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CHAPTER 1 Progress in Urban Remote Sensing: An Overview

       Xiaojun Yang

       Department of Geography, College of Social Sciences and Public Policy, Florida State University, Tallahassee, FL, USA

      Abstract

       This chapter defines the scope of the second edition of “Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment”. It begins with a discussion of the context behind the publication of this newer edition. It then discusses some major progress in urban remote sensing over the past decade, followed by an overview of the main topics covered in the volume. The chapter finally pinpoints several areas in need of further research.

1.1 INTRODUCTION

Urban remote sensing, a branch in remote sensing dedicated to the study of the urban environment, has emerged as a major interdisciplinary field of study (Yang, 2011). The rapid development of this field over the past two decades has predominately been driven by the evolving innovations in remote sensing that allow to deliver the critical information on cities in a timely and cost‐effective way to support various urban management activities and the scientific research on urban morphology, socio‐environmental dynamics, and sustainability (Seto et al., 2010, 2012).

      Since the early 2000s, the author began organizing urban remote sensing sessions at the annual meetings of the American Association of Geographers (AAG). Several major areas were identified to address the multidisciplinary needs, which included not only classic subjects, such as urban feature extraction and growth monitoring, but also emerging topics, such as urban socioeconomic indicators and urban modeling. A large number of papers were presented over a period of 10 years, which featured the research done by interdisciplinary scholars predominately from North America. The first edition of Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment published in 2011 was primarily an outcome from this decade‐long conference theme paper series. It is the first book written with a broad vision of urban remote sensing that draws upon multiple disciplines and the integration of remote sensing with relevant geospatial data and technologies. The book covered a variety of topics extending beyond urban feature extraction and into urban socioeconomic and environmental applications and predictive modeling of urbanization. It contrasts considerably in such a treatment with other dedicated books on urban remote sensing, which are largely restricted on urban feature extraction and land use interpretation (e.g. Netzband et al., 2007; Weng and Quattrochi, 2007; Gamba and Herold, 2009; Bhatta, 2010; Jürgens and Rashed, 2010; Weng et al., 2018). In addition, the book has been translated into Chinese and was published by China’s Higher Education Press in 2014.

      The time is ripe for a new edition on urban remote sensing after one decade of publishing the first version. While continuing research on some critical issues identified in the earlier volume is also examined, this new edition focuses on a variety of essential and emerging research areas in urban remote sensing including sensors, techniques, and applications. This mix should help balance the needs of professionals and students at different levels and with various backgrounds. Specifically, the book covers the following major areas:

      Note that unlike the first edition, this new volume does not explicitly include a separate part on geospatial modeling. However, various geospatial modeling techniques have been incorporated in different topics covered in this new edition, such as urban socioeconomic and environment applications.

Unlike many edited books with a contributor pool from a single event (such as a conference or workshop), this volume is written by the two groups of selected interdisciplinary scholars: those who present a scholarly paper at a recent annual meeting of the AAG or at a recent Joint Urban Remote Sensing Event (JURSE) co‐sponsored by the Geoscience and Remote Sensing Society (GRSS) and the International Society for Photogrammetry and Remote Sensing (ISPRS); and those largely identified from their recent research articles published by a variety of peer‐reviewed journals in remote sensing, machine learning, urban studies, and environmental science.

      A total of 78 authors from 17 countries or regions including Austria, Belgium, Canada, China, Denmark, France, Germany, Holland, Hong Kong, Japan, Israel, Italy, Poland, Sweden, Taiwan, Ukraine, and the United States contribute to this book. Although the book is mainly authored by the US and European scholars with case studies predominately from North America and Europe, the knowledge gained from these two major continents can be applied to other urban areas globally.

      The sections to be followed will discuss some major advances in urban remote sensing over the past decade, preview the main topics covered in the book, and pinpoint a number of areas that need further research.

1.2 ADVANCES IN URBAN REMOTE SENSING

      Over the past decade, we have witnessed the rapid advancement in urban remote sensing research amid the evolving innovations in the broad arena of Earth Observation (EO). The purpose here, however, is not to provide a comprehensive review on the progress in urban remote sensing. Rather, we simply highlight some essential and emerging areas that are shifting the directions in urban remote sensing research. Some of the major progress are summarized below.

      Firstly, a variety of remote sensors or systems have provided data essential for urban applications, which include not only some advanced ones, such as high‐resolution satellite systems, hyperspectral remote sensing, high‐resolution synthetic aperture radar (SAR), light detection and ranging (LIDAR), and nighttime satellite systems, but also several new and emergent ones, such as unmanned aerial systems (UASs) and social sensing (including street views). Inexpensive UAS (or drones) equipped with digital cameras (even with LIDAR units) and lightweight GPS units offer spatial flexibility in making sophisticated maps to support various urban applications (e.g. Kalantar et al., 2017; Khan et al., 2017; Dodge, 2018). Social sensing relies upon humans or mobile devices to collect “geotagged” information that can help improve image interpretation with additional human information (e.g. Jiang et al., 2016; Hu et al., 2016; Cai et al., 2017). Street views, such as the Google Street View (GSV) service publicly launched in 2007, offer street‐level imagery of city streetscapes that can help map urban tree cover and other features (e.g. Li et al., 2015b; Berland and Lange, 2017; Seiferling et al., 2017; Dodge, 2018).

Secondly, multi‐temporal analysis of remote sensor imagery has rapidly gained the popularity, which is essential СКАЧАТЬ