Abstract

Management of forests for biodiversity conservation requires knowledge on the habitat needs of forest-dwelling species. Important habitat factors include local stand conditions such as forest structure and tree species composition as well as the amount and distribution of suitable local habitats in a surrounding landscape. Information at both these scales can be efficiently derived from remotely sensed data.Focusing on the European boreal forest, this paper presents an analysis of the relation between the local-scale abundance and species richness of forest-dwelling birds and beetles on the one hand, and information derived from airborne laser scanning (ALS) data and satellite images on the other. The aim is to answer the following questions: 1. Can ALS-data or satellite image data or a combination of the two be used to identify important habitats for forest dwelling beetles and birds in boreal forest? 2. Which type of remote sensing data can best explain biodiversity patterns for beetle and birds species in boreal forest? 3. How accurate can different remote sensing methods predict biodiversity patterns at different spatial scales?

Published in

Title: Proceedings of the International Workshop on Remote Sensing and GIS for Monitoring of Habitat Quality
ISBN: 978-3-200-03727-4
Publisher: Department of Geodesy and Geoinformation, Vienna University of Technology

Conference

The International Workshop on Remote Sensing and GIS for Monitoring of Habitat Quality

SLU Authors

• 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

Ecology
Geosciences, Multidisciplinary
Remote Sensing


Permanent link to this page (URI)

https://res.slu.se/id/publ/63816