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
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Authors
Thomas Solvin Inger Sundheim Fløistad Gunnar Friis Proschowsky Torben Leisgaard Tiina Ylioja Marko Tynkkynen Brynjar Skúlason Hallur S. Björgvinsson Espen Stokke Marte Friberg Myhre Ellinor Edvardsson Claes UgglaAbstract
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Supplemental feeding of cervids during winter is a widespread management practice, but feeding may increase the risk of disease transmission. Therefore, legal regulations to limit supplemental feeding are often implemented when dealing with severe infectious diseases, such as chronic wasting disease (CWD) in cervids. However, it is currently unclear whether these regulations result in decreased spatial clustering and aggregation as intended. Supplemental feeding is expected to restrict the movement of cervids. Therefore, a ban on feeding may also result in wider space use and a risk of geographic spread of disease. The space use of 63 GPS-marked red deer (Cervus elaphus) was investigated before (n = 34) and after (n = 29) the implementation of a legal regulation aimed at limiting the supplemental feeding of cervids during winter in a CWD-affected region of Nordfjella, Norway. Snow depth was the main determinant of the space use for red deer. A moderate reduction in the number of GPS positions in spatial clusters was evident during periods of deep snow once the ban was in place. Sizes of core areas (Kernel 50%), home ranges (Kernel 95%), and dispersion (MCP 100%, number of 1 km2 pixels visited per deer) declined from January to March and with increasing snow depth. Dispersion (number of 1 km2 pixels visited per deer) did not depend on snow depth after the ban, and red deer used larger areas when snow depth was high after the ban compared to before. The ban on supplementary feeding had no effect on size of core areas or home ranges. Several potential factors can explain the overall weak effect of the ban on space use, including the use of agricultural fields by red deer, other anthropogenic feeding, and landscape topography. This study highlights that snow depth is the main factor determining space use during winter, and it remains to be determined whether the moderate reduction in spatial clustering during deep snow after the ban was sufficient to lower the risk of disease transmission.
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Parametric modeling of downwelling longwave irradiance under all-sky conditions (LW↓) typically involves “correcting” a clear- (or non-overcast) sky model estimate using solar-irradiance-based proxies of cloud cover in lieu of actual cloud cover given uncertainties and measurement challenges of the latter. While such approaches are deemed sound, their application in time and space is inherently limited. We report on a correction model free of solar irradiance-derived cloud proxies that is applicable at the true daily (24 hr) and global scales. The new “cloud-free” correction model demonstrates superior performance in a range of environments relative to existing cloud-free modeling approaches and to corrections based on solar-derived cloudiness proxies. Literature-based performance benchmarking indicates a performance that is often comparable to—and in some cases superior to—performances yielded by conventional parametric modeling approaches employing locally or regionally calibrated parameters, as well as to performances of satellite-based algorithms.
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Taper models, which describe the shape of tree stems, are central to estimating stem volume. Literature provides both taper- and volume models for the three main species in Norway, Norway spruce, Scots pine, and birch. These models, however, were mainly developed using approaches established over 50 years ago, and without consistency between taper and volume. We tested eleven equations for taper and six equations for bark thickness. The models were fitted and evaluated using a large dataset covering all forested regions in Norway. The selected models were converted into volume functions using numerical integration, providing both with- and without-bark volumes and compared to the volume functions in operational use. Taper models resulted in root mean squared error (RMSE) of 7.2, 7.9, and 9.0 mm for spruce, pine, and birch respectively. Bark thickness models resulted in RMSE of 2.5, 6.1, and 4.1 mm, for spruce, pine, and birch respectively. Validation of volume models with bark resulted in RMSE of 12.7%, 13.0%, and 19.7% for spruce, pine, and birch respectively. Additional variables, tree age, site index, elevation, and live crown proportion, were tested without resulting in any strong increase in predictive power.
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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.