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, ISSN 0034-4257

[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