Eva Solbjørg Flo Heggem
Sjefingeniør
(+47) 476 84 108
eva.heggem@nibio.no
Sted
Ås - Bygg O43
Besøksadresse
Oluf Thesens vei 43, 1433 Ås (Varelevering: Elizabeth Stephansens vei 23)
Sammendrag
1. Climate change is already reducing carbon sequestration in Central European forests dramatically through extensive droughts and bark beetle outbreaks. Further warming may threaten the enormous carbon reservoirs in the boreal forests in northern Europe unless disturbance risks can be reduced by adaptive forest management. The European spruce bark beetle (Ips typographus) is a major natural disturbance agent in spruce-dominated forests and can overwhelm the defences of healthy trees through pheromone-coordinated mass-attacks. 2. We used an extensive dataset of bark beetle trap counts to quantify how climatic and management-related factors influence bark beetle population sizes in boreal forests. Trap data were collected during a period without outbreaks and can thus identify mechanisms that drive populations towards outbreak thresholds. 3. The most significant predictors of bark beetle population size were the volume of mature spruce, the extent of newly exposed clearcut edges, temperature and soil moisture. For clearcut edge, temperature and soil moisture, a 3-year time lag produced the best model fit. We demonstrate how a model incorporating the most significant predictors, with a time lag, can be a useful management tool by allowing spatial prediction of future beetle population sizes. 4. Synthesis and Applications: Some of the population drivers identified here, i,e., spruce volume and clearcut edges, can be targeted by adaptive management measures to reduce the risk of future bark beetle outbreaks. Implementing such measures may help preserve future carbon sequestration of European boreal forests.
Sammendrag
Overvåkingsprogrammet i 2023 omfattet undersøkelse for tilstedeværelse av furuvednematode (Bursaphelenchus xylophilus) i hogstavfall fra furu og i furubukker av slekten Monochamus. I OK-programmets delaktivitet som omfattet kartlegging av furuvednematode i hogstavfall, ble det tatt 410 flisprøver fra hogstavfall og vindfall av furu (Pinus sylvestris) som hadde tegn på angrep av furubukker i slekten Monochamus. Prøvene ble tatt i Agder og Østfold. Flisprøvene ble inkubert ved +25°C i to uker før nematoder ble ekstrahert med Baermann-trakt og undersøkt i mikroskop. Furuvednematoden B. xylophilus ble ikke påvist, men den naturlig forekommende arten Bursaphelenchus mucronatus kolymensis ble oppdaget i 1 flisprøve. Siden overvåkingen av furuvednematode startet i 2000, har alle de analyserte flisprøvene, totalt 9334, vært negative for furuvednematode. I OK-programmets delaktivitet som omfattet kartlegging av furuvednematode i furubukker, ble feller med attraktanter for fangst av voksne, flygende furubukker satt opp i Agder, Østfold, Innlandet, Trøndelag og Ålesund. Billene ble kuttet i biter og ekstrahert med en modifisert Baermann-trakt. Suspensjonen fra ekstraksjonene ble undersøkt i mikroskop for forekomst av Bursaphelenchus spp. Ingen furuvednematoder ble påvist i de 23 undersøkte billene. Bursaphelenchus mucronatus kolymensis ble ikke heller oppdaget.
Forfattere
Geir-Harald Strand Eva Solbjørg Flo Heggem Linda Aune-Lundberg Agata Hościło Adam WaśniewskiSammendrag
Land cover maps are frequently produced via the classification of satellite imagery. There is a need for a practicable and automated approach for the generalization of these land cover classification results into scalable, digital maps while minimizing information loss. We demonstrate a method where a land cover raster map produced using the classification of Sentinel 2 imagery was generalized to obtain a simpler, more readable land cover map. A replicable procedure following a formal generalization framework was applied. The result of the initial land cover classification was separated into binary layers representing each land cover class. Each binary layer was simplified via structural generalization. The resulting images were merged to create a new, simplified land cover map. This map was enriched by adding statistical information from the original land cover classification result, describing the internal land cover distribution inside each polygon. This enrichment preserved the original statistical information from the classified image and provided an environment for more complex cartography and analysis. The overall accuracy of the generalized map was compared to the accuracy of the original, classified land cover. The accuracy of the land cover classification in the two products was not significantly different, showing that the accuracy did not deteriorate because of the generalization.
