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.
2020
Abstract
No abstract has been registered
Abstract
In this study, we aim at developing ways to directly translate raw drone data into actionable insights, thus enabling us to make management decisions directly from drone data. Drone photogrammetric data and data analytics were used to model stand-level immediate tending need and cost in regeneration forests. Field reference data were used to train and validate a logistic model for the binary classification of immediate tending need and a multiple linear regression model to predict the cost to perform the tending operation. The performance of the models derived from drone data was compared to models utilizing the following alternative data sources: airborne laser scanning data (ALS), prior information from forest management plans (Prior) and the combination of drone +Prior and ALS +Prior. The use of drone data and prior information outperformed the remaining alternatives in terms of classification of tending needs, whereas drone data alone resulted in the most accurate cost models. Our results are encouraging for further use of drones in the operational management of regeneration forests and show that drone data and data analytics are useful for deriving actionable insights. Key words: UAV, DAP, forest inventory, photogrammetry, precommercial thinning, airborne laser scanning.
Abstract
Past: In the early twentieth century, forestry was one of the most important sectors in Norway and an agitated discussion about the perceived decline of forest resources due to over-exploitation was ongoing. To base the discussion on facts, the young state of Norway established Landsskogtakseringen – the world’s first National Forest Inventory (NFI). Field work started in 1919 and was carried out by county. Trees were recorded on 10 m wide strips with 1–5 km interspaces. Site quality and land cover categories were recorded along each strip. Results for the first county were published in 1920, and by 1930 most forests below the coniferous tree line were inventoried. The 2nd to 5th inventories followed in the years 1937–1986. As of 1954, temporary sample plot clusters on a 3 km × 3 km grid were used as sampling units. Present: The current NFI grid was implemented in the 6th NFI from 1986 to 1993, when permanent plots on a 3 km × 3 km grid were established below the coniferous tree line. As of the 7th inventory in 1994, the NFI is continuous, and 1/5 of the plots are measured annually. All trees with a diameter ≥ 5 cm are recorded on circular, 250 m2 plots. The NFI grid was expanded in 2005 to cover alpine regions with 3 km × 9 km and 9 km × 9 km grids. In 2012, the NFI grid within forest reserves was doubled along the cardinal directions. Clustered temporary plots are used periodically to facilitate county-level estimates. As of today, more than 120 variables are recorded in the NFI including bilberry cover, drainage status, deadwood, and forest health. Landuse changes are monitored and trees outside forests are recorded. Future: Considerable research efforts towards the integration of remote sensing technologies enable the publication of the Norwegian Forest Resource Map since 2015, which is also used for small area estimation at the municipality level. On the analysis side, capacity and software for long term growth and yield prognosis are being developed. Furthermore, we foresee the inclusion of further variables for monitoring ecosystem services, and an increasing demand for mapped information. The relatively simple NFI design has proven to be a robust choice for satisfying steadily increasing information needs and concurrently providing consistent time series.
Authors
Stefano Puliti Marius Hauglin Johannes Breidenbach Paul M. Montesano C.S.R. Neigh Johannes Rahlf Svein Solberg Torgeir Ferdinand Klingenberg Rasmus AstrupAbstract
No abstract has been registered
2019
Abstract
No abstract has been registered
Authors
Jamal Zaherpour Nick Mount Simon N. Gosling Rutger Dankers Stephanie Eisner Dieter Gerten Xingcai Liu Yoshimitsu Masaki Hannes Müller Schmied Qiuhong Tang Yoshihide WadaAbstract
This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.
Abstract
A robust hydrological modeling at a fine spatial resolution is a vital tool for Norway to simulate river discharges and hydrological components for climate adaptation strategies. However, it requires improvements of modelling methods, detailed observational data as input and expensive computational resources. This work aims to set up a distributed version of the HBV model with a physically based evapotranspiration scheme at 1 km resolution for mainland Norway and to calibrate/validate the model for 124 catchments using regionalized parameterizations. The Penman-Monteith equation was implemented in the HBV model and vegetation characteristics were derived from the Norwegian forest inventory combined with multi-source remote sensing data at 16 m spatial resolution. The estimated potential evapotranspiration (Ep) was compared with pan measurements and estimates from the MODerate Resolution Imaging Spectrometer (MOD16) products, the Global Land Evaporation Amsterdam Model (GLEAM) and Variable Infiltration Capacity (VIC) hydrological model. There are 5 climatic zones in Norway classified based on 4 temperature and precipitation indices. For each zone, the model was calibrated separately by optimizing a multi-objective function including the Nash-Sutcliff efficiency (NSE) and biases of selected catchments. In total, there are 85 catchments for calibration and 39 for validation. The Ep estimates showed good agreement with the measurements, GLEAM and VIC outputs. However, the MOD16 product significantly overestimates Ep compared to the other products. The discharge was well reproduced with the median daily NSE of 0.68/0.67, bias of −3%/−1%, Kling-Gupta efficiency (KGE) of 0.70/0.69 and monthly NSE of 0.80/0.78 in the calibration/validation periods. Our results showed a significant improvement compared to the previous HBV application for all catchments, with an increase of 0.08–0.16 in the median values of the daily NSE, KGE and monthly NSE. Both the temporal and spatial transferability of model parameterizations were also enhanced compared to the previous application.
Authors
Jan Magnusson Stephanie Eisner Shaochun Huang Cristian Lussana Giulia Mazzotti Richard Essery Tuomo Saloranta Stein BeldringAbstract
Climate models show that global warming will disproportionately influence high‐latitude regions and indicate drastic changes in, among others, seasonal snow cover. However, current continental and global simulations covering these regions are often run at coarse grid resolutions, potentially introducing large errors in computed fluxes and states. To quantify some of these errors, we have assessed the sensitivity of an energy‐balance snow model to changes in grid resolution using a multiparametrization framework for the spatial domain of mainland Norway. The framework has allowed us to systematically test how different parametrizations, describing a set of processes, influence the discrepancy, here termed the scale error, between the coarser (5 to 50‐km) and finest (1‐km) resolution. The simulations were set up such that liquid and solid precipitation was identical between the different resolutions, and differences between the simulations arise mainly during the ablation period. The analysis presented in this study focuses on evaluating the scale error for several variables relevant for hydrological and land surface modelling, such as snow water equivalent and turbulent heat exchanges. The analysis reveals that the choice of method for routing liquid water through the snowpack influences the scale error most for snow water equivalent, followed by the type of parametrizations used for computing turbulent heat fluxes and albedo. For turbulent heat exchanges, the scale error is mainly influenced by model assumptions related to atmospheric stability. Finally, regions with strong meteorological and topographic variability show larger scale errors than more homogenous regions.
Authors
Stephanie EisnerAbstract
No abstract has been registered
Authors
B.S. Steidinger Thomas W. Crowther Jingjing Liang M. E. Van Nuland G.D.A. Werner Peter B. Reich Gert-Jan Nabuurs Sergio de-Miguel M. Zhou N. Picard Bruno Herault Xiuhai Zhao C. Zhang D. Routh Kabir G Peay Meinrad Abegg C. Yves Adou Yao Giorgio Alberti Angelica Almeyda Zambrano Esteban Alvarez-Davila Patricia Alvarez-Loayza Luciana F. Alves Christian Ammer Clara Antón Fernández Alejandro Araujo-Murakami Luzmila Arroyo Valerio Avitabile Gerardo Aymard Timothy R. Baker Radomir Bałazy Olaf Bánki Jorcely Barroso Meredith Bastian Jean-François Bastin Luca Birigazzi Philippe Birnbaum Robert Bitariho Pascal Boeckx Olivier Bouriaud Pedro H. S. Brancalion Susanne Brandl Francis Q. Brearley Roel J. W. Brienen Eben Broadbent Helge Bruelheide Filippo Bussotti Roberto Cazzolla Gatti Ricardo Cesar Goran Cesljar Robin L. Chazdon Han Y. H. Chen Chelsea L. Chisholm Emil Cienciala Connie J. Clark David Clark Gabriel Colletta Richard Condit David Coomes Fernando Cornejo Valverde Jose J. Corral-Rivas Philip Crim Jonathan Cumming Selvadurai Dayanandan André L. de Gasper Mathieu Decuyper Géraldine Derroire Ben DeVries Ilija Djordjevic Amaral Iêda Aurélie Dourdain Nestor Laurier Engone Obiang Brian J. Enquist Teresa Eyre Adandé Belarmain Fandohan Tom M. Fayle Ted R. Feldpausch Leena Finér Markus Fischer Christine Fletcher Jonas Fridman Lorenzo Frizzera Javier G. P. Gamarra Damiano Gianelle Henry B. Glick David J. Harris Andy Hector Andreas Hemp Geerten Hengeveld John Herbohn Martin Herold Annika Hillers Eurídice N. Honorio Coronado Markus Huber Cang Hui Hyunkook Cho Thomas Ibanez Ilbin Jung Nobuo Imai Andrzej M. Jagodzinski Bogdan Jaroszewicz Vivian Kvist Johannsen Carlos A. Joly Tommaso Jucker Viktor Karminov Kuswata Kartawinata Elizabeth Kearsley David Kenfack Deborah Kennard Sebastian Kepfer-Rojas Gunnar Keppel Mohammed Latif Khan Timothy Killeen Hyun Seok Kim Kanehiro Kitayama Michael Köhl Henn Korjus Florian Kraxner Diana Laarmann Mait Lang Simon L. Lewis Huicui Lu Natalia Lukina Brian S. Maitner Yadvinder Malhi Eric Marcon Beatriz Schwantes Marimon Ben Hur Marimon-Junior Andrew R. Marshall Emanuel H. Martin Olga Martynenko Jorge A. Meave Omar Melo-Cruz Casimiro Mendoza Cory Merow Abel Monteagudo Mendoza Vanessa Moreno Sharif A. Mukul Philip Mundhenk Maria G. Nava-Miranda David Neill Victor Neldner Radovan Nevenic Michael Ngugi Pascal Niklaus Jacek Oleksyn Petr Ontikov Edgar Ortiz-Malavasi Yude Pan Alain Paquette Alexander Parada-Gutierrez Elena Parfenova Minjee Park Marc Parren Narayanaswamy Parthasarathy Pablo L. Peri Sebastian Pfautsch Oliver Phillips Maria Teresa Piedade Daniel Piotto Nigel Pitman Irina Polo Lourens Poorter Axel Dalberg Poulsen John R. Poulsen Hans Pretzsch Freddy Ramirez Arevalo Zorayda Restrepo-Correa Mirco Rodeghiero Samir Rolim Anand Roopsind Francesco Rovero Ervan Rutishauser Purabi Saikia Philippe Saner Peter Schall Mart-Jan Schelhaas Dmitry Schepaschenko Michael Scherer-Lorenzen Bernhard Schmid Jochen Schöngart Eric Searle Vladimír Seben Josep M. Serra-Diaz Christian Salas Douglas Sheil Anatoly Shvidenko Javier Silva-Espejo Marcos Silveira James Singh Plinio Sist Ferry Slik Bonaventure Sonké Alexandre F. Souza Krzysztof Stereńczak Jens-Christian Svenning Miroslav Svoboda Natalia Targhetta Nadezhda M. Tchebakova Hans ter Steege Raquel Thomas Elena Tikhonova Peter Umunay Vladimir Usoltsev Fernando Valladares Fons van der Plas Tran Van Do Rodolfo Vasquez Martinez Hans Verbeeck Helder Viana Simone Vieira Klaus von Gadow Hua-Feng Wang James Watson Bertil Westerlund Susan Wiser Florian Wittmann Verginia Wortel Roderick Zagt Tomasz Zawila-Niedzwiecki Zhi-Xin Zhu Irie Casimir Zo-BiAbstract
No abstract has been registered