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Article

Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

1
Institute of Landscape and Plant Ecology, University of Hohenheim, 70599 Stuttgart, Germany
2
Ecology & Evolutionary Biology, The University of Tennessee, Knoxville, TN 37996, USA
3
National Institute for Mathematical & Biological Synthesis, Knoxville, TN 37996, USA
4
Asian School of the Environment & Earth Observatory Singapore, Nanyang Technological University of Singapore, Singapore 637551, Singapore
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1220; https://doi.org/10.3390/rs12071220
Received: 2 March 2020 / Revised: 27 March 2020 / Accepted: 8 April 2020 / Published: 10 April 2020
Monitoring 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. View Full-Text
Keywords: Elaeis guineensis; random forest; Southeast Asia; land-cover classification; synthetic aperture radar; Landsat Elaeis guineensis; random forest; Southeast Asia; land-cover classification; synthetic aperture radar; Landsat
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MDPI and ACS Style

Sarzynski, T.; Giam, X.; Carrasco, L.; Lee, J.S.H. Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine. Remote Sens. 2020, 12, 1220. https://doi.org/10.3390/rs12071220

AMA Style

Sarzynski T, Giam X, Carrasco L, Lee JSH. Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine. Remote Sensing. 2020; 12(7):1220. https://doi.org/10.3390/rs12071220

Chicago/Turabian Style

Sarzynski, Thuan, Xingli Giam, Luis Carrasco, and Janice S.H. Lee 2020. "Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine" Remote Sensing 12, no. 7: 1220. https://doi.org/10.3390/rs12071220

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