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

Braunisch, Veronika; Zellweger, Florian; Morsdorf, Felix; Purves, Ross S.; Bollmann, Kurt (2014). Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment. Biodiversity and conservation, 23(2), pp. 289-307. Springer 10.1007/s10531-013-0600-7

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE)
08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE) > Conservation Biology

UniBE Contributor:

Braunisch, Veronika

Subjects:

500 Science > 570 Life sciences; biology
500 Science > 590 Animals (Zoology)

ISSN:

0960-3115

Publisher:

Springer

Language:

English

Submitter:

Olivier Roth

Date Deposited:

30 Mar 2015 12:06

Last Modified:

08 Sep 2015 10:29

Publisher DOI:

10.1007/s10531-013-0600-7

Web of Science ID:

000329990300002

BORIS DOI:

10.7892/boris.65964

URI:

https://boris.unibe.ch/id/eprint/65964

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