气候变化领域集成服务门户(CCPortal): National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series

CCPortal

DOI	10.1016/j.rse.2020.112128
	National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series
	Frantz D.; Schug F.; Okujeni A.; Navacchi C.; Wagner W.; van der Linden S.; Hostert P.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	Urban areas and their vertical characteristics have a manifold and far-reaching impact on our environment. However, openly accessible information at high spatial resolution is still missing at large for complete countries or regions. In this study, we combined Sentinel-1A/B and Sentinel-2A/B time series to map building heights for entire Germany on a 10 m grid resolving built-up structures in rural and urban contexts. We utilized information from the spectral/polarization, temporal and spatial dimensions by combining band-wise temporal aggregation statistics with morphological metrics. We trained machine learning regression models with highly accurate building height information from several 3D building models. The novelty of this method lies in the very fine resolution yet large spatial extent to which it can be applied, as well as in the use of building shadows in optical imagery. Results indicate that both radar-only and optical-only models can be used to predict building height, but the synergistic combination of both data sources leads to superior results. When testing the model against independent datasets, very consistent performance was achieved (frequency-weighted RMSE of 2.9 m to 3.5 m), which suggests that the prediction of the most frequently occurring buildings was robust. The average building height varies considerably across Germany with lower buildings in Eastern and South-Eastern Germany and taller ones along the highly urbanized areas in Western Germany. We emphasize the straightforward applicability of this approach on the national scale. It mostly relies on freely available satellite imagery and open source software, which potentially permit frequent update cycles and cost-effective mapping that may be relevant for a plethora of different applications, e.g. physical analysis of structural features or mapping society's resource usage. © 2020 The Author(s)
英文关键词	Copernicus; Data synergy; Germany; Machine learning; Multi-sensor; Optical; Quantitative remote sensing; SAR; Urbanization
语种	英语
scopus关键词	Application programs; Cost effectiveness; Mapping; Open source software; Open systems; Radar imaging; Regression analysis; Satellite imagery; Time series; Accessible information; Consistent performance; High spatial resolution; Highly urbanized areas; Synergistic combinations; Temporal aggregation; Temporal and spatial; Vertical characteristics; Buildings; accessibility; machine learning; mapping method; physical analysis; regression analysis; satellite data; satellite imagery; Sentinel; time series analysis; urbanization; Germany
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179079
作者单位	Earth Observation Lab, Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany; Integrated Research Institute on Transformations of Human Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany; Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8/E120, Vienna, 1040, Austria; Institute of Geography and Geology, University of Greifswald, Friedrich-Ludwig-Jahn-Str. 16, Greifswald, 17489, Germany
推荐引用方式 GB/T 7714	Frantz D.,Schug F.,Okujeni A.,et al. National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series[J],2021,252.
APA	Frantz D..,Schug F..,Okujeni A..,Navacchi C..,Wagner W..,...&Hostert P..(2021).National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series.Remote Sensing of Environment,252.
MLA	Frantz D.,et al."National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series".Remote Sensing of Environment 252(2021).