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.
2023
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Sustainable forest management systems require operational measures to preserve the functional design of forest roads. Frequent road data collection and analysis are essential to support target-oriented and efficient maintenance planning and operations. This study demonstrates an automated solution for monitoring forest road surface deterioration using consumer-grade optical sensors. A YOLOv5 model with StrongSORT tracking was adapted and trained to detect and track potholes in the videos captured by vehicle-mounted cameras. For model training, datasets recorded in diverse geographical regions under different weather conditions were used. The model shows a detection and tracking performance of up to a precision and recall level of 0.79 and 0.58, respectively, with 0.70 mean average precision at an intersection over union (IoU) of at least 0.5. We applied the trained model to a forest road in southern Norway, recorded with a Global Navigation Satellite System (GNSS)−fitted dashcam. GNSS-delivered geographical coordinates at 10 Hz rate were used to geolocate the detected potholes. The geolocation performance over this exemple road stretch of 1 km exhibited a root mean square deviation of about 9.7 m compared to OpenStreetMap. Finally, an exemple road deterioration map was compiled, which can be used for scheduling road maintenance operations.
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Authors
Lennartsson, Tommy Axelsson Linkowski, Weronika Wissman, Jörgen Westin, Anna D'Amico, Marcello Kollmann, Johannes Uhe, Larissa Svenja B. Kroeger Bastianelli, Giulia Kallioniemi, Eveliina Hovstad, Knut Anders Hans Martin HanslinAbstract
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Cornelya KlutschAbstract
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Cornelya KlutschAbstract
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We tested whether windthrow damage to Nordic conifer foreststands could be reliably detected as canopy height decreasebetween a pre-storm LiDAR (Light Detection and Ranging) digitalsurface model (DSM) and a photogrammetric DSM derived froma post-storm WorldView-3 stereo pair. The post-storm ground refer-ence data consisted of field and unmanned aerial vehicle (UAV)observations of windthrow combined with no-damage areas col-lected by visual interpretation of the available very high resolution(VHR) satellite imagery. We trained and tested a thresholding modelusing canopy height change as the sole predictor. We undertooka two-step accuracy assessment by (1) running k-fold cross-validation on the ground reference dataset and examining theeffect of the potential imperfections in the ground reference data,and (2) conducting rigorous accuracy assessment of the classifiedmap of the study area using an extended set of VHR imagery. Thethresholding model produced accurate windthrow maps in dense,productive forest stands with a sensitivity of 96%, specificity of 71%,and Matthews correlation coefficient (MCC) over 0.7. However, insparse and high elevation stands, the classification accuracy waspoor. Despite certain collection challenges during the wintermonths in the Nordic region, we consider VHR stereo satelliteimagery to be a viable source of forest canopy height informationand sufficiently accurate to map windthrow disturbance in foreststands of high to moderate density.
Authors
Christoph Schürz Svajunas Plunge Michael Strauch Natalja Cerkasova Brigitta Szabó Csilla Farkas Attila Nemes Mikołaj PiniewskiAbstract
SWAT+ is a data intensive eco-hydrological model. The SWAT+ model setup procedure requires comprehensive data on topography, soils, vegetation cover, land management, or the hydrological connectivity in the landscape. Although SWAT+ allows a detailed representation of the landscape and its management, input data handling and processing can be challenging and computationally intensive. Eventually, the input files of a detailed model setup can easily comprise hundreds of thousands of lines which limits model verification and calibration. Script programming languages such as R and python are popular for data processing and analysis in many scientific disciplines. For SWAT2012 and SWAT+ several R packages and tools, such as SWATplusR and SWATfarmR have been developed over the last years which proved to be useful tools in SWAT modeling workflows. Yet, they have not been connected into consecutive workflows, which would add benefits of automation, transparency, reproducibility and flexibility. The EU funded H2020 project OPTAIN aims to create harmonized workflows for various stages of the SWAT+ model development. To achieve this, the project team improved and expanded upon existing SWAT model related R packages, and also created new R packages and scripted workflows. The developed workflows cover a wide range of modeling tasks from preparing and generating SWAT+ model input data, to setting up the model, model parametrization, creating land management schedules, verifying the model, and calibrating it. Although some of the workflows are specifically tailored to the needs of the project’s modeling case studies, most of the developed functions and workflows are generalized and can be implemented in other SWAT+ model applications. This presentation will give an overview of the available R packages and R workflows which were developed and extended within the OPTAIN framework, such as deriving soil properties , preparing weather data and the weather generator inputs with scripted workflows and the R package SWATprepR, the development of field scale connected SWAT+ model setups with SWATbuildR, scripted workflows to generate farm management crop rotation inputs employing the SWATfarmR package, or comprehensive model verification using the SWATdoctR. Further resources are presented which were compiled in the ongoing project and which can be valuable for other SWAT modelers. The presented work is a result of activities in the EU Horizon 2020 project OPTAIN (grant agreement No. 862756).
Authors
Richard Helliwell Tommy Ruud Davide Bochicchio Anne Grete Kongsted Matthias Koesling Stig Milan Thamsborg Marina Spinu Marina Stuelj Atle WibeAbstract
In summary, land, local and national markets are limiting and facilitatory factors of organic pig production. Only in Denmark did there appear to be organic pig farming operating at scale to meet export and national market demands. Whereas Otherwise, production and consumption was much more localized, often involving direct sales networks and contracts with local retailers and restaurants. Here, organic was just one of the key qualities to support sales with the transparency for customers being deemed more important in some instances. The result was diverse business structures with the scale and economic viability of production being limited due to the specifics of the market niche into which the pork was being sold. However, in all cases outdoor pig rearing was a value adding process that created additional important streams of revenue as part of diversified farm enterprises. The value of organic certification was questioned in some instances where the market was hyper localized and the practice of outdoor rearing and the transparency it produced for the consumer were considered to be of greater value. Economic conditions were challenging for all producers and with the exception of Denmark pigs were not the sole source of income. Where sales to retailers and local restaurants were not possible, or had not been established, direct sales networks were the principal source of consumer sales. However, maintaining direct sales networks could be challenging and involved significant work, including logistics. The high level of wellbeing and satisfaction of the farmer was a re-occuring theme which suggests that rearing pigs outdoors has mutual benefits for the farmer, the pigs and potentially the environment.