Dataset and methodological framework for land-use change scenario modelling in Uruguay using the Land Change Modeler.
- 1Department of Geography, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany.
- 2Integrative Research Institute THESys Transformation of Human-Environment-Systems, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany.
- 3Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany.
Abstract
This article presents a spatial dataset and methodological workflow for simulating land-system dynamics in Uruguay using the Land Change Modeler implemented in TerrSet v.2020. The dataset combines harmonized land-cover maps for 2000, 2015, and a validation dataset for 2020 with explanatory variables, transition probability matrices, and scenario-based simulations developed through a multi-layer perceptron-Markov chain approach. A key feature of the dataset is the harmonization of heterogeneous national land-use datasets into a consistent modelling framework, combined with a transparent workflow for explanatory variable selection, transition modelling, and scenario parameterization. The dataset provides the final scenario outputs together with the transition probability matrices, conservation constraint and incentive layers, validation outputs, and the methodological workflow required to reproduce the analyses and adaptation of the workflow to other regions. Three land-use policy pathways were implemented: Business-as-Usual (BAU), Multifunctional Landscape (MFL), and Grassland Conservation (GC). The dataset includes high-resolution raster layers, conservation constraint and incentive maps, explanatory variable influence assessments, and model validation outputs supporting reproducible spatial analyses and territorial planning applications. These data complement the associated Land Use Policy article, Balancing Growth and Conservation: Land-Use Policy Scenarios for Uruguay's Grasslands [1], by providing the underlying spatial datasets, methodological details, and validation framework required for reproducibility and reuse. The resources facilitate further research on grassland conservation, agroecological transitions, and scenario-based environmental assessment.
Related
- Modelling spatial trade-offs in urban sub-centre planning: A prospective scenario framework linking land value capture and residential welfare
- A Formal Framework for Measuring Institutional Collapse under a Nuclear Winter Scenario
- GLORIF1, a global river flow dataset created by integrating process-based modelling and machine learning
- An Artificial Intelligence Integrated Geospatial Decision-Support Framework for Scenario-Based Climate-Adaptive Land-Use Optimization and Environmental Risk Reduction
- Scenario-based assessment of urban community climate resilience under flood Risk: An integrated UCCRI–AHP–TOPSIS framework
BibTeX
@article{schweikart2026dataset,
title = {{Dataset and methodological framework for land-use change scenario modelling in Uruguay using the Land Change Modeler.}},
author = {Melanie Schweikart and Leonardo R Ramírez and Ina Säumel},
journal = {Data in brief},
year = {2026},
month = aug,
doi = {10.1016/j.dib.2026.113287},
url = {https://europepmc.org/article/MED/42910003},
note = {CC BY},
}
RIS
TY - JOUR
TI - Dataset and methodological framework for land-use change scenario modelling in Uruguay using the Land Change Modeler.
AU - Melanie Schweikart
AU - Leonardo R Ramírez
AU - Ina Säumel
PY - 2026
DA - 2026/08/01
JO - Data in brief
DO - 10.1016/j.dib.2026.113287
UR - https://europepmc.org/article/MED/42910003
AB - This article presents a spatial dataset and methodological workflow for simulating land-system dynamics in Uruguay using the Land Change Modeler implemented in TerrSet v.2020. The dataset combines harmonized land-cover maps for 2000, 2015, and a validation dataset for 2020 with explanatory variables, transition probability matrices, and scenario-based simulations developed through a multi-layer perceptron-Markov chain approach. A key feature of the dataset is the harmonization of heterogeneous national land-use datasets into a consistent modelling framework, combined with a transparent workflow for explanatory variable selection, transition modelling, and scenario parameterization. The dataset provides the final scenario outputs together with the transition probability matrices, conservation constraint and incentive layers, validation outputs, and the methodological workflow required to reproduce the analyses and adaptation of the workflow to other regions. Three land-use policy pathways were implemented: Business-as-Usual (BAU), Multifunctional Landscape (MFL), and Grassland Conservation (GC). The dataset includes high-resolution raster layers, conservation constraint and incentive maps, explanatory variable influence assessments, and model validation outputs supporting reproducible spatial analyses and territorial planning applications. These data complement the associated Land Use Policy article, Balancing Growth and Conservation: Land-Use Policy Scenarios for Uruguay's Grasslands [1], by providing the underlying spatial datasets, methodological details, and validation framework required for reproducibility and reuse. The resources facilitate further research on grassland conservation, agroecological transitions, and scenario-based environmental assessment.
SN - 2352-3409
ER -