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
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
Knut Ole VikenSammendrag
Kontrolltaksering av et utvalg permanente prøveflater i Landsskogtakseringen ble gjennomført i 2017, 2018 og 2019. Det er for de fleste variablene som ble sammenlignet bra samsvar mellom kontrolldataene og registreringene fra taksten, men for enkelte variable er det noe avvik. Variabler som er bestemt tidligere, og der lagleder kan bekrefte eller endre tidligere verdi/klasse, viser bra samsvar. Det gjelder grunnleggende arealklassifisering som arealtype og arealanvendelse, samt bestandsvariable som kronedekningsprosent, bonitet, bestandsalder og hogstklasse. Kronehøyde, kronetetthet, stammeklasse og blåbærdekningsprosent er variabler der det forekommer systematiske uoverensstemmelser og/eller avvik. Disse variablene har trolig forbedringspotensialer ved å kjøre grundigere opplæring, og mer kalibrering og kursing av feltarbeiderne.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Forfattere
Alexander Becker Stefania Russo Stefano Puliti Nico Lang Konrad Schindler Jan Dirk WegnerSammendrag
Monitoring and managing Earth’s forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-m resolution, using freely available satellite imagery as input. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 syntheticaperture radar images into maps of five different forest structure variables: 95th height percentile, mean height, density, Gini coefficient, and fractional cover. We train and test our model on reference data from 41 airborne laser scanning missions across Norway and demonstrate that it is able to generalize to unseen test regions, achieving normalized mean absolute errors between 11% and 15%, depending on the variable. Our work is also the first to propose a variant of so-called Bayesian deep learning to densely predict multiple forest structure variables with well-calibrated uncertainty estimates from satellite imagery. The uncertainty information increases the trustworthiness of the model and its suitability for downstream tasks that require reliable confidence estimates as a basis for decision making. We present an extensive set of experiments to validate the accuracy of the predicted maps as well as the quality of the predicted uncertainties. To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.
Forfattere
Stefano PulitiSammendrag
Det er ikke registrert sammendrag
Forfattere
Stefano PulitiSammendrag
Det er ikke registrert sammendrag
Forfattere
Johannes BreidenbachSammendrag
Det er ikke registrert sammendrag
Forfattere
Johannes BreidenbachSammendrag
Det er ikke registrert sammendrag