جستجوی مقالات مرتبط با کلیدواژه « لندفرم » در نشریات گروه « جغرافیا »

Many arid and semi-arid regions are also exposed to the risk of dust storms due to their fragile environmental stability. This environmental hazard has caused displacement of surface soil, damage to agricultural lands and vegetation and has affected the lives of people, especially vulnerable groups, close to critical centers and distant areas. The purpose of this research is to evaluate and analyze the vulnerability of social, economic and environmental criteria caused by dust storms in Kerman province. In this regard, social and economic criteria, including population density, gender structure, rural-to-urban population ratio, and rainfed agricultural lands have been considered. Also, the erodibility of geomorphological landforms, poor pastures and bare lands has been identified as an environmental criterion. The results of horizontal visibility (from weather stations) dispersion analysis show that there is a minimum average horizontal visibility in the center of the province. The amount of exposure to dust in Kerman province is between 0.73 and 8.7 meters, so the cities of Baft, Bardsir and Raber are the most exposed to this phenomenon. The results of ranking the Cocoso model showed that the cities of Kerman, Bam, Erzuye and Qalaganj are the most sensitive to dust. The results of the vulnerability of the dust phenomenon in Kerman province showed that all the cities of this province are not the same vulnerability to the dust phenomenon. The vulnerability of the dust phenomenon in Kerman province was obtained in 5 categories from very low to very high. 86% of Kerman province area is very high and high vulnerable to dust phenomenon. The results of this research can provide a basis for the development of dust storm reduction studies in order to reduce the vulnerability caused by dust storms in Kerman province for managers and decision makers.

Rising greenhouse gases due to human and natural activity have caused climate change and global warming. Consequently, these changes have exacerbated natural events such as dust activity, droughts, floods, and other natural phenomena. Climate change has a remarkable impact on the earth's hydrological cycle, surface and groundwater quality, and vegetation, and changes in these parameters play a vital role in the spread of dust storms. Many scientists have conducted research related to spatial and temporal distribution, the number of dusty phenomena with emphasis on climatic conditions, transmission routes, chemical components, and numerical simulation. Power, water, road, and other important infrastructure failures might occur as a result of sand and dust storms which can interrupt the provision of vital and critical services for the community. All these impacts can affect the sustainability and resilience of infrastructure and small and big businesses. Spatial and seasonal variations of sand-dust events and their relation to atmospheric conditions and vegetation cover in semi-arid regions of central Iran by using the Ridge Regression (RR) method and seasonal variations of precipitation, surface winds speed, air temperature, and Enhanced Vegetation Index (EVI) with Dust Storm Index (DSI) for two different periods (2001–2008 and 2009–2016) showed that the activity of sand-dust storms in the second period was greater than the first period, especially in the border region of Iran and Turkmenistan. Although many studies have been conducted on the phenomenon of dust and its relationship with vegetation, wind speed, and the origin of fine dust in some parts of the study area, no research has been done on the estimation of the vulnerability of dust storms in Kerman province. Therefore, in the current research, the amount of exposure caused by dust storms in Kerman province will be estimated by using horizontal visibility data. Then we will assess and analyze the sensitivity and vulnerability of the social, economic, and environmental criteria of Kerman province to dust storms in recent years.

The data has been used in this research such as 1. the minimum, maximum, and average horizontal visibility data (m) of meteorological stations from 2009 to 2018. 2. Precipitation in 10 years (2009-2018). 3. Vegetation 1:100000. 4. Geomorphology 1:100000. 5. Geology 1:100000. 6. Digital Elevation Model (DEM) 12.5 m PALSAR radar sensor. To prioritize each of the social, economic, and environmental criteria, we will use the Cocoso model. Finally, using the weighted linear combination method, we will estimate the vulnerability caused by dust storms. The method of estimating dust storm vulnerability in Kerman province is described below.
The combined compromise solution (CoCoSo) is a multi-criteria decision-making method that was initially proposed by Yazdani et al (2018). This method is used to rank the sensitivity of the cities of Kerman province based on overlapping criteria. This method is an integration of simple additive weighting (SAW) and weighted product models (WPM). There are 5 steps to solve a CoCoSo decision problem. The first step is to form a decision matrix, the second step is to normalize the decision matrix, the third step is to calculate SAW and WPM, the fourth step: compute aggregation strategies, and the fifth step: determine the final score and rank the options.
Vulnerability to SDS is defined as a function of exposure, sensitivity, and adaptive capacity components. Exposure refers to the nature and degree to which elements of a system are at risk of a natural or human-induced hazard; Sensitivity is another concept related to vulnerability, defined as the degree to which a system is modified or affected by hazard stimuli; and while exposure and sensitivity determine the scale and nature of likely impacts caused by hazards/perturbations, the adaptive capacity of system quantifies its ability to cope with, manage, recover from, and adapt to the potential adverse impacts of hazardous phenomenon.

The amount of exposure to dust storms in the center of Kerman province is very high and it decreases with the distance from the center of Kerman province and reaches 7.8 meters. So, in the cities of Baft, Bardsir and Raber, they have the least exposure to dust storms. The sensitivity to dust storms is generally calculated from the integration of social, economic, and environmental criteria. Social and economic criteria include residential areas (rural and urban), population density, gender ratio (male-to-female ratio), rural-to-urban population ratio, and rainfed lands. Also, the environmental criteria that are sensitive to dust storms include bare lands, poor pastures, and erodible lands. The results of the vulnerability map in Kerman province show that there are 5 levels of vulnerability in the study area, including very low, low, medium, high, and very high. The area is very large and large, with an area of 8636470 and 6545769 hectares, respectively, in the cities of Raver, Kerman, Bam, Narmashir, Jiroft, Baft, Sirjan, Shahrbabak, Rafsanjan, Bardsir, Raber, Zarand and Kohbanan. The cities of Anar, Anbarabad, Faryab, South Rudbar, and Manojan are located in the zone with high vulnerability. In general, about 95% of Kerman province is in the medium, high, and very high vulnerability category.

Nowadays, due to the effects of dust storms on human societies, many researches have been conducted on dust storms in different parts of the world. The research carried out by (Pouyan et al., 2018) showed that the cities of Regan, Fahraj, Bam, South Kerman, and Qalaganj were identified as dust sources and Menasha fine particles with high and very high class. On the other hand, the wind causes the movement of dust and leaves its effects on the neighboring cities. Therefore, it is in line with the present research. Therefore, there is a need for more planning and studies of decision-makers in the field of reducing the effects of dust in Kerman province.

Soil salinity in recent decades due to increasing population in the world and especially in developing countries, has been considered as an environmental hazard that is a very serious threat to the lives of residents of areas prone to desertification, especially in arid and semi-arid regions. This issue has intensified the process of desertification in these areas due to the decrease of precipitation and the gradual increase of temperature. However, soil salinity, like other environmental hazards, has no immediate effects (Metternich and Zink, 2013); but because of its consequences in human life, it is highly studied in today's world and is considered as a serious risk (Yu et al., 2010). At the same time, salinity is considered as one of the 7 factors of desertification and is 80% of the cause of desertification (Karam et al., 2019). Since the expansion of soil salinity and the destruction of fields and rangelands in the north of Isfahan province is one of the environmental hazards in this geographical area, the present study aims to identify and investigate this unfortunate phenomenon in this area using It deals with satellite numerical data as one of the important factors of land degradation. It may provide a platform for planning de-desertification and soil management in the region to achieve sustainable development and improve environmental conditions.

Soil salinity is one of the most important processes that limit plant growth and causes the spread of desertification and land degradation in arid and semi-arid regions. Distribution of soil salinity in different landforms and its changes over time and prediction of salinity patterns have a significant contribution in environmental management in arid and semi-arid and desert areas. The purpose of this study is to investigate the changes of soil salinity phenomena in desert landforms in the north of Isfahan province using Landsat satellite images, TM5 and OLI-TIRS sensors in the period 1987- 2020. For this purpose, 200 images were extracted in the 27 warm months of the year from June 1 to the end of August during this period. After the necessary preprocessing and mosaic and the composition of the band of the 4 images covering the image covering the study area, the salinity indices of NDSI, BI, SI-1 and SI-2 were evaluated and the relevant maps were drawn. The topographic position index and classification of landforms based on TPI were also examined.

The results showed that the trend of changes in salinity index increases from mountainous areas to low lands. The status of salinity indices in landforms extracted from TPI showed that the unit of mountains and heights have the lowest and the unit of flat plains (playa, mud and clay pan) have the highest values. The difference between these two landform units in all indicators is about 0.2. Investigation of salinity indices in the elevation tints shows that these indices decrease severely with elevation, up to the threshold of 1400 m, then the rate of the change decreases. Salinity indices decrease more with increasing slope; up to a slope of 15°, then the rate of change get gentler up to a slope of 65°. Salinity indices showed three temporal phases of change that the third phase has continued from 2008 - 2020 (12 years) with an increasing trend. The predicted maps (for the year 2030) illustrated that the southern region of Daq-E-Sorkh, including the pediments between Kashan and Ardestan, is in a critical situation, which requires the attention of environmental and natural resources managers and engineers to this issue.

The results showed that the trend of changes in salinity index increases from mountainous areas to low lands. The status of salinity indices in landforms extracted from TPI showed that the unit of mountains and heights have the lowest and the unit of flat plains (playa, mud and clay pan) have the highest values. The difference between these two landform units in all indicators is about 0.2. Investigation of salinity indices in the elevation tints shows that these indices decrease severely with elevation, up to the threshold of 1400 m, then the rate of the change decreases. Salinity indices decrease more with increasing slope; up to a slope of 15°, then the rate of change get gentler up to a slope of 65°. Salinity indices showed four temporal phases of change that the fourth phase has continued from 2014 - 2020 (6 years) with an increasing trend. The predicted maps (for the year 2030) illustrated that the southern region of Daq-E-Sorkh, including the pediments between Kashan and Ardestan, is in a critical situation, which requires the attention of environmental and natural resources managers and engineers to this issue.

Human beings have faced one of the most important problems in recent years: the water crisis and the occurrence of drought. Due to this, it is important to study the drought situation when managing water resources. In any climate, drought is caused by a lack of rainfall. However, unfortunately, defining drought and how it relates to hydrological phenomena is very difficult. First of all, drought may not affect all components of the hydrological system simultaneously. The second point is that drought does not refer to an absolute lack of moisture, but to a relative one. As a result of climate change and reduced rainfall and increased evapotranspiration in recent years, drought has become a major problem in the world, in general in arid and semi-arid regions such as Iran. Therefore, drought monitoring and management are essential. Most traditional methods rely on observations from meteorological stations and emphasize droughts. However, researchers and experts have considered the use of remote sensing techniques and satellite images as a useful tool for spatial and temporal monitoring of agricultural drought. A variety of meteorological and remote sensing indicators influence the study of droughts. Standard Index-Evapotranspiration Index (SPEI) and RDI Drought Index  are two such methods.Due to the importance of the topic of this study, the study examined drought status in the study area using remote sensing indicators (EVI, NDVI), drought prediction using Markov and Kumarkov chains, and the connection between them and drought conditions.

EVI index Hewitt and Liu introduced the EVI in 1994. As defined below, improved vegetation indices minimize atmospheric effects and differences in blue and red reflections. NIR, RED, Blue are amount of reflections in the blue, red and infrared bands.   L is the aerosol penetration coefficient and soil separation parameter which was considered equal to 1 in this equation. The coefficients C1, C2, G were considered equal to 6, 7.5, 2.5, respectively.NDVI index Normalized difference vegetation index (NDVI) is an indirect measure of photosynthetic activity. The range of this index is -1 for the minimum and +1 for the maximum amount of photosynthetic activity. NDVI is defined as follows (Tucker et al., 2010):  (2)Pnear and Pred are the reflectance values of the near infrared and red wavelengths, respectively, for pixel i during the months j and year k, respectively.

According to the results, southern regions have lower values for all vegetation indices, which indicates a lack of vegetation in these regions and the existence of drought. There is no drought in the northern areas and parts of the east of the region. Also, the results show that the region often falls into the middle class of drought based on EVI and NDVI indices. The results indicate that in 2000, 96% and 78% of the region were in the middle classes of drought, and in 2020, 98% and 93% of the region were in the middle classes of drought.
The results for the EVI index showed that 47% class 1 to class 3, 20% class 2 to class 4, 12% class 4 to class 5, 29% class 5 to class 6, 27% class 6 to class 7, 21% class 7 to Class 8, 14% to Class 8 to Class 9, and about 8% to Class 9 to Class 10, indicating an increase in drought in the area. NDVI index 49% Class 1 to Class 7, 32% Class 4 to Class 5, 19, Class 5 to Class 6, 14, Class 6 to Class 7, 11% Class 7 to Class 8 and 27% Class 8 to Class 9 has done.

Then, in order to extract the landform map of the study area, the topographic position index (TPI) was used. The results showed that high areas such as ridges and hills, near zero codes indicate flat areas or areas with low slope changes and negative codes indicate low areas such as valleys and waterways. The results showed that the highest landform in the middle drainage and plain area (about 14) percent and the lowest area is related to narrow valleys (about 1 percent). The results showed that the NDVI and EVI index in low altitude areas is lower (dry condition) and the index in high altitude areas is the highest (wet condition). Thus, the topographic situation can be used to predict the drought situation.

Alluvial fans can be known as non-equilibrium landform resulting from the spatial-temporal variability erosional and depositional processes, weathering processes, and drainage patterns on them. Formation and evolution of the alluvial fans is effectively affected by climate changes. These landforms play an important role in the exchange of carbon between the atmosphere and pedosphere. The aim of this study was to evaluate how the evolution of alluvial fan ages can affect soil carbon dynamics. The present study measured factors controlling the C mineralization ratio and carbon dioxide flux within the soil environments belonging to different alluvial fan ages (including young fan, intermediate fan, and old fan) located at the base of the Aladagh Mountain hillslope. The intermediate alluvial fans exhibited more stable environment compared with other fans. Furthermore, the position of the intermediate fans on lowlands of the old fans increased the organic carbon concentration, exchangable cattions, and fine fraction. On the other hand, the lower transport capacity of the erosion processes decreased lower micro-aggregate losses compared with other fans. As a result, the organic carbon contained in the micro-aggregates is inaccessible to microbial decomposition over long-term timescale. The old fans exhibited a significant decrease of clay fraction, which could be due to their positions on uplands and their susceptibility to gully erosion processes, causing the transportation of a larger amount of OC-rich topsoil. This condition degrades soil structure and weakens aggregate stability within the old fans, encouraging more vulnerability of organic carbon to microbial decomposition. The geomorphological differences of the alluvial fan surfaces in different ages stimulated the variation of soil carbon dynamics. This conceptual approach of combining our field survey data with an empirical model aids in a better understanding of the biochemical mechanisms controlling the C dynamics under the evolution of geomorphic landforms.

Qeshm Island is located on the southern coast of Iran in the Persian Gulf and the Strait of Hormuz, whose shores, like other coastal environments, are affected by processes and morphological changes due to sea hydrodynamics and geohydrology of the island's land environment. Mangrove forests on the north coast of Qeshm Island are a gentle environment for sedimentation of Qeshm Island runoff and sedimentation of rivers that enter this environment from the mainland. Also, along the coastline of the Strait of Hormuz and the Persian Gulf, there is the highest tidal range. Under these conditions, monitoring and digitization of temporal-spatial changes of Qeshm Island coastline can be important in sustainable coastal development and coastal strip integrity management, and by detecting and predicting it, a comprehensive plan for changes can be designed depicting developed morphodynamics, and sea retreat and forward patterns. Therefore, the present study, focusing on the coastal environment development approach, tries to classify coastal landforms and monitor the spatial changes of the coastline of Qeshm Island, which is analyzed through the Shepard classification method and the coastline system analysis tool (DSAS).

In this study, in order to classify landforms and analyze the shoreline changes of Qeshm Island based on integrated shoreline management, first by using library resources, analytical tools and field visits and by applying Shepard method, the coastal areas of Qeshm Island were classified into different zones. The data and tools used included topographic maps, geology and land use of the island, digital data and elevation model, and LANDSAT satellite imagery of the ETM + and OLI sensor series. For spatial analysis and drawing maps, ArcGIS software was used. After increasing the recovery and contrast power of water and land in ENVI software, the satellite images were transferred to Arc map software and from the images, Qeshm Island coastline polyline related to each image were drawn and the shoreline was prepared as the required layer. In this way, shorelines were prepared to monitor spatio-temporal changes with the DSAS tool. In order to statistically analyze and quantify the trend of backward and forward (moving) shorelines, statistical methods including endpoint (EPR), linear regression (LRR), weighted linear regression (WLR) and final confidence interval (NSM) embedded in DSAS tools were used in this study.

By studying the geological and topographic maps (Landsat 8 (OLI)) of the study area which was used to classify the coastline of Qeshm Island, five different morphological units including slopes, marine sediments, residential areas (man-made), terraces and the high hills in the area were identified and classified. The linear regression index (LRR) was obtained by fitting the least squares of the regression line to all points at a 95% confidence level in a particular transect, in which a positive value indicates coastal sedimentation and a negative value indicates coastal erosion. In the morphological units of the primary coasts of Qeshm Island, the highest advancement of the coastline has been in the high hills, which often ends in the hills of Basaeido port. It has flowed towards the shore and has caused sedimentation in the coastline, and its average has increased by about 11.3 meters per year during 31 years. Of course, in the coastline of Namakdan Cave Mountain, due to the lack of runoff and also the direction of the prevailing south winds, the waves have caused the coast to regress and erode; this area is located in the morphological unit of high hills, which is at least -6.3 meters. Therefore, among the morphological units of the high coastal hills, in Laseido, the LRR index is positive and indicates the advancement of the coast and sedimentation, but in the salt cave, the LRR index is negative and indicates the regression and erosion of the coast. The lowest LRR index among the primary beaches is in the morphological unit of coastal terraces (5.4 m per year) with a maximum sedimentation rate of 34 m/year in Goran in the west of the mangrove forests and a minimum of 27 meters per year in southern parts of the the island as well as the port of Laft. On Qeshm Island, the shores of mangrove forests, Dolab port, Dastko, Direstan and eastern man-made shores are considered as parts of the primary beaches. Among these morphological units, the highest rate of shoreline movement per year is LRR and EPR occurring on the shoreline of the mangrove forests, which are positive indicators and show that due to the massive sedimentation in this forest, the coast is progressing and on average annually between 21.7 up to 23.1 meters on the beach is added. The maximum of this positive displacement is about 76.6 meters per year and the minimum is 3.3 meters per year.

The surface runoff of Qeshm Island, which originates from the hills and terraces and reaches the shore, loses its initial energy by decreasing the slope and, in contact with the seawater, causes the deposition of its sedimentary load. Accumulation of these sediments has caused the shore (land) to advance towards the sea. In the mangrove forests of the north of Qeshm Island, the northeastern runoff of the island as well as marine sediments from the rivers of the mainland of Iran, especially the sediments of the Mehran River, have caused the shoreline to advance. Also, in Dolab, Basaido and Direstan ports, sediments from runoffs have caused the coastline of Qeshm Island to advance towards the sea. But on the southern coast of Qeshm Island, due to waves and winds from the west and southwest, we see regressive erosion of the coastline and the coastline recedes to the mainland by at least 27 meters annually. The results of the present study can be used in Qeshm Island coastal strip integrity management plans and experts must be notified that strong waves cause erosion of the south coast of Qeshm Island, but sedimentation of the resulting runoffs can advance the coastline in the north of Qeshm Island.

Identification of landforms and the way of their distribution is one of the basic needs in applied geomorphology and other environmental sciences and landform maps show the shapes of the earth's surface. This project aims to identify the landforms of the Qaranqu catchment area using object-oriented classification methods including nearest neighbor algorithm and thresholding. Landsat satellite imagery for 1990 (TM) and 2020 (OLI) was used for this purpose. First, to apply the classification, atmospheric and radiometric corrections were applied to the images, then to better identify and extract the phenomena, principal component analysis (PCA), and MNF algorithms were used to classify satellite images using classification methods. Object-oriented, which included the nearest neighbor method and thresholding was used. For the accuracy of the maps produced using the two methods of Kappa index and the overall accuracy of the use, the results revealed that the nearest neighbor method is more accurate than the thresholding method. The classification results showed that the highest rate of decreasing changes during 1990-2020 is related to dense rangeland because it has decreased by 12.49 percent and the highest rate of incremental changes is related to irrigate agriculture which is 10.83 percent. The most important reason for this increase is the construction of Sahand Dam over time. In the absence of well-organized planning and the adoption of appropriate policies, the destruction of rangeland will continue and turning it into arable land, which leads to irreparable environmental and economic losses in the region.

In geomorphological studies, it is important to prepare landforms for the study of forms in different regions. In the same vein, with more accurate input data, landform maps are prepared with higher accuracy. Therefore, by using digital elevation model maps with more resolution, more accurate landforms can be extracted (Shayan et al., 2005). Identifying landforms, classifying them, and identifying different geomorphic forms are important in examining the relationships between form and process in the area. By extracting landforms, various information such as climatic characteristics, soil type, and hydrology can be estimated in a watershed. Due to the significance of the issue, it is important to use a digital elevation model with more resolution to prepare landforms with more accuracy. There are several methods to increase the spatial resolution of the digital elevation model. Obtaining more detail from pixels was first proposed by the Gravity Model by Atkinson (1977). In this technique, the pixels are divided into several sub-pixels according to the values ​​of the neighboring pixels. In the gravity method, a large pixel is subdivided into sub-pixels, and a ground cover class is assigned to each sub-pixel. There is a limitation that the total number of sub-pixels of each class is directly proportional to the percentage of canopy coverage of the larger original pixel (Atkinson et al, 1997). In this way, soft input layers can be converted to hard categories with better resolution. The main problem in sub-pixel mapping is determining the location of each land cover class in larger pixels (Verhoeye, 2002). Various methods have been proposed to solve this problem, including the Hopfield network (Tatem et al., 2001; Muad and Foody 2012), the neural network after error propagation (Zhang et al., 2008; Wu et al. 2011, Nigussie et al. , 2011), linear optimization technique (Tatem et al., 2001), spatial gravity model (Mertens et al., 2006; Wang et al., 2011), pixel displacement algorithm (Kasetkasem, 2005), and genetic algorithm (Mertens et al., 2003).

Gravity model
In this model, the pixels in the digital model of altitude are named based on their position relative to the upper left pixel, known as P0.0. The same structure is used for subpixels. This means that for a scale equal to 2, it has sub-pixels p0,0, p0,1, p1,0 p1,1. So that a sub-pixel pa, b is placed inside a pixel Pi, j when the following equation is established (Xu et al., 2014):pa;b∈Pi;j⇔(aS=i)∧(bS=j)Where a is the sub-pixel row number, b is the corresponding sub-pixel column number, s is the scale factor, and i is the neighboring pixel row number, and j is the neighboring pixel column number. The neighborhoods defined in the previous step are also defined as follows:N2pa;b=Pi;j|d(pa;b.Pi;j)≤12(2S-1)Where N2 is a quadruple neighborhood model. The distance between each sub-pixel and the surrounding pixel (d) is calculated as follows (Xu et al., 2014):dpa;b.Pi;j=a+0.5-Si+0.52+b+0.5-Sj+0.52Topographic Position Index (TPI) method for landform extractionIn this study, the neighborhood method was used to study and classify landforms. Thus, the topographic position index (TPI) was used to isolate landforms in the region. TPI is the equation of each cell in a digital elevation model with the average height of neighboring cells according to the following equation. At the end of the height, the average decreases from the height in the center (Weiss, 2001).TPIi=Z0-∑n-1Zn/nZ0 is the height of the model point under evaluation, Zn is the height of the grid and n is the total number of surrounding points considered in the evaluation.

In this study, to increase the spatial resolution of the digital elevation model of southern part of Fars province, the gravity model was studied. First, the gravity model was used to increase the spatial resolution of the 30-meter DEM. In this study, four neighborhoods with different scales 2, 3 and 4 were used to find the best model to increase the spatial resolution. The results showed that the use of quadratic neighborhood (T2) with scale 2 increases the number of sub-pixels and increases the spatial resolution. According to the error values, it is determined that the best model to increase the spatial resolution is the model S = 3 for the digital model of 30 meters height. Therefore, digital elevation (DEM) model S = 3 and T = 2 were used to map the landforms of the region as input data. TPI method was used to extract the landform map of the study area. The results of applying a polynomial distribution function to select the best scale for landforms separation showed that 3 × 3 (minimum scale) and 45 45 45 (maximum scale) windows with the lowest RMSE for TPI mapping and finally landform mapping were the most suitable ones in the study area. The results showed that the TPI values ​​of the study area are between -33 to 46.77 for the 3 3 3 scale and -42.53 to 77.56 for the 45 45 45 scale (Figure 4). Indeed, in high areas such as ridges and hills, near-zero codes indicate flat areas or areas with low slope changes, and negative codes indicate low areas such as valleys and waterways. Each of the categorized landforms covers a part of the area. According to the results, it is clear that the study area includes 10 types of landforms. The results also show that the map of landforms prepared using the gravity model is more accurate.

In this study, gravity model and TPI method were used to study landforms in the south of Fars province. In this study, the resolution of images was increased using the gravity model. The results of this study showed that the gravity model with scale 3 and quadruple neighborhood has a high accuracy to increase the spatial resolution of the digital elevation model. Therefore, by using these maps with high spatial resolution, landform maps can be prepared with high accuracy. Also, by using the type of landforms and their percentage, the erosion rate in the study area can be estimated.

In this study, 5 main DEM derivatives of 12.5 m ALOS satellite (slope layer, slope direction layer, curvature layer, cumulative flow layer and altitude layer) as well as Sentinel-2 satellite images and NDVI vegetation index were used as auxiliary layers. Segmentation in this area was performed using segmentation multi resolation method. In this segmentation, the height layer was given a value of 3, the curvature layer was given a value of 2, and the other layers were given a value of 1. Then, using Layer Values ​​and Geometry algorithms and assign class commands, landforms located in the western and southwestern slopes of Zagros (Aligudarz city area) have been classified. The results showed that the use of Layer Values ​​and Geometry algorithms and assign class commands have a good ability to isolate and classify landforms, so that 8 types of landforms (slopes, ridges, water areas, precipices, peaks, ridges, lowlands and lowlands) Kappa coefficient was 0.87 and overall accuracy was 91.71%. The ridge landforms form the largest part of the region and are the dominant landforms of the region and have a good distribution in different parts, but the peak landforms with the minimum area have formed only a limited part of the study area.

Severe drought events can endanger part of the community, it is important to develop a comprehensive and spatial framework for mapping drought-prone areas and reducing risk systems (Beyaztas et al., 2018). Drought is related to hydrology and meteorology. Various environmental parameters and activities related to agriculture, vegetation, human life, wildlife, and local and national economies are affected, and the effects are often intensified by agricultural, livestock, industrial and other human activities. There are various studies conducted in the field of drought, which can be found in Aher et al., 2017; Azevedo Reis et al., 2020; and Sivakumar et al., 2020. Studies have used only climatic parameters to study droughts in these studies. Other parameters such as vegetation, soil, and topography are also affected by drought. Thus, the purpose of this study is to analyze drought using these factors in the south and east of Fars province using fuzzy methods and hierarchical analysis models. Using the Geomorphon mapping method, the topography and landforms within the study area are determined. After that, the relationship between the amount of drought and the type of landform is determined. Using a relationship between landform and the amount of drought, it is possible to determine which Delandforms will be vulnerable to drought. So, the objective of this study is to determine the degree of drought in the eastern and southern parts of Fars province and to determine the type of landforms within this region by using the geomorphon method. One of the innovations of this study is how it predicted a relationship between the type of landform and the amount of drought.

The study area is between longitudes 52 degrees and 66 minutes and 54 degrees and 18 minutes and latitudes 28 degrees and 1 minute and 30 degrees and 18 minutes. The study area covers an area of 23139.98 square kilometers. The maximum and minimum heights of the study area are respectively 3235 and 765 meters.For this study, the landforms in the region were mapped using a geomorphon method. The fuzzy method and hierarchical analysis model were also used to determine the drought status of the study area. The incremental membership functionwere used to prepare a fuzzy map for each of the parameters. The incremental membership function was used to prepare the fuzzy map for the parameters Altitude, slope, groundwater depth, land use, precipitation days, precipitation, soil texture. So values greater than the critical limit n get one and values less than m get 0, and between m and n they get x-m / n-m. For the aridity index, erosion, PET, soil salinity, and distance to river parameters, the reduction membership function was used. The values above the critical limit n were 0 and below the critical limit m were 1. The values between m and n were n-x / n-m.Then each layer was weighed using the AHP method. Weighting was done using the AHP method because each characteristic has a different effect on drought. The AHP method makes it easy to weigh parameters. AHP relies on pairwise comparisons of each parameter. Each of the factors is in the range of 1 to 9 that (Saaty & Vargas, 2001).

The results of this study showed that most areas are at risk of erosion, and most of the land use in the study area is for pasture. There is more rainfall in the western part of the study area and the drought index is higher in the eastern part. Elevations are highest in the northern half of the region while evaporation is highest in the southern parts. In the southern part, groundwater depth is highest, and rainiest days are in the western part. The soil texture in most areas is loamy clay. Using pairwise comparisons of each parameter, the results revealed rainfall and groundwater depth with weights of 0.28 and 0.01 are the most and least important parameters in determining drought-prone areas in the study area, respectively. Based on the results of fuzzy and AHP methods, areas to the east and southeast are prone to drought.

Drought forecasting is important because the annual drought causes a lot of damage in arid and semi-arid regions of Iran and leads to reduced yields of agricultural products as well as reduced drinking water and irrigation. Thus, when identifying the vulnerable areas, including the Eastern parts (eastern regions), the necessary measures must be considered, including the cultivation of low water plants, management of dams, etc.

The tourism industry can become a development tool for deprived areas as a factor of change in the economic outlook. Development of tourism in rural areas is an essential element and one of the ways to save villages from poverty, migration and social and economic problems. In the present study, coastal villages of Sirik city in Hormozgan province in the east coast of the Strait of Hormoz have been evaluated for the development of tourism due to the diversity of natural attractions. Situation and conditions of sites susceptible to tourism in studied villages and their location and location distribution through field surveys and using GPS have been transmitted to base maps. Thus, a full identity card and map of distribution of geomorphologic complications in the villages of the area were prepared In the next step, each village in the study area was considered as a geosite or geomorphosites due to the presence of various landforms Using two models of Kobalikova and Braille, they assessed the rural tourism development capability. Each geosite was valued and then the sum of values was combined and the final value of each geosite was obtained. The results of Kubalikovchr('39')s model also showed that Ghanari village with 12 points scored the highest points and kuhestak 11.75, Gheordo and Palur 8.25, respectively. The results showed that in the Braille method, the Ghanari village was scored 870 points from the total score of the points and ranked the first and Palur estates with 810 and kuhestak 775 points

Analysis of the Properties of the earth's surface, which can be influenced by climatic, hydrological, intrinsic properties of formations and neotectonic activities, plays an important role in recognizing the physical Properties of the Watershed. On the other hand, understanding the physical Properties of the Watershed is one of the first steps required in morphological and hydrological studies. This research tries to study the shape of the earth's surface in Fahliyan watershed using morphometric indices. For this purpose, 6 geomorphometric indices, total curvature, profile curvature, surface curvature, TRI index, TPI index and TWI index were selected and using the relationships for each index and using relevant extensions in ArcGis 10/3 software, each map These indicators were prepared. The results of calculating the total curvature show that more than half of the surface of the basin (52.80) has a concave curvature and 47.20 percent of it has convex curvature. Also, the lower and upper limits for curvature of the surface were -2.99 and 3.96 respectively and for the curvature of the profile were -4.87 and 4.2 respectively. Accordingly, the lower and upper limit of the TPI index, which divides the mode of nodule and bulge, was calculated as <-4.2 and >, respectively. The values of the TRI index, which are somewhat different from the height of one pixel with their adjacent eight pixels, were calculated from 0 to >14. And finally TWI index values that indicate the soil moisture condition in the order of <-1.5 to >5.5 calculated. The percentage of the area of each of these indicators in the Watershed, which was obtained in the results of this study, can be used in various watershed planning including erosion and sediment, Landform Classifcation, identifying and introducing areas involved with landslide hazard, flood studies, studying and studying resources Groundwater and… to be used.

Sand and dust storms occur when unchecked, strong or turbulent winds combine with exposed loose soil dry surfaces. These conditions are usually exacerbated by human activity and land use change in semi-arid and arid regions. The Damghan region, due to its geoecological structure, has created a special features in the arid region on the closed basin of Haj Aligholi. At present the ecosystem balance of the region is affected by anthropogenic exploitation and wind activities. The aim of this study was to investigate the priority of sand resources in the aeolian process. In this study, we were compared the composition and variations of grain size in land natural units (landforms) and human activity - based units (Landscape). In each unit of sand source were taken soil or deposit samples. The results of granulometry show that Agricultural Land Unit contain more than 60% of particles larger than 2000 microns; As a result, this unit is more resistant to wind erosion than other units. Whereas, this value is about 15% for the most sensitive landforms (alluvial plain with water erosion). On the other hand, the granulometry results of particle below 2000 micron indicate that Agricultural Land Unit contains the most vulnerable particles to wind erosion. This means that agricultural land has a high potential to provide sand resources in the process of wind erosion.

Earthquakes are one of the most important environmental hazards that always lead to a lot of damage. In addition to the effects that earthquakes on residential areas, they also have many tangible and intangible effects on landforms that can cause hazards. Due to the importance of the issue, the current study investigates the tangible and intangible effects of herd earthquakes on landforms in the Ezgeleh region. The research data includes the 30-meter SRTM digital elevation model, digital data layers, Sentinel 1 images, and information obtained through field visits. The most important research tools include ARCGIS software (for mapping and final output) and GMT (for radar interference). This research has been done in 3 stages. In the first stage, using the radar interferometry method, the amount of vertical displacement of the area is calculated. In the second stage, the vertical displacement of the landforms of the region has been evaluated and in the third stage, the tangible effects of the earthquake on the landforms of the region have been investigated. The results of the research indicate that under the influence of earthquakes, the area had a displacement of between -613 and +917 mm. Due to the vertical displacement that has occurred in the region, the landforms of the region have also faced a lot of displacement so that the plains in the region have been affected by this displacement in which the highest displacement with 382 to 917 mm elevation is related to Zahab plain. Unlike the plains of the region in which the movement and changes have been mostly imperceptible, the slopes located in the region, in addition to the imperceptible effects, have also encountered many tangible effects. Therefore, many slopes of the region, including the slopes located near the villages of Ramaki Ramazan, Meleh Kaboud and Ghouchbashi have lnadslice, as well as the slopes near Piran waterfall and Baba Yadegar valley have Debriz.

Geomorphodiversity Investigation of Damavand volcano and its surroundings based on the GmI Index Extended Abstract Introduction Geomorphodiversity defined as “the critical and specific assessment of the geomorphological features of a territory, by comparing them in an extrinsic and in intrinsic way, taking into account the scale of investigation, the purpose of the research and the level of scientific quality”. Area with high geomorphodiversity can provide a wide range of services, including provisioning (food products), regulating (erosion regulation) and supporting (land and water as a platform for human activities). The geomorphodiversity of Damavand volcano and its surroundings require special attention to identification, management and conservation, prevent degradation and construction in vulnerable areas. Promoted recognition of Geomorphodiversity can provide useful information about the management of geomorphological heritage and how to better protect them against destructive human activities and environmental hazards. Also, the preparation of Geomorphodiversity maps, the identification of areas with high potential for regional development and geotourism, provide comprehensive information for designers, planners and responsible organizations in these areas and create solutions for the environmental complexities of the study area; therefore The main objective of the present study is identification and quantitative assessment of geomorphodiversity and the preparation of geomorphodiversity maps of the study area. Methodology Damavand volcano was inscribed as Iran's first natural property in the national heritage list at the Cultural Heritage, Handicrafts and Tourism Organization on July 3, 2008. The data of this research include geology, drainage network, slope, roughness and landforms, which are related to the evolution of the physical landscape of the study area. Landsat satellite imagery, thematic maps, DEM10 meter, etc. are used to achieve the goals. In this study, geomorphodiversity quantitative index has been used for analyzing the results and the results has been validated by comparing with geomorphology map and field control. Geomorphodiversity index (GMI) is presented by Melelli et al. (2017). The value of geomorphodiversity is calculated from the sum of five factors including geological diversity, drainage network density diversity, roughness diversity, slope position index diversity, landform category diversity. All of them are grids of different terrain parameters. Results and discussion In the geomorphodiversity of the study area, the V1 class is 1.05%, V2, 25.84%, V3, 37.17%, V4, 22.14% and V5, 4.84% of the total area of the region. Distribution of geomorphodiversity of the study area is mainly related to the topography condition; the lowest value of the geomorphodiversity (V1) is related to the Damavand volcano cone, especially the lava plain in the northwest. The highest value of geomorphodiversity (V5) extends along the valley of Haraz, where different types of geomorphological processes are meeting, such as fluvial, glacial, hillslope, igneous, tectonic and etc. In other words, increasing the value of geomorphodiversity along the Haraz valley can be interpreted as a result of the frequency of the geomorphological processes presence. Validation results also showed that there is a good correlation between the geomorphodiversity index value, the number of different types of landforms and the average number of landforms. The most extensive area with the lowest value of geomorphodiversity is located in the northwest of Damavand and in the Sardagh plain. Along the Haraz valley, which has the highest geomorphodiversity, there are different types of landforms, including river terraces, lake terraces, landslide, talus, basaltic prisms, deep valleys, travertine landforms, high channel density and so on. In general, in the study area, the high value of geomorphodiversity is mainly located at the meeting place of Damavand volcanic lava with rivers. Conclusion The geomorphodiversity of Damavand volcano and its surroundings are one of the most important fortunes in the region. Geomorphological landforms have been maximized in these areas and offer a unique geomorphological complex. The geomorphological landforms density in these areas are maximized and provide a unique geomorphological collection. Areas with high geomorphodiversity are rich and unique collection of different types of landforms and geomorphological processes that require special attention for geotourism, scientific-educational, national and world heritage, geopark, and so on. In addition, the determination of areas with high geomorphodiversity is another way to promoting geomorphological heritage; In other words, geomorphodiversity are the backbone for identifying and assessing geomorphological heritage for various purposes of geotourism, national and world heritage, geopark, conservation of geomorphological heritage. To ensure that the values of these areas can be preserved for current and future generations, managing and conserving them is very important. Nevertheless, studies of geomorphodiversity are at an early stage and need more help and reflection by geomorphologists in collaboration with other researchers in the Earth sciences. Keywords; Geomorphodiversity, Landform, Geomorphological heritage, Quantitative assessment, Damavand volcano

Any land use program will be carried out at the ground level and will result in changes in landfill, surface and even underground landforms. In this regard, it is necessary to pay attention to the importance of landfills and landforms in their role in land application models., considering that geomorphologic knowledge studies the forms and processes of the Earth, it is necessary to consider the characteristics and processes affecting landforms in order to environmental Capability assessment in the land use planning In this research, the methods of environmental Capability assessment in land use planning of used in Iran from the geomorphology viewpoint have been investigated and analyzed. Investigating the methods according to the criteria used in each method showed that in the Land System Approch, the criteria of climate, fault, groundwater and environmental hazards are not considered, and also in the Natural Resources Master Plan, attention to environmental hazards And only two protection and grassland of land use are defined.In a system analysis, given that experts have different specializations, they do not take into account all the components of a system The Urban, Rural, and Industrial Development Model of McHarg and Makhdoum have a general view of nature. Therefore, the new environmental Capability assessment model was proposed based on the integration of existing methods and according to the geomorphology viewpoint, in order to evaluate the environmental Capability assessment based on the base units of the landform, and the resulting results would be more consistent with the Earth's realities. This model includes assessing ecological capability and assessing the environmental hazards. In assessing ecological capability, sub-criteria for dominant winds, slope curvature, surface water and underground water were added. Also, in the land parcel standard, a different classification of landforms for urban development was presented, and environmental risk assessment included flood potential assessment, landslide and seismicity.

In the contemporary era, vital environmental resources such as water and soil have degraded resulting from increasing population, development of environmentally harmful activities, inappropriate management and so on. Today, development beyond ecological thresholds and degrading the main factors of development and human survival has turned into one of the major challenges of human society. If this trend continues, the world society will face with various problems such as food security, environmental conservation and preservation of natural heritage. From perspective of land use planning, development of economic activities without taking into account the potentials and the degree of production capacity of land resources can bring about various environmental, social and economic problems in a specific region. Therefore, at any region, especially mountainous areas like Urmia, understanding and appropriate management of land resources and its parameters is necessary and unenviable for land sustainability Considering the physical conditions of land such as topography and landforms in Urmia region, which making sever limitations for development, it seems that analyzing of land characteristics and identifying limitations and potentials of this region can be used as an effective tool for land users in terms of developing activities. The present study is aimed at answering this critical question that: how much topographic and landform conditions can affect on land suitability for developing activities in Urmia County. The result of this study will lead a base map which can make a background for further evaluation of the land for determining various land uses in the region. In order to identify suitable classes for developing activities, the Multi Criteria Method was used. MCE is a method for combining data according to their importance in making a given decision. At a conceptual level, MCE methods involve qualitative or quantitative weighting, scoring or ranking of criteria to reflect their importance to either a single or a multiple set of objectives (Heywood and et al., 2006: 239). Over the last decade or so, a number of multiattribute (or multicriteria) evaluation methods have been implemented in the GIS environment including WLC and its variants, ideal point methods, concordance analysis, and analytic hierarchy process (Malczewski, 2004: 34). Among these procedures, the WLC and Boolean overlay operations are considered the most straightforward and the most often employed (Chen & et al., 2001: 387; Malczewski & Rinner, 2005: 250; Malczewski, 2006: 270; Lafortezza & et al., 2008: 194; Boroushaki & Malczewski, 2008: 399; Jelokhani-Niaraki & Malczewski, 2015: 493). WLC (or simple additive weighting) is based on the concept of a weighted average. The decision maker directly assigns the weights of ‘relative importance’ to each attribute map layer. A total score is then obtained for each alternative by multiplying the importance weight assigned for each attribute by the scaled value given to the alternative on that attribute, and summing the products over all attributes. When the overall scores are calculated for all of the alternatives, the alternative with the highest overall score is chosen (Malczewski, 2004: 34). Operations of overlaying layers 1. Producing Map A In order to produce map A, at first stage, slope and elevation parameters were compared. The Statistics results of this map show that the highly suitable and suitable classes cover 31.66 and 30.31% of the total area, respectively. The results also show that the combination of the slope and elevation layers has placed approximately 40% of the total areas in limited and not suitable classes.
2. Map B In order to produce map B, the standardized layer of aspect is overlaid with map A. The results of the topographic map indicate that only 21 percent of the total area is highly suitable for developing activities. In the second class, i.e. suitable, which cover 22 percent of the total area, the lands are suitable for some activities.
3. Map C By overlaying land type and map B and producing map C, more filtering was applied in structural land layers for developing activities. This phase can be regarded as the milestone of the layers overlaying process. In other words, in this step, by adding land type layer, main characteristics of land combined and the geomorphology map the region was completed. By producing geomorphology map, the area of the highly suitable was decreased, significantly. Therefore, the area of the highly suitable decreased to 13.02 percent, which indicate the highly effeteness of the land type in filtering among natural parameters for developing activities.
Producing Final Land Suitability
In final map, the drainage layer as an effective parameter was used in the analysis. The drainage map generated from river density and line density function. The results of this map show that approximately 11 and 21 percent of the total area is highly suitable and suitable, respectively. By adding drainage layer to geomorphology map, the areas of the two mentioned classes were significantly decreased. According to final land suitability map, 33 percent of the total area is highly suitable and suitable, 28 percent marginally suitable and 38 percent not suitable. The results of parameters overlay showed that in all of the steps topographic elements have significant role in filtering of land suitability for developing activities. The results of the overlaying land evaluation map and slope with proposed land suitability demonstrate that the classes of marginally, very low and not suitable, with slope of 20.18, 28.69 and 42 percent, respectively, have severe natural limitations and the possibility of developing activities is not practical in approximately 67 percent of the total area. Therefore, topographic and landform characteristics of the region are the main constraints in developing of the activities in Urmia County. Based on these results, achieving to any activity development in Urmia requires to prioritize and take into consideration environmental issues and sustainable management of natural resources.

  Urbanization is one of the major challenges of the contemporary world, and at least 0.5% of the land area is found in cities (Schneider et al., 2009, 2).Urban development as a human category occurs in the context of geomorphology.Therefore, urban geomorphology, in addition to studying geomorphic limitations in urban development (Kuek, 1984, 22), the suitability of landforms for urban use, the impact of urban activities on land surface processes and landforms created by urbanization Is evaluated and studied (Douglas & James, 2015, 52).The city of Kermanshah is located in Kermanshah Navidese plain in terms of geomorphology and is composed of different landforms.Each of these landforms plays an important role in urban development.Also, urban development has had a great impact on geomorphologic landforms and so far no studies have been carried out in these areas within the city of Kermanshah.Considering the specific geomorphological conditions of the city of Kermanshah and its risks, recognizing the interactions between geomorphology and urban development on each other will play an important role in the planning and management of Kermanshah.The purpose of this research is to evaluate the effects of geomorphology on the urban development of Kermanshah over a period of 40 years.It is impossible to achieve this without the availability of suitable data. In the present study, library, historical, cartographic and remote sensing methods are used.Map of geomorphologic complications of the city and surrounding areas of Kermanshah is based on the analysis of the land systems.Then, the extent and directions of physical development of Kermanshah city are determined during 40 years in selected years of 1977, 1986, 1996, 2006, and 2017 using Landsat satellite satellite imagery, MSS, TM and OLI.The supervised classification method has the maximum probability and selection of educational samples based on visual interpretation, false color combinations, and land surveys to determine the city's boundaries.Then, by mapping the map of geomorphologic complications with Kermanshah districts in selected years, the city's developmental extent in each of the geomorphologic traits has been determined and changes in the geomorphologic complications caused by the expansion of the city are also estimated.

The increase in the area of Kermanshah in the 40-year period has been around 5.5 times. Alluvial plain land has the largest area for the remainder of the year except for 1977 and has been affected by increasing area in all years. Alluvial fan landforms, hillside plains and regular slope have a upward trend in terms of area and area during 5 years, which suggests the development of a city in the territory of these landforms.The land flood plain has increased in terms of area, and the two hills and the irregular slopes have a limited area and have been located in urban areas only in the last two years.In Kermanshah, urban development has been affected by the existence of alluvial plain land so that the most development on this landform is in northern, northwest, northeast, western and eastern directions.The physical expansion of the city of Kermanshah has had an impact on the geomorphological prospects of Kermanshah plain.Alluvial plain landforms, flood plains alluvial fan rivers and river valleys have become urban landscapes.The subterranean rivers of the waterfall and Cham Bashir have become narrow underground channels and are no longer visible on the surface of the earth, and the development of their substrate has been stopped. Landforms of geomorphology determine how urban development is.Alluvand plain landform has the largest role in this field and more than half of the area of Kermanshah is located on this landform.The three flat landforms of alluvial plain, alluvaih fan and hillside plains account for 72% of the total area of the city of Kermanshah in 2017.The plain landform, the old town nucleus, is about 16% of the city's total area.The limiting or hazardous landforms of the flood plain, regular slopes, irregular slopes, river valleys and hill meadows account for about 12% of the area of the city of Kermanshah.The urban development of Kermanshah has had an impact on geomorphologic landforms and has led to significant changes and destruction of landforms.The drainage network of the region and Cham Bashir and Aashoran rivers have been completely destroyed, which could have adverse effects on water resources and residential areas around them.Other landforms such as alluvial plain, alluvial fan ,hillside plain and floodplain have become urban landscape and their evolutionary process has been stopped.Expansion of the city on hazardous landfills (flood plains) and limiting (irregular ranges, hill, etc.) in the future can lead to a risk to urban areas.

Geomorphometry is the science of quantitative land-surface analysis (Pike, 1995, 2000a; Raseman et al., 2004). It is an interdisciplinary field that has evolved from mathematics, the Earth sciences, and most recently computer sciences (Pike et al, 2008, 3). It is well to keep in mind the two overarching modes of geomorphometric analysis first distinguished by Evans (1972) as specific, addressing discrete surface features (i.e. Landforms), and general, treating the continuous land surface. The morphometry of landforms per se, with or without the use of digital data, is more correctly considered part of the quantitative geomorphology (Thorn, 1988; Scheidegger, 1991; Leopold et al., 1995; Rhoads &Thorn, 1996). The shape of terrain, i.e. landforms, influences flow of surface water, transport of sediments, and soil production, and determines climate on local and regional scales. Furthermore, natural phenomena like vegetation are directly influenced by landform patterns and their relative position across the landscape (Blaszczynski 1997; Blaschke & Strobl, 2003).
The Earth’s surface is structured into landforms as a result of the cumulative influence of geomorphic, geological, hydrological, ecological, and soil forming processes that have acted on over time. Landforms define boundary conditions for processes operative in the fields of geomorphology, hydrology, ecology, pedology and others (Dikau, 1989; Dikau et al., 1995; Pike, 1995, 2000a; Dehn et al., 2001). In this study, using MRS algorithms and Ecognition software, landforms in the northern slopes of Mount Sabalan have been extracted and the effects of Landform morphometry on its hydrology have been investigated The semi-automated methods refer to the automatic procedures of extracting a landform based-process. This is mainly relying on unsupervised isodata classification, pixel-based classification (supervised /subpixel classifier based on training material), the analysis of digital elevation models (DEM), algorithms, hydrological modelling, and object oriented analysis (Nabil and Moawad, 2014:42).
In this study object-oriented methods and Ecognition software were used for the classification and the extraction of landforms. The object-oriented classification was used as an alternative to traditional pixel-based classifications, to cluster grid cells into homogeneous objects, which can be classified as geomorphological features (Seijmonsbergen, 2012). In addition, the DEM and its derivation (Slope, Profile and plan curvatures, maximum and minimum curvatures), were used in order to extract landforms. Then, using fuzzy logic method, the landform, land use, NDVI index , precipitation, density of river, and lithology layers were Overlaid and the potential flooding area was obtained. In the object-oriented method, determining the scale parameter is a very important factor in the separation of different objects in an image. Scale parameter is a crucial threshold that determines the maximum allowed heterogeneity for segmentation and has a direct influence on the size of the objects to be obtained. The scale parameter, after a trial and error process, is recognized to be within a particular range (Gerçek, 2010:115). A novel method that was introduced by Dragut et al. (2010) and the ‘Estimation of Scale Parameter (ESP) that built on the idea of ‘Local Variance’ (LV) were employed to obtain the optimum scale out of a range of scales. By interpreting thresholds and prominent peaks in the ROC-LV graph, characteristic scales relative to data properties at the scene level could be found. This curve in 100 scale level was produced for the study area by using the ESP software and with respect to curve, the scale of 25 was selected for the segmentation. After segmentation, using the morphometric differences between the landforms, the landforms were extracted. After this stage, the landforms along with three layers of NDVI index, land use, and lithology was fuzzy. Finally, using gamma 0.8, they were combined and the zoning map of the potential flooding was estimated. Flood zoning map was classified into 5 classes and the percentage of each zone risk was calculated in each landform. In this research, using an object-oriented model, landforms were extracted as plain, peak, pit, ridge, channel, nose, shoulder slope, hollow shoulder, spur, planar slope, hollow, spur foot slope, and hollow foot. An assessment of the effect of landforms on the hydrology of the area revealed that three landforms of hollow, shoulder and planar slope which were respectively 67.3%, 62.9%, and 53.2% had the greatest impact on flooding and their area were zoned as high and very high flooding. On the other hand, plain and pit landforms were zoned in the form of low and very low flooding areas.

There has been always an interaction between geomorphic landforms and vegetation type and density. Alluvial fans are among landforms having considerable effect on vegetation. Sabzevar region in Khorasan Razavi Province, due to its various and typical alluvial fans, is suitable location for studying the geomorphology/vegetation relations. The aim of this study is qualitative and quantitative assessment of vegetation type and density in abandoned, old and young fans in south of Foshtanq village. Results were obtained from identification of three alluvial fan borders based on topographic maps, satellite images and field works and from determining vegetation type and canopy, soil analysis and finally the relations between mentioned parameters were identified. Results show that gullies on abandoned fan, due to their coarser texture, are adequate for shrubs. Vegetation of abandoned fan’s gullies has highest amount of canopy (10.676%) because of sufficient moisture drained from interfluves while the vegetation on active channels of old and young fans has lowest canopy (0.58 % for young fan and 0.887% for old fan). In abandoned fan, vegetation canopy is higher in gullies than interfluves, while in old fan, it is higher in interfluves than gully beds. Dominant vegetation species in old and young fans include Artemisia seiberi and Astragalus squarasus in interfluves and Pteropyrum aucheri in channels. In abandoned fan, dominant species are Artemisia seiberi and Scariola orientalis. Overall, results show that geomorphological processes such as incision and deposition influence the soil texture and fertility and hence vegetation type and density.