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Article: Oil palm mapping using Landsat and PALSAR: a case study in Malaysia

TitleOil palm mapping using Landsat and PALSAR: a case study in Malaysia
Authors
Issue Date2016
Citation
International Journal of Remote Sensing, 2016, v. 37, n. 22, p. 5431-5442 How to Cite?
Abstract© 2016 Informa UK Limited, trading as Taylor & Francis Group. Irrespective of the positive economic benefit or negative environmental impact of the rapid expansion of oil palm plantations in tropical regions, it is important to be able to create accurate land-cover maps for such areas. Optical remote sensing is vulnerable to the effects of clouds, which can limit data availability for the oil palm plantation areas in the humid tropics. The satellite-flown PALSAR (Phased Array type L-band Synthetic Aperture Radar) instrument, which provides all-day/all-weather Earth observations, offers the opportunity to identify and map oil palm plantations in cloudy regions. This study used a Support Vector Machine (SVM) classifier and a Mahalanobis distance (MD) classifier to undertake supervised classifications of Landsat, PALSAR, and combined Landsat and PALSAR data (Landsat+PALSAR) for two locations in peninsular Malaysia. Results indicate that accuracies from Landsat+PALSAR are better than accuracies from Landsat and PALSAR along for both study areas using both classifiers. The extents of the oil palm areas estimated from these maps were compared with values obtained through human photointerpretation of Google Earth™ images in previous studies. Based on the R2 statistics, it was established that the Landsat+PALSAR combination performed best for both study areas and demonstrated good potential for oil palm plantation mapping.
Persistent Identifierhttp://hdl.handle.net/10722/296798
ISSN
2020 Impact Factor: 3.151
2020 SCImago Journal Rankings: 0.918
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCheng, Yuqi-
dc.contributor.authorYu, Le-
dc.contributor.authorCracknell, Arthur P.-
dc.contributor.authorGong, Peng-
dc.date.accessioned2021-02-25T15:16:42Z-
dc.date.available2021-02-25T15:16:42Z-
dc.date.issued2016-
dc.identifier.citationInternational Journal of Remote Sensing, 2016, v. 37, n. 22, p. 5431-5442-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296798-
dc.description.abstract© 2016 Informa UK Limited, trading as Taylor & Francis Group. Irrespective of the positive economic benefit or negative environmental impact of the rapid expansion of oil palm plantations in tropical regions, it is important to be able to create accurate land-cover maps for such areas. Optical remote sensing is vulnerable to the effects of clouds, which can limit data availability for the oil palm plantation areas in the humid tropics. The satellite-flown PALSAR (Phased Array type L-band Synthetic Aperture Radar) instrument, which provides all-day/all-weather Earth observations, offers the opportunity to identify and map oil palm plantations in cloudy regions. This study used a Support Vector Machine (SVM) classifier and a Mahalanobis distance (MD) classifier to undertake supervised classifications of Landsat, PALSAR, and combined Landsat and PALSAR data (Landsat+PALSAR) for two locations in peninsular Malaysia. Results indicate that accuracies from Landsat+PALSAR are better than accuracies from Landsat and PALSAR along for both study areas using both classifiers. The extents of the oil palm areas estimated from these maps were compared with values obtained through human photointerpretation of Google Earth™ images in previous studies. Based on the R2 statistics, it was established that the Landsat+PALSAR combination performed best for both study areas and demonstrated good potential for oil palm plantation mapping.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleOil palm mapping using Landsat and PALSAR: a case study in Malaysia-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01431161.2016.1241448-
dc.identifier.scopuseid_2-s2.0-84990909840-
dc.identifier.volume37-
dc.identifier.issue22-
dc.identifier.spage5431-
dc.identifier.epage5442-
dc.identifier.eissn1366-5901-
dc.identifier.isiWOS:000385631700010-
dc.identifier.issnl0143-1161-

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