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Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia

Loss of spatial data with a long gap is a significant limitation for remote sensing analyses using satellite-based monitoring of oceanography. This limitation could not be ignored as it may affect the subsequent analysis and modeling of the data. Hence, this gap needs to be improved by filling the s...

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Main Authors: Mohamed Yussof, Fatin Nadiah, Maan, Normah, Md. Reba, Mohd. Nadzri
Format: Article
Published: Badih/Ghusayni, Ed. & Pub. 2021
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/94620/
http://ijmcs.future-in-tech.net/16.1/R-Nadiah.pdf
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spelling my.utm.946202022-03-31T15:51:35Z http://eprints.utm.my/id/eprint/94620/ Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia Mohamed Yussof, Fatin Nadiah Maan, Normah Md. Reba, Mohd. Nadzri QA Mathematics Loss of spatial data with a long gap is a significant limitation for remote sensing analyses using satellite-based monitoring of oceanography. This limitation could not be ignored as it may affect the subsequent analysis and modeling of the data. Hence, this gap needs to be improved by filling the spatial gap in the satellite datasets. In this research, Data Interpolating Empirical Orthogonal Functions (DI-NEOF) is applied to fill the spatial gap and has successfully worked in the reconstruction of missing data of chlorophyll-a for monitoring harmful algal blooms (HABs) in Sepanggar Bay located at coastal water of Kota Kinabalu, Malaysia. The original chlorophyll-a pixels are used to assess the accuracy of the predicted data. Then, the DINEOF model is compared with the Spatio-temporal Kriging model for validation purposes. The results obtained show that the DINEOF model has the highest Pearson correlation coefficient, 0.9940 and the smallest values of Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD) which are 0.2770 and 0.0155 respectively. Therefore, this proved that the DINEOF model is more effective for filling spatial long gaps. Badih/Ghusayni, Ed. & Pub. 2021 Article PeerReviewed Mohamed Yussof, Fatin Nadiah and Maan, Normah and Md. Reba, Mohd. Nadzri (2021) Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia. International Journal of Mathematics and Computer Science, 16 (1). pp. 345-356. ISSN 1814-0424 http://ijmcs.future-in-tech.net/16.1/R-Nadiah.pdf
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/
topic QA Mathematics
spellingShingle QA Mathematics
Mohamed Yussof, Fatin Nadiah
Maan, Normah
Md. Reba, Mohd. Nadzri
Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia
description Loss of spatial data with a long gap is a significant limitation for remote sensing analyses using satellite-based monitoring of oceanography. This limitation could not be ignored as it may affect the subsequent analysis and modeling of the data. Hence, this gap needs to be improved by filling the spatial gap in the satellite datasets. In this research, Data Interpolating Empirical Orthogonal Functions (DI-NEOF) is applied to fill the spatial gap and has successfully worked in the reconstruction of missing data of chlorophyll-a for monitoring harmful algal blooms (HABs) in Sepanggar Bay located at coastal water of Kota Kinabalu, Malaysia. The original chlorophyll-a pixels are used to assess the accuracy of the predicted data. Then, the DINEOF model is compared with the Spatio-temporal Kriging model for validation purposes. The results obtained show that the DINEOF model has the highest Pearson correlation coefficient, 0.9940 and the smallest values of Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD) which are 0.2770 and 0.0155 respectively. Therefore, this proved that the DINEOF model is more effective for filling spatial long gaps.
format Article
author Mohamed Yussof, Fatin Nadiah
Maan, Normah
Md. Reba, Mohd. Nadzri
author_facet Mohamed Yussof, Fatin Nadiah
Maan, Normah
Md. Reba, Mohd. Nadzri
author_sort Mohamed Yussof, Fatin Nadiah
title Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia
title_short Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia
title_full Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia
title_fullStr Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia
title_full_unstemmed Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia
title_sort reconstruction of chlorophyll-a data by using dineof approach in sepanggar bay, malaysia
publisher Badih/Ghusayni, Ed. & Pub.
publishDate 2021
url http://eprints.utm.my/id/eprint/94620/
http://ijmcs.future-in-tech.net/16.1/R-Nadiah.pdf
_version_ 1729703197894770688
score 13.211869

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