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
Abstract
Wood modification using polyesterification of sorbitol and citric acid is a novel environmentally friendly strategy for wood protection improving its dimensional stability and acts against fungal deterioration. Inelastic Raman scattering is sensitive to the molecules of high polarizability and both lignocellulose and aliphatic esters formed during the treatment are polar. Therefore, in the present study, the quality control of the treatment using a handheld Raman spectrometer equipped with 830 nm laser is suggested as a rapid and reliable approach. Raman spectra from six wood modification levels (resulting in different weight percent gain, WPG) of three different wood species (Silver birch, Scots pine and Norway spruce) as well as three sample preparation strategies (intact, sanded and milled wood samples) were collected, and further analyzed using a chemometric method. Best performing models based on Powered Partial Least Squares Regression predicted the WPG level at R2 = 0.85, 0.95 and 0.98 for birch, pine and spruce, respectively. In addition, a clear separation between hard and soft wood species was also captured. Especially for softwood species, the sample preparation method affected the model accuracy, revealing the best performance in milled material. It is concluded that by using handheld Raman spectrometer it is possible to perform accurate quality control of wood modified by polyesterification of citric acid and sorbitol.
Abstract
Det er ikke registrert sammendrag
Abstract
NIBIO har taksert elgbeite på oppdrag av Grenland Landbrukskontor i 2022-2023. Det rikeste beitetilbudet var i sørøst, mens nord var spesielt fattig på buskbeite, og sørvest mer furudominert og variabel. I rapporten er regionen delt inn i Nord, Vest og Sør. Særlig Vest hadde høy dekning av blåbærlyng, mens Sør skilte seg ut med høy dekning av høge urter og bringebær. Også Nord hadde bra dekning av blåbærlyng, men lite annet attraktivt feltsjiktbeite. Vi fant lavere enn forventet tetthet av trær i beitehøyde (30-300 cm) i alle delområdene. Takst etter samme metodikk i Kjose i 2005 indikerer at både tetthet av beitetrær og ubeita skudd-cm/tre har gått ned, til tross for yngre skog i dag. Det skyldes trolig høyt beitepress fra hjortevilt, og skogskjøtsel over tid. Se anbefalinger i utvidet sammendrag. Vi har beregnet at det er mat til maks 0.6 elg per km2 (vinterbestand) i Nord og Vest, og 1.3 per km2 i Sør. Dette er et maks optimistisk anslag, gitt at beiteplantene ikke hadde vært kuet, og hvor det må gjøres fratrekk for hjort. Et svært grovt anslag er at hjorten tar 30-50% av bæreevnen i Nord og Vest, og 15-25% i Sør. De siste 5 årene har beregnet tetthet av elg i snitt vært 0.6-0.9 elg/km2 i Grenland som helhet. Uten mer presis kunnskap om hjortens beiting er det vanskelig å si hvilken tetthet av elg som vil bidra til å friskmelde beitene. Et friskmeldt beite er en nødvendighet, men ingen garanti, for elg i god kondisjon. Elg har også andre og økende utfordringer som kan svekke kondisjonen, som et varmere klima, men beitene er fortsatt en viktig brikke i puslespillet av faktorer som bidrar til den vedvarende dårlige kondisjonen på elg i Grenland.
Abstract
Det er ikke registrert sammendrag
Abstract
Det er ikke registrert sammendrag
Authors
Tomáš Peterka Petra Hájková Martin Jiroušek Dirk Hinterlang Milan Chytrý Liene Aunina Judit Deme Melinda Lyons Hallie Seiler Harald Zechmeister Iva Apostolova Carl Beierkuhnlein Melanie Bischof Claudia Biţă-Nicolae Lisa Brancaleoni Renata Ćušterevska Jürgen Dengler Yakiv Didukh Daniel Dítě Lyubov Felbaba-Klushyna Emmanuel Garbolino Renato Gerdol Svitlana Iemelianova Florian Jansen Riikka Juutinen Jasmina Kamberović Jutta Kapfer Barbora Klímová Ilona Knollová Tiina H.M. Kolari Predrag Lazarević Ringa Luostarinen Eva Mikulášková Đorđije Milanović Luca Miserere Jesper Erenskjold Moeslund José A. Molina Aaron Pérez-Haase Alessandro Petraglia Marta Puglisi Eszter Ruprecht Eva Šmerdová Daniel Spitale Marcello Tomaselli Kiril Vassilev Michal HájekAbstract
Det er ikke registrert sammendrag
Authors
Adrian Straker Stefano Puliti Johannes Breidenbach Christopher Kleinn Grant Pearse Rasmus Astrup Paul MagdonAbstract
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.
Authors
Binbin Xiang Torben Peters Theodora Kontogianni Frawa Vetterli Stefano Puliti Rasmus Astrup Konrad SchindlerAbstract
Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy.
Abstract
Information on tree height-growth dynamics is essential for optimizing forest management and wood procurement. Although methods to derive information on height-growth information from multi-temporal laser scanning data already exist, there is no method to derive such information from data acquired at a single point in time. Drone laser scanning data (unmanned aerial vehicles, UAV-LS) allows for the efficient collection of very dense point clouds, creating new opportunities to measure tree and branch architecture. In this study, we examine if it is possible to measure the vertical positions of branch whorls, which correspond to nodes, and thus can in turn be used to trace the height growth of individual trees. We propose a method to measure the vertical positions of whorls based on a single-acquisition of UAV-LS data coupled with deep-learning techniques. First, single-tree point clouds were converted into 2D image projections, and a YOLOv5 (you-only-look-once) convolutional neural network was trained to detect whorls based on a sample of manually annotated images. Second, the trained whorl detector was applied to a set of 39 trees that were destructively sampled after the UAV-LS data acquisition. The detected whorls were then used to estimate tree-, plot- and stand-level height-growth trajectories. The results indicated that 70 per cent (i.e. precision) of the measured whorls were correctly detected and that 63 per cent (i.e. recall) of the detected whorls were true whorls. These results translated into an overall root-mean-squared error and Bias of 8 and −5 cm for the estimated mean annual height increment. The method’s performance was consistent throughout the height of the trees and independent of tree size. As a use case, we demonstrate the possibility of developing a height-age curve, such as those that could be used for forecasting site productivity. Overall, this study provides proof of concept for new methods to analyse dense aerial point clouds based on image-based deep-learning techniques and demonstrates the potential for deriving useful analytics for forest management purposes at operationally-relevant spatial-scales.
Authors
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupAbstract
The FOR-instance dataset (available at this https URL) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite the growing need for detailed tree data, automating segmentation and tracking scientific progress remains difficult. Existing methodologies often overfit small datasets and lack comparability, limiting their applicability. Amid the progress triggered by the emergence of deep learning methodologies, standardized benchmarking assumes paramount importance in these research domains. This data paper introduces a benchmarking dataset for dense airborne laser scanning data, aimed at advancing instance and semantic segmentation techniques and promoting progress in 3D forest scene segmentation. The FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections from diverse global locations, representing various forest types. The laser scanning data were manually annotated into individual trees (instances) and different semantic classes (e.g. stem, woody branches, live branches, terrain, low vegetation). The dataset is divided into development and test subsets, enabling method advancement and evaluation, with specific guidelines for utilization. It supports instance and semantic segmentation, offering adaptability to deep learning frameworks and diverse segmentation strategies, while the inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable. In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.