2020
Forfattere
Kjersti Holt HanssenSammendrag
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Sammendrag
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Forfattere
Kjersti Holt HanssenSammendrag
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Sammendrag
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Sammendrag
Skogen i Norge har et årlig netto opptak i underkant av 30 mill. tonn CO2. Størrelsen på opptaket påvirkes av forvaltningen av skogarealene, både gjennom endringer i totalarealet (avskoging og påskoging), og forvaltningen av de eksisterende skogarealene. I denne rapporten presenteres en første vurdering av syv klimatiltak som ikke tidligere er utredet, en kunnskapsoppdatering av noen tidligere utredede klimatiltak, og en framskrivning av mulige effekter på netto CO2-opptak av ulike nivå på implementerte tiltak. Rapporten er skrevet på bestilling fra Landbruksdirektoratet og Miljødirektoratet, og det er direktoratene som har gjort utvalget av tiltak....
2019
Sammendrag
Bipolar surface EMG (sEMG) signals of the trapezius muscles bilaterally were recorded continuously with a frequency of 800 Hz during full-shift field-work by a four-channel portable data logger. After recordings of 60 forest machine operators in Finland, Norway and Sweden, we discovered erroneous data. In short of any available procedure to handle these data, a method was developed to automatically discard erroneous data in the raw data reading files (Discarding Erroneous EPOchs (DESEPO) method. The DESEPO method automatically identifies, discards and adjusts the use of signal disturbances in order to achieve the best possible data use. An epoch is a 0.1 s period of raw sEMG signals and makes the basis for the RMS calculations. If erroneous signals constitute more than 30% of the epoch signals, this classifies for discharge of the present epoch. Non-valid epochs have been discarded, as well as all the subsequent epochs. The valid data for further analyses using the automatic detection resulted in an increase of acceptable data from an average of 2.15–6.5 h per day. The combination of long-term full-shift recordings and automatic data reduction procedures made it possible to use large amount of data otherwise discarded for further analyses.
Sammendrag
Automatic data collection is becoming increasingly common in cut-to-length forest operations. However, only few studies have analyzed automatically collected follow-up data from forwarders. In this study, we analyzed the driving distances of the four work elements Driving empty, Loading drive, Driving loaded, and Unloading drive (the sum of which being Total driven distance) of two forwarders operating in central Sweden. The analysis included finding the most appropriate probability density functions for each distance at the stand level (46 final felling stands in total, with one load as the unit of observation). The results showed that the mean intra-stand Total driven distance ranged 364–2393 m, and that most distances in the majority of the stands were positively skewed. Versatile probability distributions like Generalized Extreme Value and Log-logistic were the most common probability distributions. Our results provide researchers and managers a numeric understanding of the intra- and inter-stand variation of forwarding work. Hence, our study can help spread awareness of this variation to managers and foresters. With this awareness, managers, foresters, and researchers can better understand the pros and cons of follow-up data from forwarders, and how to best use and collect it. Our results can also be used by researchers as high-resolution indata during simulations of forwarding work. Additionally, the results can be used as a reference or control when determining the most suitable data distributions in future studies.
Sammendrag
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Multi-temporal Sentinel 2 optical images and 3D photogrammetric point clouds can be combined to enhance the accuracy of timber volume models on large spatial scale. Information on the proportion of broadleaf and conifer trees improves timber volume models obtained from 3D photogrammetric point clouds. However, the broadleaf-conifer information cannot be obtained from photogrammetric point clouds alone. Furthermore, spectral information of aerial images is too inconsistent to be used for automatic broadleaf-conifer classification over larger areas. In this study we combined multi-temporal Sentinel 2 optical satellite images, 3D photogrammetric point clouds from digital aerial stereo photographs, and forest inventory plots representing an area of 35,751 km2 in south-west Germany for (1) modelling the percentage of broadleaf tree volume (BL%) using Sentinel 2 time series and (2) modelling timber volume per hectare using 3D photogrammetric point clouds. Forest inventory plots were surveyed in the same years and regions as stereo photographs were acquired (2013–2017), resulting in 11,554 plots. Sentinel 2 images from 2016 and 2017 were corrected for topographic and atmospheric influences and combined with the same forest inventory plots. Spectral variables from corrected multi-temporal Sentinel 2 images were calculated, and Support VectorMachine (SVM) regressions were fitted for each Sentinel 2 scene estimating the BL% for corresponding inventory plots. Variables from the photogrammetric point clouds were calculated for each inventory plot and a non-linear regression model predicting timber volume per hectare was fitted. Each SVMregression and the timber volume model were evaluated using ten-fold cross-validation (CV). The SVMregression models estimating the BL% per Sentinel 2 scene achieved overall accuracies of 68%–75% and a Root Mean Squared Error (RMSE) of 21.5–26.1. The timber volumemodel showed a RMSE% of 31.7%, amean bias of 0.2%, and a pseudo-R2 of 0.64. Application of the SVMregressions on Sentinel 2 scenes covering the state of Baden-Württemberg resulted in predictions of broadleaf tree percentages for the entire state. These predicted values were used as additional predictor in the timber volume model, allowing for predictions of timber volume for the same area. Spatially high-resolution information about growing stock is of great practical relevance for forest management planning, especially when the timber volume of a smaller unit is of interest, for example of a forest stand or a forest districtwhere not enough terrestrial inventory plots are available to make reliable estimations. Here, predictions from remote-sensing based models can be used. Furthermore, information about broadleaf and conifer trees improves timber volume models and reduces model errors and, thereby, prediction uncertainties.