Coastal research by the team of Nelson Melo
[http://cake.fiu.edu/Melo/] at FIU and their extramural colleagues
will utilize the instrument research in the fields of oceanography and marine
remote sensing. In particular, it will provide new ways to study oceanic
surface and sub-surface phenomena. One of the challenges in airborne
remote sensing of the water and coastal environment is the interference of the
atmosphere [I. Levin; Chuanmin Hu; M. Zhang]. Because
water targets are typically dark, an accurate atmospheric correction is
required. Such a correction, however, is often difficult because of the unknown
thickness of altitude of aerosol. These unknowns are often assumed "known",
based on historical values but the errors involved on those "corrections" often
mask the data needed aerosols [Gordon, H.; Mao, Z. (2013), Mao, Z. (2014)]. The
Instrument's low flight heights (less than 500 ft)
and no-vibration, slow-flight speeds will minimize such
interference and reduce blurring of the high-spatial resolution (1-10
cm) pixels needed to obtain mutli-temporal aerial
photography (color, panchromatic and multispectral) and integrate this data
with up to 12 ancillary sensors.
The population growth and
rapid industrialization and urbanization are causing massive emission of solid
and liquid wastes into the coastal environment [Creel, L.]. Conventional
methods of water quality monitoring depend on in situ measurements and
sequential laboratory analysis of the samples [Olet,
E.; Wilde, F.D.]. These point-sampling methods may give accurate measurements
of water quality parameters, but they are expensive, time-consuming and, most
importantly, they cannot provide synchronous turbid water inputs and extension
of the plumes in coastal ecosystems for large areas. The Instrument will enable
alternative methods of water quality monitoring due to data
synchronicity, low cost, and large spatial coverage.
The Center for Imaging
Science at the Rochester Institute of Technology [http://www.cis.rit.edu/]
(see letter) will utilize the Instrument in their NSF, NASA, DOE, and
industrial research on mapping of water quality in the littoral zone for
erosion and oceanic studies.
References Cited
[LL07] I. Levin and E. Levina, "Effect of atmospheric interference and sensor
noise in retrieval of optically active materials in the ocean by hyperspectral remote sensing," Appl. Opt. 46, 6896-6906 (2007).
[HC02] C. Hu and K. Carder,
Atmospheric correction for airborne sensors: Comment on a scheme used for CASI,
Remote Sensing of Environment, Volume 79, Issue 1, January 2002, Pages 134-137,
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[RZ07] J. Ruan and W. Zhang. An efficient spectral algorithm for network community discovery and
its applications to biological and social networks. In
ICDM, 2007.
[GW94] H.R.
Gordon and M. Wang. "Retrieval of water-leaving radiance and
aerosol optical thickness over the oceans with SeaWiFS:
a preliminary algorithm." Applied optics 33, no. 3
(1994): 443-452.
[MAO+13] Z. Mao, et al. "A
new approach to estimate the aerosol scattering ratios for the atmospheric
correction of satellite remote sensing data in coastal regions." Remote
Sensing of Environment 132 (2013): 186-194.
[MAO+14] Z. Mao, et al. "A potentially
universal algorithm for estimating aerosol scattering reflectance from
satellite remote sensing data." Remote Sensing of Environment 142 (2014):
131-140.
[CR03] Creel, Liz. Ripple
effects: population and coastal regions. Washington: Population Reference
Bureau, 2003.
[OE10] E. Olet. "Water Quality Monitoring of Roxo reservoir using Landsat Images and In-situ
Measurements." (2010).
[Wil08] F.D. Wilde, 2008,
Guidelines for field-measured water-quality properties (ver. 2.0): U.S.
Geological Survey Techniques of Water-Resources Investigations, book 9, chap.
A6, section 6.0, October, available only online from http://pubs.water.usgs.gov/twri9A