Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/106862
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Detecting industrial oil palm plantations on Landsat images with Google Earth Engine
Author: Lee, J.
Wich, S.
Widayati, A.
Koh, L.
Citation: Remote Sensing Applications: Society and Environment, 2016; 4:219-224
Publisher: Elsevier BV
Issue Date: 2016
ISSN: 2352-9385
2352-9385
Statement of
Responsibility: 
Janice Ser Huay Lee, Serge Wich, Atiek Widayati, Lian Pin Koh
Abstract: Oil palm plantations are rapidly expanding in the tropics, which leads to deforestation and other associated damages to biodiversity and ecosystem services. Forest researchers and practitioners in developing nations are in need of a low-cost, accessible and user-friendly tool for detecting the establishment of industrial oil palm plantations. Google Earth Engine (GEE) is a cloud computing platform which hosts publicly available satellite images and allows for land cover classification using inbuilt algorithms. These algorithms conduct pixel-based classification via supervised learning. We demonstrate the use of GEE for the detection of industrial oil palm plantations in Tripa, Aceh, Indonesia. We performed land cover classification using different spectral bands (RGB, NIR, SWIR, TIR, all bands) from our Landsat 8 image to distinguish the following land cover classes: immature oil palm, mature oil palm, non-forest non-oil palm, forest, water, and clouds. The overall accuracy and Kappa coefficient were the highest using all bands for land cover classification, followed by RGB, SWIR, TIR, and NIR. Classification and Regression Trees (CART) and Random Forests (RFT) algorithms produced classified land cover maps which had higher overall accuracies and Kappa coefficients than the Minimum Distance (MD) algorithm. Object-based classification and using a combination of radar- and optic-based imagery are some ways in which oil palm detection can be improved within GEE. Despite its limitations, GEE does have the potential to be developed further into an accessible and low-cost tool for independent bodies to detect and monitor the expansion of oil palm plantations in the tropics.
Keywords: Elaeis guineensis; agricultural expansion; tropics; land cover classification; land use change
Description: Available online 13 November 2016
Rights: © 2016 Elsevier B.V. All rights reserved.
RMID: 0030059255
DOI: 10.1016/j.rsase.2016.11.003
Appears in Collections:Environment Institute publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.