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Showing 2 results for Pourmanafi
Maryam Karimian, Omid Beyraghdar Kashkooli, Reza Modarres, Saeid Pourmanafi,
Volume 11, Issue 2 (5-2022)
Abstract
The DINEOF algorithm is a parameter free technique based on iterative EOF analysis that is used to calculate the missing data in a given satellite data set (without requiring any prior information). In this study, the DINEOF technique has been used to fill the gaps in chlorophyll-a data series in the Persian Gulf and Oman Sea. Level 3 data (4 km spatial resolution) of chlorophyll-a concentration obtained from MODIS sensor (2003- 2020) for the study area were used. In some of the images several gaps were found in different months of the year. Images with gap in the Persian Gulf and Oman Sea were reconstructed by rtsa.gapfill R-package and DINEOF algorithm in R software. The linear regression analysis was performed between the missing and reconstructed data, and also parameters such as RMSE, MSE, MAD and SNR were calculated to evaluate the validity and performance of the DINEOF algorithm. The maximum number of the gaps in data series were found in July. Hence, the images of July have been examined and reconstructed as the case study. The original maps of chlorophyll-a concentration showed that the maximum number of the gaps were in July 2009 and 2015. Evaluation of the results showed a high accuracy of DINEOF-reconstruction method (e.g. in July 2014, R2 = 0.83, RSME = 0.34, MAD = 0.14, MSE = 0.10). The results showed that the implementation of the DINEOF algorithm (in R) to reconstruct the gaps in chlorophyll-a concentration images could serve as a rapid and efficient technique.
Maryam Karimian, Omid Beyraghdar, Reza Modarres, Saeid Pourmanafi,
Volume 11, Issue 3 (8-2022)
Abstract
Chl a is the main pigment of phytoplankton, which is an indicator of phytoplankton biomass and reflects the primary production in the marine environment. In this study, level 3 (4 km) data of Chl a concentration of Persian Gulf and Oman Sea for the period of 2003- 2018 were used. The data was converted to raster format in ArcGIS10.5 environment and then the numerical values of each pixel were extracted in R (version 4.0.2). Missing data were observed in Chl a data, to solve this problem, DINEOF algorithm was applied and non-parametric Mann-Kendall and Sen’s Stimulator tests were used to analyze Chl a concentration trends. The results showed that the maximum concentration of Chl a is in September (0.09 to 18.75 mg / m3) and October (0.23 to 18.03 mg / m3) and the minimum concentration of Chl a in May (0.22 to 5.74 mg / m3) and June (0.20 to 5.12 mg / m3). The trend of Chl a concentration variability over the study period was negative in most areas and not significant. These analyses provide an overall description of Chl a concentration variability in the Persian Gulf and Oman Sea based on satellite observations; however, further investigations based on in situ observations are needed to achieve better understanding of the patterns of of Chl a concentration alterations.
