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Chlorophyll-A Estimation from Remotely Sensed Data


Mohammed Ali, Fatima Awad Allah (2000) Chlorophyll-A Estimation from Remotely Sensed Data. Masters thesis, Universiti Putra Malaysia.


The science of remote sensing is commonly defined as method that employs electromagnetic energy to detect, record, and measure characteristics of a target. Concentrations of chlorophyll-a in water have been estimated from the spectral distribution of back-scattered light, related to reflectance. Remote sensing in general has been used much more extensively for oceans than for inland waters. Advanced image processing techniques introduced and applied using Landsat Thematic Mapper data acquired on February 22, 1 994 over the indicated region of South China Sea. The objective of the study was to calculate the chlorophyll-a concentration along Kuala Terengganu. The method was carried out to calculate the chlorophyll-a concentration in the study area that is, digital image processing which include preprocessing, display, enhancement, information extraction, and algorithm to calculate the estimated chlorophyll-a. Results of the regression analysis of DNs against referenced chlorophyll-a was used to calculate the actual chlorophyll-a concentration (calculated chlorophyll-a) of Landsat TM bands 1, 2, and 3. The results show that the chlorophyll-a concentrations in the study area are significantly correlated with band 1, 2, and 3. The lower chlorophyll-a concentration with levels (0.031-0.019) mg/m3, the higher chlorophyll-a concentration with levels (0.404-0.391) mg/m3. Finally, band 2 was the best in terms of all the parameters evaluated. In conclusion, remote sensing is an important technology for measuring chlorophyll-a concentration in the coastal water of South China Sea. From the result, TM sensor has been found a useful tool for studying chlorophyll-a concentration.

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Additional Metadata

Item Type: Thesis (Masters)
Subject: Chlorophyll
Subject: Photosynthetic bacteria
Call Number: FK 2000 5
Chairman Supervisor: Associate Professor Shattri Mansor, PhD
Divisions: Faculty of Engineering
Depositing User: kmportal
Date Deposited: 15 Apr 2011 03:04
Last Modified: 29 Mar 2024 01:43
URI: http://psasir.upm.edu.my/id/eprint/10457
Statistic Details: View Download Statistic

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