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Radar and Optical Remote Sensing - Volume:4 Issue: 4, Autumn 2021

Journal of Radar and Optical Remote Sensing
Volume:4 Issue: 4, Autumn 2021

  • تاریخ انتشار: 1401/05/24
  • تعداد عناوین: 6
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  • Ameneh Rezaei Farajabad *, Mahdi Zarrini Pages 7-29

    One of the effects of a gradual population increase in the urban areas is waste in crescent and its disposal. Among the common methods of waste management, land filling is technically, ecologically and economically important. The purpose of this study is to determine the appropriate spatial potential for sanitary landfill of waste in Rasht, based on location criteria and using GIS and remote sensing. Zoning of suitable burial sites was done based on the 4 main criteria of land use, hydrological characteristics, geological characteristics and accessibility. In a current study, SRTM sensor data, which is paired with a short time interval (to reduce changes occurring at ground level) was used to prepare a digital model of land. The maps derived from the digital model of land, included elevation and slope maps that were used to prepare the criteria of the "geological profile." After standardization and preparation of maps, in order to achieve the weight and importance of each layer to find suitable landfills, the weight of the layers used was calculated by hierarchical analysis method. Considering the effective factors in locating the municipal waste landfill and weighing each of the criteria and sub-criteria, five values of very suitable, suitable, relatively suitable, inappropriate and very inappropriate were specified. To determine the priority of the very suitable areas, the TOPSIS method was used. Among the 9 sites introduced to the TOPSIS algorithm, site four, with an area of ​​89.36 hectare, which is located in the southern parts of Rasht, was determined as the most suitable place for waste disposal.

    Keywords: Waste, hierarchical analysis, GIS, Location
  • P. Supriya * Pages 30-40

    Now a days due to the rapid advancement of economy around the world the count of vehicles increases day by day. Increase in the number of vehicles causes violation detection, road congestion, accidents at different traffic situations, uneven illumination, lighting and weather conditions. To overcome this issue license plate number is recognized but due to variations in license plate layout, font size of characters, tilted number plates, weather conditions, dirt plate and motion blur license plate recognition becomes difficult. License plate recognition has two main tasks, one is to detect the license plate and the other is to identify the license plate characters. By using region of interest license plate is detected. For recognition first tilted images are corrected using affine transformation and to improve the quality of a low-resolution image super resolution CNN is employed and connected component analysis, horizontal and vertical projection profile area used for separating each individuals characters. Each individual character image is fed to the Convolutional Neural Network (CNN) for character extraction and for classification and the license plate is recognized using convolutional neural networks. The main aim of this paper is to recognize different plate layout with different conditions with minimum data set and less processing time with maximum efficiency.

    Keywords: License plate recognition, Region of interest, Horizontal, vertical projection, convolutional neural network
  • Seyed Aghil Ebrahimi, Seyed Ali Almodaresib *, Farhad Hamzeh Pages 41-54
    Land use change and land cover are considered as one of the important and effective factors on global environmental change, so understanding and predicting the causes, processes and consequences of land use change has become a major challenge on the planet. Today, remote sensing technology and GIS are used effectively to identify and quantify land use change and its effects on the environment. The physical development of cities and the expansion of its dimensions is one of the important factors in urban land change that has many environmental, economic and social consequences. In the past few decades, the city of Tehran has faced numerous urban growth and development and surrounding towns, which has caused extensive changes in the urban lands of Tehran and surrounding areas. In this study, the trend of land use changes in Tehran in the past few decades has been studied. In the present study, using Landsat 8 satellite images, the change and transformation of lands in Tehran from 2013 to 2020 was monitored. Images were pre-processed and classified according to the LCZ model in 17 classes. Then, they were classified in SEGA GIS software and analyzed by image difference and post-classification methods. The results of image processing and classification show that urban lands are constantly growing and barren lands are increasing on a very small and slow scale. Also, land with dense vegetation has decreased from 2013 to 2020, which in itself can cause serious damage to urban planning for city managers.
    Keywords: land use, LCZ model, physical development, remote sensing
  • Amin Mohammadi Dehcheshmeh, Razieh Mirfazlullah * Pages 55-63

    Remote sensing is one of the effective tools to study the process of land use change on a large scale and in a short time. In this research, the aim is to monitor and analyze land use changes using satellite images and remote sensing from 2010 to 2020 in Sabzevar city with Landsat images. For research, preprocessing included atmospheric correction and radiometric and geometric correction. A total of 200 ground control points were collected to classify and evaluate the accuracy of the classification with the maximum probability classification algorithm in the ground visit. The classification results showed that the forest area in 2010 was equal to 68980.21 hectares, which with the change of use and its conversion to residential use, barren and rainfed agriculture in 2020 reached 66044.99 hectares, ie 2935.22 hectares, its area has decreased. Residential use with its growth in 2010 to 2020 has increased from 2855.89 to 4563.98, ie 1708.09 hectares. Land use changes in semi-dense rangeland have also decreased from 167164.89 to 153287.68 hectares, i.e. 13877.21. Kappa coefficient and overall accuracy in 2020 were 98.42 and 97.84, respectively, which was the highest value compared to previous years. In this study, it can be recommended that the government increase the vegetation of the land to protect pasture and forest uses against further changes, and to compensate for these changes, to plant fast-growing forests.

    Keywords: land use, remote sensing, Maximum Probability, Landsat
  • Mohammad Mahboub Kheirkhah * Pages 64-80
    Soil erosion is one of the environmental hazards. The aim of this study was to predict water erosion and evaluate it with CORINE and ICONA models in the study area of ​​Talesh city. In CORINE model, parameters such as topography, erodibility, erosion and land use and vegetation are required, and in ICONA model, slope, geology, land use, vegetation are required. The results showed that the highest area in the erosion risk map of 2000 is the CORINE model of the middle erosion class with an area of ​​63.22%. In 2020, the highest area is related to high erosion class with an area of ​​45.67%. These areas are seen in the western, eastern and central parts of the range. The results of the ICONA model for the period 2000 to 2020 showed that the high and very high risk classes in 2000 were 8.01 and 2.31 percent, respectively, while in 2020 these figures reached 8.80 and 3.44 percent. The results of the evaluation of the final zoning of the erosion risk map of the area, the study area with terrestrial realities, showed that in the best case, the overall image zoning accuracy of 2000 and Landsat 8 of 2020 CORINE model are equal to 0.87 and 0.91 and kappa coefficient of 0.86 and 0.89, respectively. It was estimated to be acceptable. In the ICONA model, the kappa coefficient and overall accuracy of 2000 were 0.85 and 0.86, and for 2020, 0.87 and 0.88, which were acceptable.
    Keywords: Water Erosion, CORINE model, ICONA model, Talesh city
  • Hadis Rezaei Mirghaed, Ladan Khedri Gharibvand * Pages 81-97
    Urban land use maps, in addition to different classes of land use with spatial patterns, specify the type and intensity of land use; therefore, they can be used for current and future planning of urban land. In this study, land use changes in Lali city in 30 years (1987-2017) were investigated. To evaluate the land use changes in this time interval, several spectral images of Landsat satellites 5, 7, and 8 from the years 1987, 2001 and 2017 were utilized. After collecting data and the application of necessary pre-processing on them, also for the preparation of land use maps for the specified time intervals, data analysis was carried out by Maximum Likelihood Classification Algorithm. The findings obtained each year were monitored and controlled through field operations, and land use maps in 7 classes of agriculture, rangeland, forest, mountain, residential, river, and other areas were produced. Then, the changes in each land use were determined in the specified periods during 1987 to 2001, 2001 to 2017, and eventually 1987 to 2017. While the results obtained from the final changes illustrate that the overall level of vegetation compared to the beginning of the period has declined markedly which is an indication of deforestation in the region, urban areas, agriculture, and rangelands have maintained an ascending trend which can be due to increasing urban development and rural expansion, and the growing need of residents for housing, agriculture, and gardens.
    Keywords: Lali City, Maximum likelihood, algorithm, land use