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11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFigure 1: From top to bottom and left to right, 5 m resolution LiDAR DTM, slope, Hillshade, BPI (Bathymetric PositionIndex), rock prediction, interpretation of substrate type from LiDAR, and LiDAR interpretation confidence rating.Data interpretationSonar mosaics were visually interpreted to produce polygons with homogeneous texture or grey level(Cordier, 2012). Some of these polygons were validated with ground truth data such as grabs and video tows.Afterwards the interpretation was extended to areas without field data on the basis of resemblance betweenpolygons. In empty corridors between sidescan sonar swath tracks, interpolation was carried out based onancillary data, namely a low resolution depth DTM and a 1/50,000 historic substrate map from the FrenchHydrographic survey. Interpreted and interpolated polygons were then stitched across track boundaries. In someplaces this implied tweaking contours to make them compatible, however in such cases confidence wasdecreased (see below). In all cases polygons resulting from survey data were given precedence on thoseresulting from ancillary data.For shallow waters bathymetric LiDAR was used. Méléder et al. (2007) had tested the ability of LiDAR datato characterise seabed substratum type. The methodology made use of several depth derivatives such as slope,isobaths and hill shade to visually distinguish three substrate types (rocks, soft bottom and transition zones) butthis method proved to be highly time-consuming. In order to overcome this shortcoming in Penmarc'h we testedpredictive modeling of rocky substratum. 220 ground truth points for presence or absence of rocks weremanually digitised from both the sonar interpreted map in areas where sonar and LiDAR overlapped and fromaerial photography. This ground truth dataset was randomly split into training (two thirds of the records) andevaluation (one third of the points) subsets. The modelling method used was GAM (Generalized AdditiveModel), implemented in the MGET free software (Roberts et al., 2010), which adds to ArcGIS a set of tools for152
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementGAM fitting as wall as ROC (Receiver Operating Characteristics) analysis for validation. The model was fedwith predictors (Figure 1, top and middle) that were all depth derivatives: ruggedness, BPI (BathymetricPosition Index), curvature, and hillshade. Ruggedness and BPI were respectively calculated according to theSappington et al. (2010) and Lundblad et al. (2006) algorithms, while ArcGIS native tools were used tocompute curvature and hillshade. BPI calculation allowed to identify five broad features: "broad crest", "opendepression", "broad flat", "open slope" and "near vertical wall". GAM was first run with all predictors in orderto get a backward stepwise automated model selection. This step showed that only the ruggedness variable plusthe BPI categories "broad crest" and "open slopes" were significant (p-value of 8.77e-09, 0.05 and 0.01respectively). The final model had a R² of 0.7, explained 69% of the deviance, and its validation yielded an areaunder the ROC curve (AUC) of 0.95, which indicates a strong power of prediction. The cut-off value suggestedby the ROC analysis (0.66) led to the binary classification map shown in Figure 1 (middle right). As expectedthis map brought key information to inform visual interpretation. This enabled a classification of the seabed intothree classes (Figure 1, bottom left), namely rocky, soft and heterogeneous substrate.Confidence assessmentUncertainty can be qualified in a threefold way (Congalton, 2009): (i) uncertainty on the position of the dataset; in our case where sonar and LiDAR were jointly used the latter is clearly more accurately positioned thanthe former, as no USBL (Ultra Short Base Line) was deployed, leading to an approximate 10 to 20 muncertainty on sonar mosaics; (ii) uncertainty on the contours of the interpreted units; for example sharpcontours of massive rock give high confidence on the fact that a homogeneous facies is present—as opposed tofor example a gradual transition in grain size type; (iii) uncertainty on the thematic content on the units; thishappens with subtle shades of grey or blurred texture featuring indistinct bottom type.For units where a sample or an observation was available there is full confidence whilst similar inferredclasses had a lower confidence. Confidence assessment is of particular relevance in interpolated areas where thequality of the interpretation is much lower than with survey data.As the interpreter delineated polygons, at the same time he rated confidence by applying two indices relatedto: (i) the reliability in the polygon delineation and (ii) the certainty of polygon thematic content (Alloncle et al.,2006). Although a manual operation highly dependent on the operator, this is a cost-effective approach thatcompensates for a more statistical approach. The latter is hampered by both the scarcity of ground truth data andthe very limited overlapping area between LiDAR and sonar. The map in Figure 1 (bottom right) shows thescores for the polygon thematic content of the area covered by LiDAR. The next step was merely to add the twoindices (each scoring from 1 to 3) into a single index scoring from 1 to 6. Finally, as end-users were thought toprefer a more explicit value, only three scores were kept: low, moderate and high confidence.Conclusion and next stepsThe substrate map was drafted by stitching together both the LiDAR and the sonar interpretations. Polygonsoriginating from sonar were given precedence over those coming from LiDAR, especially in deeper areas whereLiDAR capabilities diminish. In conflicting cases the confidence score was downgraded to take into accounthigher uncertainty and hence provide users with a safeguard against taking the map content at face value. Thefinal substrate map will be presented and discussed in the talk along with its confidence map. The advantage ofthis simple method is in its spatial approach: confidence is assessed for each location, whereas a statisticalmethod based on a contingency matrix for instance would only yield a global score. Its main drawback is itsstrong dependency on the interpreter, which may induce bias between two tasks. Further work is needed to makethe process semi-automatic with limited interpreter assistance.The next step will be to produce a full habitat map by incorporating biological data to this substrate map. Away of updating the confidence score to the final habitat map remains to be designed.ReferencesAlloncle N., J. Populus, C. Rollet and M.-O. Gall (2006), Benthic monitoring network (REBENT) – Brittany Region.Mapping of benthic habitats in the Glénan archipelago. RST/IFREMER/DYNECO/ AG/06-XX/REBENT.Congalton, R. G. (2006), Assessing the accuracy of remotely sensed data. Principles and practices. CRC Press. 183p.Cordier, C. (2012), V/O Haliotis: Traitement des données du sonar Geoswath R.INT. IFREMER/DYNECO/EB/12-04/CC.Gorman D., T. Bajjouk, J. Populus, M. Vasquez and A. Ehrhold (2012), “Modeling kelp forest distribution and biomassalong temperate rocky coastlines”. Marine biology. DOI 10.1007/s00227-012-2089-0.153
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CoastGIS Conference 2013: Monitorin
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Sponsors
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Steering Committee Secretariat main
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Santos Basin, BrazilA.F. Romero, R.
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Coastline development and associate
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Visual resource management system f
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Use of Terrestrial-LiDAR for quanti
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Community beach monitoring: utilisi
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Global ocean observing system for w
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ResultsMetadata11 th International
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Coastal vulnerability index to glob
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generating probabilities contours f
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maps, the most critical conditions
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Statistical and spatial toolbox for
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Global oceans and marine planning
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Analysis11 th International Symposi
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Building scenarios and visualizatio
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Using a 3D physics-based visualizat
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Documenting situated tsunami risk p
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SeaSketch11 th International Sympos
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A structured model to enable coasta
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Developing and testing approaches f
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A dynamic GIS as an efficient tool
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GIS spatio-temporal modeling of hum
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ResultsCollected data11 th Internat
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The use of GIS and geospatial techn
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Long-term continuous observations o
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Comparing endmember extraction meth
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Integration of scientific data as a
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Conclusions11 th International Symp
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