remote sensing

Climate change is a significant environmental concern due to the sensitivity of glacier melting processes and snow density to climate variations. Currently, a range of satellite sensors, including AVHRR, MODIS, GEOS, and MERIS, are employed for snow monitoring and are extensively utilized to analyze fluctuations and changes in global snow cover. The MODIS sensor is particularly favored for its extensive global spatial coverage, suitable spatial accuracy, and frequent temporal coverage across various scales. Consequently, this study utilizes snow products derived from the MODIS sensor. In this investigation, data on snow-related days from the statistical period of 1989 to 2018 were collected for three provinces: Kermanshah, Ilam, and Lorestan. This data was processed using MODIS snow cover information pertinent to the Middle Zagros region, in conjunction with remote sensing techniques. The study provides a detailed examination of snow cover changes within the specified area. The Normalized Difference Snow Index (NDSI) was employed to detect snow cover within the MODIS sensor products. To differentiate pixels and identify various phenomena, the acquired images were processed using Geographic Information Systems (GIS) technology. Analysis of seasonal snow cover changes using MODIS sensor imagery indicates a significant decreasing trend in the majority of the studied area, particularly in the elevated regions. Notably, only the western and southwestern regions of the study area exhibit no discernible decreasing trend. Furthermore, the examination of snow-covered days throughout the study period reveals a decrease in snow cover in the Middle Zagros, with these changes intensifying in recent years, especially in the snow-covered areas of the region. Changes in snow cover during the winter months, particularly in elevated areas, were observed to be more pronounced compared to other seasons and regions within the study area.

Urbanization has triggered massive development activities which led to significant changes in land use and land cover (LULC). Fundamentally, the changes in LULC have to be monitored to detect the presence of regional environmental changes and impacts. Reducing the suburban area of arable lands, destruction of vegetation, and increase of land surface temperature in urban areas, and the effects of rising temperatures are some negative consequences of urban development, building street and other malicious activities within cities. This research aims to use remote sensing to assess the consequences of destruction, and changes in land use in the city of Mashhad, was performed, and this project is trying to focus on determination and comparison of its negative effects on vegetation loss and increase of temperature and the response of city management to these changes.

The methodological framework adopted in this study involved different analytical processes of Landsat imagery to examine the effects of the LULC changes on land surface temperature. Satellite images for 1979 from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) for 2009 and 2023 were obtain and were used to analyze the changes in LULC in Mashhad. All the images were selected based on the clarity of the images with less cloud coverage; moreover, the images were all captured in the month of May for the corresponding years. In this study, Orthorectification of images for 1987 and 2009 and 2023 in this study area, which has a geographic coordinate system, was carried out by the ILWIS software capabilities, and by using affine method. Then, using resampling on bands related to 1987, their sizes were equal to the bands of 2009. Vegetation map of the study area, using satellite data and remote sensing techniques were developed. For this purpose, a vegetation index called Normalized Difference Vegetation Index or NDVI was used.

The results showed that Based on the classified the uneven distribution of LULC in Mashhad varied by year. The built-up areas expanded tremendously in almost every part of city within 36 years. This pattern was also associated with the loss of vegetation cover as the built-up category took over the green space on a large scale. However, the land use pattern in 2009 displayed an exceptional fluctuate trend of vegetation and built-up areas dispersion. From the map roughly, there was a conversion of built-up area into vegetation land from 1979 to 2009. But in 2023, it appeared plausible that the previous barren lands which was categorized as built-up areas had turned into green space for urban agriculture or urban park. Referring to the map, it was apparent that the largest transition of vegetation cover into built-up areas occurred from the central towards the north of Mashhad. In general land-use patterns can directly impact the generation of heat in the urban climate. The identification of NDVI and LST parameters could provide useful information regarding the environmental changes for sustainable urban development and management. Hence, it can be concluded that a relative increase in urban ambient air temperature in Mashhad, therefore, vegetation is changed, which can be regarded as the negative effect of land use change in this city; but due to the development of green spaces, the process of destruction has decreased in recent years.

Conjointly, rapid urbanization in Mashhad city led to the occurrence of regional heating based on the increase in LST value. Therefore, sufficient allocation of vegetation and water body in the city is recommended as a measure to reduce urban heat generation. Of course, because the development of green space has reduced the speed of changes, proper and timely intervention prevents the situation from escalating. Of course, such interventions should take into account all national and local conditions. So the study further recommends that additional factors like other LULC indices, socioeconomic variables, and other factors could be considered in future studies to produce more sturdy conclusions regarding the surface climate impact study.

Data and information play a special role in the transparency of water governance. On the other hand, witnessing contradictions in water resources data and information, inconsistent readings and narratives about water assets, outdated hardware equipment, and to some extent software enhancement in the preparation and presentation of water resources information compared to global advances, necessitates a serious review of water resources data collection and processing systems. In this regard, artificial intelligence methods, sensors, and remote sensing technologies are considered in accurate water resources accounting. This article is a systematic review of about 100 international articles that present the latest findings related to software and hardware equipment for monitoring hydrological cycle meta-indicators. These meta-indicators include precipitation, water depth/water level/flow velocity and discharge of rivers, and groundwater level. In each case, while providing a list of the most important technologies, the application level of these technologies in monitoring surface and groundwater resources in Iran was evaluated. The conducted studies prove the unfavorable application technologies in monitoring hydrological cycle in Iran. For example, out of a total of twenty-six known technologies related to surface flow measurements, only two technologies have been widely used Iran; four technologies have reached the knowledge frontier and widespread production by domestic knowledge-based companies, and eleven technologies have not yet reached the knowledge frontier Iran. In this paper, suggestions were presented to outline the path for developing new technologies for water cycle data collection and transformation in the modernization of Iran's water resources data collection and data processing infrastructure.

Remote sensing is defined as the science and art of acquiring information about objects, land, or various phenomena through the collection of data without direct contact with the phenomena under investigation. In terrestrial resources, the use of aerial photographs, space images, and satellite-derived imagery for interpreting, identifying, and obtaining information about phenomena is referred to as remote sensing. Today, understanding the environment for planning based on the ecological potential of each region, in line with assessing capabilities in specific geographical areas, is considered a crucial phase in the implementation of projects, especially at macro and national levels. Therefore, the objective of this research is to classify land cover and land use in Mobarakeh County. To create the land use map, ENVI 3.5 software, which is designed for processing and analyzing remote sensing data, particularly satellite data, was utilized. Additionally, to identify land cover, products available in the Google Earth Engine platform were employed, which support various types of widely-used satellite data offered for free. Ultimately, to evaluate the accuracy of the classified results, the produced map was compared with ground truth data using Google Earth. Furthermore, to determine the overall accuracy of the classification, the overall accuracy and Kappa coefficient were calculated. The results of this research indicate that the assessment of the produced map's accuracy, which was examined against ground reality using 388 reference points in Google Earth, yielded an overall accuracy of 95.36% and a Kappa coefficient of 0.92, indicating a reliable measure. Finally, it is suggested that future studies consider using segmentations of satellite imagery with the assistance of remote sensing software to define units and assign them to compatible zones instead of employing pixel-based methods for managing such areas.

The growth of settlements and the increase of human activities in the floodplains, especially the banks of rivers and flood-prone places, have increased the amount of capital caused by this risk. Therefore, it is very important to determine the extent of the watershed in order to increase risk reduction planning, preparedness and response and reopening of this risk. The present study uses the common pattern of the machine and the classification of Sentinel 2 images to produce land cover maps, in order to construct sandy areas and determine land issues affected by the flood of March 2018 in Aqqla city. Also, in order to check and increase the accuracy of the algorithms, three software indices of vegetation cover (NDVI), water areas (MNDWI) and built-up land (NDBI) were used using images. The different sets of setting of each algorithm were evaluated by cross-validation method in order to determine their effect on the accuracy of the results and prevent the optimistic acquisition of spatial correlation from the training and test samples. The results show that the combination of different indices in order to increase the overall accuracy of the algorithms and to produce land cover maps, the forest algorithm is used with an accuracy of 83.08% due to the use of the collection method of higher accuracy and generalizability than compared to. Other algorithms of support vector machine and neural network with accuracy of 79.11% and 75.44% of attention respectively. After determining the most accurate algorithm, the map of flood zones was produced using the forest algorithm in two classes of irrigated and non-irrigated lands, and the overall accuracy of the algorithm in the most optimal models and by combining vegetation indices (MNDWI) was 93.40%. Then, with overlapping maps of land cover and flood plains, the surface of built-up land, agricultural land and green space covered by flood was 4.2008 and 41.0772 square kilometers, respectively.

The estimation and measurement of precipitation in situ presents considerable challenges due to factors such as exorbitant costs, a scarcity of monitoring stations, point sampling limitations, and its lack of generalizability to broader surface areas. Consequently, it is imperative to evaluate the accuracy of satellite-derived precipitation products as viable alternatives to conventional field measurements. Given that precipitation is influenced by the climatic conditions and physiographic characteristics inherent to specific regions, this study aims to not only validate and verify satellite precipitation products but also to examine the impact of temperature and elevation on the efficacy of MERRA, TRMM, and CHIRPS satellite precipitation products over a monthly scale from 2005 to 2019, utilizing data from 222 synoptic stations located throughout Iran. The findings indicated that the root mean square error for the TRMM, MERRA, and CHIRPS satellites was recorded at 23.8 mm, 30.6 mm, and 35 mm respectively, suggesting a superior performance of the TRMM satellite in comparison to the other two products. Moreover, the results demonstrated that the TRMM satellite consistently outperformed the other two satellites across all temperature and elevation classifications. At elevations below 500 m and above 1500 m, as well as at temperatures less than 18 °C, MERRA exhibited superior performance relative to CHIRPS, offering more accurate estimations of actual precipitation. Overall, the results indicate that TRMM satellite products may serve as a reliable substitute for observational data, as this satellite not only demonstrates commendable performance in the assessment of satellite products but also excels across varying elevation and temperature conditions.

 Oil and gas pipeline projects face a wide range of safety and security risk factors globally, especially in oil and gas-producing countries that have unsafe environments and poor safety records. Inadequate information about the causes of pipeline failures and poor knowledge about safety and security hinder efforts to reduce such risks. Today, pipelines are widely used to transport oil and gas over long distances; Therefore, their risk assessment can help to identify related risks and take necessary measures to eliminate or reduce the resulting consequences. By reviewing the previous studies, it was found that the goal of the researchers was often to investigate the possibility and consequence of quantitative risk assessment in pipelines, while these parameters are based on the physical characteristics of pipelines and alone cannot cover all aspects of risk along the path of oil and gas pipelines. to cover At the same time, paying attention to the general Feild of the pipeline in an oil region and the uses that include these feild are very important from the environmental, sustainable development, and crisis management aspects. Finally, it seems that not paying attention to this aspect of the goal is one of the distinguishing features of this study compared to other studies. The main goal of this research is to implement a new approach to assess the riskiness of the overall Feild of pipelines on environmental resources around the Maroon oil region.

 In this research, first, the study model of pipeline risk (from the point of view of consequences after the event) was selected. In the next step, the factors affecting the riskiness of the pipeline (in case of various incidents such as fire, explosion, toxic gas leakage, etc.) were identified and quantified. Then, the initial risk zoning map of pipelines in the study area was modeled based on MATLAB software's Mamdani fuzzy inference system. In the final step, to validate the proposed model, the results and outputs of the pipeline risk zoning model based on the fuzzy inference system were compared with the general opinion of experts. Finally, the final risk zoning map of the region was prepared and combined with the route of existing pipelines in the area. became

 Based on the risk zoning of the oil and gas pipeline route from the point of view of facilities/structures, this route mainly passes through areas with low and medium risk that have an area of 223.32 Km2, this area is equivalent to 18.4% of the total area of the oil field. However, about 1.45% of the oil field area includes areas with high risk. These areas are located in the southern parts of the Maroon oil field. According to the land use map, the use of these areas includes main roads, bridges, and power lines. The reason for the high risk in these areas is that in case of accidents related to oil and gas transmission lines, the consequences of these risks in these areas can be significant. The results of risk zoning of the pipeline route from the perspective of environmental factors show that the route of pipelines passes through areas with low, medium, and high risk. But with the difference that, compared to the zoning map of facilities and structures, the passage route is more in areas with medium to high risk. The area of these areas is equal to 750.25 Km2 and includes 61.83% of the total area of the oil area, the use of these areas is consistent with the network of waterways and flowing sand (sand hills). The final risk map shows the path of oil and gas transmission lines, which is the result of combining two environmental and technical criteria. In this map, after zoning the area in terms of low, high, medium, and high risks, the route of the pipelines was adapted to it. In the final map, the route of the current pipelines in the Maroon oil region shows a great adaptation to the areas with low and medium risk. Most of the pipelines' route corresponds to areas with low and medium risk. Finally, the adaptation of the current pipeline route in the region and risk zoning in the northern and central parts of the field showed that these areas show a low and medium level of risk. According to the results, the area of high-risk areas in the oil field is 17.55 Km2, equal to 1.4% of the total area of the Maroon oil field. The total length of oil field pipelines is 249.14 km, the total of 7 km of this route, which is equivalent to 2.8% of the total length of the lines, passes through high-risk areas. Most of this route is located in the south and southeast part of the oil field.

 The results of this research show that the method of expert systems, artificial intelligence, and remote sensing can to a large extent overcome the shortcomings of the lack of zoning maps of general risk from the route of oil and gas pipelines in a vast oil area. The relationship between input and output information in the proposed fuzzy inference system was described as linguistic variables by applying expert opinion, which is more flexible and accurate compared to the classical model. Finally, based on the results of this study, the southern parts of the Maroon oil field are of high risk due to the existing pipeline route. This route is approximately 7 km long, i.e. 2.8% of the entire pipeline route. Finally, considering the above, it can be said that the above-mentioned method is effective in better decision-making by experts, because achieving a general view of the level of risk in different areas, in adopting strategies for prevention, evaluation, and correction of the area, is useful. has a lack of need for field studies, as well as reducing the burden of costs related to it, as well as saving time, are among the most important parameters that will make the research of researchers easier.

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.

Inefficient urban textures are part of the texture of cities that have stayed away from the economic life of the city because of the increase from exhaustion. In the last decade, we have seen an increase in Spatial -physical change in the cities and, after this, the inefficiency of the urban textures due to increasing urbanization. In order to tackle urban issues, planners have considered different solutions, with regeneration being the most widely accepted one. In this regard, the present research has been done with the aim of regeneration of inefficient urban textures and has tried to determine the current state of regeneration with a forward - looking approach in addition to presenting a new method for identifying inefficient urban textures and then determining possible perspectives in the regeneration of inefficient urban textures to get favorable policies. The study results have shown that the core of the city has historical textures that include neighborhoods such as Chahar bagh, Agha zaman, Chaharlan Qaleh, Ghatar chian, Mian Qaleh, and Sartpola. These areas were found to be the most inefficient part of the city and an example of unstable regeneration policies. The findings of Mic Mac show unfavorable conditions for the future of these areas, as bulldozer interventions have been carried out, destroying their texture identity without achieving the desired results for residents. It does not manage and control pollution, runoff, and sewage and does not completely provide security and comfort. As a result, we have witnessed the migration of residents and texture abandonment.

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments on the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting satellite, Suomi NPP, which was successfully launched on October 28, 2011. VIIRS represents a new generation of moderate-resolution imaging capabilities, succeeding the AVHRR at NOAA and MODIS on the Terra and Aqua satellites. It provides operational environmental monitoring and supports numerical weather prediction through its 22 radiometric imaging bands, ranging from 0.41 to 12.5 micrometers.To examine the impact of precipitation on vegetation cover in Lorestan Province, the monthly mean Standardized Precipitation Index (SPI) was calculated using rainfall data from seven meteorological stations in the province. Additionally, weekly-averaged infrared images from the Suomi NPP sensor over the period 2013 to 2021 (April 1 to the end of July) were analyzed to assess the state of vegetation cover.The results indicated that the correlation between the SPI and the vegetation indices NDVI, VCI, TCI, and VHI were 0.037, 0.048, 0.174, and 0.150, respectively. Among these indices, the TCI index exhibited the highest correlation with SPI, making it a suitable method for combining remote sensing data with meteorological station data to monitor vegetation cover conditions in Lorestan Province.The findings also revealed that vegetation experienced varying degrees of drought each year, with the most severe drought occurring in 2021 across most regions of the province, particularly in the central, southern, and northeastern areas. In contrast, drought conditions were less severe in 2013, 2015, and 2018, while vegetation cover showed better conditions in 2016, 2018, 2019, and 2020.

Deforestation is one of the biggest global environmental challenges because forests are one of the most important components of the world's ecosystem and play a key role in adjusting the climate and regulating various biochemical cycles. The forests of northern Iran are considered one of the most valuable forest ecosystems in Iran, and they are among the rich forests in terms of the diversity of plant species. Therefore, detecting changes with the help of multi-temporal data in forest levels allows us to prevent further destruction by automatically identifying these changes. The main goal of this research is to identify the thresholds and apply them to the NDVI vegetation index images in MODIS sensors and automatic monitoring of forest areas.

This research was conducted in the Central Alborz protected area with an area of more than 398 thousand hectares and very rich vegetation with more than 1100 plant species. In this research, the MOD13Q1 product of the MODIS sensor and the normalized vegetation difference index (NDVI) with a spatial resolution of 250 meters and a temporal resolution of 16 days, as well as The MCD12C1 product images of the MODIS sensor with a spatial resolution of 5600 m and a temporal resolution of one year were used in three layers with the classification schemes of the International Geosphere-Biosphere Program (IGBP), University of Maryland (UMD) and the Modis Derived Scheme (LAI). The 16-day NDVI images were converted to monthly images with the maximum value algorithm and the PCA algorithm with 25 components was applied to them to eliminate errors and noise. Using the Google Earth system, 5 random polygons were selected on virgin forest areas, in order to estimate the value of the pixels and finally determine the thresholds. Vegetation homogeneity was checked using MCD12C1 images. Forest thresholds were determined by calculating the mean and standard deviation of the sample areas during 16 years. Finally, the changes were detected by applying a threshold on the images of 2001 and 2016 and applying Boolean operations with the bolean operators on them. Then, using the Google Earth system, these points were validated.

By applying PCA to the images, the amount of noise was reduced to the optimal level while maintaining the original information of the images. The results of investigating the homogeneity of the vegetation in 5 sample areas showed that they are homogeneous in three levels and are deciduous broadleaf forests The results obtained from calculating the standard deviation and monthly average of forest values indicate that the months of June, July, and August are most suitable for assessing changes in the forest cover within the study area. The threshold for these months was obtained as 8596.55, 8000 and 8497.51 respectively. By applying these thresholds to the images captured in these three months in 2001 and 2016, and then subtracting the images from each other, the pixels that experienced a decrease in forest value were identified. Finally, after performing the Boolean operation, 200 pixels with an approximate area of 1728.23 hectares were identified as changed points with a decrease in forest cover. Upon cheching, the overall accuracy for the determined points was found to be 88.5%. Hence, the application of these thresholds enables the automatic detection of changes in forest cover areas in an up-to-date and continuous manner.

In general, the Central Alborz protected area is one of the important forest centers of the country, and according to the results, we have lost a significant number of forests during the 16 years due to various reasons. Also, the results of this research indicate the importance of using automation methods in identifying forest cover changes by applying certain thresholds on the NDVI index images of the Modis sensor in remote sensing.

Soil erosion is one of the important issues in the watersheds of Iran and can be considered as one of the most important barriers to achieving sustainable agriculture and natural resources. This study was conducted to compare the MPSYAK, EPM and BLM models using kappa statistical coefficient to assess soil erosion and sediment production in the Seymareh-e-Charnar catchment area. For this purpose, the position of 90 points of erosion points was determined in a field survey by a GPS device and these points were adapted to the final erosion maps.The evaluation of the results obtained from the models used by the Kappa statistical coefficient shows that the MPSYAK model with Kappa statistical coefficient is 0.91 compared to the APM and BLM models with a kappa statistical coefficient of 0.76 and 0.71 of greater accuracy In assessing the risk of erosion in the Seymareh Chenar catchment area. Also, the results of the overlaying of the final erosion mapwith erosion points showed that in the AP model, 81% of the points in the medium-risk zone, the BBL model had 37% of these points in the medium-risk zone and in the M-PSYAK model 90% of the specified points In the middle danger area.
Therefore, it can be said that the studied area is in the middle erosion class and the M-PSIAC model shows more accurate results for erosion evaluation in this basin. Based on the zoning done using the M-PSYAK model, 7.93, 14.45, 77.62% of the area of the area is located in very low, medium and low risk classes. Also, based on the APM model, 27.44%, 32.16%, 40.40% of the area of the area were in low, moderate, high risk classes, respectively. The results of this model estimated the coefficient of erosion intensity of 0.58 and the total sediment produced during one year was 1921183.6 cubic meters.

The characteristics of drought as one of the environmental events may affect climate changes in the future. The purpose of this study is to evaluate the effect of meteorological drought on vegetation dynamics from 2001 to 2021 in the Golestan province, northern Iran, using remote sensing satellite images. First, using MATLAB software, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated in time scales of 3, 6, 9, and 12 months for the Golestan province with a statistical period of 20 years and zoned in ArcGIS software with IDW (Inverse Distance Weighting) Interpolation Method. In the next step, we obtained the maximum value of the Enhanced Vegetation Index (EVI) per month from the MODI3Q1 product of the MODIS spectrometer. Also, using Tersest software, the correlation and line slope of EVI index changes were calculated based on SPEI index changes. The results of correlation analysis showed that in 54.19% of the area of Golestan province, 6-month SPEI changes have the maximum correlation with the EVI index compared to SPEI in other periods. As a result, the highest impact of drought on vegetation in most parts of the study area is related to the 6-month SPEI in May. The results of vegetation sensitivity to meteorological drought showed that in the south of Golestan province, which is covered with dense forest conditions and includes the eastern slopes of Alborz spread in the form of a strip from the west to the east of the province, the vegetation has relatively low sensitivity to the phenomenon of drought due to suitable climatic conditions and the relative humidity of the air. The research results can be used in long-term planning for sustainable natural resources management.

The sun is known as the source of energy, the beginning of life and the source of all other energies. The global radiation of the sun is considered one of the fundamental structures of every climate. Therefore, knowing the characteristics and predicting these basic structures has a great impact on energy-related planning. The use of satellite images and remote sensing models as a suitable and low-cost tool for estimating solar radiation has been in recent years. In order to carry out this research, the images of 2020 Landsat 8 satellite OLI sensor and TIRS sensor and Sabal algorithm were used. ENVI software was used for geometrical, atmospheric and radiometric corrections of satellite images, as well as the execution of calculations related to the Sabal model, and ArcGIS software was used for creating a database, spatial analysis, cartographic operations and finally implementing the model. The results show that the average maximum incoming shortwave radiation was 901 watts per square meter on 08/09/2020 and the lowest value was 302 watts per square meter in February. Meanwhile, the lowest amount of net radiation on 10/28/2020 is 355 watts per square meter. The difference in the amount of net radiation reaching the earth in the studied area is caused by the difference in the angle of the sun and the number of sunny hours in different months of the year. Finally, it can be concluded that the solar radiation in the region has the necessary potential for the implementation of solar photovoltaic projects in the year under review. 

Detecting the presence of vessels near the coast and far from it is one of the important issues that has many applications in military and civilian industries. This work is very useful for maritime traffic management and traffic safety at sea. Traditional methods of detecting vessels include the use of human observers or systems that transmit characteristics on vessels. New techniques that integrate data from different sources are being developed. In this research, an attempt has been made to optimize the combination of kernels (MKL) by using deep neural network for the first time and finally identify the ship in the Persian Gulf. In this research, Sentinel-1 radar sensor images were used, and finally, using K-means algorithm, 365 training samples of the sea and ships in the Persian Gulf were prepared in different weather conditions. 70% of it is introduced to the network as training data and 30% as test data. In this article, using RBF kernels and polynomials of the first, second and third degrees, features are extracted, and then using a deep neural network, the output of the kernels is combined and high-level features are extracted. The results of the introduced network showed 88% accuracy of the model in identifying and detecting vessels in the Persian Gulf region based on training data. Finally, the network was implemented for validation purposes in Bandar Abbas and Bandar Lange regions and acceptable results were obtained.

Flood hazard is one of the most destructive phenomena that affects human life worldwide. Following the activity of a powerful rainfall system on March 17, 2019, in the Gorganrood basin, a 500-yr flood occurred, and half of the Gorgan city was inundated. This research was conducted to evaluate the operation management of the Golestan Reservoir before the flood. To accomplish this study, the Landsat 8 satellite images in the period of 2002-2023 and Sentinel 2 images one month before and after the flood were processed in the Google Earth Engine (GEE) system. In total, 245 images with monthly resolution were analyzed. Five indices of AWEI_sh, NDWI, AWEInsh, WRI, and MNDWI were used to identify the water inundation area. Results show that the AWEI_sh index had the best performance in terms of a kappa coefficient of 0.985, overall accuracy of 90%, and accuracy of 100%. The results showed that while the Golestan Dam reservoir experienced less than 50% full in 90% of the months (out of 245 months), this reservoir was 68% full two days before the flood. The results of this study revealed that despite the announcement of orange-level warnings by Iran’s Meteorological Organization in 5 days before the flood event and also despite the efforts of the dam manager to exhaust the stored water in the reservoir (33 percent empty volume), these actions have not been able to protect the downstream areas of the dam from flooding. Therefore, in addition to the effect of the dam on flood control, it is necessary to revisit the locations of the human settlements downstream of the dams.

Urban areas have undergone significant changes over time, which has led to the phenomenon of "urban climate change". This study aimed to investigate the role of climatic parameters in the formation of UHII using remote sensing techniques, with a case study of Tabriz metropolis, Iran. In this study, Landsat 8/9 satellite images were used to calculate land surface temperature (LST) and urban heat island intensity (UHII) and ERA5_Land reanalysis data for meteorological parameters for the spring and summer seasons of 2023. Split Window algorithm was used to calculate LST and UHII algorithm was used to check the intensity of urban heat islands. Also, statistical analyzes of linear regression and multi-criteria or multi-variable regression and Pearson's correlation were used for the relationship between UHII and climatic parameters. The results show that bare areas without vegetation, as well as areas close to industries and airports, have the highest surface temperature and UHII. On the other hand, areas with high building density and abundant green spaces have lower surface temperatures. Analysis of meteorological parameters shows that moving from west to east in the study area, air temperature and surface pressure decrease, while precipitation increases. In addition, the wind speed increases from west to east in spring and decreases in summer, while the surface solar radiation has been constant in both seasons of spatial induction.

Iran is one of the flood-prone countries in the world, and due to climate changes in recent years, we will see more destructive floods in the future. The more prevention and measures are considered, the less human losses and economic costs will be involved. Identifying flood-prone areas is one of the primary measures to deal with them. The Kermanshah township was not separated from this and this research was conducted with the aim of preparing a flood potential map of Kermanshah township. Considering that there are many parameters that are effective in flooding, so in this research, seven parameters of height, slope and direction of slope, vegetation, land use, density of watercourse or drainage network, precipitation and distance from the watercourse are selected and the corresponding map for each one was prepared. In the next step, the maps prepared using the fuzzy AHP hierarchical analysis model were weighted and finally the layers were overlapped using the gamma fuzzy operator in ArcMap software, and the maps related to flood risk zoning in the Kermanshah township. Was obtained. The area and areas of flood risk in Kermanshah township with very high, high, medium, low and very low degrees was calculated as percentage 6.3, 9.1, 10.4, 16.7 and 28.6 respectively and the area of the regions with flood risk in Kermanshah city with very high, high, medium, low and very low degrees equal to 19.3, 26.4, 2.5, 17.3 and 34.5 respectively.