A mathematical programming approach for downscaling multi-layered multi-constraint land-use models

Land-use and land-cover change (LULCC) models are important tools for environmental policy planning. LULCC models are frequently constrained to the generation of projections at a specific resolution. However, subsequent studies or models may require finer resolutions. In this work, a downscaling method for LULCC models is proposed that uses a mathematical programming approach to disaggregate the multiple layers of the land-use change projections while respecting a series of constraints. The method is calibrated and validated with MapBiomas data for the years 2000 and 2018 converted for the GLOBIOM-Brazil model, successfully predicting land-use at a finer resolution. Also, as proof of concept, the calibrated model is also applied for GLOBIOM-Brazil projections for 2050. This paper advances the state-of-the-art by proposing and testing a downscaling method using a mathematical programming approach with spatial effects, that operates on multi-layered land-use projections with a range of constraints while allowing flexibility on the number and type of the specific layers and constraints.

Automated summary

This paper proposes a downscaling method for land-use and land-cover change (LULCC) models, using a mathematical programming approach to disaggregate multi-layered land-use change projections while respecting a range of constraints. The method is calibrated and validated with MapBiomas data and successfully predicts land-use at a finer resolution. It is also applied to future projections for 2050. This paper advances the state-of-the-art in LULCC modeling by introducing a mathematical programming approach with spatial effects, allowing flexibility in the number and type of layers and constraints.