Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 851-856, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-851-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 851-856, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-851-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Jun 2016

23 Jun 2016

MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS

A. M. Bernales1, J. A. Antolihao1, C. Samonte1, F. Campomanes1, R. J. Rojas1, A. M. dela Serna1, and J. Silapan2 A. M. Bernales et al.
  • 1University of the Phlippines Cebu Phil-LiDAR 2, Gorordo Avenue, Lahug, Cebu City, Phlippines
  • 2University of the Phlippines Cebu, Gorordo Avenue, Lahug, Cebu City, Phlippines

Abstract. The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.