Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146022
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dc.contributor.authorSarzynski, Thuanen_US
dc.contributor.authorGiam, Xinglien_US
dc.contributor.authorCarrasco, Luisen_US
dc.contributor.authorLee, Janice Ser Huayen_US
dc.date.accessioned2021-01-21T06:10:26Z-
dc.date.available2021-01-21T06:10:26Z-
dc.date.issued2020-
dc.identifier.citationSarzynski, T., Giam, X., Carrasco, L., & Lee, J. S. H. (2020). Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine. Remote Sensing, 12(7), 1220-. doi:10.3390/rs12071220en_US
dc.identifier.issn2072-4292en_US
dc.identifier.other0000-0002-5239-9477-
dc.identifier.other0000-0001-6368-6212-
dc.identifier.urihttps://hdl.handle.net/10356/146022-
dc.description.abstractMonitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationRG146/16en_US
dc.relation.ispartofRemote Sensingen_US
dc.rights© 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.subjectEngineering::Environmental engineeringen_US
dc.titleCombining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engineen_US
dc.typeJournal Articleen
dc.contributor.schoolAsian School of the Environmenten_US
dc.contributor.researchEarth Observatory of Singaporeen_US
dc.identifier.doi10.3390/rs12071220-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85084251001-
dc.identifier.issue7en_US
dc.identifier.volume12en_US
dc.subject.keywordsElaeis Guineensisen_US
dc.subject.keywordsRandom Foresten_US
dc.description.acknowledgementThis research was funded by Singapore Ministry of Education Academic Research Fund Tier 1 grant number RG146/16.en_US
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