Spatio-Temporal Modelling of the Urban Environment Quality

Document Type : Research Paper

Authors

1 MA in Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Iran

2 Assistant Professor of Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Iran

3 Assistant Professor of Environmental Planning, Management and Education, Faculty of Environment, University of Tehran, Iran

Abstract

Introduction
Fast growth of urban population and increasing demands for high living standards have intensified the pressure on natural resources and made it more difficult to answer every need. Regardless of the fact that environmental capacity, population and economy would affect the fundamental functions of the environment. Thus, analysis of the environmental quality can help us to understand the exact need for natural resources in any urban areas along with its economy and social development. The quality of urban environment is recognized as an indicator for assessing and measuring the degree of suitability in urban settlements. It is also a rate for meeting the needs of individuals and society which can be affected by several factors such as air, noise and etc. All these factors would vary by any changes in time and space. Previous studies mainly focused on spatial changes, but in this research we seek to consider seasonal changes in addition to spatial ones. We have also tried to use more complete set of indicators. Therefore, the main purpose of this study is to make a modeling of the quality of urban environment based on a set of spatio-temporal factors.
Methodology
We have used satellite imagery and some geospatial data including NDVI maps, land surface temperature, land surface moisture, land surface albedo, solar radiation, air pollution, urban heat island, building height, population density, enhanced built-up and bareness index and also noise pollution. Landsat 8 (OLI) is used to calculate NDVI indices, land surface temperature, land surface moisture, land surface albedo, urban heat island, and enhanced built-up and bareness index. A Digital Elevation Model (DEM) has been used to extract solar radiation. Finally, we have used location based field data to enhance air pollution, building height, population density and noise pollution. Because of the uncertain nature of quality measurements, we have used Fuzzy logical approach to model the quality of urban environments. One of the most important fuzzy operators for overlaying the indices is the GAMMA. Gamma operator is the general mode of multiplication and addition. In other words, the gamma fuzzy function is the product of the algebraic multiplication of two functions of collect and multiply fuzzy. This function is the result of the compatibility between the incremental effect of the fuzzy sum function and the reduction effect of the fuzzy multiplication function. Therefore, districts of 3, 6 and 11 of Tehran municipality have been selected to be measured for the quality of urban environment in Tehran along a northern-southern line. 
Results and discussion
The results of this research have indicated that a northern-southern trend in the quality of urban environment which is reducing from north to south. The environmental quality conditions of the three defined urban areas are categorized into five classes of moderate, very good, good, very low and low. According to the results, region number three has a better environmental condition than the regions six and eleven. We can also realize that most of the selected indicators have represented seasonal changes within a year in the study area. This is due to the existence of more parks and less air pollution in the northern regions. The time intervals also show a better quality in spring and summer than in autumn and winter. To investigate seasonal changes, the total area of each class was compared in different seasons and the urban environmental qualities were devised into five categories: very good, good, medium, low and very low. In the spring, a large partial of the region has a modest and good quality, and a small part of it has a very good situation. In the summer, most of the areas have a middle class situation and a small part with a very low level. This indicates the region's good status on this season. In the fall, most of the area has the lowest quality and the minimum of it has a very good level that indicates the worst condition for the urban environmental quality. In the winter, the situation is a little better. Most parts of the area are in middle level and small parts of is the area are in the lowest class. Therefore, the quality of urban environments changes dramatically within a year. At the next step, we have studied the Pearson correlation coefficient of indicators. The results of the correlations showed that the greenness is the most effective indicator of quality in urban environments. One-At-A-Time (OAT) Sensitivity Analysis was used to analyse the sensitivity of the model. Given the fact that all the changes in model outcome are less than the total percentage of input change (30% increase) for all the variables, it can be concluded that the results of the gamma fuzzy model are reliable and not affected by one or more specific variables. 
Conclusion
According to an extensive review of the literature, this study has introduced a wide array of factors in both natural and artificial environments to assess the urban environmental quality (UEQ) of Tehran. It is hopeful that this study provides a useful basis for more researches in the field of UEQ by combining both natural and built-up parts of urban areas. Further work can focus on validation and verification of the UEQ indices in the future.

Keywords

Main Subjects

References

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Human Geography Research
Volume 51, Issue 1
April 2019
Pages 229-247

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History

  • Receive Date: 05 January 2018
  • Revise Date: 18 November 2018
  • Accept Date: 18 November 2018

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