Characterizing urban subpixel composition using spectral mixture analysis - Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment

Environmental Engineering Reference

In-Depth Information

15.0

1:1

10.0

5.0

0.0

−

5.0

− 10.0

−

15.0

Reference fraction

(a)

(b)

FIGURE 8.8 (a) Scatterplot comparing reference and modeled tree fractions; each point represents the mean tree cover

fraction for a neighborhood in South Denver ( n

47). The 1:1 line represents perfect agreement. (b) Plot of residuals ( modeled

- reference ) as a function of the reference fractions. The best-fit line indicates general trends of over- and under-estimation.

References

that facilitates spectral discrimination (e.g., Herold et al ., 2004).

However, even with increased spectral resolution, some surfaces

are likely to remain confusing, such as dark, featureless surfaces

(e.g., asphalt roads and tar-shingled roofs), and highly reflective

surfaces (e.g., dry, sandy soil and concrete) (Herold et al ., 2004;

Franke et al ., 2009). One promising approach that addresses the

issues of spectral confusion and within-class variability in urban

landscapes is hierarchical (multilayer) MESMA. This method-

ology applies MESMA to multiple levels of class complexity,

from general categories (e.g., pervious or impervious) to spe-

cific urban material and vegetation species types. Hierarchical

MESMA incorporates the spatial dimension of imagery by using

the coarsest levels of analysis, associated with the highest degree

of confidence, to constrain the more complex levels (Franke

et al ., 2009). Use of MESMA to characterize urban land cover

across different levels of information will advance the develop-

ment of quantitative, generalizedmeasures needed to characterize

urban landscapes globally, while maintaining information on the

types and distributions of locally specific materials to support

urban planning and ecosystem management.

Adams, J.B., Smith, M.O. and Johnson, P.E. (1986) Spectral

mixture modeling: A new analysis of rock and soil at the

Viking Lander 1 site. Journal of Geophysical Research , 91 (B8),

8098-8112.

Adams, J.B., Sabol, D.E., Kapos, V. et al . (1995) Classification

of multispectral images based on fractions of endmembers:

application to land-cover change in the Brazilian Amazon.

Remote Sensing of Environment , 52 , 137-154.

Adams, J.B. and Gillespie, A.R. (2006) Remote Sensing of Land-

scapes with Spectral Images: A Physical Modeling Approach .

Cambridge University Press, Cambridge.

Anderson, J.R., Hardy, E.E., Roach, J.T. and Witmer, R.E. (1976)

A land use and land cover classification system for use with remote

sensor data , US Geological Survey, Professional Paper 964,

United States Government Printing Office, Washington, D.C..

Boardman, J.W., Kruse, F.A. and Green, R.O. (1995) Map-

ping target signatures via partial unmixing of AVIRIS data,

in AVIRIS Airborne Geoscience Workshop Proceedings .JPL

Publications, Pasadena.

Boucek, B. and Moran, E.F. (2004) Inferring the behavior of

households from remotely sensed changes in land cover: Cur-

rent methods and future directions, in Spatially Integrated

Social Science (eds M.F. Goodchild and D.G. Janelle), Oxford

University Press, Oxford, pp. 23-47.

Carlson, T.N. and Sanchez-Azofeifa, G.A. (1999) Satellite remote

sensing of land use changes in and around San Jose, Costa

Rica. Remote Sensing of Environment , 70 , 247-256.

Clapham Jr., W.B. (2003) Continuum-based classification of

remotely sensed imagery to describe urban sprawl on a water-

shed scale. Remote Sensing of Environment , 86 , 322-340.

DeFries, R.S., Townshend, J.R.G. and Hansen, M.C. (1999)

Continuous fields of vegetation characteristics at the global

Acknowledgments

The author is grateful to Dar Roberts, Philip Dennison and

Kerry Halligan, who have successfully transformed multiple

MESMA codes into a user friendly, open-source extension of

ENVI, known as ''Viper Tools'' and available for download

at http://www.vipertools.org. The author also acknowledges

Sharolyn Anderson for helpful comments on drafts of this

manuscript. Research in Rondonia was funded in part by a

NASA Earth System Science Graduate Student Fellowship, as

well as NASA Grant NCC5-282 as part of LBA-Ecology. Digital

videography was acquired as part of LBA-Ecology investigation

LC-07. The Landsat ETM

imagery was acquired from the Trop-

ical Rain Forest Information Center (TRFIC). The author also

thanks two anonymous reviewers for helpful comments.

Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment

Search WWH ::

Custom Search

Home