Cover: Techniques and Methods in Urban Remote Sensing by Qihao Weng

IEEE Press
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IEEE Press Editorial Board
Ekram Hossain, Editor in Chief


David Alan Grier   Andreas Molisch   Diomidis Spinellis  
Donald Heirman   Saeid Nahavandi   Sarah Spurgeon  
Elya B. Joffe   Ray Perez   Ahmet Murat Tekalp  
Xiaoou Li   Jeffrey Reed     

Techniques and Methods in Urban Remote Sensing


Qihao Weng

Indiana State University
Terre Haute, Indiana, USA










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Preface

Since the 1970s, there has been an increase in using remotely sensed data for various studies of ecosystems, resources, environments, and human settlements. Driven by the societal needs and improvement in sensor technology and image processing techniques, we have witnessed an explosive increase in research and development, technology transfer, and engineering activities worldwide since the turn of the twenty‐first century. On one hand, hyperspectral imaging afforded the potential for detailed identification of materials and better estimates of their abundance in the Earth’s surface, enabling the use of remote sensing data collection to replace data collection that was formerly limited to laboratory testing or expensive field surveys (Weng 2012). On the other hand, commercial satellites started to acquire imagery at spatial resolutions previously only possible to aerial platforms, but these satellites possess advantages over aerial imageries including their capacity for synoptic coverage, shorter revisit time, and capability to produce stereo image pairs conveniently for high‐accuracy 3D mapping thanks to their flexible pointing mechanism (Weng 2012). While radar technology has been reinvented since the 1990s due greatly to the increase of spaceborne radar programs, Light Detection and Ranging (LiDAR) technology is increasingly employed to provide high‐accuracy height and other geometric information for urban structures and vegetation (Weng 2012). These technologies are integrated with more established aerial photography and multispectral remote sensing techniques to serve as the catalyst for the development of urban remote sensing research and applications. It should further be noted that the integration of Internet technology with satellite imaging and online mapping have led to the spurt of geo‐referenced information over the Web, such as Google Earth and Virtual Globe. These new geo‐referenced “worlds,” in conjunction with GPS, mobile mapping, and modern telecommunication technologies, have sparked unprecedented interest in the public for remote sensing and imaging science.

A few global initiatives and programs have fostered the development of urban remote sensing. First, Group on Earth Observation (GEO) established in its 2012–2015 Work Plan, a new task called “Global Urban Observation and Information (GUOI)” (Weng et al. 2014). The main objectives of this task were to improve the coordination of urban observations, monitoring, forecasting, and assessment initiatives worldwide, to produce up‐to‐date information on the status and development of the urban systems at different scales, to fill existing gaps in the integration of global urban land observations, and to develop innovative concepts and techniques for effective and sustainable urban development. These objectives have been morphed into a new initiative of the GEO Work Programme of 2017–2019 (Weng et al. 2014). These GEO objectives support well the United Nations’ goals on Sustainable Urban Development (United Nations Development Programme 2015). The sustainable development goals (SDGs) represent the UN’s response to numerous societal challenges and efforts to build a sustainable Earth. GEO hopes to make cities and human settlements more inclusive, safe, resilient, and sustainable, through GUOI Initiative, by supplying objective data and information on the footprints of global urbanization and cities, developing indicators for sustainable cities, and developing innovative methods and techniques in support of effective management of urban environment, ecosystems, natural resources, and other assets, and the mitigation of adverse impacts caused by urbanization (Weng 2016). Furthermore, the International Human Dimensions Programme (IHDP) on Global Environmental Change sponsored a project on Urbanization and Global Environmental Change Project (UGEC), between 2006 and 2017, by convening an international network of scholars and practitioners working at the intersection of UGEC. UGEC has improved and expanded knowledge of the key role of these interactions to better understand the dynamic and complex challenges our societies face in the twenty‐first century and has been instrumental in shaping some international policy and science agendas, including the IPCC Fifth Assessment Report and Urban Sustainable Development Goal (Sánchez‐Rodríguez and Seto 2017). Finally, the World Bank has been supporting several groundbreaking initiatives related to urbanization, including the Urbanization Reviews. Projects have been funded to support green, inclusive, resilient, and competitive cities across the world at various stages of urban development. More importantly, the World Bank has developed its Platform for Urban Mapping and Analysis (PUMA) (puma.worldbank.org), a geospatial tool that allows users with no prior GIS experience to access, analyze, and share urban spatial data, and adapts open‐source software to the data needs of various sectors. The “PUMA – Satellite Imagery Processing Consultation” presents up‐to‐date and retrospective land use information of selected cities obtained by processing of high‐resolution satellite imagery. The rapid development of urban remote sensing has greatly benefited from the knowledges, data sets, products, and services that these programs/initiatives have to offer.

Over the past two decades, we have witnessed new opportunities continue to appear for combining ever‐increasing computational power, modern telecommunication technologies, more plentiful and capable data, and more advanced algorithms, which allow the technologies of remote sensing and GIS to become mature and to gain wider and better applications in environments, ecosystems, resources, geosciences, and urban studies. To meet the growing interests in applications of remote sensing technology to urban and suburban areas, Dr. Dale A. Quattrochi, NASA Marshall Space Flight Center, and I decided to assemble a team of experts to edit a volume on “Urban Remote Sensing” in 2006. Since its inception, the book had been serving as a reference book for researchers in academia, governmental, and commercial sectors and has also been used as a textbook in many universities. Remote sensing technology has since changed significantly and its applications have emerged worldwide, so it is feasible to write a book on the subject. I decided to write a text book on urban remote sensing, when Mr. Taisuke Soda, Wiley‐IEEE Press, Hoboken, New Jersey, sent me an invitation in 2011. It has not been easy to complete it with my increased commitments and numerous interruptions. I found it even more challenged to determine what to include in the book, as researches and applications on urban remote sensing continue to expand rapidly.

Qihao Weng

References

  1. Sánchez‐Rodríguez, R. and Seto, K. (2017). Special Announcement from UGEC Co‐Chairs Karen Seto and Roberto Sanchez‐Rodriguez. https://ugec.org/2017/01/18/special‐announcement‐ugec‐co‐chairs‐karen‐seto‐roberto‐sanchez‐rodriguez (accessed 13 January 2018).
  2. United Nations Development Programme (2015). Sustainable Development Goals (SDGs). http://www.undp.org/content/undp/en/home/sdgoverview/post‐2015‐development‐agenda.html (accessed 5 March 2016).
  3. Weng, Q. (2012). An Introduction to Contemporary Remote Sensing. New York: McGraw‐Hill Professional, pp. 320.
  4. Weng, Q. (2016). Remote Sensing for Sustainability. Boca Raton, FL: CRC Press/Taylor and Francis, pp. 366.
  5. Weng, Q., Esch, T., Gamba, P. et al. (2014). Global urban observation and information: GEO’s effort to address the impacts of human settlements. In Weng, Q. editor. Global Urban Monitoring and Assessment Through Earth Observation. Boca Raton, FL: CRC Press/Taylor and Francis, pp. 15–34.

Synopsis of the Book

This book is settled down with 12 chapters and addresses theories, methods, techniques, and applications in urban remote sensing. Both Chapters 1 and 2 address fundamental theoretical issues in remote sensing of urban areas. Because of the significance of impervious surface as an urban land cover, land use, or material, Chapter 1 examines the general requirements for mapping impervious surfaces, with a particular interest in the impacts of remotely sensed data characteristics, i.e. spectral, temporal, and spatial resolutions. The discussion is followed by a detailed investigation of the mixed pixels issue that often prevails in medium spatial resolution imagery in urban landscapes. This investigation employs linear spectral mixture analysis (LSMA) as a remote sensing technique to estimate and map vegetation – impervious surface – soil components (Ridd 1995) in order to analyze urban pattern and dynamics in Indianapolis, USA. Chapter 2 discusses another basic but pivot issue in urban remote sensing – the scale issue. The requirements for mapping three interrelated substances in the urban space – material, land cover, and land use – and their relationships are first assessed. The categorical scale is closely associated with spectral resolution in urban imaging and mapping, while the observational scale of a remote sensor (i.e. spatial resolution) interacts with the fabric of urban landscapes to create different image scene models (Strahler et al. 1986). Central to the observational scale–landscape relationship is the problem of mixed pixels and various pixel and sub‐pixel approaches to urban analysis. Next, the author’s two previous studies were discussed, both assessing the patterns of land surface temperature (LST) at different aggregation levels to determine the operational scale/the optimal scale for image analysis. Chapter 2 ends with discussion on the issue of scale dependency of urban phenomena and two case studies, one on LST variability across multiple census levels and the other on multi‐scale residential population estimation.

A large portion of the book is dedicated to the methods and techniques in urban remote sensing by showcasing a series of applications of various aspects. Typically, each application area examines with an analysis of the state‐of‐the‐art methodology followed by a detailed presentation of one or two case studies. The application areas include building extraction and impervious surface estimation and mapping (Chapters 3 and 4), LST generation, urban heat island (UHI) analysis and anthropogenic heat modeling (Chapters 5–7), cities at night (Chapter 8), urban surface runoff and ecology of West Nile Virus (WNV) (Chapters 9 and 10), assessment of urbanization impacts (Chapter 11), and estimation of socioeconomic attributes (Chapter 12).

In Chapter 3, building types are identified by using remote sensing‐derived morphological attributes for the City of Indianapolis, Indiana, USA. First, building polygons and remotely sensed data (i.e. high‐spatial‐resolution orthophotography and LiDAR point cloud data) in 2012 were collected. Then, morphological attributes of buildings were delineated. Third, a Random Forest (RF) classifier was trained using randomly selected training samples obtained from the City of Indianapolis Geographic Information System (IndyGIS) database, Google Earth Maps, and field work. Finally, the trained classifier was applied to classify buildings into three categories: (i) nonresidential buildings; (ii) apartments/condos; and (iii) single‐family houses.

Chapter 4 illustrates a few commonly used methods for estimating and mapping urban impervious surfaces. This chapter begins with a brief review of the various methods of urban impervious surface estimation and mapping, followed by two case studies. The first case study was conducted to demonstrate the capability of LSMA and the multilayer perceptron (MLP) neural network for impervious surface estimation using a single Hyperion imagery. The second case study employed a semi‐supervised fuzzy clustering method to extract annual impervious surfaces in the Pearl River Delta, southern China, from 1990 to 2014, aiming to utilizing time series Landsat imagery.

Chapters 5–7 are concerned about urban thermal landscape and surface energy balance. In Chapter 5, the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) (Weng et al. 2014) is introduced. This algorithm was developed for fusing thermal infrared (TIR) data from two satellites, the high spatial resolution Landsat Thematic Mapper (TM) and high temporal resolution MODIS data, to predict daily LST at 120‐m resolution. The second algorithm to be introduced is called “DELTA,” which stands for the five modules of Data filtEr, temporaL segmentation, periodic modeling, Trend modeling, and Gaussian, respectively (Fu and Weng 2016). This algorithm is developed to reconstruct historical LSTs at daily interval based solely on irregularly spaced Landsat imagery by taking into account some significant factors, such as cloudy conditions, instrumental errors, and disturbance events, that impact analysis of long‐term LST data.

In Chapter 6, two methods for characterizing and modeling UHI using remotely sensed LST data are introduced. A kernel convolution modeling method for two‐dimensional LST imagery will be introduced to characterize and model the UHI in Indianapolis, USA, as a Gaussian process model (Weng et al. 2011). The main contribution of this method lies in that UHIs can be examined as a scale‐dependent process by changing the smoothing kernel parameter. Furthermore, an object‐based image analysis procedure will be introduced to extract hot spots from LST maps in Athens, Greece. The spatial and thermal attributes associated with these objects (hot spots) are then calculated and used for the analyses of the intensity, position, and spatial extent of UHIs.

To gain a better understanding of UHI phenomenon and dynamics, one must examine the temporal and spatial variability of surface heat fluxes in the urban areas, especially of anthropogenic heat flux. In Chapter 7, we develop an analytical protocol, based on the two‐source energy balance (TSEB) algorithm, to estimate urban surface heat fluxes by combined use of remotely sensed data and weather station measurements. This method was applied to four Terra’s ASTER images of Indianapolis, Indiana, United States, to assess the seasonal, intra‐urban variations of spatial pattern of surface energy fluxes. In addition, anthropogenic heat discharge and energy use from residential and commercial buildings were estimated. Based on the result, the relationship between remotely sensed anthropogenic heat discharge and building energy consumption was examined across multiple spatial scales.

Nighttime light (NTL) imagery provides a unique source of Earth Observational data to examine human settlements and activities at night. In Chapter 8, a method is proposed for large‐scale urban detection and mapping by utilizing spatiotemporally adjusted NTL images across different times. Secondly, this chapter will analyze the spatiotemporal pattern of electricity consumption in the USA and China from 2000 to 2012 by using NTL imagery. This analysis offers a spatially explicit method to characterize the spatial and temporal pattern of energy consumption at regional and global scale.

Chapter 9 relates land‐use and land‐cover (LULC) change to spatially distributed hydrological modeling in order to study urban surface runoff. Two case studies will be illustrated: one in Guangzhou, China, and the other in Chicago, USA. A model widely used for estimating surface runoff was developed by the United States Soil Conservation Service, that is, the SCS model. The Guangzhou study developed a new procedure to calculate composite Curve Number, a key parameter in SCS model, based on urban compositional vegetation‐impervious surface soil (VIS) model, and then simulated surface runoff under different precipitation scenarios. The Chicago study aimed to assess the impact on water quality resulted from LULC changes in an urban watershed over a long time period, by employing the long‐term hydrologic impact assessment nonpoint source (L‐THIA‐NPS) model. This model also used the SCS curve number method to estimate runoff depth and volume.

Chapter 10 focuses on the ecology of WNV in the US urban environments. Two case studies will be presented to illustrate the applications of remote sensing and GIS techniques for analyzing and modeling the spread of WNV. The first study, by a case study of the City of Chicago, aimed to improve the understanding of how landscape, LST, and socioeconomic variables were combined to influence WNV dissemination in the urban setting, and to assess the importance of environmental factors in the spread of WNV. The second study investigated the WNV spread in the epidemiological weeks from May to October in each year of 2007–2009 in the Southern California and modeled and mapped the risk areas.

Urbanization can bring about significant changes to the environment. In Chapter 11, two case studies will be introduced to examine the impact of LULC change on LST and on surface water quality, respectively. The first study utilized 507 Landsat TM/Enhanced Thematic Mapper Plus (ETM+) images of Atlanta, Georgia, United States, between 1984 and 2011, to investigate the impact of urban LULC changes on temporal thermal characteristics by breaking down the time‐series LST observations into temporally homogenous segments. The second study simulated future land use/planning scenarios for the Des Plaines River watershed in the Chicago metropolitan area and to evaluate the response of total suspended solids to the combined impacts of future land use and climate scenarios.

The final chapter, Chapter 12, explores the feasibility of using remote sensing to estimate urban socioeconomic attributes and to analyze their changes, spatially and temporally. Specifically, methods for estimating population and assessing urban environmental quality (UEQ) will be demonstrated through two case studies, both conducted in Indianapolis, Indiana, USA. The population estimation study intended to combine the statistical‐based and dasymetric‐based methods and to redistribute census population. The objectives of this research are to compare the effectiveness of the spectral response based and the land‐use‐based methods for population estimation of US census block groups and to produce a more accurate presentation of population distribution by combining the dasymetric mapping with land‐use‐based methods. The UEQ study intended to evaluate the UEQ changes from 1990 to 2000 in Indianapolis by using the integrated techniques of remote sensing and GIS. The physical variables were extracted from Landsat images, while socioeconomic variables derived from US census data to derive a synthetic UEQ indicator.

References

  1. Fu, P. and Q. Weng. 2016. Consistent land surface temperature data generation from irregularly spaced Landsat imagery, Remote Sensing of Environment, 184(10), 175–187.
  2. Ridd, M. K. (1995). Exploring a V‐I‐S (vegetation–impervious surface–soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16, 2165–2185.
  3. Strahler, A.H., Woodcock, C.E. and Smith, J.A., 1986, On the nature of models in remote sensing. Remote Sensing of Environment, 70, pp. 121–139.
  4. Weng, Q., Fu, P. and F. Gao. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment, 145, 55–67.
  5. Weng, Q., Rajasekar, U. and X. Hu. 2011. Modeling urban heat islands with multi‐temporal ASTER images. IEEE Transactions on Geosciences and Remote Sensing, 49(10), 4080–4089.

Acknowledgments

I wish to extend my most sincere appreciation to several former and current students of Indiana State University who have contributed to this book substantially. Listed in alphabetical order of their family name, Dr. Peng Fu, Dr. Xuefei Hu, Dr. Guiying Li, Dr. Bingqing Liang, Dr. Hua Liu, Dr. Dengsheng Lu, Dr. Umamaheshwaran Rajasekar, Dr. Cyril Wilson, and Dr. Yanhua Xie. My collaborators, Dr. Lei Zhang, Dr. Iphigenia Keramitsoglou, and Dr. Fenglei Fan, have contributed to the writing of Chapters 4, 6, and 9, respectively. This book is truly a collective effort of all these great scholars. Finally, I am indebted to my family for their enduring love and support, to whom this book is dedicated. It is my hope that the publication of this book will provide stimulations to students and researchers to conduct more in‐depth work and analysis of urban remote sensing and to contribute to global urban observation and sustainable urban development goals. In the course of increased worldwide urbanization and global environment changes and with increased interest in remotely sensed Big Data, urban remote sensing has become a very dynamic field of study.

Qihao Weng
Hawthorn Woods, Indiana

About the Author

Photo of Qihao Weng.

Qihao Weng is the Director of the Center for Urban and Environmental Change and a Professor at Indiana State University and worked as a Senior Fellow at the NASA Marshall Space Flight Center from 2008 to 2009. He is currently the Lead of GEO Global Urban Observation and Information Initiative and serves as an Editor‐in‐Chief of ISPRS Journal of Photogrammetry and Remote Sensing and the Series Editor of Taylor & Francis Series in Remote Sensing Applications and Taylor & Francis Series in Imaging Science. He has been the Organizer and Program Committee Chair of the biennial IEEE‐/ISPRS‐/GEO‐sponsored International Workshop on Earth Observation and Remote Sensing Applications conference series since 2008, a National Director of American Society for Photogrammetry and Remote Sensing from 2007 to 2010, and a panelist of US DOE’s Cool Roofs Roadmap and Strategy in 2010. In 2008, Weng received a prestigious NASA senior fellowship and was also the recipient of the Outstanding Contributions Award in Remote Sensing in 2011 and the Willard and Ruby S. Miller Award in 2015 for his outstanding contributions to geography, both from the American Association of Geographers, and a recipient of Taylor & Francis Lifetime Achievement Award in 2019 and a fellowship by 2019 JSPS Invitational Fellowships for Research in Japan (Short‐term S). At Indiana State, he was selected as a Lilly Foundation Faculty Fellow in 2005 and in the following year, he received the Theodore Dreiser Distinguished Research Award. In addition, he was the recipient of 2010 Erdas Award for Best Scientific Paper in Remote Sensing (first place) and 1999 Robert E. Altenhofen Memorial Scholarship Award, both awarded by American Society for Photogrammetry and Remote Sensing. In 1998, he received the Best Student‐Authored Paper Award from International Geographic Information Foundation. Weng has been invited to give more than 110 talks by organizations and conferences worldwide and is honored with distinguished/chair/honorary/guest professorship by a dozen of universities. In 2018, he was elected a Fellow of Institute of Electrical and Electronics Engineers and a member of EU Academy of Sciences. Weng’s research focuses on remote sensing applications to urban environmental and ecological systems, land‐use and land‐cover changes, urbanization impacts, environmental modeling, and human–environment interactions, with funded support from NSF, NASA, USGS, USAID, NOAA, National Geographic Society, among others. Weng is the author of more than 230 articles and 14 books. According to Google Scholar, as of June 2019, his SCI citation has reached over 16 000 (H‐index of 59), and 39 of his publications had more than 100 citations each.