Fulltext

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Zellweger, Florian ; Morsdorf, Felix ; Purves, Ross ; Braunisch, Veronika ; Bollmann, Kurt

In: Biodiversity and Conservation, 2014, vol. 23, no. 2, p. 289-307

Ajouter à la liste personnelle
    Titre
    • Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment
    Auteur
    • Zellweger, Florian. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
    • Morsdorf, Felix. Remote Sensing Laboratories, Department of Geography, University of Zürich, Zürich, Switzerland
    • Purves, Ross. Department of Geography, University of Zürich, Zürich, Switzerland
    • Braunisch, Veronika. Conservation Biology, Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012, Bern, Switzerland
    • Bollmann, Kurt. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Biodiversity and Conservation, 2014, vol. 23, no. 2, p. 289-307. Springer Netherlands
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:324928
    Summary
    Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees(BRT) models for both variable sets separately and in combination, and compared the models' accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species' habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.