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.
2024
Authors
Erico Kutchartt José Ramón González-Olabarria Antoni Trasobares Núria Aquilué Juan Guerra-Hernández Leónia Nunes Ana Catarina Sequeira Brigite Botequim Marius Hauglin Palaiologos Palaiologou Adrian Cardil Martino Rogai Vassil Vassilev Francois Pimont Olivier Martin-Ducup Francesco PirottiAbstract
Canopy base height (CBH) and canopy bulk density (CBD) are forest canopy fuel parameters that are key for modeling the behavior of crown wildfires. In this work, we map them at a pan-European scale for the year 2020, producing a new dataset consisting of two raster layers containing both variables at an approximate resolution of 100 m. Spatial data from Earth observation missions and derived down-stream products were retrieved and processed using artificial intelligence to first estimate a map of aboveground biomass (AGB). Allometric models were then used to estimate the spatial distribution of CBH using the canopy height values as explanatory variables and CBD using AGB values. Ad-hoc allometric models were defined for this study. Data provided by FIRE-RES project partners and acquired through field inventories was used for validating the final products using an independent dataset of 804 ground-truth sample plots. The CBH and CBD raster maps have, respectively, the following accuracy regarding specific metrics reported from the modeling procedures: (i) coefficient of correlation (R) of 0.445 and 0.330 (p-value < 0.001); (ii) root mean square of error (RMSE) of 3.9 m and 0.099 kg m−3; and (iii) a mean absolute percentage error (MAPE) of 61% and 76%. Regarding CBD, the accuracy metrics improved in closed canopies (canopy cover > 80%) to R = 0.457, RMSE = 0.085, and MAPE = 59%. In short, we believe that the degree of accuracy is reasonable in the resulting maps, producing CBH and CBD datasets at the pan-European scale to support fire mitigation and crown fire simulations.
Authors
Stefano PulitiAbstract
No abstract has been registered
Abstract
Lingonberry (Vaccinium vitis-idaea L.) grows in a range of nature types in the boreal zone, and understanding factors affecting the abundance of the plant, as well as mapping its spatial distribution, is important. The abundance of the species can be an indicator of ecosystem changes, and lingonberry can also be a source for commercial utilisation of berry resources. Using country-wide data from 6404 field plots of the Norwegian national forest inventory (NFI), we modelled the relationship between lingonberry cover and airborne laser scanning (ALS) and satellite metrics and bioclimatic variables describing the forest structure, terrain, soil properties and climate using a generalised mixed-effects model with a quasipoisson distribution. The validation carried out with an independent set of 2124 NFI plots indicated no obvious bias in predictions. The most important predictors were found to be interactions between dominant tree species, stand basal area and latitude, as well as the reflectance in the near-infrared band from Sentinel-2 satellite imagery, the dominant height based on the ALS variable and the long-term mean summer (June–August) temperature. The results provide an indicator of the effects of global warming, as well as the possibility of giving forest management prescriptions that favour lingonberry and locating the most abundant lingonberry sites in Norwegian forests.
Authors
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupAbstract
No abstract has been registered
Authors
Christian Wilhelm Mohr Johannes Breidenbach Gunnhild Søgaard Oliver Moen Snoksrud Rune EriksenAbstract
No abstract has been registered
Authors
Marte Ragnhild Owren Ingvild Byskov Britta Maria Hoem Julien Jabot Hans H. Kolshus Kathrine Loe Bjønness Jakob Sandven Trude Melby Bothner Mona Irene Andersen Engedal Eirik Knutsen Lene Skyrudsmoen Berit Storbråten Kristina Vikesund Hart Evan Christian Wilhelm Mohr Gry Alfredsen Ana Aza Johannes Breidenbach Lise Dalsgaard Rune Eriksen Katharina Hobrak Christophe Moni Gunnhild SøgaardEditors
Ingeborg RønningAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Helle Ross Gobakken Mostafa Hoseini Stephan Hoffmann Jan Bjerketvedt Johannes Rahlf Rasmus AstrupAbstract
No abstract has been registered
Authors
Binbin Xiang Maciej Wielgosz Theodora Kontogianni Torben Peters Stefano Puliti Rasmus Astrup Konrad SchindlerAbstract
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.