11th ICRS Abstract book - Nova Southeastern University

Poster Mini-Symposium 17: Emerging Techniques in Remote Sensing and Geospatial Analysis
17.561
Gis Technology As A Tool To Improve Coastal Resource Management
Kim BALDWIN* 1 , Ramon ROACH 2
1 CERMES, University of the West Indies, St. Michael, Barbados, 2 Coastal Zone
Management Unit, St. Michael, Barbados
Spatial technologies, namely geographical information systems (GIS) can improve the
efficacy of coastal resource management initiatives by providing a visual framework to
link, relate and analyse both spatial and attribute data. GIS can allow for the integration
of information from a variety of sources, time periods and at multiple spatial scales; and
collate a range of natural resource data as automated spatial features which can be rapidly
merged, accessed and updated. Furthermore, GIS can be used to statistically identify
unique relationships and interactions among mapped variables and allow for a range of
innovative spatial queries, interpretation and modelling, which can improve decisionmaking
and planning within a coastal resource management framework. In Barbados, a
wide range of environmental data have been collected over the past twenty years and
analysed using conventional scientific methods to inform coastal resource management
policy. This paper will explore the advantages of using GIS as a tool to integrate and
analyse an array of coral reef information, and compare this to conventional methods
currently employed in Barbados. Ultimately, the power of utilising GIS as a functional
tool for more efficient coastal resource management in Barbados will be demonstrated.
17.562
Modelling Susceptibility Of Coral Reefs To Environmental Stress Using Remote
Sensing Data And Gis Models
Joseph MAINA* 1,2 , Valentijn VENUS 2 , Tim MCCLANAHAN 3 , Ateweberhan
MEBRAHTU 1
1 Coral Reefs Conservation Project, Mombasa, Kenya, 2 International Institute for
Geoinformation Science and Earth Observation, Enschede, Netherlands, 3 Wildlife
Conservation Society, Mombasa, Kenya
There is a need to develop methods and a decision support system to establish marine
protected areas that harbor coral reefs that are resilient to climate change. This requires
understanding patterns of coral’s susceptibility to thermal stress and how coral
communities will change with the environmental variables associated with climate
change. This study combined surface currents, wind velocity, sea surface temperature
(SST), UV radiation, photosynthetically active radiation (PAR), and chlorophyll-a
concentration for the western Indian Ocean and known relationships with coral bleaching
and mortality to derive predictor variables that correlate with thermal stress. SST
variability, UV, maximum temperature and wind speed had the highest influence on
susceptibility estimates. Two susceptibility models were developed using Spatial
Principal Component Analysis (SPCA) and cosine amplitude-AHP methods and a fuzzy
logic technique for integrating environmental variables into a measure of susceptibility.
Susceptibility models identified regional gradients in environmental stress, with the north
western Indian Ocean and some central Indian Ocean Islands as high and the southern
African coastline and islands east of Madagascar as low vulnerability regions. Half of the
strictly no take zones in the region are situated in locations with medium to high
susceptibility. Future studies using high-resolution data can better estimate the
vulnerability of specific locations and the establishment of marine protected areas where
coral reef diversity is more likely to persist through climate change.
17.563
Development Of A Hybrid Mapping Tool (Hmt) For The Characterisation Of Coral Reef
Landscapes
Samuel PURKIS* 1 , Kevin KOHLER 1 , Steven ROHMANN 2
1 National Coral Reef Institute, Nova Southeastern University, Dania Beach, FL, 2 Special
Projects Office, NOS/NOAA, Silver Spring, MD
Remote sensing is routinely used as the basis for the production of reef-scale benthic habitat
maps and is unsurpassed in terms of accuracy and cost. In cases where atmospheric status,
bathymetry and water column optical properties can be adequately described, automated
classification of benthic character is possible with high accuracy. For region-wide mapping
initiatives such an automated approach is unfeasible since optical closure of the system is
unattainable over large areas. Consequently, regional mapping has heretofore been done using a
strategy of expert-driven manual digitisation of reef habitat. Although accurate, this strategy is
expensive and time-consuming in regions of high complexity and area. The Hybrid Mapping
Tool (HMT) is being designed to aid the expert-driven process and accelerate map production
through automated delineation of habitat boundaries. It uses a suite of edge-detection methods
combined with a statistical assignment of map polygons to habitat classes on the basis of their
spectral, textural and boundary properties. By combining automated and visual techniques,
HMT will be able to produce thematically accurate maps of shallow-water benthic habitats from
a variety of image sources including multibeam sonar, LiDAR, and satellite sensors such as
Quickbird and IKONOS.
17.564
Classification Of Coral Reefs Multispectral Remote Sensing Images Using Classifiers
Combination Based in Support Vector Machines (Svm)
Antônio HENRIQUES 1 , Adrião NETO 1 , Ricardo AMARAL* 2
1 DCA, Federal University of R. G. do Norte, Natal, Brazil, 2 Federal University of R. G. do
Norte, Natal, Brazil
We report the use of derived maps from remote sensing orbital images submitted to digital
processing, at Maracajau path reef Northeastern Brazil, to optimize conservation, monitoring
and valuation actions. The accuracy reached in the mapping of submerged areas it is limited by
variation of the water column that degrades the sign received by the orbital sensor, creating
inter-classes, which introduce mistakes in the final result of the classification. The limited
capacity of the traditional methods based on conventional statistical techniques to solve the
problems related to the inter-classes, it conducted to the investigation of an alternative strategy
based on classifiers combination. The basic idea of the classifiers combination is to used a set of
classifier and combine the individual predictions with the goal of achieving improved
generalization performance if compared to the performance achievable with a single estimator.
In this work a classifiers combination is based on the serial combination of two support vector
machines set and in minimum-distance classifier algorithm were used as an alternative tool for
Classification of Remotely Sensed Image of the Coral Reefs Ecosystem. SVM is a special type
of learning machine, based on the statistical theory of the learning. The method applied has as
objective the progressive refinement of the classification process. The image was classified into
five bottom-types (accuracy %): deep water (100); under-water corals (93); inter-tidal corals
(80); algal (73); sand (100). The highest overall accuracy (89%) was obtained from SVM
trained whit 80 cases of each class. A problem observed during the development of this study it
was the similarity spectral among the class Inter-tidal corals and the class algal. The
classification accuracy, verified through field observation and photo interpretation, revealed
itself more accurate than classifications derived from the use classifier single with basic
multilayer perceptron, and kmeans algorithms.
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