جستجوی مقالات مرتبط با کلیدواژه « سنتینل 2 » در نشریات گروه « جغرافیا »

The Land surface temperature (LST) is an important Quantity that affects the cycle of energy and moisture between the earth's surface and the atmosphere. (xin et al, 2023). In fact, land surface temperature is an important index related to climatic, meteorological, hydrological, and environmental phenomena and processes and is widely used in climate change investigation, hydrological process modeling, drought monitoring, and fire risk assessment (Yang et al, 2020). One of the methods of measuring temperature is meteorological stations, which are less used due to problems such as lack of proper spatial distribution and difficult access to data, especially on a large scale. (Heidari and Akhundzadeh, 2019). Thermal infrared remote sensing is an efficient approach in this field and has attracted the attention of researchers. (Yi et al, 2023). Considering that Ardabil city, located in the northwest of the country and on the foothills of Sablan mountain, is considered one of the big cities of Iran and is affected by climate changes, including the increase in temperature (Malkian et al, 2018), it is necessary to investigate climatic factors, including temperature, in this region. The purpose of this study is to investigate the temperature in different land uses by LANDSAT9 satellites by applying the split window (SW) algorithm and Sentinel3 using the SLSTR sensor and its LST tool. Checking the temperature using two different images, by applying different algorithms and checking these temperatures in different uses in the study area can be considered as an innovation of the present study.

Ardabil city with an area of 2165 square kilometers is one of the big cities of Iran and the capital of Ardabil province. In this study, Landsat 9 and Sentinel 3 images from 20/7/2022 have been used to extract the land surface temperature (LST). For this purpose, the split window algorithm was implemented on the Landsat 9 image, and the LST algorithm of the SLSTR sensor was implemented on Sentinel 3. To check the results related to the temperature, the data from the meteorological station has been used. Also, the land use map of the region has been extracted using the Landsat 9 image on 5 residential and industrial floors, vegetation, agricultural land, water areas, and soil cover using the object-oriented technique to check the temperature in different uses.

The land use map of the studied area was extracted with overall accuracy and a kappa coefficient of 98 and 97%, respectively. Investigations showed that in Landsat image 9 agricultural land use with an average temperature of 35 degrees Celsius is the minimum temperature and soil cover use with an average temperature of 42 degrees Celsius is the maximum temperature. In the image of Sentinel 3, in the same way, agricultural land use and soil cover have minimum and maximum temperatures of 37 and 42 degrees Celsius, respectively. The minimum and maximum temperatures obtained by both sensors show close values with a difference of one degree in the maximum and 3 degrees in the minimum temperature. The temperature of the station related to Landsat 9 and Sentinel 3 is estimated to be 35 and 36 degrees, respectively, which are close to each other and show a difference of one degree. However, the temperatures obtained from the Landsat 9 sensor have a smaller difference from the temperature of the meteorological station, and this Temperature compliance is especially noticeable in the minimum temperature with a difference of 10 degrees Celsius. It should be mentioned that the spatial resolution of 30 meters of Landsat 9 and 1 km of Sentinel 3, also the location of the weather station should be considered.

The minimum and maximum temperature has been estimated using the split window algorithm on the Landsat 9 image, 22 and 51 degrees Celsius, and using the SLSTR sensor on the Sentinel 3 image, 25 and 52 degrees Celsius. The split window algorithm has high accuracy and capability and is considered one of the most effective and widely used algorithms in extracting land surface temperature in many studies (Grace et al, 2020), (Eon et al, 2023), (Zhang et al, 2019). SLSTR is also a high-precision infrared radiometer and enables more accurate LST measurements using common algorithms as well as the development of new algorithms (Kuppo et al, 2016), (Sobrino et al, 2015). The obtained temperatures show that reasonable and close values were obtained by both sensors and are by different uses. The results of this research show that the temperature calculated using the satellite images of both sensors is consistent with the data obtained from the meteorological station, however, the temperatures obtained from Landsat 9 show a greater agreement with these obtained data, especially in the minimum temperature. Regarding the maximum temperature and the station temperature, the difference between the Landsat 9 temperature and the station temperature is one degree less than the Sentinel 3 temperature. LST for each pixel in remote sensing is equivalent to the average temperature of different earth surface covers (Alavi Panah, 2017), Therefore, it seems that the high spatial and radiometric resolution of the Landsat 9 sensor has not been ineffective in the high accuracy of the obtained values.

Estimation of flood zoning is very important in terms of management and determination of flood damages. Remote sensing and the use of high-resolution images can be effective in extracting flood zoning estimator indicators. In this research, Sentinel 2 images in the year of the flood occurrence with a spatial resolution of 10 meters and Landsat 8 images at the same time in the years before and after the flood were used by environment the Google Earth Engine.  In this study, the zoning of the flood was estimated using indices of NDWI, MNDWI, and DVDI. The results showed that the MNDWI, despite the long-time interval (20 days) after the flood of 2019 and the evaporation of most of the water spread over the lands, the area of flooding was estimated better compared to the NDWI  around 330.59 ha. Also, the flood-affected area using DVDI (that indicates the destruction of vegetation due to floods showing negative values) was estimated at 3522.21 ha, which showed a small difference compared to the results provided by the results of Lorestan Governorate research (4750 hectares). Finally, the results showed although DVDI optimally estimated flood zoning due to the use of the 5-year time series of the NDVI before and after the flood if the cloud-free images of Sentinel 2 exist to extract the MNDWI, it probably could perform better than the DVDI. In general, the use of the above indicators is suggested as an important, practical, and low-cost method for management, area estimation, and flood damage determination.

Air pollution is one of the most significant challenges in today's world, particularly in developing countries. Therefore, it is necessary to monitor and control the amount of pollutants that threaten human health. In recent years, remote sensing data related to the Sentinel 5 Tropomy sensor has been considered a rich and up-to-date source of information for monitoring and investigating the temporal-spatial changes of air pollutants. In this study, the Google Earth Engine system was used to obtain Tropomi sensor products to check the amount of nitrogen dioxide pollutants in East Azerbaijan province. For this purpose, the distribution map of the NO2 was produced in four consecutive years (1397-1400). Additionally, due to the effective role of motor vehicle traffic in increasing the amount of this pollutant on the one hand and the restriction of activities and traffic of cars during the spread of the Covid-19 virus on the other hand, the changes of nitrogen dioxide in different time periods were investigated to determine the effect of restrictions on the concentration of this pollutant quantitatively. Also, in order to investigate the impact of the reopening of schools and universities after the corona pandemic, pollutant distribution maps were produced and compared for April 1400 and April 1401. The results showed that the density of NO2 in the center of the province was higher than in other cities in all time periods, and the 14-day closure in 2019 had the greatest effect in reducing the pollutant, which was about 59% in Tabriz.

The subsidence is one of the hazards that has posed a significant threat to many plains in the country in recent years. Asadabad Plain in Hamedan province is among those plains with a high potential for subsidence due to its hydro climatic conditions and geomorphology. Because of the importance of this issue, in this research the subsidence status of Asadabad Plain has been assessed and unlike many previous researches, the role of human factors in its occurrence has been analyzed. To do so, Sentinel-1 radar images, Landsat 5 and  8 satellite images, statistical information related to piezo metric wells in the region, and digital elevation layers were used as the primary data sources. Using the SBAS time series method, a subsidence map of the area from 2015 to 2022 was generated, followed by an analysis of the impact of groundwater depletion and land-use changes on subsidence. The results indicate subsidence ranging from 132 to 704 millimeters in Asadabad Plain during 2015 to 2022, with the highest subsidence occurring in the central regions of the plain. Considering the significant correlation (a correlation coefficient of 0.709) between subsidence and groundwater level decline, it can be concluded that one of the main causes of subsidence in Asadabad Plain is the depletion of groundwater resources. The assessment of land-use changes also reveals an increasing trend in agricultural land and human-built areas, corresponding to increased exploitation of groundwater resources and intensified land surface pressure, leading to exacerbated subsidence. Given the persistence of subsidence in this plain, remote sensing monitoring and the establishment of fixed GPS measurement stations facilitate monitoring the rate and extent of subsidence.

Air pollutants in Iran's metropolitan areas are among the serious challenges for managers, which also threatens the health of citizens. Monitoring air pollution is an important task in public health. Also, the unavailability of ground station data is often prevented due to the lack of network of ground monitoring stations to know the exact amount of air pollution in different parts of the country. Therefore, the use of remote sensing images in continuous monitoring of air pollutants due to their low cost and low manpower, will be appropriate and cost-effective. In this research, Sentinel 5 satellite images and MODIS sensors have been used for analytical study to detect the average trend of changes and spatial distribution of dust in Khuzestan province. The aim of this study was to use relatively high-resolution satellite data for local monitoring of air quality/air pollution and to investigate the relationship between Sentinel and MODIS image sensors. Findings indicate significant amounts of AOD in several parts of the study area such as Ahvaz, Abadan, Khorramshahr, BandarMahshahr and Bavi. Also, a small amount of fine dust is seen in the southeastern, eastern and northeastern cities of the province. According to the results of this research, as we move from the southeast, east and northeast and move towards the southern and western parts of the province, the amount of fine dust increases.

Normally, images with a high resolution (temporal or spatial) are available, while there is a limitation in accessing images which are simultaneously high spatial and temporal resolution. While, in some applications, access to images with high spatial and temporal resolution is necessary. Therefore, this study was conducted to downscaling MODIS images to Sentinel-2 spatial resolution by STARFM, ESTARFM and FSDAF spatio-temporal downscaling algorithms in different land cover classes including urban, garden, pasture, agricultural and water classes. The study area was selected with a variety of land covers around the city of Mahabad, Iran. First, the corresponding visible and near-infrared bands in Sentinel-2 and MODIS were selected and necessary pre-processes such as geometric correction were done on these images. Then, Sentinel-2 images were simulated using downscaling algorithms. The results indicated the accuracy of downscaling in the urban, garden and pasture classes compared to the agricultural and water classes. So that the ESTARFM, FSDAF and STARFM algorithms averagely showed the coefficient of determination of 88.25, 87.25 and 86.5 for the urban class, the coefficient of determination of 83.75, 83.25 and 80.5 for the garden class and the coefficient of determination of 90.75, 70.5 and 87.5 for the pasture class in all bands.

One of the types of domain instability that causes financial and human losses every year is the risk of landslides, which has social, economic and environmental consequences. The basin of Balharud Shahrestan is one of the areas prone to landslides due to its mountainous nature and steep slopes. Investigating the effective factors in the occurrence of landslides and identifying the prone areas is an important step in managing natural resources and achieving sustainable development. Radar interferometric technique is an effective method in measuring ground surface displacement. This technology has become very common in the investigation of natural hazards of the earth, such as mass movements of slopes, subsidence, earthquakes and volcanic activities. In this research, in order to identify and measure landslides, Sentil 1 radar images of 2020 and 2022 have been used. In order to process information, SARSCAPE software has been used, and the maximum landslide displacement in the opposite direction was estimated to be 0.154 cm, which was observed along the fault lines and in the center of the study area. Also, the results of this research showed that radar images have a good potential for revealing the instability of domains and calculating their displacement.

 In recent years, we have seen the importance and high demand of lithium (Li) due to its many applications, for example in the production of rechargeable lithium batteries, which are mainly related to the global markets of electric vehicle manufacturing to achieve a healthy environment and more suitable transportation. Due to this high demand, the identification of new lithium reserves is very important and the investigation of its identification and zoning methods has been the focus of many researchers, and the use of remote sensing data and image processing techniques in the detection of lithium due to cost reduction of earth exploration has increased, greatly.In this research, using modern methods, a general and intelligent approach was presented, so that with the least time and cost, after selecting the bands of the desired satellite images and zoning the area of Degh Ptergan, in Zirkoh city, South Khorasan province, as a possible area for the existence of lithium reserves, modeling was done by the supervised machine learning method, and the relative importance of the variables was determined using the trained model.Also, the relative importance of the variables was determined by the trained model, and the ability of each of the remote sensing techniques to achieve this goal has been challenged.

 Here, 13 bands of Sentinel-2 images and the region of 12 known lithium mines around the world were used as lithium presence areas, so that, by going through steps, suitable data for modeling were produced. In this way, by using the boundaries of these mines, samples were produced that can be used as input for modeling algorithms. The maximum entropy algorithm was used to model the distribution of lithium samples. Since the correlation between the input variables reduces the performance of the model and makes it difficult to interpret the results of the modeling, first, the correlation between the input variables was calculated and those with a high correlation were discarded. So that, 16 variables were used as input in the maximum entropy algorithm and finally a suitable model was obtained with the AUC (Area Under the Curve) criterion of 0.706 and by it, the study area of Degh Patregan, located in the province South Khorasan, Iran was zoned and two possible areas containing lithium resources were identified.To determine the relative importance and contribution of the input variables in the prediction map of lithium minerals, the Jacknife method was implemented. According to this method, the variables B10, B06/B08, B06/B07 and B01/B10 have a high relative importance, which shows that they have more information than the other variables. Then classic remote sensing techniques including color composition, band ratio, principal component analysis and SAM was done to zone the study area, too. The results of maximum entropy modeling were compared with these techniques and the high ability of the maximum entropy algorithm was determined.

 According to the results and prediction maps related to the classical methods, it showed that although some of these methods approximately identified the areas specified by the maximum entropy algorithm, but they had problems that is emphasized on the development of more suitable remote sensing algorithms to describe the changes associated with lithium minerals. The maximum entropy algorithm with its unique options is a powerful tool for extracting the features of satellite images and expresses their hidden information more clearly. The accuracy of this method was compared with classical techniques and it was able to provide a more appropriate classification with a low noise and with a Kappa coefficient of 0.8775 and an overall accuracy of 0.9435, and identified two areas with the possibility of the presence of lithium minerals in the study area.

Conclusion & Suggestions:

In the present research, the study area of Degh Patergan, located in South Khorasan province, Iran, was zoned, whereby two possible areas containing lithium resources were identified and the ability of classical remote sensing methods and maximum entropy algorithm was challenged. The method discussed in the research may be used as a cost-effective and technological solution with priority over field mapping for mineral exploration in remote border areas with difficult access, also an automatic approach with the maximum entropy algorithm was presented for the exploration of different mineral resources, which can be used for other exploration as well. Therefore, it is suggested to be used in different areas and to explore different sources.

Various Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro planning and information generation. Methods using air temperature recorded in weather stations to estimate the relative heat in urban areas are considered to be both time-consuming and costly. On the other hands, data with relatively high spatial resolution are capable of measuring ground surface parameters more efficiently and accurately. Thus, remote sensing technology is now considered to be a solution used to improve previously mentioned methods. Remotely sensed data are now widely used to find the quantitative relationship between patterns of vegetation cover and the elements of climate. Predicting the conditions of vegetation cover is considered to be essential for planners seeking an efficient plan for its exploitation and protection.

The present study seeks to investigate the effects of climatic factors on the vegetation trend observed in Frame forest in Mazandaran province using Sentinel 2 images and to determine the most suitable index for this area. Climatic Data collected from the nearest weather station in Farim City have been used to model climate factors (temperature and precipitation). Changes in the height above mean sea level were also considered. Following the pre-processing and processing of the Sentinel 2 images, the corresponding digital values were extracted from the spectral bands and applied as independent variables. ENVI software was used for image processing and STATISTICA and R software were used for modeling. 70% of the resulting data were used for training and the rest were used for testing or evaluating the model. Mean square error, correlation, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the presented models. Models with the highest correlation and the lowest standard error, the mean square error, the Akaike information evaluation criterion and the Bayesian evaluation criterion were selected as the best models for the studied variables.

Results & Discussion:

 A correlation coefficient of 0.43 and 0.56 was observed between temperature and precipitation and vegetation indices. AIC and BIC values equaled (565 and 3209) and (739 and 3383) respectively. Differential Vegetation Index (DVI) has proved to be the most effective parameter in relation to both temperature and precipitation factors in the region. Results indicated that differential vegetation index, green normalized difference vegetation index (GNDVI) and green difference vegetation index (GDVI) have a positive correlation with temperature, while there is a negative correlation between temperature and normalized vegetation index. Precipitation is considered to be one of the most important factors affecting vegetation. Results indicate that differential vegetation index, green difference vegetation index, green normalized difference vegetation index, non-linear vegetation index and normalized difference vegetation index have the highest impact on precipitation. In forest ecosystems, changes in climatic factors may affect trees differently. 

Collecting information about the state of vegetation cover in forests is considered to be very important. Thus, the present study has endeavored to investigate the relationship between indices of vegetation cover and climatic variables. To reach this aim, satellite data are used as a suitable and efficient tool for investigating forest ecosystems with a relatively low cost. This provides the possibility of continuously monitoring land surface. Results indicated that climatic factors affect vegetation indices in the study area. Vegetation cover protects and stabilizes the environment and thus, many researchers have tried to investigate the growth and spatial patterns of vegetation cover in different regions. It is also suggested to study the effects of climatic factors on the vegetation cover of the study areas in different geographical directions. In addition, using other climatic factors such as relative humidity, wind speed, evaporation, transpiration, and higher resolution images can increase the accuracy of the study.

Alteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.
Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal component processing (PCA) to identify the range of variations in different regions on Aster images. Gomez et al. (2005) visualized the lithological units of Namibian using the PCA algorithm on Aster data.
The exposed rock units in Muteh mining area include a series of sedimentary, volcanic, and volcanic-clastic metamorphic rocks that extends from the green schist facies to the border of green schist and amphibolites along the northeast-southwest direction. These units have been repeatedly penetrated by alkaline intrusions, especially acid and granite (Rashidenjad, Omran et al., 2002).
In general, the controlling elements of mineralization in Muteh area include structural factors (faults and fractures), alteration, and deformation. Field observations indicate the occurrence of vein mineralization and gold sulfide deposits in mylonite shear zones and fault zones in felsic to mafic metavolcanic host rocks.
Gold mineralization is mainly concentrated in highly altered metariolites containing iron and copper sulfides and within fractures as veins and deposits. Alterations in silica, sericite, and carbonation are also observed along with these sediments, which are studied as exploration keys (Moritz et al., 2006).
In this area, according to the lithology and distribution of alteration zones and the type of mineralization in Muteh gold mine, gold orogeny-type mineralizations are expected, which can be indirectly identified by recognizing the above alteration.

In this study, Aster satellite images have been used to identify, discover and separate alteration zones in ENVI 5.3 software. Also, Landsat 8 satellite images have been utilized for general investigation and identification of hydrothermal alteration zones and expansion of iron oxide minerals, and Sentinel 2 satellite data due to better spatial and radiometric resolution than the above data has been applied to increase the spatial resolution of these data and the spatial accuracy of the map from the extracted changes.
In order to validate between the field observations and spectral analysis, 24 rock samples were taken from the place of alteration, especially siliceous, argillic, and sercitic alteration around Senjedeh and Chah Khatoon deposits. 11 samples were sent to Zarazma laboratory for XRD analysis, and five samples were sent to Zarkavan Alborz Company’s laboratory for chemical analysis of 41 elements by ICP-MS method and gold element by Fire Assay method.

Considering the relationship between alteration zones and metal mineralization, it is very important to know and map these areas in the exploration of these deposits.
The results and images show that the methods used in determining and separating the altered areas in Muteh exploratory area are acceptable and the optimal and effective methods in this research, SAM and MF, have been introduced.
According to the field observations and surface sampling around Chah Khatoon and Senjedeh mineral deposits, as well as the investigation of changes, it was found that the most important changes in the region are: silicification, kaolinization, sericization, chlorination, alonation, pyrite, carbonation and so forth. This wide range shows the difference in intensity of alteration in different parts of the mineral reserve, which can be attributed to the system of joints, fractures and faults in the region.
According to the available evidence, the metariolite rock is highly silicified in the tensile zones or in places with dense seams, and the pyrite particles in the context of these rocks have turned into iron hydroxide.

By using satellite data processing, various data and information can be identified and extracted. Satellite data processing is done in two ways: visual and digital processing. By combining these two methods, the desired effects can be detected more accurately than the accuracy of satellite images. The visual method consists of preparing images of different color combinations by placing spectral bands in the red, green, and blue channels. Digital satellite image processing methods include band ratio, principal component analysis, least square regression method (Ls-Fit), spectral analysis, spectral angle mapping (SAM), and adaptive MF filter. The selection of the above methods was based on the type of information requested to extract data from images.
Aster sensor images have no blue band (spectral range 0.4-0.5 µm) and the color composition of its VNIR bands is a standard RGB (1,2,3) false color composition. In this color combination, vegetation is seen in red. Since the study area is located in a relatively arid environment without vegetation, vegetation cover was avoided in the spectral analysis.
The use and processing of Aster satellite data is one of the main features of this sensor; the use of unique spectral reflectance curves of alteration indicator minerals helped to identify and highlight these altered areas as well as finding the potential of areas prone to metal mineralization. Due to the high ability of Sentinel-2A images in identifying gossan and iron oxide ranges, the processing of these data was used to highlight these areas better.

According to the agreement of the results of geochemical and XRD studies with the distribution map of the alteration zones identified from the reference spectrum (USGS) and the spectral library (JPL), with the distribution map of lines and structural fractures of Muteh exploratory zone outside the pre-identified areas, new alteration zones were also introduced that require field research to confirm the results of stereo data processing.

In the early 1990s, radar interferometry was introduced and used as a useful tool in the study of all phenomena that cause land surface deformations. If the land surface deforms between two radar images, a surface displacement map can be created with millimeter resolution and accuracy. This paper reports the findings of the Sentinel1 –A data time series results using the SBAS algorithm to detect surface deformation in the Sangan iron ore mine. Sangan Iron Ore Mine is the largest open pit iron ore deposit in the Middle East. Due to mining activities, this mine has undergone many changes in terms of topography and geomorphology, which can intensify geomorphological processes. To detect and obtain the amount of land deformation, 48 SAR images of Sangan iron ore mine obtained by the European Space Agency's Sentinel 1-A satellite were used. The time series (2014-2020) obtained from the deformation in the range of placer mines were analyzed. The results show the average displacement rate of -20 to -35 mm per year and the maximum cumulative rate of deformations of -120 mm. Investigation of the cross-section in the two parts of the apex and the center of the alluvial fan in the placer mines during the period 2014-2017 shows the topographic changes well. To evaluating the reliability of the results, the results derived from SBAS have been compared due to the lack of data in the range of placer mines with the values measured by the total station related to the mountain unit in the years 2020-2014. The results showed that the rate of deformations from radar data using the SBAS algorithm compared to the leveling data has followed a similar pattern. However, there may be some error due to the different nature, ie in the leveling of elevation deformations measured for a point, but in interferometry the average rate is obtained from adjacent points.

This issue has become more apparent in large cities and provincial capitals, which have undergone land use changes in recent years. The main purpose of this study is to investigate the spatial relationship between the distribution of thermal islands with urban vegetation and compare the intensity of thermal islands in minimum, maximum and average values in urban areas of Kermanshah during the last 10 years. Data were analyzed using Sentinel 3 satellite imagery and SLSTR sensor with a spatial resolution of 500 m in the reflective bands and 1000 m in the thermal band using SNAP and QGIS software. Therefore, continuous monitoring of land use change is one of the main challenges of urban planning and management in metropolitan areas. Because the increase of green space in the city significantly reduces the temperature of the city and thermal islands. The key findings of this research are focused on three areas: first, focusing on the fair distribution of green spaces in the city, second, using waste spaces to increase green spaces, and finally, avoiding additional burden on the ground, because in dense areas, the possibility of creating thermal islands is much higher. It was from other places.

Subsidence is a danger that threatens many plains of Iran. Qahavand plain in Hamadan province is one of the plains that is exposed to this danger due to the unfavorable hydro-climatic conditions and the type of human activities. Considering the importance of the subject, in this research, the subsidence condition of the Ghahavand plain and the factors influencing its occurrence have been investigated. According to the subject and objectives, in this research, 31 radar images of Sentinel 1 satellite, Landsat 8 satellite image and 30 meters high digital model were used as research data. The most important research tools are GMT, ENVI and ArcGIS software. This research has been done in three stages, in the first stage, the land cover map was prepared and examined. In the second stage, using the SBAS time series method, the subsidence situation of the region was evaluated and in the third stage, the effective factors in the subsidence of the region were analyzed. According to the results of this research, the Ghahavand Plain has subsided between 15 and 78 mm during a 2-year period (from 2017/01/05 to 2019/01/19). Also, the results of this research have shown that the main cause of subsidence in the region was the sharp drop in underground water resources due to the development of irrigated agricultural lands. In fact, the highest amount of subsidence in the region has corresponded to irrigated agricultural lands

Up-to-date and accurate land use maps have played an important role in the management and planning of developing countries. The purpose of this study, in addition to preparing an accurate map of land use in Gilan province, is to compare and evaluate the maps generated using multi-season radar and optical data and also to combination these data together to improve the classification map. In this regard, data with spatial resolution of 10 meters Sentinel 2 optics and VH radar polarization of Sentinel 1 in band C were processed and analyzed to produce land Use map in the new Google Earth Engine processing environment. Processing results showed that the use of Sentinel 1 and Sentinel 2 radar data alone has kappa coefficients of 0.72 and 0.84% and overall accuracy of 78.51 and 87.41%, respectively. While the simultaneous use of Sentinel 1 and Sentinel 2 data with the approach of combining data in the Google Earth engine environment achieved very good results. The map was prepared using a simultaneous multi-season combination of Sentinil 1 and 2 with kappa coefficient and overall accuracy of 0.97 and 97.77%, respectively. In this study, Bayer user pixels in Sentinel 1 images due to similarity of redistribution and in Sentinel 2 images due to spectral similarity, caused improper performance of the support vector machine classification algorithm in separating the Bayer class and the city. In general, the results of this study indicate that the simultaneous combination of Sentinel 1 and 2 data to improve the accuracy of classification algorithms for mapping can work very well and separate land classes with high ability.

The risk of subsidence in recent years has posed serious challenges to many cities, including cities in arid and semi-arid regions of Iran. Hamedan is one of the cities that are exposed to this risk and for this reason, this study evaluated the subsidence in the city. The data included Sentinel 1 radar images, Landsat satellite images, 30 m SRTM digital model and groundwater data. Tools also included ArcGIS, GMT, and ENVI. In the first stage, land use maps of the region for the period 1991-2020 were prepared and analyzed. In the second stage, the situation of groundwater depletion in the study area was explored, and in the third stage, using radar images and SBAS time series method, the amount of subsidence in the area over a three-year period (2017 to 2020) was calculated. The results showed that the urban area and suburbs of Hamedan annually experience one meter drop in water resources and 0.7 Square kilometers of physical development. Moreover, the subsidence rate of this area during the three-year period was between 6 to 98 mm. According to the final map, the rate in western areas of Hamadan was between 60 to 98 mm, in central areas was between 30 to 60 mm, and in a big part the city subsidence is 6 to 30 mm from its eastern regions. Considering that the highest rate of physical development of Hamedan city in recent years has been in the western suburbs of the city, it can be said that the physical development along with the decline of groundwater resources are the main factors of subsidence in this city.

The purpose of this study is to use modern methods to improve the accuracy of depth measurement, first by using the proposed methods based on image fusion of sentinel-2 and Landsat 8 images and then based on hybrid fusion methods. Thus, depth measurements were performed in two studied areas, namely Chabahar Bay and Mousa Estuary, based on different scenarios. These scenarios include the traditional ratio method, the proposed methods based on image fusion using three methods of Intensity-Hue-Saturation, Gram-Schmidt transformation, and Brovey transformation, and the proposed methods based on hybrid image fusion. In order to perform depth measurement based on the proposed hybrid fusion methods, the output of the image transformation using the principal component analysis method was merged with the input image of the best bathymetry map based on image fusion at another level of fusion. Chabahar Bay depth measurement results showed that in comparison with the traditional ratio model, the proposed models based on Brovey and Gram-Schmidt methods and based on a hybrid method resulting from the integration of principal component analysis output and Gram-Schmidt method have the best depth measurement results. Regarding the depth measurement of Mousa estuary, the proposed depth measurement method based on the combination of principal component analysis and Intensity-Hue-Saturation method has created the best result in comparison with the traditional ratio model. Also, the results showed that the appropriateness of the number and distribution of depth field points with the topography of the area has a great effect on the accuracy of depth measurement.

 Landslides are one of the most complex and at the same time harmful phenomena, as a result of which the slope material is moved from the sloping surfaces and relatively deep wounds are left on the surface of the slopes. A phenomenon near cultural features, such as residential areas, roads, etc., causes financial damage and many casualties. The use of geographic information systems in the first place, followed by the use of satellite image analysis technology, is of great help to this part of management. For this purpose, the D-InSAR radar interferometry technique, with its frequent, continuous wide ground coverage and high temporal and spatial resolution, as one of the most accurate and least expensive remote sensing techniques, to display the displacements that occur in the ground surface is provided. The present study investigated the existence of landslides in the study area of Sarbaz village by means of Sentinel 1 sensor radar images and using radar interferometric technique in the period of 2017 and 2018. After various processes that were done in ENVI software and SarScape plugin, the amount of displacement and landslide of the area was obtained, and according to the results obtained, it can be said that Sarbaz area is an area with a high level of activity in terms of landslides. Also, 3 points have been introduced as points with high sensitivity, which were found to be in the medium to high risk level. The obtained results showed that Sarbaz region had a displacement of -0.069 in the period of 06/05/2017 to 12/14/2017, in fact it had a displacement of nearly 7 cm, in the period of 12/14/2017 to 12 In 06/2018, the amount of displacement was -0.035, which means that nearly 4 centimeters of displacement occurred in the region, and between 12/06/2018 and 21/12/2018, the displacement in the region was equal to -0.064, which It shows that there has been 6 cm displacement in the studied area during this time period. At the end, a map of sensitive points was prepared and presented.Deformation of the soil surface is a dynamic process on the Earth's surface, this process occurs naturally or due to human intervention. One of the forms of this deformation is landslide [7]. The definition of landslide is the general and deep movements of the entire soil layer on the surface of the mother earth, which cause heavy damages every year, sometimes it is not possible to compensate for these damages, and we need to spend a lot of time and money. The speed of their operation and their extent often creates spectacular and sometimes catastrophic phenomena, and they may affect tens or hundreds of thousands of cubic meters of rock and soil in one place. This phenomenon works mostly in isolated sediments [10]. The effects of domain instability are mass movements on a small to large scale. Range movements may be minor and unique to the fall of a single piece of rock, or they may be very large and catastrophic. The damage caused by this type of movement is more in developed countries, but 95% of the deaths caused by it happen in developing countries. Landslides are among the most devastating natural disasters in steep areas, the damage to residential areas and economic infrastructure, as well as human casualties caused by landslides are increasing worldwide [9]. Iran, with its mainly mountainous topography, tectonic activity and high seismicity, diverse geological and climatic conditions, has the main natural conditions for creating a wide range of landslides [4]. By using morphotectonic indicators, it is possible to help planners by providing maps in predicting the places that will become unstable due to future earthquakes. By using morphometric analysis and using geomorphic indices and rank parameters of drainage networks, the active tectonics of a region can be investigated [5]. Among the presented methods, we can mention tachometry, leveling, short-range photogrammetry, observations from geodetic networks [GPS], radar interferometry, and lidar. Although the first use of radar data in investigating the instability of the earth dates back to the mid-1990s, in recent decades, due to the availability of radar data with a long time span compared to the past, and the development and expansion of new methods such as techniques Interferometry has drawn the attention of researchers to the use of these methods [4]. Among the remote sensing techniques, differential radar interferometry [DINSAR], which has the ability to work in all bad weather conditions and the length of day and night, is considered as one of the effective and efficient techniques in monitoring slow changes in the earth's surface [11]. Due to the fact that natural phenomena and hazards will cause damage in different regions, therefore, it is important and necessary to investigate these issues in different time frames and regions in the country. This research was conducted for the first time in the studied region. and the purpose of doing it is to investigate the amount of landslides in Sarbaz village in Isfahan province using radar satellite images, remote sensing techniques and radar interferometry. Therefore, this research is innovative in terms of being new and up-to-date.

 In the current research, in order to monitor and determine the amount of displacement caused by the movement of the sliding mass of Sarbaz village, the radar interferometric method and Sentinle-1, radar satellite images with C-band wavelength in the ascending orbit mode have been used. Radar interferometry or interferometry is the method of using the phase difference of signals returned from the earth's surface in two SAR images taken with a time delay or with parallax from a region to extract height or information related to changes in the earth's surface. In order to implement the DInSAR method, one should first pay attention to two principles between each pair of radar images (main image and secondary image), the first is the temporal baseline and the second is the spatial baseline. After choosing suitable interferometer pairs based on temporal and spatial baselines, in the next step of processing these images, and in order to remove the topography effect from the interferometer, the method of preparing the interferometer along with the digital height model was used. The output of this stage of the work is in the form of a differential interferogram in the form of a flattened interferogram in which the stationary phase and the topographic phase are removed from the interferogram. Since the flattened interferometer contains noise that lowers the visual quality of the fringes, the adaptive filter is used for this purpose.

Identifying areas prone to landslides and zoning the potential of landslides is very necessary and important in order to avoid these areas and implement prevention and control methods. In order to implement the DINSAR method in this research, the capabilities and quality of the radar images used to prepare the interferometer were checked by calculating their temporal and spatial baseline values. Table 1. The results of numerical processing of landslides and accumulation of sediments The amount of displacement in these maps is shown in the form of a numerical range between negative values and positive values in meters(Figure 2). The level of the range and slopes and positive values indicate the accumulation of sediments at the foot of the range and slopes. According to the maps of phase transformation to displacement of the landslide and the analysis of images in different time intervals, the metamorphosis of the landslide surface can also be explained well. Figure1. General map of landslides and accumulation of sediments According to the results obtained and determining the amount of landslides in the region, we have reached a positive correlation with the results of similar works, according to the results of similar works in the field of landslides that Sadeghi et al. (2022), Casagli et al. (2016), Hammand et al.(2019), Deming et al.(2020), it is concluded that by using remote sensing and radar interferometric technique, the amount of landslides in different areas can be determined with high accuracy and low cost. achieved and provided the results to relevant authorities to provide appropriate solutions.

In the new decade, the use of new technologies in the field of urban management as well as natural disaster management has had a growing trend. The use of geographic information systems in the first place, followed by the use of satellite image analysis technology, is of great help to this part of management. The present study calculated amount of landslides in Sarbaz village in Semiram city of Isfahan province by using Sentinel 1 radar satellite data and with radar interferometric technique. It is caused by landslides or, on a smaller scale, human activities. Negative numbers and sections indicate falls on slopes and other areas.  According to the obtained results, it can be said that Sarzab region is an area with a high level of landslide activity. Also, 3 points have been introduced as high sensitivity points, which were found to be in the medium to high risk level. The obtained results showed that Sarbaz region had a displacement of -0.069 in the period of 06/05/2017 to 12/14/2017, in fact it had a displacement of nearly 7 cm, in the period of 12/14/2017 to 12 In 06/2018, the amount of displacement was -0.035, which means that nearly 4 centimeters of displacement occurred in the region, and between 12/06/2018 and 21/12/2018, the displacement in the region was equal to -0.064, which It shows that there has been 6 cm displacement in the studied area during this time period.

In recent years, maritime and aerial surveillance have become commonplace for marine pollution control; however, these methods alone cannot provide rapid and systematic monitoring due to the limitations of weather conditions, time, and location. In this regard, satellite remote sensing can play an important role in the initial detection and continuous monitoring of oil spills at sea. The synthetic aperture radar (SAR) sensor is an active microwave sensing system that can be used for oil spill detection, along with optical sensors such as MODIS, with simultaneous imaging capability. The aim of this study was to detect the oil spills around oil platforms in the northern part of the Persian Gulf on June 15, and 17, 2015, using MODIS thermal infrared imagery and Sentinel-1 images. To estimate the sea surface temperature, the split-window algorithm was applied to band 20 of MODIS. Results showed that the sea surface covered by oil spill has lower temperature than surroundings. For accurate detection of oil slicks and accuracy assessment of the results of applied image processing method on the MODIS data, the Sentinel-1 vertical polarization image and noise removal processes such as filtering and multi-looking were used. Finally, by comparing the field temperature measured by Boushehr marine waveguide and the temperature estimated for the MODIS image, and review of the geographical location of detected oil slicks, the accuracy of the results of this study and the applied image processing methods were confirmed. Application of MODIS band 20 aiming the extraction of sea-surface temperature, and its thermal infrared bands for oil spill detection at sea surface are evaluated in this study for the first time.

In recent years, maritime and aerial surveillance have become commonplace for marine pollution control; however, these methods alone cannot provide rapid and systematic monitoring due to the limitations of weather conditions, time, and location. In this regard, satellite remote sensing can play an important role in the initial detection and continuous monitoring of oil spills at sea. The synthetic aperture radar (SAR) sensor is an active microwave sensing system that can be used for oil spill detection, along with optical sensors such as MODIS, with simultaneous imaging capability. The aim of this study was to detect the oil spills around oil platforms in the northern part of the Persian Gulf on June 15, and 17, 2015, using MODIS thermal infrared imagery and Sentinel-1 images. To estimate the sea surface temperature, the split-window algorithm was applied to band 20 of MODIS. Results showed that the sea surface covered by oil spill has lower temperature than surroundings. For accurate detection of oil slicks and accuracy assessment of the results of applied image processing method on the MODIS data, the Sentinel-1 vertical polarization image and noise removal processes such as filtering and multi-looking were used. Finally, by comparing the field temperature measured by Boushehr marine waveguide and the temperature estimated for the MODIS image, and review of the geographical location of detected oil slicks, the accuracy of the results of this study and the applied image processing methods were confirmed. Application of MODIS band 20 aiming the extraction of sea-surface temperature, and its thermal infrared bands for oil spill detection at sea surface are evaluated in this study for the first time.

Introduction:

 The phenomenon of land subsidence involves collapse or sinking of the earth's surface, which can also have a slight horizontal displacement vector. This movement is not limited in terms of intensity, extent, and measure of the involved areas. Subsidence in cities leads to costly and serious damage to urban infrastructure such as buildings, roads and railway tracks. Land subsidence can occur for a variety of reasons: groundwater abstraction, Subsidence due to oil extraction in oil fields, Displacement due to landslides and collapse of internal walls of mines.Numerous techniques and methods have been implemented to study the extent, action and land expansion and behavioral measurement in relation to land subsidence. One of the most recent and the most effective methods is the radar image interference technique. Interferometric Synthetic Aperture Radar (InSAR) is a powerful technique for measuring the topography of a surface and its changes over time. Due to its broad spatial coverage and high accuracy, InSAR has become a preferred geodetic method for the study of land deformation in developed groundwater basins and provides insight into the geological and hydrological parameters that characterize the underlying aquifer systems.Varamin plain is one of the areas affected by subsidence in Iran, which is located in the alluvial fan of Jajrud river and its development and prosperity is due to the quality and fertility of the soil in this region. Irregular and over pumping of groundwater has caused many subsidence cases in the study area. This study investigates the subsidence of Jajrud alluvial fan with emphasis on Javadabad Varamin from 2016 to 2019, using permanent scatter radar interferometry method.

Materials and Methods:

 In this study, 41 images of Sentinel (2016-2019) were used to determine the rate and amplitude of land subsidence using the method of permanent dispersants. Permanent scattering points (PS) were selected based on the domain scattering index. The amplitude scattering index, according to the threshold limit is usually considered to be 0.4 or 0.42 in various studies, which means that all selected points are not located in the set of PS points and only those points to be selected that the amplitude scattering index value is exceeding the threshold. Then, at this stage, points network (PS) was prepared in order to evaluate the processing performed. The prepared spatial network in this section is derived from the Delaunay model. Due to this fact that the amount of displacement is relative in this method, therefore, one of the points in the area is selected where the amount of displacement is relatively zero in compare to other points and other PS points are applied to it. Because of seasonal temperature changes and its effect on buildings that cause elevation changes (the effect of temperature on the structure of buildings), selected point must be located on the ground. SNAP software was selected for radar interferometry processing.. In order to find the cause of subsidence, the information of piezometric wells (1996-2016) in the study area and its temporal changes were investigated.

Results and discussion:

 research findings یافته های پژوهش:The findings یافته ها:Can't load full results Try again Retrying… Retrying… Can't load full results Try again Retrying… Retrying… In general, land subsidence has occurred in two large areas. The northern region is located in the Jajrud alluvial fan. The length of this zone is 44 km and its width is 7 km, which covers an area more than 300 square kilometers. In general, we have land subsidence in two broad areas. به طور کلی ، در دو منطقه وسیع فرونشست زمین داریم. In general, the vast expanse of land subsidence have occurred. به طور کلی ، پهنه وسیع فرونشست زمین رخ داده است. Can't load full results Try again Retrying… Retrying… Its northern region is located in the Jajrud alluvial fan. منطقه شمالی آن در مخروط افکنه جاجرود واقع شده است. North Zone is located in the fan MkhrvtAfknh Jajrood. منطقه شمال در فن MkhrvtAfknh Jajrood واقع شده است. Can't load full results Try again Retrying… Retrying… The length of this zone is 44 km and its width is 7 km, which covers an area of 300 square kilometers. طول این منطقه 44 کیلومتر و عرض آن 7 کیلومتر است که مساحت آن 300 کیلومتر مربع است. Over the span of 44 km and a width of 7 meters, which is the area of over 300 square kilometers in the lie. بیش از 44 کیلومتر و عرض 7 متر ، که مساحت بیش از 300 کیلومتر مربع در دروغ است. Can't load full results Try again Retrying… Retrying… The highest amount of subsidence in this area is related to Golabbas area, which is about 120 mm per year. The presence of Jajrud alluvial fan and agricultural lands in this area is considerable.The second region is related to the southern zone. The northern part of this zone is bounded by the city of Pishva, Qala-e-Sin and the east of Varamin city. In these areas, the annual subsidence rate reaches to maximum 6 cm. The southern part of this zone is surrounded by Hesargol and Jahanabad areas. The second area is related to the southern zone. The northern part of this zone is limited to the city of Pishva, Qala-e-Sin and the east of Varamin city. منطقه دوم مربوط به منطقه جنوبی است. قسمت شمالی این منطقه محدود به شهر پیشوا ، قلعه سین و شرق شهرستان ورامین است. The second extension of the South zone is an area in the north of the city limits Rector, Xinjiang and East fort city of Varamin is limited. گسترش دوم منطقه جنوبی منطقه ای در شمال محدوده شهر رکتور ، سین کیانگ و شهر قلعه شرقی ورامین محدود است. Can't load full results Try again Retrying… Retrying… In these areas, the annual subsidence rate reaches a maximum of 6 cm. در این مناطق ، میزان فرونشست سالانه به حداکثر 6 سانتی متر می رسد. In these areas the annual subsidence reaches up to 6 cm. در این مناطق فرونشست سالانه تا 6 سانتی متر می رسد. Can't load full results Try again Retrying… Retrying… The southern part of this zone is limited to Hesargol and Jahanabad areas. قسمت جنوبی این منطقه به مناطق حصارگل و جهان آباد محدود می شود. The southern part of the zone in MntqhHay hesar goli and JhanBad is limited. قسمت جنوبی منطقه در MntqhHay hesar goli و JhanBad محدود است. Can't load full results Try again Retrying… Retrying… In these areas, the rate of land subsidence is lower compare to the central areas and it is about 20 to 40 millimeter per year. In the northern part of Jahanabad region, the maximum land subsidence is 60 mm per year. The southern zone in which the study area is located has circular shape and the amount of land subsidence increases from the periphery to the center of the region. The highest rate of land subsidence in the study area is occurred in Salman-Abad, Khaveh, Javadabad, Hesar-e-Sorkh and Zavarehvar areas, with the amount of between 160 to 200 mm per year. Areas such as Tajreh, Rostamabad and Hesar Kouchak also show significant land subsidence, that is about 120 to 160 mm per year.The result of radar interferometry studies has demonstrated that subsidence has occurred in the main parts of the Jajrud alluvial fan's surface. Javadabad region which is located in Varamin is one of the areas with significant subsidence that subsidence occurs at a rate of 20 cm per year - Javadabad region of Varamin is one of the areas with significant subsidence that occurs at a rate of 20 cm per year of land subsidence. - منطقه جوادآباد ورامین یکی از مناطقی با فرونشست قابل توجه است که با سرعت 20 سانتی متر در سال از فرونشست زمین رخ می دهد. - Regional Branch of MhdvdhHay JvadBad with significant subsidence zone at a rate of 20 cm annual event that there is land subsidence. - شعبه منطقه ای MhdvdhHay JvadBad با منطقه فرونشست قابل توجه با نرخ سالانه 20 سانتی متر که فرونشست زمین وجود دارد. Can't load full results Try again Retrying… Retrying… Studies conducted in the study area depicted that the decreases in groundwater level in the study area is linear and this is acceptable in justifying land subsidence. Studies conducted from the study area showed that the drop in groundwater level in the area is linear and this is acceptable in justifying land subsidence. مطالعات انجام شده از منطقه مورد مطالعه نشان داد که افت سطح آب زیرزمینی در منطقه خطی است و این در توجیه فرونشست زمین قابل قبول است. Studies of the area showed a decline in groundwater levels in the region is linear in this case is the justification acceptable land subsidence. مطالعات منطقه نشان داد که کاهش سطح آبهای زیرزمینی در منطقه خطی است در این مورد توجیه فرونشست زمین قابل قبول است. Can't load full results Try again Retrying… Retrying… So that the patterns obtained using the radar interferometry method in order to find the pattern of land subsidence and groundwater were consistent and somewhat uniform. In this regard, one of the main causes of land subsidence in the region in Javadabad due to the increasing depth of piezometric wells in the study period can be attributed to the high pumping of groundwater resources. The results of radar interferometry studies have shown that subsidence has occurred at most of the surface of the Jajrud alluvial fan. نتایج مطالعات تداخل سنجی راداری نشان داده است که فرونشست در بیشتر سطح مخروط افکنه جاجرود رخ داده است. As a result of TdakhlSnjy radar studies show most MkhrvtAfknh level Jajrood land subsidence occurred. در نتیجه مطالعات راداری TdakhlSnjy نشان داده شده است که بیشتر سطح مخروتافنکنه فرونشست زمین جاجرود رخ داده است. Can't load full results Try again Retrying… Retrying… According to the physiographic shape of the region and the pattern of land subsidence, the highest rate of land subsidence was observed in areas consist of low slope, including Javadabad and Varamin. In terms of risk, population, housing, roads and buildings, rail roads and important roads are involved in the phenomenon of land subsidence in this region. The railroads of Tehran, Mashhad, Garmsar and Qom are the connecting roads in this region. The occurrence of land subsidence has caused change in the local surface slope and this has caused disturbance in the drainage network and the flow path of surface water, which has ultimately result the occurrence of various types of erosion.