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.
2011
Sammendrag
Remote sensing of the activity of vegetation in relation to environmental conditions provides an invaluable basis for investigating the spatiotemporal dynamics and patterns of variability for ecosystem processes. We investigate the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) using SeaWiFS satellite observations from 1998 to 2005 and ancillary meteorological variables from the CRU-PIK dataset with a global coverage at a spatial resolution of 0.5o x 0.5o. A pixel-by-pixel spectral decomposition using Singular System Analysis leads to a global “classification” of the terrestrial biosphere according to prevalent time-scale dependent dynamics of fAPAR and its relation to meteorology. A complexity analysis and a combined subsignal extraction and dimensionality reduction reveals a series of dominant geographical gradients, separately for different time scales. At the annual scale, which explains around 50% of the fAPAR variability as a global average, patterns largely resemble the biomes of the world as mapped by biogeographical methods, and are driven by temperature and by pronounced rain seasons in the tropics. On shorter time scales, fAPAR fluctuations are exclusively driven by water supply, inducing, e.g., semiannual cycles in the equatorial belt of Africa or the Indo-Gangetic Plain. For some regions however, in particular South America, altitude, mean temperature, drought probability and fire occurrences are parameters that seem to shape the spatial patterns of fAPAR across time scales. Overall, we provide a first global multiscale characterization of fAPAR and highlight different mechanisms in land-surface-climate couplings.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Merete Wiken Dees Arild Sletten Ricardo Holgado Eldrid Lein Molteberg Tor J. Johansen May Bente Brurberg Vinh Hong Le Ragnhild Nærstad Arne HermansenSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
The effect of different light environments on trap catches of Frankliniella occidentalis and Trialeurodes vaporariorum was investigated in a commercial greenhouse rose production unit during late autumn. Two top light treatments were used: 1) High pressure sodium lamps (HPSLs) and 2) HPSLs and light emitting diodes (LEDs) with 20% blue and 80% red light. More thrips and fewer whiteflies were caught on yellow sticky traps, and more thrips were found in the flowers, in areas were LEDs were used in addition to HPSLs compared to areas where only HPSLs were used. No effect of the light treatments was found on the population level of Amblyseius swirskii, but a lower ratio of predatory mites to thrips was found on the plants where LEDs were used. The results suggest that using blue and red LEDs as interlighting, or otherwise supplementary to HPSLs, will change thrips and whitefly spatial distribution in the rose crop, and that natural enemy release rates probably need to be adjusted accordingly.
Forfattere
Lars Sekse U. Wermund R. Vidrih M. Simcic Eivind VangdalSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Erik Næsset Terje Gobakken Svein Solberg Timothy Gregoire Ross Nelson Göran Ståhl Dan Johan WeydahlSammendrag
There is a need for accurate inventory methods that produce relevant and timely information on the forest resources and carbon stocks for forest management planning and for implementation of national strategies under the United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (REDD). Such methods should produce information that is consistent across various geographical scales. Airborne scanning Light Detection and Ranging (LiDAR) is among the most promising remote sensing technologies for estimation of forest resource information such as timber volume and biomass, while acquisition of three dimensional data with Interferometric Synthetic Aperture Radar (InSAR) from space is seen as a relevant option for inventory in the tropics because of its ability to “see through the clouds” and its potential for frequent updates at low costs. Based on a stratified probability sample of 201 field survey plots collected in a 960 km2 boreal forest area in Norway, we demonstrate how total above-ground biomass (AGB) can be estimated at three distinct geographical levels in such a way that the estimates at a smaller level always sum up to the estimate at a larger level. The three levels are (1) a district (the entire study area), (2) a village, local community or estate level, and (3) a stand or patch level. The LiDAR and InSAR data were treated as auxiliary information in the estimation. At the two largest geographical levels model-assisted estimators were employed. A model-based estimation was conducted at the smallest level. Estimates of AGB and corresponding error estimates based on (1) the field sample survey were compared with estimates obtained by using (2) LiDAR and (3) InSAR data as auxiliary information. For the entire study area, the estimates of AGB were 116.0, 101.2, and 111.3 Mg ha−1, respectively. Corresponding standard error estimates were 3.7, 1.6, and 3.2 Mg ha−1. At the smallest geographical level (stand) an independent validation on 35 large field plots was carried out. RMSE values of 17.1–17.3 Mg ha−1 and 42.6–53.2 Mg ha−1 were found for LiDAR and InSAR, respectively. A time lag of six years between acquisition of InSAR data and field inventory has introduced some errors. Significant differences between estimates and reference values were found, illustrating the risk of using pure model-based methods in the estimation when there is a lack of fit in the models. We conclude that the examined remote sensing techniques can provide biomass estimates with smaller estimated errors than a field-based sample survey. The improvement can be highly significant, especially for LiDAR.