Combining point clouds from image matching with SPOT 5 multispectral data for mountain vegetation classification

There is a need to replace outdated vegetation maps over Sweden's mountain region; the ability and accuracy of creating such maps with automated methods and remotely sensed data has been a topic of recent research. While spectral information is a key data input for mapping mountain vegetation, the addition of three-dimensional (3D) data has also proven useful in classification. Point clouds from photogrammetric image matching (IM) or from airborne laser scanning (ALS) are potential 3D data sources. In this study, vegetation height and density metrics from IM and ALS data were classified both alone and in combination with SPOT 5 (Systeme Probatoire d'Observation de la Terre) satellite data and elevation data (elevation, slope, and a wetness index). A Random Forest classification was used to map alpine and subalpine vegetation over Abisko, Sweden. The most notable result in this study was higher producer's accuracy of the mountain birch classification when using IM metrics alone (98%) as compared to ALS data alone (89%). Classification of IM, SPOT, and elevation data combined gave the same overall accuracy (83%) as when using ALS, SPOT, and elevation data combined (also 83%). While most of the alpine vegetation classes were poorly classified using either the IM or ALS metrics alone, the IM point cloud appeared to contain more information for lower-growing (<2 m) vegetation than the ALS point cloud.