Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2010
Forfattere
Ievina SturiteSammendrag
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Anne Kjersti BakkenSammendrag
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Randi Berland FrøsethSammendrag
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Mette Vaarst Elisabeth Gratzer Michael Walkenhorst Silvia Ivemeyer Jan Brinkman Solveig March Lindsay k Whistance Gidi Smolders Elisabeth Stöger Johann Huber Christine Leeb Stephen Roderick Christoph Winckler Britt IF Henriksen Pip Nicholas Berit Hansen Cecilie Marie MejdellSammendrag
In: I. Darnhofer and M. Grötzer: Building sustainable rural futures, Proceedings of the 9th European IFSA Symposium, 4-7 July 2010, Vienna (Austria), WS 1.8, pp. 683-691. Universität für Bodenkultur, Vienna (ISBN 978-3-200-01908-9).
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Forfattere
Jari Vauhkonen Liviu Ene Svein Solberg Marius Hauglin Vegard Lien Petteri Packalén Terje Gobakken Erik Næsset Timo Tokola Matti MaltamoSammendrag
The aim of this study was to validate and compare single-tree detection algorithms under different forest conditions. Field data and corresponding airborne laser scanning (ALS) data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany, and pulpwood plantations in Brazil. The data represented a variety of forest types from pure Eucalyptus stands with known ages and planting densities to conifer-dominated Scandinavian forests and more complex deciduous canopies in Central Europe. ALS data were acquired using different sensors with pulse densities varying between the data sets. Field data in varying extent were associated with each ALS data set for training purposes. Treetop positions were extracted using altogether six different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation was assessed. Furthermore, the weaknesses and strengths of the methods under different forest conditions were analyzed. The results showed that forest structure and density strongly affected the performance of all algorithms. The differences in performance between methods were more pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions for which they were developed, while some could be adapted by different parameterization according to training with local data. The results of this study may help guiding the choice of method under different conditions and may be of great value for future refinement of the single-tree detection algorithms.
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