Summer land surface temperature: Small-local variation in intro-urban environment in El Paso, TX
In recent decades, numerous approaches to study the variation of land surface temperature during daytime have emerged; however, little is known about the variation during nighttime. This study addressed the spatial variation of Summer Nighttime Land Surface Temperature (NLST) and their local determinants with comparison to Daytime Land Surface Temperature (DLST) in El Paso and its neighborhoods. Images from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), ASTER Global Digital Elevation Model V002 (GDEM), and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 5 Thematic Mapper (TM 5) were used as main data sources for calculating and extracting variables, including; Land Surface Temperature (LST), Land Surface Albedo (LSA), Land Use Land Cover (LULC) classes, and Normalized Difference Vegetation Index (NDVI). Geographic Information Systems (GIS), ArcMap version 10.1, and Environment for Visualizing Images (ENVI) version 5.0 were utilized throughout this study to deal mainly with local determinants of the DLST and NLST during summer months locally. Application of Geographically Weighted Regression (GWR), a local spatial statistical technique version 4.0 employed to examine the spatially varying relationships between LST and explanatory variables including LSA, NDVI, elevation, and population density. This study also addressed the spatial distribution of social vulnerability to the LST during summer months between 1990 and 2010 using six social and biophysical indicators: total population, income, poverty, age over 65, LST, and NDVI. The results suggested that there was a strong association between the NDVI and LST, especially during daytime. Also significant positive correlation was detected between LST, population density, LSA. The population density showed comparatively higher correlation during the night when compared to daytime, which further indicated the effect of UHI. The weaker relationships observed between the elevation and LST during day and nighttime at neighborhood levels compered to pixel units, which showed relatively significant negative correlation. The LST observed high variations based on the LULC types, which showed great increase over the urban area and further indicated the effect of urban heat islands especially during nighttime. The results of GWR model indicate that four variables collectively were significant predictors of the variations of LST, which explained between 71% and 82% of the variance during daytime and totally explained ranged from 46% to 69% during nighttime. The analyses showed that vegetation played a dynamic part as a cooling factor in explaining the variation of LST during both day and nighttime, this effect tend to be stronger with the reduction of vegetation cover during daytime than nighttime. The population density was the second important variable influencing the LST during both day and nighttime which is acting as a warming factor. LSA and the elevation were the weaker explanatory variables during both day and nighttime. Spatial vulnerability was found to increase over the urban area in the last 20 years. This distribution was also highly linked to the high LST distribution which indicated that the study area will be subjected to increase the vulnerability in the future since the high percentage of this vulnerable group tend to live in urban area. In general, this dissertation casts light on an important issue in understanding the effect of built environment, biophysical and demographical factors on the local LST. Mixed methodology (correlation, descriptive, and GWR) was used in order to address this issue. The outcomes and methods used in this dissertation will be a beneficial reference for close investigation of local climate in the El Paso urban area in future work. (Abstract shortened by UMI.)
Mohamed, MacTar, "Summer land surface temperature: Small-local variation in intro-urban environment in El Paso, TX" (2013). ETD Collection for University of Texas, El Paso. AAI3565924.