keyvan ezimand

The urban heat island (UHI) as a climatic effect of urbanization can negatively impact the flora and fauna involved in urban and suburban ecosystems, the presence of pollutants, air quality, energy and water consumption, as well as human health and economy. Therefore, spatiotemporal analysis of the urban heat island changes has been considered an effective approach to understanding the impact of urbanization on the urban and suburban ecosystem, which also can support sustainable urban development and planning. Accordingly, this study contributes a novel approach to identifying the trend and predicting the pattern of UHI changes using statistical analysis, Shannon's entropy and chi-score statistics.

The study area of this research is the city of Rasht and its surroundings, a region located in the north of Iran. This research was implemented using remote sensing images from 1991 to 2021 that were collected by LANDSAT 5 and 8 with a fixed time interval of 10 years. All images were captured in the summer. In order to conduct this research in the pre-foresight stage, first, the required preprocessing, including atmospheric and radiometric corrections applied to the satellite images. Then, the surface biophysical characteristics of the study area were extracted from the satellite images. In the third step, the land surface temperature was computed using satellite images in 2021. In the fourth step, Multivariate linear regression between surface biophysical characteristics and the land surface temperature in 2021 was applied and then the cellular automata-Markov chain model was utilized to predict the land surface temperature for 2031. Finally, the pattern of changes in the urban heat island of Rasht city was investigated using statistical analysis in different geographic directions and different time periods.

The results of this study indicate that the highest positive correlation (R=0.89) was between NDBI and LST. Moreover, the highest negative correlation (R=-0.81) was between the greenness and LST. Our results also showed that the lowest correlation (R=0.42) was between the brightness and LST. The predicted LST corresponding to surface biophysical characteristics using a multivariate linear regression model illustrates the low error of this approach (RMSE=1.33K) in 2021. This means that the predicted values in 2021 are close to the real values, and therefore, this model can be trusted to predict LST in 2031. Statistical analysis of the patterns of observed and expected changes in UHI clearly illustrated that Rasht urban expansion and the UHI expansion will consistently continue to increase from 1991 to 2031. However, the expansion rate changes over time and space. Moreover, these analyses also showed that the UHI of Rasht city has a high degree of freedom and a high degree of sprawl. Thus, and as a result, its degree of goodness is negative.

The pattern of UHI changes is highly dependent on the pattern of built-up land changes: as a result, sustainable development, resilience and environmental protection of Rasht requires direct monitoring and control of the pattern of urban growth, such as preventing changes in built-up areas and agricultural lands in suburban areas by incorporating a vertical form of development as well as constructing green roofs and walls and using high-reflectance building materials.

Urbanization and urban growth have a significant impact on the natural and human environment as well as the climate at local and regional scales. For instance, the difference in the energy balance of the central and peripheral regions of cities stems from their physical characteristics and surface land cover. These characteristics in the temperate regions create the phenomenon of urban heat island, but they cause the phenomenon of the urban cold island in arid and semi-arid areas. The purpose of this study was to analyze the impacts of land-surface characteristics, land cover, built-up areas, and morphological characteristics on temperature changes in Zanjan city, Iran.

The dataset used in this study included Landsat-5 TM sensor images in 2010 and 2011 as well as statistical information at the level of building blocks. The methodology used in this study was to investigate the effects of different land covers on surface temperature. Then, to demonstrate the effects of built-up areas on surface temperature, the IBI method and Otsu threshold were used. To investigate the effects of the configuration of built-up areas on land surface temperature variations, landscape metrics such as Landscape Division Index, Fractal Dimension Index, and Percent cover of class areas were used. Finally, urban morphology has been investigated using Plot size (PS).

The results of this research showed that among all seasons, the stronger cold island was detected in summer. Moreover, the results also showed that the cold island was much better presented in summer than other seasons. The scatter plots between the land surface temperature (LST) on one hand, and the built-up area as well as the vegetation land cover, on the other hand, illustrated indirect correlations where higher Pearson correlation coefficient was observed between LST and the built-up area (r = - 0.704). Among the landscape metrics, the highest positive correlation (r = 0.72) was observed between LST and the Landscape Division Index. Moreover, a high negative correlation was found between the characteristics of urban morphology or Plot size and the LST (r = - 0.73). The results of the Pearson correlation between land cover, configuration, and morphology characteristics and LST were quite significant (P≤0.01).

From this research, it can be concluded that the configuration and morphology characteristics can model surface temperature variations better than the land cover.

The world is rapidly moving towards urbanization, and with large populations living in cities, and ever increasing population in urban areas, urban sprawl has occurred in many cities around the world. Lack of urban planning and management regarding the development of urban sprawl has been known as the source of many problems in cities around the world. Urban sprawl negatively impacts the environment, quality of life, social equity, climate change, air pollution and LST. Therefore, one of the goals of this study is to show the pattern of urban growth from the past to present, and predict the future of urban growth that may occur. It can be considered as an innovation of this study, which was less prominent in previous studies.

The study area is Rasht, where its natural attractions and tourism characteristics have increased its population and physical development. This research utilized Landsat 5 (TM), 7 (ETM+), and 8 (OLI/TIRS) images. In the first step, the pre-processing operations including geometric correction, atmospheric correction, and radiometric correction were performed on the remote sensing images. In the next step, Normalized Difference Impervious Index (NDISI) values were computed and employed to extract the impervious surface information in urban area. Then a hybrid cellular automaton–Markov (CA-Markov) model was used to predict both the quantity and spatial distribution of impervious surfaces in the city of Rasht. The performances of these methods were evaluated using 300 randomly selected samples. Finally, statistical analysis has been used to show the growth pattern of Rasht from its past to present and also to predict the future.

The results of this study indicated that the impervious surface of this city can be extracted with high accuracy (from 86.12 percent to 89.88 percent) using NDISI computed from Landsat images. Moreover, the accuracy of the CA-Markov model through prediction of impervious surface for the year 2018 was about 83.21 percent. With regard to the results of this work, the observed and expected urban growth results are not consistent with each other. The analysis of degree-of-freedom ( ) and Shannon's entropy ( ) are reflecting the urban sprawl pattern. Then, H and were used to compute the degree of goodness ( ). This parameter demonstrated that the growth pattern of Rasht was inappropriate.

This study shows that the sprawl of the city of Rasht can be characterized by a scattered growth that is expected to be worse in future, if management measures are not taken by governmental authorities. The results of this study can be useful for future urban planning and decision making such as preventing vertical land use changes.

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The environment is arena of human life and. In the last few decades, concerns about the quality of the living environment has been one of the most important problem of the society. Unsustainable and irregular development of cities that the producing the majority of the contaminants and are the center of all other human activities (Seyfadin and Mansourian, 2011, p53), cause different kind of crisis like environmental problem and reduced quality of the environment (Camp et al, 2003, p. 16). Thus improvement of the quality of urban living environment is one of the most important goals of any society. Quality of life is a complex and multi-dimensional concept that encompasses social, economic, environmental and physical dimensions of urban quality is one of the key dimensions of quality of life. Tehran is a metropolis that is in critical environmental conditions in many of the 22 regions (Farhadi and Taheri, 2009: 204). In 2017, the city ranked 199th out of 231 major cities in the world in terms of urban quality (Mercer Human Resources Advisor, 2017). At present, the continuation of the current trend can seriously affect the viability of Tehran in the not too distant future, therefore, special studies and investigations are needed to improve and improve the quality of Tehran's environment. The main objective of this study is to assess the quality of the environmental dimension of urban life with incorporation AHP-OWA method in different scenarios and based on different degrees of risk taking. As well as the analysis and study of dimensions and identifying effective indicators on the environmental dimension of urban life quality, and remarking the problems and deficiencies in order to help managers and urban planners are the goals of this study.

In terms of the nature, this study is a descriptive-quantitative and analytical one because by providing information about studied option, we describe it, then analyzes the data through different procedures.In this method first criteria are extracted using satellite images, layers of information and pollution measurement stations data that contains: greenness, the temperature of the Earth's surface, air pollution, noise pollution and vulnerability of urban buildings. In the second stage, the analysis and comparison of two for two criteria done using hierarchical analysis method for the determination of the final criteria weights. Finally, we overlap the above mentioned indicators through the AHP-OWA consolidated method in the Arc GIS and final indicator the environmental of urban life quality resulted. Region 6 of Tehran is selected as the research territory and geography and statistical population resulted from this region. This is an area with a surface of 45.2138 hectares, approximately 3.3 percent of the city surface and in view of geographic location is located in the central district of Tehran.

After providing the standard criteria affecting the final quality of the living environment, the final environmental dimension of quality of urban life derived using the analysis overlap. To calculate the weight of the criteria, Choice Expert software is used. Paired comparison matrix indicates that air pollution has the most and the temperature of the Earth's surface has the lowest importance in evaluating the quality of the urban environment. The amount of incompatibility in paired comparison criteria is 0.04 and shows being the comparison is acceptable.Findings of criteria map shows that Keshawarz Boulevard, Saei and Valfajr neighborhoods have appropriate greenness, while in Vanak neighborhood due to high density of buildings greenness is not sufficient. The Earth's surface temperature criteria suggests that Northern neighborhoods have the highest value of land surface temperature and the neighbors with sufficient vegetation have the minimum value. The amount of air pollution in the central and southern to southwest regions of Tehran is higher than north and northeast of city.In terms of noise pollution criteria, the neighborhoods located in the south of the city have worse situations than north and central localities. Finally the results of the vulnerability of the building shows that 42% of buildings have low vulnerability, 53% with the average vulnerability and 5% have high vulnerability.

Results in different scenarios suggest that 7% of the region is in very appropriate, 29% in appropriate, 17% in the medium condition, 22% in an inappropriate situation and 24% are in very bad situations. According to the environmental quality of urban life in designed scenarios, The environmental quality of urban life in the most pessimistic designed scenarios indicates that no neighborhood is in a very good group and 3 are in a very inappropriate group, 4 are in the middle group and 2 are in the appropriate group, while the most optimistic. The state has 6 neighborhoods in the appropriate group and 1 neighborhood in the inappropriate group. Also, 1 neighborhood in the inappropriate group, 2 neighborhoods in the middle group and 4 neighborhoods in the appropriate group. This indicates that in modeling the quality of life in this area even if the degree of risk taking in decision-making is increased or a very optimistic view of the environmental dimension of quality of life is still 1 neighborhood of this area namely Amirabad neighborhood with very poor quality of life and 1 neighborhood. That is, Ganjavi's Nezamy district has a poor quality of life. The results also suggest that of the south and southwest parts of the region are in better situation compared to the north and north-astern. According to the different scenarios in different degrees of risk taking, in this area, air pollution is the first and the most effective factor in reducing dimension of environmental life quality. So planning and preventive measures to reduce air pollution is proposed to solve this problem.

Environmental and climatic conditions in different geographical areas provide the basis for certain diseases. Skin cancer is one of the most common types of cancer, with a different incidence rate in geographical areas. The aim of this study is to determine the effects of climate and environmental factors on skin cancer and to map the geographical distribution of skin cancer in Iran.
This study was performed using data of patients with skin cancer, population and data of climatic and environmental factors that affect skin cancer incidence. In this study, after calculating the incidence of skin cancer rate for the whole country, we used the Geographically Weighted Regression model to establish a regression relationship between climate and environmental data and the incidence of skin cancer. The coefficient of detection between the map of incidence of skin cancer and its model map was calculated.
Correlation coefficients showed that sun UV and relative humidity had the highest positive and negative correlation with the incidence of skin cancer, respectively. The southern, eastern and central regions of Iran had the highest incidence of skin cancer rate and the northern and northwestern coasts of Iran had the lowest incidence rate. Validating of actual incidence rate map and the modeled incidence rate map indicated a coefficient of detection of 0.71.
All of the climate and environmental parameters in this study contributed to in the incidence of skin cancer.

this study examines and compares the performance of seven spectral indices in the classification and change detection of built-up lands from Landsat-7 ETM (Enhanced Thematic Mapper Plus) and Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) imageries. The Study site with an area of 68,995 hectares in this study in the Tehran It includes three mid-infrared (MIR)-based indices, i.e. the urban index (UI), the normalized difference built-up index (NDBI), and the index-based built-up index (IBI), two proposed visible (Vis)-based indices, i.e. the VrNIR-BI and VgNIR-BI or the visible red/green-based built-up indices, ,one thermal infrared (TIR)-based index, i.e. the normalized difference impervious surface index (NDISI) and visible blue/ mid-infrared (SWIR1) based built-up indices (VbSWIR1). In addition, a water index, i.e. the modified normalized difference water index (MNDWI), was also derived. Otsu’s method was used to separate water from the non-water areas on the MNDWI map. Subsequently, a water mask was produced and used to mask all the built-up index maps, leaving only the non-water areas. Using the same thresholding method, the non-water areas of all the built-up index maps were classified into built-up and non-built-up classes. The classification accuracy was assessed using 3500 reference points for each image The results show that the VbSWIR1-BI, with an overall accuracy of 92.88% (Landsat-7) and 91.68% (Landsat-8), were more robust and superior. The results also show indications that the detected spatiotemporal urban LULC changes based on the VbSWIR1-BI were also the most accurate.