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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

Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.

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Sammendrag

Coprophagy, the eating of feces, has been documented in a wide range of species but appears to be rare or difficult to detect in deer (Cervidae). Here, we report the first observation of coprophagy in moose Alces alces, which was recorded using camera collars on free-ranging moose in Norway. The footage shows an instance of allocoprophagy by an adult female moose in spring (May). We summarize the current knowledge about coprophagy in deer and briefly discuss potential drivers and possible implications for disease transmission. Further research is needed to determine whether coprophagy occurs frequently in moose and whether this behavior is positive (e.g., increased intake of nutrients) or negative (increased infection by parasites or pathogens). Alces alces, camera collar, chronic wasting disease, coprophagy, foraging, moose

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Sammendrag

This paper provides an overview of traditional hay-making structures and the related agricultural landscapes in Europe. The information was collected using a standardised questionnaire that was completed by experts from different countries. What all countries had in common was that hay production with its corresponding structures was widespread. However, the scope and importance differed among the countries today. We found differences in type and extent, in degree of awareness, and in the cultural meaning of hay-making structures. The differences were connected with built structures, as well as with other tangible and intangible aspects of cultural heritage. The distribution of the broad variety of hay-making-related structures, especially semipermanent ones, has changed throughout history, as well as the hay-making techniques, as a result of agrarian specialisation, land reclamation, and consolidation. Today, in some countries, the relevance of hay-making was mainly connected to horse keeping and landscape management (like in Germany and Hungary), while in others (like Slovakia and Slovenia), it was still predominantly used for cattle and sheep.

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Sammendrag

Monitoring and managing Earth’s forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-m resolution, using freely available satellite imagery as input. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 syntheticaperture radar images into maps of five different forest structure variables: 95th height percentile, mean height, density, Gini coefficient, and fractional cover. We train and test our model on reference data from 41 airborne laser scanning missions across Norway and demonstrate that it is able to generalize to unseen test regions, achieving normalized mean absolute errors between 11% and 15%, depending on the variable. Our work is also the first to propose a variant of so-called Bayesian deep learning to densely predict multiple forest structure variables with well-calibrated uncertainty estimates from satellite imagery. The uncertainty information increases the trustworthiness of the model and its suitability for downstream tasks that require reliable confidence estimates as a basis for decision making. We present an extensive set of experiments to validate the accuracy of the predicted maps as well as the quality of the predicted uncertainties. To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.