Remote Sensing (RS)
Heshmat Karami; Zahra Sayadi
Abstract
Extended AbstractIntroductionCoral reefs are one of the most diverse and ecologically important areas in the world. However, with increasing ocean temperatures, many coral reefs are severely threatened by bleaching events. When the water is too warm, corals expel the algae that live in their tissues, ...
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Extended AbstractIntroductionCoral reefs are one of the most diverse and ecologically important areas in the world. However, with increasing ocean temperatures, many coral reefs are severely threatened by bleaching events. When the water is too warm, corals expel the algae that live in their tissues, causing the coral to turn completely white. When a coral bleaches, it is not dead, and corals can survive a bleaching event, but they are more stressed and at risk of dying. Today, in order to predict and identify areas at risk of coral bleaching, data based on satellite remote sensing are used. In this research, using 35-year data trends, the sea surface temperature in 2022 was predicted using ArcGIS Pro tools for the Persian Gulf area and possible areas exposed to thermal stress leading to coral bleaching were identified.Materials & Methods In order to predict the bleaching of corals, the research data archive of the American National Center for Atmospheric Research (NCAR) has been used. In this analysis, the harmonic method was used to fit the trend line. A harmonic trendline is a periodically repeating curved line that is best used to describe data that follows a cyclical pattern. For anomaly analysis parameters, the average monthly temperature in each location was compared with the overall average temperature to identify anomalies. There are three mathematical methods for calculating anomaly values with the Anomaly function, in this research, the method of difference From mean was used. At the end, the dimension value or band index was extracted, in which a certain statistic is obtained for each pixel in a multi-dimensional or multi-band raster, and the final map of coral bleaching prediction was prepared, and then using the data and global maps of the National Oceanic Administration NOAA , it was evaluated.Results, discussion and conclusionThe preliminary results showed that the sea surface temperature has changed in the Persian Gulf. The range has experienced higher average temperatures since 1996, which could put the area at risk of coral bleaching. The minimum average temperature in the studied time period is 298.758 degrees Kelvin in 1991 and the maximum average temperature in 1399 is 300.737 degrees Kelvin. The parameters that were chosen for multidimensional data trend analysis include water surface temperature variable (SST) and time dimension. The obtained trend map (1980-2015) indicated that the northwestern regions of the Persian Gulf and a part of its south are more exposed to prolonged heat. In this study, frequency parameter 2 was used in the harmonic model, which uses the combination of the first-order linear harmonic curve and the second-order harmonic curve to fit the data. The accuracy of data trend fitting by harmonic regression function provided statistical parameters, R2=0.78 and RMSE=0.5. The value of R2 indicates that the observed value of sea surface temperature (SST) was predicted by the harmonic regression model by 78% and the rest remains undefined. This value of the determination coefficient confirmed the accuracy of the trend map. Another statistical parameter is the root mean square error, the lower the value, the better the fit. In the obtained results, the mean of this error is 0.5, which shows that the harmonic regression model can accurately predict the data. In this study, forecast data was analyzed to find locations where water temperatures remain warm for extended periods of time. In this context, first, anomalies in the data were calculated, anomaly or anomaly is the deviation of an observed value from its average value, and in the analysis, it shows areas that have a temperature higher than the average. As a result of this step, the anomalies in the data were calculated and the areas with higher temperature than the average were identified. In the predicted annual time frame (2022), the north-west and a part of the south of the Persian Gulf region will face a longer period of high temperature. To evaluate the accuracy of the results obtained from the analysis and the method used in predicting sea surface temperature and identifying anomalies (2022-09-03), they were compared with the maps of Nova organization on the same date and were confirmed. It is suggested that responsible organizations use methods based on remote sensing and trend analysis to assess the situation and prepare a risk map of coral reefs.
Masoud Torabi Azad; Abbas Ali Aliakbari Bidokhti; Hossein Salehianfar
Abstract
Many hydrological conditions in the seas depend on temperature variation, and the rate of this parameter is an important determinant in the environmental conditions of each area. The variations in temperature and surface wind causes changes in the density of the sea water, and the change in density affects ...
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Many hydrological conditions in the seas depend on temperature variation, and the rate of this parameter is an important determinant in the environmental conditions of each area. The variations in temperature and surface wind causes changes in the density of the sea water, and the change in density affects the stability rate and mixing of the sea water column. In this research, the mutual effect of sea surface temperature (SST) on surface wind speed in the southern Caspian region (Mazandaran province) has been investigated. First, the sea surface temperature data was collected by the AVHRR sensor of NOAA satellite and surface wind speed data was collected by QuikSCAT satellite for an area of 340 × 220 square kilometers in the southern Caspian Sea. After analyzing the satellite data for sea surface temperaturel, its monthly and seasonal variations were drawn by Tecplot software for this area. It was found that the average seasonal temperature (spring and summer) of the eastern coast of southern Caspian Sea is 0.87 degrees centigrade more than that of the eastern coast. In order to investigate the mutual effect of the sea surface temperature on surface wind speed, four stations, A and D (in the western region), B and C (in the eastern region) were selected on the southern Caspian Sea. Then, the graph of temperature time series, temperature difference between the four stations, time series of wind speed and time series of wind speed difference between the four stations from 2000 to 2005 were plotted and compared for the spring and summer seasons. The results indicate that, with increasing temperature difference between the four stations, the difference in speed also increases in 80% of the cases for the summer and in 66% of the cases in the spring season.In these two seasons, because of the decrease in the activity of atmospheric systems, the temperature difference between the two stations has a significant effect on improving the wind speed difference.Average wind speed difference in the statistical period of 2000-2005 at the stations is 0.7 m/s for the spring and 1.37 m/s for the summer season.
