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
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id	my-uitm-ir.43981
record_format	uketd_dc
spelling	my-uitm-ir.439812022-09-22T07:01:53Z Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram 2021-03-22 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 Thesis https://ir.uitm.edu.my/id/eprint/43981/ https://ir.uitm.edu.my/id/eprint/43981/1/43981.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surveying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
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
qualification_level	Bachelor degree
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
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/43981/1/43981.pdf
_version_	1783734709912600576

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

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