Incorporating landscape patterns can improve global-scale models of land use and land cover change
Incorporating landscape patterns can improve global-scale models of land use and land cover change
Environmental and socioeconomic variables that are used to predict land use and land cover (LULC) change in most global-scale land use models miss some LULC change processes, including cropland expansion in frontier areas. We tested whether incorporating landscape metrics, which measure the spatial arrangement of landscapes and vary according to complex anthropogenic and environmental factors, in global- and continental-scale machine learning models of LULC change from 1992 to 2020 improved model performance when compared to socioeconomic and biophysical variables. Two types of LULC change were modelled: cropland expansion and forest loss. Including landscape metrics always improved the accuracy of cropland expansion models but enhanced the performance of forest loss models for only coarser resolution pixels of 80 × 80 km. Therefore, landscape metrics may provide additional insights for modelling LULC change beyond socioeconomic and biophysical variables, but further research is needed to assess the impact of spatial scale on their effectiveness.
35-55
Woodman, Tamsin L.
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Alexander, Peter
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Burslem, David F.R.P.
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Travis, Justin M.J.
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Eigenbrod, Felix
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Woodman, Tamsin L.
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Alexander, Peter
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Burslem, David F.R.P.
10e7f0c6-86ca-46b3-b6e9-b8743b908729
Travis, Justin M.J.
eeb29958-d843-49e0-8583-7515a7b7708c
Eigenbrod, Felix
43efc6ae-b129-45a2-8a34-e489b5f05827
Woodman, Tamsin L., Alexander, Peter, Burslem, David F.R.P., Travis, Justin M.J. and Eigenbrod, Felix
(2026)
Incorporating landscape patterns can improve global-scale models of land use and land cover change.
Journal of Land Use Science, 21 (1), .
(doi:10.1080/1747423X.2026.2622710).
Abstract
Environmental and socioeconomic variables that are used to predict land use and land cover (LULC) change in most global-scale land use models miss some LULC change processes, including cropland expansion in frontier areas. We tested whether incorporating landscape metrics, which measure the spatial arrangement of landscapes and vary according to complex anthropogenic and environmental factors, in global- and continental-scale machine learning models of LULC change from 1992 to 2020 improved model performance when compared to socioeconomic and biophysical variables. Two types of LULC change were modelled: cropland expansion and forest loss. Including landscape metrics always improved the accuracy of cropland expansion models but enhanced the performance of forest loss models for only coarser resolution pixels of 80 × 80 km. Therefore, landscape metrics may provide additional insights for modelling LULC change beyond socioeconomic and biophysical variables, but further research is needed to assess the impact of spatial scale on their effectiveness.
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Incorporating landscape patterns - accepted manuscript
- Accepted Manuscript
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Incorporating landscape patterns can improve global-scale models of land use and land cover change (1)
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Accepted/In Press date: 21 January 2026
e-pub ahead of print date: 28 January 2026
Identifiers
Local EPrints ID: 509778
URI: http://eprints.soton.ac.uk/id/eprint/509778
ISSN: 1747-423X
PURE UUID: bfcfa126-b9b3-47d0-bc8c-91df2e192d6e
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Date deposited: 04 Mar 2026 17:55
Last modified: 07 Mar 2026 03:14
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Contributors
Author:
Tamsin L. Woodman
Author:
Peter Alexander
Author:
David F.R.P. Burslem
Author:
Justin M.J. Travis
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