Abstract

In managed landscapes, conservation planning requires effective methods to identify high-biodiversity areas. The objective of this study was to evaluate the potential of airborne laser scanning (ALS) and forest estimates derived from satellite images extracted at two spatial scales for predicting the stand-scale abundance and species richness of birds and beetles in a managed boreal forest landscape. Multiple regression models based on forest data from a 50-m radius (i.e., corresponding to a homogenous forest stand) had better explanatory power than those based on a 200-m radius (i.e., including also parts of adjacent stands). 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). Flying beetle abundance and species richness, as well as epigaeic (i.e., ground-living) beetle richness were best explained by a model including the ALS variable "maximum vegetation height" (positive) and the satellite-derived variable "proportion of pine" (negative). Epigaeic beetle abundance was best explained by "maximum vegetation height" at 50 m (positive) and "stem volume" at 200 m (positive). Our results show that forest estimates derived from satellite images and ALS data provide complementary information for explaining forest biodiversity patterns. We conclude that these types of remote sensing data may provide an efficient tool for conservation planning in managed boreal landscapes.

Keywords

biodiversity hot spot; LiDAR; ALS; kNN; epigaeic beetles, birds; beetles; boreal forest

Published in

Remote Sensing
2015, Volume: 7, number: 4, pages: 4233-4252

SLU Authors

• Lindberg, Eva

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    
  • Vienna University of Technology (TU Wien)

• 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
    

Associated SLU-program

Future Forests (until Jan 2017)
SLU Future Forests


UKÄ Subject classification

Forest Science
Ecology


Publication identifier

DOI: https://doi.org/10.3390/rs70404233

Permanent link to this page (URI)

https://res.slu.se/id/publ/66584