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
Når frisk skog avvirkes før den når hogstklasse 5, mens den løpende tilveksten fremdeles er høy, vil dette medføre redusert produksjon av virke og lavere karbonopptak på arealene. Vi har i dette prosjektet kartlagt motivene for tidlig hogst med tre undersøkelser rettet mot hhv. skogeiere, kommunale skogmyndigheter (skogbrukssjefer) og lokale virkeskjøpere (skogbruksledere). Skogeierundersøkelsen indikerer at høy virkespris, gode driftsforhold og dårlig skoghelse generelt er viktige motiver for skogsdrift. Ved tidlig hogst var skoghelseproblemer, nabodrift, og arealbruksendring de hyppigste motivene for hogsten. I Trøndelag og Nord-Norge var omdisponering motivet for tidlig hogst i 50-60% av disse tilfellene, som var langt hyppigere enn i resten av landet. Skogbrukssjefene angir at 45% og 20% av skogen som avvirket i hhv. hogstklasse 3 og 4 blir omdisponert, først og fremst til jordbruksformål. Skogkurs’ tapskalkulator for tidlig hogst er et nyttig verktøy for å bistå både skogeier og rådgiver i vurderinger av de økonomiske effektene av ulike hogsttidspunkt. Men den forutsetter at skogen har en «normal» utvikling. For vurdering av om hvorvidt skoghelseutfordringer i stående skog yngre enn hogstklasse 5 gjør det riktig å foreta en tidlig hogst, er det ingen beslutningsstøtteverktøy å støtte seg på. Skogeier er da prisgitt eget eller rådgivers skjønn i vurderingen av om skogen bør avvirkes eller vokse videre.
Forfattere
Katrin Zimmer Gro Hylen Christian Kuehne Kjersti Holt Hanssen Inger Sundheim Fløistad Aaron SmithSammendrag
«Bjørk i Norge» er sluttrapporten etter det treårige prosjekt «Flaskehalser og barrierer for økt bruk av bjørk» som er utført av en gruppe forskere ved NIBIO. Formålet med rapporten er å gi en oversikt over dagens bjørkeressurser, prognoser for volum og tilvekst samt dagens bruk av bjørk. Vi beskriver brukspotensiale, flaskehalser og barrierer for økt og kvalitetstilpasset bruk av bjørk og gjennomgår dagens kunnskap om foryngelse og skjøtsel av bjørk. I tillegg gir rapporten en oversikt over kvalitetsevalueringer av bjørk, samt en presentasjon av en studie om «forbedret kvalitetsevaluering av bjørkestokker». Vi påpeker fremtidige forskningsbehov og handlingsmuligheter for å få en bedre utnyttelse av avvirket virke, en mer kvalitetstilpasset bruk og en generell økt bruk av norsk bjørk.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Butt rot is a main defect in Norway spruce (Picea abies (L.) Karst.) trees and causes large economic losses for forest owners. However, little empirical research has been done on the effects of butt rot on harvested roundwood and the magnitude of the resulting economic losses. The main objective of this study was to characterize the direct economic losses caused by butt rot in Norway spruce trees for Norwegian forest owners. We used data obtained from seven cut-to-length harvesters, comprising ∼400,000 trees (∼140,000 m3) with corresponding stem profiles and wood grade information. We quantified the economic losses due to butt rot using bucking simulations, for which in a first case, defects caused by butt rot were included, and in a second case, all trees were assumed to be free of butt rot. 16% of trees were affected by butt rot, whereby butt rot tended to occur in larger trees. When butt rot was present in a tree, the saw log volume was reduced by 48%. Proportions of roundwood volume affected by butt rot varied considerably across harvested stands. Our results suggest that butt rot causes economic losses upwards of 7% of wood revenues, corresponding to € 18.5 million annually in Norway.
Sammendrag
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Forfattere
Morgane Merlin Rasmus Astrup Nicolas Cattaneo Nils Egil Søvde Barry Gardiner Tommaso LocatelliSammendrag
Det er ikke registrert sammendrag
Sammendrag
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.
Forfattere
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupSammendrag
The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes. In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and semantic classes. The point clouds are subdivided into five data collections representing different forests in Norway, the Czech Republic, Austria, New Zealand, and Australia. These data are meant to be used either for developement of new methods (using the dev data) or for testing of exisitng methods (test data). The data splits are provided in the data_split_metadata.csv file. A full description of the FOR-instance data can be found at http://arxiv.org/abs/2309.01279
Forfattere
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupSammendrag
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.
Forfattere
Marta Vergarechea Rasmus Astrup Knut Øistad Carolin Fischer Clemens Blattert Markus Hartikainen Kyle Eyvindson Fulvio Di Fulvio Nicklas Forsell Daniel Burgas Astor Toraño-Caicoya Mikko Mönkkönen Clara Antón FernándezSammendrag
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