Abstract

Questions: What is the optimum combination of image dates across a growing season for tree species differentiation in multi-spectral data and how does tree species composition affect overstorey canopy density? Location: Monks Wood, Cambridgeshire, eastern England. Methods: Six overstorey tree species were mapped using five Airborne Thematic Mapper images acquired across the 2003 growing season (17/03, 30/05, 16/07, 23/09, 27/10). After a series of image pre-processing procedures, supervised Maximum Likelihood classification was performed on the individual images and on all 2-, 3-, 4-, and 5-date combinations. Relationships between tree species composition and canopy density were assessed using regression analyses. Results: The image with the greatest tree species discrimination was acquired on 27/10 when the overstorey species were in different stages of leaf tinting and fall. In this image, tree species were mapped with an overall classification accuracy (OCA) of 71% (Kappa 0.63). A similar OCA was achieved from the four other images combined (OCA 72%, Kappa 0.64). The highest classification accuracy was achieved by combining three images: 17/03, 16/07, 27/10. This achieved an OCA of 84% (Kappa 0.79), which increased to 88% (Kappa 0.85) after a post-classification clump and sieve procedure. A combination of canopy height and the percentage cover of oak explained 72% of variance in canopy density. Conclusions: The ability to discriminate and map temperate deciduous tree species in airborne multi-spectral imagery is increased using time-series data. An autumn image supplemented with an image from both the green-up and full-leaf phases was optimum. The derived tree species map provides a more powerful ecological tool for determining woodland structural/compositional relationships than field-based measures.