Quantifying oil palm expansion in Southeast Asia from 2000 to 2015: A data fusion approach

Melissa Wagner, Elizabeth A. Wentz, Michelle Stuhlmacher

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The fusion of optical imagery with radar data can provide more accurate land cover change analysis of deforestation and tree-based agriculture. Radar data is limited temporally with most geographic areas not covered prior to 2007. This paper presents a new methodology to classify land cover change related to oil palm expansion that takes historic data limitations into account. Our approach utilizes Hansen’s Global Forest Cover data, optical imagery, and texture information, to extract land cover information in Sumatra and Western Malaysia, where historical data is absent. Our method demonstrates how to accurately classify oil palm without radar data with overall accuracies for optical only experiments within 4.4% of optical plus radar classifications. Our results show agricultural land use was the primary driver of land cover change with the largest increase due to oil palm expansion (6.1%). Better estimations of oil palm expansion could be used in sustainable land management policies.

Original languageEnglish (US)
Pages (from-to)26-46
Number of pages21
JournalJournal of Land Use Science
Issue number1
StatePublished - 2022


  • Data fusion
  • accuracy assessment
  • classification
  • deforestation
  • land cover change
  • land use change
  • oil palm
  • remote sensing

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes
  • Management, Monitoring, Policy and Law


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