Mapping a specific class for priority habitats monitoring from satellite sensor data

The European Union's Habitats Directive aims to protect biodiversity through the conservation of habitats. If a habitat of interest corresponds to spectrally separable land cover class(es), then this activity can benefit from the production of accurate land cover maps from remotely sensed imagery. Traditionally, the image classification techniques used assume that the set of classes has been defined exhaustively, which requires all of the classes in the region to be included explicitly in the analysis. Often, however, interest focuses on just one or a small sub-set of the classes occurring in the region that represent the habitat(s) of particular interest. Moreover, given that the size of a training set required for an image classification is typically taken to be a function of the number of classes and discriminating variables (e.g. wavebands) used in the classification, the satisfaction of the assumption of an exhaustively defined set of classes requires that much effort is directed wastefully on classes of little, if any, direct interest. Savings in training could be achieved by focusing on the class(es) of specific interest. A more appropriate approach for mapping a specific class may be to adopt a binary classification analysis that simply seeks to separate the class of interest from all others. In this way the analysis focuses on the class(es) of interest and a small training set may be used. An attractive means to achieve this is through the adoption of decision tree- and support vector machine-based approaches to classification. This paper evaluates the accuracy with which a habitat of interest to the EU Habitats Directive, fen, can be mapped from Landsat ETM+ imagery of the Norfolk Broads using such classifiers as well as, for comparative purposes, a standard maximum likelihood decision rule implemented by a discriminant analysis. All analyses yielded accurate classifications, with a conventional approach based on a maximum likelihood allocation providing an overall classification accuracy of 88.4%. However, both the decision tree- and support vector machine-based approaches provided classifications that were significantly more accurate than conventional maximum likelihood classification (p<0.05), with overall accuracies of 91.6 and 93.6%, respectively (table 3). The results highlight the ability to focus the analysis on the class of interest in a manner that is less wasteful of resources and effort and that yields a more accurate classification than the standard approach