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.
2024
Abstract
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Authors
Geir-Harald Strand Margrete Steinnes Ellen Arneberg Maria Lund Nicolai Munsterhjelm Linda Aune-Lundberg Anne RørholtAbstract
Grunnkart for bruk i arealregnskap er en sammenstilling av arealressurs- og arealbruksdata fra de norske primærdatasettene. I tillegg er det lagt inn økosysteminformasjon i henhold til Eurostats klassifikasjonssystem. Datasettet kan kobles mot andre datakilder, f.eks. arealplaner, og benyttes som grunnlag for å utarbeide ulike typer arealregnskap. Grunnkartet er et samarbeidsprosjekt mellom NIBIO, SSB, Kartverket og Miljødirektoratet.
Abstract
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Abstract
Det er ikke registrert sammendrag
Authors
Geir-Harald Strand Eva Solbjørg Flo Heggem Linda Aune-Lundberg Agata Hościło Adam WaśniewskiAbstract
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.
Abstract
Soner for pollinerende insekter i form av striper med blomster langs jordbruksareal, kom inn som et eget tiltak under regionalt miljøtilskudd i landbruket (RMP) i 2019. Hensikten er å bedre levevilkårene for ville pollinerende insekter. Tiltaket ble først iverksatt på Østlandet og i Rogaland, men senere har det også blitt tatt i bruk i flere fylker. Her beskriver vi hvordan tiltaket har vært brukt, både med hensyn til kontinuitet over tid og hvor vi finner blomsterstripene.
Authors
Belachew Gizachew Zeleke Deo D. Shirima Jonathan Rizzi Collins Byobona Kukunda Eliakimu ZahabuAbstract
Tanzania dedicates a substantial proportion (38%) of its territory to conservation, with a large number of Protected Areas (PAs) managed under various regimes. Nevertheless, the country still experiences high rates of deforestation, which threaten the ecological integrity and socio-economic benefits of its forests. We utilized the Global Forest Change Dataset (2012–2022) and implemented a Propensity Score Matching (PSM) approach followed by a series of binomial logit regression modeling. Our objectives were to evaluate (1) the likelihood of PAs in avoiding deforestation compared with unprotected forest landscapes, (2) the variability in effectiveness among the different PA management regimes in avoiding deforestation, (3) evidence of leakage, defined here as the displacement of deforestation beyond PA boundaries as a result of protection inside PAs. Our findings reveal that, despite ongoing deforestation within and outside of PAs, conservation efforts are, on average, three times more likely to avoid deforestation compared with unprotected landscapes. However, the effectiveness of avoiding deforestation significantly varies among the different management regimes. National Parks and Game Reserves are nearly ten times more successful in avoiding deforestation, likely because of the stringent set of regulations and availability of resources for implementation. Conversely, Nature Forest Reserves, Game Controlled Areas, and Forest Reserves are, on average, only twice as likely to avoid deforestation, indicating substantial room for improvement. We found little evidence of the overall leakage as a consequence of protection. These results highlight the mixed success of Tanzania’s conservation efforts, suggesting opportunities to enhance the effectiveness of many less protected PAs. We conclude by proposing potential strategic pathways to enhance further the climate and ecosystem benefits of conservation in Tanzania.
Abstract
Det er ikke registrert sammendrag
Abstract
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Abstract
Climate change is and will continue to alter plant responses to their environment. This is especially prominent concerning the adaptive tracking in reproductive phenology. For wind pollinated plants, this will substantially influence their pollen seasonality, yet there are gaps in knowledge about how environmental variation influences pollen seasonality. To investigate this, we monitored daily atmospheric pollen concentrations of seven pollen types from ecologically, economically and allergenically important plants (alder, hazel, willow, birch, pine, grass and mugwort) in twelve Norwegian locations spanning the entire country for up to 28 years. Six daily meteorological variables (maximum temperature, precipitation, wind speed, relative humidity, solar radiation and atmospheric pressure) was obtained from the MET Nordic dataset with full data cover. The pollen seasonality was then modelled using four spatial, three temporal and the six meteorological variables in a generalized linear model approach with a negative binomial distribution to investigate how each variable group thematically and individually contribute to variation in pollen seasonality. We found that the full models explained the most variation, ranging from R2 = 20.3 % to 59.5 %. The models were also highly accurate, being able to predict 54.5 % to 99.1 % of daily pollen concentrations to within 20.1 pollen grains/m3. Overall, the temporal variables were able to explain more variation than spatial and meteorological variables for most pollen types. Month, altitude and maximum temperature were the most important single variables for each category. The importance of each variable could be traced back to their individual effects of reproductive phenology, plant metabolism, species distributions and pollen release processes. We further emphasise the importance of source maps and atmospheric regional transport models in further model improvements. By understanding the relevance of environmental variation to pollen seasonality we can make better predictions regarding the consequences of climate change on plant populations.