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

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

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Official URL: http://dx.doi.org/10.1088/1755-1315/18/1/012010

Abstract

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

Item Type:Article
Uncontrolled Keywords:change vector analysis, enhanced thematic mappers, humid tropical climates, optical remote sensing
Subjects:H Social Sciences > HD Industries. Land use. Labor
Divisions:Geoinformation and Real Estate
ID Code:54381
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:05 Apr 2016 15:00
Last Modified:12 Aug 2018 11:49

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