Generalization of heterogeneous alpine vegetation in air photo-based image classification, Latnjajaure catchment, northern Sweden

Mapping alpine vegetation at a meso-scale (catchment level) using remote sensing presents difficulties due to a patchy distribution and heterogeneous spectral appearance of the plant cover. We discuss issues of generalization and accuracy assessment in this case study when using a digital CIR air photo for an automatic classification of the dominant plant communities. Spectral information from an aerial photograph was supplemented by classified plant communities in field and by topographical information derived from a DEM. 150 control points were tracked in the field using a GPS. The outcome from three alternative classifications was analysed by Kappa statistics, user’s and producer’s accuracy. Overall accuracy did not differ between the classifications although producer’s and user’s accuracy for separate classes differed together with total surface (ha) and distribution. Manual accuracy assessment when recording the occurrence of the correct class within a radius of 5 meters from the control points generated an improvement of 16 % of the total accuracy. About 10 plant communities could be classified with acceptable accuracy where the chosen classification scheme determined the final outcome. If a high resolution pixel mosaic is generalized to units that match the positional accuracy of simple GPS this generalization may also influence the information content of the image.

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