Comparison of two different Local Climate Zone mapping methods

The Local Climate Zone (LCZ) classification (Stewart and Oke 2012) is an outstanding concept for the climaterelated classification of urban areas in global scale. Although it was originally designed for meta data communication in observational urban heat island studies, its possible applications are numerous. One of the most important ones is the possibility to use these zones for the input of different climate or weather models in order to better represent urban areas. The use of this concept in these models is advantageous because this classification is based on the thermal characteristics of the urban areas, and it is connected to the most obvious alteration of the climate in urban areas, the urban heat island (Stewart 2011). The LCZ system was initially designed for the classification of urban measurement sites (Stewart and Oke 2012), but meanwhile several methods for LCZ mapping have been proposed (Bechtel and Daneke 2012, Lelovics et al. 2014, Lehnert et al. 2015, Bechtel et al. 2015). The aim of this study is to present and compare two different LCZ mapping methods, Bechtel (Bechtel et al. 2015) and Lelovics-Gal (Lelovics et al. 2014) methods. The first approach (Bechtel-method) is based on free multi-temporal remote sensing data and modern machine learning methods using classifiers like random forest. The entire workflow was implemented in the open source GIS SAGA (Bechtel et al. 2015). The second method (Lelovics-Gal method) is a GIS based automatic software tool (Lelovics et al. 2014). As an input it uses different parameters of the urban structure (like building height, sky view factor, fraction of buildings, vegetation, built up areas, albedo) acquired from different sources (e.g. satellite and aerial images, 3D building databases, CORINE land cover dataset, road databases and maps). The basic elements of this GIS method are the building block and the lot area polygon around it. The approach consists of a fuzzy preliminary classification and a post-processing scheme. Initially, all lot area polygons are assigned to a most similar and a second most similar LCZ using the parameter ranges given by the LCZ fact sheets. Consequently, the polygons are aggregated to achieve at least the minimal size of 500 m x 500 m for a single LCZs using similarity rules. The study area of this comparison is Szeged, Hungary, because in this city all of the needed input parameters are available for both methods. As a part of this comparison we analyze which are the most problematic built up types, spatial configurations, and also we try to find the advantages of the methods. Finally, we aim the integration of both approaches combining the respective advantages. Therefore, we conduct the initial classification using the Bechtel method, since it needs only few and globally available input data. As the second step, the aggregation of the Lelovics-Gal method was implemented in a JAVA tool, in order to create LCZs of sufficient size.