The suitability of agricultural lands is basic data for both sustainable agricultural planning and optimal orientation of future urbanization projects. Indeed, unplanned urban expansion leads to many social and ecological problems. The prediction of urban expansion will strongly help the decision makers and local authorities to create a suitable future urban planning while preserving the regional ecosystem and especially the agricultural lands. Using multitemporal satellite images acquired in 1997, 2001, and 2013 over the Mitidja plain in Algeria, the aim of this study is the analysis of the land use and land cover change (LULCC) spatio-temporal distributions from 1997 to 2013 to model and then predict the future LULCC in 2025. The cellular automata Markov chain model (CA-Markov) that we developed is used for LULCC prediction with a focus on urban sprawl and agricultural lands degradation over the Mitidja plain. Once calibrated and validated, the CA-Markov model generates land suitability maps that indicate the best locations for urban expansion while safekeeping the agricultural lands. The results are interesting and present valuable tools for urban planners and policymakers to ensure the sustainability management of agricultural areas over the study region.
In this paper, we present a novel fusion and classification process for remotely sensed images developed in the framework of possibility theory. Unlike probability theory, possibility theory has the ability to handle both uncertainty and imprecision of classified pixel through a possibility and a necessity measures. Proposed multisource fusion and classification process involves several steps: First of all, the probability distribution of each spectral class from the samples representing thematic classes is estimated. This estimation is based on the histogram analysis of each class. Then, the possibility distribution is estimated from probability distributions using the Klir probability-possibility transformation. Next, once the possibility of samples is determined, we apply the Lagrangian interpolation method to estimate the other observations (grey level/reflectance) of the information sources. Combination operators: conjunctive and disjunctive are then used to combine possibilities of each source. This operation is performed a pixel level. Finally, decision is taken by using a criterion based on maximization of the possibility measures in order to select the optimal class. Experimental results obtained as land cover maps and land change maps indicate by using a statistical assessment (confusion matrix, Kappa coefficient, local spectral analysis method) that the proposed possibilistic fusion and classification process outperform the existing probabilistic fusion and classification process: It is important to emphasize that classical probabilistic data fusion models do not provide an adequate combination tool to detect and label change areas. Thus, possibility theory seems to be the best methodological framework which allows the development of multisource and multitemporal image fusion and classification process.
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