جستجوی مقالات مرتبط با کلیدواژه « urban heat island (uhi) » در نشریات گروه « محیط زیست »

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.

Humans have actively managed and transformed the world’s landscapes for millennia. After the industrial revolution (between 1820 and 1840) with increase in sanitation, food security and quality of life the human population increased tremendously. Urbanization, the demographic transition from rural to urban, is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area and by 2050; this proportion will increase to 7 out of 10 people. The result was the physical growth of urban areas, be it horizontal or vertical. Urbanisation is an extreme form of Land Use and Land Cover Change (LULC) that occurs when the natural vegetation of an area is replaced with buildings and roads, which tend to have significantly higher air temperatures than their rural surrounding. This phenomenon is known as Urban Heat Island (UHI). UHIs directly and indirectly affect the thermal comfort and health of city inhabitants. UHIs cause generating more CO2 emission by increasing the energy consumption for cooling the infrastructure, but also influences water use, biodiversity change and human discomfort where all together aggravate social and environmental quality in cities which collectively contributes to global challenges. Increase in atmospheric CO2 concentration in association with LULC changes are among the main drivers to climate change.
Energy consumption, generating CO2 emissions and contributing to earth warming, in association with land use and land cover changes are amongst main drivers of global climate change on the one hand and of increasing the ‘Urban Heat Islands’(UHI) – higher temperatures in cities compared to their surroundings – on the other. Therefore, urban vegetation can have a role in mitigating the UHI effect. Urban vegetation through several mechanisms of shading, increasing albedo and evapotranspiration decreases the penetration of sun during the day. Urban vegetation also decreases summertime energy demand to cool the indoor climate, decreasing CO2 emissions as well.
Remote sensing has proved to be a useful tool for cross-scale ecological research at various spatial, temporal, and spectral scales. Remote sensing images of the apparent surface temperature of cities show the marked coolness of vegetated surfaces in general and parks in particular. Therefore ‘urban greening’ has been proposed as one approach to mitigate the human health consequences of increased temperatures resulting from climate change. However, urban vegetation not only regulates climate but also acts as an important amenity for the neighbouring communities; it support urban life and can ensure social cohesion and wellbeing. The goal of this paper focuses on the cooling effect (pattern) of urban vegetation in the city of Munich, Germany, for more than 10 years. Consequently, it is hypothesised if the urban vegetation’s cooling effect takes place during continuing years, including the warm year of 2003 in the study area?
In order to make this study happen, remote sensing data, GIS and LULC data has been used for the study area. The study area is located in the South-East of Germany and is the capital city of Bavarian state. This city is approximately 310.43 km2 with the population of 1.37 million inhabitants in 2011. Munich is a developed city and a stable region in terms of land use and land cover in the period 2002 to 2012. Distribution of green parks within the administrative area of the city (33.8 m2 per person) is the advantage of the chosen study area (fig 1).
Fig 1. Land cover map and spatial distribution of those in study area
A. Area study marked with Red, B. land cover classified in 21(CLC2006)
B.
The land surface temperature data were obtained from MYD11A2 product of MODIS sensor which is an 8-day interval data. The LST data were collected for the warm season of 2002 to 2012. The LULC data were used from the European Environment Agency (EEA) called Corine Land Cover (CLC) database which has been prepared for more than 25 European countries with 44 classes. Due to similarities in the behavior of surface temperature of different CLCs, some classes were reclassified and combined to form two major rather simplified homogenized classes; one of urban areas and the other one being the urban vegetation. The homogenized map was merged to LST data in order to compute the relationship in between. Therefore Kernel Quantile Regression (KQR) was used. KQR performs non-parametric regression and is a method for estimating functional relations between variables for all portions of a probability distribution and aims at estimating either the conditional median or other quantiles of the response variable. QR was used to calculate for the 25, 50 and 75 quantiles for each month, which illustrates the change of LST in urban areas and urban vegetation.
The results revealed that (I) a higher daytime surface temperature in dense urbanised area rather than well-vegetated and surrounding urban area, due to thermal emissivity properties of urban surfaces and heat capacity, (II) a positive and increasing trend between LST and the ratio of urban, while a negative and decreasing trend between the LST and the urban vegetation within every pixel. Estimates of Weng et al. (2007) reported as well that abundance in vegetation is one of the most influential factors in controlling LST measurements through partitioning solar radiation into fluxes of sensible and latent heat. (III) A non-linear trend between LST and the proportion of LULC within each pixel, especially for urban vegetation. Vegetation can be effective as it delivers several mechanisms of cooling simultaneously and in a complementary manner. Urban vegetation reduces heat islands through shading and evapotranspiration. Shading restricts energy storage and heating of the local environment by limiting solar penetration. Plants convert water into water vapour through evaporation; energy is being used to drive the evaporation process rather than being transferred to the sensible heat that heats up the city. As a result, cooler air temperature is observed within well-vegetated areas. Therefore, fully vegetated pixels were expected to have a cooler surface temperature. (IV) A remarkable and stronger cooling effect in terms of LST in regions where the proportion of vegetation cover was between seventy and almost eighty percent per square kilometre. Better air flow and convection, which are lower in densely vegetated areas, might be the reason for this finding. Leuzinger et al. (2010) demonstrated that trees responded differently to extremes in temperature. Results also demonstrated (V) that LST within urban vegetation was affected by the temperature of the surrounding urban area. A good example is the year 2003, when LST increased in comparison with records of previous years as a result of the well-known heat wave in Europe. The results of this study demonstrate that LST of urban vegetation is related to the temperature of its urban surrounding. Therefore, dependency may differ according to the size, shape and location of the vegetated area. Finally, (VI) the coolest places were areas far from the core of the urbanized region.
This study concluded that regional and local scale studies within the changing climate can improve our understanding of urban ecological challenges and facilitate appropriate adaptation to regional and global climate change. Therefore, this research could provide urban planners and landscapers with strategies for mitigating the UHI effect through the strategic placement of urban vegetation.