فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال دوازدهم شماره 4 (زمستان 1400)

The expansion of urbanization has increased the scale and intensity of thermal islands in cities. Investigating how cities are affected by these thermal islands plays an important role in the future planning of cities. For this purpose, this study examines and predicts the effect of land cover (LC) changes in the three classes of LC including urban areas, barren lands, and vegetation on land surface temperature (LST) in the city of Yazd during the last 30 years using Landsat 5 and 8 images. This study also examines the effect of the ratio of proximity to the barren land and vegetation classes during this period to examine how the recorded LST is affected by the mentioned ratio.

The LC maps of Yazd city were extracted using a supervised Artificial Neural Network classifier for 1990, 2000, 2010, and 2020. Terrestrial data, google earth, and ground truth maps were used to derive training data. The LST of Yazd was obtained from the thermal band of Landsat 5 and Landsat 8. After that, the LST was classified into six available classes, including 16-20, 21-25, 26-30, 31-35, 36-40, and 41-46°C which has shown that the four last classes play an important role in LST changes in Yazd city during last 30 years. To evaluate the effects of the proximity of barren land and vegetation LC classes on the LST recorded by the sensor, firstly the proximity ratio was calculated in 5×5 kernels for all image pixels. Then the mean of LST was derived based on this ratio of barren and vegetation lands.

The results of this study showed that in Yazd city, from 1990 to 2020, the area of the urban area has grown 91.5 % (33.6 km2) over the last 30 years. Barren and vegetation land, have negative growth in the area over the same period. From 1990 to 2020, barren lands in Yazd experienced a growth -79.4% (21.3 km2), which the sharp growth of urban areas justifies this negative growth in barren lands. Vegetation classes in Yazd from 1990 to 2020, have experienced a growth -68.5% (12.2 km2). The average ground temperature of this city has been constantly increasing during these 30 years. By 2020, the city of Yazd, reaching an average of 38.1°C compared to 29.2°C in 1990, has experienced a 30.4% increase in its average LST. The temperature classes of this city have also moved towards warmer temperature classes in these 30 years. As the main part of the LST area of Yazd, in 1990, in the first place, the class of 26-30 °C with 47 km2 and at the second place the class of 31-35 °C with 26.4 km2 are classified. In 2000, in a reverse trend, the main LST class was 31-35°C with 52.8 km2 as the first place and the 26-30°C class with 20 km2 as the second place. With an increased class, the LST class of 36-40 °C for both 2010 and 2020 with 40.2 and 63 km2 respectively has been recorded as the largest LST class. The LST class of 31-35 °C has been recorded as the second LST class of both years with 33.2 and 9.7 km2, respectively. The difference between these two years is in the growth -70.7% (23.5 km2) of the class area of 31-35°C and the increase of 10.3% (0.8 km2) of the hottest class of the statistical period, 41-46°C, in 2020, compared to 2010. The results of this study also showed that the highest average temperature in all year was recorded for barren lands at 37.3°C. Also, a positive correlation (mean correlation 0.95) was shown between the proximity to barren land cover and the mean LST. However, the sharp upward trend of urban areas in the whole statistical period (91.5% with 33.6 km2) as the second class with the highest average LST after the barren lands with an average of 34.1 °C versus a downward trend of 79.4% (21.3 km2) of barren lands has increased the average LST over a statistical period of 30 years. It is because the decrease of 68.5% (12.2 km2) of vegetation areas as an LC class with the lowest average LST (32.2°C) in the same period, neutralized the effect of decreasing barren lands and intensified the trend of increasing the LST. Meanwhile, a negative correlation (mean correlation -0.97) was established between the ratio of proximity to vegetation and the average LST. The results of forecasting land cover changes in 2030 in the city of Yazd indicate that in a process similar to previous periods, the class of urban areas will increase. This growth will not be significant compared to 2020, with 1.6% (1.1 km2). However, a significant decrease in green areas (vegetation) by -19.6% (1.1 km2) in the same period, along with a slight decrease in barren lands -1.8% (0.1 km2) will cause the earth’s surface to become warmer, and the area of LST classes will be increased by the year. Accordingly, the main area of the LST class in 2030 for the city of Yazd, as in 2020, is forecasted 36-40°C with 58.2 km2 (-7.6% growth compared to 2020). But the dramatic growth of the hottest class of LST over the statistical period (41-46°C) with 166.3% (14.3 km2) growth as the second major class of LST in this year (2030), as well as the negative and dramatic growth of the relatively cooler class 31-35°C with -97.9 % (9.5 km2) in this year indicates the warmer ground surface temperature in 2030.

The results of this study indicate that in 30 years in Yazd city, the decrease in vegetation in the first place, along with the increase in urban areas in the second place, has caused an increase in LST. Thus, the vegetation class reduces the LST due to its cooling effect considering its water content. In this study, it was shown that by taking all factors into account, the reduction of barren lands will lead to a decrease in LST, and also increasing urban areas with a lower impact factor than barren lands will increase the LST. However, the decrease in the area of green lands (vegetation) in recent years, along with the sharp increase in the area of urban areas has caused an increase in LST. Increasing the proximity to vegetation by creating green areas by increasing the ratio of vegetation in the vicinity of different LC and also reducing the area of barren lands, can be a good solution to deal with the impact of urbanization in recent years on ground surface temperature.

Modeling and showing the coverage of the land changes, provides a comprehensive view to researchers in various fields, including environmental and natural resources experts. One of the main methods of environmental studies is to study the land cover/use and vegetation area change. In addition to showing spontaneous changes in nature, changes affected by human activities also fall into this category. Human construction has accelerated these changes in line with its development, especially in recent decades. Today, with the development of space-related sciences and remote sensing in general, and the production of more satellite products, it is possible to display the land use of different areas without the need for field visits and easily. The different behavior of the waves received by the satellite sensor from the various phenomena, known as a spectral signature is the basis for cognition and detection of the uses of the map. Such studies in Qom province have also been considered due to the very urban growth trend and the existence of several different types of climates in the not-so-wide area of this province. Qualitative and quantitative study and display of environmental and peripheral changes in Qom province over a period of about 30 years are one of the main objectives of the present study to help identify the trend of changes in different classes and complications and to model these changes in the future. Also, recognizing the changes in the outlook of Qom province, makes possible the ground for future planning.

In the present study, study times and time steps were selected based on changes in climatic/meteorological parameters. These steps were selected 5 years apart from 1989 to 2019. The study time point was considered to be the end of spring and the beginning of summer. The reason for this was the end of the rainy season in the area. Then the images of various Landsat satellite sensors were taken at specified time steps, and these images were pre-processed, processed, and classified into 11 classes. These 11 classes included; bare land, salty land, sandy land, tree, rock, urban areas, agricultural lands, and 3 different types of range. The results were also presented quantitatively and qualitatively. Based on the available real data, which was obtained visually and by sampling from different classes, the two maximum likelihood and minimum distance classification methods in Qom province were properly evaluated, which of the two, the maximum likelihood method yielded relatively better results considering the whole province with all classes and was used in the final classification. Finally, class changes between time steps were calculated and presented as a change matrix.

The results show that between 2014 and 2019, urban, water, agriculture, and ranges (types 1 and 3) have grown significantly. Also, between the two steps of 2009 to 2014, on average, about 30% of the total rangelands, ie three different types of classified rangelands, have become barren lands. Also, in this step, the main change observed was the largest change of sandy lands to bare lands, the reasons for which need further investigation. An examination of the changes between 2004 and 2009 shows that the negative growth in urban areas is mainly due to the poor quality of Landsat 7 satellite imagery and the similarity of the spectral behavior of salt lands and urban areas. The other negatively growing classes, including lakes, have become saltier lands and rocky areas have become barren, as well as salt lands have become barren and sandy. Examining the changes between 1999 and 2004, it is concluded that the negative changes in the tree class are due to the spectral behavior of vegetation, and this class has become mainly agricultural and rangeland classes. In the lake class, a 4 % change to the salt and rocky class has been detected. Major changes in the bare land class of about 12% have been detected in the rock and sand class. Also, more than 50% of the total area of range classes has been converted to bare land class, which is significant. The study of changes from 1994 to 1999 shows that only 3 classes had positive growth and the rest of the classes have negative growth, most of which was related to the urban class and the main changes were focused on bare lands. Vegetation classes all had negative growth and also due to the spectral similarity of these classes with each other, there was no proper separation between them. 12% of the bare land class has also been turned into a sandy land class. The classification of images and the display of changes from 1989 to 1994 show that sandy soils, range type 1, trees, salt lands, and lakes have grown negatively. In total, about 34% of different types of ranges have become bare lands, which seems reasonable due to the negative change in water areas (lake) and in a way indicates a faster drought. The extent to which other classes change to the bare land class, which includes relatively large numbers, also confirms this in some way.

Considering the geographical location of Qom province and a large area of this province, especially in the eastern half of it, which includes desert lands, including barren, saline, and sandy land classes, the selection of the classes mentioned in this research makes sense. Considering the major coverage of the province, one of the problems in the present study was that almost the majority of the pixels covering the province had a lot of similar spectral behavior and this issue made the classification process difficult.  In general, the classification results related to the images taken in 2019, which is related to the recent time, show positive growth in urban, agricultural, range, and water areas according to the rainfall in early spring 2019 it was logical. Another important point related to this year is the extensive change and conversion of the class of rocky lands into different types of ranges. According to the original image taken from 2019 and the classified images, the error related to the degrees of gray is evident in those images. The software considers the similarity of the degrees of gray and the same spectrum of urban and salt classes as part of a class. These errors are also evident in bare and sandy classes.

In recent decades, land use change due to environmental and human factors has caused serious effects on the environment and the economy in Golestan province. On the other hand, wide rangelands and natural areas have been cultivated without observing ecological and scientific principles or have been exploited for special purposes and changing to other uses, while many of these lands are do not have the potential to become new land uses and they have a high potential for erosion, as a result of which we will see soil erosion, especially in sloping lands and the creation of destroyer floods. Therefore, it is necessary and essential to be aware of the type and manner of use and its possible changes over time, which will be important for planning and policy-making in the country. The aim of this study was to detection the land use changes in Golestan province during the years 1986 to 2019 and to predict the land use status of the region for 2050 using the Land Change Modeling (LCM) approach.

In order to monitor the trend of land use changes in the study area, Landsat 5 and 8 satellites (TM and OLI sensors for 1986, 2001, and 2019) were used. Interpretation and processing of satellite data were performed in ENVI software. The necessary pre-processing was performed on the images. First, the images were mosaic together and then cut according to the province boundary. In order to identify and separate the phenomena from each other, a false color image was created. Then, the supervised classification method with the maximum likelihood method was used. At this stage, five classes, including rangeland, agriculture, forestry, residential, and water areas were defined. Land use maps for 1986, 2001, and 2019 were prepared. Integration of land cover maps related to 1986, 2001, and 2019 was used as input of LCM model and digital elevation model (DEM) maps and road and stream layers to analyze area changes and prediction of land use changes of 2050. After the necessary analyzes in order to detect land use changes between the intended time periods, change maps and land use transfers were prepared. Finally, the amount of decrease and increase in each land use, the amount of net changes, the net change from other land uses to the desired class, areas without change and transfer from each land use to another land in different land cover classes in the form of maps and charts were prepared and analyzed.

The aim of this study was prediction and modeling of land use changes in a period of 33-years in Golestan province. According to the results during this period, the area of ​​the rangelands has decreased a lot, equivalent to 181181.25 hectares. Much of the decline in rangelands is due to its conversion into agricultural, which can be attributed to population growth and the need to expand crop land. The area of ​​forest lands during the mentioned years has decreased from 393018.75 to 349143.75 hectares in 2019, which has shown a decrease of 43875 hectares (2.2%). In general, the destruction of rangeland and forest areas is especially visible in developing countries due to population growth, technological growth and non-compliance with ecological principles and law enforcement. Also, the results of classified maps during the mentioned years show that the highest amount of changes in the region is related to agricultural lands, has increased to 173700 hectares equal to 8.5 % during the same period. The rate of land use changes related to the residential land class has also increased with the increasing trend from 18731.25 hectares in 1986 to 37518.75 hectares in 2019, which has increased by 18787.50 hectares (0.9%) during this period. Rapid growth of population has led to the development of residential and urban areas and the increase in this type of land use with a relatively steep slope, especially in recent years, which can be part of the government's plans for housing construction in the surrounding areas cities. This increase in the class of agricultural lands is more noticeable, especially in the central and eastern regions of the province, and can be a warning alarm for the future. It means that in an imperceptible trend, rangeland and forest lands become rainfed agricultural lands and after a while unprincipled exploitation, eventually become barren and unusable land. On the other hand, this could indicate an increase in population and demand for housing, and consequently securance of the needs of the residents of the region is a threat to rangeland lands which is necessary instead of increasing the agricultural and residential lands and turning rangeland lands into such land uses, the policy of increasing productivity in the agricultural sector should be pursued. About of water areas, it can be said that during this period, it has increased by 1.6% or 3268.75 hectares. This increase in water areas can be partly attributed to heavy rainfall and water intake and even floods in different parts of the province in 2019. Predicting the rate of land use change in 2050 indicates that in the coming years, the area of ​​rangelands and forests will be reduced by 131906.25 and 291600 hectares, respectively, and in contrast to the area of ​​agricultural land and residential areas will increase to 164137.50 and 25313.25 hectares, respectively. Therefore, the adoption of necessary measures and policies to further reduce forest and rangeland will be inevitable.

Understanding of the conditions of different land uses during the coming periods will facilitate planning for the future by creating information in terms of their spatial distribution pattern, but maintaining and creating sustainable conditions for the future both statistically and it is ecologically one of its limitations. These constraints play an important role in the safe use of different land uses in the planning process. Therefore, creating sustainable conditions in the region and modeling it in order to use the natural resources of a region regularly and sustainably is one of the preconditions for achieving upstream visions and documents, including the sustainable development plan.

In arid and semi-arid regions such as Iran, where the average annual rainfall is less than one-third of the world's average annual rainfall, groundwater is an important source of drinking water. Quantitive and irregular rainfall, limited surface water resources, and its absence in many parts of the country have led to the widespread use of groundwater. Today, increasing agricultural, horticultural and livestock activities on the one hand and industrial and workshop activities and population development along with population growth, on the other hand, excessive use of natural resources and expansion of industrial and agricultural activities and mass production of waste and scrap, groundwater resources are threatened. Seriously and has caused a lot of pollution. Qualitatively, most groundwater reservoirs are vulnerable to ministerial sources. Point sources of pollution from domestic and industrial wastewater and non-point sources of pollutants result from improper and excessive consumption of fertilizers and pesticides and their leaching into groundwater. Widespread groundwater pollution and the growth and awareness of human societies about the importance of these vulnerable resources have led to extensive efforts to protect groundwater. The process of regenerating aquifers on a regional scale in a reasonable time frame is not possible because groundwater flows very slowly. Vulnerability assessment is a way to zoning areas that are most prone to contamination; therefore, to prevent contamination and effective groundwater management, it is necessary to assess the vulnerability of aquifers because vulnerability can help determine practical and practical policies for the management of groundwater resources in the path of sustainable exploitation. In our country, in the last decade, the assessment of groundwater vulnerability to pollution has grown significantly and has had good results. Vulnerability assessment is a low-cost and powerful way to identify areas prone to contamination. Vulnerability assessment is a low-cost and powerful way to identify areas prone to contamination. In the Aisin plain of Hormozgan, due to its small area, low annual rainfall, and lack of water resources in this area, the use of groundwater resources is very important. Due to population growth, industrial activities and agricultural development, and the use of agricultural pesticides and chemical fertilizers in this plain and due to lack of knowledge or understanding of the exact level of groundwater vulnerability in this area, the need for rapid techniques to identify and assess vulnerabilities. It is underground in this area. The purpose of this research is was to map the vulnerability of groundwater in the Aisin plain of  Hormozgan using DRASTIC and GODS methods and with the help of GIS.

This research was carried out by DRASTIC and GODS methods with the help of GIS. The DRASTIC method is the most important rating method for determining vulnerability, which is more common among researchers and experts and has been used. The DRASTIC method consists of a combination of seven measurable and effective hydrogeological features effective in transferring contamination to groundwater, including groundwater depth, net recharge, aquifer, soil environment, Topography, Impact of the vadose zone, and hydraulic conductivity. Aisin plain based on available data extracted from Hormozgan Regional Water Company in GIS environment and after ranking and weighing them between 1 to 10 and superimposing them, the final vulnerability map of Aisin aquifer based on the DRASTIC model obtained Came. The GODS model, which is a very simple, practical, and experimental method for rapid assessment of groundwater pollution potential, also has four characteristics of aquifer type, unsaturated area, groundwater depth, and soil type, which were used in this study. The data used in the above models were extracted from 19 piezometric wells located in Aisin plain, which were available from 1987 to 2018. In the GODS method, like the DRASTIC method for each of the hydrogeological characteristics based on the available data, in the GIS environment, a map was prepared and ranked between 1 and 5, then after superimposing them, vulnerability maps of Aisin plain with GODS model obtained.

Since in the DRASTIC model index, the minimum possible vulnerability is 23 and the maximum is 230; However, the results of the final Aisin aquifer vulnerability map by the DRASTIC method, which is almost the most complete indicator for assessing groundwater vulnerability, Showed that the range of DRASTIC index values is between 59 and 163. The map of this index has been extracted in 6 categories from non-vulnerability to high vulnerability. Most of the area (33.66%), which covers the northeastern parts, from the center to the south of the plain, has low to moderate vulnerability. After that, moderate vulnerability (19.29%) was located in parts of the center and northwest, respectively, and also very low vulnerability (14.75%) in parts of the south and east, in parts of the east and south without vulnerability (29 11.11%), in the northern and part of the center and south with low vulnerability (10.15%) and finally, high vulnerability (10.84%) in the central and western parts were in the next categories in terms of area. In fact, according to the DRASTIC model, most of the aquifer sections of the Aisin plain were in low and medium to medium vulnerability classes in terms of vulnerability potential. Also, the results of the GODS model showed that the study area is divided into three parts including low, medium, and high vulnerability. Most of the Aisin plain (66.83%) is in the range of moderate vulnerability with ranks between 0.5 to 0.3 And the lowest level (11.31%) is related to the high vulnerability potential with a rank of 0.5 to 0.7.

In general, in both methods, the inherent vulnerability of the Aisin aquifer has been evaluated in low to high vulnerability ranges, but the extent of expansion of their vulnerability ranges has been different and the DRASTIC model of different vulnerability zones due to more characteristics and different weights based on these characteristics are different. Contamination is more accurately identified.

An earthquake is one of the most important natural events that cause a lot of financial and human losses every year around the world. An earthquake is an earthquake caused by the rapid release of energy, which often occurs due to landslides along a fault in the earth's crust. Earthquakes cause many geological-geotechnical instabilities such as multiple rockfalls, soil and rock landslides, runoff and mud flow, subsidence limestone caves, liquefaction, and expansion rupture. One of the most important effects of an earthquake is the displacement of the earth and the resulting morphological changes. Estimating the rate of land displacement and monitoring the morphological changes of this phenomenon in order to manage the crisis is one of the basic measures after the earthquake. In recent decades, extensive efforts have been made to monitor changes and displacements of the Earth's crust. With accurate alignment and ground observations, changes can be measured with great accuracy, which ground measurements are costly and can be measured sporadically. The use of remote sensing technology in the various earth sciences is very common due to the wide coverage of satellite images, the timeliness of the images, and its low cost compared to terrestrial methods. One of the applications of measurement is to show and control the movements of the earth's crust due to factors such as earthquake, drift, subsidence. The use of radar, satellite images, and radar interferometry methods, due to extensive coverage and periodic imaging and with an accuracy of about cm, is a good tool to monitor changes in the Earth's crust. Satellite imagery of the Sentinel-1 satellite system, which has been made available to the public free of charge by the European Space Agency since 2014 and is currently being continuously imaged, is a good tool for earthquake monitoring. A radar imaging technique is a new tool used for the discovery and display of land subsidence.  In the present perusal, in order to achieve the above purpose, using satellite data and radar interferometry technique, the deformation of the earth's crust due to post-seismic movements in Sarpolzahab city has been investigated. 

In this paper, using radar imagery, the deformation field due to the seismic dimension of the county is obtained from 11/ 11/ 2017 to 17/11/2017 using radar data (S _ 1 A - IW), with a baseline of 100 m.

Examination of the results of deformation of the earth's crust after an earthquake shows; The highest rate of land subsidence in the north, northwest of Sarpol-e-Zahab city (about 90 cm vertical displacements of the earth's crust) to the west, and land elevation around the epicenter (north of the herd), about 30 cm vertical displacements of the earth's crust (towards Darbandi Khan) It has happened. The effects of subsidence and uplift caused by the earthquake in the study area in addition to morphological changes in the area have also affected the hydrology of water resources in the area. For example, earthquakes have caused significant changes in the volume of water in the Strait of Hammam dam and increased the volume of water resources in the Sirvan river.

The results of this study showed that the use of radar interferometry technique, in addition to being an efficient tool in estimating the rate of crustal displacement, can be used in relatively accurate estimation of quantitative changes in water resources resulting from crustal displacement.

The seas and oceans play an important role in climate conditions as well as climate change. In addition, physical and biological phenomena are among the most important factors affecting the chemistry and environment of the sea. Therefore, it is important to know the physical processes that govern the seas and oceans, as well as the correlation between these properties and biological properties. Remote sensing algorithms use a close range of blue, green, yellow, red, and infrared, so monitoring of chlorophyll-A, the phytoplankton pigment of oceanic and coastal waters, can be measured and evaluated using state-of-the-art remote sensing technology.

In this study, the capability of remote sensing methods has been used to investigate the status of coastal water quality characteristics of Sistan and Baluchestan provinces. For this purpose, the status of chlorophyll-A has been used using OC3 bio-optical algorithms in ENVI as well as the predecessors of the Google Earth Engine platform. Google Earth Engine is an open-source spatial analysis platform that enables users to visualize and analyze planetary satellite images. Using this system, various spectral processes can be performed on different surface phenomena with different satellite data. It is also possible to perform calculations on large volumes of data without the need for high-power systems. The salinity parameter of MIRAS's SMOS satellite was used in SNAP software to investigate the parameters of chlorophyll, temperature, and organic carbon using Terra's MODIS satellite images. The time to be studied in the images used and field sampling is May 2020. In order to extract the concentration of chlorophyll-A, bio-optical algorithms based on blue and green bands (OC3) were used in ENVI software. Bio-optical models combine optical measurements of reflection or radiation with biological parameters such as chlorophyll concentration, water quality, and more. Water temperature is one of the most important factors in the life of the sea, so those marine animals can survive and reproduce only in a certain range of water temperatures. Therefore, phytoplankton is very sensitive to changes in water temperature and react to temperature. Water level can determine their frequency and distribution. In this study, the product MIR_OSUDP2 of the SMOS satellite of MIRAS on 3rd of May 2020, for the study area from  https://smos-diss.eo.esa.int/ was used.

The results showed that the amount of chlorophyll-A is higher along the shores and the stations near Joud and the estuary has a higher concentration of chlorophyll-A. The results showed the outputs of two different methods for estimating chlorophyll-A in the study area are similar. Also, according to the results, it is clear that the amount of chlorophyll-A has increased in Chabahar, Konarak, Jude, and Goater stations in recent years. In Chabahar and Konarak regions, this increase has been significant for ten years, and the sudden increase in chlorophyll in recent years in field stations requires more studies to identify the causes and should be considered. The chart below shows the rate of change in chlorophyll-A from 2019 to 2020. According to the results, the amount of organic carbon follows the amount of chlorophyll-A and in areas such as Chabahar and Konarak we see higher levels of organic carbon. Also, the highest increase in temperature in all three periods studied was in Chabahar and Konarak ports, of which human activities are one of the main factors. Also, by examining the ten-year trend, increasing temperature changes can be seen in the ports of Maidan and Jude. The general trend of temperature is decreasing to the east as expected because it is closer to open waters. Seasons when water temperatures are lower, chlorophyll-A levels are higher. Chlorophyll-A map output results by ENVI software and Google Earth Engine platform, chlorophyll-A concentrations were higher in autumn and winter than in spring and summer, high chlorophyll-A-concentrations are common in cold tropical and subtropical seasons. Also, the concentration of chlorophyll-A in the study areas along the coast is higher than the offshore areas, which is related to the chlorophyll-A harvesting algorithm in type 1 waters; In other words, coastal areas have more value than offshore areas due to shallow depth, high turbidity and suspended sediments. Because there is no river discharge in this area, these areas are mostly affected by hydrodynamic processes such as wind direction and sea currents. The lowest chlorophyll-A concentrations were observed in the region from May to September, which was contrary to fluctuations in water surface temperature, which could be due to rising currents. The amount of organic carbon is one of the most important factors for evaluating the performance of aquatic ecosystems, which determines the potential of ecosystems for fishery products; The results of the study of organic carbon showed that the amount of organic carbon as chlorophyll-A in the two seasons of autumn and winter was higher than spring and summer so that the trend of changes in organic carbon also followed the trend of changes in chlorophyll-A. There is a correlation between temperature fluctuations and chlorophyll-A, this correlation indicates the importance of water surface temperature in changes in the growth rate of phytoplankton as one of the climatic factors and has made the most important parameter affecting chlorophyll-A, water surface temperature. According to the obtained results, the trend of temperature changes in the last ten years is increasing and the hottest stations are Chabahar and Konarak stations. In terms of salinity, areas with lower salinity had higher chlorophyll-A levels. Comparison of the data obtained from this study with the above indicates that the range of recorded fluctuations of the quality parameters studied in the natural waters of the region and is consistent with similar studies in the study area by other experts.

The results of this study show the acceptable accuracy of the results compared to the data of similar researchers in addition to the speed and ease of the method. Therefore, with the help of remote sensing science, timely monitoring of the quality parameters of water areas can prevent major crises and save time and money, problems that may be irreversible if they occur.