Biodiversity and Conservation

, Volume 23, Issue 2, pp 289–307

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

  • Florian Zellweger
  • Felix Morsdorf
  • Ross S. Purves
  • Veronika Braunisch
  • Kurt Bollmann
Original Paper

DOI: 10.1007/s10531-013-0600-7

Cite this article as:
Zellweger, F., Morsdorf, F., Purves, R.S. et al. Biodivers Conserv (2014) 23: 289. doi:10.1007/s10531-013-0600-7

Abstract

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.

Keywords

Airborne laser scanningBonasa bonasiaHabitat modelMountain forest Remote sensingSpecies conservation

Supplementary material

10531_2013_600_MOESM1_ESM.doc (57 kb)
Online Resource 1Description and definition of all field variables, including the sampling reference within the sampling plot. Supplementary Fig. 1 (DOC 57 kb)
10531_2013_600_MOESM2_ESM.doc (69 kb)
Online Resource 2Detailed description and flow chart of the LiDAR data processing and variable extraction. Supplementary Fig. 2 (DOC 69 kb)
10531_2013_600_MOESM3_ESM.doc (42 kb)
Online Resource 3Description and definition of all LiDAR variables. Supplementary Fig. 3 (DOC 42 kb)
10531_2013_600_MOESM4_ESM.doc (66 kb)
Online Resource 4Statistical overview of field and LiDAR variables. Supplementary Fig. 4 (DOC 65 kb)
10531_2013_600_MOESM5_ESM.doc (38 kb)
Online Resource 5Moran’s I correlogram on residuals of the combined BRT model for the analysis of potential spatial autocorrelation in the data. Supplementary Fig. 5 (DOC 38 kb)

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Florian Zellweger
    • 1
  • Felix Morsdorf
    • 2
  • Ross S. Purves
    • 3
  • Veronika Braunisch
    • 4
    • 5
  • Kurt Bollmann
    • 1
  1. 1.Swiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
  2. 2.Remote Sensing LaboratoriesDepartment of Geography, University of ZürichZürichSwitzerland
  3. 3.Department of Geography, University of ZürichZürichSwitzerland
  4. 4.Conservation Biology, Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
  5. 5.Forest Research Institute of Baden-WürttembergFreiburgGermany