Techniques and Methods in Urban Remote Sensing

Qihao Weng

• Publisher: Wiley-IEEE Press
• Year: 2019
• Print ISBN: 9781118217733
• eBook ISBN: 9781119307334
• Edition: 1
• Topic: Tecnologia e ingegneria
• Subtopic: Ingegneria civile

An authoritative guide to the essential techniques and most recent advances in urban remote sensing

Techniques and Methods in Urban Remote Sensing offers a comprehensive guide to the recent theories, methods, techniques, and applications in urban remote sensing. Written by a noted expert on the subject, this book explores the requirements for mapping impervious surfaces and examines the issue of scale. The book covers a range of topics and includes illustrative examples of commonly used methods for estimating and mapping urban impervious surfaces, explains how to determine urban thermal landscape and surface energy balance, and offers information on impacts of urbanization on land surface temperature, water quality, and environmental health.

Techniques and Methods in Urban Remote Sensing brings together in one volume the latest opportunities for combining ever-increasing computational power, more plentiful and capable data, and more advanced algorithms. This allows the technologies of remote sensing and GIS to become mature and to gain wider and better applications in environments, ecosystems, resources, geosciences, geography and urban studies. This important book:

• Contains a comprehensive resource to the latest developments in urban remote sensing
• Explains urban heat islands modeling and analysis
• Includes information on estimating urban surface energy fluxes
• Offers a guide to generating data on land surface temperature

Written for professionals and students of environmental, ecological, civic and urban studies, Techniques and Methods in Urban Remote Sensing meets the demand for an updated resource that addresses the recent advances urban remote sensing.

1
Urban Mapping Requirements

1.1 Introduction

Urban landscapes are typically a complex combination of buildings, roads, parking lots, sidewalks, garden, cemetery, soil, water, and so on. Each of the urban component surfaces possesses unique biophysical properties and relates to their surrounding environment to create the spatial complexity of urban ecological systems and landscape patterns. To understand the dynamics of patterns and processes and their interactions in heterogeneous landscapes such as urban areas, one must be able to quantify accurately the spatial pattern of the landscape and its temporal changes (Wu et al. 2000). In order to do so, it is necessary (i) to have a standardized method to define theses component surfaces and (ii) to detect and map them in repetitive and consistent ways, so that a global model of urban morphology may be developed, and monitoring and modeling their changes over time be possible (Ridd 1995).

Impervious surfaces are anthropogenic features through which water cannot infiltrate into the soil, such as roads, driveways, sidewalks, parking lots, rooftops, and so on. In the past two decades, impervious surface has emerged not only as an indicator of the degree of urbanization, but also a major indicator of environmental quality (Arnold and Gibbons 1996). Impervious surface is a unifying theme for all participants at all watershed scales, including planners, engineers, landscape architects, scientists, social scientists, local officials, and others (Schueler 1994). The magnitude, location, geometry, and spatial pattern of impervious surfaces, and the pervious–impervious ratio in a watershed have hydrological impacts. Although land‐use zoning emphasizes roof‐related impervious surfaces, transport‐related impervious surfaces could have a greater impact. The increase of impervious cover would lead to the increase in the volume, duration, and intensity of urban runoff (Weng 2001), and an overall decrease of groundwater recharge and baseflow but an increase of stormflow and flood frequency (Brun and Band 2000). Furthermore, imperviousness is related to the water quality of a drainage basin and it’s receiving streams, lakes, and ponds (Hurd and Civco 2004). In addition, the areal extent and spatial occurrence of impervious surfaces may significantly influence urban climate by altering sensible and latent heat fluxes within the urban canopy and boundary layers (Yang et al. 2003). Therefore, estimating and mapping impervious surface is significant to a range of issues and themes in environmental science central to global environmental change and human–environment interactions and has been regarded as a key variable in urban remote sensing studies. The data sets of impervious surfaces are valuable not only for environmental management, e.g. water quality assessment and storm water taxation, but also for urban planning, e.g. building infrastructure and sustainable urban development.

Remote sensing technology has been widely applied in urban land‐use and land‐cover (LULC) classification and change detection. However, it is rare that the classification accuracy of greater than 80% can be achieved by using per‐pixel classification (so‐called “hard classification”) algorithms (Mather 1999, p. 10). Therefore, the “soft”/fuzzy approach of LULC classifications has been applied, in which each pixel is assigned a class membership of each LULC type rather than a single label (Wang 1990). Nevertheless, as Mather (1999) suggested, either “hard” or “soft” classifications was not an appropriate tool for the analysis of heterogeneous landscapes. Mather (1999) maintained that identification/description/quantification, rather than classification, should be applied in order to provide a better understanding of the compositions and processes of heterogeneous landscapes such as urban areas. Ridd (1995) proposed a major conceptual model for remote sensing analysis of urban landscapes, i.e. the vegetation–impervious surface–soil (V‐I‐S) model. It assumes that land cover in urban environments is a linear combination of three components, namely, vegetation, impervious surface, and soil. Ridd believed that this model can be applied to spatial‐temporal analyses of urban morphology, biophysical, and human systems. Having realized that the V‐I‐S model may be used as a method to define standardized urban landscape components, this chapter employs linear spectral mixture analysis (LSMA) as a remote sensing technique to estimate and map V‐I‐S components in order to analyze urban pattern and dynamics. The case study will be conducted in Indianapolis, United States, from 1991 to 2000, by using multi‐temporal satellite images, i.e. Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) imagery of 1991, 1995, and 2000. Because of the significance of impervious surface as an urban land cover, land use, or material, this chapter will start with examining data requirements for remote sensing of impervious surfaces, with a particular interest in the impacts of remotely sensed data characteristics (i.e. spectral, temporal, and spatial resolutions).

1.2 Spectral Resolution Requirement

Remote sensing of impervious surfaces should consider the requirements for mapping three interrelated entities or substances on the Earth surface (i.e. material, land cover, and land use) and their relationships. Mapping of each entity/substance must consider the spectral resolution of a remote sensor. The spectral features include the number, locations, and bandwidths of spectral bands. The number of spectral bands can range from a limited number of multispectral bands (e.g. 4 bands in SPOT data and 7 for Landsat TM) to a medium number of multispectral bands (e.g. Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER] with 14 bands and Moderate Resolution Imaging Spectroradiometer [MODIS] with 36 bands) and to hyperspectral data (e.g. AVIRIS and EO‐1 Hyperion images with 224 bands). A large nu...

Table of contents

• Cover
• Table of Contents
• Preface
• Synopsis of the Book
• Acknowledgments
• About the Author
• 1 Urban Mapping Requirements
• 2 The Scale Issue
• 3 Building Extraction and Classification
• 4 Estimation and Mapping of Impervious Surfaces
• 5 Land Surface Temperature Data Generation
• 6 Urban Heat Islands Modeling and Analysis
• 7 Estimation of Urban Surface Energy Fluxes
• 8 Cities at Night
• 9 Urban Runoff Modeling and Prediction
• 10 Urban Ecology of West Nile Virus
• 11 Impacts of Urbanization on Land Surface Temperature and Water Quality
• 12 Remote Sensing of Socioeconomic Attributes
• Index
• End User License Agreement