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.
2012
Abstract
No abstract has been registered
Abstract
The ingrowth core method is widely used to assess fine root (diameter < 2 mm) production but has many inherent deficiencies. In this study, we modified this method by adopting mini ingrowth cores (diameter 1.2 cm), extending sample intervals to a growing season, and developing new models to quantify the concurrent production, mortality and decomposition, and applied them to a secondary Mongolian oak (Quercus mongolica Fischer ex Ledebour) forest. Annual fine root production, mortality and decomposition estimated by our method were 2.10 ± 0.23, 1.78 ± 0.20 and 0.85 ± 0.13 t ha−1, respectively, and 33.3% of the production was decomposed in the growing season. The production estimate using our method was significantly higher than those using two long-term ingrowth core (sample interval >2 months) methods. However, it was significantly lower than that using the short-term ingrowth core (sample interval <2 months) method, presumably due to the lower root competition and less decomposition occurring in the short-term cores. The fine root estimates using our method in the growing season were generally higher than those using the forward and continuous inflow methods but lower than those using the backward method. Our method reduces the disturbances in roots and soil, minimizes the sampling frequency and improves the quantification of fine root decomposition during the sample intervals. These modifications overcome the limitations associated with the previous ingrowth core methods. Our method provides an improved alternative for estimating fine root production, mortality and decomposition.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
We exploit two recently developed informationtheoretic quantities (or measures) designed to quantify information content and complexity of ordered data (time series), respectively. Both are based on order statistics of given data sets, and probe into the shortterm structure of the data only due to finite length restrictions. Their usage requires fixing one parameter, word length or order depth D. The information measure is the orderbased Shannon entropy HS, and the complexity measures is the JensenShannon divergence CJS. The latter requires a chosen reference distribution, i.e. CJS represents a class of measures. Entropy HS and complexity CJS of data series may be represented against each other in a twodimensional diagram which we will refer to as ComplexityEntropy Causality Plane, or CECP. Very long realizations of classic stochastic processes and chaotic deterministic maps each obey one location in the CECP, specific for the process. This can be used to differentiate chaos from correlated noise (Rosso et al. 2007), which is notoriously difficult otherwise. For observed data, a mixture of deterministic (signal) and stochastic (noise) parts is to be expected. We use an ensemble of longterm river runoff time series as example, which are known to exhibit powerlaw decaying longrange correlations. We compare these data with a longrange correlated candidate process, the k noise, from the perspective of order statistics and the CECP. Although these processes resemble runoff series in their correlation behavior and may be even tuned to any runoff series by changing the value of k, the CECP locations and in particular the order pattern statistics reveals qualitative differences between them. We give a detailed account of these differences, and use them to conclude on the deterministic nature of the (shortterm) dynamics of the runoff time series. The proposed methodology also represents a stringent test bed for hydrological or other environmental models. http://dames.pik-potsdam.de/Abstracts.pdf
Authors
Harri Kaartinen Juha Hyyppa Xiaowei Yu Mikko Vastaranta Hannu Hyyppa Antero Kukko Markus Holopainen Christian Heipke Manuela Hirschmugl Felix Morsdorf Erik Næsset Juho Pitkanen Sorin Popescu Svein Solberg Bernd Michael Wolf Jee-Cheng WuAbstract
No abstract has been registered
Abstract
This study investigates the relationship between Leaf Area Index (LAI) reduction in pine stands caused by pine sawfly (Neodiprion sertifier) larva and reflectance change measured using multitemporal optical satellite data. The study was carried out in 552 Scots Pine (Pinus sylvestris)-dominated stands in southern Norway (60° 41′ N, 12° 18′ E). Post-damage Satellite Pour l'Observation de la Terre (SPOT) satellite data were calibrated to surface reflectance using reflectance products of the moderate-resolution imaging spectroradiometer (MODIS). Standwise reflectance change was then computed by subtracting a pre-damage SPOT image that had been relative calibrated to the post-damage image using histogram matching. The reflectance changes were related to changes in LAI obtained from multitemporal lidar data calibrated with field measurements made with a LiCOR LAI-2000 plant canopy analyser. The reduced needle biomass growth due to the insect damage caused an increase in reflectance on the order of 0.002–0.015 in the visible and short-wave infrared SPOT bands and a decrease of 0.01 in the near infrared (NIR) band compared with a large reference data set with normally developed stands. A cross-validated discriminant analysis showed that 79% of the damaged stands could be separated from the undamaged stands by using the SPOT data.
Abstract
Harvest activity directly impacts timber supply, forest conditions, and carbon stock. Forecasts of the harvest activity have traditionally relied on the assumption that harvest is carried out according to forest management guidelines or to maximize forest value. However, these rules are, in practice, seldom applied systematically, which may result in large discrepancies between predicted and actual harvest in short-term forecasts. We present empirical harvest models that predict final felling and thinning based on forest attributes such as site index, stand age, volume, slope, and distance to road. The logistic regression models were developed and fit to Norwegian national forest inventory data and predict harvest with high discriminating power. The models were consistent with expected landowners behavior, that is, areas with high timber value and low harvest cost were more likely to be harvested. We illustrate how the harvest models can be used, in combination with a growth model, to develop a national business-as-usual scenario for forest carbon. The business-as-usual scenario shows a slight increase in national harvest levels and a decrease in carbon sequestration in living trees over the next decade.
Authors
Ryan Bright Francesco Cherubini Rasmus Astrup Neil Bird Annette L. Cowie Mark J. Ducey Gregg Marland Kim Pingoud Ilkka Savolainen Anders Hammer StrømmanAbstract
No abstract has been registered
Authors
Jari Vauhkonen Liviu Theodor Ene Sandeep Gupta Johannes Heinzel Johan Holmgren Juho Pitkänen Svein Solberg Yunsheng Wang Holger Weinacker Marius Hauglin Vegard Lien Petteri Packalén Terje Gobakken Barbara Koch Erik Næsset Timo Tokola Matti MaltamoAbstract
Airborne laser scanning data and corresponding field data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany and tropical pulpwood plantations in Brazil. Treetop positions were extracted using six different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation was assessed. Furthermore, the weaknesses and strengths of the methods under different types of forest were analyzed. The results showed that forest structure strongly affected the performance of all algorithms. Particularly, the success of tree detection was found to be dependent on tree density and clustering. The differences in performance between methods were more pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions for which they were developed, while some could be adapted by different parameterization according to training with local data. The results of this study may help guiding the choice of method under different forest types and may be of great value for future refinement of the single-tree detection algorithms.