In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest a...In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.展开更多
Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island effect.Ther...Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island effect.Thermal remote sensors measure the radiation emitted by ground objects,which can be used to estimate the land surface temperature and are beneficial for studying urban heat island effects.The present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter seasons.The study also identifies hot spots and cold spots within the study area.In this study,a significant land surface temperature difference was observed between the urban and rural areas,predominantly at night,indicating the presence of urban heat island at night.These diurnal land surface temperature fluctuations are also detected seasonally,with a relatively higher temperature intensity during the summer.The trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than 0.01.By using the spatial autocorrelation method with the urban heat island index as the key parameter,hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area.A hot spot with 95 and 90 percent confidence level was identified outside the urban area.This spike in temperature for a particular region in the rural area is due to industry and the associated built-up area.The study also identified cold spots with a 90 percent confidence level within the rural area.However,cold spots with a 95 and 99 percent confidence level were not identified within the study area.展开更多
Given the rapid urbanization worldwide, Urban Heat Island(UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat i...Given the rapid urbanization worldwide, Urban Heat Island(UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat island(SUHI) in China’s Meihekou City, a combination method of Monte Carlo and Random Forest Regression(MC-RFR) is developed to construct the relationship between landscape pattern indices and Land Surface Temperature(LST). In this method, Monte Carlo acceptance-rejection sampling was added to the bootstrap layer of RFR to ensure the sensitivity of RFR to outliners of SUHI effect. The SHUI in 2030 was predicted by using this MC-RFR and the modeled future landscape pattern by Cellular Automata and Markov combination model(CA-Markov). Results reveal that forestland can greatly alleviate the impact of SUHI effect, while reasonable construction of urban land can also slow down the rising trend of SUHI. MC-RFR performs better for characterizing the relationship between landscape pattern and LST than single RFR or Linear Regression model. By 2030, the overall SUHI effect of Meihekou will be greatly enhanced, and the center of urban development will gradually shift to the central and western regions of the city. We suggest that urban designer and managers should concentrate vegetation and disperse built-up land to weaken the SUHI in the construction of new urban areas for its sustainability.展开更多
文摘In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.
基金funded this research through grant NITT/R&C/SEED GRANT/2021e22/P.14.
文摘Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island effect.Thermal remote sensors measure the radiation emitted by ground objects,which can be used to estimate the land surface temperature and are beneficial for studying urban heat island effects.The present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter seasons.The study also identifies hot spots and cold spots within the study area.In this study,a significant land surface temperature difference was observed between the urban and rural areas,predominantly at night,indicating the presence of urban heat island at night.These diurnal land surface temperature fluctuations are also detected seasonally,with a relatively higher temperature intensity during the summer.The trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than 0.01.By using the spatial autocorrelation method with the urban heat island index as the key parameter,hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area.A hot spot with 95 and 90 percent confidence level was identified outside the urban area.This spike in temperature for a particular region in the rural area is due to industry and the associated built-up area.The study also identified cold spots with a 90 percent confidence level within the rural area.However,cold spots with a 95 and 99 percent confidence level were not identified within the study area.
基金Under the auspices of National Natural Science Foundation of China(No.41977411,41771383)Technology Research Project of the Education Department of Jilin Province(No.JJKH20210445KJ)。
文摘Given the rapid urbanization worldwide, Urban Heat Island(UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat island(SUHI) in China’s Meihekou City, a combination method of Monte Carlo and Random Forest Regression(MC-RFR) is developed to construct the relationship between landscape pattern indices and Land Surface Temperature(LST). In this method, Monte Carlo acceptance-rejection sampling was added to the bootstrap layer of RFR to ensure the sensitivity of RFR to outliners of SUHI effect. The SHUI in 2030 was predicted by using this MC-RFR and the modeled future landscape pattern by Cellular Automata and Markov combination model(CA-Markov). Results reveal that forestland can greatly alleviate the impact of SUHI effect, while reasonable construction of urban land can also slow down the rising trend of SUHI. MC-RFR performs better for characterizing the relationship between landscape pattern and LST than single RFR or Linear Regression model. By 2030, the overall SUHI effect of Meihekou will be greatly enhanced, and the center of urban development will gradually shift to the central and western regions of the city. We suggest that urban designer and managers should concentrate vegetation and disperse built-up land to weaken the SUHI in the construction of new urban areas for its sustainability.
