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Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms

Oil palm is one of the major crops in Malaysia; it accounts for 47% of the global palm oil supply. Equatorial climate has provided Malaysia with the potential to produce oil palm biomass, which is one of the major contributors to the local economy. The utilisation of oil palm biomass as a source of...

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Main Authors: Shaharum, Nur Shafira Nisa, Mohd Shafri, Helmi Zulhaidi, Wan Ab. Karim Ghani, Wan Azlina, Samsatli, Sheila, Yusuf, Badronnisa
Format: Article
Language:English
Published: Universiti Putra Malaysia Press 2019
Online Access:http://psasir.upm.edu.my/id/eprint/69699/1/10%20JST-S0506-2019.pdf
http://psasir.upm.edu.my/id/eprint/69699/
http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2027%20(S1).%202019/10%20JST-S0506-2019.pdf
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spelling my.upm.eprints.696992019-07-08T07:16:53Z http://psasir.upm.edu.my/id/eprint/69699/ Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms Shaharum, Nur Shafira Nisa Mohd Shafri, Helmi Zulhaidi Wan Ab. Karim Ghani, Wan Azlina Samsatli, Sheila Yusuf, Badronnisa Oil palm is one of the major crops in Malaysia; it accounts for 47% of the global palm oil supply. Equatorial climate has provided Malaysia with the potential to produce oil palm biomass, which is one of the major contributors to the local economy. The utilisation of oil palm biomass as a source of renewable energy is one of the effective methods to promote green energy. Therefore, there is a need to have sufficient data related to oil palm biomass such as yield estimation, oil palm distributions, and locations. The aim of this study was to produce a land cover map on the distribution of oil palm plantations on three districts located in Selangor. Landsat 8 images of resolutions 15 x 15 m were used and classified via machine learning and non-machine learning algorithms. In this study, three different classifier algorithms were compared using support vector machines, artificial neural networks, and maximum likelihood classifications in which the values obtained for overall accuracy were 98.96%, 99.39%, and 15.30% respectively. The output showed that machine learning algorithms, support vector machines and artificial neural networks gave rise to high accuracies. Hence, the mapping of oil palm distributions via machine learning algorithm was better than that via non-machine learning algorithm. Universiti Putra Malaysia Press 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/69699/1/10%20JST-S0506-2019.pdf Shaharum, Nur Shafira Nisa and Mohd Shafri, Helmi Zulhaidi and Wan Ab. Karim Ghani, Wan Azlina and Samsatli, Sheila and Yusuf, Badronnisa (2019) Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms. Pertanika Journal of Science & Technology, 27 (S1). pp. 123-135. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2027%20(S1).%202019/10%20JST-S0506-2019.pdf
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Oil palm is one of the major crops in Malaysia; it accounts for 47% of the global palm oil supply. Equatorial climate has provided Malaysia with the potential to produce oil palm biomass, which is one of the major contributors to the local economy. The utilisation of oil palm biomass as a source of renewable energy is one of the effective methods to promote green energy. Therefore, there is a need to have sufficient data related to oil palm biomass such as yield estimation, oil palm distributions, and locations. The aim of this study was to produce a land cover map on the distribution of oil palm plantations on three districts located in Selangor. Landsat 8 images of resolutions 15 x 15 m were used and classified via machine learning and non-machine learning algorithms. In this study, three different classifier algorithms were compared using support vector machines, artificial neural networks, and maximum likelihood classifications in which the values obtained for overall accuracy were 98.96%, 99.39%, and 15.30% respectively. The output showed that machine learning algorithms, support vector machines and artificial neural networks gave rise to high accuracies. Hence, the mapping of oil palm distributions via machine learning algorithm was better than that via non-machine learning algorithm.
format Article
author Shaharum, Nur Shafira Nisa
Mohd Shafri, Helmi Zulhaidi
Wan Ab. Karim Ghani, Wan Azlina
Samsatli, Sheila
Yusuf, Badronnisa
spellingShingle Shaharum, Nur Shafira Nisa
Mohd Shafri, Helmi Zulhaidi
Wan Ab. Karim Ghani, Wan Azlina
Samsatli, Sheila
Yusuf, Badronnisa
Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
author_facet Shaharum, Nur Shafira Nisa
Mohd Shafri, Helmi Zulhaidi
Wan Ab. Karim Ghani, Wan Azlina
Samsatli, Sheila
Yusuf, Badronnisa
author_sort Shaharum, Nur Shafira Nisa
title Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
title_short Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
title_full Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
title_fullStr Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
title_full_unstemmed Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
title_sort mapping the distribution of oil palm using landsat 8 data by comparing machine learning and non-machine learning algorithms
publisher Universiti Putra Malaysia Press
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/69699/1/10%20JST-S0506-2019.pdf
http://psasir.upm.edu.my/id/eprint/69699/
http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2027%20(S1).%202019/10%20JST-S0506-2019.pdf
_version_ 1643839558594330624
score 13.211869

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