Remote Sensing for Understanding the Vertical Dimension of Urban Landscapes: A review on data, products, methods, and prospects

Quantifying the vertical structure of urban landscapes is the prerequisite to understand its social and ecological impacts. Remote sensing provides an important tool for data acquisition and pattern analysis on the vertical structure of urban landscapes. Therefore, a comprehensive review of how remote sensing can facilitate the quantification of the vertical structure of urban landscapes is not only desirable for the remote sensing community but also for the increasing number of scholars who are broadly interested in 3D related issues. Here, we present a comprehensive review of the current statuses of data, products, methods, and applications for quantifying the vertical dimension of urban landscapes using remote sensing. We further discussed the challenges now facing and future research needed. Specifically, we first reviewed and compared the remote sensing data that could be used to extract the vertical information of urban landscapes, including passive optical data, LiDAR data, SAR data, and street view imagery. We then presented a detailed list of the digital surface model (DSM) and ground object height products derived from remotely sensed data that are readily used for quantifying the vertical structure of urban landscapes, ranging from global-scale products to local ones with finer resolution. We also reviewed key approaches for urban vertical structure analysis, which included urban land cover classification, analysis of the vertical structure of urban architectures, greenspace, and comprehensive landscapes, as well as analysis of the dynamics of urban vertical structure. Additionally, we synthesized applications of urban vertical structure in six core fields: urban heat islands and microclimates, disaster risks, biodiversity, energy and resource consumption, human settlements and social equity, and urban management and planning. Finally, we discussed current challenges and future opportunities, such as limitations and tradeoffs of remote sensing technologies for vertical analysis, extracting vertical information by fusing multi-source remote sensing data, deep learning for high-resolution and high-quality DSM generation, establishing a comprehensive index system for urban vertical structure quantification, monitoring the dynamics of 3D urban landscapes based on long-time-series data, exploring the human perception of 3D urban landscapes, and addressing uncertainty and robustness. To our knowledge, this paper is the first work that comprehensively integrates these elements around the urban vertical structure. It provides abundant information on the quantification of the vertical structure of urban landscapes using remote sensing and offers valuable insights for the remote sensing and 3D research community.