The biophysical and socioeconomic factors are thought to have close relationships with the Land Surface Temperature (LST) due to the physical characteristics of green areas, building spaces and the influences of socially-related and economically-related factors. This study spatially and quantitatively examines the influences of biophysical and socioeconomic data on LST using Landsat TM image and census data in Hong Kong, and attempts to model the LST by a few but effective integration methodologies. The selected biophysical factors include Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index (NDBI). The socioeconomic data includes population density, per capita income, percentage of college graduates, percentage of modern building, percentage of old buildings and village houses, and building density. Data integration using the Principal Components Analysis (PCA) and Stepwise Regression (SR) revealed that the NDVI and NDBI are the prominent factors on LST. The NDVI has an inverse relationship with the LST while the NDBI has a positive correlation. These support the facts that green areas provide a cooling effect to the surroundings in a city and the increase in building spaces leads to a rise in LST. It is also noted that high-income, highly educated families prefer to live in green and open space areas, and old buildings and antique villages in Hong Kong still preserve green areas. The backward SR method shows the strongest correlation (r=0.836 and s.d.=0.586) exists between LST and the biophysical/socioeconomic parameters. These findings can be used for investigating other urban thermal environments and as measures for analyzing the mitigating effects of urban heat island caused by sociorelatedfactors.
|Number of pages||10|
|Journal||International Journal of Geoinformatics|
|Publication status||Published - 1 Mar 2010|
ASJC Scopus subject areas
- Geography, Planning and Development
- Earth and Planetary Sciences (miscellaneous)