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Fig. 3 | BMC Ecology

Fig. 3

From: Classifying development stages of primeval European beech forests: is clustering a useful tool?

Fig. 3

Stem position maps of the primeval forest Mirdita with k-means clustering solutions of the structural data highlighted (3 clusters). Coloring of the background images indicates areas which were assigned to the same cluster (gray tone) and how well a point is represented by its cluster (silhouette coefficient, red tone). A moving window approach of several observation scales (200 m2, 500 m2, 1000 m2, 1500 m2, panels a to d) was applied to aggregate the structural datasets (7 attributes, Table 2) which was used by the clustering algorithm A uniform kernel was used for the moving window (equal weighting of all objects within the window). For results of a bivariate normal kernel (weighting of objects by their distance to the window center) see Additional file 3: Figure S3

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