Title: Oil palm (Elaeis guineensis) mapping with details: smallholder vs. industrial plantations and their extent in Riau, Sumatra
Authors: DESCALS ADRIASZANTOI ZOLTANMEIJAARD ERIKSUTIKNO HARSONORINDANATA GURUHWICH SERGE
Citation: REMOTE SENSING vol. 11 no. 21 p. 2590
Publisher: MDPI
Publication Year: 2019
JRC N°: JRC118053
ISSN: 2072-4292 (online)
URI: https://www.mdpi.com/2072-4292/11/21/2590
https://publications.jrc.ec.europa.eu/repository/handle/JRC118053
DOI: 10.3390/rs11212590
Type: Articles in periodicals and books
Abstract: 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, 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.
JRC Directorate:Sustainable Resources

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