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Impervious Surface Mapping with Multi-Spectral Remote
Sensing
Dr. Qihao Weng
Associate Professor of Geography; Director, Ctr. Urban & Environmental Change
Indiana State [email protected]
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Acknowledgement
• This research is sponsored by NSF (BCS-0521734), and by the NASA ISGC program (NGTS-40114-4), and the USGS IndianaView program for a project entitled “Indiana Impervious Surface Mapping Initiative”.
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Impervious Surfaces and Watershed Quality
• Impervious surfaces: Anthropogenic features through which water cannot infiltrate into the soil, such as roads, driveways, sidewalks, parking lots, rooftops, and so on.
• A major indicator of the degree of urbanization, and environmental quality (Arnold and Gibbons, 1996).
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Impervious Surfaces and Watershed Quality
• Watersheds: Natural integrator of hydrological, biological, and geological processes. Human-watershed interactions (planning and policy). Scale dependency. Require an integrated approach to data analysis, in which GIS, remote sensing, and GPS are ideal tools.
• Impervious surfaces: A unifying theme for all participants – planners, engineers, landscape architects, scientists, social scientists, local officials, and others at all watershed scales (Schueler, 1994).
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Impervious Surfaces and Watershed Quality
• Impervious surfaces relate to watersheds in: hydrology (Brun and Band, 2000; Weng, 2001), water quality (Brabec et al. 2002; Hurd and Civco, 2004 ), habitat structure (Booth, 1991; Shaver et al. 1994), biodiversity of aquatic systems (Black and Veatch, 1994; Gillies et al. 2003), land surface temperature (Weng et al. 2006; Lu and Weng, 2006), and water temperature (Galli, 1991).
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Impervious Surfaces and Watershed Quality
• Transport-related vs. roof-related impervious surfaces: land use zoning emphasizes the latter, but the former has a greater hydrological impact.
• The magnitude, location, geometry and spatial pattern of impervious surfaces, and pervious/impervious ratio (landscape structure) in a watershed.
• Threshold: stream quality declines at 10%-15% of imperviousness (Schueler, 1994).
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Impervious Surfaces and Watershed Quality
Ranking of Steam Health (Arnold and Gibsons, 1996)
•Less than 10% imperviousness – protected;
•10%-30% - impacted;
•Over 30% - degraded.
Figure created by Prisloe et al. 2001.
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Estimating and Mapping Impervious Surfaces
• Field survey with GPS - expensive, time-consuming, but accurate.
• Manual digitizing from hard-copy maps or remote sensing imagery (especially aerial photographs) - become increasingly automated (e.g., scanning and feature extraction).
• Remote sensing methods using spectral data.
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Estimating and Mapping Impervious Surfaces
• Most traditional studies correlate impervious surface percentage with land use and land cover (LULC) type/class.
• Limitations of this approach – Intra-variation of imperviousness within the same class; Vary with use density (Brabec et al. 2002); Inconsistent and not replicable.
• When LULC data derived from per-pixel image classification, imperviousness data would be limited by the pixel resolution (Clapham, 2003).
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Remote Sensing Methods
• (1) Multiple regression - relates percent impervious surface to remote sensing and/or GIS variables (Chabaeva et al. 2004; Bauer et al. 2004).
• (2) Sub-pixel algorithms - decompose an image pixel into fractional components (Ridd, 1995; Ji and Jensen, 1999; Wu and Murray, 2003; Lu and Weng, 2004).
• (3) Artificial neural network - applied advanced machine learning algorithms to derive impervious surface coverage (Flanagan and Civco, 2001. Output: per-pixel impervious predictions; Training data: Landsat TM spectral reflectance values)
• (4) Classification and regression tree (CART) algorithm - produced rule-based models for prediction based on training data, and yielded estimates of subpixel percent imperviousness (Yang et al. 2003).
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Landscape as a Continuum
• A continuum model better suited -Continuously varying landscapes, e.g. agricultural land in Midwest USA; residential areas; semi-arid areas, urban and suburban areas, etc.
• A continuum model – pixel measurement regarded as a sum of spectral interactions among the elements weighted by their concentrations.
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Landscape as a Continuum
• A continuum model suited for L-resolution image scenes (Strahler et al. 1986). Scene elements not detectable. Medium resolution (10-100 m) imagery for heterogeneous landscapes.
• Description/quantification vs. classification: e.g., Composition of soil - sand, silt, and clay.
• A continuum model can provide description/quantification of landscapes, but can also be used for classification (Adams et al. 1995; Roberts et al. 1998; Lu and Weng, 2004).
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Linear Spectral Mixture Analysis
• LSMA: Remote sensing implementation of the continuum model.
• LSMA assumes: The spectrum measured by a sensor is a linear combination of the spectra of all components (fractions, endmembers) within a pixel.
• Fraction images used for: biophysical description, landscape characterization, classification, change detection, etc.
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LSMA Model
i
n
kikki RfR
1
Where: i is the number of bands used; k = 1, …, n is number of endmembers; is the spectral reflectance of band i of a pixel; is proportion of endmember k within the pixel; is the spectral reflectance of endmember k within the pixel on band i, and is the error for band i.
iR
kf
ikR
i
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Research Objective
• To develop an approach for estimating and mapping impervious surfaces from multi-spectral Landsat imagery
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Study Area – Marion County, Indiana, USA
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Remote Sensing Data Used
• Landsat ETM+ image of June 22, 2000 (11:14 AM).
• High resolution air photos: 2002 digital orthophotography.
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Methods
• Spectral mixture analysis of optical bands (endmembers calculated: green vegetation, soil, low albedo, and high albedo).
• Impervious surface estimation.
• LST calculation from Landsat thermal infrared data.
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Fraction images
computed from six ETM+
reflective bands using LSMA (A: high albedo; B: low albedo, C:
soil; and D: green
vegetation)
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Feature spaces
between the minimum
noise fraction components,
illustrating potential
endmembers
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0
50
100
150
200
250
1 2 3 4 5 6
ETM+ bands
ET
M+
DN
val
ues
Vegetation Soil Low albedo high albedo
hi
ETM+ spectral features of the selected four endmembers
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A: based on combination of high-albedo and low-albedo fraction images.
B: Improved impervious surface image by combined use of land surface temperature and fraction images.
The values of impervious surface range from 0 to 1, with the lowest values in black and highest values in white.
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Procedure for refining impervious surfaces based on data integration of land surface temperature and fraction images.
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Sample selection for accuracy assessment of impervious surfaces.
Digitized impervious surface polygons within the selected sample plot on the digital orthophoto.
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Overall RMSE = 9.22%, system error = 5.68%.
76 sample plots (300m*300m)
For plots with less than 30% impervious surface,
RMSE = 9.98%, system error = 8.59%.
For plots with greater than or equal to 30% impervious surface,
RMSE = 8.36%, system error = 2.77%.
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Conclusions
• The continuum model suitable for estimating and mapping impervious surfaces from multispectral satellite imagery;
• Impervious surfaces derived from satellite imagery applicable to various applications.
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Reference Books
• Remote Sensing of Impervious Surfaces, by Qihao Weng, CRC Press/Taylor & Francis, ISBN: 1420043749, $129.95, to be published in Sept. 2007.
• Urban Remote Sensing, 2006, By Qihao Weng and Dale Quattrochi, CRC Press/Taylor & Francis, ISBN: 0849391997, $99.95.
• Order at www.crcpress.com