3D SPATIAL REPRESENTATION O 3D GEOSPATIAL SOLUTI

2. 3D SPATIAL REPRESENTATION O

ECOSYSTEM Pelagic marine features are characterize boundaries, dynamic, and full 3D structure 1995; Shyue and Tsai, 1996. These restrictive for data acquisition, as well modelling and representation. A furth oceanographic data sets is the frequent anis of data, due primarily to logistics and co expensive sampling at sea. Development of 3D geospatial modelling of the marine pe consequently challenging. This might explai analysis of oceanographic phenomena is tr environment, limited to static cutting plane vertical sections either contoured or colou various parameters Head et al., 1997. Oceanic physical parameters, such as tempe define distinct water masses with more or les Vertical and horizontal distribution of th influences the oceanic carbon cycles, whi important role in regulating global cli objective of the Malina oceanographic cam better understanding of these interactions. spatial analyses in a 3D geospatial model o can then be of great value. Such a model co use in a resource management or conservatio A summary of some common and specialise this work are listed in Table 1. These ha according to criteria for their suitability modelling of the pelagic environment. Th Criteria ArcGIS 10 Commercial GIS 3D Interpolation - 3D raster representation - 3D vector representation Visualization cuts - Visualization iso-surfaces - Visualization volumes - 3D statistical analyses - 3D spatial analyses - Table 1. Review of five geospatial m

3. 3D GEOSPATIAL SOLUTI

REPRESENTATION OF WATER DISTRIBUTION: MALINA CAS In the south-eastern Beaufort Sea, several typ can be identified, such as the nutrient Halocline Water UHW. The fractional pre obtained for each of the 243 sampling accordingly to a method described by Lans geospatial voxel model of this water mass w Paradigm GOCAD, a scientific visualization 3D geological spatial modelling. This spati by a grid of 150 x 75 x 100 voxels in x, compressed vertically between water surfa surface. UHW values were attributed to each through a 3D interpolation of sampling po kriging. OF THE PELAGIC ized by their fuzzy re Gold and Condal, e characteristics are ell as for geospatial rther problem with nisotropic distribution costs associated with of tools available for pelagic ecosystem is lain why conventional traditionally in a 2D anes in horizontal and lour-coded to present perature and salinity, less fuzzy boundaries. these water masses hich in turn play an climate. One partial campaign was to gain ns. Visualization and l of these phenomena could also be of good tion perspective. lised tools reviewed in have been evaluated ty for 3D geospatial This review indicates that recent efforts of 3D develop mostly focused on object centred co structures for example: ArcGIS Indeed, several research teams h adequate 3D marine GIS Arsenau 2009. We consider that an adequ pelagic continuous phenomena volumetric field representations. T more developed in geomodelling t the use of 3D raster-based mode voxel VOlume piXEL structures beginning of 1990, they are still GIS the GIS open-source GRA exception to this generality, offerin It is also worth noticing that the m Voronoï tessellations, whose adva various academic works Beni et 2008, are absent so far in comm tools. A draw-back with the geomo paper is its limited ability for im imperative in oceanographic resea use of remote sensing. Another ess marine geospatial modelling tool static cuts in vertical direction. E might seem trivial and does not re method, this operation is not poss and very limited with EnterVol ArcGIS that permits volumetric Finally, none of the tools eval consideration the dynamic natur pelagic phenomena or to assess a of spatial 3D models, such as cross 2007. Fledermaus HabitatSpace CTech EnterVol Commercial marine GIS Academic prototype GIS Extension to ArcGI Limited Kriging Kriging limited, ID Limited ? Limited - Limited - ? - Limited l modelling tools from commercial and academic GIS as well a TION TO ER MASSES ASE STUDY types of water masses t rich pacific Upper presence of UHW was ing points x, y, z nsard et al. 2012. A s was constructed with ion tool developed for atial model was built x, y, z direction and rface and bathymetric ach voxel in the model points with ordinary Figure 2. Kriging variance of sp Upper Halocline Water in the Beau sampling p lopment in the GIS field have conceptual design using vector IS version 10, Fledermaus. have recognized the lack of ault et al., 2004; Mesick et al., quate representation of marine na needs fully developed That kind of representation is g tools. The general solution is dels, commonly referred to as res. Although in use since the ll mostly absent in commercial RASS might however be an ring limited volume rendering. more dynamic data structures - vantages have been stressed in et al., 2011; Ledoux and Gold, mmercial geospatial modelling modelling tool reviewed in this image treatment and analyses, search considering the common essential function for an optimal ool is the visualization of 2D . Even though such a function require a true 3D interpolation ossible at present with ArcGIS ol, commercial extension to tric representation Table 1. valuated permit to take into ture and fuzzy boundaries of a general predictive capability oss-validation e.g. Foglia et al. ol Paradigm Gocad cGIS Geomodelling tools , IDW Kriging, IDW, other. ll as from geomodelling. spatial 3D model for pacific Beaufort Sea. Black dots indicate points. 7th International Conference on 3D Geoinformation, May 16-17, 2012, Québec, Canada 22 of 24 The kriging variogram’s dependent predictiv in Figure 2. In general, the lower the e location, the better is the prediction of the sp The final spatial model permits us to visual volumes as well as cuts in any plane of the Figure 3. Geospatial representation of Up approximately the upper 500 m of the constituted of UHW and voxels containing black dots. Vertical exaggeration in fig

4. DISCUSSION AND CONCL