Image Processing Reference
In-Depth Information
Roof types for energy use and fire danger prevention
Urban energy balance and local climate in the urban boundary layer
Urban ecology and biotope/habitat assessment
Exercises
•
Consider you follow a photon emitted by the sun that travels through the atmo-
sphere, reflects at the earth's surface and is then acquired by a satellite remote
sensor (see Fig.
4.1
). In this context, discuss the main factors and processes
influencing the spectral signal acquired by the remote sensing systems.
Download a set of example spectra from the Santa Barbara urban spectral library
•
that have been published (see Herold et al.
2003
, http://www.geogr.uni-jena.
de/~c5hema/spec/ieee_fig2.zip)
The spectra are best viewed with MS EXCEL or similar programs. Interpret the
•
spectral signatures to gain understanding of their characteristics resulting from
known absorption/reflection processes. What similarities and differences exist
between the different spectral signatures and how does this affect remote sensing
applications?
You are asked to map the roads within an urban area. What spectral challenges
•
would you face and what kind of remote sensing data would you choose to be
successful?
Wood shingle roofs are important in assessing the fire hazard danger in urban
•
areas. Your local fire department is interested in looking at remote sensing to
update their maps. What potentials and limitations of this technology would you
offer them?
From your understanding of spectral urban characteristics, how would you go
•
about mapping urban land uses like residential, commercial and industrial, and
recreational from remote sensing data. Is a pure spectral signal sufficient enough
to separate them? What other sources of image information might be useful for
this task?
Obviously there is no “standard” spectrum for urban impervious surfaces.
•
Most linear spectral unmixing approaches, however, require a representative
endmember spectrum for built areas. How would you approach spectral mixture
analysis in urban areas? You might want to look at the work of Rashed et al.
(
2001
) and Wu and Murray (
2003
) to help you with your answer.
References
Ben-Dor E, Levin N, Saaroni H (2001) A spectral based recognition of the urban environment
using the visible and near-infrared spectral region (0.4-1.1 m): a case study over Tel-Aviv. Int
J Remote Sens 22:2193-2218
Clark RN (1999) Spectroscopy of rocks and minerals and principles of spectroscopy. In: Rencz AN
(ed) Manual of remote sensing. Wiley, New York, pp 3-58
Clark RN, Green RO, Swayze GA, Meeker G, Sutley D, Hoefen TM, Livo KE, Plumlee G, Pavri B,
Sarture C, Wilson S, Hageman P, Lamothe P, Vance JS, Boardman J, Brownfield I, Gent C,
