A Formally and Algorithmically Efficient LULC change Model-Building Environment

François-Rémi Mazy (1) , Pierre-Yves Longaretti (1)

1 STEEP - Sustainability transition, environment, economy and local policy

The use of spatially explicit land use and land cover (LULC) change models is widespread in environmental sciences and of interest in public decision-help. However, it appears that these models suffer from significant biases and shortcomings, the sources of which can be mathematical, conceptual or algorithmic. We formalize a modeling environment that distinguishes a calibration-estimation module and an allocation module. We propose an accurate calibration-estimation method based on kernel density estimation and detail an unbiased allocation algorithm. Moreover, a method of evaluation of LULC change models is presented and allows us to compare them on various fronts (accuracy, biases, computational efficiency). A case study based on a real land use map but with known (enforced) transition probabilities is used. It appears that the estimation error of the methods we propose is substantially improved over the best existing software. Moreover, these methods require the specification of very few parameters by the user, and are numerically efficient. This article presents an overview of our LULC change modeling framework; its various formal and algorithmic constituents will be detailed in forthcoming papers.

Mots clés: Land Use Change; Land Cover Change; LULC; Model Development; Model Evaluation; Model Accuracy; Density Estimation; Calibration; Allocation; Map Simulation

Domaines: Applications [stat.AP]; Milieux et Changements globaux; Environnement et Société; Modélisation et simulation

François-Rémi Mazy, Pierre-Yves Longaretti. A Formally and Algorithmically Efficient LULC change Model-Building Environment. GISTAM 2022 - International Conference on Geographical Information Systems Theory, Applications and Management, Apr 2022, Lisbon, Portugal. pp.1-12. ⟨hal-03626612⟩