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Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra

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CREAF, Cerdanyola del Vallès, 08193 Barcelona, Spain
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European Commission, Joint Research Centre, 20127 Ispra, Italy
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Stellenbosch University, Stellenbosch 7602, South Africa
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Borneo Futures, Bandar Seri Begawan BA 2711, Brunei Darussalam
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Durrell Institute of Conservation and Ecology, University of Kent, Canterbury CT2 7NR, UK
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School of Biological Sciences, University of Queensland, Queensland 4072, Australia
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Austindo Nusantara Jaya Tbk, Kawasan Mega Kuningan, Jakarta 12950, Indonesia
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School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom, Street, Liverpool L3 3AF, UK
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Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park, 904, 1098 XH Amsterdam, The Netherlands
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2590; https://doi.org/10.3390/rs11212590
Received: 30 September 2019 / Revised: 31 October 2019 / Accepted: 3 November 2019 / Published: 5 November 2019
Oil 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 with remote sensing data, none of these studies considered the discrimination between industrial and smallholder plantations and, furthermore, between young and mature oil palm stands. This study assesses the feasibility of mapping oil palm plantations, by typology (industrial versus smallholder) and age (young versus mature), in the largest palm oil producing region of Indonesia (Riau province). The impact of using optical images (Sentinel-2) and radar scenes (Sentinel-1) in a Random Forest classification model was investigated. The classification model was implemented in a cloud computing system to map the oil palm plantations of Riau province. 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, to obtain the best model performance (OA = 90.2% and kappa = 87.2%). These features are texture images that capture contextual information, such as the dense harvesting trail network in industrial plantations. The study also shows 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, certification bodies, and stakeholders can adequately detect mature oil palm stands over large regions without training complex classification models and with Sentinel-1 features as the only predictive variables. 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. View Full-Text
Keywords: oil palm; smallholder plantation; industrial plantation; Sentinel-1; Sentinel-2; image classification; cloud computing oil palm; smallholder plantation; industrial plantation; Sentinel-1; Sentinel-2; image classification; cloud computing
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MDPI and ACS Style

Descals, A.; Szantoi, Z.; Meijaard, E.; Sutikno, H.; Rindanata, G.; Wich, S. Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra. Remote Sens. 2019, 11, 2590.

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