Title: Can Airborne Laser Scanning (ALS) and Forest Estimates Derived from Satellite Images Be Used to Predict Abundance and Species Richness of Birds and Beetles in Boreal Forest?
Language: English
Authors: Lindberg, Eva
Roberge, Jean-Michel 
Johansson, Therese 
Hjältén, Joakim 
Category: Research Article
Issue Date: 2015
Journal: Remote sensing
ISSN: 2072-4292
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
DOI: 10.3390/rs70404233
Library ID: AC11360591
URN: urn:nbn:at:at-ubtuw:3-2131
Organisation: E120 - Department für Geodäsie und Geoinformation 
Publication Type: Article
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