AVIS-IBIS

Birds of Indian Subcontinent

Modelling bird richness and bird species presence in a boreal forest reserve using airborne laser-scanning and aerial images

Publication Type:Journal Article
Year of Publication:2014
Authors:Eldegard, K, Dirksen, JWirkola, Ørka, HOle, Halvorsen, R, Næsset, E, Gobakken, T, Ohlson, M
Journal:Bird Study
Volume:61
Issue:2
Date Published:2014
ISBN Number:0006-3657
Keywords:Phylloscopidae, Phylloscopus, Phylloscopus trochilus, Regulidae, Regulus, Regulus regulus, Troglodytes, Troglodytes troglodytes, Troglodytidae
Abstract:Capsule Variables obtained from airborne laser-scanning (ALS) enabled slight or fair predictions of bird presence, and including multispectral data further improved predictions slightly.Aims To assess the usefulness of ALS as a tool for predicting species richness and single-species presence, and to investigate if including information from multispectral aerial images further improved predictability of bird presence.Methods Bird presence data were sampled in a Norwegian boreal forest reserve. Prediction models were developed for species richness and presence of the eight most abundant species by the use of two different modelling approaches: generalized linear models and the machine learning method random forest. Predictor variables were descriptors of three-dimensional forest structure obtained by ALS, and descriptors of tree species composition obtained from multispectral aerial images.Results Cross-validation of the prediction models indicated overall slight or fair predictive capability. Best predictions were obtained for Goldcrest, Wren, and Willow Warbler. Inclusion of spectral variables derived from the aerial imagery slightly improved the predictive performance of several models, most notably for Willow Warbler.Conclusion We suggest that predictability of species richness and presence of single bird species can be improved by better matching of the scale of recording for birds and the predictor variables obtained by remote sensing.Capsule Variables obtained from airborne laser-scanning (ALS) enabled slight or fair predictions of bird presence, and including multispectral data further improved predictions slightly.Aims To assess the usefulness of ALS as a tool for predicting species richness and single-species presence, and to investigate if including information from multispectral aerial images further improved predictability of bird presence.Methods Bird presence data were sampled in a Norwegian boreal forest reserve. Prediction models were developed for species richness and presence of the eight most abundant species by the use of two different modelling approaches: generalized linear models and the machine learning method random forest. Predictor variables were descriptors of three-dimensional forest structure obtained by ALS, and descriptors of tree species composition obtained from multispectral aerial images.Results Cross-validation of the prediction models indicated overall slight or fair predictive capability. Best predictions were obtained for Goldcrest, Wren, and Willow Warbler. Inclusion of spectral variables derived from the aerial imagery slightly improved the predictive performance of several models, most notably for Willow Warbler.Conclusion We suggest that predictability of species richness and presence of single bird species can be improved by better matching of the scale of recording for birds and the predictor variables obtained by remote sensing.
URL:http://dx.doi.org/10.1080/00063657.2014.885492
Short Title:Bird Study
Taxonomic name: 
Phylloscopidae (Birds), Phylloscopus (Birds), Phylloscopus trochilus (Birds), Regulidae (Birds), Regulus (Birds), Regulus regulus (Birds), Troglodytes (Birds), Troglodytes troglodytes (Birds), Troglodytidae (Birds)
Scratchpads developed and conceived by (alphabetical): Ed Baker, Katherine Bouton Alice Heaton Dimitris Koureas, Laurence Livermore, Dave Roberts, Simon Rycroft, Ben Scott, Vince Smith