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
Authors
Jaime Candelas Bielza Lennart Noordermeer Erik Næsset Terje Gobakken Johannes Breidenbach Hans Ole ØrkaAbstract
Tree species composition is essential information for forest management and remotely sensed (RS) data have proven to be useful for its prediction. In forest management inventories, tree species are commonly interpreted manually from aerial images for each stand, which is time and resource consuming and entails substantial uncertainty. The objective of this study was to evaluate a range of RS data sources comprising airborne laser scanning (ALS) and airborne and satellite-borne multispectral data for model-based prediction of tree species composition. Total volume was predicted using non-linear regression and volume proportions of species were predicted using parametric Dirichlet models. Predicted dominant species was defined as the species with the greatest predicted volume proportion and predicted species-specific volumes were calculated as the product of predicted total volume multiplied by predicted volume proportions. Ground reference data obtained from 1184 sample plots of 250 m2 in eight districts in Norway were used. Combinations of ALS and two multispectral data sources, i.e. aerial images and Sentinel-2 satellite images from different seasons, were compared. The most accurate predictions of tree species composition were obtained by combining ALS and multi-season Sentinel-2 imagery, specifically from summer and fall. Independent validation of predicted species proportions yielded average root mean square differences (RMSD) of 0.15, 0.15 and 0.07 (relative RMSD of 30%, 68% and 128%) and squared Pearson's correlation coefficient (r2) of 0.74, 0.79 and 0.51 for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and deciduous species, respectively. The dominant species was predicted with median values of overall accuracy, quantity disagreement and allocation disagreement of 0.90, 0.07 and 0.00, respectively. Predicted species-specific volumes yielded average values of RMSD of 63, 48 and 23 m3/ha (relative RMSD of 39%, 94% and 158%) and r2 of 0.84, 0.60 and 0.53 for spruce, pine and deciduous species, respectively. In one of the districts with independent validation plots of mean size 3700 m2, predictions of the dominant species were compared to results obtained through manual photo-interpretation. The model predictions gave greater accuracy than manual photo-interpretation. This study highlights the utility of RS data for prediction of tree species composition in operational forest inventories, particularly indicating the utility of ALS and multi-season Sentinel-2 imagery.
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
Christian Wilhelm Mohr Johannes Breidenbach Gunnhild Søgaard Oliver Moen Snoksrud Rune EriksenAbstract
No abstract has been registered
Abstract
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
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.