11th ICRS Abstract book - Nova Southeastern University

Poster Mini-Symposium 17: Emerging Techniques in Remote Sensing and Geospatial Analysis
17.577
Deep Atris: A New Towed System For Unobtrusive Mapping Of Benthic Habitats
And Organisms
David G. ZAWADA* 1 , Philip THOMPSON 1 , Jerry BUTCHER 1
1 Center for Coastal and Watershed Studies, U.S. Geological Survey, St. Petersburg, FL
Geo-positioned observations of coral reefs and associated habitats are critical to many
resource-conservation, monitoring, and research projects. Applications for these types of
data include characterizing essential habitat, assessing changes, monitoring the progress
of restoration efforts, and ground-truthing acoustic, lidar, and satellite imagery.
Acquiring such imagery for large areas can be expensive and time-consuming. To
enhance its mapping capabilities and provide a more efficient alternative, the U.S.
Geological Survey has developed the Deep Along-Track Reef-Imaging System (Deep
ATRIS), a towed sensor package deployable from boats of moderate size (~8 m). Deep
ATRIS is based on a light-weight, computer-controlled, towed vehicle that is capable of
following a programmed diving profile. The vehicle is 1.3 m long with a 63-cm wing
span, a maximum tow speed of 2.6 m/s, and an operating tow-depth limit of 27 m,
extendable to 90 m. Transect lengths of 56 km can be surveyed in 6 hr. Deep ATRIS
can carry a wide variety of instruments, including conductivity-temperature-depth
sensors, fluorometers, transmissometers, and cameras. The current payload consists of a
high-speed (20 frames/s), color digital camera, custom-built light-emitting diode lights, a
compass, a 3-axis orientation sensor, a pressure sensor, and a nadir-looking altimeter.
Images are displayed and archived in real time on the surface computer, along with the
corresponding GPS coordinates. The first sea trial was conducted in a coral reef setting
within Biscayne National Park, Florida, USA, in July 2007. Several example geo-located
mosaics will be presented to illustrate the high quality of Deep ATRIS imagery. Types of
information that can be obtained from the rich dataset include percent cover, species
abundance and richness, and morphological characteristics. The images also reveal the
potential for unobtrusive animal observations; fish and sea turtles imaged seem
unperturbed by the presence of Deep ATRIS.
17.578
Using Remotely Sensed Lidar Data To Examine The Relationship Between Habitat
Complexity And Fish Assemblage Structure in Hawaii
Lisa WEDDING* 1 , Alan FRIEDLANDER 2
1 Department of Geography, University of Hawaii at Manoa, Honolulu, HI, 2 NOAA
Biogeography Branch, Honolulu, HI
Remotely sensed LIDAR (Light Detection and Ranging) data has recently been utilized
in coral reef ecosystems to derive rugosity, a measure of habitat complexity. We used
LIDAR-derived rugosity to examine the relationship between habitat complexity and
various fish assemblage metrics (numerical abundance, diversity, richness, and biomass)
in Hawaii. We established significant positive associations between LIDAR-derived
rugosity and these measures of fish assemblage structure in hard bottom habitat. We also
demonstrated that LIDAR-derived rugosity was a good predictor of fish biomass and
found that different components of the fish assemblage responded to different spatial
scales of LIDAR-derived rugosity depending on their size and mobility. Habitat
complexity derived from remotely sensed data can be used to predict the fish assemblage
of an area and can therefore aid in the optimal location and design of marine protected
areas by identifying specific areas that offer great natural protection. The results of our
study suggest that LIDAR data has the potential to assist in prioritizing areas for
conservation and management in the Main Hawaiian Islands and similar insular tropical
ecosystems.
17.579
Image Time Series Analysis For The Inference Of Coral Reef Ecosystem Health
Alicia SIMONTI* 1 , Ronald EASTMAN 1 , John ROGAN 1
1 Graduate School of Geography, Clark University, Worcester, MA
Recent evidence of changes in global climate leads to the question of the geographic extent and
impacts on ocean productivity and the status of tropical coral reefs. New methods in remote
sensing of coral reefs have proved to be imperative. In light of current obstacles in the remote
sensing of coral reef health due to fine spatial heterogeneity, water column interference, and
differentiation of varying benthic substrata, this research endeavors to apply newly developed
methods of image time series analysis to various image series related to coral health. Time
series analysis of remotely sensed imagery has traditionally been limited to Principal
Components Analysis which decomposes the series into its major spatial and temporal
dimensions of variability. New developments in image time series analysis include spatial and
temporal Fourier and Wavelet Spectral Analysis which investigate the oscillatory behavior and
several parametric and non-parametric image trend procedures based on the Mann-Kendall test
and the robust Theil-Sen slope which determine linear and monotonic trends. Additionally,
phenological trends in ocean productivity can be investigated with a procedure based on trend
analysis of annual harmonic regression coefficients. Furthermore, this software development is
capable of removing seasonality in order to better investigate change over time and space.
These newly developed methodologies can prove quite beneficial in the study of coral reef
health via remote sensing indirectly by investigating numerous factors which impact corals.
These factors can be investigated and correlated simultaneously, including, but not limited to,
chlorophyll a concentration, sea surface temperature, ocean currents, aerosols, sea surface
height, and non-remotely sensed data such as climatic teleconnections. Initial findings show
significant trends in chlorophyll a concentration, sea surface temperature, and sea level height
that could have implications for coral reef health.
17.580
Remote Sensing Of Seagrass Biomass in Case Of Spectrally Variable Bottom Types
Ele VAHTMÄE* 1 , Tiit KUTSER* 1
1 Estonian Marine Institute, University of Tartu, Tallinn, Estonia
We developed an in situ method for fast estimation of seagrass biomass (dry weight) based on
comparing of photos of study area with photo-library of quadrates with known seagrass
biomass. The method was used to get seagrass biomass estimates over a spatially heterogeneous
coral reef area (Ngederrak Reef, Palau) where the bottom below the seagrass varied from bare
sand to almost 100% coral or macroalgal cover. The biomass estimates along four one hundred
meters long transects were compared to QuickBird and Ikonos data. The results show that it is
possible to estimate seagrass biomass in seagrass beds where the substrate is bare sand. High
macroalgal or coral cover makes it difficult to estimate seagrass biomass when multispectral
data is used.
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