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Abstract: Effective land cover mapping often requires the use of multiple data sources and data interpretation methods, particularly when no one data source or interpretation method provides sufficiently good results. Method-oriented approaches are often only effective for specific land cover class/data source combinations, and cannot be applied when different classification systems or data sources are required or available. Here we present a method, based on Endorsement Theory, of pooling evidence from multiple expert systems and spatial datasets to produce land cover maps. Individual ‘experts’ are trained to produce evidence for or against a class, with this evidence being categorised according to strength. An evidence integration rule set is applied to evidence lists to produce conclusions of different strength regarding individual classes, and the most likely class identified. The only expert system design implemented currently within the methodology is a neural network model, although the system has been designed to accept information from decision trees, fuzzy k-means and Bayesian statistics as well. We have used the technique to produce land cover maps of Scotland using three classification systems of varying complexity. Mapping accuracy varied between 52.6% for a map with 96 classes to 88.8% for a map with eight classes. The accuracy of the maps generated is higher than when individual datasets are used, showing that the evidence integration method applied is suitable for improving land cover mapping accuracy. We showed that imagery was not necessarily the most important data source for mapping where a large number of classes are used, and also showed that even data sources that produce low accuracy scores when used for mapping by themselves do improve the accuracy of maps produced using this integrative approach. Future work in developing the method is identified, including the inclusion of additional expert systems and improvement of the evidence integration, and evaluation carried out of the overall effectiveness of the approach. [Copyright &y& Elsevier]

Britain contains some of the largest areas of blanket peatland in the world and the monitoring of this resource is vital. This study has investigated whether Landsat ETM+ can be used to identify types of blanket peatland on Lewis. This was done using Principal Component Analysis (PCA) on composites of band ratios and single band variables, and using neural network classification. The distinction between peatland and non-peatland was easily accomplished, but the identification of different peatland types was more difficult. PCA on a composite of spectral bands 1 to 9 was the most useful composite, but did not improve over the use of NDVI-related band ratios. An overlap was found between peatland classes caused by similar spectral signatures of peat banks and eroded peatland. This was confirmed by a separate study examining the variation between different blanket bog classes in the Land Cover of Scotland 1988 dataset. It is suspected that this problem will remain with Landsat ETM+ -based classification of peatland because the spatial resolution is insufficient to capture the heterogeneous nature of the terrain and vegetation types. The paper discusses further methodologies and information sources which, in combination with Landsat ETM+ data, could improve the ability to classify peatland. [ABSTRACT FROM AUTHOR]