فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 117 (پاییز 1400)

Dust and air pollution from dust are of important social and everyday issues in society. According to the Earth Observatory website dust storms are considered natural hazards, which affect ecosystems for short time intervals ranging from a few hours to a few days. Dust outbreaks have a significant impact on climate, human health and ecosystems, and numerous studies have been conducted worldwide with different instrumentation and techniques to investigate of such events. In addition to human health problems, these phenomena impose much damages to industrial and agricultural installation, population centers and communication ways. The recognition of source regions, creation and expansion style of dust storms and their relation with atmospheric circulation patterns are fundamental factors in reduction of their damages this has affected major decision-makings and policies. Dust particles enter the atmosphere under the influence of various factors such as weather conditions, ground surface characteristics such as topography, surface moisture, roughness and rough length and vegetation and soil characteristics (texture, density and composition) and land use (agriculture). Dust particles enter the atmosphere from the soil surface, rocks, volcanic lavas or environmental pollution and can lead to reduced evaporation, lowering the surface temperature and affect the precipitation process. Dust storms are one of the destructive climatic phenomena which are affected by various climatic elements such as pressure, precipitation, wind, temperature and evaporation. Dust storms are more common in arid and semi-arid regions and are far more important. Khorasan Razavi province has an arid and semi-arid climate with several dust storms occurring on its surface every year.

Statistical study of dust storms and tracking and revealing the path of these storms is important. In this study, statistical analysis of dust storms was performed using data from Khorasan Razavi Synoptic Meteorological Station. According to the World Meteorological Organization (WMO) guidelines, when wind speeds exceed 30 knots per station and horizontal visibility is less than one kilometer due to the dust phenomenon, a dust storm is reported and, the WW = 30-35 codes related to dust or sand storms are introduced, the occurrence of these codes in Khorasan Razavi synoptic stations was investigated. In the next step, the source of the dust storm event was investigated as a case study on 10/17/2017 using the image of MODIS satellite in Mashhad. Monitoring of dust source region, transport pathways and plume characteristics is only possible from satellites because ground-based measurements are very limited in space and time. Therefore, it is important to identify, also for prognostic purposes, the atmospheric circulation patterns facilitating the transport of dust particles from their source regions over distances of thousands of kilometers downwind. Compared to ground-based measurements, satellite observations offer a more efficient way of determining key characteristics of aerosols at temporal and spatial scales that are needed to study and monitor aerosol impacts upon the climate system. The Modis sensor was used to detect the path of the phenomenon and to examine satellite images. Compared to other sensors, MODIS measures the entire earth's surface in 36 bands, covering from the visible band (0.415 micrometers) to the thermal infrared (14235 micrometers). The Modis sensor is a high radiometric resolution (12-bit) device which is carried by two American satellites, Terra and Aqua. The crossing time of the two Terra and Aqua satellites along the equator is at 10:30 and 13:30 local time. In this study, images of the MODIS visible True color band with a resolution of one kilometer on the date under study in the area were received from NASA. Aerosol Optical Depth (AOD) is one of the most important parameters in the study of dust. The Aerosol Optical Depth actually refers to the distribution of dust aerosols in the atmosphere. This wavelength-dependent quantity is defined as the decrease in light per unit length on a given path Aerosol Optical Depth (AOD) is the degree to which aerosol particles prevent the transmission of light. It is defined as the integrated extinction coefficient over a vertical column of unit cross-section. It is an indirect measurement of the size and number of concentrations of aerosol particles present in a given column of air. The spectral dependency of AOD contains information about the dominance of fine and coarse mode particles, the aerosol source regions, the modeling of aerosol radiative effects, the air quality (through monitoring of particulate matter), and the correction for aerosol effects in satellite remote sensing of the Earth’s surface. Aerosol Optical Depth (AOD) maps were obtained with a spatial resolution of 0.1 by 0.1 degree and were received at three-hour time intervals from the Barcelona Forecast Center for the study. After the dust storm was detected and the source areas were identified, the tracing of the dust particles to Mashhad was determined using the HYSPLIT mode. The HYSPLIT model, which is a dual model, was used to calculate the dust trajectory, dispersion, and simulate it. HYSPLIT is a complete model for computing trajectories, complex dispersion, and deposition simulations using either puff or particle approaches. It is plausible to detect transport pathways through monitoring the dust source region in HYSPLIT. Developed by National Oceanic and Atmospheric Administration (NOAA), it is a Lagrangian model. This model is widely used for air parcel dispersion, transportation, and deposition simulation.

The results showed that the highest amount of dust storms is associated with Sabzevar with 136 mild storms and 79 severe storms and Sarakhs and Gonabad were in the next ranks. The dust phenomenon was observed case-by-case by detection and tracking that, this phenomenon has been formed in Turkmenistan in the early hours and affected Mashhad by penetrating the eastern borders of the country. According to satellite imagery, the origin of the dust storm in history has been in parts of Turkmenistan northeast of Mashhad. The output of HYSPILT maps shows good overlap with satellite imagery. This path has also been overlapped in AOD and Trajectory_Wind survey. In general, it can be stated that initially the primary dust cores were formed in the Turkmen desert and the density of dust increases as it moves west, and it has penetrated to the west and on the eastern borders of Iran and then to the city of Mashhad.

Dust storms are observed and recorded at meteorological stations as a weather phenomenon that is categorized according to the degree of visibility deterioration. In this study, the days with dust storm (observation code 30 to 35, present weather), the synoptic analysis of wind speed, horizontal visibility and weather conditions have been adopted. The results showed that the highest amount of dust storms is associated with Sabzevar with 79 severe storms and Sarakhs and Gonabad were in the next ranks. Studieswith the various methods have shown that initially the primary dust cores were formed in the Turkmen desert and the density of dust increases as it moves west, and it has penetrated to the west and on the eastern borders of Iran and then to the city of Mashhad.

Tropospheric aerosol particles play an important role in the Earth's radiative energy balance directly by scattering and absorbing solar radiation and indirectly by modulating the microphysical and radiative properties of clouds. Aerosol optical depth (AOD) based on satellite remote sensing data is a quantitative estimate of the amount of aerosol in the atmosphere and can be used as an indicator of aerosol particle concentration. In general, the review of previous studies indicates the high importance of aerosol products based on satellite remote sensing data in modeling the spatial-temporal patterns of dust storms and in particular the identification of dust sources. The advantages of using satellite AOD to identifying dust events are possible in arid areas with relatively little cloud cover. The presence of clouds in the sky also severely limits AOD terrestrial and satellite measurements. Thus, AOD datasets sometimes have a gap due to factors such as cloudiness. Since the possibility of monitoring and measuring aerosols in cloudy conditions is limited, the use of proxy datasets to fill the gap will be an advantage. In this regard, several studies based on the analysis of satellite data have emphasized the association between climatic parameters and dust events (specifically AOD) in different regions. Therefore, considering the relationship between climatic parameters and AOD, these parameters can be used as a proxy data set to estimate AOD values for areas without data or with cloud cover. Also, using the predicted values of climatic parameters, AOD values can be predicted. Accordingly, in order to achieve reliable AOD prediction results, it is necessary to use a generalizable approach that can model the complex relationships between large data sets and satisfactorily solve the mentioned problems. For this purpose, one of the efficient data mining algorithms called M5P was considered to analyze and extract the relationships between climatic parameters and AOD to obtain predictive models. The M5P algorithm is a combination of tree and regression models with capabilities such as high prediction accuracy and ease of interpreting results.

In this study, in order to derive AOD predictive models based on climatic parameters, M5P data mining algorithm based on tree structure and multivariate linear regression analysis were used. Accordingly, a spatial database of remote sensing time series data related to 4 climatic parameters (as independent variables) including surface air temperature (SAT), precipitation (P), surface relative humidity (SRH) and wind speed (WS), and AOD (as dependent variable) was generated. WEKA software was used to implement the M5P model. After analyzing the relationships between independent and dependent variables through the tree model structure and linear multivariate regression, AOD predictive rules were extracted. Statistical indicators were used to validate the linear predictive models.

After pre-processing the time series data of climatic parameters and AOD as training data set, the input independent and dependent variables of the M5P were defined. Implementation steps of the M5P algorithm, including homogenization of independent input data sets by forming decision-making trees based on a series of "if-then" rules, multivariate linear regression analysis in homogeneous classes, and finally validation of the model results was performed in WEKA software. Thus, a total of four linear models (LM) or predictive rules for estimating AOD based on the values of climatic parameters were extracted. Finally, by placing the values of climatic parameters in the obtained linear models, the AOD value can be estimated based on the thresholds defined by the M5P algorithm. The obtained linear models are able to predict AOD values in different conditions (based on climatic parameters). Validation of the results of the M5P algorithm based on correlation analysis between input variables and evaluation of prediction errors through MAE and RMSE statistics shows the acceptable performance and accuracy of linear models in relation to AOD prediction. Given the dynamics of aerosol particles (especially dust) and their ability to transportability by the wind even at very far distances from their source of emission, it is likely that the amount of measured AOD for a pixel by a satellite sensor, does not exactly belong to the same area on earth. Therefore, in relation to the prediction error of the models, it should be noted that this may be due to the ability of the aerosol particles to be carried by the wind. Due to the strong correlation between AOD and climatic parameters, possible discrepancies may be due to the mentioned reason. Because a dust storm arising from a source may have no relation with the values of the climatic parameters at the destination.

In general, in this study, the capability of M5P data mining algorithm in order to AOD prediction was evaluated. Using the M5P algorithm based on inductive learning and using remote sensing time series data, through the formation of decision trees based on the set of "if-then" rules, four linear predictive models based on climatic parameters were extracted. Predictive models were extracted and validated using a data set for Ahvaz city. AOD, as an indicator of the state of the atmospheric aerosol, has great importance for dust storms studies.Access to AOD data is restricted in some parts of the world and in some seasons due to some limitations such as cloud cover. On the other hand, it is important to be aware of future spatial-temporal patterns of dust storms in order to adopt crisis management measures. Using the obtained predictor linear models in this study, it is possible to make an acceptable estimation of AOD in some areas, there are restrictions on access to AOD. Also, by entering the predicted values of climatic parameters, it is possible to estimate the future spatial-temporal patterns of AOD.Dust storms generally occur as a function of a wide range of environmental conditions, including atmospheric properties, as well as surface parameters such as vegetation, soil moisture, and soil texture. With this background, only considering the atmospheric conditions and their impacts on the spatial-temporal patterns of AOD may sometimes not produce the desired results. Therefore, it is recommended in future studies in this field, in addition to climatic parameters, which are mostly indicators of the atmospheric condition; ground surface parameters should also be used in modeling. By doing so expected to increase the accuracy of linear models for predicting AOD.

One of the important consequences of climate change is a change in the frequency and intensity of rainfall. In fact, it can be said that as a result of this phenomenon (climate change), in many parts of the world, the frequency and intensity of maximum rainfall have increased. However, the type and severity of these changes vary from region to region (IPCC, 2012: 5). In recent decades, in the framework of climate change, several studies have been conducted on the trend of rainfall in most parts of the world. Most of these studies are based on parametric or non-parametric methods such as linear regression analysis, Spearman's test, age gradient test, and Mann-Kendall test. However, the use of these methods requires some limiting assumptions. In addition, these tests only show the trend of changes in the average time series, while they do not provide any information about the trend of changes in different time series classes. In this regard, Sen (2012: 1044) proposed the ITA method, which addresses the above-mentioned problems in addition to allowing a trend to be identified in a series of "low", "medium" and "high" values.

In this study, in order to achieve the defined goal, the basic method of percentiles was initially used. Since the choice of the threshold value of the base percentile is a matter of taste and does not reflect the intensity-frequency of heavy rainfall, the type 1 equation probabilistic distribution function (Gamble) was used to determine the heavy precipitation threshold to provide a criterion that results in intensity-frequency of occurrence, while it is based on time series distribution of the precipitation data. For this purpose, first the data of rainy days related to 6 synoptic stations in the west of the country from 1961-2019 were obtained from the Meteorological Organization. Then, in order to calculate the maximum amount of daily rainfall during different return periods, the Gamble distribution function was used, based on which the heavy rainfall threshold was defined for the stations in the region and the maximum daily rainfall values during the return periods of 2, 5, 10, 15, 20, 25, 50 and 100 years were calculated. Finally, MK and ITA tests were used to determine the trend of these precipitations. The ITA method was proposed by Sen. Despite its simplicity, it does not require any presuppositions and is more capable than other non-parametric methods because this method is able to identify hidden trends and internal trends in time series in addition to uniform trends. Therefore, in this study, ITA method was used to identify the uniform and non-uniform trends in the maximum daily rainfall and the total annual rainfall in the west of the country.

After determining the thresholds of heavy rainfall using the Gamble distribution function, it became clear that the average rainfall of more than 37 mm in the western region of the country is considered heavy rainfall. The heavy rainfall index of Dezful station was higher than other stations, which indicates the high intensity of daily rainfall in this station. In this study, heavy precipitation threshold was calculated for all stations by applying the percentile method on rainy days with a minimum threshold of 1 mm. Then, ITA method was used to analyze the trend and determine the behavior of total annual rainfall and maximum daily rainfall in the study area. Application of this method on the total annual rainfall of Khorramabad station showed that middle floor precipitation (430-650) of this station is decreasing, while upper and lower floor precipitation was decreasing. In Hamedan station, the precipitation of the middle class (240-240) showed a significant decreasing trend, but no significant trend was observed in the lower class (less than 240) and the upper class (above 440). In Kermanshah, Dezful, Ahvaz and Abadan stations, the trend specified in all classes was decreasing and uniform. The application of Man-Kendall method on the total annual rainfall in the study area showed that the trend of these rains is decreasing in all stations and is significant in Khorramabad, Hamedan, Kermanshah and Dezful at the level of 95%. Regarding the application of ITA method for maximum daily rainfall of Gamble, the results showed that in Khorramabad station, rainfall with a return period of 5, 10, 15, 20 and 25 years of this station increased and precipitation with a return period of 2 years showed no particular trends. At Abadan station, rainfall with a return period of 50 years showed a decreasing trend. In Dezful and Ahvaz stations, the trend marked in all classes was a decreasing trend. In general, no specific trend was observed in Hamedan and Kermanshah stations. Regarding the maximum daily rainfall of Gamble, the results of Mk showed a negative trend in Hamedan, Kermanshah, Ahvaz and Abadan stations and a positive trend in Khorramabad and Hamedan, but only in Ahvaz station, the trend was significant at 95%. In the present study, some conflicting results have been obtained by comparing the ITA and MK methods. This shows the advantage of this method over other process tests. For example, while the Mann-Kendall test on Khorramabad station showed a significant decrease in the total annual rainfall of this station, the Sen method showed a different trend from this method. In fact, according to ITA, it was found that the total rainfall in Khorramabad had an uneven trend, which was divided into three classes, and the precipitation class showed 430 to 650 decreasing trends less than 430 and more than 650 increasing trends in this station.

The results of ITA method showed that on an annual scale, there is a non-uniform trend in rainfall in Khorramabad and Hamedan. But in Kermanshah, Dezful, Ahvaz and Abadan stations, the trend marked in all classes was decreasing and uniform. On a daily scale, the results showed that precipitation with a return period of 5, 10, 15, 20 and 25 years of this station is an increasing trend and precipitation with a return period of 2 years is without a trend. At Abadan station, rainfall with a return period of 50 years showed a decreasing trend. In Dezful and Ahvaz stations, the trend marked in all classes was a decreasing trend. In Hamedan and Kermanshah stations, in general, no specific trend was observed, but in more detail, it can be said that the trend of rainfall on the upper floor of Hamedan station was increasing, whereas the trend was decreasing in Kermanshah station. The results of Mann-Kendall test also showed that on an annual scale, all stations have a decreasing trend and on a daily scale, Hamedan, Kermanshah, Ahvaz and Abadan stations have a negative trend, although Khorramabad and Hamedan stations showed a positive trend in precipitation. In fact, when there is a non-uniform trend in the time series, the MK test shows that the trend is insignificant, but the ITA method detects such non-uniform trends and makes hidden time series information available.

Land surface temperature (LST) plays an important role in surface energy balance. A set of environmental parameters, such as temporal and geographical changes, thermal properties, biophysical properties, climatic parameters and subsurface conditions can cause heterogeneous spatio-temporal distribution of LST and its anomalies to be. LULCC-induced surface temperature anomalies have important implications for understanding the physical mechanisms associated with the surface to changes in various biophysical factors, including albido and surface roughness (also known as aerodynamic resistance). The purpose of this study is to evaluate the seasonal changes and abnormalities of daytime and nighttime land surface temperature in Iran based on LST derived from satellite data.

In this study, the following steps were performed:A study area:The whole country of Iran was wanted. To better reveal the behavior of surface temperature anomalies in Iran, the data has been converted to a seasonal scale and also for the first time in the country, surface temperature anomalies have been studied separately for night and day.B) DataB-1) Moderate Resolution Imaging Spectroradiometer(MODIS)To investigate the anomaly of land surface temperature, the MODIS sensor data of Terra satellite MOD_LSTAD and MOD_LSTAN products were used for day and night data with a horizontal separation of 10 km and the statistical period of 2001-2018, respectively.C) Calculate trend and trend slope using non-parametric Mann-Kendall and Sen’s testsIn order to evaluate the abnormal trend of land surface temperature in Iran, non-parametric Mann-Kendall (M-K) test was used. The non-parametric Sen's method was used to estimate the slope of the process in the time series of land surface temperature anomalies and day and night in Iran.

The results showed that the mean anomaly of daytime land surface temperature in Iran (LSTAD) in the three seasons of winter, spring and autumn is negative and in summer is positive. Also, the long-term mean anomaly of night surface temperature (LSTAN) is negative in cold seasons (winter and autumn) and positive in warm seasons. The positive maximum of LSTAD in Iran was 0.172 in summer and its negative maximum was -0.672 in autumn. The same statistical quantity was obtained for LSTAN positive anomaly in summer 0.266 and in autumn 0.244. The minimum LSTAD was calculated between -1.942 to -3.097 and the maximum was calculated between 1.047 to 2.865. For night, it showed a minimum between -0.748 to -1.296 and a maximum between 1.597 to 2.189. The average statistical trend of Iran LSTAD and LSTAN in all seasons except autumn is increasing. This amount, despite being incremental, is not significant. During the day, the maximum average trend of increasing abnormality is obtained in summer (0.744) and at night in spring (1.038). The minimum and maximum trends in both day and night in Iran are significant at the alpha level of 0.01 and in terms of trend intensity, the warm seasons are more intense. The highest computational Z-score of Mann-Kendall test was obtained at night with the value of 4.097 (spring). Also, the same maximum amount per day was calculated with the amount of 3.917 in summer.

In this study, we have evaluated the day and night land surface temperature anomaly of Iran using Terra satellite MODIS sensor data during a long-term statistical period (2001-2018). The non-parametric Mann-Kendall test was used to study the trend and the non-parametric Sen test was used to calculate the trend slope. The positive anomaly of Iran's land surface temperature is higher at night than during the day and this amount is also significant in the warm seasons of the year. The maximum positive anomaly was obtained during the day during the summer with a value of 0.172 degrees Celsius and for the night with a value of 0.266 degrees Celsius. The average anomaly trend of land surface temperature during the day and night in winter to summer is increasing and only in autumn this amount is decreasing. The minimum and maximum trend in each period of time is significant at the alpha level of 0.01 and the intensity of the trend is more at night than during the day. The main focus of negative anomalies is recognizable in low-lying dry areas, inland arid regions located in the east and southeast of Iran and inland holes of Iran. While the increasing anomaly in the highlands and high latitudes of Iran is significant. Also, the dominant upward trend can be seen in the highlands of Iran, except in autumn; In this regard (Fallah Ghalhari, Shakeri and Dadashi Roudbari,2019) who used three methods of microcirculation SDSM, MarkSimGCM and CORDEX simulated the minimum and maximum temperature of Iran under the models CanESM2, GFDL-ESM2M and MPI-ESM-LR up to 2100 ; It was concluded that the annual temperature anomalies of the selected models are at high latitudes and mountainous highlands, which is in line with the results obtained in this study. One of the most important roles of land surface temperature and its anomaly is changes in convective processes, mixture layer depth and wind speed. Therefore, increasing the anomaly of land surface temperature in Iran can increase convection on the one hand and change the regional wind speed. (Dadashi Roudbari,1399) in explaining the role of surface temperature and climate change has stated that the warm surface of convection increases and causes the mixing of surface air and high surface air. Since the velocities of horizontal winds at land level are zero and at higher levels, the vertical mixing of horizontal winds causes wind speeds close to the earth's land surface to increase and wind speeds at high levels to decrease. Variability in surface temperature also changes the air temperature near the surface. In addition to what has been said, land surface warming in the highlands of Alborz and Zagros also affects the carbon cycle; Because surface heating accelerates the melting of snow and ice in these areas, resulting in the release of excess carbon (Fili, Roir, Gotha, & Pregent, 2003). Therefore, it is worthwhile to pay more attention to policies related to carbon stabilization as well as programs related to water resources and dam construction based on what was addressed in this study.

Monitoring seasonal changes of Meighan wetland using SAR, thermal and optical remote sensing imagesAbstractThe aim of this study is to monitor the seasonal changes of Meighan wetland located in Markazi province in Iran. This is a multi-sensor approach; Sentinel-1 and Landsat 8 images were captured from May 2019 to January 2020. Modified Normalized Difference Water Index (MNDWI) and Land surface temperature were computed based on spectral bands of Landsat 8. Backscattering values in VH and VV polarimetric bands of Sentinel 1 images were also considered. Different wetland land cover classes were extracted based on these three measures. The results of each season were further compared with the classification output with support vector machines. The wetland main water body reaches its maximum extent in May 2019 (61.18 square kilometers) and its minimum extent is reported in August 2019 with an extent of 19.25 square kilometers. The outputs of the support vector machine classification were more compatible with MNDWI index. The results of this study show that the multi-sensor approach can efficiently be used in monitoring seasonal changes of wetland.

Wetlands are one of the natural ecosystems that play an important role in plant and animal diversity conservation. Wetlands are very sensitive to environmental changes because they are located in an intermediate zone between land and marine ecosystems. Their constant monitoring is of great importance especially in wetlands with seasonal changes pattern. The Wetland ecosystems are influenced by anthropogenic and natural factors. Drought, reduced rainfall, unsustainable management of water resources, overexploitation, and dam construction threaten wetlands. Field surveying and mapping of natural resources are generally not cost-effective because these methods are expensive and time-consuming. Also, it is not possible to repeat it periodically with a constant interval. Therefore, the use of remote sensing data such as optics and radar data is necessary in the study of natural resources. However, natural landscapes are complex and composed of various land cover types. Optical multispectral images are not always able to classify such a landscape, perfectly. This source of data is also affected by atmospheric conditions; the presence of clouds or fog block capturing these images. SAR sensors unlike optics sensors are capable of capturing images in all weather conditions. In fact, the use of each satellite image has advantages and disadvantages and in many applications they complement each other. Multi-sensor approaches beneficiate from the capabilities of different satellite images. Researches have shown that a multi-sensor approach in natural resources studies, especially wetlands is of great value. The multi-source approach and the seasonal variations discussed in this study have not been followed in any research on Meighan wetland. The benefits of Sentinel-1 characteristics; such as suitable spatial and radiometric resolutions and free access highlight the finding of this research.

Meighan wetland is located in the center of Iran in Markazi province. This wetland has ecological and economical importance in the region. In the last two decades, one road is constructed on it and divided it into two parts; this changes the wetland into a calm environment and subsequently the evaporation has been increased. In this study, the seasonal changes of Meighan wetland were investigated using Landsat 8 and Sentinel-1 images. The images in each season were selected in such a way that the minimum possible difference exist between their acquisition date. The preprocessing steps were done independently on each optic and SAR image. Sentinel-1 SAR images have been calibrated and the digital numbers were converted into the corresponding backscattering values (in decibel) in each polarimetric band. Although, from spectral reflectance values in different Landsat bands, Modified Normalized Difference Water Index (MNDWI) were calculated in each season. Land surface temperatures were also calculated from thermal bands. Five different land cover classes are observed in the wetland and its surroundings; main water body of the wetland, shallow water zone, saline soil, surrounding area and remaining land covers (known as others). These areas were also extracted based on MNDWI index, land surface temperature (LST) and backscattering values in VH and VV sentinel-1 polarimetric bands. Then, the whole area is classified by the support vector machine classifier. In the last step, the extracted regions from different methods were compared with the land cover classification results in each season. The differences and similarities of the extracted areas were discussed further.

The findings of this study show that the main wetland body reaches its maximum extent in May 2019 based on the SVM classification results. In this month, MNDWI index-based results were closer to the one obtained with the support vector machine classification. The support vector machine classification results and MNDWI index achieved similar results in the delineation of the wetland water zone, the shallow water zone and saline soil. In August 2019, the wetland water area was reduced based on the support vector machine classification. In May 2019 and January 2020, when the wetland water area was larger in comparison to other months, the results of the MNDWI index are close to the results of the support vector machine classification. The extracted area of shallow water class and saline soil class show the highest difference between classification results and MNDWI results. The same results have been obtained by comparison of extracted area based on the backscattering values of VH and VV polarimetric bands and MNDWI index; the maximum differences are observed in shallow water and saline soil classes. This could be related to the sensitivity of SAR backscattering values to moisture content. Over the year, the moisture content varies in response to temperature, rainfall, and evapotranspiration. The changes in moisture content affect the dielectric constant of the material. The dielectric constant governs the magnitude of backscattering values. The moisture changes cause variation in SAR backscattering values over the year.

Long-term wetland change detection is frequently studied with optical remote sensing images. Although, wetlands show the seasonal pattern in response to temperature and rainfall changes over the year, however, wetland seasonal variations are not fully explored. In this study, Sentinel 1 and Landsat8 images covering the study area were captured over the year. The results of the present study showed that the seasonal variation of wetland can be monitored based on a multi-sensor approach. In May 2019, the Meighan main water body reached the highest extent and the smallest area was observed in August 2019. In addition, in January 2020, the wetland water area increased again. Also some differences are observed between the extracted areas based on the MNDWI index, VH and VV polarizations, and the support vector machine classification results in different seasons. These differences are observed more in the spring. The performance of MNDWI index in wetland water area extraction in most seasons is very close to the classification results of the support vector machine. This shows the high capabilities of MNDWI spectral index in monitoring wetlands. In addition, the main water body of the wetland can be well separated by backscattering values of VH and VV Sentinel 1 polarimetric bands.

An earthquake is the movement of the surface of the Earth resulting from a sudden release of energy in the Earth's lithosphere that creates seismic waves. Earthquakes are one of the most unpredictable and dangerous natural phenomena that cause many financial and human losses every year. Due to the great importance of this natural crisis, several studies have been conducted to investigate this phenomenon. Many of these studies show that the earthquakes phenomenon is highly related to the deformation of the earth, rising ground temperatures, gases and aerosols, and electromagnetic disturbances in the atmosphere. The land surface temperature is highly dependent on the interactions of the earth's surface layers. When an earthquake occurs, stresses and activities in the fault range increase, causing significant temperature changes compared to normal temperatures. These temperature changes manifest themselves as anomalies in place or time.Regarding the materials and methods, in this research, using MODIS thermal products and shapefile of Iran’s faults, seven earthquakes with the intensity of more than 6 Ms have been investigated. First the preprocessing was performed on LST data so that thermal noise signals caused by seasonal changes be removed from the original data. This was done by using a linear model made from the previous year data which no seismic activities were reported during its 40 days of investigation. Then, using the formation of a three-dimensional picture of time-temperature-distance in the earthquake-related fault as input, two methods for detecting thermal anomalies have been investigated on the data. The mean standard deviation method, which is a threshold method using two parameters, and the interquartile method, which is similar to the previous method but uses different statistical parameters as input, are the two algorithms used in this research. Finally, using the results of the best method for detecting anomalies, severity parameter of each earthquake is estimated using artificial neural networks.Regarding the results and discussion, it should be noted that the results of anomaly detection algorithms show that both methods of thermal anomaly detection have detected thermal anomalies related to each earthquake on the day of the earthquake in a radius closest to the fault. In some cases like fahraj earthquake some anomalies were detected aside the anomaly detected on the day of the earthquake. However, results of the mean-standard deviation method gives more false alarms as an earthquake thermal anomaly than the interquartile method. Although these anomalies could be related to the earthquake it cannot be a certain fact. So in order to have a better outcome we use the results of interquartile anomaly detection method as input for training of artificial neural network. The results in mathematical modeling have a relatively high accuracy in the case of seismic intensity parameter using artificial neural network with the total accuracy of 0.73. These results indicate that the best accuracy belongs to Azgalah and the one with least accuracy belongs to fahraj study case. Although the number of earthquakes studied for neural network training has been relatively small, but the availability of large amounts of data on each earthquake has provided appropriate accuracy. In conclusion, this study shows that thermal anomalies is one of the most significant precursors for earthquake’s investigations. Using the relevant fault and anomalies with respect to the buffer zones in different distances can help us increase the accuracy dramatically. Since many previous studies that investigated thermal anomalies connected to the earthquakes, explored areas around the epicenter, in this study we show that the corresponding fault is just as important as epicenter.Finally, it should be noted that the indicator of surface temperature changes and thermal anomalies alone cannot be sufficient to fully investigate the parameters of the earthquake or have the necessary accuracy to analyze the earthquake. However, due to the low volume of thermal data and the simplicity of working with them, it is recommended that they be used for initial earthquake surveys, and if it is partially confirmed for further analysis, use other methods and indicators that require the application of heavy and complex algorithms and processes. It is also possible to combine the results of this precursor with the results of other precursors to achieve sufficient accuracy. Regarding the results and discussion, it should be noted that the results of anomaly detection algorithms show that both methods of thermal anomaly detection have detected thermal anomalies related to each earthquake on the day of the earthquake in a radius closest to the fault. In some cases like fahraj earthquake some anomalies were detected aside the anomaly detected on the day of the earthquake. However, results of the mean-standard deviation method gives more false alarms as an earthquake thermal anomaly than the interquartile method. Although these anomalies could be related to the earthquake it cannot be a certain fact. So in order to have a better outcome we use the results of interquartile anomaly detection method as input for training of artificial neural network. The results in mathematical modeling have a relatively high accuracy in the case of seismic intensity parameter using artificial neural network with the total accuracy of 0.73. These results indicate that the best accuracy belongs to Azgalah and the one with least accuracy belongs to fahraj study case. Although the number of earthquakes studied for neural network training has been relatively small, but the availability of large amounts of data on each earthquake has provided appropriate accuracy. In conclusion, this study shows that thermal anomalies is one of the most significant precursors for earthquake’s investigations. Using the relevant fault and anomalies with respect to the buffer zones in different distances can help us increase the accuracy dramatically. Since many previous studies that investigated thermal anomalies connected to the earthquakes, explored areas around the epicenter, in this study we show that the corresponding fault is just as important as epicenter.Finally, it should be noted that the indicator of surface temperature changes and thermal anomalies alone cannot be sufficient to fully investigate the parameters of the earthquake or have the necessary accuracy to analyze the earthquake. However, due to the low volume of thermal data and the simplicity of working with them, it is recommended that they be used for initial earthquake surveys, and if it is partially confirmed for further analysis, use other methods and indicators that require the application of heavy and complex algorithms and processes. It is also possible to combine the results of this precursor with the results of other precursors to achieve sufficient accuracy. 

Biogeomorphology, defined as the two-way interaction between geomorphology and ecology in different scales. Every landform is comprised of several micro-scale landform units such as peak, ridge, shoulder, etc. landform units can create various microhabitats and enhance heterogeneity in ecosystems. This information is obtained by extracting patterns from plants, processes, and landforms in the landscape. Differences in landforms are followed by differences in biological factors (type of cover, plenty, pattern, density) and non-biological factors (form, soil, geological, climatic). One of the important factors is the chemical and physical properties of the soil. Because soil not only provides the environment, water, and minerals for the plant but also affects the pattern and distribution, type, and dynamics of the plant. Soil properties in landforms cause changes in pattern, density, and vegetation composition. So, soil properties are influenced by vegetation at smaller spatial scales. Small-scale landform patterns play a major role in determining the plant distribution pattern and are a good tool for evaluating Macro-scale bio geomorphological relationships.

The variables examined in this study include the type of landform element, height, chemical and physical properties of soil, and vegetation characteristics (pattern and density). We tested the hypothesis that landform unit features to determine the spatial distribution of vegetation patterns in the case study. This study was aimed to determine the relationship between vegetation properties (vegetation pattern and density) and landform unit type and soil characterize in Hablehroud. Hablehroud basin, that is located between 35°16' 6"- to 35°57' 22" North latitude and 52°15'43" to 53°-8'-53" East longitude (the area about 3200 square km) Between Semnan and Tehran provinces. Our study is based on remote sensing coupled with field observations and laboratory studies.We prepared geomorphic classification of landform unit, vegetation map, and Eco geomorphology map. Using the Geomorphon method of landforms shows the geomorphon-based maps of landforms. Based on geomorphon technique Classification used DEM 12.5 M resolution in SAGA7.5. Geomorphon map includes most common landform elements. In the next step, the vegetation map of the area was prepared using the vegetation index (SAVI). All calculations were performed in ENVI.5.3 software in the next stage after the matching of these two maps; a new Eco geomorphological map was prepared. Where landform-plant units were identified. A field survey was conducted from Jun to July in 2020. We plotted a total of 40 stands within the four micro-landform units in the study basin According to field surveys, and the percentage of vegetation cover four units were identified. Soil samples were collected from a depth of 20cm for all 40 plots (1×1m) some physio-chemical analyses were conducted on them including (PH, EC, wetness, Organic, Texture). According to google earth, field survey, CAD software four types of vegetation patterns includes (spot-dense, gap-dense, spot-scatter, gap-scatter) were identified for each plot. Statistical analyses were calculated using Minitab18 software. We investigate the significance and correlation, principal component analysis, and stepwise regression.Results and discussion Geomorphon map includes the 10 most common landform elements: peak, ridge, shoulder, spur, slope, hollow, foot slope, valley, pit, and flat obtained from 498 patterns. In the geomorphon map, a pattern of various landscapes has been created. The vegetation index (SAVI) of the area was prepared using Landsat8 – July 2020. The map of eco- geomorphological units includes four types of geomorphon: Slope, Hollow, Foot slope, Spur which are extracted with dense to scattered vegetation. Field studies of soil sampling have been done to measure the physical and chemical properties of the soil, plot and photograph the plots to extract plant characteristics (pattern and density). Four patterns were extracted: dense point, dense gap, scattered point, and scattered gap for 40 plots. After data collection, type of landform, height, soil properties (chemical and physical) and vegetation (pattern extraction and density) for statistical analysis and analysis of biogeomorphology in Minitab18 including correlation, factor analysis, and multivariate and stepwise regression has been.

showed that there is a significant relationship between the type of landform and density and pattern of vegetation. Among four landform unit which includes (hollow, foot slope, slope, spur). Foot slope and Hallow have the highest density and spot-dense pattern. The correlation between pattern and vegetation density with soil moisture and landform unit type is significant with value (p <0.003) and landform type with value (p <0.007). The value of R2 indicates that the predictor variables explain 72.32% of the variance in the vegetation pattern of the sign. The results of regression equations showed that vegetation pattern as a dependent variable is influenced by the four variables, first landform type, and wetness, organic, sand percentage. Regression model 70.50% of the variations in vegetation pattern was related to these four variables. Because landform units have a direct and indirect role in other factors of plant growth and distribution such as moisture absorption, heat, amount of organic matter, erosion, soil texture, and activity of microorganisms. Each of the landform units, according to its shape and characteristics, plays a role in the pattern and density of vegetation. The domain landform generally has four extraction patterns (spot-dense, gap-dense, spot-scatter, gap-scatter) in the study area. The differences in landform-unit area, climate, and topographic features show different patterns of vegetation distribution. The pattern and density of vegetation in the spur are often scattered and in the hollow and the foot slope are spot dense, which is due to the morphometric and topographic features of the units.

The results showed that changes in plant distribution patterns are well related to landform type and soil properties. In this study, four types of landform elements (hollow, foot slope, slope, spur) along with chemical and physical properties of soil about the pattern and density of vegetation were analyzed. So that the type of vegetation pattern has a positive correlation with the amount of sand, PH, EC and has a negative correlation with the rest of the variables. The results of the factor analysis and regression model showed that approximately 69 to 70% of changes in vegetation patterns could be predicted by the variables mentioned in the study. Among the independent variables of landform unit type, Soil moisture, Organic matter, and Height have a significant relationship with the dependent variable and state that different patterns of vegetation in different parts of the Hablehroud basin are related to landform type, height, and different soil characteristics. The effect of landform elements is quite different depending on how much it is affected by soil properties. About vegetation, the most spot-dense pattern at the foot slope, hollow, the slope, and the least occurred in the spur. The spot-scatter pattern had the biggest portion on the slopes, the spur, the hollow, and the foot slope. Gap-scatter pattern, the portion of slopes was higher than other landform elements, and the spot-scatter pattern had the least repetition on the desired landform elements, which is generally observed in the slopes.Therefore, the effects of geomorphic processes of vegetation characteristics are inevitable.

Urban heat island monitoring with remote sensing data is increasing and one of the most important reasons is to provide more spatial information of urban temperature than terrestrial data. The heat island resulting from this data is called the surface urban heat island. Different methods can show the intensity of Surface urban heat island in a city differently. Furthermore, consider of the temporal and spatial variations in temperature can cause error in calculating urban heat island. The relationship between factors such as vegetation index, land use, altitude and meteorological factors with urban heat island has been investigated and proven in previous researches. In this regard, predicting the land surface temperature in and around the cities to simulate the intensity of the urban heat island in the coming years has been of interest to researchers because reliable predictions of the difference between urban and rural areas are essential for planning about cities. Different cities may affected by different factors depending on the climate in which they are established. Therefore, in the study of the heat island of Tabriz and Urmia, land use is investigated as a determining factor. In addition, temporal variations in the area of Lake Urmia will be studied to assess the relationship between the extent changes of this water body and the intensity of surface urban heat island in Tabriz and Urmia.

In this study, MODIS land surface temperature data (MOD11A1) in tile No. h21, v06 has been used to investigate the urban heat island. This tile covers northwestern Iran. The common time series used in MODIS Terra and Aqua is from 2003 to 2019. Terra and Aqua each monitor the entire earth twice a day. In this study, all four observations have been used in order to evaluate the diurnal variations of the urban heat island. Second MODIS data that used in this study is the land cover type (MCD12Q1). To identify the types of land cover, the FAO Land Cover Classification System has been selected among the existing classification layers, which has been generated by applying the supervised classification method to the MODIS reflectance data. Another MODIS product has been used to study the changes in the area of Lake Urmia. This product provides a time series of the world's lakes extent, depth and reservoir variations. The data were obtained from the detection of water and land pixels using a machine-learning algorithm.Urban area and type of pervasive land cover around the city has been obtained using MODIS land cover type data. The pixels that belonged to a specific land cover in more than 75% of the study period (temporal frequency of land use species) were considered as the representative pixels of that land cover. To determine the urban and rural area, an area equal to the size of the urban extent around it has been selected as rural area. The land covers were examined among the rural pixels. The pixels that cover more than two thirds of the rural area have been identified (spatial frequency of land covers). The intensity of the heat island has been estimated according to each of the dominant land covers. Then, the intensity of surface urban heat island in relation to each of the land covers of rural district has been compared. This process also has been done once for the whole area of the rural pixels.

Evaluations of land cover type in rural area of Urmia and Tabriz cities showed that land cover type of cropland and natural herbaceous has the largest area with more than 75% of land cover frequency. The surface urban heat island in Tabriz has annual cycles. In the warm period of the year, the cropland shows a more intense heat island rather than natural herbaceous and all rural area. In addition, at this time of the year, the estimate of urban heat island in relation to the area of natural herbaceous in most cases indicates the cold heat island in Tabriz. These conditions are inverted during the cold period of the year and the urban heat island in cropland and natural herbaceous shows the cold and heat island, respectively. The intensity of the urban heat island of Urmia in the land cover of cropland and natural herbaceous is well separated and show completely different annual cycles, but its annual variations is the same as in Tabriz. The use of natural herbaceous as a rural land cover in Urmia shows a more severe cold island in the warm period of the year than Tabriz.The urban heat island of Urmia at night shows obvious differences compared to Tabriz. First, the annual in the Urmia heat island cycles are well seen, which indicates the increase of the night heat island in the warm period of the year and its decrease in the cold period. The second major difference is the urban heat island values related to different rural land cover type. The heat island of Urmia, although in smaller numbers, often shows more intensity than Tabriz. It may be due to the smaller size of Urmia city compared to Tabriz and its shorter distance from the lake that cause to more affect by water extent variation of the Urmia Lake. Because of this condition, the daytime urban heat island in Urmia occurred more frequently. In addition, there is a significant difference between urban heat island of rural land covers in Urmia. While this difference is less in Tabriz and less in nighttime than daytime.

Calculation of the surface urban heat island with MODIS data showed that in some cases, especially during the daytime cold island occurs in some parts of the two cities of Tabriz and Urmia. The calculated heat or cold island was determined by selected type of land covers in rural area. In addition, the selected type of land cover in rural area has a great effect on estimating the intensity of the urban heat island. Cropland as a rural area during the night shows more intense heat island than natural herbaceous while during the daytime the opposite condition was happened. The use of all type land covers as rural area shows the intensity of the heat island between cropland and natural herbaceous as rural area.Due to the large effect of heterogeneous surfaces on the measurement of surface temperature during the daytime, measurements at nighttime can provide the intensity of the urban heat island with better accuracy. In this regard, nighttime observations of MODIS land surface temperature, especially in Aqua, which is the closest observation to minimum temperatures, can be useful in monitoring the intensity of the urban heat island and its temporal-spatial changes, especially in warm period of the year.