Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends

By Qihao Weng. 2012

1,193 citations recorded by the source corpus.

Abstract The knowledge of impervious surfaces, especially the magnitude, location, geometry, spatial pattern of impervious surfaces and the perviousness–imperviousness ratio, is significant to a range of issues and themes in environmental science central to global environmental change and human–environment interactions. Impervious surface data is important for urban planning and environmental and resources management. Therefore, remote sensing of impervious surfaces in the urban areas has recently attracted unprecedented attention. In this paper, various digital remote sensing approaches to extract and estimate impervious surfaces will be examined. Discussions will focus on the mapping requirements of urban impervious surfaces. In particular, the impacts of spatial, geometric, spectral, and temporal resolutions on the estimation and mapping will be addressed, so will be the selection of an appropriate estimation method based on remotely sensed data characteristics. This literature review suggests that major approaches over the past decade include pixel-based (image classification, regression, etc.), sub-pixel based (linear spectral unmixing, imperviousness as the complement of vegetation fraction etc.), object-oriented algorithms, and artificial neural networks. Techniques, such as data/image fusion, expert systems, and contextual classification methods, have also been explored. The majority of research efforts have been made for mapping urban landscapes at various scales and on the spatial resolution requirements of such mapping. In contrast, there is less interest in spectral and geometric properties of impervious surfaces. More researches are also needed to better understand temporal resolution, change and evolution of impervious surfaces over time, and temporal requirements for urban mapping. It is suggested that the models, methods, and image analysis algorithms in urban remote sensing have been largely developed for the imagery of medium resolution (10–100 m). The advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms.

Subfields: Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4444b708bf0b07592efe913cd813c9ccf3e738f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research (2016, neutral): “For example, Lu and Weng (2007) and Weng (2012) discussed details of major image classi-ﬁ cation approaches and their main steps, classi ﬁ cation accuracy improvement techniques and issues affecting classi ﬁ cation performance.”
A review of regional science applications of satellite remote sensing in urban settings (2013, neutral): “…systems, satellite imagery is available dating back to the 1970s, and the imagery is well suited to studying and monitoring urban changes and growth trends (Pham, Yam-aguchi, & Bui, 2011; Rashed et al., 2005; Sabet, Ibrahim, & Kanaroglou, 2011; Van de Voorde, Jacquet, & Canters, 2011; Weng, 2012).”
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery (2014, background): “Furthermore, use of multisource data including optical, thermal and microwave data in urban land classification should be systematically evaluated [52,53].”
Urban heat island impacts on plant phenology: intra-urban variability and response to land cover (2016, methodology): “Spectral mixture analysis is a widely used technique for estimating relative composition of different land cover classes by comparing the spectral signature of each pixel to spectra from userdefined reference endmembers (Small 2001, Wu and Murray 2003, Wu 2004, Buyantuyev et al 2007, Weng 2012).”
A critical synthesis of remotely sensed optical image change detection techniques (2015, background): “…involve the analysis of: (1) Key 539 absorption and reflection features present in the visible, near-infrared and especially short-wave infrared 540 regions (Weng, 2012), (2) Fine scale textures (Perry & Nawaz, 2008), and lastly (3) The image scene’s 541 contextual and 3D parameters (Herold, 2008).”
Derivation of an urban materials spectral library through emittance and reflectance spectroscopy (2014, neutral): “Urban applications of such classiﬁcations (Weng, 2012) cover a wide range of purposes; such as mapping of urbanisation (Deng et al., 2009), identiﬁcation of impervious surface cover for the quantiﬁca-tion of ﬂood risk (Schueler, 1994), assessment of concrete quality (Brook and Ben-Dor, 2011), and…”