Parsch, Christoph, Wagner, Benjamin, Engert, Jayden e., Panjaitan, Rawati, Laurance, William F., Nitschke, Craig R., and Kreft, Holger (2025) Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata. Science of the Total Environment, 970. 178864.

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Abstract

The island of New Guinea harbors some of the world’s most biologically diverse and highly endemic tropical ecosystems. Nevertheless, progressing land-use change in the region threatens their integrity, which will adversely affect their biodiversity as well as carbon stocks and fluxes. Our objectives were to (1) compare deforestation drivers between Indonesian New Guinea and Papua New Guinea, (2) identify areas with a high risk of future deforestation under different development scenarios, and (3) evaluate the effects of potential deforestation scenarios on carbon pools. We integrated machine learning and cellular automata to model and forecast deforestation across New Guinea. We assessed the potential loss of irrecoverable carbon stocks for four deforestation scenarios ranging from 4.8 % (business-as-usual) to 28 % (high development scenario) forest loss between 2020 and 2040. Areas of high deforestation risk were consistently forecasted in lowland regions across the four deforestation scenarios. In Indonesian New Guinea, 75 % of deforestation was forecasted below ~380 m a.s. l., but ranged higher in Papua New Guinea (<750 m a.s.l.). Land change-induced carbon loss varied largely across the four scenarios and ranged between 156 and 918 Mt in Indonesian New Guinea and between 223 and 1082 Mt in Papua New Guinea, respectively. Our analysis reveals promising potential for integrating random forests and cellular automata models to forecast high-resolution deforestation over large spatial extents. Our models reveal the vulnerability of New Guinea’s lowlands to future deforestation, emphasizing the need to protect key areas where deforestation conflicts with the conservation of carbon stocks, ecosystem functions, and biodiversity.

Item ID:	84913
Item Type:	Article (Research - C1)
ISSN:	1879-1026
Keywords:	Carbon, Cellular automata, Deforestation, Indonesia, Land change, Papua New Guinea, Random forests
Copyright Information:	© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Date Deposited:	28 Oct 2025 23:50
FoR Codes:	41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 50% 40 ENGINEERING > 4013 Geomatic engineering > 401302 Geospatial information systems and geospatial data modelling @ 50%
SEO Codes:	18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180603 Evaluation, allocation, and impacts of land use @ 100%
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