International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W16
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W16, 85–92, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W16-85-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W16, 85–92, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W16-85-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  01 Oct 2019

01 Oct 2019

GEOSPATIAL ASSESSMENT AND MODELING OF URBAN HEAT ISLANDS IN QUEZON CITY, PHILIPPINES USING OLS AND GEOGRAPHICALLY WEIGHTED REGRESSION

C. A. Alcantara1, J. D. Escoto1, A. C. Blanco1,2, A. B. Baloloy2, J. A. Santos2, and R. R. Sta. Ana2 C. A. Alcantara et al.
  • 1Dept. of Geodetic Engineering, University of the Philippines, Diliman, Quezon City 1101, Philippines
  • 2Training Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, Quezon City 1101, Philippines

Keywords: UHI, UTFVI, Land Surface Temperature, NDBI, NDVI

Abstract. Urbanization has played an important part in the development of the society, yet it is accompanied by environmental concerns including the increase of local temperature compared to its immediate surroundings. The latter is known as Urban Heat Islands (UHI). This research aims to model UHI in Quezon City based on Land Surface Temperature (LST) estimated from Landsat 8 data. Geospatial processing and analyses were performed using Google Earth Engine, ArcGIS, GeoDa, and SAGA GIS. Based on Urban Thermal Field Variance Index (UTFVI) and the normalized mean per barangay (village), areas with strong UHI intensities were mapped and characterized. high intensity UHIs are observed mostly in areas with high Normalized Difference Built-up Index (NDBI) like the residential regions while the weak intensity UHIs are noticed in areas with high Normalized Difference Vegetation Index (NDVI) near the La Mesa Reservoir. In the OLS regression model, around 69% of LST variability is explained by Surface Albedo (SA), Sky View Factor (SVF), Surface Area to Volume Ratio (SVR), Solar Radiation (SR), NDBI and NDVI. OLS yield relatively high residuals (RMSE = 1.67) and the residuals are not normally distributed. Since LST is non-stationary, Geographically Weighted Regression (GWR) regression was conducted, proving normally and randomly distributed residuals (average RMSE = 0.26).