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Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

2016; Taylor & Francis; Volume: 49; Issue: 1 Linguagem: Inglês

10.5721/eujrs20164914

2279-7254

Anton Kuzmin, Lauri Korhonen, Terhikki Manninen, Matti Maltamo,

Forest ecology and management

Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery. The forest floor was covered by snow so that the contrast between the crowns and the background was maximized. The images were taken from a helicopter flying at low altitude so that fine details of the canopy structure could be distinguished. Segments created by an object-oriented image processing were used as a basis for a linear discriminant analysis, which aimed at separating the three dominant tree species occurring in the area: Scots pine, Norway spruce, and downy birch. In a cross validation, the classification showed an overall accuracy of 81.9%, and a kappa coefficient of 0.73.