Keywords
LUCC modelling, agent-based modelling, initialization step, Northern Ecuadorian Amazon
Start Date
17-9-2020 1:40 PM
End Date
17-9-2020 2:00 PM
Abstract
Spatial agent-based models (ABMs) are increasingly used to study land use and land cover changes (LUCC). This process-based approach allows formalizing the interactions between society and environment through the modelling of human decision-making processes regarding to the land use and its impact on regional land cover. Data-driven ABMs, which have emerged over the past decade, imply the integration of data for getting models more realistic, in a descriptive approach (KIDS). Most of the data are integrated at the initialization step, where agents of the simulation are created and their attributes values (including location) are initialized. We argue that this step is crucial, particularly because of the well-known phenomena of path dependence and dependence over initial conditions but also for getting a more comprehensive simulation model i.e. more realistic initial conditions of agents and landscape, a necessity for testing the model facing known empirical data using calibration. Thus, we introduce a LUCC-ABM of deforestation dynamics in Northern Ecuadorian Amazon for which initialization has been optimized. Our model workflow can be described as follow: first, 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 generate an agricultural landscape by specifying a patchwork of farming activities in each cadastral parcel 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 reproduce LUCC patterns due to human activities over eight years in Northern Ecuadorian Amazon as they appeared on land cover maps.
Building an agent-based model of Land Use and Land Cover Changes to simulate changes between two land cover maps: lessons from an initialization step optimization
Spatial agent-based models (ABMs) are increasingly used to study land use and land cover changes (LUCC). This process-based approach allows formalizing the interactions between society and environment through the modelling of human decision-making processes regarding to the land use and its impact on regional land cover. Data-driven ABMs, which have emerged over the past decade, imply the integration of data for getting models more realistic, in a descriptive approach (KIDS). Most of the data are integrated at the initialization step, where agents of the simulation are created and their attributes values (including location) are initialized. We argue that this step is crucial, particularly because of the well-known phenomena of path dependence and dependence over initial conditions but also for getting a more comprehensive simulation model i.e. more realistic initial conditions of agents and landscape, a necessity for testing the model facing known empirical data using calibration. Thus, we introduce a LUCC-ABM of deforestation dynamics in Northern Ecuadorian Amazon for which initialization has been optimized. Our model workflow can be described as follow: first, 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 generate an agricultural landscape by specifying a patchwork of farming activities in each cadastral parcel 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 reproduce LUCC patterns due to human activities over eight years in Northern Ecuadorian Amazon as they appeared on land cover maps.
Stream and Session
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