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dc.contributor.authorDESCALS ADRIAen_GB
dc.contributor.authorSZANTOI ZOLTANen_GB
dc.contributor.authorMEIJAARD ERIKen_GB
dc.contributor.authorSUTIKNO HARSONOen_GB
dc.contributor.authorRINDANATA GURUHen_GB
dc.contributor.authorWICH SERGEen_GB
dc.date.accessioned2020-01-14T01:05:08Z-
dc.date.available2020-01-13en_GB
dc.date.available2020-01-14T01:05:08Z-
dc.date.created2020-01-09en_GB
dc.date.issued2019en_GB
dc.date.submitted2019-10-07en_GB
dc.identifier.citationREMOTE SENSING vol. 11 no. 21 p. 2590en_GB
dc.identifier.issn2072-4292 (online)en_GB
dc.identifier.urihttps://www.mdpi.com/2072-4292/11/21/2590en_GB
dc.identifier.urihttps://publications.jrc.ec.europa.eu/repository/handle/JRC118053-
dc.description.abstractOil palm is rapidly expanding in Southeast Asia and represents one of the major drivers of deforestation in the region. This includes both industrial scale and smallholder plantations, the management of which entails specific challenges, with either operational scale having its own particular social and environmental challenges. Although past studies addressed the mapping of oil palm, none of these studies considered the discrimination between industrial and smallholder plantations and, moreover, between young and mature oil palm stands. We studied the feasibility of mapping oil palm plantations, by typology and age, in the largest palm oil producing region of Indonesia (Riau province). We investigated the impact of using optical images (Sentinel-2) and radar scenes (Sentinel-1) in a Random Forest classification model. The classification model was implemented in a cloud computing system. Our results show that the mapping of oil palm plantations by typology and age requires a set of optimal features derived from optical and radar data for obtaining the best performance (OA = 90.2% and kappa = 87.2%). These features are texture images that capture the dense harvesting trail network in industrial plantations. Moreover, we also show that the mapping of mature oil palm trees, without distinction between smallholder and industrial plantations, can be done with high accuracy using only Sentinel-1 data (OA = 93.5% and kappa = 86.9%) because of the characteristic backscatter response of palm-like trees in radar scenes. This means that researchers, certifying bodies, and stakeholders can coarsely detect mature oil palm stands over large regions without training complex classification models. The results over Riau province show that smallholders represent 49.9% of total oil palm plantations, which is higher than reported in previous studies. This study is an important step towards a global map of oil palm plantations at different production scales and stand ages that can frequently be updated. Resulting insights would facilitate a more informed debate about optimizing land use for meeting global vegetable oil demands from oil palm and other oil crops.en_GB
dc.description.sponsorshipJRC.D.6-Knowledge for Sustainable Development and Food Securityen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherMDPIen_GB
dc.relation.ispartofseriesJRC118053en_GB
dc.titleOil palm (Elaeis guineensis) mapping with details: smallholder vs. industrial plantations and their extent in Riau, Sumatraen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.3390/rs11212590 (online)en_GB
JRC Directorate:Sustainable Resources

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