Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli

Recent developments in high resolution remote sensing have created a wide array of potential new mangrove applications. In this study the concept of Pleiades is applied to mapping and exposes the current system developments and spatial industry needs to delineate individual tree canopy. By exploring...

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Main Author:	Rosli, Sharul Nizam
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Remote Sensing
Online Access:	https://ir.uitm.edu.my/id/eprint/31247/1/TD_SHARUL%20NIZAM%20ROSLI%20AP%20R%2019_5.pdf
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id	my-uitm-ir.31247
record_format	uketd_dc
spelling	my-uitm-ir.312472020-06-16T04:19:22Z Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli 2020-06-16 Rosli, Sharul Nizam Remote Sensing Recent developments in high resolution remote sensing have created a wide array of potential new mangrove applications. In this study the concept of Pleiades is applied to mapping and exposes the current system developments and spatial industry needs to delineate individual tree canopy. By exploring developments in a Pleiades technology and investigating the use of the technology in mapping, a lot of advantages for spatial industry have been explored. Along advancements in technology, there were various methods have been developed to delineate individual tree canopy. The Pleiades image which is 0.63 m resolution was used. The study area was covered in mangrove are at Bagan Datuk, Perak. The major research strategy used in this project, are detecting, classify, and analyze the classification on mangrove family. Segmentation and classification approach were developed for this delineation canopy in the study area. Method that being used are Support Vector Machine (SVM) and K-Nearest Neighborhood (K-NN) that being apply in Object Based Image Analysis (OBIA). The information was used to identify individual tree canopies and delineated their boundaries. The results of segmentation and classification were used to know which classifier have the highest accuracy assessment in the study area that correspond with the result images obtained. This research show that SVM has the highest accuracy with 63.8156% overall accuracy and 0.5513 kappa coefficient better than K-NN that has 59.8303% overall accuracy and 0.5018 kappa coefficient. 2020-06 Thesis https://ir.uitm.edu.my/id/eprint/31247/ https://ir.uitm.edu.my/id/eprint/31247/1/TD_SHARUL%20NIZAM%20ROSLI%20AP%20R%2019_5.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surverying, Universiti Teknologi Mara Perlis
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Remote Sensing
spellingShingle	Remote Sensing Rosli, Sharul Nizam Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli
description	Recent developments in high resolution remote sensing have created a wide array of potential new mangrove applications. In this study the concept of Pleiades is applied to mapping and exposes the current system developments and spatial industry needs to delineate individual tree canopy. By exploring developments in a Pleiades technology and investigating the use of the technology in mapping, a lot of advantages for spatial industry have been explored. Along advancements in technology, there were various methods have been developed to delineate individual tree canopy. The Pleiades image which is 0.63 m resolution was used. The study area was covered in mangrove are at Bagan Datuk, Perak. The major research strategy used in this project, are detecting, classify, and analyze the classification on mangrove family. Segmentation and classification approach were developed for this delineation canopy in the study area. Method that being used are Support Vector Machine (SVM) and K-Nearest Neighborhood (K-NN) that being apply in Object Based Image Analysis (OBIA). The information was used to identify individual tree canopies and delineated their boundaries. The results of segmentation and classification were used to know which classifier have the highest accuracy assessment in the study area that correspond with the result images obtained. This research show that SVM has the highest accuracy with 63.8156% overall accuracy and 0.5513 kappa coefficient better than K-NN that has 59.8303% overall accuracy and 0.5018 kappa coefficient.
format	Thesis
qualification_level	Bachelor degree
author	Rosli, Sharul Nizam
author_facet	Rosli, Sharul Nizam
author_sort	Rosli, Sharul Nizam
title	Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli
title_short	Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli
title_full	Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli
title_fullStr	Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli
title_full_unstemmed	Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli
title_sort	assessment of pleiades satellite image for mangrove forest classification / sharul nizam rosli
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surverying, Universiti Teknologi Mara Perlis
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/31247/1/TD_SHARUL%20NIZAM%20ROSLI%20AP%20R%2019_5.pdf
_version_	1783734109383688192

Assessment of pleiades satellite image for mangrove forest classification / Sharul Nizam Rosli

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