جستجوی مقالات مرتبط با کلیدواژه « remote sensing » در نشریات گروه « جغرافیا »

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.

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.

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. 

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.

Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh glacier’s snout is known as one of the most dynamic parts of glaciers in Takht-e-Soliman height due to the yearly advance and retreat of glacier movement causing substantial volumes of various glacial deposits to collapse into their downstream areas. Nowadays, the advancements of satellite imagery, aerial photos, and Unmanned Automated vehicles (UAV) pave the path for accurately extracting and evaluating these changes. Therefore, the objectives of this research are: (a) evaluating the use of new and cost-effective technologies (UAVs) in comparison to satellite imagery for monitoring glacier changes, (b) identifying spatiotemporal glacier elevation changes, and (c) evaluation of the elevation change rate of the Alamkooh glacier snout from 2010 to 2020 using high spatial resolution remotely sensing data. In this context, the elevation changes of the snout of Alamkooh Glacier, as the hazardous activist part of this glacier, were assessed using Digital elevation models (DEMs) differences of 2010, 2018, and 2020.

Alamkooh Glacier is located on the northern hillside of Alamkooh Summit in the Takht-e-Soliman region. The snout of this glacier is situated in a steep valley known as Lizbonak and its high activity changes the shape and morphology of this area. In this paper, spatial and temporal elevation changes of Alamkooh Snout were identified and evaluated using DEMs subtraction derived from aerial laser scanning (LiDAR) data in 2010, and from images captured by UAV in 2018 and 2020. Before elevation change analysis, the DEMs obtained through UAVs in 2018 and 2020 were carried out using approximately 40 and 20 ground control points, respectively. The resulting outputs displayed a reliable accuracy of around 15 cm for these DEMs. In addition, for assessing elevation changes precisely, the all of extracted DEMs were preprocessed and orthorectified and then subsequently subtracted pairwise. Then after, the accuracy of elevation changes was appraised based on non-glacial area elevation change. The outcomes of elevation change in this region signify a high level of accuracy in the 10-year time span. According to the results, the average and standard division elevation change of non-glacial area was ±0.05 cm and 0.34 cm respectively. Moreover, the average error assessment on the non-glacial area indicates that within eight years from 2010 to 2018 the average error was ±0.16 cm, and within two years it was ±0.11 cm from 2018 to 2020.

Results of DEMs pairwise differences show significant elevation changes in this part of Alamkooh Glacier from 2010 to 2020. The average and the maximum elevation change rates in this period are -0.8 (m/yr.) and -2.31(m/yr.) respectively. The major elevation changes in the snout of Alamkooh happened in the initial period from 2010 to 2018 where the yearly and the maximum mean elevation change rates were -1.03 (m/yr.) and –2.77 (m/yr.) respectively. On the contrary, the elevation changes from 2018 to 2020 were lower than the first period whereas the yearly mean elevation change was about +0.1 (m/yr.) and the maximum elevation change rate was -1.85 (m/yr.). The positive rate of elevation change from 2018 to 2020 is due to debris and ice cubes flowing from upstream and accumulation downstream. Moreover, the Spatial analysis of elevation changes results show a heterogeneous distribution whereas the most significant elevation change in the snout of Alamkooh glacier has occurred predominantly across and along the largest existing valley rather than being evenly spread out across the entire area. The elevation change domain in this valley is between +1.3±0.05 to -23.05±0.05 and the average elevation change of in ten years from 2010 to 2020 is about -8.01 ± 0.05 meters. These changes mostly were negative with decreasing and eroding rates. In contrast, the elevation changes in other valleys only occurred at the exit area of the glacier and just the entrance of the snout area, and the margins did not show a considerable change. When considering all valleys in the snout of Alamkooh the elevation changes distribution across the snout varies between +0.45 to -13.2 (m) with an average of -7.8 (m) which is less than alongside changes at the main valley.

The results show elevation changes in the Almakooh snout do not have constant rate and largely fluctuate in different years and regions. The maximum elevation changes occurred from 2010 to 2018 and along with the main steepest valley. The main valley plays a vital role in elevation change analysis and flowing debris down. This area is also known as the depletion area of ​​the Alamkooh glacier and its drastic elevation changes are caused due to ice and snow melt.  The tremendous historical flood of the SardAbrood River occurred in June 2011 was created and affected by elevation changes in this area. Therefore, the tongue of Alamkooh Glacier is considered one of the most dangerous areas regarding natural hazards, and morphological change studies require precaution regarding approaching or visiting this area. This research also confirms that using time-series of remote sensing data such as UAV and Lidar images is very helpful and cost-effective data for identifying, extracting, and monitoring the spatiotemporal changes of glaciers, debris flow directions, and natural hazards.

One of the suitable methods for evaluating water quality is the use of water quality indicators. Qualitative indicators are a powerful management tool for different levels of decision-making. With the availability of satellite images, besides the use of field data, it is possible to study the quality of surface water with greater power and increase the dimensions and scope of the work and reduce the cost of the study. Therefore, in this research, considering Taking the mentioned cases and with the aim of investigating the spatial-temporal changes in the water quality of Yamchi Dam under the Balkhali Chai river basin and using the WQI water quality index and remote sensing, a quantitative study has been done in the area of the informed case. The area investigated in this research is under the river basin leading to Yamchi dam located in Ardabil province. Yamchi dam is closed on the path of Balkhali Chai river from the main branches of Qara-Su river. The main branches of Yamchi Dam are one of the important dams due to passing through important residential areas such as Nair and villages in the region, as well as the location of some industrial and recreational centers near the boundaries of these rivers and because of the provision of drinking water and agriculture in the region. The region is Azerbaijan. Due to the prevailing conditions and serious threats caused by industrial effluents and domestic sewage, it is always under the threat of pollution.

Land use and land cover change includes the conversion of natural land cover into impermeable surfaces as a product of the rapid growth of urban and rural areas. One of the topics that has attracted the attention of researchers in recent years is the impacts of these changes on the temperature of the earth's surface as a vital parameter for the environment and human life. In this study, the relationship between the changes in land cover and the temperature of the earth's surface has been investigated using Madis sensor satellite images, focusing on Mazandaran province. In order to analyze the data, Moran's index and hot spot analysis have been used from 2012 to 2022. The findings showed that following the changes of the land cover in the studied area, the surface temperature of the province has increased by 1.46 degrees during the studied decade. Also, during the same period, the night temperature of the province has increased by 1.02 degrees Celsius and its daily temperature has increased by 1.89 degrees Celsius. Also, more than 98% of the urban lands, which are considered as the hottest parts of the province, are located in hot clusters. The results of this study can play an important role in better planning the regional development of Mazandaran province and create healthier residential environments and provide very useful information to help manage and plan the development of residential land in order to achieve environmental sustainability.The results of this study can play an important role in better planning the regional development of Mazandaran province and create healthier residential environments and provide very useful information to help manage and plan the development of residential land in order to achieve environmental sustainability.

Dust storm is a complex process that it was affected by relation between earth-atmophere system and point of veiw climatologist and meteorologist that they assessing atmospheric and climatic change, in general of world veiw, monitoring from dust cover is a need structures. The western region of Iran is the study area. The data used in this study are divided into two categories: ground-based observations in 27 synoptic stations extracted from the Iran’s Meteorological Organization during period (1998-2010) and satellite MODIS images during the first to fourth days of July 2008. Finally, the aim has analyzed using Arc GIS and ENVI softwares and NDDI index. According to results, interpolated map for the number of dusty days during the study period over the western half of Iran showed that extent of case study have not a equal system aspect quantity of occuring from dust phenomenon and how is it trend. The number of dust days increase from north toward south and sites located in northen proprotion of studied area have experienced a lower dust events. While, maximum hotspots are occuring over southwestern sites such as: Ahvaz, Ilam, Boushehr and Shiraz. Therefore, principle offspring of dust input has been out of country boundaries and arrived far way area. On based resultes obtined on satellite images using NDDI index also idicate that maximun of intense cover dust is observed over Fars, Ilam, Boushehr and Ahvaz provinces on the first, second, thrid and forth of July. But, the lowest rate of index situated in extent far such as: Eastern Azarbayjan, Western Azarbayjan provinces. Thus, parts located on the north of the study area experienced less dusty days and the maximum dust core was located in the southwestern (mostly ahvaz). The long-term result was consistent with the use of NDDI index and the daily average of NDDI index in the whole study area indicated the hotspot areas (Ilam, Ahvaz, Omidieh, Bushehr and Shiraz) during the first to fourth days July 2008. However, in the region has reduced the level of dust cover when a wet and cloudy synoptic system pass over the central and northwestern parts of the study area.

This study aimed to assess the habitat quality of the Tajan watershed in northern Iran through land use changes from 1992 to 2052 and to investigate its relationship with landscape metrics, including number of patches (NP), patch density (PD), edge density (ED), largest patch index (LPI), landscape shape index (LSI), and splitting index (SPLIT). Landsat 8 and 4 images were processed to produce land use maps for 1992, 2022, and 2052 using maximum likelihood, cross-combination, and CA-Markov methods in ENVI and TerrSet software. Habitat quality was also assessed using InVEST in three scenarios based on the land use maps. Relationships were analyzed using least squares regression and Spearman's correlation test. The results showed that from 1992 to 2052, forest and agricultural areas had the most decreasing (-82,460 hectares) and increasing (76,392 hectares) changes, respectively. Habitats in the central part of the watershed had higher quality than those in the northern and southern parts. The relationship between habitat quality and land use changes is significant and inverse (-0.95

Monitoring of land use changes and destruction of vegetation as one of the dominant parameters in soil erosion is one of the important issues for assessment and control in natural resource management. The Hyrcanian forests of Gilan province, over the past years, have deteriorated due to neglect and have taken on a different face. So; The purpose of this research is to reveal the changes in land use and the destruction of forest cover and its effects on soil erosion in the watershed of Ghaleroodkhan Fuman. For this purpose, the changes in land use that took place between 1371 and 1402 were extracted using Landsat images and object-oriented classification techniques and were classified (agriculture, forest, pasture, water, and residential). In the next step, by identifying the effective factors in the erosion of the area and preparing the information layers of each criterion in GIS, the standardization of the layers was done using the fuzzy membership function, the weighting of the criteria using the CRITIC method and the final modeling was done using the MARCOS multi-criteria analysis method. The study of the changes in watershed use shows that the forest cover in 1992, with an area of 222.17 square kilometers, had the largest area among the land uses, and in 2023, its area decreased to 205.03 square kilometers. Also considering the results; Residential use with an increase of 27.17 square kilometers has changed the most during the 30 years of study. According to the erosion zoning map, respectively; The area of the floor with very high and high erosion potential has increased from 18.04 and 31.05 percent in 1992 to 22.52 and 32.34 percent in 2023. According to the obtained results, it is possible to reduce the forest cover and convert it into residential areas, agricultural lands, and pastures, as well; He considered the conversion of agricultural lands to residential areas and the increase of residential and agricultural use in the boundaries and riverbeds as the most important factors involved in increasing the soil erosion potential of the basin.

Monitoring of water changes in human settlements especially in rural areas is essential because of its importance and vital role in sustainability as well as its effect on the natural environment of the village. The current research has investigated the effects of the Zayandeh Rud River drying on the Land Surface Temperature (LST) and vegetation in the years 1990 to 2023 using Landsat-5 and Landsat-8 multispectral and thermal images. For this purpose, two indices of the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), together with LST, and Hot Spot were calculated, and their changes in three levels of the river, One-kilometer buffer from the river and rural settlements were investigated. Based on the results, the years 2000 and 2008 were identified as the years in which the river flow was drier than the other years, and the years 1990, 2013, 2018, 2022, and 2023 were identified as the years with more water flow. The results showed that in dry years, NDWI and NDVI values tended to have small positive values, and in wet years tended to have smaller negative values. Also, dry years had higher LST and occupied a larger area of warm temperature classes. Dry years also had more hot spots than cold spots compared to wet years. The results of the correlation between parameters also showed a negative relationship between LST and NDVI and NDWI in all years and a positive relationship between NDVI and NDWI in drier years. Also, the correlation of the distance from the river was only significant in 1990 and below 100 meters, positive with LST and negative with NDWI. The results of the present study are useful for the planning and proper management of water resources and rivers in Iran

Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R = -0.85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0.58, indicating good accuracy of the model.

In the present study, using Landsat (6 images), MODIS (6 images), and SEBAL method, surface albedo values were estimated for Ahar City. Also, ENVI4.8 and ArcGIS10.3 software were used for calculations. Based on the obtained results, the mean albedo during the studied years is 0.42 and 0.38, respectively, for Landsat and MODIS images. Also, in Landsat images, the minimum albedo with a numerical value of 0.02 belonged to 2021/5/29 and the maximum albedo with a value of 0.84 belonged to 2018/8/28. The highest mean albedo with a value of 0.55 belonged to 28/8/2018 and the lowest albedo with a numerical value of 0.33 belonged to 2021/5/29. In the MODIS images, the minimum albedo with a numerical value of 0.02 belonged to 220/6/2, the maximum albedo with a value of 0.77 belonged to 2018/8/28, and the highest mean albedo with a value of 0.47 belonged to 2019/6/11 and the lowest mean albedo with a numerical value of 0.28 belonged to 220/6/2. It was also found that the northern areas of Ahar City, which includes the forests of Arsbaran and where there are agricultural lands or pasture lands, have a lower albedo value, while urban and barren areas have more albedo than agricultural lands and pastures.

The moisture content of sugarcane sheath is a crucial parameter during the crop's growth period, as it plays a key role in understanding water stress and field irrigation management. Traditional methods of measuring crop moisture levels involve time-consuming and expensive processes like obtaining wet and dry weights, followed by calculating moisture content, which are impractical for large areas. Recent advancements in remote sensing technology have enabled the monitoring of plant tissue moisture content in large fields. Remote sensing data have a high capacity to update crop growth monitoring systems.  In this regard, it is possible to use satellite images that provide a wealth of information to users. This research aims to evaluate sugarcane leaf sheath moisture using satellite images and generate moisture maps based on the best model.

The sugarcane fields, which represent the largest agricultural operations in Khuzestan, have an area of over 84,000 hectares. It covers over 9,670 hectares are cultivated by the Amir Kabir Agriculture and Industry company, the focus of this research. The study area is located at a latitude of 31° 00' 20' N and a longitude of 48° 15' 22' E. A total of 18 farms of the sugarcane variety CP69-1062 were utilized for this research. Five points were selected from each farm, and the coordinates of the points were recorded using a GPS device. The study was carried out between July and September. Ground data were collected nearly simultaneously with the Sentinel-2 satellite imaging of the target area. The moisture content of each collected sample was determined gravimetrically in the laboratory. For each image, indices and spectral bands were calculated using QGIS software and the output was saved as Excel and TIF files. The indices and bands obtained from Sentinel-2 satellite images were used to estimate and monitor the moisture status of sugarcane leaf sheath. In the next step, a variance inflation factor (VIF) analysis was implemented to check the collinearity between indices and bands. Finally, the indices of NDVI, EVI, SRWI, Clgreen and single bands B2, B3, B4, B5, B6, B11 and B12 were entered as input to four GRNN, RF, SVR and PLSR models. The Bayes algorithm was employed to optimize the parameters of the model.

The results demonstrated that the SVR model exhibited a superior ability to estimate leaf sheath moisture compared to other models. Additionally, the sensitivity analysis revealed that the SRWI, Clgreen, NDVI, B5, B12, B11, B4, B3, EVI and B2 parameters are effective parameters in the moisture content modelling process. In the final stage, the leaf sheath moisture was classified into five stress classes, namely irrigation time, low moisture, medium moisture, and high moisture, in the order from low to high. The results of the moisture maps and the irrigation schedule for each date indicate that the combined output of B2, B3, B4, B5, B6, B11, B12, NDVI, EVI, SRWI and Clgreen indices and bands has a superior performance. These indices were utilized in the preparation of irrigation plans. This method was employed to assess the potential of S2 MSI spectral indices for the estimation of leaf sheath moisture in the sugarcane growth stage.

Based on sensitivity analysis, the SRWI parameter was found to be the most effective index in the modelling process. Consequently, it can be concluded that a combination of indices and bands of NDVI, EVI, SRWI, Clgreen, B2, B3, B5, B4, B11, and B12 provides a more accurate estimate of sugarcane sheath moisture than any single input. Thus, processing and analysis of Sentinel-2 satellite images can be used to enhance the methodologies employed for the monitoring of sugarcane sheath moisture content in expansive fields.