Inference of Local Climate Zones from GIS Data, and Comparison to WUDAPT Classification and Custom-Fit Clusters

Abstract

A GIS-based approach is used in this study to obtain a better LCZ map of Berlin in comparison to the remote-sensing-based WUDAPT L0 approach. The LCZ classification of land use/cover can be used, among other applications, to characterize the urban heat island. An improved fuzzy logic method is employed for the purpose of classification of the zone properties to yield the GIS-LCZ map over 100 m × 100 m grid tiles covering the Berlin region. The zone properties are calculated from raster and vector datasets with the aids of the urban multi-scale environmental predictor (UMEP), QGIS and Python scripts. The standard framework is modified by reducing the threshold for the zone property impervious fraction for LCZ E to better detect paved surfaces in urban areas. Another modification is the reduction in the window size in the majority filter during post-processing, compared to the WUDAPT L0 method, to retain more details in the GIS-LCZ map. Moreover, new training areas are generated considering building height information. The result of the GIS-LCZ approach is compared to the new training areas for accuracy assessment, which shows better overall accuracy compared to that of the WUDAPT L0 method. The new training areas are also submitted to the LCZ generator and the resulting LCZ-map gives a better overall accuracy value compared to the previous (WUDAPT) submission. This study shows one shortcoming of the WUDAPT L0 method: it does not explicitly use building height information and that leads to misclassification of LCZs in several cases. The GIS-LCZ method addresses this shortcoming effectively. Finally, an unsupervised machine learning method, k-means clustering, is applied to cluster the grid tiles according to their zone properties into custom classes. The custom clusters are compared to the GIS-LCZ classes and the results indicate that k-means clustering can identify more complex city-specific classes or LCZ transition types, while the GIS-LCZ method always divides regions into the standard LCZ classes.