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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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.
Sammendrag
Inthis study, we introduce Point2Tree, a modular and versatile framework that employs a three-tiered methodology, inclusive of semantic segmentation, instance segmentation, and hyperparameter optimization analysis, designed to process laser point clouds in forestry. The semantic segmentation stage is built upon the Pointnet++ architecture and is primarily tasked with categorizing each point in the point cloud into meaningful groups or ’segments’, specifically in this context, differentiating between diverse tree parts, i.e., vegetation, stems, and coarse woody debris. The category for the ground is also provided. Semantic segmentation achieved an F1-score of 0.92, showing a high level of accuracy in classifying forest elements. In the instance segmentation stage, we further refine this process by identifying each tree as a unique entity. This process, which uses a graph-based approach, yielded an F1-score of approximately 0.6, signifying reasonable performance in delineating individual trees. The third stage involves a hyperparameter optimization analysis, conducted through a Bayesian strategy, which led to performance improvement of the overall framework by around four percentage points. Point2Tree was tested on two datasets, one from a managed boreal coniferous forest in Våler, Norway, with 16 plots chosen to cover a range of forest conditions. The modular design of the framework allows it to handle diverse pointcloud densities and types of terrestrial laser scanning data.
Sammendrag
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Sammendrag
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Sammendrag
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Sammendrag
Denne rapporten gir en oversikt over tilstanden i skog som var vernet per. 1.1.2021. Datamaterialet som er utgangspunktet for rapporten er registreringer gjennomført av Landsskogtakseringen, gjennom «Overvåkingsprogrammet for skog i verneområder». Registreringene er utført i løpet av femårsperioden 2017-2021. Utvalgte resultater: • Vernet skog omfattet per 1.1. 2021 totalt 592 481 ha, tilsvarende 4,9 prosent av det totale skogarealet. • 3,7 prosent av den produktive skogen, og 7,9 prosent av den uproduktive skogen i landet finnes i vernet skog, der skogbruk ikke er tillatt. • I produktiv skog er andelen skogareal i klassene middels og høy+ svært høy bonitet underrepresentert samlignet med skogen generelt. • Skogen i verneområder er generelt eldre og har en større andel skog i senere utviklingstrinn. Biologisk gammel skog etter Landsskogtakseringens definisjon utgjør nærmere tre ganger så høy andel i den produktive delen av vernet skogareal som i produktiv skog totalt. • Det stående volumet i verneområdene utgjør 4,1 prosent av totalt stående volum. • Gjennomsnittlig tilvekst i skog som var vernet per 1.1.2016 er lavere enn gjennomsnittet for skog generelt. • Vernet skog inneholder mer volum død ved per hektar i gjennomsnitt enn øvrig skog. • I produktiv skog er MiS-livsmiljøene “liggende dødved” og “stående dødved”, samt “gamle trær” vanligere i verneområdene. For uproduktiv skog er “gamle trær” vanligere. I tillegg presenteres resultater som viser hvordan den vernede skogen har utviklet seg siden forrige taksering, som ble gjennomført 2012-2016.
Forfattere
Gry Alfredsen Lone Ross Michael Altgen Igor A. Yakovlev Anne-Cathrine Flyen Mari sand Austigaard Johan Mattsson Nanna Bjerregaard PedersenSammendrag
Det er ikke registrert sammendrag
Forfattere
Gry Alfredsen Lone Ross Michael Altgen Igor A. Yakovlev Anne-Cathrine Flyen Mari sand Austigaard Johan Mattsson Nanna Bjerregaard PedersenSammendrag
Det er ikke registrert sammendrag