1016/j.rse.2008.01.018.http://linkinghub.elsevier.com/retrieve/pii/S003442570800059X
doi: 10.1016/j.rse.2008.01.018
[61] Serneels S, Lambin E F, 2001. Proximate causes of land-use change in Narok District, Kenya: A spatial statistical model.Agriculture, Ecosystems and Environment, 85: 65-81. doi: 10.1016/S0167-8809(01)00188-8.http://www.sciencedirect.com/science/article/pii/S0167880901001888
doi: 10.1016/S0167-8809(01)00188-8
[62] Shahraki M R, Barani H, 2012. Examining factors on the destruction of Golestan province rangelands.Journal of Conservation and Utilization of Natural Resources, 1(3): 59-78. (in Persian)
[63] Sheehy D P, Damiran D, 2012. Assessment of Mongolian rangeland condition and trend (1997-2009). Final Report for the World Bank and the Netherlands-Mongolia Trust Fund for Environmental Reform (NEMO), 0-47.
[64] Tabari H, AghaKouchak A, Willems P, 2014. A perturbation approach for assessing trends in precipitation extremes across Iran.Journal of Hydrology, 519: 1420-1427. doi: 10.1016/j.jhydrol.2014.09.019.https://linkinghub.elsevier.com/retrieve/pii/S0022169414007008
doi: 10.1016/j.jhydrol.2014.09.019
[65] The Presidential Deputy Office of Strategic Planning and Control(PDOSPC), 2011. Law for the Fifth Development Plan of the Islamic Republic of Iran (IR038). Retrieved from .http://www.wipo.int
[66] Thomlinson J R, Bolstad P V, Cohen W B, 1999. Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products.Remote Sensing of Environment, 70: 16-28. doi: 10.1016/S0034-4257(99)00055-3.http://linkinghub.elsevier.com/retrieve/pii/S0034425799000553
doi: 10.1016/S0034-4257(99)00055-3
[67] Turner B L, 2002. Toward integrated land-change science: Advances in 1.5 decades of sustained international research on land-use and land-cover change. In: Challenges of a Changing Earth. Berlin, Heidelberg: Springer, 21-26.
[68] Ward M D, Gleditsch K S, 2008. Spatial Regression Models (Vol. 155). Thousand Oaks, CA: Sage, 112.
[69] White R P, Wanasselt W, 2000. Grasslands in Pieces: Modification and Conversion Take a Toll. World Resource Institute: Washington,DC, USA, 1-4.
[70] Wilber D N, 2014. Iran,Past and Present:From Monarchy to Islamic Republic (Vol. 529).Princeton University Press, 398pp.http://www.jstor.org/stable/j.ctt7ztp39
doi: 10.1515/9781400857470
[71] Yuan F, Sawaya K E, Loeffelholz B Cet al., 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing.Remote sensing of Environment, 98: 317-328. doi: 10.1016/j.rse.2005.08.006.http://linkinghub.elsevier.com/retrieve/pii/S0034425705002646
doi: 10.1016/j.rse.2005.08.006
[72] Zhang R, Du Q, Geng Jet al., 2015. An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study.Habitat International, 46: 196-205. doi: 10.1016/j.habitatint.2014.12.001.https://linkinghub.elsevier.com/retrieve/pii/S0197397514002069
doi: 10.1016/j.habitatint.2014.12.001
[73] Zhou Y, Wang Y, Gold A Jet al., 2014. Assessing impact of urban impervious surface on watershed hydrology using distributed object-oriented simulation and spatial regression.GeoJournal, 79: 155-166. doi: 10.1007/ s10708-013-9506-x.http://link.springer.com/10.1007/s10708-013-9506-x
doi: 10.1007/s10708-013-9506-x
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