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.
2022
Forfattere
Bjørn Egil FløSammendrag
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Forfattere
Sigridur DalmannsdottirSammendrag
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Sammendrag
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Forfattere
Trond MæhlumSammendrag
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Sammendrag
The preservation of the functionality of forest soil is a key aspect in planning mechanized harvesting operations. Therefore, knowledge and information about stand and soil characteristics are vital to the planning process. In this respect, depth-to-water (DTW) maps were reviewed with regard to their potential use as a prediction tool for wheel ruts. To test the applicability of open source DTW maps for prediction of rutting, the ground surface conditions of 20 clear-cut sites were recorded post harvesting, using an unmanned aerial vehicle (UAV). In total, 80 km of machine tracks were categorized by the severity of occurring rut-formations to investigate whether: i) operators intuitively avoid areas with low DTW values, ii) a correlation exists between decreasing DTW values and increasing rut severity, and iii) DTW maps can serve as reliable decision-making tool in minimizing the environmental effects of big machinery deployment. While the machine operators did not have access to these predictions (DTW maps) during the operations, there was no visual evidence that driving through these areas was actively avoided, resulting in a higher density of severe rutting within areas with DTW values <1 m. A logistic regression analysis confirmed that the probability of severe rutting rapidly increases with decreasing DTW values. However, significant differences between sites exist which might be attributed to a series of other factors such as soil type, weather conditions, number of passes and load capacity. Monitoring these factors is hence highly recommended in any further follow-up studies on soil trafficability.
Forfattere
Eva Narten HøbergSammendrag
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Forfattere
Åsmund Mikalsen KvifteSammendrag
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Forfattere
Yngve RekdalSammendrag
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Sammendrag
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Forfattere
Misganu Debella-GiloSammendrag
Parts of the limited agricultural land area in Norway are taken up by buildings, roads, and other permanent changes every year. A method that detects such changes immediately after they have taken place is required in order to monitor the agricultural areas closely. To that end, Sentinel-2 satellite image time series (SITS) acquired during the summer of 2019 were used to detect the agricultural areas taken up by permanent changes such as buildings and roads. A deep-learning algorithm using 1D convolutional neural network (CNN), with the convolution in the temporal dimension, was applied to the SITS data. The training data was collected from the building footprints dataset filtered using a mono-temporal image aided with the areal resource map (AR5). The deep-learning model was trained and evaluated before being used for prediction in two regions of Norway. Procedures to reduce overfitting of the model to the training data were also implemented. The trained model showed a high level of accuracy and robustness when evaluated based on a test dataset kept out of the training process. The trained model was then used to predict new built-up areas in agricultural fields in two Sentinel-2 tiles. The prediction was able to detect areas taken by new buildings, roads, parking areas and other similar changes. The prediction was then evaluated with respect to the existing building footprints after a few post-processing procedures. A high percentage of the buildings were detected by the method, except for small buildings. The details of the methods and the results obtained, together with brief discussion, are presented in this paper.