High-resolution land-use maps from 1960 to 2100

Anthropogenic land-use and land-cover change is a major driver of climate change and biodiversity loss. Land-use and land-cover change also leads to changes in the spatial pattern of Earth’s landscapes, which have further negative impacts on environmental processes. For example, deforestation can increase the distance between forest patches, making it harder for species to move between them. Harmonized land-use and land-cover datasets contain standardized maps of global land use and land cover through time, without any gaps or sudden changes between historic and future periods. Harmonized datasets are important for addressing the negative consequences of land-use and land-cover change because they are used in environmental models to predict its impacts on processes such as carbon emissions and species movement. However, existing harmonized datasets do not project the spatial pattern of land-use and land-cover change into the future and have coarse resolutions that do not match the fine scales on which environmental processes occur. We present a high-resolution, global-scale harmonized land-use and land-cover dataset for five future scenarios that spans the period 1960–2100. The spatial pattern of land-use and land-cover change was accounted for when generating the future maps to ensure that they have realistic spatial patterns. This new dataset will be suitable for integration with a range of environmental models, such as those that model biodiversity, climate, and fire, and therefore offers an important tool for understanding the effects of land-use and land-cover change and developing solutions to environmental challenges.

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Publication Details

Journal
One Earth
Published
2025-12-05
DOI
https://doi.org/10.1016/j.oneear.2025.101525
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

High-resolution land-use maps from 1960 to 2100

Karina Winkler, David F. R. P. Burslem, Justin M. J. Travis, Roslyn Henry et al.
One Earth
Remote Sensing in Agriculture
article

High-resolution land-use maps from 1960 to 2100

Karina Winkler, David F. R. P. Burslem, Justin M. J. Travis, Roslyn Henry, Felix Eigenbrod, Tamsin L. Woodman, Peter Alexander, Bartlomiej Arendarczyk
article en

Abstract

Anthropogenic land-use and land-cover change is a major driver of climate change and biodiversity loss. Land-use and land-cover change also leads to changes in the spatial pattern of Earth’s landscapes, which have further negative impacts on environmental processes. For example, deforestation can increase the distance between forest patches, making it harder for species to move between them. Harmonized land-use and land-cover datasets contain standardized maps of global land use and land cover through time, without any gaps or sudden changes between historic and future periods. Harmonized datasets are important for addressing the negative consequences of land-use and land-cover change because they are used in environmental models to predict its impacts on processes such as carbon emissions and species movement. However, existing harmonized datasets do not project the spatial pattern of land-use and land-cover change into the future and have coarse resolutions that do not match the fine scales on which environmental processes occur. We present a high-resolution, global-scale harmonized land-use and land-cover dataset for five future scenarios that spans the period 1960–2100. The spatial pattern of land-use and land-cover change was accounted for when generating the future maps to ensure that they have realistic spatial patterns. This new dataset will be suitable for integration with a range of environmental models, such as those that model biodiversity, climate, and fire, and therefore offers an important tool for understanding the effects of land-use and land-cover change and developing solutions to environmental challenges.

One EarthVol. 9(2)
Universitas Gadjah Mada (ID), University of Aberdeen (GB), Alpenforschungsinstitut (Germany) (DE), University of Southampton (GB), University of Edinburgh (GB)
Openalex Percentile: Top 62%
Remote Sensing in Agriculture
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