Urban Planning
Rahman Zandi; Fatemeh Shahriyar
Abstract
This research aims to evaluate the relationship between time series of land use changes and land surface temperature in desert cities in Yazd using time series satellite images of 1987-2022 in Google Earth Engine system. To calculate (LST), using Landsat 5, 7 and 8 thermal band data in these two time ...
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This research aims to evaluate the relationship between time series of land use changes and land surface temperature in desert cities in Yazd using time series satellite images of 1987-2022 in Google Earth Engine system. To calculate (LST), using Landsat 5, 7 and 8 thermal band data in these two time periods, in addition to the supervised classification method, from the separate window algorithm method, and to calculate the vegetation cover from the normalized index (NDVI) has been used. The results of the supervised classification method showed; By comparing the changes in land use area between 2022 and 1987, it was determined that in 1987, desert areas had the largest area with (1815/1416) square kilometers, and in 2022, residential areas had the largest area with (74/1861) square kilometers. The lowest area in 1987-2022 is related to garden and forest lands with (34.4934) square kilometers and in 2022 with (2.5281) square kilometers. The amount of vegetation changes in 1987 with (11.9916) square kilometers, compared to 2022 with (13.0455) square kilometers, had the lowest area. The results of temperature changes showed that the maximum and minimum temperature of 1987 was equal to (60-61) degrees Celsius, compared to 2022 with values of (19-33) degrees Celsius, there were temporal and spatial changes. Therefore, by examining the average annual temperature and precipitation in different seasons of the year until the horizon of 2045, it was determined that with the increase in annual temperature in the future, this city will face a decrease in rainfall in different rainy seasons of the year. Therefore, the highest temperature occurred in the spring season and the lowest rainfall occurred in the autumn season.
Urban Planning
Abolfazl Ghanbari; Mir Hossein Pourbagher
Abstract
In this study, using images of Landsat-8, Landsat-7 and Sentinel-2 satellites in the coding environment of Google Earth Engine, their uses and changes during the two periods before and after urbanization (from 2000 to 2008 and from 2008 to 2019) will be categorized and then the next five-year development ...
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In this study, using images of Landsat-8, Landsat-7 and Sentinel-2 satellites in the coding environment of Google Earth Engine, their uses and changes during the two periods before and after urbanization (from 2000 to 2008 and from 2008 to 2019) will be categorized and then the next five-year development forecast of Sahand city (until 2025) will be made. Perceptron multilayer artificial neural network (MLP) method has been used as a method for predicting spatial multi-criteria decision making (MCDM). The independent variables used in the present study in predicting the physical development of the city are land price, type of use, slope, slope direction, altitude, distance from urban areas, distance from waterway network, distance from fault, distance from network Passages (main and secondary). The results of classification of satellite images showed that the physical development of Sahand new city has been done in order to turn barren lands into urban land. In addition, physical development was built to turn cheaper land into areas. The built lands have been greatly developed and from 64,155 square meters in 2000 to 682,192 square meters in 2019. Among the image classification methods for land use extraction, the SVM method was the best method and also the Sentinel-2 satellite images had the highest accuracy. The multilayer perceptron artificial neural network was used to predict the future physical development of the new city of Sahand, which according to studies, the development is predicted in directions that are based on the cheapness of the land and the limitations. Geomorphological is like slope and altitude.
Urban Planning
Hassan Mahmoudzadeh; Mostafa Mahdavifard; Majid Azizmoradi; zanjani zanjani sani
Abstract
Introduction
Urbanization as a revolution in human culture has transformed human interactions with one another. As the urbanization population grows, the use of the environment is intensified. Studies have shown that increasing population and expanding urbanization are turning urban green spaces into ...
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Introduction
Urbanization as a revolution in human culture has transformed human interactions with one another. As the urbanization population grows, the use of the environment is intensified. Studies have shown that increasing population and expanding urbanization are turning urban green spaces into rough and impermeable concrete surfaces, and this trend is especially serious in developing countries and the Third World. Since urban growth is a complex phenomenon in which a number of variables interact nonlinearly, the use of ANNs to model urban development and growth is perfectly reasonable. Artificial neural networks with nonlinear mapping structure have been developed for modeling interconnected systems such as the brain consisting of neurons. The artificial neural network is independent of the statistical distribution of data and does not require any specific statistical variables, so this feature facilitates the combination of remote sensing data and GIS. Currently, remote sensing science is changing a fundamental paradigm in which one- or two-image interpretation approaches pave the way for a wide array of data-rich applications. These improvements are facilitated by the GEE Satellite Image Processing System. The purpose of this research is to introduce a new system (GEE), to investigate and analyze this web portal, its application in monitoring and evaluation of human habitat changes (GHSL) and to map the relationship created using MLP model to predict physical development changes in Tabriz.
Materials and Methods
In this study, the Google Earth Engine (GEE) satellite image processing online system was used to process and extract the global GHSL product, and then the MLP model of Terset was used to predict changes.
Results and Discussion
In this study, it was attempted to analyze and analyze Landsat satellite images in a few minutes in order to prepare physical development map of Tabriz city without using hard data and to predict future development changes using the data available in Google Inheritance Satellite Image Processing System. Physically measure the city using the MLP model. GEE online processor has been able to map the growth of urbanization in the Tabriz city over the past six years. With the increase in urbanization over the past 40 years in the city of Tabriz, we have seen the destruction of about 38% of gardens and agriculture in the city, and even this system of rapid population growth in recent years (2014) on the outskirts of Tabriz as the main center of recent earthquakes.
Conclusion
It has shown the city of Tabriz and is also witnessing a growing trend towards physical development of the city in this part of Tabriz. The results of the MLP model show that the physical development of Tabriz in the future is northeastward and on the outskirts of Mount Aoun bin Ali.
