Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1431-1436, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1431-2016
© 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, 1431-1436, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1431-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  14 Oct 2016

14 Oct 2016

IDENTIFYING LOCAL SCALE CLIMATE ZONES OF URBAN HEAT ISLAND FROM HJ-1B SATELLITE DATA USING SELF-ORGANIZING MAPS

C. Z. Wei and T. Blaschke C. Z. Wei and T. Blaschke
  • Department of Geoinformatics - Z_GIS, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria

Keywords: Urban heat island, Local climate zones, Impervious surface area, Land surface temperature, Surface albedo, Self-organization maps

Abstract. With the increasing acceleration of urbanization, the degeneration of the environment and the Urban Heat Island (UHI) has attracted more and more attention. Quantitative delineation of UHI has become crucial for a better understanding of the interregional interaction between urbanization processes and the urban environment system. First of all, our study used medium resolution Chinese satellite data-HJ-1B as the Earth Observation data source to derive parameters, including the percentage of Impervious Surface Areas, Land Surface Temperature, Land Surface Albedo, Normalized Differential Vegetation Index, and object edge detector indicators (Mean of Inner Border, Mean of Outer border) in the city of Guangzhou, China. Secondly, in order to establish a model to delineate the local climate zones of UHI, we used the Principal Component Analysis to explore the correlations between all these parameters, and estimate their contributions to the principal components of UHI zones. Finally, depending on the results of the PCA, we chose the most suitable parameters to classify the urban climate zones based on a Self-Organization Map (SOM). The results show that all six parameters are closely correlated with each other and have a high percentage of cumulative (95%) in the first two principal components. Therefore, the SOM algorithm automatically categorized the city of Guangzhou into five classes of UHI zones using these six spectral, structural and climate parameters as inputs. UHI zones have distinguishable physical characteristics, and could potentially help to provide the basis and decision support for further sustainable urban planning.