Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra
| Autor: | Zoltan Szantoi, Serge A. Wich, Erik Meijaard, Adrià Descals, Harsono Sutikno, Guruh Rindanata |
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| Jazyk: | angličtina |
| Předmět: |
S1
010504 meteorology & atmospheric sciences Image classification Science 0211 other engineering and technologies Biodiversity Smallholder plantation 02 engineering and technology Elaeis guineensis 01 natural sciences oil palm Deforestation Palm oil Contextual information Cloud computing 021101 geological & geomatics engineering 0105 earth and related environmental sciences smallholder plantation industrial plantation Industrial plantation GE biology Land use Agroforestry sentinel-2 sentinel-1 cloud computing biology.organism_classification Vegetable oil Geography Oil palm General Earth and Planetary Sciences Sentinel-1 Predictive variables Sentinel-2 image classification |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname Dipòsit Digital de Documents de la UAB Universitat Autònoma de Barcelona Remote Sensing, Vol 11, Iss 21, p 2590 (2019) Remote Sensing Volume 11 Issue 21 Pages: 2590 Recercat: Dipósit de la Recerca de Catalunya Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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