éclairer le choix des outils de simulation des changements des modes d’occupation et d’usages des sols. Une approche comparative

Mas, Jean-François; Kolb, Mélanie; Houet, Thomas; Paegelow, Martin; Olmeda, Maria Teresa Camacho

doi:10.3166/rig.21.405-430

Cited by 3 publications

(6 citation statements)

References 12 publications

(18 reference statements)

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“…Leur fonctionnement « type » se résume en trois étapes (Mas et al, 2011 ; : (1) à partir de ces données, ils estiment les tendances de changements futurs en terme de quantité de changements ainsi que les probabilités de transition d'une classe d'occupation du sol vers une autre ; (2) à l'aide de données géographiques traduisant les facteurs explicatifs de la localisation des changements, ils produisent, pour chaque transition et à l'aide de méthodes statistiques plus ou moins complexes (régression logistique, réseau neuronal, etc. ), une carte des probabilités de localisation des changements ; (3) ils utilisent des méthodes dynamiques et spatialement explicites (automate cellulaire, système multiagents, etc.)…”

Section: La Place De La Calibration Des Modèlesunclassified

“…Kolb et al (2013) ont mis en évidence que ceci n'était jamais démontré même dans le cas où aucun facteur de changement supplémentaire soit intervenu. De plus, les méthodes utilisées peuvent modifier le poids relatif de chacun d'eux dès lors qu'un nouveau facteur est intégré ; -à partir d'un même jeu de données et de règles, les modèles peuvent également produire des simulations spatio-temporelles différentes en raison des méthodes mobilisées (Mas et al, 2011) et donc engendrer une incertitude propre (Fig. 1c).…”

Section: La Place De La Calibration Des Modèlesunclassified

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Usages des modèles spatiaux pour la prospective

Houet

2015

Rev. Int. Geomat.

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Section: La Place De La Calibration Des Modèlesunclassified

Usages des modèles spatiaux pour la prospective

Houet

2015

Rev. Int. Geomat.

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“…When CA_MARKOV uses a spatial filter, the simulation does not necessarily select the highest ranking pixels from the original suitability maps, because the spatial filter modifies the original suitability maps. Mas et al (2011Mas et al ( , 2014 showed that LCM and DINAMICA tend to simulate new patches when the explanatory variables produce disjoint patches that have high transition potential. Neural networks use non-linear functions and consider interactions among variables.…”

Section: Allocation Of Changementioning

confidence: 99%

“…For example, the proportion at the year 2006 is 0.1696. A more detailed description of the MARKOV module is in the Idrisi Selva Help System, Mas et al (2011Mas et al ( , 2014 and Sinha and Kumar (2013).…”

Section: Components Of Modelsmentioning

confidence: 99%

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Comparison of simulation models in terms of quantity and allocation of land change

Olmedo

Pontius

Paegelow

et al. 2015

Environmental Modelling & Software

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-François Mas. Comparison of simulation models in terms of quantity and allocation of land change. Environmental Modelling and Software, Elsevier, 2015, pp.214-221. 10.1016/j.envsoft.2015 Comparison of simulation models in terms of quantity and allocation of land change a b s t r a c tOur article illustrates how to compare the outputs from models that simulate transitions among categories through time. We illustrate the concepts by comparing two land change models: Land Change Modeler and Cellular Automata Markov. We show how the modeling options influence the quantity and allocation of simulated transitions, and how to compare output maps from pairs of model runs with respect to a reference map of transitions during the validation interval. We recommend that the first step is to assess the quantity of each transition and to determine the cause of the variation in quantity among model runs. The second step is to assess the allocation of transitions and to determine the cause of the variation in allocation among model runs. The separation of quantity and allocation of the transitions is a helpful approach to communicate how models work and to describe pattern validation.

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A Bayesian Spatial Model Highlights Distinct Dynamics in Deforestation from Coca and Pastures in an Andean Biodiversity Hotspot

Chadid

Dávalos²,

Molina

et al. 2015

Forests

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Abstract:The loss of tropical forests has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. Here, we examine the conversion of forests to pastures and coca crops (illicit activity) on the San Lucas Mountain Range, Colombia for 2002-2007 and 2007-2010. Land use maps and biophysical variables were used as inputs to generate land use and cover change (LUCC) models using the DINAMICA EGO software. These analyses revealed a dramatic acceleration of the pace of deforestation in the region, with rates of conversion from forest to pasture doubling from the first to the second period. Altitude, distance to other crops, and distance to rivers were the primary drivers of deforestation. The influence of these drivers, however, differed markedly depending on whether coca cultivation or pastures replaced forest. Conversion to coca was more probable farther from other crops and from settlements. In contrast, proximity to other crops and to settlements increased conversion to pasture. These relationships highlight the different roles of coca and pastures in forest loss, OPEN ACCESSForests 2015, 6 3829 with coca tending to open up new forest frontiers, and pastures tending to consolidate agricultural expansion and urban influence. Large differences between LUCC processes for each period suggest highly dynamic changes, likely associated with shifting underlying causes of deforestation. These changes may relate to shifts in demand for illicit crops, land, or mining products; however, the data to test these hypotheses are currently lacking. More frequent and detailed monitoring is required to guide actions to decrease the loss of forest in this highly vulnerable biodiversity hotspot in the Northern Andes.

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éclairer le choix des outils de simulation des changements des modes d’occupation et d’usages des sols. Une approche comparative

Cited by 3 publications

References 12 publications

Usages des modèles spatiaux pour la prospective

Usages des modèles spatiaux pour la prospective

Comparison of simulation models in terms of quantity and allocation of land change

A Bayesian Spatial Model Highlights Distinct Dynamics in Deforestation from Coca and Pastures in an Andean Biodiversity Hotspot

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