L. Chiang, Correlation of fuzzy sets, Fuzzy Sets and Systems, vol.102, issue.2, pp.221-226, 1991.
DOI : 10.1016/S0165-0114(97)00127-9

. Choi, Reliability-based Structural Design, 2006.

. Chutta, Sensitivity analysis of atmospheric dispersion model-Rimpuff using Hartley-like measure, 2013.

. Cihlar, ]. J. Jansen, L. J. Cihlar, and . Jansen, From Land Cover to Land Use: A Methodology for Efficient Land Use Mapping over Large Areas, The Professional Geographer, vol.53, issue.2, pp.275-289, 2001.
DOI : 10.1080/00330124.2001.9628460

. Clarke, A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area, Environment and Planning B: Planning and Design, vol.24, issue.2, pp.247-261, 1997.
DOI : 10.1068/b240247

. Clarke, ]. K. Gaydos, L. Clarke, and . Gaydos, Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore, International Journal of Geographical Information Science, vol.60, issue.7, pp.699-714, 1998.
DOI : 10.1017/S0003975600001132

. Cockx, Quantifying uncertainty in remote sensing-based urban land-use mapping, International Journal of Applied Earth Observation and Geoinformation, vol.31, pp.154-166, 2014.
DOI : 10.1016/j.jag.2014.03.016

. Crosettoa, Uncertainty propagation in models driven by remotely sensed data, Remote Sensing of Environment, vol.76, issue.3, pp.373-385, 2001.
DOI : 10.1016/S0034-4257(01)00184-5

P. N. Dadhich and S. Hanaoka, Remote sensing, GIS and Markov's method for land use change detection and prediction of Jaipur district, J Geomatics, vol.4, pp.9-15, 2010.

F. De, ]. L. Stolfi, J. De-figueiredo, and . Stolfi, Affine arithmetic : Concepts and applications, Numerical Algorithms, vol.37, issue.1, pp.147-158, 2004.

]. W. Dunn and I. , A Quick Proof That the Least Squares Formulas Give a Local Minimum, The College Mathematics Journal, vol.36, issue.1, pp.64-65, 2005.
DOI : 10.2307/30044823

]. R. Eastman, Idrisi Taiga, Guide to GIS and Image Processing, Manual Version 16.02, p.342, 2009.

M. A. Ahmadi, K. F. Hikoei, and N. Rashidinia, Application of Fuzzy Decision Tree Analysis for Prediction Asphaltene Precipitation Due Natural Depletion : Case Study, Australian Journal of Basic and Applied Sciences, vol.6, pp.190-197, 2012.

]. D. Evans, An Application of Numerical Integration Techniques to Statistical Tolerancing, Technometrics, vol.9, issue.3, pp.441-456, 1967.
DOI : 10.2307/1266512

. Falahatkar, Integration of remote sensing data and GIS for prediction of land cover map, Intern. J. Geom. and Geosci, vol.1, issue.4, 2011.

. Ferchichi, An Intelligent Possibilistic Approach to Reduce the Effect of the Imperfection Propagation on Land Cover Change Prediction, International Conference on Computational Collective Intelligence, vol.22, issue.3, pp.520-529, 2015.
DOI : 10.1007/s11222-011-9274-8

URL : https://hal.archives-ouvertes.fr/hal-01226249

. Ferchichi, Towards an uncertainty reduction framework for land-cover change prediction using possibility theory, Vietnam Journal of Computer Science, vol.187, issue.57, pp.1-15, 2016.
DOI : 10.1080/1747423X.2010.519059

URL : https://hal.archives-ouvertes.fr/hal-01505793

. Ferchichi, Propagating aleatory and epistemic uncertainty in land cover change prediction process, Ecological Informatics, vol.37, pp.24-37, 2017.
DOI : 10.1016/j.ecoinf.2016.11.006

URL : https://hal.archives-ouvertes.fr/hal-01511263

. Ferchichi, Reducing uncertainties in land cover change prediction models using sensitivity analysis. Knowledge And Information Systems, 2017.
DOI : 10.1007/s10115-017-1102-9

. Ferson, ]. S. Ginzburg, L. R. Ferson, and . Ginzburg, Different methods are needed to propagate ignorance and variability, Reliability Engineering & System Safety, vol.54, issue.2-3, pp.133-144, 1996.
DOI : 10.1016/S0951-8320(96)00071-3

D. Guyonnet, B. Bourgine, D. Dubois, H. Fargier, and B. , Hybrid Approach for Addressing Uncertainty in Risk Assessments, Journal of Environmental Engineering, vol.129, issue.1, pp.68-78, 2003.
DOI : 10.1061/(ASCE)0733-9372(2003)129:1(68)

. Hagashi, ]. M. Klir, G. J. Hagashi, and . Klir, MEASURES OF UNCERTAINTY AND INFORMATION BASED ON POSSIBILITY DISTRIBUTIONS, International Journal of General Systems, vol.10, issue.4, pp.43-58, 1983.
DOI : 10.1016/S0020-7373(78)80003-0

. Haihua, SENSITIVITY ANALYSIS OF HIERARCHICAL HYBRID FUZZY - NEURAL NETWORK, International Journal on Smart Sensing and Intelligent Systems, vol.8, issue.3, pp.1837-1854, 2015.
DOI : 10.21307/ijssis-2017-832

L. Hasofer, Exact and Invariant Second Moment Code Format, Journal of the Engineering Mechanics Division, vol.100, issue.1, pp.111-121, 1974.

. Hebert, Fuzzy rank correlation between fuzzy numbers, 10th IFSA World congress, pp.224-227, 2003.

. Heijungs, ]. R. Frischknecht, R. Heijungs, and . Frischknecht, Representing Statistical Distributions for Uncertain Parameters in LCA. Relationships between mathematical forms, their representation in EcoSpold, and their representation in CMLCA (7 pp), The International Journal of Life Cycle Assessment, vol.10, issue.4, pp.248-254, 2005.
DOI : 10.1065/lca2004.09.177

]. J. Helton and F. J. Davis, Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliability Engineering & System Safety, vol.81, issue.1, pp.23-69, 2003.
DOI : 10.1016/S0951-8320(03)00058-9

. Helton, An exploration of alternative approaches to the representation of uncertainty in model predictions, Reliability Engineering & System Safety, vol.85, issue.1-3, pp.39-71, 2004.
DOI : 10.1016/j.ress.2004.03.025

. Helton, Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.1414-1434, 2006.
DOI : 10.1016/j.ress.2005.11.055

. Helton, Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.10-11, 2006.
DOI : 10.1016/j.ress.2005.11.017

. Heuvelink, Propagation of errors in spatial modelling with GIS, International journal of geographical information systems, vol.3, issue.4, pp.303-322, 1989.
DOI : 10.1007/978-1-4612-5090-6_1

H. Et-mcdermid, ]. J. Hird, and G. J. Mcdermid, Noise reduction of NDVI time series : An empirical comparison of selected techniques, Remote Sensing of Environment, vol.113, issue.1, pp.248-258, 2009.

. Jadidi, Spatial Representation of Coastal Risk: A Fuzzy Approach to Deal with Uncertainty, ISPRS International Journal of Geo-Information, vol.23, issue.3, pp.1077-1100, 2014.
DOI : 10.1007/s10584-006-0329-3

. Jakeman, Ten iterative steps in development and evaluation of environmental models, Environmental Modelling & Software, vol.21, issue.5, pp.602-614, 2006.
DOI : 10.1016/j.envsoft.2006.01.004

S. J. Jantz, M. K. Goetz, and . Shelley, Using the Sleuth Urban Growth Model to Simulate the Impacts of Future Policy Scenarios on Urban Land Use in the Baltimore-Washington Metropolitan Area, Environment and Planning B: Planning and Design, vol.8, issue.2, pp.251-271, 2003.
DOI : 10.1068/b1288

. Jellouli, Forest fire modelling using cellular automata: application to the watershed Oued Laou (Morocco), Mathematical and Computer Modelling of Dynamical Systems, vol.8, issue.5, pp.493-507, 2016.
DOI : 10.1080/00207729608929265

. Jiang, An evidence-theory model considering dependence among parameters and its application in structural reliability analysis, Engineering Structures, vol.57, pp.12-22, 2013.
DOI : 10.1016/j.engstruct.2013.08.028

. Jimenez-munoz, ]. J. Sobrino, J. A. Jimenez-munoz, and . Sobrino, Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data, International Journal of Remote Sensing, vol.12, issue.5, pp.999-1014, 2006.
DOI : 10.1109/36.602541

. Kang, Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics, Chinese Journal of Aeronautics, vol.29, issue.3, pp.571-579, 2016.
DOI : 10.1016/j.cja.2016.04.004

URL : https://doi.org/10.1016/j.cja.2016.04.004

]. G. Klir-et-wiermann, M. J. Klir, and . Wiermann, Uncertainty Based Information . Elements of Generalised Information Theory, 1998.

. Koi, . D. Murayama-2010-]-d, Y. Koi, and . Murayama, Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam, Remote Sensing, vol.61, issue.5, pp.1249-1272, 2010.
DOI : 10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2

. Kolb, Evaluating drivers and transition potential models in a complex landscape in southern Mexico, Int. J. Geogr. Inf. Sci, 2013.

[. Duy, Uncertainty analysis by Dempster-Shafer theory in probabilistic risk assessment, Proceeding of the ESREL Conference, 2010.

[. Duy, An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications, Proc IMechE Part O : J Risk and Reliability, vol.0, issue.0, pp.1-20, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00868014

V. Levashenko and E. Zaitseva, Fuzzy Decision Trees in Medical Decision Making Support System, Proceedings of the Federated Conference on Computer Science and Information Systems, pp.213-219, 2012.

. Leyk, ]. S. Zimmermann, N. E. Leyk, and . Zimmermann, Improving land change detection based on uncertain survey maps using fuzzy sets, Landscape Ecology, vol.8, issue.1, pp.257-272, 2007.
DOI : 10.1007/978-3-7908-1869-7

]. W. Li, Dynamic monitoring of land use/cover changes based on CBERS imagery, 2011 19th International Conference on Geoinformatics, pp.1-5, 2011.
DOI : 10.1109/GeoInformatics.2011.5981155

. Li, Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information, Entropy, vol.18, issue.11, pp.5738-5752, 2014.
DOI : 10.1002/hyp.1465

URL : http://www.mdpi.com/1099-4300/16/11/5738/pdf

. Li, ]. X. Yeh, A. G. Li, and . Yeh, Modelling sustainable urban development by the integration of constrained cellular automata and GIS, International Journal of Geographical Information Science, vol.14, issue.2, pp.131-152, 2000.
DOI : 10.1038/311419a0

]. D. Lindley, Understanding uncertainty, 2010.
DOI : 10.1002/0470055480

. Liu, Dynamic evidential reasoning for change detection in remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.5, pp.1955-1967, 2012.
DOI : 10.1109/TGRS.2011.2169075

S. M. Lloyd and R. Ries, Characterizing, Propagating, and Analyzing Uncertainty in Life-Cycle Assessment: A Survey of Quantitative Approaches, Journal of Industrial Ecology, vol.4, issue.3-4, pp.161-179, 2007.
DOI : 10.1017/CBO9780511840609

. Logsdon, Probability mapping of land use change: A GIS interface for visualizing transition probabilities, Computers, Environment and Urban Systems, vol.20, issue.6, pp.389-398, 1996.
DOI : 10.1016/S0198-9715(97)00004-5

. Longley, Remote Sensing and Urban Analysis, pp.245-258, 2001.
DOI : 10.4324/9780203306062_chapter_13

. Luo, Continuum topology optimization with non-probabilistic reliability constraints based on multi-ellipsoid convex model, Structural and Multidisciplinary Optimization, vol.89, issue.1???3, pp.297-310, 2008.
DOI : 10.1007/BF01650949

. Magliocca, From meta-studies to modeling: Using synthesis knowledge to build broadly applicable process-based land change models, Environmental Modelling & Software, vol.72, pp.10-20, 2015.
DOI : 10.1016/j.envsoft.2015.06.009

URL : https://hal.archives-ouvertes.fr/hal-01194535

]. S. Manson, Challenges in Evaluating Models of Geographic Complexity, Environment and Planning B: Planning and Design, vol.4, issue.1, pp.245-260, 2007.
DOI : 10.1126/science.284.5411.83

. Mas, A Suite of Tools for ROC Analysis of Spatial Models, ISPRS International Journal of Geo-Information, vol.7, issue.3, 2013.
DOI : 10.1007/s10708-004-5049-5

. Mas, Inductive pattern-based land use/cover change models: A comparison of four software packages, Environmental Modelling & Software, vol.51, pp.94-111, 2014.
DOI : 10.1016/j.envsoft.2013.09.010

URL : https://hal.archives-ouvertes.fr/hal-01187569

M. Masson, P. A. Hebert, and T. Denoeux, Corrélation de rang entre nombres flous, Actes des Rencontres Francophones sur la Logique Floue et ses Applications (LFA'03), pp.137-142, 2003.

]. R. Melchers, Importance sampling in structural systems, Structural Safety, vol.6, issue.1, pp.3-10, 1989.
DOI : 10.1016/0167-4730(89)90003-9

]. A. Ménard and D. J. Marceau, Simulating the impact of forest management scenarios in an agricultural landscape of southern Quebec, Canada, using a geographic cellular automata, Landscape and Urban Planning, vol.79, issue.3-4, pp.3-4, 2007.
DOI : 10.1016/j.landurbplan.2006.02.016

F. Merry, B. S. Soares-filho, D. Nepstad, G. Amacher, and H. Rodrigues, Balancing Conservation and Economic Sustainability: The Future of the Amazon Timber Industry, Environmental Management, vol.297, issue.5586, pp.395-407, 2009.
DOI : 10.1111/j.1365-2486.2007.01323.x

. Mishra, . Susaki-2014-]-b, J. Mishra, and . Susaki, Sensitivity Analysis for L-Band Polarimetric Descriptors and Fusion for Urban Land Cover Change Detection, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.10, pp.4231-4242, 2014.
DOI : 10.1109/JSTARS.2014.2354675

. Mishra, Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India, Zbornik radova Geografskog instituta Jovan Cvijic, SANU, vol.64, issue.1, pp.111-127, 2014.
DOI : 10.2298/IJGI1401111M

. Miura, Evaluation of sensor calibration uncertainties on vegetation indices for MODIS, IEEE Transactions on Geoscience and Remote Sensing, vol.38, issue.3, pp.1399-1409, 2000.
DOI : 10.1109/36.843034

P. K. Mondal, N. Garg, M. Sharma, and . Kappas, Cellular automata (ca) markov modeling of LULC change and sensitivity analysis to identify sensitive parameter(s), Proceedings of the 27th International Cartographic Conference, 2015.

]. R. Moore, Interval Analysis, 1966.

H. Morgan, Uncertainty : a guide to dealing with uncertainty in quantitative risk and policy analysis, Morgan et M. Henrion, 1992.
DOI : 10.1017/CBO9780511840609

. Mousivanda, Global sensitivity analysis of the spectral radiance of a soil???vegetation system, Remote Sensing of Environment, vol.145, pp.131-144, 2014.
DOI : 10.1016/j.rse.2014.01.023

]. R. Muller-et-middleton, J. Muller, and . Middleton, A Markov model of land-use change dynamics in the Niagara region, Landscape Ecol, vol.9, pp.151-157, 1994.

. Munoz-carpena, Global Sensitivity and Uncertainty Analyses of the Water Quality Model VFSMOD-W, Transactions of the ASABE, vol.50, issue.5, pp.1719-1732, 2007.
DOI : 10.13031/2013.23967

D. Nepstad, B. S. Soares-filho, F. Merry, A. Lima, P. Moutinho et al., The End of Deforestation in the Brazilian Amazon, Science, vol.440, issue.7083, pp.1350-1351, 2009.
DOI : 10.1038/nature04389

B. Neumann, Theory of self reproducing automata, 1966.

T. A. Nigussie and A. Altunkaynak, Assessing the Hydrological Response of Ayamama Watershed from Urbanization Predicted under Various Landuse Policy Scenarios, Water Resources Management, vol.28, issue.10, pp.3427-3441, 2016.
DOI : 10.1007/s11269-014-0642-y

. Nouri, Predicting Urban Land Use Changes Using a CA???Markov Model, Arabian Journal for Science and Engineering, vol.2, issue.7, 2014.
DOI : 10.3390/rs2061549

M. Oberguggenberger, J. King, and B. Schmelzer, Classical and imprecise probability methods for sensitivity analysis in engineering: A case study, International Journal of Approximate Reasoning, vol.50, issue.4, pp.680-693, 2009.
DOI : 10.1016/j.ijar.2008.09.004

. Oberkampf, Error and uncertainty in modeling and simulation, Reliability Engineering & System Safety, vol.75, issue.3, pp.333-357, 2002.
DOI : 10.1016/S0951-8320(01)00120-X

]. W. Oberkampf and C. J. Roy, Verification and validation in scientific computing, 2010.
DOI : 10.1017/CBO9780511760396

A. Olsson, G. Sandberg, and O. Dahlblom, On Latin hypercube sampling for structural reliability analysis, Structural Safety, vol.25, issue.1, pp.47-68, 2003.
DOI : 10.1016/S0167-4730(02)00039-5

C. Paegelow, ]. M. Olmedo, M. T. Paegelow, . Camacho, and . Olmedo, Modelling Environmental Dynamics Advances in Geomatic Simulations. Series Environmental Science, 2008.
DOI : 10.1007/978-3-540-68498-5_1

I. Park and R. V. Grandhi, Quantification of model-form and parametric uncertainty using evidence theory, Structural Safety, vol.39, pp.44-51, 2012.
DOI : 10.1016/j.strusafe.2012.08.003

. Parker, Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review, Annals of the Association of American Geographers, vol.1, issue.2, pp.314-337, 2003.
DOI : 10.1038/35065672

. Parker, Illustrating a new 'conceptual design pattern' for agent-based models of land use via five case studies-the Mr. Potatohead framework Agent-based modelling in natural resource management, pp.23-51, 2008.

. Parsa, Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran, Modeling Earth Systems and Environment, vol.67, issue.4, p.178, 2016.
DOI : 10.1016/s0169-2046(03)00036-7

. Peng, The cloud albedocloud droplet effective radius relationship for clean and polluted clouds from RACE and FIRE, ACE. J. Geophys. Res, vol.107, issue.D11, 2002.

M. Pesaresi and J. A. Benediktsson, A new approach for the morphological segmentation of high-resolution satellite imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.2, pp.309-320, 2002.
DOI : 10.1109/36.905239

]. R. Peters, A new algorithm for image noise reduction using mathematical morphology, IEEE Transactions on Image Processing, vol.4, issue.5, pp.554-568, 1995.
DOI : 10.1109/83.382491

. Pianosi, Sensitivity analysis of environmental models: A systematic review with practical workflow, Environmental Modelling & Software, vol.79, pp.214-232, 2016.
DOI : 10.1016/j.envsoft.2016.02.008

. Pijanowski, Addressing the interplay of poverty and the ecology of landscapes: a Grand Challenge Topic for landscape ecologists?, Landscape Ecology, vol.84, issue.2, 2010.
DOI : 10.1017/CBO9780511618581

]. R. Pontius and L. Schneider, Land-use change model validation by a ROC method for the Ipswich watershed, Ecosystems and Environment, vol.85, pp.1-3, 2001.

. Pontius, Useful techniques of validation for spatially explicit land-change models, Ecological Modelling, vol.179, issue.4, pp.445-61, 2004.
DOI : 10.1016/j.ecolmodel.2004.05.010

G. R. Pontius and J. Malanson, Comparison of the structure and accuracy of two land change models, International Journal of Geographical Information Science, vol.68, issue.2, pp.243-265, 2005.
DOI : 10.1016/S0167-8809(01)00189-X

R. G. Pontius and B. Parmentier, Recommendations for using the relative operating characteristic (ROC), Landscape Ecology, vol.18, issue.4, pp.367-382, 2014.
DOI : 10.1023/A:1026115807529

R. G. Pontius and M. Millones, Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment, International Journal of Remote Sensing, vol.68, issue.15, pp.4407-4429, 2011.
DOI : 10.1080/01431160701352113

. Pu, Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery, International Journal of Remote Sensing, vol.72, issue.12, pp.3285-3308, 2011.
DOI : 10.3390/s8031613

URL : http://www.geo.umass.edu/faculty/yu/2011PuIJRS.pdf

. Puertas, Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago Metropolitan Area, Land Use Policy, vol.38, pp.2010-2045, 2014.

]. R. Quinlan, Programs for Machine Learning, 1993.

. Rahman, ]. S. Xu, H. Rahman, and . Xu, A univariate dimension-reduction method for multi-dimensional integration in stochastic mechanics, Probabilistic Engineering Mechanics, vol.19, issue.4, pp.393-408, 2004.
DOI : 10.1016/j.probengmech.2004.04.003

. Ramankutty, . Foleyn, J. A. Ramankutty, and . Foley, Estimating historical changes in global land cover: Croplands from 1700 to 1992, Global Biogeochemical Cycles, vol.76, issue.1, pp.997-1027, 1999.
DOI : 10.1007/BF00478339

S. S. Rao, J. P. Rao, and . Sawyer, Fuzzy finite element approach for analysis of imprecisely defined systems, AIAA Journal, vol.106, issue.12, pp.2364-2370, 1995.
DOI : 10.1016/0020-0255(91)90031-O

D. K. Ray, J. M. Duckles, and B. C. Pijanowski, The Impact of Future Land Use Scenarios on Runoff Volumes in the Muskegon River Watershed, Environmental Management, vol.32, issue.3, 2010.
DOI : 10.1111/j.1752-1688.1995.tb04035.x

R. Real, A. L. Márquez, J. Olivero, and A. Estrada, Species distribution models in climate change scenarios are still not useful for informing policy planning : an uncertainty assessment using fuzzy logic 2010, pp.33304-314

A. Rienow and R. Goetzke, Supporting SLEUTH ??? Enhancing a cellular automaton with support vector machines for urban growth modeling, Computers, Environment and Urban Systems, vol.49, pp.1-16, 2012.
DOI : 10.1016/j.compenvurbsys.2014.05.001

. Rinderknecht, Bridging uncertain and ambiguous knowledge with imprecise probabilities, Environmental Modelling & Software, vol.36, pp.122-130, 2012.
DOI : 10.1016/j.envsoft.2011.07.022

. Saeidi, Fusion of Airborne LiDAR With Multispectral SPOT 5 Image for Enhancement of Feature Extraction Using Dempster–Shafer Theory, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.10, pp.6017-6025, 2014.
DOI : 10.1109/TGRS.2013.2294398

. Sala, Global Biodiversity Scenarios for the Year 2100 , Science, vol.287, issue.5459, pp.1770-1774, 2000.
DOI : 10.1126/science.287.5459.1770

. Saltelli, Sensitivity Analysis in Practice : A Guide to Assessing Scientific Models, 2004.
DOI : 10.1002/0470870958

. Samardzic-petrovic, Modeling Urban Land Use Changes Using Support Vector Machines, Transactions in GIS, vol.29, issue.3, pp.718-734, 2016.
DOI : 10.1007/s00477-014-0942-z

A. L. Canales, A. Benito, M. Passuello, G. Terrado, V. Ziv et al., Sensitivity analysis of ecosystem service valuation in a Mediterranean watershed, Science of The Total Environment, vol.440, pp.140-153, 2012.
DOI : 10.1016/j.scitotenv.2012.07.071

. Sander, Uncertain Numbers and Uncertainty in the Selection of Input Distributions?Consequences for a Probabilistic Risk Assessment of Contaminated Land, Risk Analysis, vol.4, issue.5, pp.1363-1375, 2006.
DOI : 10.1111/j.0272-4332.2004.00455.x

. Sarmento, Incorporating Uncertainty in the Accuracy Assessment of Land Cover Maps Using Fuzzy Numbers and Fuzzy Arithmetic, 2013.

A. Sengupta and T. K. , On comparing interval numbers, European Journal of Operational Research, vol.127, issue.1, pp.28-43, 2000.
DOI : 10.1016/S0377-2217(99)00319-7

. Sexton, A model for the propagation of uncertainty from continuous estimates of tree cover to categorical forest cover and change, Remote Sensing of Environment, vol.156, pp.418-425, 2015.
DOI : 10.1016/j.rse.2014.08.038

]. G. Shafer, A Mathematical Theory of Evidence, 1976.

. Shan, Genetic Algorithms for the Calibration of Cellular Automata Urban Growth Modeling, Photogrammetric Engineering & Remote Sensing, vol.74, issue.10, pp.1267-1277, 2008.
DOI : 10.14358/PERS.74.10.1267

. Shi, ]. W. Ehlers, M. Shi, and . Ehlers, Determining uncertainties and their propagation in dynamic change detection based on classified remotely-sensed images, International Journal of Remote Sensing, vol.52, issue.14, pp.2729-2741, 1996.
DOI : 10.1080/01431169608949103

. Silvestrini, Simulating fire regimes in the Amazon in response to climate change and deforestation, Ecological Applications, vol.21, issue.5, pp.1573-1590, 2011.
DOI : 10.1016/j.rse.2004.08.015

. Smith, The effect of neglecting correlations when propagating uncertainty and estimating the population distribution of risk, Risk Analysis, 1992.

. Soares-filho, dinamica???a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier, Ecological Modelling, vol.154, issue.3, pp.217-235, 2002.
DOI : 10.1016/S0304-3800(02)00059-5

R. Filho, D. Silvestrini, P. Nepstad, H. Brando, A. Rodrigues et al., Forest fragmentation, climate change and understory fire regimes on the Amazonian landscapes of the Xingu headwaters, Landscape Ecology, vol.6, issue.24, pp.585-598, 2012.
DOI : 10.5194/bg-6-235-2009

B. A. Song, K. I. Bryan, G. Paul, and . Zhao, Variance-based sensitivity analysis of a forest growth model, Ecological Modelling, vol.247, pp.135-143, 2012.
DOI : 10.1016/j.ecolmodel.2012.08.005

. Subedi, Application of a Hybrid Cellular Automaton ??? Markov (CA-Markov) Model in Land-Use Change Prediction: A Case Study of Saddle Creek Drainage Basin, Florida, Applied Ecology and Environmental Sciences, vol.1, issue.6, pp.126-132, 2013.
DOI : 10.12691/aees-1-6-5

F. Sutton, W. Sutton, and . Fahmi, Cairo's urban growth and strategic master plans in the light of Egypt's 1996 population census results, Cities, vol.18, issue.3, pp.135-149, 2001.
DOI : 10.1016/S0264-2751(01)00006-3

. Tayyebi, A Spatial Logistic Regression Model for Simulating Land Use Patterns, A Case Sturdy of the Shiraz Metropolitan Area of Iran Advances in Earth Observation of Global Change, 2010.

. Tayyebi, Assessing uncertainty dimensions in land-use change models: using swap and multiplicative error models for injecting attribute and positional errors in spatial data, International Journal of Remote Sensing, vol.60, issue.5, pp.149-170, 2014.
DOI : 10.1016/j.engstruct.2004.12.006

M. Thapa, Urban growth modeling of Kathmandu metropolitan region, Nepal, Thapa et Y. Murayama, pp.25-34, 2011.
DOI : 10.1016/j.compenvurbsys.2010.07.005

]. K. Thompson, Variability and Uncertainty Meet Risk Management and Risk Communication, Risk Analysis, vol.23, issue.2, pp.647-654, 2002.
DOI : 10.1023/A:1011184119153

URL : http://www.ce.ncsu.edu/risk/pdf/thompson.pdf

. Turner, Land-Use and Land-Cover Change, IGBP Report, vol.35, issue.7, 1995.

]. T. Václavík and J. Rogan, Identifying Trends in Land Use/Land Cover Changes in the Context of Post-Socialist Transformation in Central Europe: A Case Study of the Greater Olomouc Region, Czech Republic, GIScience & Remote Sensing, vol.46, issue.1, pp.54-76, 2009.
DOI : 10.2747/1548-1603.46.1.54

. Valbuena, A method to define a typology for agent-based analysis in regional land-use research, Agriculture, Ecosystems & Environment, vol.128, issue.1-2, pp.27-36, 2008.
DOI : 10.1016/j.agee.2008.04.015

. Van and . Kwast, Uncertainty analysis and data-assimilation of remote sensing data for the calibration of cellular automata based land-use models. International Environmental Modelling and Software Society, pp.997-1004, 2012.

V. Van, A fuzzy set approach to assess the predictive accuracy of land use simulations, J. van Vliet Ecological Modelling, vol.261, pp.32-42, 2013.

]. A. Veldkamp and L. O. Fresco, CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica, Ecological Modelling, vol.91, issue.1-3, pp.231-248, 1996.
DOI : 10.1016/0304-3800(95)00158-1

A. Veldkamp and E. F. Lambin, Predicting land-use change, Agriculture, Ecosystems & Environment, vol.85, issue.1-3, pp.1-6, 2001.
DOI : 10.1016/S0167-8809(01)00199-2

. Verburg, A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use, Ecological Modelling, vol.116, issue.1, pp.45-61, 1999.
DOI : 10.1016/S0304-3800(98)00156-2

. Verburg, Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model, Environmental Management, vol.30, issue.3, pp.391-405, 2002.
DOI : 10.1007/s00267-002-2630-x

. Verburg, Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: A study for land use in Europe, Journal of Environmental Management, vol.127, pp.132-144, 2013.
DOI : 10.1016/j.jenvman.2012.08.038

. Verstegen, Detecting systemic change in a land use system by Bayesian data assimilation, Environmental Modelling & Software, vol.75, pp.424-438, 2016.
DOI : 10.1016/j.envsoft.2015.02.013

]. W. Walker, P. Harremoes, J. Rotmans, J. P. Van-der-sluijs, M. B. Van-asselt et al., Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support, Integrated Assessment, vol.4, issue.1, pp.5-17, 2003.
DOI : 10.1076/iaij.

. Wang, Generalized Neumann Expansion and Its Application in Stochastic Finite Element Methods, Mathematical Problems in Engineering, vol.117, issue.10, pp.1-13, 2013.
DOI : 10.1016/j.probengmech.2003.11.017

URL : http://doi.org/10.1155/2013/325025

. Wei, . Hua-2013-]-h, Y. Wei, and . Hua, EFAST Method for Global Sensitivity Analysis of Remote Sensing Models Parameters, Remote Sensing Technology and Application, vol.28, issue.5, pp.836-843, 2013.

. White, The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics, Environment and Planning B: Planning and Design, vol.24, issue.3, pp.323-343, 1997.
DOI : 10.1068/b240323

. White, Developing an urban land use simulator for European cities, Proceedings of the Fifth EC GIS Workshop : GIS of Tomorrow. European Commission Joint Research Centre, pp.179-190, 2000.

W. Xiao, D. Zhao, H. Zhou, and . Gong, Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales, IEEE Transactions on Geoscience and Remote Sensing, pp.1-11, 2013.

. Xua, Integrating the system dynamic and cellular automata models to predict land use and land cover change, International Journal of Applied Earth Observation and Geoinformation, vol.52, pp.568-579, 2016.
DOI : 10.1016/j.jag.2016.07.022

K. Wang, X. Wu, and . Yu, Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set : A Case Study of Wuhan City, Remote Sens, vol.8, issue.4, pp.304-321, 2016.

. Yin, Changes in urban built-up surface and population distribution patterns during 1986???1999: A case study of Cairo, Egypt, Computers, Environment and Urban Systems, vol.29, issue.5, pp.595-616, 2005.
DOI : 10.1016/j.compenvurbsys.2005.01.008

Z. Youn, L. J. Xi, P. Wells, and . Wang, Enhanced Dimension-Reduction (eDR) Method for Sensitivity-Free Uncertainty Quantification, 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2006.
DOI : 10.1002/nme.1135

. Zargar, Dempster-Shafer Theory for Handling Conflict in Hydrological Data: Case of Snow Water Equivalent, Journal of Computing in Civil Engineering, vol.26, issue.3, pp.434-447, 2012.
DOI : 10.1061/(ASCE)CP.1943-5487.0000149

. Zhang, Using Markov chains to analyze changes in wetland trends in arid Yinchuan Plain, China, Mathematical and Computer Modelling, vol.54, issue.3-4, pp.924-930, 2011.
DOI : 10.1016/j.mcm.2010.11.017