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Article: Towards a global oil palm sample database: Design and implications

TitleTowards a global oil palm sample database: Design and implications
Authors
Issue Date2017
Citation
International Journal of Remote Sensing, 2017, v. 38, n. 14, p. 4022-4032 How to Cite?
Abstract© 2017 Informa UK Limited, trading as Taylor & Francis Group. Global oil palm plantations have expanded in the last few decades, resulting in negative impacts on the environment. Satellite remote sensing plays an important role in monitoring the expansion of oil palm plantations, but requires high-quality ground samples for training and validation. To facilitate the monitoring of oil palm plantations on a large scale, we propose an oil palm sample database that includes the five countries with the largest areas of oil palm plantations: Indonesia, Malaysia, Nigeria, Thailand, and Ghana. In total, 45,896 samples were collected using a hexagonal sampling design. High-resolution images from Google Earth, the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and Landsat optical images were used to identify oil palm plantations and other types of land cover (croplands, forests, grasslands, shrublands, water, hard surfaces, and bare land). The characteristics of oil palm cover and its environment, including PALSAR backscattering coefficients, terrain, and climate recorded in this database are also discussed. The results indicate that using the PALSAR band algebra threshold alone is not recommended to distinguish oil palm from other land-cover/use types.
Persistent Identifierhttp://hdl.handle.net/10722/296829
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.authorZhao, Yuanyuan-
dc.contributor.authorXu, Yidi-
dc.contributor.authorHackman, Kwame-
dc.contributor.authorCracknell, Arthur P.-
dc.contributor.authorGong, Peng-
dc.date.accessioned2021-02-25T15:16:46Z-
dc.date.available2021-02-25T15:16:46Z-
dc.date.issued2017-
dc.identifier.citationInternational Journal of Remote Sensing, 2017, v. 38, n. 14, p. 4022-4032-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296829-
dc.description.abstract© 2017 Informa UK Limited, trading as Taylor & Francis Group. Global oil palm plantations have expanded in the last few decades, resulting in negative impacts on the environment. Satellite remote sensing plays an important role in monitoring the expansion of oil palm plantations, but requires high-quality ground samples for training and validation. To facilitate the monitoring of oil palm plantations on a large scale, we propose an oil palm sample database that includes the five countries with the largest areas of oil palm plantations: Indonesia, Malaysia, Nigeria, Thailand, and Ghana. In total, 45,896 samples were collected using a hexagonal sampling design. High-resolution images from Google Earth, the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and Landsat optical images were used to identify oil palm plantations and other types of land cover (croplands, forests, grasslands, shrublands, water, hard surfaces, and bare land). The characteristics of oil palm cover and its environment, including PALSAR backscattering coefficients, terrain, and climate recorded in this database are also discussed. The results indicate that using the PALSAR band algebra threshold alone is not recommended to distinguish oil palm from other land-cover/use types.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleTowards a global oil palm sample database: Design and implications-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01431161.2017.1312622-
dc.identifier.scopuseid_2-s2.0-85026668087-
dc.identifier.volume38-
dc.identifier.issue14-
dc.identifier.spage4022-
dc.identifier.epage4032-
dc.identifier.eissn1366-5901-
dc.identifier.isiWOS:000401460800003-
dc.identifier.issnl0143-1161-

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