Management Applications of Optical Remote Sensing in the Active River Channel - Fluvial Remote Sensing for Science and Management - page 34

Table 2.4 Common issues in river remote sensing. The issues highlighted below commonly arise in the context of using optical
remote sensing to map river variables. The table does not summarize issues that are common to remote sensing across all
applications, such as atmospheric and geometric corrections, standard image processing issues, or large data storage requirements.

Category Common Issues in River Applications Potential Solutions

- acquire your own aircraft
- use satellite imagery
- use existing imagery for historical analysis

Logistics: see Aspinall et al.,
2002; Marcus and
Fonstad, 2008

Timing of flights: Aircraft-based imagery
cannot be acquired when variable needs to
be measured

- schedule field data collection during periods
of relatively stable stream conditions (e.g.
low flow period)

Timing of field data: Field data collected under
conditions different than those under which
the image data were acquired

Location: Field sites inaccessible or too
dangerous to collect ground validation data

- substitute data from similar settings
- collect data at less dangerous time (e.g. use
post-flood indicators of depths)
- model data to simulate plausible values

Expertise: Requires personnel with significant
expertise in remote sensing

- contract with existing experts
- use existing web-based data sets (e.g. for
flooding)
- seek out automated approaches

Optical environment: see
Legleiter et al., 2009;
Marcus and Fonstad, 2008

Obstructed view of river : from above - use low elevation platforms (e.g. hand held

balloon, drones)
- use active device (LiDAR, radar)

Turbidity : blocks light penetration of water
column

- use ground-based measurements (e.g. total
station surveys, sonar, ground-based radar)

Shadows : create different radiance values over
identical features

- mask out shadowed areas
- develop algorithms for shadow removal

- plan image acquisition to avoid unfavorable
conditions
- use algorithms to normalize lighting
conditions

Sun-target-sensor geometry : obscures features
(e.g. reflections, sun glint)

Local variations: turbulence, substrate color,
SAV, etc. generate different reflectances for a
given measure (e.g. depth)

- use band ratios to normalize for variations
- stratify image to measure variable separately
in each category

Imagery & ground data: see
Aspinall et al., 2002;
Legleiter et al., 2002, 2009;
Marcus et al., 2003

Location precision : must be high to match small
features on imagery and in streams

- map directly to the imagery
- set up benchmark/targets to tightly
co-registered imagery to ground surveys

Spatial resolution : may not be sufficient to
detect small stream features

- acquire imagery with high spatial resolution
for smaller streams
- use pixel unmixing for features with distinct
spectra (e.g. wood)

Spectral coverage: Narrow spectral resolution
over a broad spectral range sometimes
needed to separate features (e.g. biotypes,
SAV) that have similar compositions

- use hyperspectral imagery
- use mapping algorithms that do not rely to
such a large extent on between-spectra
variations (e.g. semi-variograms, kriging)