Abstract

In managed landscapes, conservation planning requires effective methods to identify high-biodiversity areas. Satellite images and airborne laser scanning (ALS) can provide information about vegetation over large areas. This study evaluates the potential of detailed vegetation structure derived from ALS data for predicting the stand-scale abundance and species richness of birds and beetles in a managed boreal forest landscape. Earlier results have shown that bird abundance and species richness were best explained by the ALS variables "maximum vegetation height" and "vegetation cover between 0.5 and 3 m" (both positive), while the abundance and species richness of flying beetles as well as epigaeic (i.e., ground-living) beetles were best explained by a model including the ALS variable "maximum vegetation height" (positive) and the satellite-derived variable "proportion of pine" (negative). Now we include new explanatory variables describing the size of the tree crowns and the patchiness of the canopy derived from ALS data. The results demonstrate the level of detail in vegetation structure that can be obtained from ALS data and the importance of this information compared to other variables derived from remote sensing on a coarser scale.

Published in

Title: Book of Abstracts, IUFROLE WG Conference in Tartu, Estonia, 2015 : Sustaining ecosystem services in forest landscapes
ISBN: 978-9949-9715-0-3
Publisher: IUFROLE WG Conference

Conference

IUFRO Landscape Ecology Conference 2015

SLU Authors

• Lindberg, Eva

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Roberge, Jean-Michel

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    

• Löfroth, Therese

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    

• Hjältén, Joakim

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Remote Sensing
Ecology


Permanent link to this page (URI)

https://res.slu.se/id/publ/69037