Publications 👨🏻‍🔬

2023

1. Thèse

   

   Modélisations rétroprospectives des dynamiques de déforestation de l’Amazonie équatorienne : vers une hybridation des approches pattern et process

   Romain Mejean

   Université Toulouse le Mirail - Toulouse II , Nov 2023

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2021

1. GAMA Days

   

   Landscape generation for a Land Use and Land Cover Change Agent-Based Model, with GAMA platform

   Romain Mejean, Kevin Chapuis, Mehdi Saqalli , and 2 more authors

   Juin 2021

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   Land change models have emerged over the past few decades to explore the causes and consequences of land use and land cover changes (LUCC) and to better forecast them in order to guide public policies. Agent-based models of LUCC (LUCC-ABMs) allows formalizing the interactions between society and environment through the modelling of heterogeneous stakeholders involved in land change and their individual decision-making process regarding to the land use, which has an impact on regional land cover. We introduce a LUCC-ABM of deforestation dynamics in Northern Ecuadorian Amazon, built with GAMA platform. The purpose of the model is to reproduce LUCC patterns due to human activities over eight years as they appeared between two land cover maps. To this end, we have developed a workflow based on i) a strong input of both qualitative and quantitative data at initialization step by a landscape generator based on census and survey data ii) livelihood strategies to typologize land use behaviours. Our model workflow can be described as following: after the data processing, we generate a spatially and socially structured population with a synthetic population generation library (GENSTAR) on the basis of census, cadastral and land cover data. Then, we assign a livelihood strategy to each household through a multi-criteria evaluation according to qualitative and spatial criteria from field surveys. Next, once again with GENSTAR, we generate an agricultural landscape for the entire study area by specifying a patchwork of farming activities in each plot assigned to these agents according to their livelihood strategy, on the basis of field survey data in a sparse remote sensing data context. Lastly, we calibrate parameters related to agent decision-making processes in order to better reproduce environmental dynamics. In addition to a contribution on the integration of data in LUCC-ABMs, this work aims to investigate the connection between pattern-based and process-based modelling of LUCC.

2020

1. AGILE

   

   Improving Business-as-Usual Scenarios in Land Change Modelling by Extending the Calibration Period and Integrating Demographic Data

   Romain Mejean, Martin Paegelow, Mehdi Saqalli , and 1 more author

   In Geospatial Technologies for Local and Regional Development , Juin 2020

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   Land use and land cover change (LUCC) models are increasingly being used to anticipate the future of territories, particularly through the prospective scenario method. In the case of so-called trend or Business-as-Usual (BAU) scenarios, the aim is to observe the current dynamics and to extend them into the future. However , as they are implemented as baseline simulation in most current software packages , BAU scenarios are calibrated from a training period built from only two dates. We argue that this limits the quantitative estimation of future change intensity, and we illustrate it from a simple model of deforestation in Northern Ecuadorian Amazon using the Land Change Modeler (LCM) software package. This paper proposes a contribution to improve BAU scenarios calibration by mainly two enhancements: taking into account a longer calibration period for estimating change quantities and the integration of thematic data in change probabilities matrices. We thus demonstrate the need to exceed the linear construction of BAU scenarios as well as the need to integrate thematic and particularly socio-demographic data into the estimation of future quantities of change. The spatial aspects of our quantitative adjustments are discussed and tend to show that improvements in the quantitative aspects should not be dissociated from an improvement in the spatial allocation of changes, which may lead to a decrease in the predictive accuracy of the simulations.

2. Towards ”Socializing the pixel” and ”Pixelizing the social” with GAMA platform : an Agent-Based Model of Land Use and Land Cover Changes in Northern Ecuadorian Amazon

   Romain Mejean, Kevin Chapuis, Doryan Kaced , and 2 more authors

   In 10th IEMS Conference , Jun 2020

2019

1. MABS

   

   PASHAMAMA: An Agricultural Process-Driven Agent-Based Model of the Ecuadorian Amazon

   Doryan Kaced, Romain Mejean, Aurélien Richa , and 2 more authors

   In Multi-Agent-Based Simulation XIX , Jun 2019

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   This article presents the PASHAMAMA model that aims at studying the situation in the northern part of the Amazonian region of Ecuador in which the intensive oil extraction has induced a high rise of population, pollution, agricultural work and deforestation. It simulates these dynamics impacts on both environment and population by examining exposure and demography over time thanks to a retro-prospective and spatially explicit agent-based approach. Based on a previous work that has introduced roads, immigration and pollution (induced by the oil industry) dynamics, we focus here on the agricultural and the oil salaried work sides of the model. Unlike many models that are highly focused on the use of quantitative data, we choose a process-based approach and rest on qualitative data extracted from interviews with the local population: farmers are not represented by highly cognitive agents, but only attempt to fulfill their local objectives by fulfilling sequentially their constraints (e.g. eating before earning money). We also introduce a new evaluation method based on satellite pictures that compares simulated to “real” data on a thematic division of the environment.

2018

1. Contaminations et Conflits Environnementaux Contrastés En Amazonie Équatorienne : Une Approche Par La Cartographie Participative

   Romain Mejean, Clément De Meyer, Doryan Kaced , and 1 more author

   Jun 2018