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Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram

Mangroves forest are important in providing services and goods to its surrounding and environment. There are differences in mangrove age for each tree and according to previous studies the factors that cause the differences in the age class is because of lightning and it is more frequent in the matu...

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Main Author: Faris Francis Singaram, Fareena
Format: Thesis
Language:English
Published: 2021
Subjects:
Remote Sensing
Geographic information systems
Online Access:https://ir.uitm.edu.my/id/eprint/43981/1/43981.pdf
https://ir.uitm.edu.my/id/eprint/43981/
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spelling my.uitm.ir.439812022-09-22T07:01:53Z https://ir.uitm.edu.my/id/eprint/43981/ Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram Faris Francis Singaram, Fareena Remote Sensing Geographic information systems Mangroves forest are important in providing services and goods to its surrounding and environment. There are differences in mangrove age for each tree and according to previous studies the factors that cause the differences in the age class is because of lightning and it is more frequent in the mature forests and in the zone that are protected. The studies on mangroves age estimation are rare and limited even though there are many studies on mangroves species and the deforestation of mangroves in Malaysia. Therefore, this study aimed to used OBIA method with selected machine learning algorithm to estimate the mangrove age by using Sentinel 2A image. The parameters involved to estimate the mangrove age are differences feature selection and different supervised machine learning algorithm. The support vector machine (SVM) which is one of the machines learning algorithms for object-based image analysis (OBIA) method is used in this study for the classification of the mangrove from other LULC. The supervised machine learning algorithm, SVM and Decision Tree are used for the estimation of the mangrove age into young and mature. The study used Sentinel 2A images which is one of the high-resolution satellite images that is used for monitoring the changes of mangrove forest in previous studies and it is used in this study in evaluating mangroves age. The results of the age estimation shows that SVM classifier are more suitable for age estimation than Decision Tree with SVM obtaining higher accuracy than DT. Overall, this study has a great potential for the future management of mangroves forest in Malaysia. The significant of this study is to prove that the application of object-based image analysis classification in evaluating mangroves age are suitable and have great potential for the future management of mangroves forest in Malaysia especially in Pulau Tuba, Kedah. Furthermore, the mangroves age map can help to analyse the age of the mangroves in order to maintain its existence and growth. 2021-03-22 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/43981/1/43981.pdf Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram. (2021) Degree thesis, thesis, Universiti Teknologi Mara Perlis. <http://terminalib.uitm.edu.my/43981.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Remote Sensing
Geographic information systems
spellingShingle Remote Sensing
Geographic information systems
Faris Francis Singaram, Fareena
Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
description Mangroves forest are important in providing services and goods to its surrounding and environment. There are differences in mangrove age for each tree and according to previous studies the factors that cause the differences in the age class is because of lightning and it is more frequent in the mature forests and in the zone that are protected. The studies on mangroves age estimation are rare and limited even though there are many studies on mangroves species and the deforestation of mangroves in Malaysia. Therefore, this study aimed to used OBIA method with selected machine learning algorithm to estimate the mangrove age by using Sentinel 2A image. The parameters involved to estimate the mangrove age are differences feature selection and different supervised machine learning algorithm. The support vector machine (SVM) which is one of the machines learning algorithms for object-based image analysis (OBIA) method is used in this study for the classification of the mangrove from other LULC. The supervised machine learning algorithm, SVM and Decision Tree are used for the estimation of the mangrove age into young and mature. The study used Sentinel 2A images which is one of the high-resolution satellite images that is used for monitoring the changes of mangrove forest in previous studies and it is used in this study in evaluating mangroves age. The results of the age estimation shows that SVM classifier are more suitable for age estimation than Decision Tree with SVM obtaining higher accuracy than DT. Overall, this study has a great potential for the future management of mangroves forest in Malaysia. The significant of this study is to prove that the application of object-based image analysis classification in evaluating mangroves age are suitable and have great potential for the future management of mangroves forest in Malaysia especially in Pulau Tuba, Kedah. Furthermore, the mangroves age map can help to analyse the age of the mangroves in order to maintain its existence and growth.
format Thesis
author Faris Francis Singaram, Fareena
author_facet Faris Francis Singaram, Fareena
author_sort Faris Francis Singaram, Fareena
title Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
title_short Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
title_full Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
title_fullStr Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
title_full_unstemmed Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
title_sort comparison of machine learning algorithms for estimating mangrove age using sentinel 2a at pulau tuba, kedah, malaysia / fareena faris francis singaram
publishDate 2021
url https://ir.uitm.edu.my/id/eprint/43981/1/43981.pdf
https://ir.uitm.edu.my/id/eprint/43981/
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score 13.211869

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