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Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate

Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction of Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution of cloud contaminations, are always a c...

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Main Authors: Hashim, Mazlan, Pour, Amin Beiranvand, Onn, C. H.
Format: Article
Language:English
Published: Institute of Physics Publishing 2014
Subjects:
HD Industries. Land use. Labor
Online Access:http://eprints.utm.my/id/eprint/54381/1/MazlanHashim2014_Optimizingcloudremovalfromsatellite.pdf
http://eprints.utm.my/id/eprint/54381/
http://dx.doi.org/10.1088/1755-1315/18/1/012010
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spelling my.utm.543812018-08-12T03:49:29Z http://eprints.utm.my/id/eprint/54381/ Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate Hashim, Mazlan Pour, Amin Beiranvand Onn, C. H. HD Industries. Land use. Labor Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction of Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution of cloud contaminations, are always a critical task in the tropical climate. This paper suggests a fast and alternative approach to remove cloud and shadow contaminations for Landsat Enhanced Thematic Mapper+ (ETM+) multi temporal datasets. Band 3 and Band 4 from all the Landsat ETM+ dataset are two main spectral bands that are very crucial in this study for cloud removal technique. The Normalise difference vegetation index (NDVI) and the normalised difference soil index (NDSI) are two main derivatives derived from the datasets. Change vector analysis is used in this study to seek the vegetation dynamics. The approach developed in this study for cloud optimizing can be broadly applicable for optical remote sensing satellite data, which are seriously obscured with heavy cloud contamination in the tropical climate Institute of Physics Publishing 2014 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/54381/1/MazlanHashim2014_Optimizingcloudremovalfromsatellite.pdf Hashim, Mazlan and Pour, Amin Beiranvand and Onn, C. H. (2014) Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate. 8th International Symposium of The Digital Earth (ISDE8), 18 (1). ISSN 1755-1315 http://dx.doi.org/10.1088/1755-1315/18/1/012010 DOI: 10.1088/1755-1315/18/1/012010
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic HD Industries. Land use. Labor
spellingShingle HD Industries. Land use. Labor
Hashim, Mazlan
Pour, Amin Beiranvand
Onn, C. H.
Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate
description Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction of Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution of cloud contaminations, are always a critical task in the tropical climate. This paper suggests a fast and alternative approach to remove cloud and shadow contaminations for Landsat Enhanced Thematic Mapper+ (ETM+) multi temporal datasets. Band 3 and Band 4 from all the Landsat ETM+ dataset are two main spectral bands that are very crucial in this study for cloud removal technique. The Normalise difference vegetation index (NDVI) and the normalised difference soil index (NDSI) are two main derivatives derived from the datasets. Change vector analysis is used in this study to seek the vegetation dynamics. The approach developed in this study for cloud optimizing can be broadly applicable for optical remote sensing satellite data, which are seriously obscured with heavy cloud contamination in the tropical climate
format Article
author Hashim, Mazlan
Pour, Amin Beiranvand
Onn, C. H.
author_facet Hashim, Mazlan
Pour, Amin Beiranvand
Onn, C. H.
author_sort Hashim, Mazlan
title Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate
title_short Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate
title_full Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate
title_fullStr Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate
title_full_unstemmed Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate
title_sort optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate
publisher Institute of Physics Publishing
publishDate 2014
url http://eprints.utm.my/id/eprint/54381/1/MazlanHashim2014_Optimizingcloudremovalfromsatellite.pdf
http://eprints.utm.my/id/eprint/54381/
http://dx.doi.org/10.1088/1755-1315/18/1/012010
_version_ 1643653515874140160
score 13.211869

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