فصلنامه پژوهش های ژئومورفولوژی کمی
سال نهم شماره 3 (پیاپی 35، زمستان 1399)

Desertification should be considered as the destruction of the fragile balance generating human, plant, and animal life in arid, semi-arid, and sub-humid arid areas. During the recent decade, Urmia catchment and the surrounding area of Lake Urmia have encountered imbalance by experiencing severe environmental fluctuations.Thus, due to the ecological and eco-systemic significance of this area, studying the occurrence of desertification seems essential. Thus, the present study including:1. the evaluation of the occurrence of desertification in the surrounding area of Lake Urmia using the spectral indices of vegetation, Albedo, tasseled cap, 2) Identifying a base pixel relationship between different biophysical indices (normalized vegetation difference, greenery rate, humidity rate, surface Albedo, brightness degree) and detection are among the best study pairs for evaluating the status of desertification due to the highest negative correlation among them.The studied area The studied is part of Urmia catchment located on northwestern Iran with geographical coordinates of 44 degrees and 0 minute to 47 degrees and 0-minute east longitude and also 37 degrees and 0 minute to 38 degrees and 20 minutes’ north latitude and has an area of 14395 square kilometers.

In the first step, Satellite images of Sentinel-2 (Level-1C) were downloaded from Copernicus Open Access Hub (https://scihub.copernicus.eu). The images were acquired on July 2018. The cloud- free images were selected on July because it is the period when natural and annual vegetation is minimal and crops are harvested. Desertification during this period is best assessed to avoid confusion with seasonal vegetation. In the second step the images were pre-processed and processed using QGIS software. Then, ArcGIS 10.3, SAGA-GIS, and SPSS (22) software were used for the statistical regression analysis between vegetation and Albedo (the first pair of spectral index), greenery and brightness coefficient (the second pair of spectral index), as well as humidity rate and brightness coefficient (the third pair of spectral index) were used to identify the pair of spectral indices with higher negative correlation.

Based on the results, the high rate of NDVI index and greenery rate are related to the points like the Alluvial fans located at the foot of Misudagh hillside.However, the low rate of the above-mentioned indices is related to the areas like aquatic zones (Lake Urmia). The Albedo rate in the studied area is between -0.01 to 0.9.The low values of Albedo index are related to the areas full of vegetation and aquatic bodies. The high values of Albedo and brightness rate are related to bright soils.The high rates of humidity are related to aquatic bodies, the areas with vegetation, and the humid lands around Lake Urmia while its low values are related to the soils with bright texture, the land without vegetation, and the lands and salt marsh without humidity resulted from the retrogression of Lake Urmia and poor soils without organic materials.In order to evaluate the regression relationship between the spectral indices, the SAGA-GIS was used. The analysis showed a strong negative correlation between the spectral index pair of humidity rate and brightness coefficient (r=0.37).After the spectral index pair of brightness coefficient-humidity rate, the spectral index pair of normalized difference Vegetation-Albedo is equal to r=0=1.14. The negative correlation is between the spectral index rate of brightness coefficient- greenery rate (r=4.9) allocating higher rate to itself than the previous spectral index pair.Thus, first the spectral index pair of brightness coefficient and humidity rate as well as normalized difference vegetation and Albedo was normalized.

After normalizing the two above-mentioned index pairs, the correlation coefficient for normalized vegetation -Albedo is equal to -0.34 and for humidity rate-brightness coefficient is equal to -0.31. Due to the close correlation obtained for the above-mentioned indices, the map for desertification risk was prepared based on the two above-mentioned pairs and then verified using the Confusion Matrix using Ground truth ROI. Kappa coefficient obtained for the map is equal to 0.89 and the obtained accuracy rate is equal to 90.1. The achieved desertification map was classified in five categories of without risk, poor, average, relatively severe, and severe. From the total 14350.81 square kilometers of the studied area, 557.391 square kilometers (3.9%) is at the severe risk of desertification, 2015.330 square kilometers (14.04$) is at the relatively severe risk, 4007.073 square kilometers (27.92%) is at the average risk, 3660.534 square kilometers (25.50%) is at the poor risk and 4110.473 square kilometers (28.64%) is at the class of no risk for desertification .Based on the above-mentioned issues, any effort for exploiting the lands located on the studied area should be made with full cation and based on sufficient knowledge on the conditions of these lands.

The occurrence of landslides is the result of the interaction of complex and diverse environmental factors. These factors are divided into the trigger and the primary cause. Landslide occurrence triggers include weathering, earthquakes, rainfall and snow melting. Human activity like construction of roads and buildings on steep slopes and dispersal of water from supply systems and sewers could also trigger the occurrence of the phenomena (Cubito et al., 2005). In this Investigation will be using data layers Region, Identifying of the most important factors landslide prone areas in Heyran road and in this Investigation effectiveness of methods used to be examined.

In this study landslide sensitivity preparation mapping, position had landslide occurred in the study area bye GPS it got recorded and using geology Astara map Layer was extracted fault. Besides these layers used were layers of elevation, slope and aspect of the DEM with a resolution of 30 m. To do this research using the data above according to SVM algorithm landslide susceptibility maps sensitive communication axis was determined Heyran. SVM algorithm is based on statistical learning theory. According to this theory can be bound for to data error rate unclassified machine learning, to be considered as a generalized error rate. In this study, a better estimate of landslide susceptibility neural network method was used. After preparation of the layer of elevation, slope, aspect, geology, faults, land use and other factors data inputted in software SPSS Modeler. Software output for each one of the factors was just small amounts in continue investigation process landslide susceptibility map used software GIS was prepared.

In the zoning of landslide sensitivity, the most important part of the work is the preparation of a layer of dispersion of landslides in the region. The accuracy and precision of zoning is main due to this part of the work. In order to assess the accuracy and correctness of the zoning of field works, the study of landslides is an integral part and field works were carried out to identify landslides in the region. GPS is most important tool at this stage. A total of 42 cases of major landslides were recorded during Heyran. In this research, the Heiran road was investigated using neural network methods and vector machine algorithm. The layers used is elevation, slope, aspect, geological formations, faults, land use. Most affected by slope instability, mainly related to land use pasture, farmland and roads linking within the elevation is 1,400 meters high. In the overall evaluation of the performance of the models, sigmoid kernel model have better performance due to the layers used and the conditions of the axis of communication. So the results of these two models can be the basis of zoning.

Slope instability as one of the most important geomorphological hazards in some areas has made significant and has created serious problems for residents. This research has been carried out to identify areas of potential Landslide in the Heyran-Astara. This communication road is very important for landslide occurrence. In addition to road hazards, there are multiple slides along the road. It was necessary to study and compare accurate zoning methods for proper evaluation in this range. In this study, neural network models and four models vector machine algorithm has been evaluated and compared. According to the results of the sigmoid kernel and sigmoid kernel models, more than 80 percent of the 42 landslides are recorded in a large and very high class. In the overall assessment of the performance of the models, the sigmoid kernel and Neural network models are more consistent with the layers used and the conditions of the Heyran-Astara communication road and the registered position of the slides. So the results of these two models can be the basis of zoning.

River bank instability, erosion and sediment transportation are natural processes in rivers, causing destruction of agricultural fields and human infrastructures. The channel bank erosion is one of the sources of sediment production in rivers. The bank erosion causes not only increase in the sediment yield but also the river instability as well as change in the flow regime and channel pattern. During recent decades, sediment load and river bank instability have raised major global concerns, and a lot of expenses have been allocated to the river bank stability. The occurrence of occasional floods and the erosion of channel bed and banks cause great damages to the residential areas around streams every year. Therefore, the river bank protection against erosion is of great importance in environmental planning. Destruction and failure of river banks is associated not only with the type and intensity of erosion, but also with the bank properties such as the shape of structures in banks as well as the mechanical characteristics of the constituent materials of banks. So far, some researches have evaluated the stability and instability of river banks based on different parameters. The aim of this paper is study the potential of river bank erosion risk and also instability in the Bidvaz River. The Bidvaz River is a branch of Kalshor River located in the North Khorasan Province.

In this study, topthe 1:50,000 topographic maps of Iranian National Geography Organization were used to extract topographic data, and the 1:100,000 geological maps of Geological Survey & Mineral Exploration of Iran were used to obtain geological data. The identification of geomorphological landforms of the study area was carried out by the Google Earth™ images and fieldworks. Evaluation of the river bank erosion was determined by the Johnson (2006) method. In this research, a segment of Bidvaz River with a length of 6 km was selected and 9 cross-sections were surveyed to analyze the instability of the channel. In the Johnson method, 13 indicators were used to determine the stability of river channel stability: (1) watershed and flood plain activity and characteristics; (2) flow habit; (3) channel pattern; (4) entrenchment/ channel confinement; (5) bed material; (6) bar development; (7) obstructions; (8) bank soil texture and coherence; (9) average bank slope angle; (10) vegetative or engineered bank protection; (11) bank cutting; (12) mass wasting or bank failure; (13) upstream distance to bridge from meander impact point. The score (ranging from 1 to 12) for each indicator was determined and, based on the sum of scores from all 13 indicators, the overall stability of the channel was obtained in four classes: excellent, good, fair, and poor. The scoring to parameters was based on the fieldworks, satellite imagery, laboratory tests and on the expert’s knowledge and experiences. In order to evaluate the river morphology, a total of 9 topographic cross-sections were prepared using a theodolite. Finally, the rate of channel instability of 9 sections was analyzed.

The result of this study shows that, in section 1, the flow habit (parameter 2) and watershed and flood plain activity and characteristics (parameter 1) are the most influential factors on the channel instability, whereas the upstream distance to bridge from meander impact point (parameter 13) has the lowest effect on channel instability. Overall, parameters 1 and 2 are effective factors in channel instability in all sections. Nevertheless, the two mentioned parameters are not the most important in all sections. For example, in section 2, vegetative or engineered bank protection (parameter 10) is the most important factor in channel instability, whereas the most effective parameter in channel instability in section 3 is the entrenchment/ channel confinement (parameter 4). Result of this study reveal that some parameters had less or without effect on channel instability. For instance, parameter 13 had the minimum effect in channel instability in sections 1, 2, 3, 4, 5, 8 and 9. In section 6, parameters 4, 5, 8, and 12 are not important factors in channel instability. In section 7, vegetative or engineered bank protection (parameter 10) had the lowest impact on channel instability. Overall, the results of Johnson method and soil texture test show that the Bidvas River is inherently susceptible to bank erosion. Nevertheless, the rates of channel banks and bed erosion increase from section 1 (upstream) towards section 9 (downstream).

The study area has an arid and dry climate in which the irregular and intense precipitations prevail. The Bidvas is a seasonal river and the occasional flooding is one the marked characteristic of this river. The occurrence of occasional floods and the significant variations of its discharge in Bidvas River have resulted in the erosion of river banks and bed. Data show that the instability of Bidvaz channel varies in its different sections. Results of Johnson method demonstrate that the rates of channel banks and bed erosion increase from upstream downstream. The higher rate of instability in downstream sections is attributed to the higher slopes of channel banks and their lower soil coherence. Among 13 parameters evaluated in the Johnson method, the flow habit (parameter 2) and watershed and flood plain activity and characteristics (parameter 1) had more effective effects in channel instability than other parameters, in all sections. The parameter 13 (upstream distance to bridge from meander impact point) had the lowest impact in channel instability.

Mathematical models are one of the important tools for predicting sediment yield in rivers and reservoirs. The purpose of this study was to investigate sediment transport and bed profile changes along the longitudinal and transverse directions in the Yelfan River Ekbatan dam using GSTARS 1.2 model. The studies show that the amount of erosion in the upstream sections is increased compared to the downstream sections and is almost in line with the results of the model in cross section analysis. The model estimated the volume of the sediment to be 1.36 million cubic meters and its proximity to the measured value of 1.42 million cubic meters showed that the Young's equation in the Gstars model presented 1.2 acceptable results.

Landslides are among the most dangerous and hazardous natural hazards in mountainous areas, which are mainly caused by earthquakes or rains, destroying people's lives and property every year. The increasing population and expansion of human settlements in mountainous areas, the difficulty of predicting the time of landslide occurrence, and the numerous factors contributing to the occurrence of this phenomenon, reveal the necessity of landslide hazard zoning.Landslide hazard zoning is crucial for quick and safe actions and strategic planning for the future. Therefore, scientific study of landslide phenomena and risk zoning mapping are important on the one hand to identify landslide potential areas in human activities area and on the other hand to identify safe locations for development. New habitats or other human uses such as roads, power and energy paths, power plants, etc., are of interest to planners at various scales.Golestan province in the north of Iran, with 200 registered mass movements, is one of the most active landslide areas, which is not excluded from the study area between Tuskestan forest to Gorgan. The present study, while zoning the landslide risk in the study area, evaluates and compares fuzzy operator zoning methods using two methods (Qs) and (P), has provided a suitable model for the assessment and evaluation of landslide risk. Therefore, the main purpose of this study is to select an appropriate and optimal model of fuzzy set of operators for mapping landslide hazard in the study area.

The zoning and preparation of landslide hazard maps in this study is based on the integration of landslides with effective criteria in landslide events. Initially, the landslide distribution data were collected in the study area, then transformed into sliding zones using high spatial resolution satellite imagery and Google Earth images.The layer obtained from landslide zones as the most important layer used in the present study is the dependent variable in the implementation of zoning models. Then, the parameters affecting the occurrence of landslides in the distance between Tuskestan forest to Gorgan were identified. Effective layers include elevation, slope, aspect, distance to road, geology, distance to river, land use and vegetation extracted from maps and images and then processed using various functions in Arc GIS software.After preparing these layers as effective variables, they were integrated with the landslide layer. integration and overlap layers were performed using the gamma function, which is a combination of multiplication and multiplication fuzzy multiplication, using the Raster Calculator tool in Arc Map environment. Next, the relative weight of each factor and its related classes was calculated using frequency ratio model. Then, after determining the final weights of the layers, by crossing the zone landslide map and hazard zoning maps, we evaluate and compare landslide hazard zoning methods using the Quality Sum Method and (Qs) and accuracy (P) were investigated and a suitable model was selected according to the study area.

Investigation of the results of fuzzy membership values (frequency ratio) and relationship between factors affecting landslide occurrence and landslides occurred in the study area shows that the lowest and highest altitudes were highest and lowest, respectively. The fuzzy membership of the effective slope classes indicates that as the slope increases, the threshold of slope instability also increases and the likelihood of mass movements such as landslides is increased.The north aspects play a more effective role in generating landslide motions due to their higher rainfall and moisture content. Zones with marl formations have the highest fuzzy membership among the other lithological units in the region. In terms of vegetation density factor, fuzzy membership value of 1 was obtained for parts of the region with lower vegetation density. The effective factor of the distance to road shows its prominent role in the occurrence of landslides with fuzzy membership values obtained for its different classes; the less than 250 m distance of the road having the most The fuzzy membership value is the lowest fuzzy membership category with distances greater than 1,800 meters. The combination of landslides in the stream network density layer indicates that the highest landslide distribution is in the low density class (1200 - 75 m / km 2) per unit area. Therefore, zones with loose formations close to the road have greater potential for landslides. As the study area has this discrepancy and the impact of most road and geological factors has overshadowed how other factors are affected.The zoning map with 0.7 gamma shows that the area of very high risk zone between Tuskestan forest to Gorgan is 0.47, high risk zone 0.82, medium risk zone 1.65 and relatively low classes risk and very low risk are 2.54 and 8.95 square kilometers, respectively. the landslide hazard zonation map with 0.8 gamma, the areas of very high, high, medium, relatively low and very low risk areas were 1.05, 1.74, 2.56, 4.43 and 4, 63 km2, respectively. The area of these zones in the zoning map with 0.9 gamma are 1.19, 1.94, 2.49, 4.62 and 4.18 km2, respectively. The value of Quality sum (Qs), which compares and evaluates the methods compared to each other, indicates that the 0.7 with 2.42 fuzzy gamma operator has the highest Qs among the other gamma operators. Therefore, this operator is introduced as the optimal operator in landslide hazard zonation of the study area.

The results show that zones with loose formations, close to the roads and zones with higher rainfall have more potential for landslides. Also, the results of the modeling using selected methods showed different accuracy of them in preparing the final map of landslide zoning in the study area. But Fuzzy Gamma Operator with Landau 0.7 has better utility in landslide zoning than other methods presented. Therefore, any planning and management of the environment should be done according to the results of this model.

Tectonic geomorphology is defined as the study of landforms produced by tectonic processes, or the application of geomorphic principles to the solution of tectonic problems. (zovoili,2004). These indices have been developed as basic reconnaissance tools to identify areas experiencing rapid tectonic deformation (keller & Pinter, 1996). Geomorphological analyses allow the study of modifications that affect river basins, particularly modifications due to active tectonics, and investigate the morphotectonic evidence of the area. Attempts to quantify tectonic deformation from landscape analyses have been performed for decades (e.g., Bull and McFadden, 1977; Wells, et al., 1988; Pérez-Peña et al., 2010; Sarp and Duzgun, 2015; Gao et al., 2013; Demoulin et al., 2015; Luirei et al., 2015; Topal et al., 2016; Cheng et al., 2016; Mathew et al., 2016; Topal, 2019; Obaid and Allen 2019). The aim of this paper is to extract information on active tectonic, situation of the fault lineaments and landscape evolution of the study area.

In this paper we used Aerial image, topographical map, geological map, Digital Elevation model (DEM), Radar Topography Mission (SRTM) data and applied software such as ArcGIS, Google Earth, ENVI and Global mapper. The first step for calculating morphometric analysis in the region is to digitize topographical maps with the scale 1:25000 by ArcGIS software to extract required data and then morphometrical data is formulized and calculated exactly and the results is interpreted. The DEM data of 30 m have been used to generate the drainage basin. For study of morphotectonic of region, we must receive the geomorphic indices. These indices are particularly used to study active tectonics. The indices: stream-gradient index (SL), drainage basin asymmetry (Af), drainage basin shape (Bs), hypsometric integral (Hi), valley floor width-valley height ratio (Vf), Transverse River Sinuosity Index(S) and mountain-front sinuosity (Smf) were calculated using GIS technique in Khrramroud Basin. From these indices the relative active tectonics index value (Iat) was determined. The acquired values and classes are according to El-Hamdouni et al. (2008) and enclosed references. In this study, by means of remote sensing methods and ETM images and based on surface deformation like curved, truncated and offset structures the lineament which are related to the activity of subsurface or conceal faults are mapped. Ultimately, the results of these quantitative indices were compared to analyzing of the fractal dimension of the study area.

The rivers are highly sensitive to subtle landscape fluctuations induced by tectonic activity and can assist in differentiating active segments of geologic structures. Because drainage basins represent dynamic systems that may retain records of formation and progression since most tectono-geomorphic processes occur within its confines. Therefore, morphometric analyses of river networks, drainage basins and relief using geomorphic indices, as well as geostatistical analyses of topographical data have become useful tools for investigating landform evolution. As part of the Alpine–Himalayan orogenic system, the Zagros Orogen represents a mountainous region along ∼1500 km with an extensive active crustal deformation and intense seismic activity in a northwest–southeast direction (Gürbüz and Saein, 2018).Zagros fold-thrust belt is a foreland portion of Zagros orogeny in SW Iran. Khorramroud basin is in the Zagros fold-thrust belt at Lorestan subzone. In this paper we are undertaking a tectonic geomorphology of Khorramroud River catchment. The aim of this paper is to determine the most geomorphic indices and the analysis of the fractal dimension using the Box Counting method. Results of the calculation of geomorphologic indices: The value of the SL index varies from 9.25 to 574 in the region with low and high tectonic activity, respectively. Af index, 15 sub-basins are classified in class 1 (high activity), 24 sub-basins in class 2 (moderate activity) and 8 basins in class 3 (low activity). The presence of active fault system in these regions can be attributed to this asymmetry and tilting. The values calculated from the Bs index are classified in the classes 1, 2 and 3 which indicate the asymmetry of sub-basins. In 3 basins, the values of this index are classified in class 1, in 12 sub-basins in class 2 and 32 sub-basins in class 3. Vf index, 5 sub-basins are classified in class 1, 13 sub-basins in class 2 and 29 basins in class 3. S index, 5 sub-basins are classified in class 1 and 41 sub-basins in class 2. Hi index, 16 sub-basins are classified in class 1, 22 sub-basins in class 2 and 9 sub-basins in class 3. The mountain front of the study area is divided into 20 sections along the study area, in order to assessment of the Smf index. Then, this index is calculated for different sections. Measured values of the Smf index for most part of the study area show high relative activity. The classification used in this paper for each geomorphic index is calculated from El Hamdouni's method. By using relative tectonic activity Index (Iat) the area was investigated into 4 classes of tectonic activities as very high, high, medium and low. Based on this classification, the north, north-east and south, south-west regions have very high to high tectonics activities.In this study, by means of remote sensing methods and ETM images and based on surface deformation like curved, truncated and offset structures the lineament which are related to the activity of subsurface or conceal faults are mapped. Next, regarding to the study area, is created 6 squares with dimensions of 23.9 km, in order to applying the Box Counting method. In the fractal analysis, the fault lineaments of each square are evaluated separately. The fractal dimension is quantified for each square. Finally, they are drowned on the log-log graphs. N2 and N5 zones indicate the maximum fractal dimensions. These values are 1.7806 and 1.8264, respectively.

According to the values of the calculated indices, to determine the total tectonic activity, the relative active tectonics index (Iat) was evaluated. Based on the results of this study, the north, north-east and south, south-west regions of the basin have very high to high tectonics activities which are confirmed by the fractal analysis.

Geotourism is one of the relatively new branches of tourist attraction perception and is an interdisciplinary science based on geosciences and tourism whose major goals are to identify, prioritize, evaluate and manage land or geomorphic heritage.Geomorphosites encompass a variety of spatial and temporal scales of forms and processes. Geomorphosites are believed to have an important role in understanding the birth record and history of the Earth through its evidence. With these interpretations, geomorphic landscapes, such as forms derived from intrinsic (volcanic, fault ...) and outer (glacial, river, wind ...) processes, have a special place. In so far as the interpretation of the structure and functions of these forms and processes, together with their management in the form of a geomorphosite, are regarded as the principal aspects of the geotourism knowledge framework. Currently, most studies on the planning and management of geomorphosites and the structure of geotourism knowledge in our country are in their early stages. For this reason, the main nature of this study is to formulate and explain a geotourism management model based on the geomorphosites of desert areas. Accordingly, a structural study of geotourism along with a more scientific revision, especially with regard to entrepreneurship, local community development and environmental sustainability, is more than ever necessary. This study tries to formulate a geotourism management model for desert areas, especially the desert territory of Tabas city, while evaluating the capability of the arid geomorphosites of Tabas city by selecting a suitable method, management issues and geotourism management model of this area for principally exploiting and providing these areas. Edit and adjust.

In this section, desert geomorphosites in the region, despite the lack of a specialized desert evaluation method (in European researchers' studies), have not been developed yet. However, a method has been attempted to somewhat cover the characteristics of the deserts, especially the conditions of the study area. Among the available methods, the method of BrochI et al (2007) has been used. In this method, three sets of criteria are developed for designing and designing the quantitative parametric model: IQ = scientific aspects, based on the apparent and intrinsic quality of the geomorphosites (score between 1 and 3); P = potential for use as a cultural resource, Tourism and education (Score 1 to 3); C = Potential threats and protection needs and needs; For these three sections, Brochey (2007) 19 Indicators and Values Using Expert Opinions He obtained and suggested that these models were used in this model. The final quality of each geomorphosite is measured using the three main criteria and summing their values. Numeric points and values are between 1 and 1. Therefore, the benchmarks need to be normalized. In this method, however, the values set prior to the evaluation were to be valued by different experts and then normalized to the values obtained from the model evaluation, here due to the unavailability of the required number of experts on one hand and the lack of Their acquaintance with the geomorphological conditions of the Tabas desert was neglected and only the results of geomorphocyte evaluation were considered as the criteria.The second part of the process of preparing the communication in the managerial model will be as follows: Preparation of an Intellectual Model: At this stage a set of factors and elements affecting geotourism that includes effective processes in the field will be identified and identified.

The purpose of this section is to evaluate the geomorphologic nature of desert terrain in Tabas. In this regard, 24 field geomorphosites from desert and desert areas were selected for evaluation. In the next step, these geomorphosites were evaluated by 19 criteria in the model proposed by Broch et al in 2007, the final results of which are shown in Table 3. As the results show, the sandstones were selected as the superior geomorphosites, and the truncated rock outcrops and old low-lying Kalmar Mountains were the next priorities.Tabas city contains archive of all kinds of desert geomorphosites, desert, glacier, karstic, mountainous ... etc is protected and not protected. And each geomorphous site is a suitable geosystem with different geomorphic actions and reactions, and therefore each management style will be unique. Land Surface Systems in the Desert Territory in the Tabas City Geotourism Area, provided they have complementary values as desert geomorphosites. Step Two: At this stage, desert geomorphs were identified, selected and evaluated. Step Three: Selected geomorphocytes were evaluated by the method proposed by Brochey and desert geomorphocytes that are qualified to implement in a managerial paradigm and can play a role in realizing geomorphology, environmental sustainability, entrepreneurship, and community economic development. , Were selected based on the scores obtained. Step Four: Camel Ridge Geomorphosites, truncated rock outcrops, and old low-altitude Clamford Mountains, scored favorably. At this time, it is necessary to identify and characterize the management challenges of these areas in terms of human and natural geography with the knowledge of the study area, prior studies and applied geography knowledge. Until the desert geomorphosites overcome these challenges, the challenges are set out in the following pattern before they are implemented. Step Five: After going through the filtering and considering the human and natural management challenges, it is now time to examine the potential status of these geomorphosites in terms of services and infrastructures, practices, safeguards, vulnerabilities, and vulnerability thresholds. And presenting future research perspectives were studied. Step 6: At this stage, the implementation of the Goals of Step Five has been examined by government management namely correspondence with the Governorate of Tabas and the Governorate of South Khorasan, and in particular with local management ie local councils and the indigenous Tabas community. Fortunately, the management area of selected desert geomorphosites in Tabas County has a better performance. Step 7: Finally, by correctly combining Step 5 and Step 6, a proper return to the transcendent goals of geotourism will be possible and the third step can be revived.

Part of the studies of geomorphology is dedicated to the automated, semi-automated identification, segmentation and classification of landscapes and landforms at different scales. Each of the landscape classification systems, includes a number of smaller units or landforms. Some methods have been used to identify and recognize Landforms, while others have been dedicated to the landscape classifications. Landscape classification is applicable in a wide range of geomorphological studies such as, mapping geomorphological maps, zonation and environmental potential in the field of ecotourism, environmental exploitation and sustainable development, and also in the field of economic geography, natural hazard assessment and arranging the land use planning documents of the country and many other fields which is directly and indirectly applicable. In this study, an attempt has been made to present a new system in the landscape classification which be able to recognize and classify, landscapes of the terrain surface using, digital elevation models considering the ease and simplicity of the procedures. For this purpose, tangential, plan and profile curvature and slope extracted from digital elevation model, these three curvature combined together to get mean curvature and these factors including elevation, slope and mean curvature used to classify landscapes of the terrain. In the next step, each of the three components of the above-mentioned was divided into two parts based on 5 methods of thresholding, including: geometrical interval, quantile, natural breaks, standard deviation and weighted average. Finally, all three components have been coded and named with a special system and the area of the Iran, was classified into 8 landscapes units and the results were presented as a color map

The reduced groundwater levels of plains have led to increased water extraction cost, increased energy consumption, reduced water quality, and the appearance of subsidence. Structural subsidence in plains could be directly resulted from reduced level of groundwater as well as destruction of alluvial texture in aquifers. Although it results from the compacted underlying layers of soil, subsidence represents unpleasant outcomes in the future. The compacted underlying layers and reduced water table in groundwater basins reduce the water storage space. In other words, reduced groundwater storage is a rational consequence of subsidence (Sharifikia et al., 2014). Scientific investigation and various instruments, including mathematical models, are required to overcome the problems of groundwater resources (Gaura et al., 2011; Hu et al., 2010). Mathematical models allow for investigating changes in the current and future situations of groundwater tables by incorporating different influential factors (Yaoutia et al., 2008; Zhang, 2010). Therefore, using mathematical models, the situation of an aquifer can be simulated by collecting information on the inputs and outputs of the groundwater system at a small cost in a short time. Such simulation models can provide the mutual effects of surface water and groundwater in short- and long-term periods. MODFLOW is among the most important mathematical models (Lachaal et al., 2012).In this current paper the approaches to minimize subsidence in Abhar plain is evaluated that based on an empirically derived relationship between cumulative subsidence rates and groundwater levels is used with common groundwater model software MODFLOW. Also, the vertical changes in the ground structure were nonlinearly modeled based on the cellular changes of the finite difference method. To determine the essential factor of the changes, the fuzzy model approach and spatial-statistical analysis were adopted. The largest effect on the appearance of subsidence was studied by the regression relationships of the corresponding points in the spreadsheet setting.

Abhar Plain is located in the northwest of the Namak Lake Basin. It occupies an area of 1926.5 km2, 1040.92 km2 of which is plain, while the remaining area is composed of mountains. The maximum and minimum elevations of Abhar Plain are 2166 and 749 m, respectively. The saturated area of the plain is approximately 657.9 km2 – the corrected area is 657.4 km2. The groundwater extraction area of the plain includes 1359 water wells with an annual discharge of 233.36 million m3, 169 fountains with an annual discharge of 4.88 million m3, and 101 aqueducts with an annual discharge of 1.3 million m3.Considering that parameters involved in the calculation of the final subsidence layer had reduction or enhancement effects on each other and given the descriptions on the overlapping functions, the current study employed the gamma operator. Computation was performed by the MODFLOW-2005 engine in GMS v.10. The study period was selected to be 119 months, which could be made more accurate and rebuilt based on the maximum available data. The computation engine of PCG2 with 100 outer and inner iterations, a critical convergence variation limit of 0.01 m, and a critical convergence error limit of 0.01 m3 per day was selected. 75% of the interval length was used for calibration in non-stable conditions. After seven executions of the calibration model with a certain number of internal iterations, the optimal final number of surface feeding, horizontal hydraulic conductivity and horizontal hydraulic conductivity anisotropy, specific yield in the form of pilot extraction points, and transferability parameters in the boundaries, and the waterway network in the form of the bunches of lines. The layers were also used to develop the fuzzy subsidence model.According to the described fuzzy theory, each of the basic parameters influencing or representing subsidence was transformed into a standard raster map in the range of 0-1 by a linear equation. The fuzzified layers included water level reduction, the difference between the initial and final water levels, horizontal hydraulic conductivity anisotropy, horizontal hydraulic conductivity, saturated aquifer depth, surface feeding, extraction flow rate, and specific yield. Direct fuzzification was applied to all the parameters, except for surface feeding, to which inverse fuzzification was applied. Table 2 shows the zoning errors of the selected layers. The parameters of Table 2 were used to develop the fuzzy model. The coefficients of the aquifer were merely extracted from the last calibration round of the groundwater flow model.In the next step, to investigate the spatial variations of subsidence occurrence, the land-use layer was utilized as the statistical analysis basis of the subsidence raster output.

The results of the current study indicated that subsidence modeling can be carried out by linear regression analysis with a determination correlation coefficient of 70%. Among the influential parameters, merely the spotted aquifer depth layer had a correlation of 30%. The fuzzified variations of the layers with the enhancement-reduction gamma combination had the highest correlations with the spotted subsidence layer of the MODFLOW output. However, the correlation was noticeably lower than expected. In the subsidence model, the final raster layer was transferred to the GIS setting using dispersed points. After zoning using the regional analysis command in two manners, the separate indicators of land-uses and a basic land-use set were extracted. These results revealed that the urban land-use accounted for 26% of subsidence, even though it occupied merely 4% of the aquifer’s area. The agricultural land-use (including gardens), which accounted for 42% of the aquifer’s area, involved 56% of subsidence events. Finally, occupying 54% of the aquifer’s area, the idle land-use accounted for merely 19% of the vertical changes of the aquifer’s structure. The high subsidence in urban areas was noticeable. Considering the alluvial groundwater yield, the effect of water level reduction can be seen with small spatial distances.

One of the types of wide-ranging processes that cause many casualties and financial losses in many parts of the world and Iran every year is landslides. Landslide is a mass movement of soil or rock due to gravity on slopes, which is one of the important geological hazards. Landslides are morphodynamic processes that have a complex structure and various factors and variables play a role in its creation, so it is very difficult to assess the risks of this phenomenon. This natural phenomenon causes the destruction or damage to residential areas, all kinds of vital structures and arteries such as roads, power lines, water, gas, pastures, forests, and agricultural lands and will have many destructive environmental and social effects. Landslide mechanisms and their main mechanisms, in addition to internal and external factors (climate), are also affected by human (entropic) activities. In today's world, many methods have been introduced for zoning the risk of landslides, which are generally divided into three categories: statistical methods (two-dimensional, multivariate, logistic regression, information value), experimental (Anbalagan, Mora-Warson, Stevenson) and a combination. (Artificial neural network and fuzzy logic) are divided. Most of these methods are experimental and are presented regionally for specific conditions. In Iran, there are many landslides every year that cause a lot of damage (Afjeh Nasrabadi et al., 2008). Alamut River catchment area with an area of 74.82 square kilometers, with coordinates of 15 23 23 ° 50 to 54 52 52 ° 50 eastern longitude and 12 17 ° 36 to 17 33 ° 36 north latitude in northeastern Qazvin province in one The mountainous region is located. Due to the mountainous nature of the area and the great difference in altitude, various geological formations and a wide variety of lithology, have a great talent for creating wide-ranging movements, especially landslides. In this study, the potential for landslide risk in the Alamut River catchment area has been investigated using the perceptron artificial neural network method. Neural networks are computer algorithms that can extract our important relationships between a large number of linear and nonlinear parameters from a given bank. Pradhan Vali, 2010). Artificial neural network is one of the effective models in zoning the landslide. In this model, complex statistical analyzes have been avoided and based on nonlinear functions, each of the effective factors in landslides has been assigned weight. This model is based on the training of effective factors in landslides and by dividing the data in educational and experimental classes and using sigmoid functions, it proceeds to zoning the susceptible areas. Perceptron networks consist of an input layer, a number of hidden layers, and an output layer. In multilayer perceptron networks, the number of hidden layers can be any number, although in most applications a hidden layer is sufficient. To conduct this research, slides were first identified through the interpretation of aerial photographs and satellite images and field visits and a map of their distribution was prepared. In the next step, according to the location of landslides, seven effective factors of slope, slope direction, height, precipitation, land use, lithology and distance from fault were investigated and information layers were prepared by GIS and in MATLAB environment suitable structure for Landslide zoning was written using the artificial neural network method with a multilayer perceptron structure. In order to use the neural network method in MATLAB software, the following steps have been performed in order. 1. Provide data. 2. Normalize the data (0 to 1). 3. Squaring the area to 100 * 100 sides. 4. Teaching the neural network (using the previous slips and that the areas with a slope below 5 degrees and the areas inside the waterway do not have slips). 5. Convert data to Excel. 6. Enter the data into the network and get the output. 860 pixels of data were used to train and test the network throughout the region. Of these, 674 pixels were used for training and 184 pixels for network testing.The results of the artificial neural network outcomes in the experimental phase show that the network created was able to report 36 out of the 38 sliding pixels correctly, indicating of 91% sensitivity. Also, out of 150 non-slip pixels, the network was able to detect 143 experimental samples, which again achieved an accuracy of about 91%. Therefore, the total accuracy was calculated to be 91%. The number of repetitions was changed from 1000 to 15000, which was calculated with the number of repetitions of 10,000 as the minimum error value. According to the results of the neural network method, only 25.76% of the basin area is in the middle and upper class and 72.17% of the basin is in the low and very low class. Out of a total area of 435 square kilometers, landslides are 121 square kilometers, about 21.81 percent are in very low, low and medium classes and 314 kilometers, about 72.81 percent are in high and very high classes. Areas with high and very high risk classes are often located in the eastern and northern parts of the basin, and areas with very low and low risk classes are mostly located in the western and central areas.

The Soil and Water Assessment Tool – SWAT (Arnold et al. 1998; Arnold et al., 2012) is a well-established physically based, semi-distributed hydrological model for river basin scale application (Brighenti et al., 2019). is a hydrological model to assess sediment (Yesuf et al., 2015; Vigiak et al., 2017; Abdelwahab et al., 2018), impact of land use (Zeiger and Hubbart, 2016; Choto and Fetene, 2019; stream flow generation (Hallouz et al., 2018; in-stream water quality, climate change and, water quality and quantity variation (Jiao et al., 2014; Francesconi et al., 2016; Yang et al., 2018 ). A number of studies used laboratory, field scales and modeling studies to understand soil erosion and sediment dynamics in various regions. Several worldwide studies have been done using the SWAT model. The objective of this study is to apply the Soil and Water Assessment Tool (SWAT) model to predict surface runoff generation patterns and soil erosion hazard in Badavar catchment and to prioritize most degraded sub-catchment in order to adopt the appropriate management intervention. The specific objective of the study is validation of the SWAT model to assess its capability in predicting sediment yield transport in Badavar catchment.

Study area The study area is Badavar catchment (58311 ha) in the west of Iran and the northern part of Lorestan Province. Badavar catchment is one of the sub-catchments of of the Karkheh River. The average height of the catchment is 1941 m. The annual average precipitation is 527 mm, and the annual average temperature is about 10.5 °C. The climate of study area is in the category of sub-humid, characterized by cold winters and temperate summers. Most of the study area has slope 0-5 degree. Rain farming and week rangeland are the dominate land use in this area.The soil erosion is critical in the Badavar catchment. soil erosion in addition to damages to the land, it will reduce the capacity of the downstream dam of this catchment (the Karkheh dam). So in this study, SWAT model is used to identify the risk of erosion in Badavar catchment and various factors affecting of the erosion.SWAT Model: Model parameters: For preparation of input data, the GIS "version10.3"and ArcMap "ArcSWAT2012" was used. The following basic data were selected as SWAT model inputs:1) Digital Elevation Model (DEM): it is extracted from 1:50000 topography map with a spatial resolution of 30 m. 2) Landuse: it was obtained by Forests, Range and Watershed Management Organization of Iran. 3) Slope: it is extracted from DEM. Slope map shows that the Badavar catchment consists mainly of the plain with low slope (0-5 degree). 4) Hydro-meteorological data: The data required includes rainfall, river discharge, and climate data such as temperatures, solar radiations, humidity, and wind speed. These hydrological and meteorological data are collected from two organizations; the Iran Meteorological Organization and the Ministry of Energy.5) Soil data: SWAT model requires different soil textural and physico-chemical properties. These include soil texture, available water content, hydraulic conductivity, bulk density, and organic carbon content for different layers of each soil type. All of these soil characteristics and the soil map of the catchment were prepared by field survey and 39 systematic random samples of soil and laboratory works. Also the soil texture data was used to extract hydrological soil groups that were linked with FAO’s texture classification. This was then linked with the SWAT database using the soil layers and soil type. 6) Delineation of sub-catchments and HRUs:SWAT uses two types of functional units: the subbasin and the Hydrologic Response Unit (HRU) (Neitsch et al., 2011). The sub-basin is a spatially defined region that comprises a main reach and its contributing area, which is composed by one or more HRUs (Vigiak et al., 2017). On the other hand, analysis the catchment is allowed by SWAT as a whole or by subdividing it into sub-basins containing the same portions called Hydrological Response Units (HRU) (Briak et al., 2016). The HRU is a land unit of homogeneous environmental properties (soil, land use/cover, management, and topography) and hydrologic behavior (Vigiak et al., 2017). 33 sub-catchments and 53 Hydrological Response Units (HRU) have been generated for Badavar catchment.After simulation, factor analysis was used to determine the most important factors in sediment production. Factor analysis attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. The purpose of factor analysis is to reduce the complexity within the similarity matrix of a multivariate data collection, transforming it into a simpler and more easily interpreted factor matrix.Finally, for comparisons between observed and simulated sediment loads, four model evaluation statistics were selected; Correlation coefficient (R2), Root Mean Squared Error (RMSE), Index of Agreement (D) and Correlation determination (r).

The most important inputs of the model are: soil information, land use, slope, elevation, geology, weather information (rainfall, background and temperature, relative humidity, dew point, solar radiation and wind speed). Also factor analysis was used to determine the most important factors in sediment production. The simulation results showed that amount of sediment output from the basin is 7170 tons per year. After implementation of the model, the amount of sediment simulated and observation sediment was compared. Correlation coefficient (R2), Root Mean Squared Error (RMSE), Index of Agreement (D) and Correlation determination (r ) were validated with 95%, 03%, 97% and 97%, respectively which indicated the high accuracy of the results. Also the results of factor analysis showed that the role of land use in sedimentation of the study area is more than other factors.

The comparisons between observed and simulated sediment loads showed that the model has fairly acceptable accuracy for Badavar catchment. A more accurate estimation of erosion and sediment yield can be made by providing accurate data and the best management practice is highly recommended for the dam sustainability, because of the proximity of Badavar cahchment erosion to the Karkheh dam.
Key word: Erosion, Sediment, Factor Analysis, SWAT, Badavar.

Surface sediment's particles diameter is closely related to dominant processes of developing surface morphology of landforms. This study investigates the relation between sediments texture and gullies development in southern plains of eastern Alborz using statistical analysis and measuring the particles' diameter in four pilots in Garmsar-Seyyed Abad, Iran. The pilot sites were selected based on differences and similarities of surface morphology using satellite imageries and field studies. Samples were collected from bottom, middle, and upper part of gullies. After measuring the weight and doing sieve analysis of representative samples, the results were analyzed using GRADISTAT software in the form of graphs and tables. In addition the morphometric index was measured collecting 800 samples from 5*5 m boxes. The results showed that the surface sediments are derived from multiple sources which indicates the difference of effective processes over time (sites 1, 2 and 4, covered by gravel pavement called sandy gravel, sited 3 covered by gravely sand. Site 1 and 4 are derived from two sources, site 2 is three sources, and site 3 is single source). Poorly sorted sediments indicate that the size and type of gully's surface and deep sediments have not been able to fully affect on gullies development in all sites. Therefore, gullies development is not only related to sediments texture and diameter, but also consideration of more important factors such as tectonics processes is required in these sites.

Tectonic geomorphology is the knowledge that can quantify the impact of active tectonics using geomorphic indices as quantitative measurements and descriptions of landforms and landscapes on rivers; thus, quantitative conditions measurements Provides them with the opportunity to identify the status of areas with active tectonic structure. Extraction of geomorphic indices using Digital Elevation Models (DEM) in GIS software environment in recent decades has been accurate and reliable method in drainage basin analysis, as one of these indices for rapid evaluation of activity. In order to study the active tectonics in the study area, the morphotectonic parameters of the rivers have been used. With the study of topographic landforms and the model of drainage systems by using geomorphic indices and the geological structure of each area, it is possible to evaluate the active tectonic performance and to determine the absence of active tectonic movements. The quantitative measurements provide conditions that allow them to identify the status of active tectonics areas. Along with the advancement of tectonic science of geomorphology, scientists have found that active tectonic processes can affect the shape and function of rivers being one of the most important observations that occur rapidly. And consistently respond to deformation caused by active tectonics at the surface reflecting minor changes in topography, thus examining drainage pattern and river diversion provides important information on structural expansion and evolution of the area. the Alborz orogenic belt is a part of the named area, and the placement of the studied area in the central Alborz has caused the area to be affected by this tectonic movements. This mountain range is the result of two orogenic movements, one of them is Precambrian ores (Acinitic), the course of which is essentially a metamorphism that leads to the interconnection and hardening of the paving stones in the Precambrian, The second one is the Alpine orogeny movements that it happens in Mesozoic and Cenozoic periods. This mountain range is approximately 600 kilometers long and 100 kilometers wide along the south side of the Caspian Sea. The northern margin of the Alborz line is usually sloping. General trend of study area is NE-SW. Firstly, Rivers and basins of the area were extracted using STRAHLER method using 30 m accuracy digital elevation model in Arc GIS software. Then the necessary modifications to the wells and extraction basins were carried out using topographic maps and satellite images and finally the study area was divided into 18 sub-basins. Finally, for the extracted basins, geomorphic indices including hierarchical anomaly indices (Δa), longitudinal river gradient (SL), basin shape (Ff), drainage density (Dd) and relative prominence (Bh) in 18 drainage basins were calculated and finally the relative active tectonic index (IAT) was measured. A tectonic activity zoning map was prepared for each indicator in the study area and the results of the indices were analyzed. Based on the calculations obtained from the hierarchical anomaly index, the index shows very high and high values in sub-basins 2, 6, 9, 10 and 13 along the Khazar, North Alborz and Azarak faults. The values obtained from the basin shape coefficient index calculations are also very high and high along the mentioned faults and in the aforementioned basins. In basins 12, 13 and 15 that lie along the Hassan Gile fault, the values obtained from the relative prominence indices and drainage basin density are very high and high. The extension of these faults in basins 12, 13 and 15 increases the longitudinal gradient index and thus indicates a high rate of morphotectonic anomalies in the area. Finally, by calculations with relative active tectonic person and comparing it with other calculated indices, it was found that sub-basins 12, 13 and 15 affected by Hassan Gil fault activity Very high and sub-basin 6, which is affected by the Azarak fault activity, shows high index. It should be noted that other minor faults formed due to high tectonic activity in the area have a significant impact on the increase of morphotectonic indices and have caused some sub-basins. High levels of indicators and sometimes very high. Studies in this part of central Alborz show that recent relative tectonic activity is high and very high due to the active faults in the region such as Azarak, Caspian and northern Alborz. About 66.5 percent of the area is dominated by these faults, as well as other minor faults formed by recent tectonic movements, suggesting moderate to high tectonic activity. So it can be understand that this area of Alborz totally having the high active tectonic based on morphometric indices.

Animated sand dunes are a major threat to wind erosion, causing severe damage to transportation networks, agricultural products, water resources and residential areas ) Ahmadi, 2006). Sand hills are also one of the most important facets of wind erosion. Sometimes the speed and direction of the winds in the area can help a lot to understand the morphometric properties of sand dunes (Pourmand et al., 2015). The use of remote sensing over the past few decades as a tool to study the characteristics of sand dunes and study its changes over time has made it possible to extract former climatic diets and monitor marginal areas that are prone to desertification (Otterman, 1981; Tucker et al., 1994). Image processing is one of the techniques used in data analysis and interpretation of satellite data to study natural hazards such as sand dunes. In general, monitoring the changes and spatial-temporal progress of sand dunes as one of the most important environmental hazards in arid regions of the world is essential. Therefore, Sistan plain in eastern Iran in recent decades due to environmental and human factors, influx The flow of sand and the advance of the sand dunes in it have stopped human activities in most parts of it and have caused irreparable damage. Studying this environmental risk in this area will not be without merit to reduce its destructive effects and sustainable management in the area. This study uses the time series of Landsat satellite data to monitor the trend of changes in the sand dunes of Sistan plain and its effects on people's social life. To do this, Landsat 5 and 7 satellite images (July and August images between 1995 and 2018) were used. After obtaining the required data from USGS site and forming a database, the steps of correction and analysis were applied to them with the help of GIS and ENVI software, and then four Landsat image tiles were mosaiced together to completely cover the study area. . In this study, a field visit was made to identify the location of hills and other sand forms and during the maximum likelihood classification in Envi.5.3 software environment, the affected areas of hills and other sand forms were identified and The changes in this phenomenon were compared between 1995 and 2018. Several factors affect the movement of quicksand in the Sistan plain. One of the most important is the 120-day winds in the dry seasons, which burn at 110 to 170 kilometers per hour. The results of the Golbad drawing for July are shown in Figure 3. July, like June, which is affected by the 120-day winds of Sistan, offers a similar pattern both in terms of direction and speed of wind. This month's wind direction is northwest to southeast. Satellite images were taken over six periods to study how sand dunes developed and spread in the region in July and August. Between 1995 and 2000, with the drying up of Hamoon Wetland, the spread of sand dunes on the shores of the lake is clearly visible. Although Lake Hamoon has been flooded in 2015, the spread of sand dunes affected by the lake's dryness in the past is still visible, due to strong winds and the loss of vegetation during the pre-basin period. It's time. In 2010 images, the lake's flooding during this period had a positive effect on stabilizing and reducing the surface of the sand dunes. However, in the following years, with the decrease of the lake's water level, the amount of sand dunes in Sistan plain has increased again, which can be clearly traced from the prepared images. Analysis of meteorological data on wind direction and speed in this area showed that the prevailing winds determine the movement of sand dunes, 120-day winds, and there is a direct relationship between wind speed and the rate of movement and progress of sand dunes. Landscape sandstone expansion plans from the Landsat Measures Processing for July and August, the most turbulent months in terms of winds, were developed in 1995 and 2018 to monitor changes in the region's sand dunes. Which clearly depicts the dynamics of sand dunes in different years. According to the results of a study of Landsat satellite remote sensing data, the area of sand dunes in August increased from 8.23 percent in 1995 to 11 percent, and in July 2018 from 7.55 percent to 10 percent of the range Cover the study. Which indicates an almost dramatic increase in sand dunes. Also, the drastic changes in the area of Lake Hamoon from 1995 to 2018 can be clearly seen in the prepared maps, and it is said that the expansion of sand dunes in different years is directly related to changes in lake level at different times. Field studies show that during the frequent droughts in the Sistan region, the movement of sand dunes has been so high that it has buried a large number of rural houses and destroyed agricultural lands. This has led to the unemployment of a large number of farmers in the region and has caused severe damage to water supply facilities and canals, so that its compensation requires a lot of time and money. Many people in the area have migrated to escape the situation, and many have resorted to false jobs to earn a living, including fuel smuggling, smuggling and tourism. Comparing the results of this study with the study of writing (2007) that examined the damages caused by the relocation of sand dunes in eastern Zabul, shows almost the same results, with the difference that this study generally considers the Sistan plain. Data and a complete study of the trend of changes in sand dunes in Sistan plain show, but the study study in 2007 was only focused on sand dunes in the east of Zabol.