Abstract
Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.
Highlights
Local Climate Zones (LCZs) have been established as an interdisciplinary scheme to describe urban morphology on a neighborhood scale [1]
As a first step for large-scale or even global LCZ mapping, we focus on the globally available imagery provided by the Sentinel-2 and Landsat-8 mission [27], as well as the Global Urban Footprint (GUF), OSM and Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light layers, using a Residual convolutional neural Network (ResNet) [28,29], as a framework for our investigations
This paper presents an investigation of the applicability and importance of the datasets and features for LCZ classification, focusing on the globally available Sentinel-2 and Landsat-8 imagery
Summary
Local Climate Zones (LCZs) have been established as an interdisciplinary scheme to describe urban morphology on a neighborhood scale [1]. The 17 LCZ classes are based on climate-relevant surface properties on the local-scale, mainly related to 3D surface structure (e.g., height and density of buildings and trees), surface cover (e.g., vegetation or paved), as well as anthropogenic (anthropogenic heat output) parameters. The scheme contains ten “built” and seven “natural” classes, which are depicted, intended to be universal and applicable in cities all over the world, offering the possibility to compare different areas of different cities with trenchant distinctions representing the heterogeneous thermal behavior within an urban environment [2]. 7: Lightweight low-rise 10 Heavy industry C: Bush, scrub. The scheme contains ten “built” and seven “natural” classes, which are depicted in Figure 1, intended to be universal and applicable in cities all over the world, offering the possibility to compare different areas of different cities with trenchant distinctions representing the heterogeneous thermal behavior within an urban environment [2].
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