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.
2011
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Holger LangeAbstract
In ecosystem research, data-driven approaches to modeling are of major importance. Models are more often than not shaped by the spatiotemporal structure of the observations: an inverse modeling approach prevails. Here, I investigate the insights obtained from Recurrence Quantification Analysis of observed ecosystem time series. As a typical example of available long-term monitoring data, I choose time series from hydrology and hydrochemistry. Besides providing insights into the nonstationary and nonlinear dynamics of these variables, RQA also enables a detailed and temporally local model-data comparison.
Authors
Miguel D. Mahecha Markus Reichstein Nuno Carvalhais Gitta Lasslop Holger Lange Sonia I. Seneviratne Rodrigo Vargas Christof Ammann M. Altaf Arain Alessandro Cescatti Ivan A. Janssens Mirco Migliavacca Leonardo Montagnani Andrew D. RichardsonAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Erik Næsset Terje Gobakken Svein Solberg Timothy Gregoire Ross Nelson Göran Ståhl Dan Johan WeydahlAbstract
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.
Authors
Tim J. Mullin Bengt Andersson Jean-Charles Bastien Jean Beaulieu Rowland D. Burdon W.S. Dvorak John N. King T. Kondo Jodie Krakowski Steve J. Lee Steve E. McKeand Luc Pâques Annie Raffin John H. Russel Tore Skrøppa Michael Stoehr Alvin YanchukAbstract
This chapter reviews the historical context, economic importance, objectives and achievements to-date for many of the more important conifers undergoing domestication through genetic improvement programmes around the world. These provide examples of the context in which genomic technologies will have an impact in forestry. Unlike many other crop plants and livestock animals, forest trees have only been exposed to a few cycles of breeding and selection, and most retain very large amounts of genetic variation in natural populations. These factors present both opportunities and hurdles in the effective application of genomic technologies to existing operational breeding programmes.
Authors
Tore SkrøppaAbstract
No abstract has been registered
Authors
Tore SkrøppaAbstract
No abstract has been registered