Mapping tree species in temperate deciduous woodland using time-series multi-spectral data

Questions: What is the optimum combination of image dates across a growing season for tree species differentiation in multi-spectral data and how does species composition affect overstorey canopy density? Location: Monks Wood, Cambridgeshire, eastern England, UK. Methods: Six overstorey tree species were mapped using five Airborne Thematic Mapper images acquired across the 2003 growing season (17 March, 30 May, 16 July, 23 September, 27 October). After image pre-processing, supervised maximum likelihood classification was performed on the images and on all two-, three-, four- and five-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 other four images combined (OCA 72%, kappa 0.64). The highest classification accuracy was achieved by combining three images: 17 March, 16 July, 27 October. This achieved an OCA of 84% (kappa 0.79), increasing to 88% (kappa 0.85) after a post-classification clump and sieve procedure. Canopy height and 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.