Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques

Rapid urbanization and the risk of climatic variations, including a rise in temperature and increased rainfall, have urged research in the development of methods and techniques to monitor the modification of land use/land cover (LULC). This study employed the normalized differencing vegetative ind...

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Main Authors:	Syeda Maria, Zaidi, Akbari, Abolghasem, Azizan, Abu Samah, Ngien, S. K., Gisen, J. I. A.
Format:	Article
Language:	English
Published:	Pjoes 2017
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/19825/ http://umpir.ump.edu.my/id/eprint/19825/ http://umpir.ump.edu.my/id/eprint/19825/ http://umpir.ump.edu.my/id/eprint/19825/1/Pol.J.Environ.Stud.Vol.26.No.6.2833-2840.pdf

id	ump-19825
recordtype	eprints
spelling	ump-198252018-01-15T03:40:23Z http://umpir.ump.edu.my/id/eprint/19825/ Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques Syeda Maria, Zaidi Akbari, Abolghasem Azizan, Abu Samah Ngien, S. K. Gisen, J. I. A. TA Engineering (General). Civil engineering (General) Rapid urbanization and the risk of climatic variations, including a rise in temperature and increased rainfall, have urged research in the development of methods and techniques to monitor the modification of land use/land cover (LULC). This study employed the normalized differencing vegetative index (NDVI) and semi-supervised image classification (SSIC) integrated with high-resolution Google Earth images of the Kuantan River Basin (KRB) in Malaysia. The Landsat-5 (TM) images for the years 1993, 1999, and 2010 were selected. The results from both classifications provided a consistent accuracy of assessment with a reasonable level of agreement. However, SSIC was found to be more precise than NDVI. Overall accuracy was 82% for 1993 and 1999, and 80% for 2010, with the kappa values ranging from 0.789 to 0.761. Meanwhile, NDVI accuracy was attained at 64% with kappa value at 0.527 for 1999. In addition, 70% and 72% accuracy were obtained for 1993 and 2010 with estimated kappa values of 0.651 and 0.672, respectively. The study is anticipated to assist decision makers for better emergency response and sustainable land development action plans, thus mitigating the challenges of rapid urban growth Pjoes 2017-10-30 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19825/1/Pol.J.Environ.Stud.Vol.26.No.6.2833-2840.pdf Syeda Maria, Zaidi and Akbari, Abolghasem and Azizan, Abu Samah and Ngien, S. K. and Gisen, J. I. A. (2017) Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques. Polish Journal of Environmental Studies, 26 (6). pp. 2833-2840. ISSN 1230-1485 http://www.pjoes.com/pdf/26.6/Pol.J.Environ.Stud.Vol.26.No.6.2833-2840.pdf 10.15244/pjoes/68878
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Syeda Maria, Zaidi Akbari, Abolghasem Azizan, Abu Samah Ngien, S. K. Gisen, J. I. A. Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques
description	Rapid urbanization and the risk of climatic variations, including a rise in temperature and increased rainfall, have urged research in the development of methods and techniques to monitor the modification of land use/land cover (LULC). This study employed the normalized differencing vegetative index (NDVI) and semi-supervised image classification (SSIC) integrated with high-resolution Google Earth images of the Kuantan River Basin (KRB) in Malaysia. The Landsat-5 (TM) images for the years 1993, 1999, and 2010 were selected. The results from both classifications provided a consistent accuracy of assessment with a reasonable level of agreement. However, SSIC was found to be more precise than NDVI. Overall accuracy was 82% for 1993 and 1999, and 80% for 2010, with the kappa values ranging from 0.789 to 0.761. Meanwhile, NDVI accuracy was attained at 64% with kappa value at 0.527 for 1999. In addition, 70% and 72% accuracy were obtained for 1993 and 2010 with estimated kappa values of 0.651 and 0.672, respectively. The study is anticipated to assist decision makers for better emergency response and sustainable land development action plans, thus mitigating the challenges of rapid urban growth
format	Article
author	Syeda Maria, Zaidi Akbari, Abolghasem Azizan, Abu Samah Ngien, S. K. Gisen, J. I. A.
author_facet	Syeda Maria, Zaidi Akbari, Abolghasem Azizan, Abu Samah Ngien, S. K. Gisen, J. I. A.
author_sort	Syeda Maria, Zaidi
title	Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques
title_short	Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques
title_full	Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques
title_fullStr	Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques
title_full_unstemmed	Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques
title_sort	landsat-5 time series analysis for land use/land cover change detection using ndvi and semi-supervised classification techniques
publisher	Pjoes
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/19825/ http://umpir.ump.edu.my/id/eprint/19825/ http://umpir.ump.edu.my/id/eprint/19825/ http://umpir.ump.edu.my/id/eprint/19825/1/Pol.J.Environ.Stud.Vol.26.No.6.2833-2840.pdf
first_indexed	2023-09-18T22:28:25Z
last_indexed	2023-09-18T22:28:25Z
_version_	1777416122588463104

Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques

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