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

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.

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.

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.

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.

unplanned urban physical development inflicts irreparable damages such as Land use change causes, vegetation loss, an increase in the temperature of the earth's surface, an increase in the price of residential land, and the tendency to marginalization becomes unplanned. In this research, To investigate urban changes during a 25-year study period from 1995 to 2020 to prevent the mentioned damages due to physical development without a plan three Landsat images for analysis were obtained from the USGS website. Pre-processing and processes to identify the changes on the images to estimate the physical development were done in ArcGIS and ENVI software, the surface temperature was extracted, the marginal areas were determined, the change map was obtained and finally the results were validated. The results showed that in 2020 and 2015, the temperature of the earth's surface in the northern and northeastern regions is higher than in other directions of the city, during 2020-1995, about 1850 hectares have been added to the area of urban areas. The main reason for the physical development of Zahedan city in the east and northeast direction is the expansion of marginalization in agricultural lands. Also, one of the reasons for the expansion of the city of Zahedan in the south and southwest direction is the presence of industrial towns and administrative centers such as universities. Therefore, due to the increase of built-up lands (marginal areas), the agricultural land in the north and northeast of Zahedan City has decreased and its temperature has increased. It is necessary to continuously monitor urban changes so that urban managers can carefully plan for the physical development of cities to prevent uncontrolled urban physical development and its possible future risks.

One of the key priorities of the Ministry of Agriculture Jihad is the mapping of croplands to estimate crop acreage and annual yield. In recent decades, remote sensing technology has proven to be highly effective in estimating the extent of crop cultivation through the use of timely images and synchronized data with diverse spatial, temporal, and spectral resolutions, leveraging advanced machine-learning algorithms. This study presented a framework for crop mapping in Marvdasht, Fars Province, by utilizing time series of Landsat-8 satellite images and advanced machine-learning algorithms. The employed algorithms included Decision Tree (DT), Random Forest (RF), Rotation Forest (RoF), Support Vector Machine (SVM), and Dynamic Time Warping (DTW) analysis. The results indicated that the dynamic time warping and random forest methods outperformed others, achieving significantly higher accuracy (with an overall accuracy improvement of 10-12%) in generating the agricultural land-use map of the study area. Furthermore, this research demonstrated the effectiveness of Bands 2-5 of Landsat-8 satellite in confidently identifying all crops in this region using the mentioned methods.

Morphometric study of a watershed plays a very important role in land structure. Remote sensing methods provide a good tool for studying and extracting stream networks. One of the common methods for extracting waterway networks and conducting a morphometric study of watersheds is the use of Digital Elevation Models (DEMs) with a high spatial resolution. The purpose of this study was to extract stream networks by using the DEMs of high spatial resolution, such as ALOS-1 and Sentinel-1, and those of medium spatial resolution like SRTM and TanDemX. To produce the DEM by using Sentinel-1 images, the InSAR method was applied. Finally, to validate the accuracy of this DEM for checking Sentinel-1 ability to extract stream networks, the ALOS-1 DEM with the spatial resolution of 12.5 m was used. The results revealed that the produced DEMs by using Sentinel-1 images had a high correlation of about 0.99 with the reference data of ALOS-1, thus showing the high capability of the DEM for extracting stream networks. The results of extracting the waterway networks demonstrated that each of the two DEMs of Sentinel-1 and ALOS-1 with high spatial resolutions could extract 9 waterway networks, while the digital models of SRTM and TanDemX with medium resolutions could only extract 7 and 6 stream networks, respectively. The studies indicated that the baseline parameters, as well as the time difference between the two Master and Slave images in InSAR, had to be highly considered by researchers to improve the quality of the Sentinel-1 DEM.

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.

Snow studies drastically influences on managing water resources and preventing the dangers caused by floods. Moreover, the modeling of snow melt runoff is faced with the lack of snow density data in the basin due to many changes in the distribution of snow masses, and creating snow measurement stations at high altitudes are difficult and expensive work. Considering the issues, this study attempted to invent a new equation and use remote sensing techniques in a simpler and more physical way to determine the degree-day factor (α) and then the simulation was performed by defining its calculated values using classical and new methods along with the annual and seasonal values ​​of the recession coefficient (k) for the snow-melt runoff model (SRM). The outcomes were evaluated and compared. In order to evaluate the performance of the model and its parameters, simulations were performed for the calibration and validation periods for the water years 2011-2012 and 2012-2013, respectively, and the MODIS snow cover product (MOD10A1) was used to estimate the snow-covered area and to estimate the radiation values. NEO net radiation product (CERES-NETFLUX-E) was used. The results showed that the best method for simulating runoff in the calibration period (2011-2012) was the new degree-day factor calculation method and the two values of the seasonal recession coefficient with the coefficient of determination of 0.72 and the volume difference of 4.17. In the validation period (2012-2013), the runoff simulation method with the new degree-day factor method and an annual recession coefficient with a coefficient of determination of 0.51 and the volume difference of 4.38 provided the best results in terms of assessment of the model accuracy criteria.

Soil erosion is the most common type of land degradation occurring in arid and semi-arid areas, as well as in flooded areas, due to natural and human processes. Erosive factors lead to the loss of land fertility, transforming damaged areas into abandoned lands and disrupting the balance of natural ecosystems. Soil erosion, as an important environmental issue, poses a threat to the sustainability of natural resources and soil productivity, turning it into a major problem in many parts of the world. To address this challenge, evaluating soil erodibility and preparing a risk zoning map is one of the most important preventive measures. One of the methods for estimating soil erosion is the SLEMSA model, which utilizes basic environmental information to assess the amount of soil loss. Consequently, the present research aims to determine soil loss values and create zoning maps for high erosion areas using the SLEMSA model and remote sensing data. The results obtained from this study can assist organizational managers and executive departments in formulating effective plans to control soil erosion and contribute to the achievement of the country's sustainable development goals.

The SLEMSA model divides the soil erosion environment into four physical systems: crops, climate, soil, and topography. These control variables are then integrated into three sub-models: the soil erodibility sub-model, topography sub-model, and vegetation sub-model. In this study, the topographic factor map is generated using two parameters: slope and domain length. These parameters are derived from the Digital Elevation Model (DEM), obtained from the SRTM Digital Elevation Data version 4 of the Google Earth Engine system, with a spatial resolution of 90 meters. To create the soil erodibility map, rain kinetic energy maps and soil erodibility factors are required. The Google Earth Engine System’s Global Precipitation Measured (GPM) v6 data was used to extract rain kinetic energy. Simultaneously, the soil erodibility factor was obtained from a geological formations map acquired from the World Soil Database (HWSD) for the year 2022. Additionally, a vegetation factor map was generated using ESA-CCI global land use data.

The topographic factor map was created using slope and domain length maps. Based on this factor's map, it is evident that the highest values are in the high mountain areas of the basin. The soil erodibility factor map is prepared based on soil erodibility and rain kinetic energy. The results of the soil erosion factor map for this basin show that erosion is more evident in areas with weak and unstable formations mainly affected by the kinetic energy of rainfall. The vegetation factor map was prepared using released energy maps and land use maps. Considering the direct relationship between the vegetation factor and erosion, it can be concluded that the highest soil losses occur in areas with a higher vegetation factor. Consequently, the combination of topographical factors, erodibility, and vegetation indicates the amount of soil erosion in terms of tons per hectare per year. The research findings indicate that the marginal areas from the northeast to the southeast of the basin, in the altitude range of 1880 to 3622 meters, lack suitable vegetation due to the high altitude and slope. Also, due to the high amount of precipitation in these areas, an increase follows the speed of surface runoff. Additionally, sensitive formations in these areas contribute to intensifying soil erosion. Approximately 11.7% of the basin area is in the erosion risk group, with the erosion rate ranging from 37.1 to 67.7 tons per hectare per year. Planning, management, and control measures are needed to address soil erosion and protection in these high-risk areas.

The results of this research indicate that approximately 96.61% of the region has a low to moderate erodibility rate. Meanwhile, the lowest erosion values are observed in the middle and outlet areas of the basin, characterized by lower altitudes and better vegetation conditions. These areas are more favorable in terms of sensitivity to erosion, and the presence of annual rainfall between 460 and 600 mm leads to a decrease in runoff, consequently reducing the rate of erosion. On the other hand, the highest amount of erosion can be seen in the Doab and Kakareza watersheds, attributed to inappropriate vegetation, annual rainfall exceeding 600 mm, and structures with low resistance. Finally, it can be said that in Iran, especially in Lorestan province, the lack of models to estimate erosion with limited data has become a significant challenge. Therefore, the purpose of this research was to investigate soil erosion in the Karkheh watershed of Lorestan province using the SLEMSA model. Utilizing simple environmental data, this model shows promising potential for estimating soil erodibility in the Karkheh watershed of Lorestan province.

Soil moisture can be considered in the control of desertification, agricultural activities, watershed management and optimal management of water resources.   Since the country of Iran is facing many problems in these fields, the expansion of studies in the field of accurate estimation of soil moisture becomes important (Mehrabi et al., 2019). The GLDAS system may have a high error compared to the measured data in some areas. Therefore, it is necessary before the data and results of this product are used as a decision-making tool in the region.  The quality of these data should be evaluated locally using ground-measured data (Polo et al, 2016 & Sanchez-Lorenzo et al, 2013 & Zhang, 2019).In this study, Terra MODIS data was used to estimate soil moisture due to higher spatial resolution (1 km). Due to the fact that it is difficult to estimate the humidity time series in the field, and radar remote sensing methods produce humidity maps with low spatial resolution. Therefore, in this study, a new method was introduced to prepare a soil moisture map with higher spatial resolution based on NDVI and LST MODIS products.The purpose of this study is to estimate soil moisture in Jiroft city using the products of the Morris sensor and NDVI and LST indices. Considering that Jiroft plain is one of the agricultural poles of Iran. Estimating the time series of soil moisture and then providing a drought index based on soil moisture is a useful method for investigating agricultural drought in the study area.

LST and NDVI have high relative importance in arid regions (Park et al, 2016). In the study area, an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created using OLS method between soil moisture observations (2021 and 2022) and NDVI and LST time series data. Based on the results, both independent variables NDVI and LST were significant at the 99% level) p-value<0.01) .VIF values were less than 10. Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. In general, R2 = 0.78 and it shows the accuracy of soil moisture estimation method. R2 was obtained as 0.74 and 0.8 in the years 2021 and 2022, respectively, which indicates the accuracy of the predicted values.

Estimation of spatial and temporal changes of soil moisture is an important issue in low data areas such as Iran.  We proposed a multiple regression model based on Modis NDVI and LST to obtain surface soil moisture at a regional scale. The results showed that the multivariate linear regression method can be used to estimate soil moisture products with high resolution in areas with little data in the surface layers of the soil. Park et al. (2016) stated that LST and NDVI have high relative importance in arid regions. In the studied area, according to previous studies (Bai et al, 2020 & Wang et al, 2007), an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series became. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created between soil moisture observations (2021 and 2022) and NDVI and LST time series data using OLS method.  Based on the results of simulated soil moisture changes and the OLS method in estimating soil moisture (0-30 cm), both independent variables NDVI and LST were significant at the 99% level (p-value <0.01)). VIF values are less than 10 Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. Overall, R2 was 0.7  And it showed the accuracy of the soil moisture estimation method, which is consistent with the results of (Khanmohammadi et al, 2008 & Lin et al, 2015).

  As one of the most essential needs of living beings, clean air quality has been threatened by natural and human activities. In recent years, dust storms have been increasing spatially and temporally, causing numerous damages to social, economic, and environmental health for the residents of the southern and southwestern regions of Iran. In the present study, MODIS sensor data were used to investigate dust storms and detect horizontal optical depth.

The  advantages of MODIS sensor data include high spectral and temporal resolution. Additionally, meteorological station data were collected based on the study period. After preprocessing the data and preparing field observations, the necessary features for modeling were extracted using the differential method between selected bands of each MODIS sensor image, along with features extracted from ground-based meteorological station sensors. After further investigations and evaluations and using the viewpoints of meteorological experts, 36 differential features from various MODIS image bands and six features from ground-based meteorological station data, totaling 42 features, were extracted. Subsequently, using feature selection techniques, the best features were identified. A novel method named ML-Based GMDH, which improves the GMDH neural network by altering partial functions with machine learning models, was employed to detect dust concentration and horizontal optical depth. To achieve optimal accuracy, the hyper-parameters of this model were heuristically tuned using the TLBO optimization algorithm. Additionally, machine learning methods such as Basic GMDH, SVM, MLP, MLR, RF, and their ensemble models were implemented to compare with the main approach. According to the results, the TLBO-tuned ML-Based GMDH method provided superior accuracy in detecting dust concentration compared to the aforementioned machine-learning methods.

The SVM-PSO method was selected as the best method in the feature selection phase, the RF method was chosen as the best method among basic classification methods, and the Ensemble SVM and Ensemble RF methods were selected as the best methods in the ensemble and classification phase. It was also observed that using the ensemble approach led to a desirable improvement in horizontal optical depth classification. In the second approach, a method titled ML-Based GMDH, which improves the GMDH neural network by altering partial functions with machine learning algorithms, was used for estimating dust concentration. Additionally, to achieve suitable accuracy, the hyper-parameters of this model were finely tuned using the TLBO optimization algorithm. The results showed that this method provided appropriate accuracy in estimating dust concentration and horizontal optical depth, out performing the best-selected methods from the first approach

One of the most important steps towards sustainable development is the protection of land integrity, so that a part of the land is changed annually for several reasons, and the withdrawal of such lands from the production path creates irreparable damages. Given the high intensity of land use change in Mazandaran province, including the Haraz plain, which represents a significant environmental issues at both macro and spatial scales, the monitoring and analysis of theese changes can be considered a valuable tool for for the management and planning of land use. Considering that Haraz plain has not been spared from the crisis of destructive land use changes in recent decades, the need to monitor, highlight and process these changes as one of the most important management factors in this region is confirmed.

In order to investigate land use changes, it is necessary to integrate a number of layers over a specific period of time. This research aims to investigate land use changes in Haraz Plain from 1980 to 2021. Therefore, Landsat data was employed to quantify the changes. By applying atmospheric, geometric, and radiometric corrections, image enhancement operations were performed and land use change maps were produced based on the supervised classification method, maximum likelihood algorithm and basis component analysis functions. The type of land use changes was determined from the difference function of the identification images and the accuracy of the maps using the overall accuracy test and the Kappa statistic.

The results showed that from 1980 to 1990, the area of forest lands decreased by 4 km2. The rangeland area also decreased from 450 to 436 km2. From 2000 to 2010, the area of forest land decreased from 272 to 270 km2 and rangeland decreased from 432 to 420 km2. Finally, between 2011 and 2021, the area of forest lands decreased by 9 km2 and the rangeland area decreased by 5 km2. The results indicated a reduction in the area of forest and rangeland, accompanied by an increase in the area of agricultural land and residential areas.

The results obtained and the defined goals allow us to conclude that the area in question underwent changes in terms of its use during the considered statistical period (1980-2021). These changes were noticeable. Therefore, human factors interventions play a pivotal role in land use changes. The results of this study can assist planners in identifying the factors influencing land use changes and in making appropriate management decisions in the future.

 The amim od this research is to investigate the effects of land use changes on vegetation in Mian-ab watershed  in Shushtar city  from 2000 to 2020.

 In this research, Landsat 5 and 8 satellite images for 2000 and 2020, respectively, were downloaded from the United States Geological Survey (USGS) website. After extracting the images, necessary pre-processing, including atmospheric, radiometric, and geometric corrections, was done on them in the Envi-5.3 software environment. To prepare the land use map, supervised classification algorithms, including maximum likelihood, minimum distance from the mean, parallel surfaces, and Mahalanoi distance, were used.

  The results show, in the period of 2020, changes in agricultural indicators (46.1%), range land (1.14%), water areas (1.54%), and a decreasing trend in rainfed lands (1.94%) and residential areas (3.63%) have an increasing trend compared to 2000. Dams, industrial centers, polluting factories, and temperature increases have caused most areas of good condition of vegetation indicators to become average.

 Based on the research findings, residential areas are rarely observed where the basin has a steep and slippery slope (15-30%) since leveling operations are not carried out in them. The natural situation and fluctuations in the height of the Shushtar watershed have produced a variety of plants.

Innovation:

 The results of the conversion of agricultural and barren lands and lands that are already part of the river bed were applied, and the percentage of changes in the area of the index (SAVI) was lower than other indices, which is why the soil surface moisture of this index is affected by crop irrigation.

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, 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 816 watts per square meter in July and the lowest value was 302 watts per square meter in February. Meanwhile, the highest amount of net radiation in June is in the range of 602-680 W/m2 with a value of 32%. 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.

Wetlands as an integral part of the global ecosystem in flood prevention or mitigation, feeding aquifers and providing unique habitat for plants and animals and other services and benefits are key elements of a regional conservation strategy. Anzali International Wetland in Guilan Province is one of the 10 most valuable wetlands in the world, which has undergone many changes in land use and vegetation due to structural changes resulting from man-made processes, and its nature and ecological functions have been endangered. The purpose of this study is to investigate the application of remote sensing data in mapping changes in the spatial pattern of the landscape with the help of field work training areas at the bed of the wetland and to analyze the changes of territorial cohesion based on the metrics of the landscape. First, satellite data were analyzed and Sentinel-2 images from 2016 to 2020 were classified by training areas. Then, a map of land cover in 7 classes of agriculture, barren, reed, forest, rangeland, water and urban area was created for mapping and analysis of land use metrics. After extracting class-level and landscape-level metrics in Fragstats software and determining appropriate metrics using PCA method with R and Canoco software, LPI, LSI, ENN_MN, CA, TE, NP, SHAPE_MN, PARA_MN, IJN, ARE_ Applications were selected for better analysis of the area. Analysis of metrics indicates that, in general, the landscape is fragmented, more complex and irregular in form, and more discontinuous in terms of the degree of integration of structural elements.

The Central Plateau of Iran, due to climate changes and the reduction of available water resources on one hand, and the increase in population and the consequent increase in demand on the other hand, is facing a severe water crisis. The science of remote sensing and the availability of numerous satellite products have made it possible to monitor the process of changes in various environmental parameters, especially surface and underground water sources, with appropriate accuracy. For this purpose, using the Google Earth Engine system, 16 different satellite products including different environmental parameters such as precipitation, temperature, evaporation and transpiration, soil moisture, runoff, total water storage (GRACE), vegetation cover index and water surface area were received and prepared for the time period 2000-2022. Then, using the non-parametric Mann-Kendall test and the Sen’s slope estimator, the change trend of these parameters was investigated. According to the results, the changes in earth's gravity, which indicates the level of underground water, as well as the area of water surfaces, which indicates surface water resources, and soil moisture, showed a significant decreasing trend. On the other hand, maximum temperature, minimum temperature, potential evaporation and transpiration and NDVI index have a significant increasing trend. Despite the decrease in water surface area, the vegetation cover index has increased, which indicates the increase in the area under cultivation of agricultural products and excessive harvesting of underground water resources, which is also confirmed by the decreasing trend of the GRACE satellite product. The correlation coefficients between parameters with significant trends also showed that there is a significant correlation between GRACE and NDVI parameters, minimum temperature, maximum temperature, soil moisture and area of surface water bodies and and in future researches, satellite products related to these parameters can be used to evaluate the condition of surface and underground water resources in other regions and basins of Iran.