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Geospatial Analysis of Urban Heat Island Effects and Tree Equity
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作者 Jillian Gorrell Sharon R. Jean-Philippe +3 位作者 Paul D. Ries Jennifer K. Richards Neelam C. Poudyal Rochelle Butler 《Open Journal of Forestry》 2024年第1期1-18,共18页
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
关键词 Spatial Analysis Land Cover Urban Heat Island Effect (UHIE) EVAPOTRANSPIRATION Tree Canopy Impervious Surface gis Prediction Model gis machine learning
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