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.
2022
Authors
Knut ØistadAbstract
No abstract has been registered
Abstract
Lack of national soil property maps limits the studies of soil moisture (SM) dynamics in Norway. One alternative is to apply the global soil data as input for macro-scale hydrological modelling, but the quality of these data is still unknown. The objectives of this study are 1) to evaluate two recent global soil databases (Wise30sec and SoilGrids) in comparison with data from local soil profiles; 2) to evaluate which database supports better model performance in terms of river discharge and SM for three macro-scale catchments in Norway and 3) to suggest criteria for the selection of soil data for models with different complexity. The global soil databases were evaluated in three steps: 1) the global soil data are compared directly with the Norwegian forest soil profiles; 2) the simulated discharge based on the two global soil databases is compared with observations and 3) the simulated SM is compared with three global SM products. Two hydrological models were applied to simulate discharge and SM: the Soil and Water Integrated Model (SWIM) and the Variable Infiltration Capacity (VIC) model. The comparison with data from local soil profiles shows that SoilGrids has smaller mean errors than Wise30sec, especially for upper soil layers, but both soil databases have large root mean squared errors and poor correlations. SWIM generally performs better in terms of discharge using SoilGrids than using Wise30sec and the simulated SM has higher correlations with the SM products. In contrast, the VIC model is less sensitive to soil input data and the simulated SM using Wise30sec is higher correlated with the SM products than using SoilGrids. Based on the results, we conclude that the global soil databases can provide reasonable soil property information at coarse resolutions and large areas. The selection of soil input data should depend on the characteristics of both models and study areas.
Abstract
No abstract has been registered
Abstract
Tree diameter increment (ΔDBH) and total tree height increment (ΔHT) are key components of a forest growth and yield model. A problem in complex, multi-species forests is that individual tree attributes such as ΔDBH and ΔHT need to be characterized for a large number of distinct woody species of highly varying levels of occurrence. Based on more than 2.5 million ΔDBH observations and over 1 million ΔHT records from up to 60 tree species and genera, respectively, this study aimed to improve existing ΔDBH and ΔHT equations of the Acadian Variant of the Forest Vegetation Simulator (FVS-ACD) using a revised method that utilize tree species as a random effect. Our study clearly highlighted the efficiency and flexibility of this method for predicting ΔDBH and ΔHT. However, results also highlighted shortcomings of this approach, e.g., reversal of plausible parameter signs as a result of combining fixed and random effects parameter estimates after extending the random effect structure by incorporating North American ecoregions. Despite these potential shortcomings, the newly developed ΔDBH and ΔHT equations outperformed the ones currently used in FVS-ACD by reducing prediction bias quantified as mean absolute bias and root mean square error by at least 11% for an independent dataset and up to 41% for the model development dataset. Using the revised ΔDBH and ΔHT estimates, greater prediction accuracy in individual tree aboveground live carbon mass estimation was also found in general but performance varied with dataset and accuracy metric examined. Overall, this analysis highlights the importance and challenges of developing robust ΔDBH and ΔHT equations across broad regions dominated by mixed-species, managed forests.
Authors
Peter Annighöfer Martina Mund Dominik Seidel Christian Ammer Aitor Ameztegui Philippe Balandier Ieva Bebre Lluis Coll Catherine Collet Tobias Hamm Franka Huth Heike Schneider Christian Kuehne Magnus Löf Any Mary Petritan Ion Catalin Petritan Peter Schall Jürgen BauhusAbstract
Just as the aboveground tree organs represent the interface between trees and the atmosphere, roots act as the interface between trees and the soil. In this function, roots take-up water and nutrients, facilitate interactions with soil microflora, anchor trees, and also contribute to the gross primary production of forests. However, in comparison to aboveground plant organs, the biomass of roots is much more difficult to study. In this study, we analyzed 19 European datasets on above- and belowground biomass of juvenile trees of 14 species to identify generalizable estimators of root biomass based on tree sapling dimensions (e.g. height, diameter, aboveground biomass). Such estimations are essential growth and sequestration modelling. In addition, the intention was to study the effect of sapling dimension and light availability on biomass allocation to roots. All aboveground variables were significant predictors for root biomass. But, among aboveground predictors of root biomass plant height performed poorest. When comparing conifer and broadleaf species, the latter tended to have a higher root biomass at a given dimension. Also, with increasing size, the share of belowground biomass tended to increase for the sapling dimensions considered. In most species, there was a trend of increasing relative belowground biomass with increasing light availability. Finally, the height to diameter ratio (H/D) was negatively correlated to relative belowground biomass. This indicates that trees with a high H/D are not only more unstable owing to the unfavorable bending stress resistance, but also because they are comparatively less well anchored in the ground. Thus, single tree stability may be improved through increasing light availability to increase the share of belowground biomass.
Authors
Katrin ZimmerAbstract
No abstract has been registered
Authors
Katrin ZimmerAbstract
No abstract has been registered
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
Jingjing Liang Javier G. P. Gamarra Nicolas Picard Mo Zhou Bryan Pijanowski Douglass F. Jacobs Peter B. Reich Thomas W. Crowther Gert-Jan Nabuurs Sergio de-Miguel Jingyun Fang Christopher W. Woodall Jens-Christian Svenning Tommaso Jucker Jean-Francois Bastin Susan K. Wiser Ferry Slik Bruno Hérault Giorgio Alberti Gunnar Keppel Geerten M. Hengeveld Pierre L. Ibisch Carlos A. Silva Hans ter Steege Pablo L. Peri David A. Coomes Eric B. Searle Klaus von Gadow Bogdan Jaroszewicz Akane O. Abbasi Meinrad Abegg Yves C. Adou Yao Jesús Aguirre-Gutiérrez Angelica M. Almeyda Zambrano Jan Altman Esteban Alvarez-Dávila Juan Gabriel Álvarez-González Luciana F. Alves Bienvenu H. K. Amani Christian A. Amani Christian Ammer Bhely Angoboy Ilondea Clara Antón Fernández Valerio Avitabile Gerardo A. Aymard Akomian F. Azihou Johan A. Baard Timothy R. Baker Radomir Balazy Meredith L. Bastian Rodrigue Batumike Marijn Bauters Hans Beeckman Nithanel Mikael Hendrik Benu Robert Bitariho Pascal Boeckx Jan Bogaert Frans Bongers Olivier Bouriaud Pedro H. S. Brancalion Susanne Brandl Francis Q. Brearley Jaime Briseno-Reyes Eben N. Broadbent Helge Bruelheide Erwin Bulte Ann Christine Catlin Roberto Cazzolla Gatti Ricardo G. César Han Y. H. Chen Chelsea Chisholm Emil Cienciala Gabriel D. Colletta José Javier Corral-Rivas Anibal Cuchietti Aida Cuni-Sanchez Javid A. Dar Selvadurai Dayanandan Thales de Haulleville Mathieu Decuyper Sylvain Delabye Géraldine Derroire Ben DeVries John Diisi Tran Van Do Jiri Dolezal Aurélie Dourdain Graham P. Durrheim Nestor Laurier Engone Obiang Corneille E. N. Ewango Teresa J. Eyre Tom M. Fayle Lethicia Flavine N. Feunang Leena Finér Markus Fischer Jonas Fridman Lorenzo Frizzera André L. de Gasper Damiano Gianelle Henry B. Glick Maria Socorro Gonzalez-Elizondo Lev Gorenstein Richard Habonayo Olivier J. Hardy David J. Harris Andrew Hector Andreas Hemp Martin Herold Annika Hillers Wannes Hubau Thomas Ibanez Nobuo Imai Gerard Imani Andrzej M. Jagodzinski Stepan Janecek Vivian Kvist Johannsen Carlos A. Joly Blaise Jumbam Banoho L. P. R. Kabelong Goytom Abraha Kahsay Viktor Karminov Kuswata Kartawinata Justin N. Kassi Elizabeth Kearsley Deborah K. Kennard Sebastian Kepfer-Rojas Mohammed Latif Khan John N. Kigomo Hyun Seok Kim Carine Klauberg Yannick Klomberg Henn Korjus Subashree Kothandaraman Florian Kraxner Amit Kumar Relawan Kuswandi Mait Lang Michael J. Lawes Rodrigo V. Leite Geoffrey Lentner Simon L. Lewis Moses B. Libalah Janvier Lisingo Pablito Marcelo López-Serrano Huicui Lu Natalia V. Lukina Anne Mette Lykke Vincent Maicher Brian S. Maitner Eric Marcon Andrew R. Marshall Emanuel H. Martin Olga Martynenko Faustin M. Mbayu Musingo T. E. Mbuvi Jorge A. Meave Cory Merow Stanislaw Miscicki Vanessa S. Moreno Albert Morera Sharif A. Mukul Jörg C. Müller Agustinus Murdjoko Maria Guadalupe Nava-Miranda Litonga Elias Ndive Victor J. Neldner Radovan V. Nevenic Louis N. Nforbelie Michael L. Ngoh Anny E. N’Guessan Michael R. Ngugi Alain S. K. Ngute Emile Narcisse N. Njila Melanie C. Nyako Thomas O. Ochuodho Jacek Oleksyn Alain Paquette Elena I. Parfenova Minjee Park Marc Parren Narayanaswamy Parthasarathy Sebastian Pfautsch Oliver L. Phillips Maria T. F. Piedade Daniel Piotto Martina Pollastrini Lourens Poorter John R. Poulsen Axel Dalberg Poulsen Hans Pretzsch Mirco Rodeghiero Samir G. Rolim Francesco Rovero Ervan Rutishauser Khosro Sagheb-Talebi Purabi Saikia Moses Nsanyi Sainge Christian Salas-Eljatib Antonello Salis Peter Schall Dmitry Schepaschenko Michael Scherer-Lorenzen Bernhard Schmid Jochen Schöngart Vladimír Šebeň Giacomo Sellan Federico Selvi Josep M. Serra-Diaz Douglas Sheil Anatoly Z. Shvidenko Plinio Sist Alexandre F. Souza Krzysztof J. Stereńczak Martin J. P. Sullivan Somaiah Sundarapandian Miroslav Svoboda Mike D. Swaine Natalia Targhetta Nadja Tchebakova Liam A. Trethowan Robert Tropek John Tshibamba Mukendi Peter Mbanda Umunay Vladimir A. Usoltsev Gaia Vaglio Laurin Riccardo Valentini Fernando Valladares Fons van der Plas Daniel José Vega-Nieva Hans Verbeeck Helder Viana Alexander C. Vibrans Simone A. Vieira Jason Vleminckx Catherine E. Waite Hua-Feng Wang Eric Katembo Wasingya Chemuku Wekesa Bertil Westerlund Florian Wittmann Verginia Wortel Tomasz Zawiła-Niedźwiecki Chunyu Zhang Xiuhai Zhao Jun Zhu Xiao Zhu Zhi-Xin Zhu Irie C. Zo-Bi Cang HuiAbstract
The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.