Towards global oil palm plantation mapping using remote-sensing data

Yuqi Cheng, L. Yu, Yidi Xu, Xiaoxuan Liu, Hui Lu, Arthur P. Cracknell, Kasturi Kanniah, Peng Gong

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In recent decades, palm oil, which forms one of the world’s major bulk feedstock and oil crops, has been cultivated at an increasing scale to meet new demand. Oil palm expansion has driven not only socio-economic development but also serious ecological problems and environmental pollution through deforestation and fires to clear the forests. Uneconomic oil palm plantations can influence the balance of regional ecosystems and the carbon cycle. Many countries report national statistics on the area of oil palm, but few document the extent and locations of oil palm plantations. In this study, we produce and make available oil palm maps that include 15 countries with more than 10,000 ha of planted oil palms. Phased Array Type L-band Synthetic Aperture (PALSAR-2) images and high-resolution (<2.5 m) images in Google Earth were used to produce oil palm maps by supervised classification and visual interpretation. Two independent verification systems were used to evaluate map accuracy. The characteristics of oil palm plantations distribution and their environment suitability including terrain and climate conditions of the global oil palm planted regions are also discussed. The results indicate that the total area of oil palm in global in 2016 was estimated to be 29.49 million hectares (Mha) although the mapping result showed a good correlation with other records, but relatively large uncertainty in Africa. Most oil palm trees grow in warm (24–29.5°C), wet conditions (1000–4000 mm p.a. of precipitation), flat terrain (slope less than 8°), and low elevation (0–800 m); however, these growing conditions are slightly different in different continents.

Original languageEnglish
Pages (from-to)5891-5906
Number of pages16
JournalInternational Journal of Remote Sensing
Volume39
Issue number18
Early online date9 Jul 2018
DOIs
Publication statusPublished - 2018

Fingerprint Dive into the research topics of 'Towards global oil palm plantation mapping using remote-sensing data'. Together they form a unique fingerprint.

  • Cite this

    Cheng, Y., Yu, L., Xu, Y., Liu, X., Lu, H., Cracknell, A. P., Kanniah, K., & Gong, P. (2018). Towards global oil palm plantation mapping using remote-sensing data. International Journal of Remote Sensing, 39(18), 5891-5906. https://doi.org/10.1080/01431161.2018.1492182