LiDAR as a rapid tool to predict forest habitat types in Natura 2000 networks |
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Authors: | Claus Bässler Jutta Stadler Jörg Müller Bernhard Förster Axel Göttlein Roland Brandl |
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Institution: | 1.Bavarian Forest National Park,Grafenau,Germany;2.Department of Community Ecology,Helmholtz Centre for Environmental Research, UFZ,Leipzig,Germany;3.Strategic Landscape Planning and Management,Technische Universit?t München,Freising,Germany;4.Department of Ecology,Technische Universit?t München,Freising,Germany;5.Animal Ecology, Department of Ecology, Faculty of Biology,Philipps-Universit?t Marburg,Marburg,Germany |
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Abstract: | Management strategies for the conservation of biodiversity can be developed only with precise information on the spatial distribution
of organisms on relevant, mostly regional, spatial scales. Current surrogates for approximating the distribution of biodiversity
are habitats mapped within a number of national and international frameworks (e.g., Natura 2000), even though conventional habitat mapping is time consuming and requires well-trained personnel. Here we evaluated the use
of light detection and ranging (LiDAR) to map forest habitat types to simplify the process. We used available data of habitat
types for the Bavarian Forest National Park as a basis to predict habitat types with LiDAR-derived variables. Furthermore,
we compared these results with predictions based on extensive ground-based climate, soil and vegetation data. Using linear
and flexible discriminant analyses, we found that LiDAR is able to predict forest habitat types with the same overall accuracy
as the extensive ground data for climate, soil and vegetation composition. Subtle differences in the vegetation structure
between habitat types, particularly in the vertical and horizontal vegetation profiles, were captured by LiDAR. These differences
in the physiognomy were in part caused by changes in altitude, which also influence tree species composition. We propose that
the most-efficient way to identify forest habitat types according Natura 2000 is to combine remote-sensing LiDAR data with well-directed field surveys. |
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