فصلنامه پژوهش های فرسایش محیطی
سال نهم شماره 2 (پیاپی 34، تابستان 1398)

 Coastal systems are very dynamic, and their movement is relatively fast due to the collision of onshore and marine environment. The majority of the world's population is concentrated along with the coastal areas. Hormuz Strait coasts are affected by morphological variables due to the hydrodynamics of the sea and the dynamics of coastal and onshore environments. Destruction, transport, and displacement of sediments, Settlement of destruction's materials, are the most prominent features of this case study. Coastal areas of Bandar Abbas are occupied by the dense of human constructions and residential people. The northern coast of the Hormoz strait has the highest tidal range in comparison with the shoreline of the Oman Sea and the Persian Gulf. Therefore, the effects of seawater in this area are more obvious than other places, and all of these factors cause coastal changes. In general, the research goal is to study the shoreline changes in a 47 years period and also find the most important factor in shoreline changes in that period, finally to predict the shoreline changes in the future.

 In this study, Landsat satellite images, MSS, TM, ETM +, OLI sensors from 1972 to 2019 were used to monitoring the shoreline changes in the northwest and west of the Hormuz Strait. In the next step, the necessary preprocesses (radiometric and atmospheric corrections) were applied to the images in ENVI 5.3 software. Next, the NDWI index was used to process satellite images and to separate water and land index. After that, to improve the clarity of shoreline changes, the High Pass filter was applied on each image. After applying the filter, the shoreline was extracted in each year. After extracting the shoreline, the shoreline zones turned into Vector in ENVI software and then moved to Arc GIS10.5 software. After transferring to Arc GIS10.5 software, the coast was divided into 4 zones, by using satellite images and field visits and also according to natural and human factors. Furthermore, Digital Shoreline Analysis System (DSAS) was used to analyze the shoreline variations. After calculating shoreline variations through the Digital Shoreline Analysis System, the MAPE and RMSE criteria were used to evaluate the error in the change ratio, accuracy, and positioning of shorelines in 2029 and 2039.

 Linear Regression Rate (LRR) is derived from fitting the position of the shoreline to the shoreline time. The slope of the linear regression equation shows the displacement rate in meters per year at a confidence level of 95%. For zone A, which includes the Delta of Hasan Langi and the Shur river, the coastal displacement rate is between 0.05 to 11.04 meters per year. The positive value of the slope in the regression equation indicates the progression of the shoreline to the sea (the sedimentation process). This progress is related to the extension of two important rivers, Hasan Langhi and Shur, and the amount of sediment carried by these rivers. For zone B, the shoreline displacement is between 1.78 to 7.7 meters per year, which the coastal constructions that have expanded over the past 30 to 40 years are one of the most important factors in this case. The study of transects in this zone shows the stability of this zone because most of the shoreline changes are less than 1 meter per year. For zone C, which includes the Kol river delta, the shoreline displacement is between -0.9 to 8.58 meter per year, which the sedimentation was seen in different parts of this zone by calculations and the review of transects. In zone D that covers the area of Mangrove forests and the Mehran river delta, the shoreline displacement from -3.6 to 6.84 meter per year. Due to the values obtained from the validation of LRR and EPR parameters, the EPR parameter has less error in both MAPE and RMSE criteria and is more proper for shoreline prediction.
 

 During the 47 years, Linear Regression Rate (LRR) in the zones of A, B, C, and D was respectively 12.6, 1.65, 2.63 and 0.8 meters per year. According to the results, shorelines have more progress toward the sea, showing that the sedimentation is more than erosion. The changes were calculated by the transect method during four periods, and then using the statistical analysis, and finally the calculation of MAPE and RMSE. As a result, the EPR parameter was selected as the basis for predicting shorelines. The predicted shorelines for the next 10 to 20 years also show the progress of the shoreline toward the sea and the continuation of this sedimentation process. In addition, it is not possible to ignore the zones that the erosion process occurred, especially in the Hara forests in the Khour Khoran and parts of the Kol River delta and in the west zone of Bandar Abbas. The results of this study give an indication of the vulnerability of various coastal areas and essential needs for planning to protect the shoreline in different zones.

Wind erosion as an “environmental threat” has caused serious problems in the world. Identifying and evaluating areas affected by wind erosion can be an important tool for managers and planners in the sustainable development of different areas.  nowadays there are various methods in the world for zoning lands affected by wind erosion. One of the most important methods is the use of satellite images and various classification methods. Satellite imagery with features such as wide coverage, repeatability and continuous updating is particularly important in determining land cove.  classification methods include pixel based, object oriented and map decision tree. Field studies on the spatial development of wind erosion sites are difficult and expensive to replicate and monitoring studies in these areas are not possible. the purpose of this study is to evaluate the classification methods in the detection of the Sarakhs plain sandy zones in order to identify endangered sources of these zones.

In this study, the Landsat ETM + satellite data was used from USGS web site and all the processed satellite images was done with ENVI software and Arcmap 10.3 GIS. After pre-processing the images, including geometric, atmospheric and radiometric corrections, the land use map was prepared using a supervised classification method in six classes. These classes include agricultural lands, barren lands, sand dunes, wind deposition, lakes and rangelands. Classification was performed based on all the algorithms of pixel based, object oriented and map decision tree methods. These algorithms include maximum likelihood, minimum distance, neural network, and support vector machine in the pixel based method and The object-oriented approach used the nearest-neighbor algorithms on the scales of 1, 3, 5, 7 and the support of vector machine. The final classification was done by a decision tree method map. Parameters used for validation of the results include total accuracy, kappa coefficient, accuracy matrix of the producer and the produced, quantity and allocation disagreement.

The results of the classification show that the number of pixels in the training samples is 25049 pixels obtained by random sampling. The number of ground points used to estimate the overall accuracy of the produced maps are 90 control points from Google Earth satellite imagery, 50 control points from 1: 50000 topographic maps and 45 field control points. The evaluation of classification methods showed that higher accuracy percentage for decision tree method is 87%, kappa index 82%, Quantity disagreement 6.7% and allocation disagreement 5.6% Compare to other methods. These coefficients in the pixel based method are respectively 83%, kappa coefficient 78%, quantity disagreement 10.4% and allocation disagreement 6.1% And in object-oriented method, the overall accuracy is 80%, Kappa index 75%, quantity disagreement 83% and allocation disagreement 7.6%. The least producer and user accuracy in all three methods is related to sand dune class and the highest amount of quantitative disagreement is assigned to the pixel based method for the class of wind deposition and rangelands, In the object-oriented method, it is related to the class of agricultural lands and sand dunes, and in the decision-tree method, it is related to the classes of agricultural lands and rangelands. This may be due to the lack of acceptable separation of the sand dune class.

In this study, it was assumed that the object-oriented classification method would more accurately classify sand dunes and zones but since the sand dunes of Sarakhs do not follow specific morphology and geometry and they are more longitudinal Therefore, the classification of these zones was performed better with the pixel based method. But the land use, such as agricultural that follows geometric shapes, was more accurately classified in the object-oriented method. The area of sandy lands, including hills and sandy zones, was estimated to be about 1349 km2. Most of these lands are located in the central part of the study area in the vicinity of biological and physical elements. Also, the comparison of the area maps shows that the area of land using water levels and agricultural lands are close to each other. And the area differences are mostly related to rangelands, barren lands and sandy areas. Based on the results of this study, it can be suggested that decision tree method is more suitable than pixel based and object oriented methods for classifying land cover and detecting sandy zone changes and the most important reason is the use of both algorithms.

Soil moisture curve is an important characteristic of soil and its measurement is necessary for determining soil available water content for plant, evapotranspiration and irrigation planning. Direct measurements of soil moisture coefficients are time-consuming and costly. But it is possible to estimate these characteristics from readily available soil properties. The purposes of this study were: 1) development of pedotransfer functions (PTFs) for estimating of soil moisture content at field capacity (FC) and permanent wilting point (PWP) conditions by artificial neural networks system (ANN) and multivariate regression method and 2) investigation effects of using soil primary particles, aggregates and porosity fractal dimensions as a predictor for increasing the accuracy and reliability of these PTFs.

For this reason, 90 soil samples from three regions (Agricultural land of the Ardabil plain, Forest of the Fandoglo and Rangelands of the Sareyn, which were located in Ardabil province) were collected in random design sampling method. Then FC and PWP coefficients of these soils were measured using pressure plates apparatus. As well as, some readily available properties of soils such as fractal dimensions (primary particles, aggregates, and soil pores), texture, bulk density and particles density, porosity, organic carbon and calcium carbonate equivalent (CCE) were determined by routine laboratory method. Then data were divided into two datasets randomly: Training dataset (including 72 soil samples) and test dataset (including 18 soil samples). Regression-PTFs for estimating FC and PWP were developed once by using and once without using of the fractal dimension of primary particles (DS), the fractal dimension of aggregates (Df) and fractal dimensions of soil pores (Dy) as independent variables. The predictors of Regression-PTFs once again were used for development of the ANNs-PTFs. Therefor two PTFs were developed for predicting each dependent variable (FC and PWP). Statistical and Neurosolution softwares were used for development of the Regression-PTFs and ANN-PTFs, respectively. Finally, the accuracy and reliability of PTFs were investigated.

Results showed that FC has a positive significant correlation with soil silt (r= 0.52**) and organic carbon content (r= 0.86**), and a negative significant correlation with sand (r= -0.50**), CCE (r= -0.74**), bulk density (r= -0.64**), particles density (r= -0.79**) and Df (r= -0.47**). As well as, there are positive significant correlation between PWP and other soil properties such as soil silt (r= 0.48**) and organic carbon content (r= 0.77**), and negative significant correlation with sand (r= -0.50**), CCE (r= -0.74**), bulk density (r= -0.70**), particles density (r= -0.80**) and Df (r= -0.52**). Results also showed that there is a positive significant correlation between FC and PWP (r= 0.84**). When fractal dimensions used as independent variables for estimating of FC, three variables (bulk density (ρb), particles density (ρp), and fractal dimension of soil pores (Ds)) included as a predictor in PTFs and these predictors could explain 80% and 98% of variation of FC, at Regression- and ANN-PTFs, respectively. But when fractal dimensions didn’t used in modeling, PTFs was developed with four predictors (ρb, ρp, dg and σg) and these predictors could explain 81% and 92% of the variation of FC, at Regression- and ANN-PTFs, respectively. Results also showed that there were no significant differences between the Regression- and ANN-PTF which achieved for the estimation of FC values. As well as, Regression-PTF by using fractal dimensions as independent variables for the estimation of PWP was developed with three predictors (ρb, ρp and Ds) and these predictors could explain 76% and 92% of the variation of PWP, at Regression- and ANN-PTFs, respectively. But when fractal dimensions weren’t used as independent variables, PTFs was developed with two predictors (ρb and ρp), and these predictors could explain 71% and 85% of the variation of PWP, at Regression- and ANN-PTFs, respectively. Results of the investigation of accuracy and reliability of the PTFs showed that when fractal dimensions used as independent variables for estimating of PWP, only the accuracy and reliability of the ANN-PTF was increased.

ANN-PTFs were more accurate than Regression-PTFs. When fractal dimensions of soil primary particles, aggregates, and pores were used as independent variables in modeling for the prediction of FC and PWP, only the fractal dimension of soil pores included as a predictor and increased the accuracy of ANN-PTFs, but it could not increase the accuracy of Regression-PTFs.

 Land use includes all types of land uses to meet different human needs. In other words, land use refers to the type of human use of land, and this type of use is related to the value of the land and (its) natural characteristics. To understand and identify, land use changes using satellite data to provide a broad and integrated view of an area, reproducibility, easy access, high accuracy of data obtained and high analytical speed, as well as performing the classification process a suitable way to map land use. It is particularly widespread in geographical areas. These changes include changes in the hydrological system, effects on erosion, changes in soil physical and chemical properties, and vast changes in land surface morphology, so studying land use changes is one of the (necessities). (The) rain study is the cognition of the face of the earth. Identifying timely and precise land use changes is the basis for a better understanding of the relationships and interactions between humans and land resources. Soil erosion is one of the most important soil (in)fertility factors that nowadays is increasing because of poultry manure loss.

 The data needed in this method include topographic maps, land use, hydrological basin, soil, digital elevation model, slope of the area, as the input to the required model. Soil information is one of the most basic data needed for soil erodibility. WLC model requires soil map to scale with different soil physico-chemical properties such as soil texture, soil moisture percentage, hydraulic conductivity, bulk density. 1: 40,000 was prepared and used by Ardebil Province Natural Resources Department. Digital elevation map was prepared using 1: 25000 topographic map of the study area. In this research, using the topographic map of 1: 25000 scale and digital elevation modeling, the slope map of Agh Laghan Chay Watershed was prepared. The lithology map of the study area was prepared using the 1: 100,000 Geological Survey of Iran Geological Survey. In addition, the standardization-criticalization and weighing methods have been used.

 The results show that in 1990 the overall accuracy was 95% and the kappa coefficient was 0.93, in 2000 the overall accuracy was 90% and the kappa coefficient was 0.97 and in 2018 the overall accuracy was 93% and the kappa coefficient was 0.91. During the years (1990-2000-2018), significant changes are noticeable, most notably the rangelands and the waste land, which, due to intensive exploitation, gradually shifts its land to other uses such as residential and agricultural areas, dry land, that have been assigned. Increased area of land use and cropland and agricultural areas in 2000 and 2018, compared to 1990, indicate the degradation of rangelands and the reduction of waste land, which will cause significant changes in the morphological systems of the region, mainly to increase the rate erosion and sedimentation in watersheds, reduction of groundwater recharge, destructive floods and other morphological processes will be due to erosion zoning maps in the study area that in 1990 was very high risk area of   1758/82 hectares. This class of danger per year 2000 and 2018, respectively 08/1912 and 25/1914 hectare is increased and the high class area in 1990, 59/4018, 78/4219 and 31/4481 to ha respectively in 2000 and 2018 is increased. In the erosion map of the years 1990-2000-2018, mainly high-risk and high-risk areas are located in agricultural, orchard and residential land uses; therefore, different land use changes in the area have caused changes in the morphological trends of the area.

 Knowing the ratios of land uses and how they change over time is one of the most important issues in planning and policy making. Soil erosion is a global problem that threatens land-use such as changes in water resources. Land use changes are one of the most important issues in the recent world which causes many changes in land surface systems, including geomorphic systems. Land use is one of the most important factors in soil erosion. The results show more accuracy of object-oriented classification. Studies also show that monitoring land use changes using object-oriented methods yields better results when observing all parameters. In the study of land use changes over the years 1990–2012, the results showed that there were major changes in this period of time and It is related to dense rangelands that, due to intensive exploitation, have gradually devoted their land to other uses, such as residential and agricultural areas, and land use, and wasteland has declined over time and has become land and agricultural land. According to soil erosion zoning maps in the study area of   Agh Laghan Chay, In the years (1990-2000-2018), mainly high risk and high risk areas are in land use, agricultural, orchard, vegetation and high risk areas. Comfy and very comfy are located in rangelands and man-made areas. The results also showed that the area of   high risk class in the years (1990 - 2000 - 2018) was 11.20, 12.20 and 12.22%, respectively, and the area of   high risk class in the years (1990-2000-2018), respectively. The order is 25.59, 26.65 and 28.29, which is increasing like many high-risk classes, due to the increase in residential area. It seems necessary to preserve natural areas, stabilize and legalize land use, erosion control and soil and water conservation practices in the context of high erosion potentials, within the framework of other conservation schemes. Get it. Civilians and governmental and non-governmental organizations in the region can manage and monitor land use changes.

Soil erosion is the most important cause of land degradation and the cause of water loss, soil loss, sedimentation in water resources, and maximum flood intensification (Liu et al., 2019). Models are the most important tools for estimating and mapping of erosion at the watershed level. As the experimental models are dependent on used coefficients and region conditions, physical models based on the soil erosion process more accurately predicting soil erosion development (Yuan and Yu, 2017). The MPSIAC model is one of the most important experimental models that is widely used to estimate soil erosion in Iran's watersheds, also reported in literature the Morgan Morgan Finney (MMF) is one of the most efficient physical models in soil erosion estimation. In this study, MPSIAC experimental model and MMF physical model were used to estimate the erosion and determine its distribution at Ardebil Agh Gouni area and compared the efficiency of two models in estimating soil erosion.

Agh Gouni watershed with an area of ​​1800 hectares located at 10 km south of Ardebil city was selected as the study area. At 100 points of the watershed with 300m intervals, soil sampling and field measurements of vegetation, soil and rock cover percentage were done and field data required for MPSIAC and MMF models were obtained. Soil erodibility index was determined by Williams et al (1983) by measuring the percentage of sand, silt and clay particles as well as the percentage of organic carbon, bulk density and particle density of soil samples. Precipitation and hydrology data were also obtained using meteorological data and estimated runoff using curve number (CN) method. Geological and topographic information was also obtained from the maps. Field visit, interpretation of aerial photos, and satellite imagery were performed to identify the watershed and determine the status of erosion. With soil, runoff, topography, geology and meteorological data, required inputs for the two models were obtained and soil erosion estimation was performed for 100 selected points at the watershed. Then, soil erosion interpolated between the points by inverse distance weighting (IDW) method and prepared soil erosion map of watershed.

The results showed that the means of soil erosion in the studied area was estimated by MPSIAC and MMF model of 5.06 and 3.79 ton/ha/year, respectively. Also, the erosion map obtained from the estimation of MPSIAC model showed that higher erosion rates occur in areas with high slope and greater gully erosion density. The erosion map obtained from the estimation of MMF model also showed that there is more agreement between surface runoff flow and annual erosion estimation with this model. In the MMF model, only surface erosion caused by runoff and raindrops is modeled, while in MPSIAC, in addition to surface erosion, gully erosion is also considered as one of the 9 factors in the scoring model. Therefore, the estimated result by this model is higher. In the erosion map of the MMF model the least estimation of erosion is related to the upstream of the watershed in the south and west of the watershed, which has minimal runoff flow in these areas and has relatively flat topography with respect to the slope map, so in this area the kinetic energy of the raindrops is the domain reason of soil erosion. As the outflow portion of the watershed gradually increases the effect of runoff to soil erision. It was also observed that the kinetic energy factor (E) of the MMF model was uniform in most parts of the watershed and did not vary significantly, while the surface runoff volume factor (Q) from the upstream to the downstream and outlet of watershed gradually increased, which is the result of an increase in volume and velocity of runoff to outlet side.

It was generally observed that the estimation of soil erosion with MPSIAC model is more than MMF model and due to the use of gully erosion as one of the factors of erosion, the erosion distribution with this model is very consistent with the gully erosion distribution. Therefore, although the MPSIAC model can be used to estimate long-term erosion in the region, estimating the annual erosion that usually results of sheet and rill erosion the MMF model is more accurate. As well as observed to obtain annual erosion distribution map, using the MMF model that is the basis of estimating rainfall and runoff energy is more accurate.

Investigating the relationship between landslides in sediment production in watersheds is one of the most important issues in the management of watersheds. The purpose of this research is to introduce a suitable model for the effect of landslide on sediment load in Gol Gol watershed in Ilam province, with the assumption that the linear relationship between the indices of landslide influence on the sediment load of the basin is dominant. Therefore, the data of Flood Basin sedimentation in two observation and annual observation methods were estimated by using the sediment curve within the groups during the 30-year period. Active basin landslides had been identified using satellite imagery and field analysis and had been analyzed using spatial correlation models in the GIS software environment. The results showed that Moran spatial autocorrelation model is the best model and landslide of cluster pattern. After analysis of factors in the overlapping model of indices, the cause of cluster pattern of landslides were introduced marl lithology of Gurpi Formation. The results of quantitative analysis of variables in the statistical software, correlated in one and two-variable regressions, showed a linear relationship between the indicators of landslide on sediment load of the basin, but this correlation in multivariate regressions showed that nonlinear relationship between the indicators of influence Landslide dominates the sediment load in this basin, and the mean slope index of landslides with a coefficient of explanation of 0.997 and landslide area with a coefficient of explanation of 0.870 has the most effect on sediment load in this basin. Of course, the use of this research method can provide better results in  future research.

Assuming that the linear relationship between the landslide indexes is dominant over sediment load of the basin. Flood discharge basin data were analyzed in two observational and annual methods using the sediment curve of the middle classes during the 30-year period. The active lake landslides have been analyzed using spatial correlation model of mooran and this spatial analysis has shown that landslides are of cluster pattern and spatial autocorrelation is related to the indices of landslide and sediment load in the basin. There are several methods for estimating suspended sediment load. In this study, the power relationship between flow discharge and suspended sediment flux, known as depositional curve, is used. The data of water flow and discharge of the Seduce Glommed hydrometric stations were obtained from the regional waters of Ilam province. At first, daily, monthly and annual sedimentation rates were calculated over the entire statistical period. Then, the method of drawing the curve of sediment was used as the middle method. The sediment curve is plotted using statistical data of discharge flow and sediment discharge. The data series for the statistical period were used to plot the sediment yield curve. According to the available hydrometric station data, in which the annual discharge of the deposit was recorded simultaneously with the flow rate several times, the sediment curve was plotted on a logarithmic scale.

The results of 9 active landslides in the basin area are shown as independent variables and sediment as a dependent variable in the mud GOLGOL  basin. These data are analyzed in SPSS21 software and the data in the software is bi-dimensional zed to have the same unit to not have the degree of impact under the single domain. In the next step, using regression models are analyzed that step models Stepwise, forward or forward, are the best models for analyzing the correlation of variables. In general, the mean of the correlation of  data is significant and in the multivariate (multiple), regression is a non-linear relationship, while if a variable or even two independent variables are analyzed with a dependent variable of the deposition, the linear relationship is shown, so that it cannot be judged , for example, the variable area has a direct relation with the increase or decrease of sediment, but it is necessary to be analyzed in the conditions of the catchment area, and taking into account the other variables that influence the landslide in the sedimentation of this process. Therefore, nonlinear relationship.

The results showed that the cluster pattern of landslides was marl lithology of the Gourpi Formation and the best model was Moran spatials autocorrelation. Correlation in one and two-variable regressions shows a linear relationship between the indices of landslides influence on sediment load of the basin, but correlation in multivariate regression showed that non-linear relationship between the indices of landslide on sediment load in this basin is dominant and The mean slope index of landslides with a coefficient explanation of 0.997 and landslides area with a coefficient of explanation of 0.870 had the most effect on sediment load in this basin.