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The classification of remotely sensed images such as aerial photographs or satellite sensor images for deriving ecosystem-related maps (e.g., land cover, land use, vegetation, soil) is generally based on clustering of spatial entities within a spectral space. In most cases, Boolean logic is applied in order to map landscape patterns. One major concern is that this implies an ability to divide the gradual variability of the Earth's surface into a finite number of discrete non-overlapping classes, which are considered to be exhaustively defined and mutually exclusive. This type of approach is often inappropriate given the continuous nature of many ecosystem properties. Moreover, the standard data processing and image classification methods used will involve the loss of information as the continuous quantitative spectral information is degraded into a set of discrete classes. This leads to uncertainty in the products resulting from the use of remote sensing tools. It follows that any estimated ecosystem property has an associated error and/or uncertainty of unknown magnitude, and that the statistical quantification of uncertainty should be a core part of scientific research using remote sensing. In this paper we will review recent attempts to take explicitly into account uncertainty when mapping ecosystems.
