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.
2023
Authors
Sofia Junttila Jonas Ardö Zhanzhang Cai Hongxiao Jin Natascha Kljun Leif Klemedtsson Alisa Krasnova Holger Lange Anders Lindroth Meelis Mölder Steffen M. Noe Torbern Tagesson Patrik Vestin Per Weslien Lars EklundhAbstract
Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of Northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD1732 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69–0.78, RMSE = 1.97–2.28 g C m−2 d−1, and NRMSE =9-11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m−2 d−1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75–0.80, RMSE = 2.23–2.46 g C m−2 d−1, NRMSE = 11–12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m−2 d−1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underestimation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.
Authors
Marleen Pallandt Bernhard Ahrens Marion Schrumpf Holger Lange Sönke Zaehle Markus ReichsteinAbstract
Soil organic carbon (SOC) is the largest terrestrial carbon pool, but it is still uncertain how it will respond to climate change. Especially the fate of SOC due to concurrent changes in soil temperature and moisture is uncertain. It is generally accepted that microbially driven SOC decomposition will increase with warming, provided that sufficient soil moisture, and hence enough C substrate, is available for microbial decomposition. We use a mechanistic, microbially explicit SOC decomposition model, the Jena Soil Model (JSM), and focus on the depolymerization of litter and microbial residues by microbes. These model processes are sensitive to temperature and soil moisture content and follow reverse Michaelis-Menten kinetics. Microbial decomposition rate V of the substrate [S] is limited by the microbial biomass [B]: V = Vmax * [S] * [B]/(kMB + [B]). The maximum reaction velocity, Vmax, is temperature sensitive and follows an Arrhenius function. Also, a positive correlation between temperature and kMB-values of different enzymes has been empirically shown, with Q10 values ranging from 0.71-2.80 (Allison et al., 2018). Q10 kMB-values for microbial depolymerization of microbial residues would be low compared to those of a (lignified) litter pool. An increase in kMB leads to a lower reaction velocity (V) and V becomes less temperature sensitive at low substrate concentrations. In this work we focus on the following questions: “how do temperature and soil moisture changes affect modelled heterotrophic respiration through the Michaelis-Menten term? Is there a temperature compensation effect on modelled decomposition rate because of the counteracting temperature sensitivities of Vmax and kMB?” We model these interactions under a mean warming experiment (+3.5 °K) as well as three soil moisture experiments: constant soil moisture, a drought, and a wetting scenario.
Authors
Holger Lange Jaana Bäck Georg Jocher Natascha Kljun Anne Klosterhalfen Alexander Knohl Natalia Kowalska Adam Kristensson Corinna Rebmann Teresa Saura-Yera Alberto VilagrosaAbstract
Utilizing forest ecosystems to mitigate climate change effects and to preserve biodiversity requires detailed insights into the feedbacks between forest type, climatic and soil conditions, and in particular forest management history and practice. Analysis of long-term observations at the site level, remote sensing proxies and understanding relevant biogeochemical and biophysical processes are key to achieving these insights. In the recently started EU H2020 project “CLimate Mitigation and Bioeconomy pathways for sustainable FORESTry” (CLIMB-FOREST), we address these issues based on intensely monitored sites with flux measurements (ICOS, Fluxnet), other ecosystem research and observation networks (eLTER, National Forest Inventories), remotely sensed observations and process understanding. This presentation outlines the activities of CLIMB-FOREST regarding (1) carbon stocks and fluxes according to stand age, species distribution, management and disturbance history; (2) biophysical effects of forest structure; (3) effects and importance of short-lived climate forcers (e.g. BVOCs) and (4) management and extreme event (drought, fire) impact on SOC and N dynamics. We also outline how the gained knowledge informs scenario runs of the Vegetation and Earth System Model RCA-GUESS in the project.
Authors
Peter Zubkov Barry Gardiner Bjørn Egil Kringlebotn Nygaard Sigmund Guttu Svein Solberg Tron Haakon EidAbstract
Forest damage caused by heavy wet snow accumulation in the canopy is the second most important abiotic forest disturbance agent in Nordic conifer stands after wind. The extent and frequency of snow damage in the future climate in the Nordic region is a major uncertainty. Few mechanistic models of snow damage risk to trees exist that could support forest management scenario analysis and decision making. We propose a snow damage risk model consisting of a numerical weather prediction-based snow accumulation model for forest canopies and a mechanistic critical snow load model. Snow damage probability predictions were validated on snow breakage data from the winters of 2016 and 2018 covering 3.5 million individual trees in south-eastern Norway derived from pre- and post-damage aerial laser scanning campaigns. The proposed model demonstrated satisfactory damage and no-damage class separation with an AUC of 0.72 and 0.77 in Norway spruce and Scots pine, respectively, and an F1 score of 0.7 in conifers taller than 10 m that suffered moderate stem breakage. The model achieved a classification accuracy that is comparable to that of statistical models but is simpler and requires fewer inputs.
Abstract
We tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair. The post-storm ground reference data consisted of field and unmanned aerial vehicle (UAV) observations of windthrow combined with no-damage areas collected by visual interpretation of the available very high resolution (VHR) satellite imagery. We trained and tested a thresholding model using canopy height change as the sole predictor. We undertook a two-step accuracy assessment by (1) running k-fold cross-validation on the ground reference dataset and examining the effect of the potential imperfections in the ground reference data, and (2) conducting rigorous accuracy assessment of the classified map of the study area using an extended set of VHR imagery. The thresholding model produced accurate windthrow maps in dense, productive forest stands with a sensitivity of 96%, specificity of 71%, and Matthews correlation coefficient (MCC) over 0.7. However, in sparse and high elevation stands, the classification accuracy was poor. Despite certain collection challenges during the winter months in the Nordic region, we consider VHR stereo satellite imagery to be a viable source of forest canopy height information and sufficiently accurate to map windthrow disturbance in forest stands of high to moderate density.
Abstract
Questions Observations in permanent forest vegetation plots in Norway and elsewhere indicate that complex changes have taken place over the period 1988–2020. These observations are summarised in the “climate-induced understorey change (CIUC)” hypothesis, i.e. that the understorey vegetation of old-growth boreal forests in Norway undergoes significant long-term changes and that these changes are consistent with the ongoing climate change as an important driver. Seven testable predictions were derived from the CIUC hypothesis. Location Norway. Methods Vegetation has been monitored in a total of 458 permanently marked plots, each 1 m2, in nine old-growth forest sites dominated by Picea abies at intervals of 5–8 years over the 32-year study period. For each of the 52 combinations of site and year, we obtained response variables for the abundance of single species, abundance and species density of taxonomic–ecological species groups and two size classes of cryptogams, and site species richness. All of these variables were subjected to linear regression modelling with site and year as predictors. Results Mean annual temperature, growing-season length and the number of days with precipitation were higher in the study period than in the preceding ca. 30-year period, resulting in increasingly favourable conditions for bryophyte growth. Site species richness decreased by 13% over the 32-year study period. On average, group abundance of vascular plants decreased by 24% (decrease in forbs: 38%). Patterns of group abundance change differed among cryptogam groups: although peat-moss abundance increased by 39%, the abundance of mosses, hepatics and lichens decreased by 13%, 49% and 67%, respectively. Group abundance of small cryptogams decreased by 61%, whereas a 13% increase was found for large cryptogams. Of 61 single species tested for abundance change, a significant decrease was found for 43 species, whereas a significant increase was found only for 6 species. Conclusions The major patterns of change in species richness, group species density and group abundance observed over the 32-year study period accord with most predictions from the CIUC hypothesis and are interpreted as direct and indirect responses to climate change, partly mediated through changes in the population dynamics of microtine rodents. The more favourable climate for bryophyte growth explains the observed increase for a few large bryophyte species, whereas the decrease observed for small mosses and hepatics is interpreted as an indirect amensalistic effect, brought about by shading and burial in mats of larger species and accelerated by reduced fine-scale disturbance by microtine rodents. Indirect effects of a thicker moss mat most likely drive the vascular plant decline although long-term effects of tree-stand dynamics and former logging cannot be completely ruled out. Our results suggest that the ongoing climate change has extensive, cascading effects on boreal forest ecosystems. The importance of long time-series of permanent vegetation plots for detecting and understanding the effects of climate change on boreal forests is emphasised.
Authors
Inger Martinussen Mathias Amundsen Aksel Granhus Antje Gonera Marius Hauglin Anne Linn Hykkerud Laura Jaakola Mikko Kurttila Jari Miina Rainer Peltola Gesine Schmidt Josefine Skaret Baoru Yang Kjersti AabyAbstract
Almost 95% of the area in Norway is wilderness and 38% of the land area is covered by woods. These areas are abundant in valuable renewable resources, including wild berries. In our neighbouring countries, Sweden and Finland, wild berries are already a big industry. At the same time, on the market the Norwegian wild berries are almost non-existent and berries are left unexploited. Lingonberry (Vaccinium vitis-idaea) is one of the most abundant and economically important wild berries in the Nordic countries. Nevertheless, lingonberry has a large untapped potential due to its unique health effects and potential for increased value creation. It is estimated that 111,500 t of lingonberry are produced in the Norwegian woods. Norway is a long and diverse country with a range of climatic conditions. Adaptations to different conditions can give differences in both yield and quality of wild berries. Yields vary enormously from year to year and among different locations. A steady supply, predictable volumes and high quality are vital for successful commercialization of wild berries. To increase the utilization of berries, there is a need for increased knowledge regarding availability and quality variation of the berries. In addition, the Norwegian market suffers from high labour costs and cannot compete in product price. Innovative solutions and new knowledge on quality aspects can open possibilities for value creation. Toward achieving this goal, we have created a project called “WildBerries”, the main objective of which is to produce research-based knowledge that will create the basis for increased commercial utilization of Norwegian wild berries.
Authors
Jyrki Jauhiainen Juha Heikkinen Nicholas Clarke Hongxing He Lise Dalsgaard Kari Minkkinen Paavo Ojanen Lars Vesterdal Jukka Alm Aldis Butlers Ingeborg Callesen Sabine Jordan Annalea Lohila Ülo Mander Hlynur Óskarsson Bjarni D. Sigurdsson Gunnhild Søgaard Kaido Soosaar Åsa Kasimir Brynhildur Bjarnadóttir Andis Lazdins Raija LaihoAbstract
We compiled published peer-reviewed CO2, CH4, and N2O data on managed drained organic forest soils in boreal and temperate zones to revisit the current Tier 1 default emission factors (EFs) provided in the IPCC (2014) Wetlands Supplement: to see whether their uncertainty may be reduced; to evaluate possibilities for breaking the broad categories used for the IPCC EFs into more site-type-specific ones; and to inspect the potential relevance of a number of environmental variables for predicting the annual soil greenhouse gas (GHG) balances, on which the EFs are based. Despite a considerable number of publications applicable for compiling EFs being added, only modest changes were found compared to the Tier 1 default EFs. However, the more specific site type categories generated in this study showed narrower confidence intervals compared to the default categories. Overall, the highest CO2 EFs were found for temperate afforested agricultural lands and boreal forestry-drained sites with very low tree stand productivity. The highest CH4 EFs in turn prevailed in boreal nutrient-poor forests with very low tree stand productivity and temperate forests irrespective of nutrient status, while the EFs for afforested sites were low or showed a sink function. The highest N2O EFs were found for afforested agricultural lands and forestry-drained nutrient-rich sites. The occasional wide confidence intervals could be mainly explained by single or a few highly deviating estimates rather than the broadness of the categories applied. Our EFs for the novel categories were further supported by the statistical models connecting the annual soil GHG balances to site-specific soil nutrient status indicators, tree stand characteristics, and temperature-associated weather and climate variables. The results of this synthesis have important implications for EF revisions and national emission reporting, e.g. by the use of different categories for afforested sites and forestry-drained sites, and more specific site productivity categories based on timber production potential.
Abstract
Key message We studied size distributions of decay-affected Norway spruce trees using cut-to-length harvester data. The harvester data comprised tree-level decay and decay severity recordings from 101 final felling stands, which enabled to analyze relationships between size distributions of all and decay-affected trees. Distribution matching technique was used to transfer the size distribution of all trees into the diameter at breast height (DBH) distribution of decay-affected trees. Context Stem decay of Norway spruce (Picea abies [L.] Karst.) results in large economic losses in timber production in the northern hemisphere. Forest management planning typically requires information on tree size distributions. However, size distributions of decay-affected trees generally remain unknown impeding decision-making in forest management planning. Aims Our aim was to analyze and model relationships between size distributions of all and decay-affected Norway spruce trees at the level of forest stands. Methods Cut-to-length harvester data of 93,456 trees were collected from 101 final felling stands in Norway. For each Norway spruce tree (94% of trees), the presence and severity of stem decay (incipient and advanced) were recorded. The stand-level size distributions (diameter at breast height, DBH; height, H) of all and decay-affected trees were described using the Weibull distribution. We proposed distribution matching (DM) models that transform either the DBH or H distribution of all trees into DBH distributions of decay-affected trees. We compared the predictive performance of DMs with a null-model that refers to a global Weibull distribution estimated based on DBHs of all harvested decay-affected trees. Results The harvester data showed that an average-sized decay-affected tree is larger and taller compared with an average-sized tree in a forest stand, while trees with advanced decay were generally shorter and thinner compared with trees having incipient decay. DBH distributions of decay-affected trees can be matched with smaller error index (EI) values using DBH (EI = 0.14) than H distributions (EI = 0.31). DM clearly outperformed the null model that resulted in an EI of 0.32. Conclusions The harvester data analysis showed a relationship between size distributions of all and decay-affected trees that can be explained by the spread biology of decay fungi and modeled using the DM technique. Keywords Root and butt rot, Heterobasidion spp., Armillaria spp., Cut-to-length harvester, Forest management and planning
Authors
Xiande Li Zhilu Sun Giovanna Ottaviani Aalmo Fangfang Cao Divina Gracia P. Rodriguez Chen Qian Yongxun Zhang Knut ØistadAbstract
Agricultural extension services are integral to technology adoption where they play a key role in delivering relevant agricultural information and technologies to farmers. In China, agricultural extension services are provided through experimentation, demonstration, training, and consulting. In Norway, agricultural extension is focused on collecting, developing, and coordinating agricultural knowledge to farmers. This chapter focuses on why agricultural extension is needed, how it is developed, and what services agricultural extension provides to its clients. It discusses experiences from China and Norway where agricultural extension has led to or is necessary for boosting agricultural productivity, increasing food security and safety, and improving the well-being of farmers.