Tropical deforestation modelling: comparative analysis of different predictive approaches. The case study of Peten, Guatemala

Follador, Marco; Villa, Nathalie; Paegelow, Martin; Renno, Fernanda; Rossion, Bruno

doi:10.1007/978-3-540-68498-5_3

Cited by 6 publications

(3 citation statements)

References 14 publications

(4 reference statements)

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“…Besides which, the proposed model includes variables, such as forest production, degraded forests, and areas for restoration, that have not been included in other studies to determine the most suitable areas for deforestation or reforestation. The weighting criteria and the weights assigned to categorical variables also differ from other studies that have used MCE [32,67,68]. The weights of categorical variables were assigned from two points of view: the qualitative criteria, which was based on the opinion of experts and the quantitative criteria, which considered spatial computations.…”

Section: Discussionmentioning

confidence: 99%

Geospatial Simulation Model of Deforestation and Reforestation Using Multicriteria Evaluation

et al. 2020

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Deforestation is an anthropic phenomenon that negatively affects the environment and therefore the climate, the carbon cycle, biodiversity and the sustainability of agriculture and drinking water sources. Deforestation is counteracted by reforestation processes, which is caused by the natural regeneration of forests or by the establishment of plantations. The present research is focused on generating a simulation model to predict the deforestation and reforestation for 2030 and 2050 using geospatial analysis techniques and multicriteria evaluation. The case study is the North Pacific Basin, which is one of the areas with the greatest loss of forest cover in Mexico. The results of the spatial analysis of forest dynamics determined that the forest area in 2030 would be 98,713.52 km2, while in 2050 would be 101,239.8 km2. The mean annual deforestation and reforestation expected in the study area is 115 and 193.84 km2, for the 2014–2030 period, while mean annual deforestation and reforestation values of 95 and 221.31 km2 are expected for the 2030–2050 period. Therefore, considering the forest cover predicted by the deforestation and reforestation model, a carbon capture of 16,209.67 ton/C was estimated for the 2014–2030 period and 587,596.01 ton/C for the 2030–2050.

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Section: Discussionmentioning

confidence: 99%

Geospatial Simulation Model of Deforestation and Reforestation Using Multicriteria Evaluation

et al. 2020

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“…In the recent years, the artificial neural networks (ANNs), frequently applied to the study of complex systems for solving engineering problems of artificial intelligence such as problems related to the various technological fields and, alongside to satellite data, are used to estimate the thematic information at a sub-pixel scale, for the risk analysis (Foody 1997), to classify images, to quantify the land use changes (Kanungo et al 2006) and finally for the prediction of the environmental dynamics (Follador et al 2008).…”

Section: The Soft Computing Methods and The Analysis Neural Network (Ann)mentioning

confidence: 99%

The artificial neural network for the rockfall susceptibility assessment. A case study in Basilicata (Southern Italy)

Losasso

Sdao

2018

Geomatics, Natural Hazards and Risk

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This paper presents the results obtained by the elaboration of an artificial neuronal network for the creation of a rockfall susceptibility map. The analysis was carried out by analysing the predisposing and triggering factors of the rockfall phenomenon. The parameters considered for this study and representing the input data of the artificial neural network are factors such as: gradient, soil use, lithology, rockfall source areas and kinetic energy values obtained by considering the probable pathways of the blocks through simulations with dedicated softwares, DEMs and niches of the rockfalls that have already occurred in the past. The processing of this data (required in a versatile dedicated software for the realization of the artificial neural network in ASCII format) is done using GIS softwares, useful tools for the creation of hazard maps. An important step is the realization of the rockfall inventory map: it allows to identify the training set (consisting of 50% of the pixels relative to the rockfall niches) for the network training and the testing set (considering the remaining 50% of the pixels relative to the rockfall niches) to assess the network accuracy by overlaying the rockfall niches belonging to the testing set with the obtained susceptibility map.

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“…A cela s'ajoute une comparaison de l'aptitude des outils à simuler des changements d'occupation et d'usages des sols. Cette analyse comparative s'appuie sur une base de données volontairement simplifiée qui intègre des types de dynamiques théoriques de changements d'usages des sols, aussi bien présentes sur des fronts pionniers (Follador et al, 2008) que dans des territoires sujets à l'étalement urbain (Aguejdad et Houet, 2008). La procédure d'évaluation est basée sur la comparaison de la carte simulée pour une date donnée avec une carte de référence (la réalité) à la même date.…”

Section: Approche Comparative : Processus De Modélisation Fonctionneunclassified

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

Mas¹,

Kolb²,

Houet³

et al. 2011

Revue internationale de géomatique

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Tropical deforestation modelling: comparative analysis of different predictive approaches. The case study of Peten, Guatemala

Cited by 6 publications

References 14 publications

Geospatial Simulation Model of Deforestation and Reforestation Using Multicriteria Evaluation

Geospatial Simulation Model of Deforestation and Reforestation Using Multicriteria Evaluation

The artificial neural network for the rockfall susceptibility assessment. A case study in Basilicata (Southern Italy)

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

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