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
The use of logging residues for bioenergy is encouraged in many countries, due to an increasing demand for renewable energy. However, there is concern that removal of logging residues may cause a long-term reduction in soil nutrient availability, reducing forest growth in the remaining stand. To quantify the growth response of Norway spruce and Scots pine to whole-three harvesting at first thinning a series of eight field experiments was set up in SE Norway in the seventies. Results after 25 years showed that whole-tree thinning lead to a decrease in forest growth. The effect was present more or less immediately after thinning, and was still present after 25 years. The average reduction in growth was around 10 % after 25 years in the spruce stands, while in the pine stands a non-significant average growth reduction of 4 % was found. The results are generated under experimental conditions, and in practice a share of the residues is left on site, decreasing the nutrient loss.
Abstract
Four Norway spruce stands treated with single tree selection were studied 11 years after the cuttings. In each of the stands we performed four strengths of cuttings in 0.2 ha plots, with removals ranging from zero to 70 % of the basal area. We investigated accumulated and annual growth, changes in stand structure, tree age and tree damage. 10-20 % of the living trees were still damaged 11 years after the cutting. The diameter distribution displayed a reverse J-curve in all plots both before and after the cuttings. Eleven years later, the curve is only slightly changed. Annual ring widths from 300 increment cores were analysed. Most trees started to increase the growth two or three years after the cutting. This improved growth accelerated the following six or seven years with 20-80 % increase. Both small and large trees reacted, including severely suppressed trees. The initial crown volume and crown vitality after cutting is essential for the increased growth since several years are necessary to build up a larger and better crown. A reduced volume per hectare provided an increased growth for each of the remaining trees and indicates less competition for nutrients and light after cutting. The observed growth during the 11-year post-harvest period was about 10 % less than the estimated yield capacity for even-aged stands.
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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
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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.