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.
Mohammad Kazemi Garaje; Khalil Valizadeh Kamran
Abstract
1- Introduction Direct measurement of physical parameters of water, such as sea surface temperature and water depth through traditional methods is very time-consuming and costly. Thus, new cost-effective methods, such as remote sensing technology, have always been of interest to experts, managers and ...
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1- Introduction Direct measurement of physical parameters of water, such as sea surface temperature and water depth through traditional methods is very time-consuming and costly. Thus, new cost-effective methods, such as remote sensing technology, have always been of interest to experts, managers and decision-makers. Satellite imagery is used to estimate sea surface temperature and water depth. Therefore, the present study seeks to calculate sea surface temperature and water depth and investigate their relation using satellite imagery. 2- Materials and Methods In the present study, Landsat 8 satellite images of Urmia and Van Lake were retrieved from USGS website for August 16th, and August 23rd, 2018. Information about water temperature and water depth of 3 meteorological stations in the study area were also obtained from the Artemia Research Center and the Meteorological Organization of West Azerbaijan Province for a period of three months. In the next step, geometric and atmospheric corrections were performed on the images using ENVI5.3 software. In thermal remote sensing, thermal bandwidth of satellite imagery cannot reflect black-body radiation. Moreover, electromagnetic spectrum of radiation used in the Boltzmann relationship covers a range of 3 to 300 micrometers. This is while the thermal spectrum range of thermal sensors is generally between 10.5 to 12.5 micrometers.Thus, the split-window algorithm was used to calculate the land surface temperature. Water emission coefficient equals 0.98. Multiplying the amount of water emission by the amount of land surface temperature (LST) and subtracting the results from zero Kelvins (-273), we can obtain sea surface temperature in Celsius degrees. 2-1- Calculating relative depth of water As one of the dynamic characteristics of water, water depth has an important role in the management and optimal use of marine resources. Water depth measurement refers to the underwater study of oceans, lakes and rivers. Therefore, Stump Method was used to calculate water depthin the present study. 2-2- Accuracy assessment In order to estimate the accuracy, information about water surface temperature and relative water depth in three stations in Lake Urmia, namely Qalqachi, MalekAshtar and Ashk stations, were collected from the Artemia Research Center and the Meteorological Organization of West Azerbaijan Province. 3- Results Results indicate high accuracy of remote sensing methods in sea surface temperature and water depth measurements. The lowest RMSE of sea surface temperature measurement is related to MalekAshtar station (1/1). This station also has the lowest amount of RMSE (1/5) obtained in water depthmeasurement. According to the results, a negative correlation coefficient is observed between the values of sea surface temperature and water depthvariables. The correlation between sea surface temperature and water depth in Lake Van equals -0.52, while this correlation equals -0.24in Lake Urmia. 4- Discussion Despite their relatively high accuracy, usinginformation collected from meteorological stations to calculate physical parameters of water,such as water surface temperature and water depth, has some limitations. However, new technologies such as remote sensing can overcome the limitations of traditional methods. Remote sensing technology has made estimating the physical parameters of water on a regional to a global scale possible. Results of the present study indicate high accuracy of remote sensing technology in measuring physical parameters of water such as surface temperature and depth. In this regard, shallow water bodies have the highest surface temperature and deeper water show lower temperatures. The results also indicate that fluctuations in the water surface temperature and water depth can increase or decrease the correlation coefficient between these two variables. Thus, higher correlation coefficient between water surface temperature and water depth in Lake Van compared to Lake Urmia is due to its greater depth of water. 5- Conclusion Results indicate that the upstream of Lake Urmia is deeper than itsdownstream and also has a higher level of salinity which reduce evapotranspiration in the upstream of the lake. Thus, theupstreamof Lake Urmia has not been as severely affected by the drought. The correlation coefficient between water surface temperature and water depth of Lake Van also shows that this lake has a relatively lower water surface temperature compared to Lake Urmia due to its greater depth. Therefore, the rate of evapotranspiration in this lake is less than Lake Urmia and the drying process is negligible. Due to the fact that Lake Urmia and Van are in the same climate, the high temperature of the water level of Lake Urmia due to its shallower depth can be one of the causes of Lake Urmiadrying. The amount of water in the lake can be increased by increasing the volume of water entering the lake.This can be achieved by destroying a number of dams built on the rivers flowing into the lake or by water transfer from adjacent water bodies. Therefore, increasingwater depth and reducingwater surface temperature can be considered as one of the main solutions to prevent the drying of Lake Urmia.