Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

This paper presents an approach for reducing uncertainty related to the process of land-cover change (LCC) prediction. LCC prediction models have, almost, two sources of uncertainty which are the uncertainty related to model parameters and the uncertainty related to model structure. These uncertainties have a big impact on decisions of the prediction model. To deal with these problems, the proposed approach is divided into three main steps: (1) an uncertainty propagation step based on possibility theory is used as a tool to evaluate the performance of the model; (2) a sensitivity analysis step based on Hartley-like measure is then used to find the most important sources of uncertainty; and (3) a knowledge base based on machine learning algorithm is built to identify the reduction factors of all uncertainty sources of parameters and to reshape their values to reduce in a significant way the uncertainty about future changes of land cover. In this study, the present and future growths of two case studies were anticipated using multi-temporal Spot-4 and Landsat satellite images. These data are used for the preparation of prediction map of year 2025. The results show that our approach based on possibility theory has a potential for reducing uncertainty in LCC prediction modeling.

[1]  E. HereherMohamed,et al.  Analysis of urban growth at Cairo, Egypt using remote sensing and GIS , 2012 .

[2]  Fei Yang,et al.  Study of remote sensing based parameter uncertainty in production Efficiency Models , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[3]  Célia Ghedini Ralha,et al.  A multi-agent model system for land-use change simulation , 2013, Environ. Model. Softw..

[4]  Didier Dubois,et al.  Formal Representations of Uncertainty , 2010, Decision-making Process.

[5]  Leonid M. Ivanov,et al.  Sensitivity Analysis of nonlinear Models to parameter perturbations for Small Size Ensembles of Model Outputs , 2011, Int. J. Bifurc. Chaos.

[6]  G. Klir.  Measure of Uncertainty and Information , 2006 .

[7]  A. Valk.  The Dutch Planning Experience , 2002 .

[8]  Tapan Kumar Pal,et al.  On comparing interval numbers , 2000, Eur. J. Oper. Res..

[9]  Laurent Durieux,et al.  A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data , 2008 .

[10]  B. Solaiman,et al.  A data mining based approach to predict spatiotemporal changes in satellite images , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Wadii Boulila,et al.  An Intelligent Possibilistic Approach to Reduce the Effect of the Imperfection Propagation on Land Cover Change Prediction , 2015, ICCCI.

[12]  Behzad Saeedi Razavi.  Predicting the Trend of Land Use Changes Using Artificial Neural Network and Markov Chain Model (Case Study: Kermanshah City) , 2014 .

[13]  Amin Tayyebi,et al.  Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS , 2014, Int. J. Geogr. Inf. Sci..

[14]  Önder Halis.  ERROR ESTIMATION OF ORTHORECTIFICATION OF SMALL SATELLITE IMAGES BY DIFFERENTIAL SENSITIVITY ANALYSIS , 2010 .

[15]  Marta Schuhmacher,et al.  Sensitivity analysis of ecosystem service valuation in a Mediterranean watershed. , 2012, The Science of the total environment.

[16]  K. Sutton,et al.  Cairo's urban growth and strategic master plans in the light of Egypt's 1996 population census results , 2001 .

[17]  D. Dubois,et al.  When upper probabilities are possibility measures , 1992 .

[18]  Harvey J. Miller,et al.  Decentralized and coordinate-free computation of critical points and surface networks in a discretized scalar field , 2014, Int. J. Geogr. Inf. Sci..

[19]  Isabelle Bloch,et al.  Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing , 1997, IEEE Trans. Geosci. Remote. Sens..

[20]  William F. Caselton,et al.  Decision making with imprecise probabilities: Dempster‐Shafer Theory and application , 1992 .

[21]  George J. Klir,et al.  Uncertainty-Based Information , 1999 .

[22]  Wadii Boulila,et al.  A Probabilistic Collocation Method for the Imperfection Propagation: Application to Land Cover Change Prediction , 2014, J. Multim. Process. Technol..

[23]  Dominique Salameh,et al.  Short-term relationships between emergency hospital admissions for respiratory and cardiovascular diseases and fine particulate air pollution in Beirut, Lebanon , 2015, Environmental Monitoring and Assessment.

[24]  Rituparna Chutia,et al.  SENSITIVITY ANALYSIS OF ATMOSPHERIC DISPERSION MODEL-RIMPUFF USING THE HARTLEY-LIKE MEASURE † , 2013 .

[25]  Javier Montero,et al.  Fuzzy sets in remote sensing classification , 2008, Soft Comput..

[26]  Scott Ferson,et al.  Sensitivity analysis using probability bounding , 2006, Reliab. Eng. Syst. Saf..

[27]  Florencio Ballestores,et al.  An integrated parcel-based land use change model using cellular automata and decision tree , 2012 .

[28]  Wei Wang,et al.  Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information , 2014, Entropy.

[29]  Peter Nijkamp,et al.  Trapped between antiquity and urbanism – a multi-criteria assessment model of the greater Cairo Metropolitan area , 2011 .

[30]  Önder Halis Bettemir.  ERROR ESTIMATION OF ORTHORECTIFICATION OF SMALLSATELLITE IMAGES BY DIFFERENTIAL SENSITIVITY ANALYSIS , 2010 .

[31]  Alfred Stein,et al.  Application of random sets to model uncertainties of natural entities extracted from remote sensing images , 2010 .

[32]  Arika Ligmann-Zielinska,et al.  Applying time-dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change , 2010, Int. J. Geogr. Inf. Sci..

[33]  D. Datta,et al.  Measures of sensitivity and uncertainty with the fuzzy model of solute transport through groundwater , 2011, 2011 International Conference on Communication and Industrial Application.

[34]  Valentin,et al.  Some Peculiarities of the Preprocessing of Spectral Data and Images , 2013 .

[35]  Jon C. Helton,et al.  Survey of sampling-based methods for uncertainty and sensitivity analysis , 2006, Reliab. Eng. Syst. Saf..

[36]  Zhi-Yong Yin,et al.  Changes in urban built-up surface and population distribution patterns during 1986-1999: A case study of Cairo, Egypt , 2005, Comput. Environ. Urban Syst..

[37]  Anthony Gar-On Yeh,et al.  Neural-network-based cellular automata for simulating multiple land use changes using GIS , 2002, Int. J. Geogr. Inf. Sci..

[38]  N. Lam,et al.  Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata , 2015, Environmental Monitoring and Assessment.

[39]  G. Klir,et al.  MEASURES OF UNCERTAINTY AND INFORMATION BASED ON POSSIBILITY DISTRIBUTIONS , 1982 .

[40]  A. P. Jacquin,et al.  Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff , 2010 .

[41]  José A. Sobrino,et al.  Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data , 2006 .