Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends
Highlights
► Comprehensive review on methods to extract, estimate and map impervious surfaces. ► Discussions on the mapping requirements of urban impervious surfaces. ► Problems and prospects in remote sensing of impervious surfaces in the urban areas. ► Impact of new sensing systems on the models and algorithms in urban remote sensing. ► First to discuss about research traditions in urban remote sensing.
Introduction
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 recent years, impervious surface has emerged not only as an indicator of the degree of urbanization, but also a major indicator of environmental quality (Arnold & 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). Watersheds with large amounts of impervious cover may experience an overall decrease of groundwater recharge and baseflow and an increase of stormflow and flood frequency (Brun & Band, 2000). Furthermore, imperviousness is related to the water quality of a drainage basin and its receiving streams, lakes, and ponds. Increase in impervious cover and runoff directly impact the transport of non-point source pollutants including pathogens, nutrients, toxic contaminants, and sediment (Hurd & Civco, 2004). Increases in runoff volume and discharge rates, in conjunction with non-point source pollution, will inevitably alter in-stream and riparian habitats, and the loss of some critical aquatic habits (Gillies et al., 2003). 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). As impervious cover increases within a watershed/administrative unit, vegetation cover would decrease. The percentage of land covered by impervious surfaces varies significantly with land use categories and sub-categories (Soil Conservation Service, 1975). 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. The datasets 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.
Many techniques have been applied to characterize and quantify impervious surfaces using either ground measurements or remotely sensed data. Field survey with GPS, although expensive and time-consuming, can provide reliable information on impervious surfaces. Manual digitizing from hard-copy maps or remote sensing imagery (especially aerial photographs) have also been used for mapping imperviousness. Later, this technique has become more heavily involved with automation methods such as scanning and the use of feature extraction algorithms. From the 1970s to 1980s, satellite imagery started to gain popularity in natural resources and environmental studies, and was used in the interpretive applications, spectral applications, and modeling applications of impervious surfaces (Slonecker et al., 2001). In reviewing the methods of impervious surface mapping, Brabec et al. (2002) identified four different approaches, i.e., using a planimeter to measure impervious surface on aerial photography, counting the number of intersections on the overlain grid on an aerial photography, conducting image classification, and estimating impervious surface coverage through the percentage of urbanization in a region. These reviews concluded that in the 1970s and 1980s, aerial photography was the main source of remote sensing data for estimating and mapping impervious surfaces (Brabec et al., 2002, Slonecker et al., 2001).
A literature search via Scopus, the largest abstract and citation database of peer-reviewed literature, indicates that in the 1990s the number of publications on remote sensing of impervious surface was limited (Fig. 1). This is largely due to the lack of remote sensors suitable for detecting and estimating various types of impervious surfaces, immature digital image processing techniques, and constrained computing power. Then, at the turn of the 21st century, remote sensing of impervious surfaces was rapidly gaining interest in the remote sensing community. Fig. 1 shows that annual publications and citations on the subject increased exponentially. The average annual citation (number of citation per article per year) on remote sensing of impervious surfaces was 0.82 between 2001 and 2010, while the number of citations per year for the whole field of remote sensing was 0.55 for the same period. This comparison indicates that remote sensing of impervious surfaces has become one of the more dynamic fields in remote sensing. All major remote sensing journals in the world have published articles on this subject. Table 1 lists most relevant peer-refereed journals, along with most prolific authors and major research groups. Several factors contribute to the increase of the literatures and their significance. The advent of high-resolution imagery, especially those less than 5 m resolution, and more capable image processing techniques, have both driven the technologic advance in remote sensing of impervious surfaces. Driven by the concerns over global environmental change, societal needs of impervious surface data, and enhanced computing and internet technology, many municipal government agencies and non-government organizations have started to collect and map impervious surface data for civil and environmental uses. Given increasing importance in the field of remote sensing, it becomes an urgent need to systematically examine the current state of the research and to trace its future trends. This review begins with examining data requirements for remote sensing of impervious surfaces, with a particular interest in the impacts of remotely sensed data characteristics (i.e., spatial, spectral, and temporal resolutions, and LiDAR data). Next, various digital methods for extracting and estimating impervious surfaces are assessed. In addition, the author will address future developments by looking into how emerging algorithms in digital image processing will influence the field of remote sensing in general and impervious surfaces estimation and mapping in particular.
Section snippets
Spatial resolution
Spatial resolution is a function of sensor altitude, detector size, focal size and system configuration (Jensen, 2005). It defines the level of spatial detail depicted in an image, and it is often related to the size of the smallest possible feature that can be detected from an image. This definition implies that only objects larger than the spatial resolution of a sensor can be picked out from an image. However, a smaller feature may sometimes be detectable if its reflectance dominates within
Major methods of estimation and mapping
Many factors must be taken into account in selecting an image processing method for use. Researchers may have to consider the user's need, research objectives, remotely sensed data available, compatibility with previous work, availability of image processing algorithms and computer software, and time constraints (Lu & Weng, 2007). Among these factors, the selection of suitable remote sensing data is the first important step for a successful application (Jensen and Cowen, 1999, Phinn, 1998,
Data and image fusion
Data and image fusion can be implemented between different sensors, wavelength regions, spatial, spectral, and temporal resolutions. Images from different sensors contain distinctive features. Data fusion or integration of multi-sensor or multi-resolution data takes advantage of the strengths of distinct image data for improvement of visual interpretation and quantitative analysis. In general, three levels of data fusion may be identified, i.e., pixel (Luo & Kay, 1989), feature (Jimenez et al.,
Conclusions
Existing remote sensing literature has regarded impervious surface as a type of surface material, land cover, or land use. The discrepancy in conceptual view has stimulated research into three major directions. Various sub-pixel algorithms applied largely to medium-resolution but less frequently to high-resolution imagery to estimate and mapping impervious surfaces as a type of surface material. Per-pixel algorithms were employed for all sorts of images at various spatial resolutions to
Acknowledgments
The author wishes to thank Drs. Xuefei Hu, Dengsheng Lu, and Mrs. Jing Han for their assistance in research, which contributes to this review. Dr. Marvin Bauer kindly provides editing and valuable comments for improving the manuscript. The author would also like to thank anonymous reviewer for the constructive comments and suggestions.
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