Change Detection in Mangrove Forest Area Using Local Mutual Information

This thesis unveils the potential and utilization of similarity measure for forest change detection. A new simple similarity approach based on local mutual information is used to detect any significant changes in the image of forest areas. Point similarity measure is defined as a measure which is u...

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Main Author:	Jahari, Mahirah
Format:	Thesis
Language:	English English
Published:	2010
Subjects:	Mangrove forest Crops - Growth Forest biodiversity - Climatic factors
Online Access:	http://psasir.upm.edu.my/id/eprint/19711/1/ITMA_2010_5_F.pdf
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id	my-upm-ir.19711
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spelling	my-upm-ir.197112013-05-27T08:02:54Z Change Detection in Mangrove Forest Area Using Local Mutual Information 2010-11 Jahari, Mahirah This thesis unveils the potential and utilization of similarity measure for forest change detection. A new simple similarity approach based on local mutual information is used to detect any significant changes in the image of forest areas. Point similarity measure is defined as a measure which is used to calculate the similarity of individual pixels. The basic idea of the proposed method is that any change pixel will be maximally dissimilar, i.e. the value of similarity of these pixels will be low. The method has been tested to detect and identify changes caused by plant growth and plant loss in four sub-areas of Matang Mangrove Forest, Perak. Image of SPOT 5 satellite taken from band 1, band 2,band 3, and band 4 with the resolution of 10meter dated on 5 August 2005 and 13 June 2007 has been used to test the method. It is then compared with the results of Principal Component Analysis 1 (PCA 1). The plant loss areas has been successfully identified as any pixel with the value of local mutual information less than and equals to zero. The method has been refined to accurately detect changes caused by the growth areas by thresholding the histogram of the average percentage of difference between joint probability and marginal probability. Results from the experiment showed that a threshold value of zero is the best threshold value to identify between changed and unchanged areas in all cases of the images. In overall, band 3 gives the best results of forest change detection compared to the other bands in all cases. Compared to the image differencing and normalized differenced vegetation index (NDVI), the proposed method not only can solve the problem on selecting the threshold value but also provides the highest percentage of successful classification at the fourth, second and first study area with the value of 95.07%, 89.47% and 87.66% respectively. From the results, it has been concluded that local mutual information is not only can be effectively used for change detection technique but also can be used to classify the plant growth and plant loss areas. Mangrove forest Crops - Growth Forest biodiversity - Climatic factors 2010-11 Thesis http://psasir.upm.edu.my/id/eprint/19711/ http://psasir.upm.edu.my/id/eprint/19711/1/ITMA_2010_5_F.pdf application/pdf en public masters Universiti Putra Malaysia Mangrove forest Crops - Growth Forest biodiversity - Climatic factors Institute of Advance Technology English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Mangrove forest Crops - Growth Forest biodiversity - Climatic factors
spellingShingle	Mangrove forest Crops - Growth Forest biodiversity - Climatic factors Jahari, Mahirah Change Detection in Mangrove Forest Area Using Local Mutual Information
description	This thesis unveils the potential and utilization of similarity measure for forest change detection. A new simple similarity approach based on local mutual information is used to detect any significant changes in the image of forest areas. Point similarity measure is defined as a measure which is used to calculate the similarity of individual pixels. The basic idea of the proposed method is that any change pixel will be maximally dissimilar, i.e. the value of similarity of these pixels will be low. The method has been tested to detect and identify changes caused by plant growth and plant loss in four sub-areas of Matang Mangrove Forest, Perak. Image of SPOT 5 satellite taken from band 1, band 2,band 3, and band 4 with the resolution of 10meter dated on 5 August 2005 and 13 June 2007 has been used to test the method. It is then compared with the results of Principal Component Analysis 1 (PCA 1). The plant loss areas has been successfully identified as any pixel with the value of local mutual information less than and equals to zero. The method has been refined to accurately detect changes caused by the growth areas by thresholding the histogram of the average percentage of difference between joint probability and marginal probability. Results from the experiment showed that a threshold value of zero is the best threshold value to identify between changed and unchanged areas in all cases of the images. In overall, band 3 gives the best results of forest change detection compared to the other bands in all cases. Compared to the image differencing and normalized differenced vegetation index (NDVI), the proposed method not only can solve the problem on selecting the threshold value but also provides the highest percentage of successful classification at the fourth, second and first study area with the value of 95.07%, 89.47% and 87.66% respectively. From the results, it has been concluded that local mutual information is not only can be effectively used for change detection technique but also can be used to classify the plant growth and plant loss areas.
format	Thesis
qualification_level	Master's degree
author	Jahari, Mahirah
author_facet	Jahari, Mahirah
author_sort	Jahari, Mahirah
title	Change Detection in Mangrove Forest Area Using Local Mutual Information
title_short	Change Detection in Mangrove Forest Area Using Local Mutual Information
title_full	Change Detection in Mangrove Forest Area Using Local Mutual Information
title_fullStr	Change Detection in Mangrove Forest Area Using Local Mutual Information
title_full_unstemmed	Change Detection in Mangrove Forest Area Using Local Mutual Information
title_sort	change detection in mangrove forest area using local mutual information
granting_institution	Universiti Putra Malaysia
granting_department	Institute of Advance Technology
publishDate	2010
url	http://psasir.upm.edu.my/id/eprint/19711/1/ITMA_2010_5_F.pdf
_version_	1747811448790188032

Change Detection in Mangrove Forest Area Using Local Mutual Information

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