Integrating GIS and remote sensing for vegetation analysis and modeling: methodological issues

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Abstract
GIS and remote sensing have emerged as distinct spatial data handling technologies with their own methods of data representation and analysis. Combining them as tools to support vegetation analysis and modeling thus presents a number of challenges. The paper begins by describing the major data sources, applications, and software characteristics of each technology, and then compares them within a consistent terminological framework that emphasizes the digital representation of continuously varying spatial data. Because the spatial continuum can be discretized in many different ways, and because each can only approximate the truth, both GIS and remote sensing are subject to error and uncertainty. Integration, and subsequent analysis and modeling, require that explicit attention be directed to uncertainty. The paper reviews the models of error that have been developed in recent years for spatial data and examines their use in the interface between GIS and remote sensing. The paper looks at the functional requirements of modeling, and includes discussion of error propagation.

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