A persistent problem for GIS researchers is the quantitative assessment of the overall error, or uncertainty, of a GIS analysis that requires the combination of heterogeneous data sets. Rather than strive for a global solution, this chapter outlines a stepwise approach to a combined error analysis for the integration of diverse data sets in GIS. The presented method was developed for the integration of information derived from remotely sensed data into a GIS database. Even for such a simple analysis, which is routinely performed in environmental GIS applications, an analytical error-handling strategy is still missing, and researchers have made intensive use of simulation techniques. The purpose of this study is to develop an analytical model to address common errors that can be associated with the generation of GIS layers, such as manual digitizing (positional) errors and automated classification (thematic) errors. As a first step, the error model is restricted to the case of point digitizing and maximum likelihood classification. However, this model can be extended to address integrated error analyses of multisource GIS data or multitemporal remote sensing data sets.
|Number of pages||10|
|Publication status||Published - 1 Mar 1996|
ASJC Scopus subject areas
- Earth-Surface Processes