فصلنامه پژوهش های فرسایش محیطی
سال دهم شماره 2 (پیاپی 38، تابستان 1399)

The process of identifying landforms is a subject that has been researched by many researchers. All the definitions of geomorphology emphasize the study and identification of landforms. Understanding landforms and how they are distributed are some sort of essential requirements in applied geomorphology and other environmental sciences (Shayan et al., 2012). On the other hand, remote sensing is a powerful tool for studying different ecosystems of the earth to produce valuable temporal and spatial data (Rezaei Moghaddam and Saghafi, 2006). Arekhi (2014) used ETM+ digital data to map the land use of the Abbas plain. To classify images, artificial neural network, supporting vector machine and maximum likelihood were used. Based on the results, neural network classification method has the highest accuracy of land cover mapping. Also, De laet et al. (2007), by studying the development and stability of desert pebbles in Turkey, concluded that desert pebbles in this area are formed likely (in situ) by mechanical erosion of the surface fragments and minimal Tophonomic effects, in sediment with diameters greater than 2 cm. The purpose of this study is to evaluate the efficiency of Landsat imagery in identifying and classifying desert pavement comprehensively using satellite imagery classification strategies.

The studied area with an area of ​​47645/98 ha, in Semnan city is located in 53° 28¢ to 53° 53¢ 43° east and 35° 20¢ to 35° 40¢north. ETM+ multispectral satellite data were selected for this study because of spatial, temporal and especially radiometric resolution. The data of this sensor comprises seven spectral bands, obtained from the USGS site. In order to distinguish different types of desert pebbles in terms of cover density using EDRISI Selva and ENVI 4.5 software and ETM+ sensor images of Landsat satellite, 4 methods including supervised classification methods, maximum likelihood, Mahalani distance, minimum distance from mean and Parallel surfaces were used. Each classification method was compared for classification accuracy using overall accuracy coefficients, kappa, user accuracy and producer accuracy.
The error matrix table was also presented for each method. In this matrix, the degree of compatibility of each class with the ground reality is shown, in which the degree of overlap of one class in the other classes can be observed. The error matrix diameter and the percentage of correctly classified classes and other cells show the number of assigned errors (column of each class in the error matrix) and deleted row of each class in the error matrix) (Lillesand et al. 2004, Ahmadpour et al. 2011). Finally, the spatial mapping of each method was plotted in Arc GIS 10.2.

Supervised Maximum Likelihood (MXL) classification method According to the results, the mentioned method has less interference than the other classification methods and except for the class with 40-70% pavement density which has 22.41% interference with the class with 70-90% density, the rest of the applications have less than 8% interference. According to the results, desert pavement with 20-40% density had the highest percentage of 56.62%.  Minimum Distance to Average (MinDis) supervised classification method The overall kappa coefficient for the minimum distance from the mean is 75.54% and the overall accuracy is 81.61%. According to the results, this method has less interference in two classes of 20-40 and 70-90% of desert pavement density than the other two classes. According to the results, desert pavement with a density of 40-70% had the highest percentage of area with 41.88% (9935.79 ha) and the exposed rock with 537.7 ha (1.12%) compose the lowest area in the region.Mahalani Distance Monitoring Classification Method (MahD)In this method, there is a high degree of overlap between the classes, with the highest interference being between 40-70% and 70-90% of the desert pavement, which accounts for 25.86% of pixels for the 70-90 class. According to the zoning, the desert pavement with a density of 20-40% with an area percentage of 53.55 have the widest class in this method. Also, the lowest percentage of area with 13.39% (6376.43 ha) of the whole area is related to the rock outcrop.Supervised Classification Method of Parallel SurfacesThe overall kappa coefficient for this method was 21.06% and the overall accuracy was 41.25%. The estimated coefficients indicate the inefficiency of the model in separating the pavement classes. The lack of recognition and separation of the two rock outcrops and pavement classes with density of 20-40% has increased the area belonging to the two classes of pavement and the mismatch with real conditions and ground reality, so that, with pavement with 70-90% density and the area covers of 34/99,995 ha (73.52%) of the region.

Given that for optimal use of multispectral data, it is necessary to identify the best band composition. Choosing the best bonding combination is difficult and time consuming visual comparison. Therefore, a technique called Optimal Determination Factor (OIF) can be used to determine the most appropriate band composition to produce the best false color image and to determine the most appropriate bands for digital classification (Alavi Panah, 2000). The results of applying the optimum index in the present study showed that the best band composition for the detection and separation of the desert pavement in the south of Semnan is a combination of 6-4-3 with the optimum index value equal to 71.45 that are located in visible and infrared thermal band (VNIR +TIR). Climatic and geographical differences, satellite harvest time, physicochemical and biological properties of landforms and other effects in the area can produce different results. On the other hand, the classification methods used in this study differ in terms of the structure and complexity of the algorithm, which were calculated to evaluate the performance of the methods, kappa coefficients and overall accuracy. The classification results show that the coefficients of the classification results accuracy obtained from the used methods are considerable. Since the classes, bands, and other conditions used for all methods are the same, the difference in accuracy depends only on the computational algorithms of the methods. In all the investigated methods (except for the least distance from the mean), the class with desert pavement of 20-40% has the lowest kappa coefficients and accuracy, indicating the low ability of the investigated methods in spectral resolution of this class. In general, spectrally separable classes show pretty high accuracy in all methods, and vice versa. The use of other object-based classification methods as well as better spatial and spectral resolution sensors images and the incorporation of properties such as particle diameter can be effective in mapping desert pavement classes.

The forests of the Zagros vegetation area cover about six million hectares (40%) of Iranchr('39')s forest area, which is dominated by oak species. These forests are exposed to a number of threats, including land use change, deforestation, over grazing, deliberate and unintentional fires, pest and disease attacks, and climate change. Although accurate and timely identification of changes at the Earthchr('39')s surface is the basis for understanding the relationship between human functions and natural events and the use of natural resources, it is important to gather information about continuous changes in forest cover. Field operations require a lot of time and money. Therefore, the study of forest cover changes using satellite imagery has become one of the most important sub-branches in forestry science and is a tool for monitoring and controlling various changes in forest ecosystems. On the other hand, soil erosion in forest areas is lower than the other areas such as agricultural and rangeland areas, but extensive deforestation to produce wood and crops in recent years has significantly reduced sediments. Typically, the concentration of suspended sediments is estimated by the method of measuring the scale of sediment and discharge. Such curves are often generated using linear logarithmic regression of sedimentation and discharge data or conventional data curve in which the relationship between discharge and suspended sedimentation is a logarithmic linear equation. In the present study, land use changes in three forest watersheds in Kohgiluyeh and Boyer-Ahmad and Fars provinces were investigated using satellite imagery. Then, the effect of land use changes, especially good forest and rangelands on the values of sediment measurement equations in the study areas were analyzed.

The studied area with an area of 2400 square kilometers is part of the Zagros forests, which includes three forest watersheds of Seyedabad and Shah Bahram in Kohgiluyeh and Boyer-Ahmad provinces and Shivzohreh catchments in Fars province. At first, forest cover map of the last three decades (1987-2017) was prepared using Landsat satellite images. After initial pre-processing of Landsat images, the Maximum Likelihood (ML) supervised algorithm was used to prepare the land use map and 11 land use classes were separated. An error matrix was used to evaluate the classification accuracy of the land use classes, which includes criteria of producer accuracy, user accuracy, overall accuracy and Kappa index. Based on this, 532 field samples were prepared using a combination of field visits and Google Earth. After preparing the land use change maps in four time periods of 1987, 1997, 2007 and 2017, the annual discharge changes and the sediment rating curve were extracted in three ten-year periods using the data of the hydrometric station at the output of the catchments. For this purpose, rating curves were drawn in the three periods of the mentioned decade and the values of equation coefficients of the curve were compared during the mentioned periods.

The results showed that the total accuracy of the land use map of 1987, 1997, 2007 and 2017 was 87.34, 87.62, 87.07 and 90.4%, respectively, and their Kappa coefficient was 0.85, 0.86, 0.85 and 0.89, respectively. The forest land use had a good producer and user accuracy. Also, in Seyedabad area during the first two decades, soil erodibility (coefficient α) increased, but in the period 2007-2017 due to a significant increase in the area of mostly good forest lands (F1), has decreased. In the catchments of Shah Bahram and Shivzohreh, with decreasing forest lands of mainly weak (F3) and pastures of mainly good (R1), during three consecutive decades, soil erodibility has decreased. On the other hand, with the decrease in annual discharge, especially in the period of 2007-2017 in Seyedabad and Shah Bahram catchments, the value of coefficient b has decreased, but in the Shivzohreh catchment, despite the decrease in discharge in three consecutive decades, it seems that due to the hydrological changes of the river as a result of human activities and the availability of new sedimentation resources, the value of the coefficient b of the sediment rating curve has increased.

Since the rate of erosion in forest lands is lower than rangeland and agricultural lands, so in Seyedabad catchment, despite increasing the area of ​​medium rangeland lands (R2), with decreasing area of ​​good rangeland lands (R1) along with reducing forest lands during the first two decades of the year, soil erodibility (α coefficient) increased and therefore the value of α coefficient of sediment rating curve during the period 1997-2007 compared to the previous period 1987-1997 increased by about 0.05. However, in the period 2007-2017, despite a slight increase in the area of ​​rangeland lands, due to a significant increase in the area of ​​mostly good forest lands (F1), soil erodibility has decreased and therefore the value of the α coefficient of the sediment rating curve compared to the former period has decreased by 0.11. In the Shah Bahram catchment, over the last 30 years, the area of forest lands has steadily decreased by about 43 square kilometers every three decades, while the area of rangelands has increased by about 39 square kilometers. However, the α-coefficient of the sediment rating curve has been steadily decreasing during the studied periods, because on one hand the area of mostly weak forest lands (F3) has decreased over 10-year periods and on the other hand the area of mostly good rangelands (R1) has increased, so soil erodibility (α coefficient) has also decreased. In Shivzohreh catchment, although the area of forest lands has decreased by about 8 square kilometers in the last 30 years, these forest lands are mainly related to weak forest lands (F3). On the other hand, the area of rangeland lands has increased by about 39 square km, which is mainly related to good rangeland lands (R1), so such changes in the coverage of this area have reduced soil erodibility and therefore the value of the α-coefficient of the curve has been steadily declining over three decades.

Various measures and activities have been taken to reduce and prevent the direct and indirect effects of soil erosion but due to limited human and financial resources, it is often not possible to carry out these activities in a whole erosion-sensitive area; therefore, identification of areas requiring special attention to conservation is essential. Without prioritizing the watershed, many financial resources will be wasted, so mapping, monitoring, and prioritizing areas for erosion control will be required to avoid wasting funds. Therefore, prioritizing conservation areas at risk of soil erosion is an important consideration in natural resource management planning that allows decision makers to implement management strategies that are more sustainable in the long term. A review of the research background showed that almost no quantitative field measurements of soil erosion were performed using standard protocols in southern part of Bahmaei County. Also, no study has been conducted in Bahmaei County that has analyzed the spatial trends of erosion and its relationship with the effective factors. There is no information about erosion patterns and its dynamics in Bahmaei County in any study. Therefore, this study uses a relatively simple qualitative method by analyzing the interaction of three indicators of slope, vegetation and land use with the aim of helping to fill the existing gap for mapping and prioritizing lands in the southern part of Bahmaei County based on their sensitivity.

The study area is southern part of Bahmaei County in Kohgiluyeh and Boyerahmad province with an area of about 40306 hectares and an average elevation of 623 meters above sea level. The average rainfall in this region is about 400 mm. About 60% of Southern part of Bahmaei County is composed of erosion-sensitive formations of Gachsaran, Mishan and Aghajari consisting of clay, marl and gypsum, which is also in a very weak condition in terms of vegetation. However, developing soil erosion management strategies in this area is very limited due to lack of data.  The rate and amount of soil erosion is influenced by topographic factors, vegetation, rainfall and runoff, soil erodibility, and land cover (Wang et al., 2013). The intensity, duration and frequency of rainfall are important factors in soil loss that are affected by climate, while vegetation and slope are factors that determine soil resistance to erosion. In this study the risk of soil erosion was evaluated on the basis of the reaction between the slope angle, vegetation cover and land use and, according to the SL 190-2007 standard, classified and ranked in very low, moderate, severe, very severe and highly inflated classes.

Investigation of the parameters affecting soil erosion showed that slope class 0 to 5 ° had the highest percentage of area (40%) and slope class greater than 35 ° had the lowest area percentage (1%) in the study area. This is indicative of the low slope and lowland area and the area should have little soil erosion. In 2003, class 45 to 60% had the highest amount (92%) and class less than 30% had the lowest amount (0.0007%) of vegetation, and in 2017, class 30 to 45% had the highest amount (81.6%) and class more than 75% have the lowest amount (0.065%) of vegetation. This indicates a decrease in vegetation over time in the study area. Land use maps prepared from the study area show that the percentage of rangeland, farmland, garden and residential land uses in 2003 were 84.7%, 12.6%, 0.8% and 1.8%, respectively, and in 2017 it was reached 44.6, 49.5, 3.2 and 2.5%. In other words, in the last 14 years (2003 to 2017), about 16143.5 hectares of rangelands area has been reduced, 14863.9 hectares have been added to farmland, 972.5 hectares to gardens and 307.1 hectares to residential use. In 2003, rangeland use had the highest percentage of area (84.7%) and garden use the lowest percentage of area (0.8%) and in 2017, farmland use had the highest percentage of area (49.5%) and residential use had the lowest percentage of area (2.5%) in the study area, which indicates a significant increase in agricultural growth, including agriculture and horticulture in the study area in recent years. The results showed that in 2003, the very low erosion risk class had the highest percentage of area (41%) and the risk of extreme erosion, the lowest percentage of area (12.2%), and in 2017, the very low erosion risk class has the highest percentage of area (40%) and the risk of extreme erosion class, the lowest percentage of area (29.2%) in the study area. In the study of changes in the erosion zones in the study period in the study area, it was determined that the very low and low erosion class from 2003 to 2017 has been decreasing trend and the rest of the classes have been increasing trend, which indicates an increase in the amount of erosion in the study area.

In a separate analysis of the studied parameters, the results showed that about 80% of the study area has a slope of less than 30%, in this regard, the area should have little soil erosion. The study of vegetation in the study period showed that in 2003, about 8% of the study area has more than 60% vegetation, while in 2017 this amount of vegetation is only about 0.2% of the area. The study concluded that this reduction in vegetation over time could increase the risk of soil erosion. In the study of land use changes, it was found that agricultural and residential use has increased by 37%, and in this regard, it seems that land use changes can also lead to increased soil erosion in the region. Based on the analysis of the interaction of the studied parameters in the preparation of the erosion risk map, it was found that the amount of moderate to extremely severe erosion risk zones has increased by about 20% in the study period in the study area. In soil erosion studies, the risk of erosion usually determines the relative probability of erosion occurring in one site compared to other sites by qualitatively analyzing the interaction of impact factors, Therefore, the results of this study can be a basis for government organizations in order to prioritize the implementation of soil protection activities, allocation of funds and land management.

Dust is a term used in meteorology to refer to very small, solid, light particles of silt, clay, or sand created by wind erosion and desertification. It is transported over long distances. Dust is more common in arid and semi-arid lands, which is related to the climatic characteristics of these areas and has become one of the main problems in these arid areas. Studying the effects and consequences of this phenomenon, as well as proper management to reduce the effects of dust and focusing on areas which are the origin of the storm can be important in identifying areas as the source of dust. In other words, proper identification of dust sources is of great importance due to its impact on the environment. Soil erosion is the most important cause of dust, and the most important parameters for soil erosion include vegetation, soil moisture, surface roughness, and land use.

To identify the potential of dust generation in areas of Hamadan province and the effected ranges, a multi-criteria evaluation method was applied. Land use information, vegetation, roughness, and soil moisture were extracted from the Landsat 8 (OLI) and Landsat 5 (TM) images for three periods of 2001, 2009 and 2018. Images were corrected radiometrically. ENVI 5.3 software was used to correct the radiometric and atmospheric images. All of the layers were standardized to a range of 0-255 as a fuzzy method. to standardize the land use layer, the user-defined function was used and fallow and uncultivated lands were given a value of 255 while, 150 to low-density pastures, 95 to fallow grounds, 75 to dryland farming, 25 to built-up areas, 6 to Mountain and rocky outcrop, 3 to gardens, 2 to irrigated agriculture and zero value to snow and water cover. The NDVI index was used to extract the vegetation map and standardized using the Linear Symmetric function. the soil moisture index was obtained by the NDMI index, and Linear Monotonically decreasing function was applied to standardization this layer. The surface roughness coefficient was extracted from ASTER imagery and a Linear Monotonically decreasing was employed to standardize this layer. The WLC approach was applied to integrate the above criteria. According to expert choice, the humidity score is 5 compared to vegetation cover, humidity to roughness is 7, moisture to land use is 9, vegetation to roughness is 3, vegetation to land use is 5 and roughness to land use is 3. Then the fuzzy maps of the area were merged so that higher values ​​and closer to 255 meant more desirability as dust sources and lower values ​​meant less desirability. Finally, to select the potential hotspots, using the dust output desirability image, the threshold levels were adjusted based on the threshold.

The results showed that the incompatibility rate from the comparisons made between the factors was estimated to be 0.66, which is acceptable. The final map of possible dust sources was prepared based on utility levels using threshold values ​​for 2001, 2009 and 2018. By contractually dividing the suitability map into 5 classes based on the threshold, areas with the highest potential (255-225), high potential (225-200), medium potential (150-200), low potential (150-100) and lowest potential (100-0) were identified and the 5th category (255-225) was selected as the most likely sources. The soil erosion map was then overlaid with the dust hotspot map and the result showed that of the total 227,483 hectares of land identified as dust hotspot, 121023 hectares are located in lands with catastrophic erosion, 56956 hectares in lands with extraordinary erosion, 17718 hectares in lands with Extreme erosion and 24272 hectares in lands with high erosion, respectively. In other words, more than 96 percent of the identified hotspots are situated in areas with moderate erosion and higher.

The results of this study showed that four-defined factors including soil moisture, vegetation, land surface roughness, and land use type can have a high ability to identify potential dust sources. Overlaying identified areas with erosion maps, which show an overlap of about 97 percent between these sources and areas with above-average erosion, confirms this fact that the main internal sources of dust are due to soil degradation on lands. Accordingly, the lack of proper management can change these potentially identified areas to actual dust production centers. Therefore, it is necessary to plan protection programs to prevent dust storms such as planting vegetation around the borders of the province or county and to prevent pasture overgrazing.

Active tectonics is defined as neotectonic movements that are likely to occur in the future and threaten human societies (Burbank et al, 2001). Active tectonic studies are important topics in the earth sciences and their results are widely used to assess natural hazards and land use development and management programs in densely populated areas (Pedrera et al, 2009).  Hisami et al (2006) estimates shortening of the northwestern Zagros to a maximum of 5 mm per year, and Mirzaei (1997) estimates that more than 50% of the recorded earthquakes in Iran occur in the Zagros Zone and is the most seismic-prone area in Iran. Shabani (2004) has identified the Kandand fault as an earthquake source in Kermanshah province. The west of the Kerend Basin is in the folded Zagros Zone and the Kereend seismic fault is located in this basin. Therefore, it seems that tectonics of the region is active and considering the location of the city of West Kereend and many villages and human settlements in the basin, evaluation and estimation of its active tectonics are necessary. The purpose of this study was to evaluate and estimate the active tectonics of the Kereend West basin using drainage network analysis.

The data of this research include (30 meters) ASTER DEM, geological map 1: 100000, topographic maps 1: 50000. Then, during field visits, the geomorphological features of the West Kerend basin were examined. Then, using the DEM of the area, the area of West Kerend basin and its drainage network were extracted and the waterways were ranked according to the Straler method. Then the geometric features, drainage network and topography of the West Kereend basin were calculated. Then, linear morphometric, shape and uneven morphometric parameters (Table 1) and geomorphic indices for this basin were calculated. The values of morphometric and geomorphic parameters are classified according to Table (2) and have scores of 1, 2 and 3, which indicate low, medium and high tectonic activity, respectively. The classification of the amount of technical activity in the West Kereend Basin is based on the CR index. This index is the sum of the scores of morphometric and geomorphic parameters used (Table 3) and its high values indicate the most active tectonic conditions (Shukla et al, 2014).

The results of two linear morphometric parameters indicate the location of the West Kereend basin at the end of the youth stage of the erosion cycle and the anomaly of the drainage network and the low impact of these indicators on lithological conditions. The results of the shape parameter indicate the high roughness and elongation of the shape of the West Kereend basin due to tectonic uplift of the anticlines of the studied basin. Based on the values obtained from three morphometric parameters, the roughness of the West Kereend basin has moderate tectonic activity. The results of geomorphic indices also indicate the tectonic activity of the West Kereend basin. The value of index (Af) in the West Kereend basin indicates active tectonics and increase on the left side of the West Kereend River, which is due to the uplift of the Kereend Anticline due to the shortening of the Zagros and the Kereend drift movement on its southern edge. The index (T) of West Kereend basin also indicates the topographic asymmetry of this basin and indicates active tectonic intervention and the elevation of the left bank of the river. Based on the results of CR index, West Kereend basin is in the class with high tectonic activity.

The results of linear morphometric parameters indicate the location of the western Kereend basin at the end of the youth stage of the erosion cycle. The values of the shape parameter indicate the elongation of the basin and the elevation of the anticlines of the studied basin and the results of the morphometric parameters of the roughness also indicate the relative roughness of the West Kereend basin. The results of geomorphic indices also indicate the asymmetry of the basin and the existence of active formation on the left side of the river in the West Kereend basin. The results of CR parameter show that West Kereend basin is in the class with high tectonic activity. In general, the active tectonic status of the West Kereend basin includes active tectonics and the general uplift of the basin due to the shortening of the Zagros zone due to the Arabian plate pressure and the asymmetry of the basin and the uplift of the left bank of the river due to the westbound drift activity. This is consistent with the results of studies by Blank et al. (2003), Bachmanov (2003), and Hesami et al. (2006), who believe in the uplift of the northwestern Zagros. The tectonic activity of the Kereend West basin as well as the entire northwest Zagros range can cause active tectonic hazards such as earthquakes.

Iran is one of the countries that is witnessing an increase in dust events and wind erosion recently. Although wind erosion is a global phenomenon, its severity depends on the environmental circumstances (Chorley et al., 2000). In the arid and semi-arid regions, wind is the main factor in desertification process (Yan, 2004). Coastal zones are dynamic parts of the Earth. They and arid lands are the most favorable areas for wind processes (Mahmoodi, 2002). Experiments have shown that in dry areas, wind velocities of up to 4.5 meters per second can be considered as an erosive factor. From this point on, changes in wind erosion rates will be followed by changes in velocities (Mahmoudi, 2002). Of course, the transport of sediment by wind is the result of the interaction between the wind and the earthchr('39')s surface, which depends on the size of the sediment grains (Bagnold, 1941). Highly fine grains (<60 – 70 µm) are transported in suspension, or they are transported over long distances via the turbulent airflow (Lancaster, 1995). In addition to the characteristics of surface soil, the sources of soil moisture supply from the atmosphere are the main factors affecting wind erosion (Shayan et al., 2014). There is also an undeniable relationship between dust emission and geomorphological features and land cover. Studies in various parts of the world, including West Texas in the United States (Leea et al., 2012), Hamoon in Iran (Dahmardeh et al., 2019) confirm this. In arid regions, dust events are one of the most important threats to the human environment (Kermani et al., 2016). According to the studies by the Iranian National Plan for the Identification of Crisis Centers of Wind Erosion, over 20 million hectares of Iran are in a critical wind erosion area. About 13 million hectares are in the origin zone, about 2 million hectares are transport or transit areas and the remaining 5 million hectares are sedimentation zones or ergs (Desert Affairs Office, 2002). The western part of Mackoran coastal plains exposed to wind erosion, as a part of the wind erosion critical area, due to its topographic and soil characteristics (Asadpour & Akbarian, 2009; Akbarian et al.,2008). The present study attempted to identify the role of geomorphological factors that affected the dust events in the western part of Makran coastal plain as a dry coastal region. The study area is located in the western coastal plain of Mackoran, southern Iran at 25°31´N - 27°09´N, 56°54´ - 59°19´E, which is north of the Gulf of Oman and east of the Strait of Hormuz. Generally, the region consists of dry land with very little rain, windy with sandstorms, torrents, thunderstorms, high humidity, and fog. Geologically, the area is affected by the general structure of the Makoran Mountains, and is mainly composed of shale, marl, and sandstone layers. The Mackoran Plain is divided into six parts by five relatively high hills from the remnants of the Mackoran Foothills, which reach the coast in the form of capes. Based on the position of the first cape in Bunji (west of Kuh Mobarak), the region of the research is divided into two main sections that have completely different orientations (Figure 1). The west of Kuh Mobarak includes the shores of the Strait of Hormuz and has a north-south trend; The eastern part also includes the coasts of Jask county with the western-eastern trend.

The data collected and processed in this study include Modis satellite images, the wind velocities, the percentage of relative humidity and precipitation in the period from 1/1/1993 to 12/31/2018, as well as the granulometric information about the plain surface sediments, including wind depositions, bed of dry rivers and coastal plain landforms. The research tools included topographic and geomorphological maps of the area, GPS, laboratory instruments of granulometry, and computer software such as Envi. After referring to the research records, 31 samples were taken. The samples were granulometric and the diameter characteristics of the particles were determined. From the relationship between particles’ diameter and threshold velocity, wind erosion threshold velocity was determined and with experimental formulas, threshold velocity was obtained in humid air conditions. Also, the probability of the occurrence of wind erosion in different months during the selected time period was calculated and analyzed. Finally, by processing the optical depth of terra satellite images from the MODIS sensor, the possible dust days were investigated and compared with a map of geomorphology in two parts of the research area. 

The study of the geomorphological map indicates the existence of two different trends north-south and west-east in the stretch of plains and wind-sensitive landforms, respectively in the eastern part of the Strait of Hormuz and the coast of the Sea of Oman, east of Hormozgan province. The diameter of sediment particles in the research area varies between 94 and 375 micrometers. Most of the lowland landscapes’ sediments have a diameter of 187.57 μm, so according to the Zakhar table (Table 1), the threshold of wind erosion at a rate of 7.21 ms-1 will be result in dry conditions. The wind erosion threshold in terms of humidity changes, varies between 7.21 and 12.31 meters per second during the research period. The months of February, March, and April respectively have the highest probability of erosion; October, November, and September respectively have the lowest probability of wind erosion. Image processing shows different concentrations of dust in the western and southern parts of the region. On dusty days, the concentration of dust in the western plain is far more than of the southern plain.

According to the results, it seems that the spatial difference in dust concentration in the western plain of Mackoran is affected by the geomorphological arrangement of landforms against the prevailing wind and their erodibility to wind is not a determining factor. In the western plains, where sensitive zones extend perpendicular to the wind direction, the wind is able to remove large volumes of suspended particles without being saturated. This process is not applicable to the southern plains, where sensitive forms have a narrow width and long elongation on wind direction.