Abstract
In the context of climate change and urban heat islands, the concept of local climate zones (LCZ) aims for consistent and comparable mapping of urban surface structure and cover across cities. This study provides a timely survey of remote sensing-based applications of LCZ mapping considering the recent increase in publications. We analyze and evaluate several aspects that affect the performance of LCZ mapping, including mapping units/scale, transferability, sample dataset, low accuracy, and classification schemes. Since current LCZ analysis and mapping are based on per-pixel approaches, this study implements an object-based image analysis (OBIA) method and tests it for two cities in Germany using Sentinel 2 data. A comparison with a per-pixel method yields promising results. This study shall serve as a blueprint for future object-based remotely sensed LCZ mapping approaches.
Highlights
Urbanization is an important global socioeconomic phenomenon, with approximately55% of the world’s total population currently living in cities, a proportion that is estimated to reach 66% by 2050 [1]
Stewart and Oke [7] proposed the concept of the “local climate zone” (LCZ) classification system, which has since received wide attention and became a global standard for classifying urban structure, with applications in diverse fields [8,9,10,11]
We will briefly analyze the limitation of the per-pixel analysis approach that is used in almost all studies, while Section 4 will briefly highlight the advantages of the object-based image analysis (OBIA) methodology
Summary
Urbanization is an important global socioeconomic phenomenon, with approximately. 55% of the world’s total population currently living in cities, a proportion that is estimated to reach 66% by 2050 [1]. Various impacts of urbanization (e.g., changes in surface features, anthropogenic activities, and energy consumption structure) led to local climate changes [2] such as heat island effects [3] As an important means of acquiring surface feature information, remote-sensing technology has been widely applied to monitor impervious urban surfaces [4,5,6]. LCZ classification is an emerging field, with many studies on remote-sensing mapping conducted in recent years [14,15,16]field,. LCZ classification is an emerging with studies urban remote sensing focuses on the detection of impervious surfaces but has not yet urban been remote-sensing mapping conducted in recent years [14,15,16]. Traditional on LCZ remote-sensing mapping conducted in recent years [14,15,16] (Figure 2). Due to the lack of current research on object-based LCZ classification, we apply the object-based LCZ classification to two cities in Germany in order to compare its performance with common per-pixel methods in LCZ mapping
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