Mapping woodland species composition and structure using airborne spectral and LiDAR data

Abstract

Tree and shrub species composition and vegetation structure are key components influencing the quality of woodland or forest habitat for a wide range of organisms. This paper investigates the unique thematic classes that can be derived using integrated airborne LiDAR and spectral data. The study area consists of a heterogeneous, semi‐natural broadleaf woodland on an ancient site and homogeneous broadleaf and conifer woodland on an adjoining plantation. A parcel‐based unsupervised classification approach was employed, using the first two Principal Components from 12 selected wavebands of HyMap data and a Digital Canopy Height Model extracted from LiDAR data. The resultant 52 data clusters were amalgamated into 10 distinct thematic classes that contain information on species composition and vegetation structure. The thematic classes are relevant to the National Vegetation Classification (NVC) scheme for woodlands and scrub of Great Britain. Furthermore, in distinguishing structural subdivisions within the species‐based NVC classes, the thematic classification provides greater information for quantifying woodland habitat. The classes show degeneration from and regeneration to mature woodland communities and thus reflect the underlying processes of vegetation succession and woodland management. This thematic classification is ecologically relevant and is a forward development in woodland maps created from remote sensing data.

Acknowledgements

We are grateful to the Environment Agency of England and Wales for funding and acquiring the ALTM 1210 data used in this study. The HyMap data were acquired during the SAR and Hyper‐spectral Airborne Campaign (SHAC 2000) and are courtesy of the Natural Environment Research Council (NERC) and the British National Space Centre (BNSC). HyMap data processing was carried out by Laine Skinner, University of Wales, Swansea. We are grateful to Richard Wadsworth (CEH Monks Wood) for advice on LiDAR processing methods, and to David Gaveau and Jennifer Grant (formerly of CEH Monks Wood) for assistance with LiDAR data pre‐processing. We are also grateful for helpful comments on the manuscript by Geoff Smith, Andrew Wilson, and Shelley Hinsley (of CEH Monks Wood) and by the anonymous reviewers.