جستجوی مقالات مرتبط با کلیدواژه "سنتینل 1" در نشریات گروه "جغرافیا"

The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of urban areas is one of the main drivers of global changes that have important direct and indirect effects on environmental conditions and biodiversity. In the process of physical development of the city, due to the transformation of natural and semi-natural ecosystems into impermeable surfaces, it often causes irreversible environmental changes. One of the new approaches in urban planning is the use of remote sensing techniques and geographic information system. The emergence of remote sensing and machine learning techniques offers a new and promising opportunity for accurate and efficient monitoring and analysis of urban issues in order to achieve sustainable development. The process of processing satellite images can generally be divided into two approaches: pixel-based image analysis and object-based image analysis. The pixel-based analysis technique is performed at the level of each pixel of the image and uses only the spectral information available in each pixel. On the other hand, the object-based analysis approach is performed on a homogeneous group of pixels, taking into account the spatial characteristics of the pixels. One of the basic problems in urban remote sensing is the heterogeneity of the urban physical environment. The urban environment usually includes built structures such as buildings and urban transportation networks, several different types of vegetation such as agricultural areas, gardens, as well as barren areas and water bodies. Therefore, in the pixel-based processing approach, the existence of heterogeneity in the urban biophysical environment causes spectral mixing and also spectral similarities in the classification operation of satellite images in such a way that in a place where a pixel is If the surrounding environment is different, it causes Salt and Pepper Noise. Therefore, according to the problems in the pixel-based processing approach, the aim of this research is to compare the accuracy of machine learning algorithms based on object-based processing of satellite images in extracting the physical development area of Hamedan city using Sentinel 2 satellite image.

  The remote sensing data used in this research is a multi-spectral satellite image with a spatial resolution of 10 meters from the Sentinel 2 satellite, including bands 2 (blue), 3 (green), 4 (red) and 8 (near infrared) related to the date is the 23 of August 2023 in the city of Hamadan. The image of the Sentinel 2 satellite was downloaded from the website of the European Space Agency. In ENVI software, the pre-processing operation was performed on the satellite image. Then, in the eCognition software, the segmentation process was performed based on the appropriate scale, shape factor, and compression factor with the aim of producing image objects. After segmenting and converting the image into image objects, using machine learning classifiers based on object-oriented processing of satellite images including Bayes classification algorithms, k-nearest neighbor, support vector machine, decision tree and random trees, the classification process was carried out and maps of urban physical development area were produced. After the segmentation operation and the production of visual objects, three classes of built-up urban land, vegetation and barren land were defined, and some of the built objects in the segmentation stage were selected as training points and some were selected as ground Truth points.Results & DiscussionAfter downloading the satellite image from the website of the European Space Organization, in order to apply the radiometric correction of the image and also with the aim of matching the value of the gray levels of the image with the value of the real pixels of the terrestrial reflection, the gray levels are converted to radiance and then, using atmospheric correction, to coefficients. They became terrestrial reflections. In order to apply radiometric correction, Radiometric Calibration tool was used, and to apply atmospheric correction, FLAASH model was used in ENVI software. In order to classify the satellite image based on machine learning algorithms based on object-based processing, eCognition software was used. The satellite image of the study area, which was pre-processed and saved in TIFF format, was called in the environment of this software and saved as a project. In order to produce visual objects, segmentation operations were performed in different scales, shape factor and compression ratio to reach the most appropriate segmentation mode. In this step, the multiple resolution segmentation method was used to segment the image. The most appropriate segmentation included the scale of 100 and the shape factor of 0.6 and the compression factor of 0.4. Because in scales higher than 100, the construction of the visual object was not done correctly, so that several distinct complications were placed in one piece, and in scales less than 100, in some cases, one complication was placed in several pieces. In order to classify the generated image objects, machine learning algorithms were defined separately and after training each algorithm, the classification operation was performed. In this step, the classification was done based on the nearest neighbor method and by selecting the average and standard deviation parameters for each image band. After producing a map of the city physical development range through machine learning classifiers based on object-based processing of satellite images, the classification accuracy of each of the used algorithms was calculated. In order to calculate the accuracy of the above algorithms in eCognition software, using selected ground Truth control points, the overall accuracy and kappa coefficient were calculated for each of the algorithms.

Based on the results of the research, it is possible to produce a map of Hamedan's urban physical development using machine learning algorithms based on object-based processing of satellite images with acceptable accuracy. Also, among all the algorithms used in this research, k-nearest neighbor with overall accuracy of 97% and kappa coefficient of 0.96 provided more accuracy.

 Land subsidence is an invisible danger that has faced many cities in Iran, including Pishva, with a serious challenge. The importance of subsidence has led to the evaluation of the amount of subsidence in the urban area and outskirts of Pishva using radar images and the SBAS time series method (2020-2022) in this research.

 In this research, Landsat satellite images, Sentinel 1 radar images, and library information were used as research data to achieve the desired goals. The working method has been that first, using satellite images, the trend of land use changes in the region has been evaluated, and then, based on the available information, the state of the decline of underground water resources in the region has been investigated. In the following, the subsidence rate of the region has been calculated using the SBAS time series method.

 The results of this research have shown that the studied area has subsided between 107 and 411 mm during a 2-year period (2020 to 2022), based on which it can be said that the maximum annual subsidence of the area is more than 20 cm.  The results of the evaluation of land use changes in the region have shown that during the 30 years (1991 to 2021), the land use of manufactured areas has increased from 2.2 to 9.6 square kilometers. Also, the level of piezometric wells in the region has faced an annual drop of more than 1 meter.

 Considering that the development of manufactured uses is associated with an increase in the pressure on the earth's surface, and also the development of residential and industrial areas is associated with an increase in the use of underground water resources, the changes in land uses during recent years, the context has provided for subsidence in the region.Innovation: The method's high precision allows the research results to be used for various construction planning.

The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable and environmentally friendly method to quickly identify and investigate the distribution of heavy metals in soil and thus identify suspected contaminated areas (Scheuber & Köhl, 2003:33). Remote sensing is one of the ways that can provide a cost-effective and quick solution to investigate the distribution of heavy metals on a large scale using spectroscopic techniques (Bi et al., 2009:16). Habibi et al. (2023:4) also measured and evaluated the concentration of heavy metals in the aerial parts and soil of the tree species of Bandar Abbas city and also identified the species that has the highest potential for absorbing heavy metals. The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn>Zn>Pb>Cd. (Nikolaevich, 2023:30) they addressed the modeling of heavy metal pollution in Central Russia based on satellite images and machine learning. Al, Fe, and Sb contamination were predicted for 3000 and 12100 grid nodes in an area of 500 km2 for the Central Russian region for 2019 and 2020. Estimating the amount of this pollution requires time and high cost. Considering the traffic on the Aleshtar -Khorramabad highway near Kakareza forests and the effect of heavy metal concentration in the soil and leaves of the oak species which can be caused by natural and human pollution, the accumulation of heavy metals in the species Iranian oak is a serious threat to this forest. Therefore, it is necessary to study and discuss pollutants and their effects on the environmental cycle. In this regard, considering the cost and time-consuming nature of traditional methods and since remote sensing methods are a suitable complement to traditional methods; the aim of the present research is to use remote sensing techniques and spectral analyses to evaluate and model the accumulation of heavy metals in Iranian oak species.

The present study is located on the road of Aleshtar -Khorramabad, 20 kilometers northwest of Khorramabad. For this purpose, five transects were created at distances adjacent to the road, 500 and 1000 meters on both sides of the road, and 10 x 10 m sample pieces were planted. Inside the sample plots, 30 soil samples were randomly collected and 30 leaf samples were collected from trees in all directions of the crown. To extract heavy metals from soil samples and plant samples, the acid digestion method was used and the physical characteristics of the soil were measured using standard methods. After preparing the samples, the concentration of Pb, Cu, and zinc heavy metals in soil and leaves was measured and the index of biological concentration of heavy metals from soil to leaves was calculated. Then the relationship between the concentration of heavy elements measured and the reflectance in different bands or band ratios at the corresponding sampling points was obtained. Non-parametric methods and generalized multiple linear regression models were used in order to model quantitative variables and spectral values corresponding to sample parts in satellite data. ArcGIS software was used to implement sample parts on the image, ENVI software was used for image processing, and STATISTICA software was used for modeling.

Cu and Pb in Iranian oak leaves had significant differences at different distances at the 0.05 level, but Cu did not have significant differences at different distances at the 0.05 level. Cu and Pb did not have significant differences in different soil intervals at the 0.05 level, but Cu had significant differences in different soil intervals at the 0.05 level. The bioconcentration factor was obtained as (0.2, 0.5, 0.2) mg/kg. The study of modeling of non-parametric methods using Sentinel-2 satellite data showed that the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for the three metals Cu, Pb, and Cu, respectively. The artificial neural network (ANN) algorithm obtained the highest accuracy. Also, according to the results of the random forest algorithm, for the three mentioned metals, PSRI, HMSSI, and PSRI indices are the most important in modeling.Based on the findings, the concentration values of Cu and zinc were significantly different at different distances, but the Cu values were not significantly different at different distances. In this regard, Mansour concluded in 2014 that there is a significant difference between the concentration of Cu and zinc in the leaves of the species, which can be attributed to traffic density and human activities, and the high amount of zinc metal in this study is the wear of car tires؛ and stated that the concentration of Cu is caused by the production of greenhouse gases and the use of vehicles using Cu gasoline. Based on the findings, the values of Cu and zinc concentrations at different distances did not have significant differences, but the Cu values had significant differences at different distances. Sources of input of Cu element to the soil are urban, industrial, and agricultural waste, fertilizers, and chemicals that add it to the soil through liquid, solid, or mineral fertilizers. These findings are with the results of some researchers including Wu and colleagues (2010:38), Botsou et al. (2016:17) are consistent. Based on the findings obtained from the calculation of the bioconcentration index and their comparison with the classification proposed by Ma et al. (2001:25) for Iranian oak species plants in relation to Cu, zinc, and Cu metals from soil to leaves, it acts as an accumulating plant. In accordance with the results of this research, in the study of Khodakarmi et al. (2009:15), Iranian oak was included in the category of superabsorbent plants in relation to the accumulation of Cu pollutants, which has a high capacity in terms of root absorption. Also, Madejón et al. (2006:25) stated that oak leaves are more resistant than olive leaves. The concentrations of elements in leaves and fruits decrease with time and the risk of toxicity in the food web is reduced. The review and comparison of five algorithms showed that (ANN) the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for three metals, Cu, Zn, and Cu, respectively. Considering the importance of the PSRI synthetic band in increasing the accuracy of modeling with satellite images and the influence of the visible and near-infrared bands, the amount of reflection measured by the spectroscopic method showed that with the increase in the concentration of heavy elements, the amount of reflection in the visible and infrared range decreases (Liu et al., 2011:24).

The results showed that Sentinel-2 images along with artificial intelligence techniques have a relatively good ability to model the level of biological pollution index in the region. In line with the obtained results, it is suggested that the Iranian oak species is used to reduce pollution on highways because it accumulates heavy metals.

Snow depth plays a critical role as a key input parameter in various agricultural, hydrological, and climatological models. Nevertheless, the process of estimating snow depth through optical remote sensing tools is subject to uncertainties stemming from constraints within the imaging technique. Consequently, the primary objective of this study is to employ active microwave remote sensing technology for the purpose of snow depth estimation in regions characterized by mountainous terrain. The radar interferometric technique employing active microwave imagery was utilized for the specific objective of examining the microwave signal's interaction with snow accumulation. Utilizing Sentinel 1 satellite images of the Zagros mountains in Iran during the months of February 2017, March 2019, and 2020, relevant data was acquired. Furthermore, field measurements of snow depth were conducted to validate the proposed algorithm. In order to enhance the accuracy of snow depth estimations, the data from both VV and VH channels was integrated by applying a weighting factor determined based on the local radiation angle. The comparison between the outcomes of the suggested approach and the field data revealed a correlation coefficient of 0.86. Furthermore, the calculated values for RMSE and P-Value were 14.37 cm and 0.009, correspondingly. Based on the statistical metrics derived from the validation process of the proposed technique, it demonstrated a satisfactory performance in the estimation of snow depth.

Soil erosion is a global risk that seriously threatens water and soil resources, and land changes, especially the destruction and change of forests, are important factors involved in increasing the potential of its occurrence. Therefore, planners and managers must be aware of forest cover changes and their role in causing soil erosion over some time. The evidence shows that with the increase in population and to meet the needs of the residents, the extent of forest lands in the Ghaleroodkhan basin has decreased and the amount of soil erosion has increased. Based on this, the current research seeks to evaluate the effect of changes in forest cover on soil erosion in the Ghaleroodkhan Fuman basin.

In this research, to investigate land use changes (especially forest), Landsat satellite images from (OLI-TM) sensors for the years (1992-2023) were obtained from the American Geology website in August. Then, to prepare the images, geometric and atmospheric corrections were made on the images using the Flash method and Envi5.3 software. In the next step, using the object-oriented classification method and the nearest neighbor algorithm by Ecognition software, land use maps were extracted in 1992 and 2023. In the next step, by identifying the effective factors involved in the erosion of the region (including; land use, slope, lithology, soil, distance from the road, distance from the river, and rainfall) and preparing the information layers of each criterion in GIS, valuing and standardizing the layers using Fuzzy membership function and criteria weighting were done using Critique method. Eventually; The final analysis and modeling was done using MABAC multi-criteria analysis method.

It can be said that the largest area of use in 1992 is related to forest cover with an area of 222.17 square kilometers, which has decreased to 205.03 square kilometers in 2023. The next land use, which has the largest area in the Ghaleroodkhan basin, is the use of agricultural land. The amount of agricultural area in 1992 was about 161.03 square kilometers, which decreased by about 11 square kilometers in 2023 and reached 150.05 square kilometers. The land use of residential areas has changed the most in the last 30 years, in the starting year (1992) was about 33.01 square kilometers, and in 2023 it reached the area of 60.18 square kilometers.According to the obtained results, the criteria of slope, land use, and lithology, respectively; In 1992, with weight coefficients of 0.164, 0.154, and 0.145, and in 2023, with weight values of 0.169, 0.158, and 0.142, they received the highest value of weight coefficient in bothstudied periods. . Also, according to the erosion zoning maps of the studied basin, it can be seen that in 1992, the area of the layer with very high and high erosion potential was 98.92 and 118.19 square kilometers, respectively. in 2023 respectively; 132.39 and 119.94 square kilometers of the basin surface have very high erosion potential.

The results of the study show a decrease in the area of forest cover in the Ghaleroodkhan basin during the study period. So about 1700 hectares of the forest lands of the basin have been reduced and converted to other uses. Mainly, a large area of the forested lands of the basin has been converted into non-forested lands such as agriculture, residential, and pastures, which indicates the not very suitable situation for protecting the region.It can be acknowledged that according to the trend of land use changes during the studied period, the reduction of forest cover and its conversion to agricultural areas, pastures, and residential lands, as well as encroachment on the privacy and river bed in the form of changes in the use of the river to agriculture and residential. Along with other natural factors of the range, such as; The presence of sensitive and erodible formations, high slope, adequate rainfall, and the abundance of waterways have played the greatest role in increasing the soil erosion potential of the Ghaleroodkhan basin. Due to the severity of destruction in the study area, necessary management measures should be taken as soon as possible to prevent and reduce further damage to the forests of the area, as well as to reduce erosion. Eventually; It should be acknowledged that the use of the land use change map and the erosion risk zoning map obtained in the present study is a practical guide to know the state of forest destruction in the region and the erosion potential of the basin.

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.

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.

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.