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.
2023
Sammendrag
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Sammendrag
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Forfattere
Daniel Ibañez Michael Salka Vicente Guallart Stefano Boeri Livia Shamir Maria Lucrezia De Marco Sofia Paoli Maria Chiara Pastore Massimo Fragiacomo Lone Ross Sylvain BouletSammendrag
Realising the transition to BioCities, which function as holistically integrated systems similar to natural ecosystems, will require not just the creation of green and blue infrastructure (GI) (Davies et al. 2015), associated land-use patterns, and urban forests (UF) (Konijnendijk et al. 2006), but also utilising appropriate design of built structures and landscapes that support nature-based solutions (NBS) and ecosystem services (ESS).
Sammendrag
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Forfattere
Sebastian EiterSammendrag
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Forfattere
Grete H. M. Jørgensen Inger Hansen Finn Arne Haugen Magnhild Garte Høiberg Lise Grøva Gabriela WagnerRedaktører
Per StålnackeSammendrag
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Sammendrag
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Forfattere
Gunnhild JaastadSammendrag
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Forfattere
Alexander Tischer Julian Klaus Christian Kuehne Bernd Hoffmann Martin Hartig Robert SchmidtSammendrag
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Forfattere
Adrian Straker Stefano Puliti Johannes Breidenbach Christopher Kleinn Grant Pearse Rasmus Astrup Paul MagdonSammendrag
Fine-grained information on the level of individual trees constitute key components for forest observation enabling forest management practices tackling the effects of climate change and the loss of biodiversity in forest ecosystems. Such information on individual tree crowns (ITC's) can be derived from the application of ITC segmentation approaches, which utilize remotely sensed data. However, many ITC segmentation approaches require prior knowledge about forest characteristics, which is difficult to obtain for parameterization. This can be avoided by the adoption of data-driven, automated workflows based on convolutional neural networks (CNN). To contribute to the advancements of efficient ITC segmentation approaches, we present a novel ITC segmentation approach based on the YOLOv5 CNN. We analyzed the performance of this approach on a comprehensive international unmanned aerial laser scanning (UAV-LS) dataset (ForInstance), which covers a wide range of forest types. The ForInstance dataset consists of 4192 individually annotated trees in high-density point clouds with point densities ranging from 498 to 9529 points m-2 collected across 80 sites. The original dataset was split into 70% for training and validation and 30% for model performance assessment (test data). For the best performing model, we observed a F1-score of 0.74 for ITC segmentation and a tree detection rate (DET %) of 64% in the test data. This model outperformed an ITC segmentation approach, which requires prior knowledge about forest characteristics, by 41% and 33% for F1-score and DET %, respectively. Furthermore, we tested the effects of reduced point densities (498, 50 and 10 points per m-2) on ITC segmentation performance. The YOLO model exhibited promising F1-scores of 0.69 and 0.62 even at point densities of 50 and 10 points m-2, respectively, which were between 27% and 34% better than the ITC approach that requires prior knowledge. Furthermore, the areas of ITC segments resulting from the application of the best performing YOLO model were close to the reference areas (RMSE = 3.19 m-2), suggesting that the YOLO-derived ITC segments can be used to derive information on ITC level.