Much of the current semantic interoperability discussion centers around finding methods for explicitly defining objects in order to overcome different definitions of similar terms or similar definitions of different terms. For information communities such as land managers where defined objects with specific attributes and properties comprise the most important type of entity in their spatial decision making activities, these discussions are critical. However, there is a quite different aspect to the semantic interoperability issue for those information communities such as environmental modelers who deal more frequently with continuously distributed phenomena such as soils, vegetation and rainfall. In these communities, definitions of identified objects are often acknowledged as transient results of specific analytical procedures rather than stable, real objects. In these communities, continuous reality may be the only stable, real entity.
A preliminary study conducted in 1996 at CSIRO in Australia (Kemp 1997) which sought to identify and describe the different conceptual spatial models used in various disciplines of environmental modeling concluded that there are no fundamental differences between these scientists' conceptual models. Environmental determinism is a fundamental principle in the prediction of the occurrences of most environmental phenomena. Since many environmental phenomena are fields (phenomena for which a value exists at all locations and which may vary continuously across space), continuity provides a common context.
Continuity in the environmental sciences
In many sciences, traditional data collection and representation techniques have relied on the discretization of both space and the phenomena being studied. This is particularly true in soil science, geology and vegetation ecology. In these cases, data collection requires experts who interpret the environmental clues, some of them unspecified and unmeasureable, and make conclusions about the distribution of classes of the phenomenon being mapped. The data which is ultimately recorded (i.e. mapped) is not the fundamental observed phenomena, but an inferred classification. An assumption of continuous change across space does not exist in these data collections.
However, it has long been recognized that this assumption of discontinuity, of homogeneous regions with distinct boundaries, in disciplines such as soils or vegetation science is invalid (see for example Burrough et al 1977; MacIntosh 1967). These phenomena which are strongly influenced by environmental gradients do vary significantly over space. For many environmental modeling purposes, classified data collection techniques do not result in satisfactory digital records of the phenomena. They do not, in fact, match the scientists’ conceptual models of their phenomena.
Fortunately, the ability to store and manipulate large spatial data bases and the powerful new spatial technologies have begun to allow environmental modelers to move the digital representations closer to these continuous conceptual models. At several different locations, researchers are now working to develop models of soil formation and vegetation growth which are based on continuous environmental determinants such as elevation and rainfall (see for example Burrough et al 1992; Gessler et al 1996; Kavouras 1996; Lees 1996; Mackey 1996). These environmental models allow soils or vegetation to be described by a number of different parameters, and, only when necessary, classified accordingly. Classes can be extracted for any set of criteria using various statistical techniques. Hence, classes and their explicitly defined spatial objects are only temporary representations of a continuous reality.
All of this is not to argue that defined objects have no function in environmental applications. At the management end of modeling applications, continuous results are often too difficult to integrate conceptually, particularly when there are several environmental gradients involved. Classification allows many different factors to be summarized and understood in the abstract, though not necessarily analytically. Thus, the need for objects remains though their definition may be ephemeral.
Semantic interoperability works best when based on a common conceptual reality. To achieve this, objects and phenomena can be conceptualized within the context of their real, physical environment. With reality forming the interface between different environmental models and spatial databases, all data can be passed through this reality interface, conceptually returning it to its expression in the natural physical environment before it is redefined as required for specific software or other data models.
Critical issues for further study
Burrough, P. A., Brown, L., and Morris, E. C. (1977). Variations in vegetation and soil pattern across the Hawkesbury Sandstone plateau from Barren Grounds to Fitzroy Falls, New South Wales. Australian Journal of Ecology, 2:137-59.
Burrough, P. A., MacMillan, R. A., and vanDeursen, W. (1992). Fuzzy classification methods for determining land suitability from soil profile observations and topography. Journal of Soil Science, 43(2):193-210.
Gessler, P., McKenzie, N., and Hutchinson, M. (1996). Progress in Soil-landscape Modelling and Spatial Prediction of Soil Attributes for Environmental Models. In Proceedings of Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM. National Center for Geographic Information and Analysis, University of California, Santa Barbara, CA.
Kavouras, M. (1996). Geoscience Modelling: From Continuous Fields to Entities. In Geographic Objects with Indeterminate Boundaries, P. A. Burrough and A. U. Frank, eds., Taylor &Francis, pp. 313-323.
Kemp, K. K. (1997). Integrating traditional spatial models of the environment with GIS. In Proceedings of 1997 ACSM/ASPRS Annual Convention and Exposition, Auto-Carto 13, Seattle, WA. American Society of Photogrammetry and Remote Sensing and American Congress on Surveying and Mapping. pp. 23-32.
Lees, B. (1996). Improving the spatial extension of point data by changing the data model. In Proceedings of Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM. National Center for Geographic Information and Analysis, University of California, Santa Barbara.
MacIntosh, R. P. (1967). The continuum concept of vegetation. Botanical Review, 33:130-187.
Mackey, B. (1996). The role of GIS and environmental modelling in the conservation of biodiversity. In Proceedings of Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM. National Center for Geographic Information and Analysis, University of California, Santa Barbara, CA.