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
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
Abstract
No abstract has been registered
2022
Authors
Johannes Schumacher Håvard Toft Larsen Paul McLean Marius Hauglin Rasmus Astrup Johannes BreidenbachAbstract
The number of people affected by snow avalanches during recreational activities has increased over the recent years. An instrument to reduce these numbers are improved terrain classification systems. One such system is the Avalanche Terrain Exposure Scale (ATES). Forests can provide some protection from avalanches, and information on forest attributes can be incorporated into avalanche hazard models such as the automated ATES model (AutoATES). The objectives of this study were to (i) map forest stem density and canopy-cover based on National Forest Inventory and remote sensing data and, (ii) use these forest attributes as input to the AutoATES model. We predicted stem density and directly calculated canopy-cover in a 20 Mha study area in Norway. The forest attributes were mapped for 16 m × 16 m pixels, which were used as input for the AutoATES model. The uncertainties of the stem number and canopy-cover maps were 30% and 31%, respectively. The overall classification accuracy of 52 ski-touring routes in Western Norway with a total length of 282 km increased from 55% in the model without forest information to 67% when utilizing canopy cover. The F1 score for the three predicted ATES classes improved by 31%, 9%, and 6%.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Pekka E. Kauppi Gustav Stål Lina Arnesson-Ceder Isabella Hallberg Sramek Hans Fredrik Hoen Arvid Svensson Iddo K. Wernick Peter Högberg Tomas Lundmark Annika NordinAbstract
Planting new forests has received scientific and political attention as a measure to mitigate climate change. Large, new forests have been planted in places like China and Ethiopia and, over time, a billion hectares could become available globally for planting new forests. Sustainable management of forests, which are available to wood production, has received less attention despite these forests covering at least two billion hectares globally. Better management of existing forests would improve forest growth and help mitigate climate change by increasing the forest carbon (C) stock, by storing C in forest products, and by generating wood-based materials substituting fossil C based materials or other CO2-emission-intensive materials. Some published research assumes a trade-off between the timber harvested from existing forests and the stock of C in those forest ecosystems, asserting that both cannot increase simultaneously. We tested this assumption using the uniquely detailed forest inventory data available from Finland, Norway and Sweden, hereafter denoted northern Europe. We focused on the period 1960 – 2017, that saw little change in the total area covered by forests in northern Europe. At the start of the period, rotational forestry practices began to diffuse, eventually replacing selective felling management systems as the most common management practice. Looking at data over the period we find that despite significant increases in timber and pulp wood harvests, the growth of the forest C stock accelerated. Over the study period, the C stock of the forest ecosystems in northern Europe increased by nearly 70%, while annual timber harvests increased at the about 40% over the same period. This increase in the forest C stock was close to on par with the CO2-emissions from the region (other greenhouse gases not included). Our results suggest that the important effects of management on forest growth allows the forest C stock and timber harvests to increase simultaneously. The development in northern Europe raises the question of how better forest management can improve forest growth elsewhere around the globe while at the same time protecting biodiversity and preserving landscapes.
Authors
Saheba Bhatnagar Stefano Puliti Bruce Talbot Joachim Bernd Heppelmann Johannes Breidenbach Rasmus AstrupAbstract
Wheel ruts, i.e. soil deformations caused by harvesting machines, are considered a negative environmental impact of forest operations and should be avoided or ameliorated. However, the mapping of wheel ruts that would be required to monitor harvesting operations and to plan amelioration measures is a tedious and time-consuming task. Here, we examined whether a combination of drone imagery and algorithms from the field of artificial intelligence can automate the mapping of wheel ruts. We used a deep-learning image-segmentation method (ResNet50 + UNet architecture) that was trained on drone imagery acquired shortly after harvests in Norway, where more than 160 km of wheel ruts were manually digitized. The cross-validation of the model based on 20 harvested sites resulted in F1 scores of 0.69–0.84 with an average of 0.77, and in total, 79 per cent of wheel ruts were correctly detected. The highest accuracy was obtained for severe wheel ruts (average user’s accuracy (UA) = 76 per cent), and the lowest accuracy was obtained for light wheel ruts (average UA = 67 per cent). Considering the nowadays ubiquitous availability of drones, the approach presented in our study has the potential to greatly increase the ability to effectively map and monitor the environmental impact of final felling operations with respect to wheel ruts. The automated mapping of wheel ruts may serve as an important input to soil impact analyses and thereby support measures to restore soil damages.
Authors
Emily Lines Matthew Allen Carlos Cabo Kim Calders Amandine Debus Stuart Grieve Milto Miltiadou Adam Noach Harry JF Owen Stefano PulitiAbstract
With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets. Many studies use their own data at small spatio-temporal scales, and demonstrate an application of an existing or adapted data science method for a particular task. This approach often involves intensive and time-consuming data collection and processing, but generates results restricted to specific ecosystems and sensor types. There is a lack of widespread acknowledgement of how the types and structures of data used affects performance and accuracy of analysis algorithms. To accelerate progress in the field more efficiently, benchmarking datasets upon which methods can be tested and compared are sorely needed.Here, we discuss how lack of standardisation impacts confidence in estimation of key forest properties, and how considerations of data collection need to be accounted for in assessing method performance. We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data. We list a set of example large-scale datasets that could contribute to benchmarking, and present a vision for how community-driven, representative benchmarking initiatives could benefit the field.
Abstract
UAV, drone, ortho photo, image analysis, deep learning, wheel rut, monitoring, GIS, method
Authors
Thomas Gschwantner Iciar Alberdi Sébastien Bauwens Susann Bender Dragan Borota Michal Bosela Olivier Bouriaud Johannes Breidenbach Jānis Donis Christoph Fischer Patrizia Gasparini Luke Heffernan Jean-Christophe Hervé László Kolozs Kari T. Korhonen Nikos Koutsias Pál Kovácsevics Miloš Kučera Gintaras Kulbokas Andrius Kuliešis Adrian Lanz Philippe Lejeune Torgny Lind Gheorghe Marin François Morneau Thomas Nord-Larsen Leónia Nunes Damjan Pantić John Redmond Francisco C. Rego Thomas Riedel Vladimír Šebeň Allan Sims Mitja Skudnik Stein Michael TomterAbstract
Wood resources have been essential for human welfare throughout history. Also nowadays, the volume of growing stock (GS) is considered one of the most important forest attributes monitored by National Forest Inventories (NFIs) to inform policy decisions and forest management planning. The origins of forest inventories closely relate to times of early wood shortage in Europe causing the need to explore and plan the utilisation of GS in the catchment areas of mines, saltworks and settlements. Over time, forest surveys became more detailed and their scope turned to larger areas, although they were still conceived as stand-wise inventories. In the 1920s, the first sample-based NFIs were introduced in the northern European countries. Since the earliest beginnings, GS monitoring approaches have considerably evolved. Current NFI methods differ due to country-specific conditions, inventory traditions, and information needs. Consequently, GS estimates were lacking international comparability and were therefore subject to recent harmonisation efforts to meet the increasing demand for consistent forest resource information at European level. As primary large-area monitoring programmes in most European countries, NFIs assess a multitude of variables, describing various aspects of sustainable forest management, including for example wood supply, carbon sequestration, and biodiversity. Many of these contemporary subject matters involve considerations about GS and its changes, at different geographic levels and time frames from past to future developments according to scenario simulations. Due to its historical, continued and currently increasing importance, we provide an up-to-date review focussing on large-area GS monitoring where we i) describe the origins and historical development of European NFIs, ii) address the terminology and present GS definitions of NFIs, iii) summarise the current methods of 23 European NFIs including sampling methods, tree measurements, volume models, estimators, uncertainty components, and the use of air- and space-borne data sources, iv) present the recent progress in NFI harmonisation in Europe, and v) provide an outlook under changing climate and forest-based bioeconomy objectives.