Sayyad Asghari Saraskanrood; Mostafa Omidifar; Ehsan Ghale
Abstract
Identification of landforms and the way of their distribution is one of the basic needs in applied geomorphology and other environmental sciences and landform maps show the shapes of the earth's surface. This project aims to identify the landforms of the Qaranqu catchment area using object-oriented classification ...
Read More
Identification of landforms and the way of their distribution is one of the basic needs in applied geomorphology and other environmental sciences and landform maps show the shapes of the earth's surface. This project aims to identify the landforms of the Qaranqu catchment area using object-oriented classification methods including nearest neighbor algorithm and thresholding. Landsat satellite imagery for 1990 (TM) and 2020 (OLI) was used for this purpose. First, to apply the classification, atmospheric and radiometric corrections were applied to the images, then to better identify and extract the phenomena, principal component analysis (PCA), and MNF algorithms were used to classify satellite images using classification methods. Object-oriented, which included the nearest neighbor method and thresholding was used. For the accuracy of the maps produced using the two methods of Kappa index and the overall accuracy of the use, the results revealed that the nearest neighbor method is more accurate than the thresholding method. The classification results showed that the highest rate of decreasing changes during 1990-2020 is related to dense rangeland because it has decreased by 12.49 percent and the highest rate of incremental changes is related to irrigate agriculture which is 10.83 percent. The most important reason for this increase is the construction of Sahand Dam over time. In the absence of well-organized planning and the adoption of appropriate policies, the destruction of rangeland will continue and turning it into arable land, which leads to irreparable environmental and economic losses in the region.
Geomorphology
Mousa Abedini; Ehsan Ghale
Abstract
1-IntroductionDue to increasing land-use changes, mainly for human activities, it is necessary to monitor vegetation changes, evaluate their trends and their environmental impacts for future planning and resource management. With the increase in population and the development of technologies, human beings ...
Read More
1-IntroductionDue to increasing land-use changes, mainly for human activities, it is necessary to monitor vegetation changes, evaluate their trends and their environmental impacts for future planning and resource management. With the increase in population and the development of technologies, human beings are, currently, considered the most important and powerful tool of environmental change in the biosphere. Land use is the type of land use in the current situation, which includes all land uses in various sectors of agriculture, natural resources, and industry. Due to the provision of a wide and integrated view of an area, reproducibility, easy access, high accuracy of information obtained, and high speed of analysis, using satellite data is a good way to prepare a land-use map, especially in large geographical areas. One of the most widely used methods of extracting information from satellite images is classification, which allows users to generate different information. According to the type of classification method of the study area, the characteristics of the educational points get different results to separate the thematic phenomena and extract information more accurately.2-MethodologyMordagh River, which is known as Mordi Chai in the region, originates from the southern slope of Sahand Mountain located in East Azerbaijan and flows south. By connecting the sub-branches, it continues its way to the city of Maragheh, passes through the city of Malekan, and enters Lake Urmia. In the present study, Landsat satellite images, TM, and OLI sensors from 2000 and 2020 were used to identify the area and prepare a land-use map. To prepare for classification and processing on them, the necessary pre-processing was first done on the images. Images were pre-processed in ENVI5.3 software using the FLAASH method. Finally, ENVI5.3 software was used to classify the base pixel and eCognition Developer 64 software was used for object-oriented classification. To evaluate the classification results, the Kappa coefficient and overall accuracy were used to evaluate the classification accuracy of the maps. 3-Results and DiscussionAccording to the obtained results, it is observed that the most area in the study area in 2000 with the method of minimum distance belongs to the use of medium and dense rangeland. The lowest area for the year 2000 is the use of residential areas. In 2020, the highest area of land use is 173.875 square kilometers. The lowest area is related to the use of snow with a rate of 0.199 square kilometers and the use of residential areas, which compared to 2000, has an increase of up to 5.54 square kilometers. In the maximum likelihood method in 2000 and 2020, the highest areas are related to medium rangeland and soil uses, respectively. The lowest area for 2000 is related to vegetation and for 2020 is snow use. In addition, in the support vector machine method, the highest and lowest areas for 2000 are related to medium rangeland and vegetation uses, respectively, and for 2020, medium rangeland and snow uses have the highest and lowest areas, respectively. According to the maps obtained from the object-oriented method, the highest area in 2000 is related to medium rangeland with 156.406 square kilometers and then dense rangeland with 96.514 square kilometers. The lowest area is related to the use of residential areas with 11.141 square kilometers. In 2020, the highest area is related to the use of dense rangeland (126.907 square kilometers). In addition, the lowest area is snow use with an amount of 5.199 square kilometers.4-Conclusions According to the results of this study and other studies, it can be suggested that the object-oriented classification method for land-use change studies is a more appropriate and accurate method than the pixel-based method. One of the most important reasons for achieving high accuracy in the object-oriented classification method is that in this method, in addition to spectral information, information related to texture, shape, position, and content is also used in the classification process. The study of pixel-based classification showed that in selecting educational examples, the more uniform the user is and free of mixed pixels, the more accurate the classification process is. So that the land use classification and vegetation in the pixel-based method had the highest accuracy, which due to the uniform surface of both land use and homogeneous texture, the selection of training samples in these uses with the highest accuracy and have played an important role in improving overall accuracy and kappa coefficient. Based on the results of the extent of different classes related to the land use of the basin studied in 2000 and 2020, we see a decreasing trend of dense rangeland, medium rangeland, and vegetation and increasing land use of residential areas and soil. What is very clear in these maps is the excessive reduction of pastures and their conversion to other uses.Given the growing population and the need for food and economic issues, this transformation is obvious and it cannot be said that this change can be prevented.
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 ...
Read More
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.
