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
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.
Sammendrag
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Sammendrag
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Sammendrag
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Sammendrag
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Forfattere
Lisa M. Schüler Juline Walter Hidehiko Kato Hirono Suzuki Christopher Jonathan Hulatt Ralf Rautenberger Sofia Navalho Benjamin Schmid Joao Varela Kiron Viswanath Peter Simon Claus SchulzeSammendrag
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Forfattere
Lampros LamprinakisSammendrag
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Sammendrag
Harvest weed seed control takes advantage of seed retention at maturity by collecting weed seeds as they pass through the harvester. We assessed the seed production and shedding pattern of common weed species in two wheat and two oat fields in Denmark. The aim was to evaluate the possibility of harvesting retained seeds on weeds at crop harvest by a combine harvester based on estimation of weeds seed retention. Before flowering, ten plants of each weed species were selected and surrounded by a seed trap comprising of a porous net. When the plants started shedding seeds, the seeds were collected from the traps and counted weekly until crop harvest. Just before crop harvest, the retained seeds on the plants were counted and the ratio of harvestable seeds and shed seeds during the growing season were determined. The seed production and shedding patterns varied between the species. In oat, Anagallis arvensis L., Capsella bursa-pastoris (L.) Medik., Chenopodium album L., Fallopia convolvulus (L.) Á. Löve, Geranium molle L., Persicaria maculosa Gray, Polygonum aviculare L., Silene noctiflora L., Sinapis arvensis L., Sonchus arvensis L., Spergula arvensis L., Stellaria media (L.) Vill.,Veronica persica Poir., and Viola arvensis Murray retained on average 61, 52, 67, 44, 58, 32, 59, 95, 67, 23, 45, 56, 51, and 33%, respectively, of their produced seeds at crop harvest. In wheat, Alopecurus myosuroides Huds. and Apera spica-venti (L.) P. Beauv. retained on average 34 and 33%, respectively, of their seeds at harvest. Silene noctiflora was classified as a good target for harvest weed seed control; A. myosuroides, A. spica-venti, C. bursa-pastoris, C. album, F. convolvulus, G. molle, P.maculosa, Sinapis arvensis, Sonchus arvensis, Spergula arvensis and V. arvensis were classified as intermediate targets; and A. arvensis, P. aviculare, S. media and V. persica were classified as poor targets. The research shows that there is a great potential to reduce the input of weed seeds to the soil seed bank by harvest weed seed control. Keywords: Harvest weed seed control; Soil seed bank ; Weed seed retention
Sammendrag
Harvest Weed Seed Control (HWSC) systems are used to collect and/or kill weed seeds retained on the weed plants at crop harvest. The effect of HWSC methods depends on the weeds seed retention at harvest. Therefore, delay in crop harvest reduces the efficiency of HWSC. In 2018, we studied the seed production and shedding pattern of Alopecurus myosuroides in a semi-field experiment in Taastrup, Denmark, to find the seed shedding time range of this species. In 2017 and 2018, we also followed the seed shedding pattern of A. myosuroides in a wheat field. Seeds of A. myosuroides were planted in pots in a greenhouse with a constant temperature of 5°C. In December 2017, the seedlings were transplanted in a box (120 × 80 cm2) located outdoor. In spring 2018, the number of plants was reduced to 14 providing a space of 685 cm2 for each plant. We surrounded each plant with a porous net to collect the seeds. The nets were checked once a week to record the beginning of the seed shedding period. Hereafter, seeds were collected weekly using a portable vacuum cleaner. Plants in the box started seed shedding in the second week of June and seed shedding continued for 12 weeks (end of August). In the wheat field, A. myosuroides plants surrounded by a net started to shed seeds in the third week of June and continued until wheat harvest on 31 July in 2017 and in the second week of July and continued until wheat harvest on 15 August in 2018. We found a significant difference between the weekly number of shed seeds in all three experiments (P