Modeling Earth Systems and Environment

, Volume 3, Issue 4, pp 1215–1228 | Cite as

An ensemble of spatially explicit land-cover model projections: prospects and challenges to retrospectively evaluate deforestation policy

  • Andrew V. Bradley
  • Isabel M. D. Rosa
  • Amintas BrandãoJr.
  • Stefano Crema
  • Carlos Dobler
  • Simon Moulds
  • Sadia E. Ahmed
  • Tiago Carneiro
  • Matthew J. Smith
  • Robert M. Ewers
Original Article

Abstract

Ensemble techniques, common in many disciplines, have yet to be fully exploited with spatially explicit projections from land-change models. We trial a land-change model ensemble to assess the impact of policies designed to conserve tropical rainforest at the municipality scale in Brazil, noting the achievements made and challenges ahead. Four spatial model frameworks that were calibrated with the same predictor variables produced 21 counterfactual simulations of the actual landscape. Individual projections with a uniform calibration period gave estimates that between 29 and 68% of the simulated deforestation was saved, but lacked an uncertainty estimate, whilst batch projections from two different model frameworks provided a more dependable mean estimate that 38 and 49% deforestation was prevented with an uncertainty range of 1900 and 1000 km2. The consensus ensembles used agreement between the projections and found that the seven examples with a uniform calibration period produced an error margin of ±435.94 km2 and a prevented forest loss estimate of 50%. Using all 21 projections with diverse calibration periods improved these errors to ±179.26 km2 with a 53% estimate of prevented forest loss. Whilst we achieved a method of combining projections of different frameworks to reduce uncertainty of individual modelling frameworks, demonstrating a control model and accounting for non-linear conditions are challenges that will provide better confidence in this method as an operational tool. Such retrospective evidence could be used to make timely rewards for efforts of governments and municipalities to support tropical forest conservation and help mitigate deforestation.

Keywords

Land-cover modeling Deforestation Environmental policy SimiVal Brazil Green Municipality 

Notes

Acknowledgements

Spatial data sets were supplied by Imazon, Brazil, the socioeconomic data from Instituto Nacional de Pesquisas Espaciais, Brazil, and prepared at Imperial College by Igor Lysenco. Funding for the land-cover modelling Tansley working group was from Natural Environment Research Council Biodiversity & Ecosystem Service Sustainability grant, NERC/PR100027. AVB, IMDR and RME were supported by European Research Council grant 281986. This paper represents a contribution to Imperial College’s initiative in Grand Challenges in Ecosystems and the Environment. We also thank the feedback of the anonymous referees on earlier drafts of this paper.

Supplementary material

40808_2017_376_MOESM1_ESM.docx (31 kb)
Supplementary material 1 (DOCX 32 KB)

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Copyright information

© Crown Copyright 2017

Authors and Affiliations

  • Andrew V. Bradley
    • 1
  • Isabel M. D. Rosa
    • 1
    • 2
    • 3
  • Amintas BrandãoJr.
    • 4
    • 5
  • Stefano Crema
    • 6
  • Carlos Dobler
    • 7
  • Simon Moulds
    • 8
    • 9
  • Sadia E. Ahmed
    • 10
    • 11
  • Tiago Carneiro
    • 12
    • 13
  • Matthew J. Smith
    • 14
  • Robert M. Ewers
    • 1
  1. 1.Department of Life SciencesImperial College of LondonAscotUK
  2. 2.Biodiversity ConservationGerman Centre for Integrative Biodiversity Research (iDiv)LeipzigGermany
  3. 3.Martin Luther University Halle-WittenbergHalleGermany
  4. 4.Imazon, Travessa Dom Romualdo de SeixasBelémBrazil
  5. 5.Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental StudiesUniversity of WisconsinMadisonUSA
  6. 6.Clark LabsClark UniversityWorcesterUSA
  7. 7.Graduate School of GeographyClark UniversityWorcesterUSA
  8. 8.Centre for Water SystemsUniversity of ExeterExeterUK
  9. 9.Department of Civil and Environmental EngineeringImperial College of LondonLondonUK
  10. 10.Computational Science LaboratoryMicrosoft ResearchCambridgeUK
  11. 11.AB Sustain, 64 Innovation WayPeterboroughUK
  12. 12.Environmental Change InstituteOxford University Centre for the EnvironmentOxfordUK
  13. 13.TerraLAB, Computer Science DepartmentFederal University of Ouro PretoOuro PretoBrazil
  14. 14.Microsoft Digital, MicrosoftLondonUK

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