Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2023
Authors
Isabell EischeidAbstract
No abstract has been registered
Authors
Isabell Eischeid Jesper Madsen Rolf Anker Ims Bart A. Nolet Åshild Ønvik Pedersen Kees H.T. Schreven Eeva Marjatta Soininen Nigel Gilles Yoccoz Virve RavolainenAbstract
Arctic tundra vegetation is affected by rapid climatic change and fluctuating herbivore population sizes. Broad-billed geese, after their arrival in spring, feed intensively on belowground rhizomes, thereby disturbing soil, mosses, and vascular plant vegetation. Understanding of how springtime snowmelt patterns drive goose behavior is thus key to better predict the state of Arctic tundra ecosystems. Here, we analyzed how snowmelt progression affected springtime habitat selection and vegetation disturbance by pink-footed geese (Anser brachyrhynchus) in Svalbard during 2019. Our analysis, based on GPS telemetry data and field observations of geese, plot-based assessments of signs of vegetation disturbance, and drone and satellite images, covered two spatial scales (fine scale: extent 0.3 km2, resolution 5 cm; valley scale: extent 30 km2, resolution 10 m). We show that pink-footed goose habitat selection and signs of vegetation disturbance were correlated during the spring pre-breeding period; disturbances were most prevalent in the moss tundra vegetation class and areas free from snow early in the season. The results were consistent across the spatial scales and methods (GPS telemetry and field observations). We estimated that 23.4% of moss tundra and 11.2% of dwarf-shrub heath vegetation in the valley showed signs of disturbance by pink-footed geese during the study period. This study demonstrates that aerial imagery and telemetry can provide data to detect disturbance hotspots caused by pink-footed geese. Our study provides empirical evidence to general notions about implications of climate change and snow season changes that include increased variability in precipitation.
Abstract
Soil management is important for sustainable agriculture, playing a vital role in food production and maintaining ecological functions in the agroecosystem. Effective soil management depends on highly accurate soil property estimation. Machine learning (ML) is an effective tool for data mining, selection of key soil properties, modeling the non-linear relationship between different soil properties. Through coupling with spectral imaging, ML algorithms have been extensively used to estimate physical, chemical, and biological properties quickly and accurately for more effective soil management. Most of the soil properties are estimated by either near infrared (NIR), Vis-NIR, or mid-infrared (MIR) in combination with different ML algorithms. Spectroscopy is widely used in estimation of chemical properties of soil samples. Spectral imaging from both UAV and satellite platforms should be taken to improve the spatial resolution of different soil properties. Spectral image super-resolution should be taken to generate spectral images in high spatial, spectral, and temporal resolutions; more advanced algorithms, especially deep learning (DL) should be taken for soil properties’ estimation based on the generated ‘super’ images. Using hyperspectral modeling, soil water content, soil organic matter, total N, total K, total P, clay and sand were found to be successfully predicted. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties. An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for water, total organic C, extractable phosphorus, and total N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness. More precise and detailed soil property estimation will facilitate future soil management.
Authors
Jiangsan ZhaoAbstract
No abstract has been registered
Authors
Linn VassvikAbstract
No abstract has been registered
Authors
Linn VassvikAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Ruben Erik Roos Johan Asplund Tone Birkemoe Aud Helen Halbritter Rechsteiner Siri Lie Olsen Linn Vassvik Kristel van Zuijlen Kari KlanderudAbstract
No abstract has been registered
Authors
Nhat Strøm-AndersenAbstract
No abstract has been registered