High-resolution land use/cover forecasts for Switzerland in the 21st century.

ABSTRACT: We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21^st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using > 200 predictors and accounting for climate and policy changes. We improved model performance by using a data augmentation algorithm that synthetically increased the number of cells of underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good prediction accuracy. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using t

SUBMITTER: Butikofer L 

PROVIDER: S-EPMC10891137 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature