Floodplain
Modeling Based on Data
Fusion of Polarimetric SAR,
Interferometry, and Laser
Altimetry
Principal
Investigators:
Jakob van Zyl and Yunjin Kim, NASA Jet Propulsion
Laboratory
Melba Crawford and Jay Famiglietti, University of
Texas at Austin
The
human and economic impact of
flooding in both inland and
coastal lowlands is
enormous. Inland riverine
watersheds are subject to
episodic rainfall events,
while low relief coastal
watersheds are prone to
flooding from both uplands
and from the sea.
Hydrologic models can
be used to predict the
occurrence of flooding
provided that the required
input fields are accurately
described.
Of these inputs, some
of the most important
include an accurate
description of the
topography of the
floodplain, as well as the
state of soil moisture and
vegetation cover.
The main objective of
this research is to provide
these critical input fields
for a hydrological model to
enable more accurate
prediction of flooding
events and flood extent in
riverine and coastal
floodplains.
Recent
advances in polarimetric SAR
show promise for augmenting
the capability of
traditional interferometric
SAR and laser altimetry.
In particular, a
polarimetric topography
technique provides useful
slope information, and
polarimetric interferometry
may be used to decompose the
topographic response into
vegetation and ground
surface contributions.
All of these sources
of remotely sensed
topography, land cover
characterization, and even
soil moisture provide
valuable information for
hydrologic mapping for flood
prediction and management.
Unfortunately,
currently available analysis
techniques are typically
applied to individual data
sources, and the information
is later integrated
subjectively. Methodology which is capable of jointly and objectively
extracting and using
information from a suite of
data types in mapping
terrestrial features over
extensive areas is
critically needed, but not
yet developed.
Development of an
integrated approach that
utilizes the combined
capability of polarimetric
interferometry, polarimetric
topographic mapping, and
laser altimetry for
topographic mapping of
flood-prone areas is the
primary objective of the
proposed research.
Additionally, a
secondary goal is to provide
soil moisture and land cover
characterization inputs from
the same remotely sensed
data.
As such, new
techniques are being
developed to exploit the
capabilities of remote
sensing to provide the
required high resolution
data sets, with the goal of
exploring the utility of
these remotely-sensed
products for improving the
predictive reliability of
flood hydrological models.
For this project,
data from two primary sites,
one on the Finke River of
Australia and the other from
a riverine system in Texas
are being analyzed.