Geomorphology
leila aghayary; sayyad Asghari Saraskanrood; Batool Zeynali
Abstract
control and monitoring solutions. By using field studies, geological and topographical maps, and by reviewing the researches and studies done in this field, as well as examining the existing conditions in the studied area, 9 factors of elevation, slope, slope direction, lithology, distance from the fault. ...
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control and monitoring solutions. By using field studies, geological and topographical maps, and by reviewing the researches and studies done in this field, as well as examining the existing conditions in the studied area, 9 factors of elevation, slope, slope direction, lithology, distance from the fault. , the distance from the river, the distance from the communication roads, land use and rainfall were investigated as factors affecting the occurrence of landslides. Therefore, the purpose of this research is to investigate and analyze the most important factors involved in creating the risk of landslides in Garami city and to identify the prone areas that will probably be involved in landslides in the near future. In this research, the zoning of prone areas was done with the Aras multi-criteria algorithm in the Edrisi software environment, and according to the results of landslide risk zoning; The criteria of land use, slope, and lithology are the most important factors involved in creating the risk of landslides in the study area with weight coefficients of 0.187, 0.152, 0.152, and 0.142, respectively, and are 361.99 and 450.32, respectively. A square kilometer of the area has a very high probability of danger. Finally, it can be said that the most important factor involved in increasing the amount and potential of landslides in Germi city is the change of land use and the increase of agricultural land and livestock pastures.
Batool Zeynali; Ehsan Ghale; Shiva Safari
Abstract
1-IntroductionOne of the most important water sources in mountainous areas is snow cover, which significantly affects the amount of runoff on the ground. Moreover, seasonal snow cover influences biotic components and water quality in rivers. Snow cover is one of the most important sources of fresh water ...
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1-IntroductionOne of the most important water sources in mountainous areas is snow cover, which significantly affects the amount of runoff on the ground. Moreover, seasonal snow cover influences biotic components and water quality in rivers. Snow cover is one of the most important sources of fresh water and affects the hydrological system of different altitudes in mountainous areas. Climate change has a major impact on the diversity of snow cover, thereby having adverse effects on snowmelt runoff and glacier mass balance. Remote sensing, due to its advantages, can control large areas with high spatial and temporal resolution. This technology provides the ability to quantitatively measure the physical properties of snow and water in remote and inaccessible areas where ground surveying may be expensive and dangerous. Therefore, it can be said that in basins with no accurate information on snow cover, this technology can be used to extract snow cover.2-MethodologyThe study area is Sabalan Mountain located in Ardabil province and its surroundings. In this study, Landsat 8 satellite images for 2020 and Landsat 5 images for 2000 were used for February due to the presence of sufficient snow to extract the snow-covered area. It was tried to select images with minimal errors. The images were mosaicked after ensuring the absence of common errors and atmospheric correction using the FLAASH model in ENVI5.3 software, then a part of the image was cut based on the research. In the eCognition software, the images were classified into three classes of water, soil, and snow using NDSI and NDSINW, then the classification result was transferred to ArcGIS software and the snow cover area was calculated. The NDSI was proposed based on the normalization of the green band difference and SWIR1 on MODIS images. NDSI and MNDWI are among the most widely used indices for implementing SCG maps. 3-Results and DiscussionIn this research, in order to obtain a snow cover map and its area, an object-oriented classification and NDSI and NDSINW have been used. The snow-covered areas extracted using the object-oriented method for the years 2000 and 2020 were calculated as 2500 and 1954 square kilometers, respectively. The values of 2557 and 1937 square kilometers were extracted as snow-covered area by applying NDSINW and 2610 and 2577 square kilometers were extracted by applying NDSI. The NDSI shows a larger snow and ice cover than it exists because it considers water as snow (Commission Error). Therefore, it is not suitable for distinguishing water from snow or extracting snow-covered area in areas where water exists. In contrast, the NDSINW is able to extract snow cover in areas with aquatic terrains because it uses near-infrared and middle-infrared bands and the difference between them in snow reflection to remove water-covered area. The classification maps were validated using samples taken from the satellite images and for both 2000 and 2020 images, overall accuracy coefficient and the kappa coefficient of the classification were estimated 0.99 and 99%, respectively.4-Conclusions In the present study, the object-oriented classification method was applied for detecting and extracting the snow-covered area based on the combination of optical bands on the Landsat 8 and Landsat 5 images of Sabalan region in Ardabil province. Then, the normalized difference snow index (NDSI) and the normalized difference snow index with no water information (NDSINW) were applied and the results of them were compared to identify the snow cover using the accurate object-oriented classification method. According to the results of the object-oriented classification map and the applied indices, it was found that both indices were able to extract snow cover compared to the object-oriented method in cold and winter area. However, the NDSI index had some error in extracting the snow-covered area due to not limiting aquatic terrains and water-covered areas and considering them same as the snow-covered areas, especially in areas where the presence of water is significant. Therefore, in areas with little or no water, it can be a very good index for extracting the snow-covered area.Keywords: Object Oriented Classification, Snow-covered Area, NDSI and NDSINW Spectral Indicators, Sabalan Mountain. 5-References Custodio, E., Cabrera, M.D.C., Poncela, R., Puga, L.O., Skupien, E., & Del Villar, A. (2016). Groundwater intensive exploitation and mining in Gran Canaria and Tenerife, Canary Islands, Spain. Hydrogeological, environmental, economic and social aspects, Science of the Total Environment, 557, 425–437.Donmez, C., Çiçekli, S.Y., Cilek, A., & Arslan, A. (2020). Mapping snow cover using landsat data: toward a fine-resolution water-resistant snow index. Meteorology and Atmospheric Physics. 10.1007/s00703-020-00749-y.Manickam, S., & Barros, A. (2020). Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behavior and Sensitivity to Snow Wetness and Land cover. Journal remote sensing, 12(483), 1-31.Parajka J., Holko, L., & Kostka, Z. (2001). Distributed modelling of snow water equivalent-Coupling a snow accumulation and melt model and GIS. Institute of Hydrology. Slovak Academy of Sciences, 14, 86-102.Sood, V., Singh, S., Taloor, A., Prashar, SH., & Kaur, R. (2020). Monitoring and mapping of snow cover variability using topographically derived NDSI model over north Indian Himalayas during the period 2008–19.Thomas, A.C., Reager, J.T., Famiglietti, J., & Rodell, M. (2014). A GRACE-based water storage deficit approach for hydrological drought characterization. Geophysical Research Letters, 41(5), 1537–1545.Voss K.A., Famiglietti, J., Lo, M., de Linage, C., Rodell, M., & Swenson, S. (2013). Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris-Euphrates-Western Iran region. Water Resource Research, 49: 27-39.Taylor, R.G., Scanlon, B., Doll, P., Rodell, M., van Beek, R., Wada, Y., Longuevergne, L., Leblanc, M., Famiglietti, J., Edmunds, M., Konikow, L., Green, T.R., Chen, J.Y., Taniguchi, M., Bierkens, M.F.B., MacDonald, A., Fan, Y., Maxwell, R.M., Yechieli, Y., Gurdak, J.H., Allen, D., Shamsudduha, M., Hiscock, K., Yeh, P.J.F., Holman, I., & Treidel, H. (2013). Ground water and climate change. Nature Climate Change, 3(4): 322–329.
Ardashir Yousefzadeh; Battol Zeynali; Khalil Valizadeh Kamran; Saayad Asghari Sar Eskanrood
Volume 6, Issue 18 , June 2019, , Pages 181-205
Abstract
Introduction According to Cornelsen (2015), soil moisture is one of the most important variables in the hydrological cycle. In Manson's studies (2010), soil moisture was identified as one of the major climatic variables by the World Meteorological Organization, the Global Climate Observing System, and ...
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Introduction According to Cornelsen (2015), soil moisture is one of the most important variables in the hydrological cycle. In Manson's studies (2010), soil moisture was identified as one of the major climatic variables by the World Meteorological Organization, the Global Climate Observing System, and the Observational Satellite Observatory. Remote sensing provides a powerful tool for detecting and monitoring soil moisture near the Earth's surface (0 to 5 cm). Also, according to Babaeian research (2015), optical reflection of the soil and thermal emission to Eliit (1979) and Microwave backed by Das researches (2008) is related on soil moisture. Remote sensing techniques based on microwave waves are effective techniques for estimating soil moisture. Surface water levels can be extracted using the NDVI index in Landsat images (Maryam Khosravian et al., 2012, p. 115), and user variations in time series can also be identified. (Malian et al., 1395, p. 49). Due to the limitation of access to radar information, the focus of the study is on the near-visible infrared range and the amount of heat from the surface of the earth is measured from 3.5 to 14 micrometers (Curran, 1985). Soil moisture content with this method requires the estimation of soil surface temperature and vegetation index (Wang & Co, 2009). Vegetation and surface temperature have a complex dependence on soil moisture (Carlson, 1994). According to Gillies et al. (1997), the combination of these two indicators can be used to estimate soil moisture with an acceptable accuracy.In 2017, a model for estimating soil moisture using a visual distance assay was proposed based on the linear physical relationship between soil moisture and the short-range infrared reflection (STR), which is based on the distribution of pixels inside the surface temperature space and the normalized vegetation index (STR-VI) (Sadegi et al., 2017). A trapezoid or triangle model is one of the models used in remote sensing to estimate soil moisture. The study area is the Simineh River basin which is one of the sub basins of Lake Urmia Basin, with an of 3279 km2. Methodology The main data in this study are Landsat 8 satellite imagery. After applying atmospheric and radiometric corrections, the processing of images, between 2016-2017, was done according to the process of view of Figure 1. Figure (1) Research process (Source: Writers) -Thermal-Optical Trapezoid Model (TOTRAM) This model is based on the distribution of pixels in the surface temperature and vegetation cover space that is fitted to estimate soil moisture using a linear equation in space (LST-VI) (Sadegi et al., 2017). Equation (1) -Optical Trapezoid Model (OPTRAM) The base of this model is the insertion of surface temperature to estimate the soil moisture in the visible wavelength range. In this physical model, the linear relationship between soil moisture and infrared reflection is expressed. Equation (2) Result According to the results of this study, the lowest average temperatures of satellite images were respectively -3.23 and 2.12 C in 2015 and 2016, indicating an increase in temperature. In 2017, the highest amount of vegetation density was 0.66. The correlation between the OPTRAM model in 2015 and the STR and NDVI variables, were positive and the correlation indices were respectively 0.709 and 1. These figures for STR and NDVI in 2016 were respectively -0.648 and 1, which indicated a negative correlation between STR and soil moisture; soil moisture decreased with increasing STR and increased with increasing NDVI. And the positive correlation between OPTRAM model and NDVI confirmed it. In 2017, the positive correlation between STR and NDVI with soil moisture were respectively 0.672 and 1. The TOTRAM model in 2015 had a negative correlation with the LST and NDVI indices and they were respectively -0.574 and -1. It indicated low accuracy of this model compared to the OPTRAM model in estimating soil moisture. In 2016, the correlation between LST and NDVI with soil moisture were respectively -0.974 and 0.409. They respectively reached -0.940 -0.787 in 2017. Discussion and Conclusion In this research, due to the limitations of the field information, soil moisture was extracted without the use of ground control points. The comparison of the accuracy of the two models in the region was investigated. The results indicated that soil moisture can be extracted from the STR index with high accuracy, compared to LST index, based on NDVI Triangular space. Due to the low cost and the availability of visible images, radar images were accurately obtained and the correlation between OPTRAM model and soil moisture estimation was confirmed. According to the extraction results, the OPTRAM model can estimate the soil moisture better than the TOTRAM model, due to the fact that it is not influenced by environmental factors and global parameters. According to research results, TOTRAM has two main constraints. First, it cannot be used for a satellite without thermal bonding. Secondly, in addition to soil moisture, the LST depends on environmental factors to be calibrated for each image. To overcome the limitations of the TOTRAM model as well as the empirical visibility of indicators, a new physical trapezoidal model, called OPTRAM, is proposed. It is based on the physical relationship developed between soil moisture and the "reflected infrared reflection" (Sadegi et al., 2015).
Sayyad Asghari Sarskanroon; Batool Zeinali; Nader Poornariman
Volume 2, Issue 3 , January 2017, , Pages 1-20
Abstract
Rivers are dynamic systems that lateral boundaries and their morphologic characteristics are changing in time continuously. This instability is created by erodibility of river bed and consequently river patterns changing. Case study of this research is Germi Chay in East Azerbaijan province. ...
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Rivers are dynamic systems that lateral boundaries and their morphologic characteristics are changing in time continuously. This instability is created by erodibility of river bed and consequently river patterns changing. Case study of this research is Germi Chay in East Azerbaijan province. The purposes of this study are investigation of river patterns and determining erodibility of river route. For these used Landsat satellite images, digital elevation model (DEM), vegetation, geologic and land use maps. In order to determining of river pattern and its Effective factors were used Sinuosity, Central angle indexes and longitudinal profile analysis. Finally river erodibility classes were determined in 5 classes by overlaying effective layers in erodibility. Results indicated pattern of studied river is meandering. Longitudinal profile analysis in both intervals indicated that changing of situation is not seen in longitudinal profile and these changes are in a normal state. This is indicator regular trend in effective factors of river morphological actions. Also results indicated that erodible areas of Medium to high and high are in parts with Structures sensitive to erosion (mainly Quaternary sediments), the lack of suitable and dense vegetation that are caused slope movements in river bed.
Sayyad Asghari; Batool Zeinali; Saleh Asghari
Volume 3, Issue 7 , October 2016, , Pages 39-57
Abstract
Sayyad Asghari[1]* Batool Zeinali[2] Saleh Asghari[3] Abstract The location of human settlements and other facilities created by human are affected by Environmental factors, particularly geomorphology and geology. Today, as a result of population growth, development of construction is inevitable and ...
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Sayyad Asghari[1]* Batool Zeinali[2] Saleh Asghari[3] Abstract The location of human settlements and other facilities created by human are affected by Environmental factors, particularly geomorphology and geology. Today, as a result of population growth, development of construction is inevitable and the adverse impact of human needs on the ground as well as operation of areas around city and villages for creating of home and economic and industrial facilities have increasing expansion. Meanwhile, a plurality of geomorphological factors and dynamics of the natural environment makes difficult possibility of assessment all factors in order to recognize the best location for the placement elements of development. So the use of efficient methods of evaluation will be the most important measures for better planning. Accordingly, the aim of present study is using from Topsis method to locate the best places of natural and geomorphologic structure for future development of Urmia. In this study with entering of area data layers to the ARC GIS and based on topographic factors, the most important constraint of morphological Urmia, was diagnosed three sites suitable for development that proposed sites using natural and morphological components and by techniques Fuzzy ANTROPY (for index weighting) and TOPSIS (to prioritize sites) were evaluated. According to research, site C in the eastern part of the city by a factor of 0.76877 CI as the best place in Urmia is intended for future development. [1]- Assistant of Geomorphology, Urmia University, (Corresponding Autor), Email:s.asghari@urmia.ac.ir. [2]- Assistant of Climatology, University of Mohaghegh Ardabili.. [3]- Ph.D Student of Geography and Rural Planning, Kharazmi University.
