Towards a Generic Theoretical Framework for Pattern-Based LUCC Modeling | Towards a Generic Theoretical Framework for Pattern-Based LUCC Modeling: An Accurate and Powerful Calibration-Estimation Method Based on Kernel Density Estimation
2022
Mazy, François-Rémi | Longaretti, Pierre-Yves | Sustainability transition, environment, economy and local policy (STEEP) ; Inria Grenoble - Rhône-Alpes ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) ; Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) ; Université Grenoble Alpes (UGA) | Institut de Planétologie et d'Astrophysique de Grenoble (IPAG) ; Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ) ; Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France
International audience
显示更多 [+] 显示较少 [-]英语. Several modeling strategies have been proposed to study Land Use and Land Cover changes (LUCC). However, substantial discrepancies have been noted between different models for the same problem, questioning their overall reliability and reproducibility. To address this challenge, we elaborate a generic, formally correct, theoretical framework for pattern-based LUCC modeling, which is implemented in our own software, CLUMPY (Comprehensive Land Use [and cover] Modeling in PYthon).The present work focuses on calibration. We devise a kernel density calibration-estimation method (Bayes-eKDE) that is shown on synthetic artificial data to be both accurate and algorithmically efficient. We also introduce a generic evaluation method that allows us to compare the calibration efficiency of existing models. The gain in precision and computational time of our calibration method is precisely quantified in this way.
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