Knowledge and Information Systems

, Volume 55, Issue 3, pp 719–740 | Cite as

Reducing uncertainties in land cover change models using sensitivity analysis

  • Ahlem Ferchichi
  • Wadii Boulila
  • Imed Riadh Farah
Regular Paper


Land cover change (LCC) models aim to track spatiotemporal changes made in land cover. In most cases, LCC models contain uncertainties in their main components (i.e., input parameters and model structure). These uncertainties propagate through the modeling system, which generates uncertainties in the model outputs. The aim of this manuscript is to propose an approach to reduce uncertainty of LCC prediction models. The main objective of the proposed approach is to apply a sensitivity analysis method, based on belief function theory, to determine parameters and structures that have a high contribution in the variability of the predictions of the LCC model. Our approach is applied to four common LCC models (i.e., DINAMICA, SLEUTH, CA-MARKOV, and LCM). Results show that uncertainty of the model parameters and structure has meaningful impacts on the final decisions of LCC models. Ignoring this uncertainty can lead to erroneous decision about land changes. Therefore, the presented approach is very useful to identify the most relevant uncertainty sources that need to be processed to improve the accuracy of LCC models. The applicability and effectiveness of the proposed approach are demonstrated through a case study based on the Cairo region. Results show that 13% of the agriculture and 3.8% of the desert lands in 2014 would be converted to urban areas in 2025.


LCC prediction models Input parameters uncertainty Model structure uncertainty Belief function theory Sensitivity analysis Estimation 


  1. 1.
    Sklar FH, Costanza R (1991) The development of dynamic spatial models for landscape ecology: a review and prognosis. Quantitative methods in landscape ecology. Springer, New York, pp 239–288Google Scholar
  2. 2.
    Xu X, Du Z, Zhang H (2016) Integrating the system dynamic and cellular automata models to predict land use and land cover change. Int J Appl Earth Obs Geoinf 52:568–579CrossRefGoogle Scholar
  3. 3.
    Hu Z, Lo C (2007) Modeling urban growth in Atlanta using logistic regression. Comput Environ Urban Syst 31(6):667–688CrossRefGoogle Scholar
  4. 4.
    Zhang R, Tang C, Ma S, Yuan H, Gao L, Fan W (2011) Using Markov chains to analyze changes in wetland trends in arid Yinchuan plain, China. Math Comput Model 54:924–930CrossRefGoogle Scholar
  5. 5.
    Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93(2):314–337CrossRefGoogle Scholar
  6. 6.
    Huang B, Xie C, Tay R (2010) Support vector machines for urban growth modelling. Geoinformatica 14(1):83–99CrossRefGoogle Scholar
  7. 7.
    Boulila W, Ettabaa KS, Farah IR, Solaiman B, Ghzala HB (2009) Towards a multi-approach system for uncertain spatio-temporal knowledge discovery in satellite imagery. ICGST Int J Graph Vis Image Process (GVIP) 9(6):19–25Google Scholar
  8. 8.
    Boulila W, Farah IR, Solaiman B, Ghzala HB (2011) Interesting spatiotemporal rules discovery: application to remotely sensed image databases. VINE J Inf Knowl Manag Syst 41(2):167–191Google Scholar
  9. 9.
    Boulila W, Farah IR, Ettabaa KS, Solaiman B, Ben Ghzala H (2011) A data mining based approach to predict Spatio-temporal changes in satellite images. Int J Appl Earth Observ Geoinform 13(3):386–395CrossRefGoogle Scholar
  10. 10.
    Razavi BS (2014) Predicting the trend of land use changes using artificial neural network and Markov chain model (Case Study: Kermanshah City). Res J Environ Earth Sci 6(4):215–226Google Scholar
  11. 11.
    De Souza FJ, Velloso MLF, Fonseca OLH (2002) Change-detection of land cover using fuzzy sets and remotely sensed data. IEEE Int Geosci Remote Sens Symp 6:3381–3383CrossRefGoogle Scholar
  12. 12.
    Ferchichi A, Boulila W, Farah IR (2016) Propagating aleatory and epistemic uncertainty in land cover change prediction process. Ecol Inform 37:24–37CrossRefGoogle Scholar
  13. 13.
    Fordham DA, Haythorne S, Brook BW (2016) Sensitivity analysis of range dynamics models (SARDM): quantifying the influence of parameter uncertainty on forecasts of extinction risk from global change. Environ Model Softw 83:193–197CrossRefGoogle Scholar
  14. 14.
    Foody GM (2003) Uncertainty, knowledge discovery and data mining in GIS. Prog Phys Geogr 27(1):113–121CrossRefGoogle Scholar
  15. 15.
    Aerts JCJH, Goodchild MF, Heuvelink GBM (2003) Accounting for spatial uncertainty in optimization with spatial decision support systems. Trans GIS 7(2):211–230CrossRefGoogle Scholar
  16. 16.
    Verstegen JA (2016) Quantifying and reducing uncertainty in land use change model projections : Case studies on the implications of increasing bioenergy demands. Thesis, Utrecht UniversityGoogle Scholar
  17. 17.
    Hoffman FO, Hammonds JS (1994) Propagation of uncertainty in risk assessment: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk Anal 14(5):707–712CrossRefGoogle Scholar
  18. 18.
    Al-sharif AAA, Pradhan B (2015) Spatio-temporal prediction of urban expansion using bivariate statistical models: assessment of the efficacy of evidential belief functions and frequency ratio models. Appl Spatial Anal Policy 9:213–231CrossRefGoogle Scholar
  19. 19.
    Ferchichi A, Boulila W, Farah IR (2016) Towards an uncertainty reduction framework for land-cover change prediction using possibility theory. Vietnam J Comput Sci 4:195–209CrossRefGoogle Scholar
  20. 20.
    Jebur MN, Pradhan B, Tehrany MS (2015) Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. IEEE J Sel Top Appl Earth Observ Remote Sens 8(2):674–690CrossRefGoogle Scholar
  21. 21.
    Liu Z, Dezert J, Mercier G, Pan Q (2012) Dynamic evidential reasoning for change detection in remote sensing images. IEEE Trans Geosci Remote Sens 50(5):1955–1967CrossRefGoogle Scholar
  22. 22.
    Kruger C, Lakes T (2015) Bayesian belief networks as a versatile method for assessing uncertainty in land-change modeling. Int J Geogr Inf Sci 29(1):111–131CrossRefGoogle Scholar
  23. 23.
    Tayyebi AH, Tayyebi A, Khanna N (2014) Assessing uncertainty dimensions in land-use change models: using swap and multiplicative error models for injecting attribute and positional errors in spatial data. Int J Remote Sens 35(1):149–170CrossRefGoogle Scholar
  24. 24.
    van der Kwast J, Poelmans L, Van de Voorde T, de Jong K, Uljee I, Karssenberg D, Canters F, Engelen G (2012) Uncertainty analysis and data-assimilation of remote sensing data for the calibration of cellular automata based land-use models. In: International environmental modelling and software society, pp 997–1004Google Scholar
  25. 25.
    Verburg PH, Tabeau A, Hatna E (2013) Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: a study for land use in Europe. J Environ Manag 127:S132–S144CrossRefGoogle Scholar
  26. 26.
    Mondal MS, Garg PK, Sharma N, Kappas M (2015) Cellular automata (CA) markov modeling of LULC change and sensitivity analysis to identify sensitive parameter(s). In: Proceedings of the 27th international cartographic conference, vol 38 (818)Google Scholar
  27. 27.
    Saltelli A, Tarantola S, Campolongo F, Ratto M (2004) Sensitivity analysis in practice: a guide to assessing scientific models. Joint Research Centre of the European Commission, Ispra, ItalyGoogle Scholar
  28. 28.
    Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T (2016) Sensitivity analysis of environmental models: a systematic review with practical workflow. Environ Model Softw 79:214–232CrossRefGoogle Scholar
  29. 29.
    Sánchez-Canales M, Benito AL, Passuello A, Terrado M, Ziv G, Acuña V, Schuhmacher M, Elorza FJ (2012) Sensitivity analysis of ecosystem service valuation in a Mediterranean watershed. Sci Total Environ 440:140–153CrossRefGoogle Scholar
  30. 30.
    Bettemier ÖH (2010) Error estimation of orthorectification of small satellite images by differential sensitivity analysis. J Aeronaut Space Technol 4(4):65–74Google Scholar
  31. 31.
    Haihua X, Xianchuan Y, Dan H, Sha D (2015) Sensititvity analysis of hierchical hybrid fuzzy-neural network. Int J Smart Sens Intell Syst 8(3):1837–1854Google Scholar
  32. 32.
    Zielinskaa AL, Sunb L (2010) Applying time-dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change. Int J Geogr Inf Sci 24(12):1829–1850CrossRefGoogle Scholar
  33. 33.
    Helton JC, Johnson JD, Sallaberry CJ, Storlie CB (2006) Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliab Eng Syst Saf 91(10–11):1175–1209CrossRefGoogle Scholar
  34. 34.
    Li C, Wang W, Xiong J, Chen P (2014) Sensitivity analysis for urban drainage modeling using mutual information. Entropy 16:5738–5752CrossRefGoogle Scholar
  35. 35.
    Wei H, Hua Y (2013) EFAST method for global sensitivity analysis of remote sensing models parameters. Remote Sens Technol Appl 28(5):836–843Google Scholar
  36. 36.
    Xiao Y, Zhao W, Zhou D, Gong H (2013) Sensitivity analysis of vegetation reflectance to biochemical and biophysical variables at leaf, canopy, and regional scales. IEEE Trans Geosci Remote Sens 52:1–11Google Scholar
  37. 37.
    Homma T, Saltelli A (1996) Importance measures in global sensitivity analysis of nonlinear models. Reliab Eng Syst Saf 52(1):1–17CrossRefGoogle Scholar
  38. 38.
    Song X, Bryan BA, Paul KI, Zhao G (2012) Variance-based sensitivity analysis for a forest growth model. Ecol Model 247:135–143CrossRefGoogle Scholar
  39. 39.
    Gerardino-Neira C, Goodman J, Velez-Reyes M, Rivera W (2008) Sensitivity analysis of a hyperspectral inversion model for remote sensing of shallow coastal ecosystems. IGARSS I-98–I-101Google Scholar
  40. 40.
    Ferson S, Tucker WT (2006) Sensitivity analysis using probability bounding. Reliab Eng Syst Saf 91(1011):1435–1442CrossRefGoogle Scholar
  41. 41.
    Sengupta A, Pal TK (2000) Theory and methodology: on comparing interval numbers. Eur J Oper Res 127:28–43CrossRefzbMATHGoogle Scholar
  42. 42.
    Ali T, Boruah H, Dutta P (2012) Sensitivity analysis in radiological risk assessment using probability bounds analysis. Int J Comput Appl 44(17):1–5Google Scholar
  43. 43.
    Hall JW (2006) Uncertainty-based sensitivity indices for imprecise probability distributions. Reliab Eng Syst Saf 91(101):1443–1451CrossRefGoogle Scholar
  44. 44.
    Helton J, Johnson J, Oberkampf W, Sallaberry C (2006) Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty. Reliab Eng Syst Saf 91:1414–1434CrossRefGoogle Scholar
  45. 45.
    Oberguggenberger M, King J, Schmelzer B (2009) Classical and imprecise probability methods for sensitivity analysis in engineering: a case study. Int J Approx Reason 50:680–693CrossRefGoogle Scholar
  46. 46.
    Ferson S, Tucker WT (2006) Sensitivity in risk analysis with uncertain numbers. Technical Report SAND2006-2801, Sandia National Laboratories, Albuquerque, NMGoogle Scholar
  47. 47.
    Mas JF, Kolb M, Paegelow M, Olmedo MTC, Houet T (2014) Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Model Softw 51:94–111CrossRefGoogle Scholar
  48. 48.
    Pontius GR, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19(2):243–265CrossRefGoogle Scholar
  49. 49.
    Sutton K, Fahmi W (2001) Cairo’s urban growth and strategic master plans in the light of Egypt’s 1996 population census results. Cities 18(3):135–149CrossRefGoogle Scholar
  50. 50.
    De Almeida CM, Monteiro AMV, Cmara G, Cerqueira GC, Pennachin CL, Batty M (2005) GIS and remote sensing as tools for the simulation of urban land-use change. Int J Remote Sens 26(4):759–774CrossRefGoogle Scholar
  51. 51.
    Filho BSS, Filho LC, Cerqueira GC, Araujo WL (2003) Simulating the spatial patterns of change through the use of the DINAMICA model, Anais XI SBSR, Belo Horizonte, Brasil, 05–10 April, INPE, pp 721–728Google Scholar
  52. 52.
    Soares-Filho BS, Cerqueira GC, Pennachin CL (2002) DINAMICAa stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecol Model 154:217–235CrossRefGoogle Scholar
  53. 53.
    Bihamt N, Soffianian A, Fakheran S, Gholamalifard M (2015) Using the SLEUTH urban growth model to simulate future urban expansion of the Isfahan Metropolitan Area, Iran. J Indian Soc Remote Sens 43(2):407–414CrossRefGoogle Scholar
  54. 54.
    Hua L, Tang L, Cui S, Yin K (2014) Simulating urban growth using the Sleuth Model in a coastal peri-urban district in China. Sustainability 6(6):3899–3914CrossRefGoogle Scholar
  55. 55.
    Jantz CA, Goetz SJ, Donato D, Claggett P (2010) Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Comput Environ Urban Syst 34:1–16CrossRefGoogle Scholar
  56. 56.
    Arsanjani JJ, Kainz W, Mousivand AJ (2011) Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran. Int J Image Data Fusion 2:329–345CrossRefGoogle Scholar
  57. 57.
    Gong W, Li Y, Fan W, Stott P (2015) Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata-Markov modelling. Int J Appl Earth Obs Geoinf 34:207–216CrossRefGoogle Scholar
  58. 58.
    Abuelaish B, Olmedo MTC (2016) Scenario of land use and land cover change in the Gaza strip using remote sensing and GIS models. Arab J Geosci 9:274–288CrossRefGoogle Scholar
  59. 59.
    Mishra VN, Rai PK, Mohan K (2014) Prediction of land use changes based on land change modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. J Geogr Inst Cvijic 64(1):111–127CrossRefGoogle Scholar
  60. 60.
    Tewolde MG, Cabral P (2011) Urban sprawl analysis and modelling in Asmara, Eritrea. Remote Sens 3:2148–2165CrossRefGoogle Scholar
  61. 61.
    Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38:325–339MathSciNetCrossRefzbMATHGoogle Scholar
  62. 62.
    Shafer GA (1976) Mathematical theory of evidence. Princeton University Press, PrincetonzbMATHGoogle Scholar
  63. 63.
    Balch MS (2012) Mathematical foundations for a theory of confidence structures. Int J Approx Reason 53(7):1003–1019MathSciNetCrossRefzbMATHGoogle Scholar
  64. 64.
    Bain L, Engelhardt M (1991) Introduction to probability and mathematical statistic, 2nd edn. Duxbury, Pacific GrovezbMATHGoogle Scholar
  65. 65.
    Ferson S, Kreinovich V, Ginzburg L, Myers D, Sentz K (2003) Constructing probability boxes and DempsterShafer structures, Technical report, Sandia National LaboratoriesGoogle Scholar
  66. 66.
    Miller LH (1956) Table of percentage points of Kolmogorov statistics. J Am Stat Assoc 51:111121Google Scholar
  67. 67.
    Lilliefors H (1967) On the Kolmogorov–Smirnov test for normality with mean and variance unknown. J Am Stat Assoc 62:399402CrossRefGoogle Scholar
  68. 68.
    El-Sadek A, Irvem A (2014) Evaluating the impact of land use uncertainty on the simulated streamflow and sediment yield of the Seyhan River basin using the SWAT model. Turk J Agric For 38:515–530CrossRefGoogle Scholar
  69. 69.
    Nigussie TA, Altunkaynak A (2016) Assessing the hydrological response of Ayamama watershed from urbanization predicted under various landuse policy scenarios. Water Resour Manag 30:3427–3441CrossRefGoogle Scholar
  70. 70.
    R Development Core Team (2012) R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  • Ahlem Ferchichi
    • 1
  • Wadii Boulila
    • 1
    • 2
  • Imed Riadh Farah
    • 1
    • 2
  1. 1.RIADI Laboratory, National School of Computer SciencesUniversity of ManoubaManoubaTunisia
  2. 2.ITI DepartmentTELECOM-BretagneBrestFrance

Personalised recommendations