Towards global oil palm plantation mapping using remote-sensing data

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 de...

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Main Authors: Cheng, Yuqi, Yu, Le, Xu, Yidi, Liu, Xiaoxuan, Lu, Hui, Cracknell, Arthur Philip, Kanniah, Kasturi Devi, Gong, Peng
Format: Article
Published: Taylor and Francis Ltd. 2018
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Online Access:http://eprints.utm.my/id/eprint/85295/
http://dx.doi.org/10.1080/01431161.2018.1492182
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.852952020-03-17T08:04:50Z http://eprints.utm.my/id/eprint/85295/ Towards global oil palm plantation mapping using remote-sensing data Cheng, Yuqi Yu, Le Xu, Yidi Liu, Xiaoxuan Lu, Hui Cracknell, Arthur Philip Kanniah, Kasturi Devi Gong, Peng G70.39-70.6 Remote sensing 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. Taylor and Francis Ltd. 2018-07-09 Article PeerReviewed Cheng, Yuqi and Yu, Le and Xu, Yidi and Liu, Xiaoxuan and Lu, Hui and Cracknell, Arthur Philip and Kanniah, Kasturi Devi and Gong, Peng (2018) Towards global oil palm plantation mapping using remote-sensing data. International Journal of Remote Sensing, 39 (18). pp. 5891-5906. ISSN 0143-1161 http://dx.doi.org/10.1080/01431161.2018.1492182 DOI:10.1080/01431161.2018.1492182
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic G70.39-70.6 Remote sensing
spellingShingle G70.39-70.6 Remote sensing
Cheng, Yuqi
Yu, Le
Xu, Yidi
Liu, Xiaoxuan
Lu, Hui
Cracknell, Arthur Philip
Kanniah, Kasturi Devi
Gong, Peng
Towards global oil palm plantation mapping using remote-sensing data
description 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.
format Article
author Cheng, Yuqi
Yu, Le
Xu, Yidi
Liu, Xiaoxuan
Lu, Hui
Cracknell, Arthur Philip
Kanniah, Kasturi Devi
Gong, Peng
author_facet Cheng, Yuqi
Yu, Le
Xu, Yidi
Liu, Xiaoxuan
Lu, Hui
Cracknell, Arthur Philip
Kanniah, Kasturi Devi
Gong, Peng
author_sort Cheng, Yuqi
title Towards global oil palm plantation mapping using remote-sensing data
title_short Towards global oil palm plantation mapping using remote-sensing data
title_full Towards global oil palm plantation mapping using remote-sensing data
title_fullStr Towards global oil palm plantation mapping using remote-sensing data
title_full_unstemmed Towards global oil palm plantation mapping using remote-sensing data
title_sort towards global oil palm plantation mapping using remote-sensing data
publisher Taylor and Francis Ltd.
publishDate 2018
url http://eprints.utm.my/id/eprint/85295/
http://dx.doi.org/10.1080/01431161.2018.1492182
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