This paper presents a model (SLEUTH*) for projecting contrasting urban growth futures. It is derived from the SLEUTH model, which has been modified in order to incorporate an additional spatially explicit factor, and to be used in a fully controlled forecasting mode. Our aim is to spatially allocate urban growth, its amount and pattern, according to predefined prospective scenarios and assuming a non path-dependency approach. This modelling approach aims at being used under a "story and simulation" (SAS) approach, which constrains the model validation. To assess model efficiency, three types of tests have been undertaken: (1) sensitivity tests; (2) reproduction of known changes over a past period; and (3) simulation of changes that break trends. Results show that SLEUTH* conveniently simulates expected urban changes for exploring contrasting scenarios that are the basis for land planning strategies.

The temporal non-stationarity of land use and cover change (LUCC) processes is one of the main sources of uncertainty that may influence the calibration and the validation of spatial path-dependent LUCC models. In relation to that, this research aims to investigate the influence of the temporal non-stationarity of land change on urban growth modeling accuracy based on an empirical approach that uses past LUCC. Accordingly, the urban development in Rennes Metropolitan (France) was simulated using fifteen past calibration intervals which are set from six training dates. The study used Idrisi’s Cellular Automata-Markov model (CA-Markov) which is an inductive pattern-based LUCC software package. The land demand for the simulation year was estimated using the Markov Chain method. Model validation was carried out by assessing the quantity of change, allocation, and spatial patterns accuracy. The quantity disagreement was analyzed by taking into consideration the temporal non-stationarity of change rate over the calibration and the prediction intervals, the model ability to reproduce the past amount of change in the future, and the time duration of the prediction interval. The results show that the calibration interval significantly influenced the amount and the spatial allocation of the estimated change. In addition to that, the spatial allocation of change using CA-Markov depended highly on the basis land cover image rather than the observed transition during the calibration period. Therefore, this study provides useful insights on the role of the training dates in the simulation of non-stationary LUCC.

The energy balance differences between urban and rural areas give rise to a representative phenomenon of urban climate change called the urban heat island (UHI). The UHI phenomenon, which is exacerbated by global warming effects and urban expansion, exposes urban areas and populations to thermal stress and overheating. Therefore, measuring the intensity of UHI vulnerability is needed to mitigate its effects on health, comfort, energy consumption and infrastructure. Additionally, urban decision makers are looking for methodologies to identify the most vulnerable areas in urban planning. This research aims to investigate areas prone to urban overheating in Montpellier Mediterranean metropolitan area as a representative case study of the French urban planning context. The objective is to develop a methodology for assessing UHI vulnerability based on the interaction of exposure and sensitivity to urban planning policies. Accordingly, a set of fifteen indicators is developed to visualize urban planning policies, exposure and sensitivity to urban overheating. A statistical-based method is applied to determine the individual weights based on the indicators’ variance, which is calculated using principal component analysis (PCA). The spatial and urban planning distribution of vulnerability is then analyzed. The results indicate that the areas with the highest urban planning challenges are most vulnerable to UHI. In fact, the results reveal that these areas have urban planning characteristics likely to aggravate urban overheating. Therefore, this research proposes a methodology for integrating mitigation solutions into urban planning: increase in permeable and green areas; improvement of urban ventilation and heat exchange and decrease in anthropogenic heat generation. It demonstrates that combining urban planning policies with exposure and sensitivity is a new perspective for vulnerability studies. The findings of this study can be utilized by decision makers to better visualize areas susceptible to UHI and plan appropriate mitigation strategies.

L’objectif de cette recherche est d’explorer l’apport de l’utilisation combinée de la télédétection à haute résolution spatiale et des métriques paysagères pour la caractérisation des formes spatiales et le suivi de l’expansion urbaine à l’échelle d’une métropole de taille moyenne, Rennes Métropole. L’application porte sur une période de vingt-et-un ans (1984 à 2005) à partir d’images Landsat. Les trajectoires d’évolution des changements d’occupation et d’usage des sols ont d’abord été reconstruites à partir de la classification des images satellitales selon une approche orientée-objet. Ensuite, des métriques paysagères ont été calculées afin d’analyser les principales dynamiques spatio-temporelles de l’expansion urbaine et leur impact sur les structures paysagères. Les résultats révèlent une nette progression du tissu urbain qui est inégalement réparti dans l’espace, et à des rythmes légèrement différents selon la période considérée. Cette progression de la consommation foncière, d’un taux moyen annuel de 190 ha/an, s'est principalement effectuée au détriment des terres agricoles tandis que la tache urbaine continue d’intégrer de plus en plus d’espaces verts périphériques. L’analyse des métriques spatiales illustre le passage d’une phase de fragmentation accélérée du paysage vers une phase d'expansion par étalement continu de l’urbain existant.

L’objectif de cet article est d’évaluer les apports et les limites d’usage des modèles SLEUTH, LCM et NEDUM-2D pour spatialiser des scénarios prospectifs d’aménagement du territoire. Les deux modèles de changements d’occupation et d’usage des sols spatialement explicites SLEUTH et LCM sont basés sur une approche inductive de simulation de patrons spatiaux tandis que NEDUM-2D est un modèle économique d’expansion urbaine à base de processus. L’application porte sur l’aire urbaine de Toulouse dont les trajectoires d’évolution des changements d’occupation et d’usage des sols entre 1990 et 2006 ont préalablement été reconstruites. Le but est de suivre la dynamique spatio-temporelle de l’expansion urbaine et de calibrer les deux modèles SLEUTH et LCM. Après une présentation détaillée de la démarche méthodologique de construction de scénarios contrastés, cohérents et plausibles de développement urbain, le fonctionnement et les hypothèses propres à chacun des trois modèles de spatialisation sont présentés. Ensuite, une analyse comparative des résultats des deux modèles SLEUTH et LCM est menée à travers la spatialisation d’un scénario tendanciel élaboré à l’horizon 2025. Les résultats obtenus montrent d’importantes différences entre les simulations issues des deux modèles. L’influence de ces derniers concerne à la fois la quantité de changement et son allocation spatiale. Les résultats indiquent aussi que les deux modèles empiriques SLEUTH et LCM, calibrés à l’aide des trajectoires passées, ne permettent pas de spatialiser des scénarios de rupture avec les tendances passées. A l’issue de cette analyse, nous proposons de développer un nouveau modèle hybride couplant l’approche économique et l’approche géographique, utilisable de façon contrôlée conformément à la définition de scénarios normatifs.

The rapid expansion of built-up areas (BUAs) requires effective spatial and temporal monitoring, being a crucial practice for urban land use planning, resource allocation, and environmental studies, and spectral indices (SIs) can provide efficiency and reliability in automating the process of BUAs extraction. This paper explores the use of nine spectral indices and sixteen thresholding methods for the automatic mapping of BUAs using Landsat 8 imagery from a semi-arid climate in Morocco during spring and summer. These indices are the Normalized Difference Built-Up Index (NDBI), the Vis-red-NIR Built-Up Index (VrNIR-BI), the Perpendicular Impervious Surface Index (PISI), the Combinational Biophysical Composition Index (CBCI), the Normalized Built-up Area Index (NBAI), the Built-Up Index (BUI), the Enhanced Normalized Difference Impervious Surfaces Index (ENDISI) and the Built-up Land Features Extraction Index (BLFEI). Results show that BLFEI, SWIRED, and BUI maintain high separability between built-up and each of the other land cover types across both seasons, as evaluated via the Spectral Discrimination Index (SDI). The lowest SDI values for all three indices were observed for bare soil against BUAs, with BLFEI recording 1.21 in the wet season and 1.05 in the dry season, SWIRED yielding 1.22 and 1.08, and BUI showing 1.21 and 1.08, demonstrating their robustness in distinguishing BUAs from other land covers under varying phenological and soil moisture conditions. These indices reached overall accuracies of $93.97\%$, $93.39\%$ and $92.81\%$, respectively, in wet conditions, and $91.57\%$, $89.17\%$ and $89.67\%$, respectively, in dry conditions. The assessment of thresholding methods reveals that the Minimum method resulted in the highest accuracies for these indices in wet conditions, where bimodal medium peaked histograms were observed, whereas the use of Li, Huang, Shanbhag, Otsu, K-means, or IsoData was found to be the most effective under dry conditions, where more peaked histograms were observed.