Accession Number:

ADA572943

Title:

Development and Testing of Physically-Based Methods for Filling Gaps in Remotely Sensed River Data: Annual Report Year 2

Descriptive Note:

Annual rept.

Corporate Author:

GEOLOGICAL SURVEY GOLDEN CO

Personal Author(s):

Report Date:

2012-09-30

Pagination or Media Count:

16.0

Abstract:

The long-term goal of the work described here is to develop and test a general methodology for predicting unmeasured river characteristics using a variety of potentially incomplete remotely sensed data sets. Rather than addressing the problem using various geostatistical techniques to interpolate and extrapolate the remotely sensed data, we are developing two physically based techniques, each of which can be used to fill in missing or incorrect segments of remotely sensed data sets. The first method is based on using the conservation equations for mass and momentum to fill in various kinds of missing information and the second is based on using computational morphodynamics coupled flow and bed evolution predictions to identify and fix errors in remotely sensed bathymetry. Both methods develop estimates of hydraulic and morphologic variables that satisfy conservation of mass and momentum. Importantly, we believe these methods can integrate a variety of different kinds of information, rather than concentrating on a single input data set or a desired output variable. Thus, although most of our initial work is aimed at resolving bathymetry, our goals are more general. Our work in this area has been motivated by our earlier efforts in characterizing errors in bathymetric data in rivers collected using remote sensing i.e., bathymetric LiDAR and various optical correlation techniques using multi- and hyperspectral scanning, as reported in Wright and Brock 2002, Kinzel et al., 2007, Legleiter and Roberts 2005, and Legleiter et al., 2004 . Comparison of the remotely sensed techniques with ground truth data obtained using conventional surveying techniques showed systematic errors that are associated with missing andor incomplete information, especially in deeper areas where our remote sensing techniques fail due to attenuation in the case of LiDAR and due to a simple lack of resolution for the optical scanning techniques.

Subject Categories:

  • Hydrology, Limnology and Potamology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE