Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine
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 f...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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2021
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| Online Access: | https://hdl.handle.net/10356/146022 |
| _version_ | 1811684245080375296 |
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| author | Sarzynski, Thuan Giam, Xingli Carrasco, Luis Lee, Janice Ser Huay |
| author2 | Asian School of the Environment |
| author_facet | Asian School of the Environment Sarzynski, Thuan Giam, Xingli Carrasco, Luis Lee, Janice Ser Huay |
| author_sort | Sarzynski, Thuan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:25:33Z |
| format | Journal Article |
| id | ntu-10356/146022 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:25:33Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1460222023-02-28T16:40:30Z Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine Sarzynski, Thuan Giam, Xingli Carrasco, Luis Lee, Janice Ser Huay Asian School of the Environment Earth Observatory of Singapore Engineering::Environmental engineering Elaeis Guineensis Random Forest 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. Ministry of Education (MOE) Published version This research was funded by Singapore Ministry of Education Academic Research Fund Tier 1 grant number RG146/16. 2021-01-21T06:10:26Z 2021-01-21T06:10:26Z 2020 Journal Article Sarzynski, 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/rs12071220 2072-4292 0000-0002-5239-9477 0000-0001-6368-6212 https://hdl.handle.net/10356/146022 10.3390/rs12071220 2-s2.0-85084251001 7 12 en RG146/16 Remote Sensing © 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/). application/pdf |
| spellingShingle | Engineering::Environmental engineering Elaeis Guineensis Random Forest Sarzynski, Thuan Giam, Xingli Carrasco, Luis Lee, Janice Ser Huay Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine |
| title | Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine |
| title_full | Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine |
| title_fullStr | Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine |
| title_full_unstemmed | Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine |
| title_short | Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine |
| title_sort | combining radar and optical imagery to map oil palm plantations in sumatra indonesia using the google earth engine |
| topic | Engineering::Environmental engineering Elaeis Guineensis Random Forest |
| url | https://hdl.handle.net/10356/146022 |
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