Object-oriented Image Analysis and Scale-space: Theory and Methods for Modeling and Evaluating Multiscale Landscape Structure

Landscapes are Complex Systems, which by their very nature necessitate a multiscale approach in their monitoring, modeling and management. To assess such broad extents, remote sensing technology is the primary provider of landscape sized data sets, and while tremendous progress has been made over the last thirty years in terms of improved resolution, data availability, and public awareness, the vast majority of remote sensing analytical applications still rely on basic image processing concepts: in particular, per-pixel classification in multi-dimensional feature space. In this paper we describe and compare two technically and theoretically different image processing approaches, both of which facilitate multiscale pattern analysis, exploration, and the linking of landscape components based on methods that derive spatially-explicit multiscale contextual information from a single resolution of remote sensing imagery. Furthermore, we suggest how both methods may be integrated for improved results.