International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 275–282, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-275-2019
© 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/W19, 275–282, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-275-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Dec 2019

23 Dec 2019

INTRA-URBAN HEAT ISLAND DETECTION AND TREND CHARACTERIZATION IN METRO MANILA USING SURFACE TEMPERATURES DERIVED FROM MULTI-TEMPORAL LANDSAT DATA

K. P. Landicho1 and A. C. Blanco1,2 K. P. Landicho and A. C. Blanco
  • 1Training Center for Applied Geodesy and Photogrammetry, University of the Philippines Diliman, Quezon City 1101, Philippines
  • 2Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines

Keywords: Urbanization, Local Moran’s I, Google Earth Engine, Emissivity, Hotspot, Coldspot, Histogram, Trendlines

Abstract. Unprecedented urbanization in Metro Manila has led to the proliferation of the urban heat island (UHI) effect. This is characterized by a prominent difference in the temperatures of the urban and its surrounding rural and less urbanized areas. Temperature differences occur within these UHI’s indicating the existence of intra-urban heat islands (IUHI). UHI’s and IUHI’s are well-documented indicators of urban environmental degradation and therefore puts the population of Metro Manila at risk. In anticipation of these effects, their detection and the characterization of their behaviour through time can contribute to proper urban planning thus mitigating harmful effects. Google Earth Engine was used to retrieve land surface temperatures (LST) from Landsat data from 1997 to 2019 using emissivity estimation. The Local Moran’s I statistic was then used to identify cluster and outlier types (COT). A histogram with 10 bins representing the net COT frequencies per barangay was then used to identify IUHI’s. Annual temperature measurements and COT areas were plotted against time and based on linear-fit trend lines they characterize the study area as to having an annual increase in temperature of roughly 0.18 °C and hotspot area extent of around 0.03 km2, and a decrease in coldspot area extent around 0.01 km2. Hotspots were found to be frequent in the cities of Caloocan, Manila, Pasay, and Quezon while coldspots were found to be frequent in the cities of Caloocan, Las Piñas, Malabon, Navotas, and Valenzuela. In conclusion, IUHI’s were detected with statistical basis, both spatially and temporally.