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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