Spatio-temporal modeling of the urban environment quality

This research modeling of the Urban Environmental Quality (UEQ) of districts of 3, 6 and 11 of Tehran using an integrated Geographic Information System and remote sensing approach. The identified indicators in this study contain both natural and artificial aspects in order to model the quality of urban environments. The current study aims to develop a method for spatiotemporal modelling of UEQ. For this purpose fuzzy logic method is used. The results show the existence of a fairly regular pattern as an increase in desirability of UEQ from south to north of the area. The seasonal changes of UEQ show the improvement of environmental condition in spring and summer compared to autumn and winter. Fast growth of the 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 to the fact of the environmental capacity, population and economy would effect on the fundamental functions of the environment. So, analysis of the environmental quality can help us to understand the exact need for natural resources in any urban areas along with considering its economy and social development scale. 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 have mainly focused on spatial changes, but in this paper we decided to consider seasonal changes in addition to spatial ones. Also, we tried to use more complete set of indicators. So, the main purpose of this study is modeling the quality of urban environment based on a set of spatio-temporal factors. We used satellite imagery and some geospatial data such as NDVI index 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) used to extract solar radiation. Finally, we used based location field data to enhance Air pollution, Building height, population density and noise pollution. Because of the uncertain nature of quality measurements, we used Fuzzy logical approach to model the quality of urban environments. One of the most important fuzzy operators for overlapping 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 decreasing 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 with northern-southern line. The results show 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, moderate, ‘very good’, ‘good’, ‘very low’ and ‘low’. As the results show, region number three has a better environmental condition than the regions of six and eleven. We can also realize that the most of the selected indicators have shown seasonal changes within a year in our study area. This is due to the existance of more parks and less air pollution in the northern regions. Also, time intervals show a better quality situation in spring and summer than in autumn and winter. To investigate seasonal changes, the total area of each class was compared in a different season 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 area has a middle class situation and a small part with a very low level, which indicates the region's good status on this season. In the fall, we have maximum of the area with the lowest quality and the minimum of it with 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 levels and small parts of it is in the lowest class. therefore, the quality of urban environments changes dramatically within a year. At the next step, we studied the Pearson correlation coefficient of indicators and the results showed that the greenness is the most effective indicator of quality in urban environments. One-At-A-Time (OAT) Sensitivity Analysis were used to analyze the sensitivity of the model. Results show that the effect of 30% increasement on all inputs is between 2% and 17%. By considering the fact that all the changes in model outcome is 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. According to an extensive review of the literature, this study selects a wide range of factors in both natural and artificial environments to assess the urban environmental quality (UEQ) of Tehran. It is hoped that this study provides a useful basis for a more researches in the field of UEQ, combining both natural and built-up parts of urban zones. Further work will focus on validation and verification of the UEQ indices.