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
Forfattere
Risto Päivinen Rasmus Astrup Richard A. Birdsey Johannes Breidenbach Jonas Fridman Annika Kangas Pekka E. Kauppi Michael Köhl Kari T. Korhonen Vivian Kvist Johannsen François Morneau Thomas Riedel Klemens Schadauer Iddo K. WernickSammendrag
Det er ikke registrert sammendrag
Forfattere
Volkmar Timmermann Isabella Børja Nicholas Clarke Jostein Gohli Ari Hietala Jane Uhd Jepsen Paal Krokene Harald Haga Lislegård Nina Elisabeth Nagy Hans Nyeggen Sverre Solberg Halvor Solheim Thomas Solvin Arvid Svensson Mari Mette Tollefsrud Ole Petter Laksforsmo Vindstad Bjørn Økland Wenche AasSammendrag
Skogens helsetilstand påvirkes i stor grad av klima og værforhold, enten direkte ved tørke, frost og vind, eller indirekte ved at klimaet påvirker omfanget av soppsykdommer og insektangrep. Klimaendringene og den forventede økningen i klimarelaterte skogskader gir store utfordringer for forvaltningen av framtidas skogressurser. Det samme gjør invaderende skadegjørere, både allerede etablerte arter og nye som kan komme til Norge i nær framtid. I denne rapporten presenteres resultater fra skogskadeovervåkingen i Norge i 2022 og trender over tid for følgende temaer: (i) Landsrepresentativ skogovervåking; (ii) Intensiv skogovervåking; (iii) Overvåking av bjørkemålere i Troms og Finnmark; (iv) Barkbilleovervåkingen; (v) Furuvednematode; (vi) Askeskuddsyke; (vii) Andre spesielle skogskader i 2022.
Sammendrag
Key message We studied size distributions of decay-affected Norway spruce trees using cut-to-length harvester data. The harvester data comprised tree-level decay and decay severity recordings from 101 final felling stands, which enabled to analyze relationships between size distributions of all and decay-affected trees. Distribution matching technique was used to transfer the size distribution of all trees into the diameter at breast height (DBH) distribution of decay-affected trees. Context Stem decay of Norway spruce (Picea abies [L.] Karst.) results in large economic losses in timber production in the northern hemisphere. Forest management planning typically requires information on tree size distributions. However, size distributions of decay-affected trees generally remain unknown impeding decision-making in forest management planning. Aims Our aim was to analyze and model relationships between size distributions of all and decay-affected Norway spruce trees at the level of forest stands. Methods Cut-to-length harvester data of 93,456 trees were collected from 101 final felling stands in Norway. For each Norway spruce tree (94% of trees), the presence and severity of stem decay (incipient and advanced) were recorded. The stand-level size distributions (diameter at breast height, DBH; height, H) of all and decay-affected trees were described using the Weibull distribution. We proposed distribution matching (DM) models that transform either the DBH or H distribution of all trees into DBH distributions of decay-affected trees. We compared the predictive performance of DMs with a null-model that refers to a global Weibull distribution estimated based on DBHs of all harvested decay-affected trees. Results The harvester data showed that an average-sized decay-affected tree is larger and taller compared with an average-sized tree in a forest stand, while trees with advanced decay were generally shorter and thinner compared with trees having incipient decay. DBH distributions of decay-affected trees can be matched with smaller error index (EI) values using DBH (EI = 0.14) than H distributions (EI = 0.31). DM clearly outperformed the null model that resulted in an EI of 0.32. Conclusions The harvester data analysis showed a relationship between size distributions of all and decay-affected trees that can be explained by the spread biology of decay fungi and modeled using the DM technique. Keywords Root and butt rot, Heterobasidion spp., Armillaria spp., Cut-to-length harvester, Forest management and planning
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Volkmar Timmermann Isabella Børja Nicholas Clarke Rune Eriksen Jostein Gohli Gro Hylen Jane Uhd Jepsen Paal Krokene Holger Lange Helge Meissner Nina Elisabeth Nagy Jørn-Frode Nordbakken Sverre Solberg Halvor Solheim Ole Petter Laksforsmo Vindstad Bjørn Økland Wenche AasSammendrag
Skogens helsetilstand påvirkes i stor grad av klima og værforhold, enten direkte ved tørke, frost og vind, eller indirekte ved at klimaet påvirker omfanget av soppsykdommer og insektangrep. Klimaendringene og den forventede økningen i klimarelaterte skogskader gir store utfordringer for forvaltningen av framtidas skogressurser. Det samme gjør invaderende skadegjørere, både allerede etablerte arter og nye som kan komme til Norge i nær framtid. I denne rapporten presenteres resultater fra skogskadeovervåkingen i Norge i 2021 og trender over tid for følgende temaer: (i) Landsrepresentativ skogovervåking; (ii) Skogøkologiske analyser og målinger av luftkjemi på de intensive overvåkingsflatene; (iii) Overvåking av bjørkemålere i Troms og Finnmark; (iv) Barkbilleovervåkingen 2021 og mulig overgang til to generasjoner; (v) Asiatisk askepraktbille – en dørstokkart? (vi) Overvåking av askeskuddsyke; (vii) Andre spesielle skogskader i 2021.
2022
Forfattere
Johannes Schumacher Håvard Toft Larsen Paul McLean Marius Hauglin Rasmus Astrup Johannes BreidenbachSammendrag
The number of people affected by snow avalanches during recreational activities has increased over the recent years. An instrument to reduce these numbers are improved terrain classification systems. One such system is the Avalanche Terrain Exposure Scale (ATES). Forests can provide some protection from avalanches, and information on forest attributes can be incorporated into avalanche hazard models such as the automated ATES model (AutoATES). The objectives of this study were to (i) map forest stem density and canopy-cover based on National Forest Inventory and remote sensing data and, (ii) use these forest attributes as input to the AutoATES model. We predicted stem density and directly calculated canopy-cover in a 20 Mha study area in Norway. The forest attributes were mapped for 16 m × 16 m pixels, which were used as input for the AutoATES model. The uncertainties of the stem number and canopy-cover maps were 30% and 31%, respectively. The overall classification accuracy of 52 ski-touring routes in Western Norway with a total length of 282 km increased from 55% in the model without forest information to 67% when utilizing canopy cover. The F1 score for the three predicted ATES classes improved by 31%, 9%, and 6%.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Rapporten gir en oversikt over skogressurser og skogtilstand i Møre og Romsdal for referanseåret 2018 (data innsamlet i løpet av femårsperioden 2016-2020). Nye resultater settes også sammen med resultat fra tidligere takster, for å vise historisk utvikling. Resultatene er basert på data registrert i Landsskogtakseringens permanente prøveflatenett, supplementært med temporære prøveflater for å oppnå tilfredsstillende nøyaktighet på fylkesnivå.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Saheba Bhatnagar Stefano Puliti Bruce Talbot Joachim Bernd Heppelmann Johannes Breidenbach Rasmus AstrupSammendrag
Wheel ruts, i.e. soil deformations caused by harvesting machines, are considered a negative environmental impact of forest operations and should be avoided or ameliorated. However, the mapping of wheel ruts that would be required to monitor harvesting operations and to plan amelioration measures is a tedious and time-consuming task. Here, we examined whether a combination of drone imagery and algorithms from the field of artificial intelligence can automate the mapping of wheel ruts. We used a deep-learning image-segmentation method (ResNet50 + UNet architecture) that was trained on drone imagery acquired shortly after harvests in Norway, where more than 160 km of wheel ruts were manually digitized. The cross-validation of the model based on 20 harvested sites resulted in F1 scores of 0.69–0.84 with an average of 0.77, and in total, 79 per cent of wheel ruts were correctly detected. The highest accuracy was obtained for severe wheel ruts (average user’s accuracy (UA) = 76 per cent), and the lowest accuracy was obtained for light wheel ruts (average UA = 67 per cent). Considering the nowadays ubiquitous availability of drones, the approach presented in our study has the potential to greatly increase the ability to effectively map and monitor the environmental impact of final felling operations with respect to wheel ruts. The automated mapping of wheel ruts may serve as an important input to soil impact analyses and thereby support measures to restore soil damages.