Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation
Remote sensing techniques using airborne scanning laser altimetry (LiDAR) and high resolution multi-spectral imagery allow spatially fine-grained predictor variables to be measured over large geographical areas and thus facilitate testing of the spatial generality of organism-habitat models. These techniques are considered using the skylark as an example species. A range image segmentation system for LiDAR data is described which allows measurement of skylark habitat predictor variables such as within-field vegetation height, boundary height and shape for individual fields within the LiDAR image. Additional variables such as field vegetation type and fractional vegetation ground cover may be obtained from co-registered multi-spectral data. These techniques could have wide application in testing the generality of relationships between populations and habitats, and in ecological monitoring of change in habitat structures and the associated effects on wildlife, over large geographical areas.
Document Type: Research Article
Affiliations: 1: Environmental Systems Science Centre (ESSC), The University of Reading, Harry Pitt Building, 3 Earley Gate, Whiteknights, PO Box 238, Reading RG6 6AL, UK 2: Ecology and Behaviour Group, Edward Grey Institute of Field Ornithology, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK 3: Royal Society for the Protection of Birds, The Lodge, Sandy, Bedfordshire. SG19 2DL, UK
Publication date: 2003-06-01