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...