Publikasjoner
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2024
Sammendrag
Lingonberry (Vaccinium vitis-idaea L.) grows in a range of nature types in the boreal zone, and understanding factors affecting the abundance of the plant, as well as mapping its spatial distribution, is important. The abundance of the species can be an indicator of ecosystem changes, and lingonberry can also be a source for commercial utilisation of berry resources. Using country-wide data from 6404 field plots of the Norwegian national forest inventory (NFI), we modelled the relationship between lingonberry cover and airborne laser scanning (ALS) and satellite metrics and bioclimatic variables describing the forest structure, terrain, soil properties and climate using a generalised mixed-effects model with a quasipoisson distribution. The validation carried out with an independent set of 2124 NFI plots indicated no obvious bias in predictions. The most important predictors were found to be interactions between dominant tree species, stand basal area and latitude, as well as the reflectance in the near-infrared band from Sentinel-2 satellite imagery, the dominant height based on the ALS variable and the long-term mean summer (June–August) temperature. The results provide an indicator of the effects of global warming, as well as the possibility of giving forest management prescriptions that favour lingonberry and locating the most abundant lingonberry sites in Norwegian forests.
Sammendrag
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Forfattere
Anne Rørholt Margrete Steinnes Erik Engelien Gunnhild Søgaard Rune Eriksen Arvid SvenssonSammendrag
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Forfattere
Christian Wilhelm Mohr Johannes Breidenbach Gunnhild Søgaard Oliver Moen Snoksrud Rune EriksenSammendrag
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Sammendrag
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Forfattere
Marte Ragnhild Owren Ingvild Byskov Britta Maria Hoem Julien Jabot Hans H. Kolshus Kathrine Loe Bjønness Jakob Sandven Trude Melby Bothner Mona Irene Andersen Engedal Eirik Knutsen Lene Skyrudsmoen Berit Storbråten Kristina Vikesund Hart Evan Christian Wilhelm Mohr Gry Alfredsen Ana Aza Johannes Breidenbach Lise Dalsgaard Rune Eriksen Katharina Hobrak Christophe Moni Gunnhild SøgaardRedaktører
Ingeborg RønningSammendrag
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Sammendrag
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Forfattere
Helle Ross Gobakken Mostafa Hoseini Stephan Hoffmann Jan Bjerketvedt Johannes Rahlf Rasmus AstrupSammendrag
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Forfattere
Binbin Xiang Maciej Wielgosz Theodora Kontogianni Torben Peters Stefano Puliti Rasmus Astrup Konrad SchindlerSammendrag
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.