24 Aug 2020

24 Aug 2020

Review status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

High-resolution global map of smallholder and industrial closed-canopy oil palm plantations

Adrià Descals1, Serge Wich2,3, Erik Meijaard4,5,6, David L. A. Gaveau7,8, Stephen Peedell9, and Zoltan Szantoi9,10 Adrià Descals et al.
  • 1CREAF, Cerdanyola del Vallès, 08193 Barcelona, Spain
  • 2School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom, Street, Liverpool L3 3AF, UK
  • 3Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park, 904, 1098 XH, Amsterdam, the Netherlands
  • 4Borneo Futures, Bandar Seri Begawan BA 2711, Brunei Darussalam
  • 5Durrell Institute of Conservation and Ecology, University of Kent, Canterbury CT2 7NR, UK
  • 6School of Biological Sciences, University of Queensland, Queensland 4072, Australia
  • 7Center for International Forestry Research, P.O. Box 0113 BOCBD, Bogor, Indonesia
  • 8TheTreeMap, Bagadou Bas 46600 Martel, France
  • 9European Commission, Joint Research Centre, 20127 Ispra, Italy
  • 10Stellenbosch University, Stellenbosch 7602, South Africa

Abstract. Oil seed crops, especially oil palm, are among the most rapidly expanding agricultural land uses, and their expansion is known to cause significant environmental damage. Accordingly, these crops often feature in public and policy debates, which are hampered or biased by a lack of accurate information on environmental impacts. In particular, the lack of accurate global crop maps remains a concern. Recent advances in machine learning and remotely-sensed data access make it possible to address this gap. We present an up-to-date map of closed-canopy oil palm (Elaeis guineensis) plantations by typology (industrial vs. smallholder plantations) at the global scale and with an unprecedented detail (10-meter resolution). Sentinel-1 and Sentinel-2 data were used to train a DeepLabv3+ model, a convolutional neural network (CNN) for semantic segmentation. The characteristic backscatter response of closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn the spatial patterns, such as the harvest road networks, allowed the distinction between industrial and smallholder plantations globally (overall accuracy = 97.5 % and kappa = 84.9 %). The user's accuracy in industrial and smallholders was 73.8 % and 89.4 %, and the producer's accuracy was 85.6 % and 78.8 % respectively. The global oil palm layer reveals that oil palm plantations are found in 47 tropical countries. Southeast Asia ranks as the main producing region with 17.47 × 106 ha, or 90 % of global plantations. Our analysis confirms significant regional variation in the ratio of industrial versus smallholder growers, but also that, from a typical land development perspective, large areas of legally defined smallholder oil palm resemble industrial-scale plantings. The overall oil palm surface per country is similar to the harvested area reported by FAO, except for countries in Western Africa, where our estimates are lower due to the omission of feral oil palm plantations. In Indonesia, the world's largest producer, our planted area estimate is higher because FAO does not report unregistered landholdings. Our model identifies primarily closed-canopy oil palm stands and misses young or sparsely planted oil palm stands. An accurate global map of planted oil palm can help to shape the ongoing debate about the environmental impacts of oil seed crop expansion, especially if other crops can be mapped to the same level of accuracy. As our model can be regularly rerun as new imagery is published, it can be used to reliably to monitor the expansion of a crop. The global oil palm layer for the second half of the year 2019 at a spatial resolution of 10 meters can be found at (Descals et al., 2020).

Adrià Descals et al.

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Adrià Descals et al.

Data sets

High resolution global industrial and smallholder oil palm map for 2019 Adrià Descals, Serge Wich, Erik Meijaard, David L.A. Gaveau, Stephen Peedell, and Zoltan Szantoi

Adrià Descals et al.


Total article views: 1,194 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
779 402 13 1,194 69 20 23
  • HTML: 779
  • PDF: 402
  • XML: 13
  • Total: 1,194
  • Supplement: 69
  • BibTeX: 20
  • EndNote: 23
Views and downloads (calculated since 24 Aug 2020)
Cumulative views and downloads (calculated since 24 Aug 2020)

Viewed (geographical distribution)

Total article views: 800 (including HTML, PDF, and XML) Thereof 788 with geography defined and 12 with unknown origin.
Country # Views %
  • 1


Latest update: 04 Mar 2021
Short summary
Sustainable vegetable oil production requires accurate global oil crop maps. We used high-resolution, global satellite data to train a deep learning model that accurately classified industrial and smallholder oil palm, the main oil producing crop. Our results outperformed previous studies and proved the suitability of deep learning for land use mapping. The global map reveals 19.5 MHa planted oil palm, however, young plantations (<3 years) are not included in this estimate.