Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2023
Forfattere
Grete H. M. Jørgensen Inger Hansen Finn Arne Haugen Magnhild Garte Høiberg Lise Grøva Gabriela WagnerRedaktører
Per StålnackeSammendrag
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Forfattere
Gunnhild JaastadSammendrag
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Forfattere
Alexander Tischer Julian Klaus Christian Kuehne Bernd Hoffmann Martin Hartig Robert SchmidtSammendrag
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Forfattere
Adrian Straker Stefano Puliti Johannes Breidenbach Christopher Kleinn Grant Pearse Rasmus Astrup Paul MagdonSammendrag
Fine-grained information on the level of individual trees constitute key components for forest observation enabling forest management practices tackling the effects of climate change and the loss of biodiversity in forest ecosystems. Such information on individual tree crowns (ITC's) can be derived from the application of ITC segmentation approaches, which utilize remotely sensed data. However, many ITC segmentation approaches require prior knowledge about forest characteristics, which is difficult to obtain for parameterization. This can be avoided by the adoption of data-driven, automated workflows based on convolutional neural networks (CNN). To contribute to the advancements of efficient ITC segmentation approaches, we present a novel ITC segmentation approach based on the YOLOv5 CNN. We analyzed the performance of this approach on a comprehensive international unmanned aerial laser scanning (UAV-LS) dataset (ForInstance), which covers a wide range of forest types. The ForInstance dataset consists of 4192 individually annotated trees in high-density point clouds with point densities ranging from 498 to 9529 points m-2 collected across 80 sites. The original dataset was split into 70% for training and validation and 30% for model performance assessment (test data). For the best performing model, we observed a F1-score of 0.74 for ITC segmentation and a tree detection rate (DET %) of 64% in the test data. This model outperformed an ITC segmentation approach, which requires prior knowledge about forest characteristics, by 41% and 33% for F1-score and DET %, respectively. Furthermore, we tested the effects of reduced point densities (498, 50 and 10 points per m-2) on ITC segmentation performance. The YOLO model exhibited promising F1-scores of 0.69 and 0.62 even at point densities of 50 and 10 points m-2, respectively, which were between 27% and 34% better than the ITC approach that requires prior knowledge. Furthermore, the areas of ITC segments resulting from the application of the best performing YOLO model were close to the reference areas (RMSE = 3.19 m-2), suggesting that the YOLO-derived ITC segments can be used to derive information on ITC level.
Forfattere
Tatsiana EspevigSammendrag
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Forfattere
Lidong Mo Constantin M. Zohner Peter B. Reich Jingjing Liang Sergio de Miguel Gert-Jan Nabuurs Susanne S. Renner Johan van den Hoogen Arnan Araza Martin Herold Leila Mirzagholi Haozhi Ma Colin Averill Oliver L. Phillips Javier G. P. Gamarra Iris Hordijk Devin Routh Meinrad Abegg Yves C. Adou Yao Giorgio Alberti Angelica M. Almeyda Zambrano Braulio Vilchez Alvarado Esteban Alvarez-Dávila Patricia Alvarez-Loayza Luciana F. Alves Iêda Amaral Christian Ammer Clara Antón Fernández Alejandro Araujo-Murakami Luzmila Arroyo Valerio Avitabile Gerardo A. Aymard Timothy R. Baker Radomir Bałazy Olaf Banki Jorcely G. Barroso Meredith L. Bastian Jean-Francois Bastin Luca Birigazzi Philippe Birnbaum Robert Bitariho Pascal Boeckx Frans Bongers Olivier Bouriaud Pedro H. S. Brancalion Susanne Brandl Francis Q. Brearley Roel Brienen Eben N. Broadbent Helge Bruelheide Filippo Bussotti Roberto Cazzolla Gatti Ricardo G. César Goran Cesljar Robin L. Chazdon Han Y. H. Chen Chelsea Chisholm Hyunkook Cho Emil Cienciala Connie Clark David Clark Gabriel D. Colletta David A. Coomes Fernando Cornejo Valverde José J. Corral-Rivas Philip M. Crim Jonathan R. Cumming Selvadurai Dayanandan André L. de Gasper Mathieu Decuyper Géraldine Derroire Ben DeVries Ilija Djordjevic Jiri Dolezal Aurélie Dourdain Nestor Laurier Engone Obiang Brian J. Enquist Teresa J. Eyre Adandé Belarmain Fandohan Tom M. Fayle Ted R. Feldpausch Leandro V. Ferreira Leena Finér Markus Fischer Christine Fletcher Lorenzo Frizzera Damiano Gianelle Henry B. Glick David J. Harris Andrew Hector Andreas Hemp Geerten Hengeveld Bruno Hérault John L. Herbohn Annika Hillers Eurídice N. Honorio Coronado Cang Hui Thomas Ibanez Nobuo Imai Andrzej M. Jagodziński Bogdan Jaroszewicz Vivian Kvist Johannsen Carlos A. Joly Tommaso Jucker Ilbin Jung Viktor Karminov Kuswata Kartawinata Elizabeth Kearsley David Kenfack Deborah K. Kennard Sebastian Kepfer-Rojas Gunnar Keppel Mohammed Latif Khan Timothy J. Killeen Hyun Seok Kim Kanehiro Kitayama Michael Köhl Henn Korjus Florian Kraxner Dmitry Kucher Diana Laarmann Mait Lang Huicui Lu Natalia V. Lukina Brian S. Maitner Yadvinder Malhi Eric Marcon Beatriz Schwantes Marimon Ben Hur Marimon-Junior Andrew R. Marshall Emanuel H. Martin Jorge A. Meave Omar Melo-Cruz Casimiro Mendoza Irina Mendoza-Polo Stanislaw Miscicki Cory Merow Abel Monteagudo Mendoza Vanessa S. Moreno Sharif A. Mukul Philip Mundhenk María Guadalupe Nava-Miranda David Neill Victor J. Neldner Radovan V. Nevenic Michael R. Ngugi Pascal A. Niklaus Jacek Oleksyn Petr Ontikov Edgar Ortiz-Malavasi Yude Pan Alain Paquette Alexander Parada-Gutierrez Elena I. Parfenova Minjee Park Marc Parren Narayanaswamy Parthasarathy Pablo L. Peri Sebastian Pfautsch Nicolas Picard Maria Teresa F. Piedade Daniel Piotto Nigel C. A. Pitman Axel Dalberg Poulsen John R. Poulsen Hans Pretzsch Freddy Ramirez Arevalo Zorayda Restrepo-Correa Mirco Rodeghiero Samir G. Rolim Anand Roopsind Francesco Rovero Ervan Rutishauser Purabi Saikia Christian Salas-Eljatib Philippe Saner Peter Schall Mart-Jan Schelhaas Dmitry Schepaschenko Michael Scherer-Lorenzen Bernhard Schmid Jochen Schöngart Eric B. Searle Vladimír Seben Josep M. Serra-Diaz Douglas Sheil Anatoly Z. Shvidenko Javier E. Silva-Espejo Marcos Silveira James Singh Plinio Sist Ferry Slik Bonaventure Sonké Alexandre F. Souza Krzysztof J. Stereńczak Jens-Christian Svenning Miroslav Svoboda Ben Swanepoel Natalia Targhetta Nadja Tchebakova Hans ter Steege Raquel Thomas Elena Tikhonova Peter M. Umunay Vladimir A. Usoltsev Renato Valencia Fernando Valladares Fons van der Plas Tran Van Do Michael E. van Nuland Rodolfo M. Vasquez Hans Verbeeck Helder Viana Alexander C. Vibrans Simone Vieira Klaus von Gadow Hua-Feng Wang James V. Watson Gijsbert D. A. Werner Susan K. Wiser Florian Wittmann Hannsjoerg Woell Verginia Wortel Roderik Zagt Tomasz Zawiła-Niedźwiecki Chunyu Zhang Xiuhai Zhao Mo Zhou Zhi-Xin Zhu Irie C. Zo-Bi George D. Gann Thomas W. CrowtherSammendrag
Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2,3,4,5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.
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Formålet med dette prosjektet har vært å se på hvordan data som samles inn i Arealrepresentativ overvåking av semi-naturlig eng i Norge (ASO) kan brukes i Naturindeks. Dette har vi gjort ved først å se hvordan artsforekomster kan brukes direkte som indikatorer for endringer i biologisk mangfold, og om variabler om bruksregime er egnet for å vurdere utviklingen av det biologiske mangfoldet knyttet til åpent lavland. Til slutt har vi laget fremgangsmåter for de forskjellige datatypene i ASO for å bruke disse som indikatorer.
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