Monitoring Red Tide Outbreaks in the Arabian Gulf Using MODIS Data

Abstract

Detecting and monitoring harmful algae blooms (HABs) outbreaks, also known as red tide, are facing significant challenges at both local and regional scales. Red tide outbreaks in the Arabian Gulf have caused a number of negative effects during the last few years including the spread of bad smell in the air resulting from the organic decomposition of dead plant algae, death of large quantities of fishes and crustaceans, disturbance of fishing operations and suspending desalination plants operations. Recent red tide outbreaks have also caused the closure of a number of beaches and have severely affected the fishery and tourism industry in the UAE. The objective of this project is to validate the existing chlorophyll MODIS algorithms and to recalibrate some of these models for MODIS Satellite to detect and monitor red tide outbreaks in the Arabian Gulf. Therefore, the recalibrated model will be tested and used as early warning and daily monitoring tools of red tide outbreaks in the Arabian Gulf. Although several ocean color algorithms such as OC3, FAI, CI and FLH were tested, using the in situ data provided by different organizations, however, their performance was not as expected. Therefore, OC3 was recalibrated to increase the performance of detecting the red tide and finding the appropriate coefficients for the model so that it delivers desired results for the Arabian Gulf region. The highest calibration correlation coefficient was found to be 0.45. Unfortunately, this value is still low. A new logarithmic model for the water deeper than 10 m was proposed in order to avoid the effect of the bottom reflectance. It shows a correlation coefficient of 0.74 using 27 data points. The performance of the proposed model, compared with in situ data, is better than OC3.

Date of Award	2012
Original language	American English
Supervisor	Mohamed Hosni Ghedira (Supervisor)