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.
2020
Sammendrag
Det er ikke registrert sammendrag
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....
Forfattere
Gunnhild Søgaard Gry Alfredsen Clara Antón Fernández Rasmus Astrup Helmer Belbo Nicholas Clarke Rune Eriksen Aksel Granhus Kjersti Holt Hanssen Ari Hietala Frans Kockum Christian Wilhelm Mohr Jørn-Frode Nordbakken Jogeir N. Stokland Jennie Sverker Tonje ØklandSammendrag
Det årlige netto opptaket i skogen i Norge økte frem til 2009 (over 35 mill. tonn), og har etter det vist en avtakende trend. I 2018 var det et netto opptak på i underkant av 28 millioner tonn CO2- ekvivalenter. Størrelsen på opptaket påvirkes av forvaltningen av skogarealene, både gjennom endringer i totalarealet (avskoging og påskoging), og forvaltningen av de eksisterende skogarealene. I en første rapport til Klimakur 2030 – skrevet på oppdrag fra Miljødirektorat og Landbruksdirektoratet - ble det presentert en første vurdering av syv klimatiltak som ikke tidligere var utredet, samt en kunnskapsoppdatering for noen tidligere utredede klimatiltak. I denne rapporten presenteres ytterligere vurderinger av fire av disse tiltakene; ungskogpleie, grøfterensk, stubbebehandling mot råte og gjødsling med treaske. Rapporten er skrevet på bestilling fra Landbruks- og matdepartementet (LMD) og Klima- og miljødepartementet (KLD), og det er departementene som har gjort utvalget av tiltak som skulle vurderes videre...
2019
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Global Forest Watch (GFW) provides a global map of annual forest cover loss (FCL) produced from Landsat imagery, offering a potentially powerful tool for monitoring changes in forest cover. In managed forests, FCL primarily provides information on commercial harvesting. A semi-autonomous method for providing data on the location and attributes of harvested sites at a landscape level was developed which could significantly improve the basis for catchment management, including risk mitigation. FCL in combination with aerial images was used for detecting and characterising harvested sites in a 1607 km2 mountainous boreal forest catchment in south-central Norway. Firstly, the forest cover loss map was enhanced (FCLE) by removing small isolated forest cover loss patches that had a high probability of representing commission errors. The FCLE map was then used to locate and assess sites representing annual harvesting activity over a 17-year period. Despite an overall accuracy of >98%, a kappa of 0.66 suggested only a moderate quality for detecting harvested sites. While errors of commission were negligible, errors of omission were more considerable and at least partially attributed to the presence of residual seed trees on the site after harvesting. The systematic analysis of harvested sites against aerial images showed a detection rate of 94%, but the area of the individual harvested site was underestimated by 29% on average. None of the site attributes tested, including slope, area, altitude, or site shape index, had any effect on the accuracy of the area estimate. The annual harvest estimate was 0.6% (standard error 12%) of the productive forest area. On average, 96% of the harvest was carried out on flat to moderately steep terrain (<40% slope), 3% on steep terrain (40% to 60% slope), and 1% on very steep terrain (>60% slope). The mean area of FCLE within each slope category was 1.7 ha, 0.9 ha, and 0.5 ha, respectively. The mean FCLE area increased from 1.0 ha to 3.2 ha on flat to moderate terrain over the studied period, while the frequency of harvesting increased from 249 to 495 sites per year. On the steep terrain, 35% of the harvesting was done with cable yarding, and 62% with harvester-forwarder systems. On the very steep terrain (>60% slope), 88% of the area was harvested using cable yarding technology while harvesters and forwarders were used on 12% of the area. Overall, FCL proved to be a useful dataset for the purpose of assessing harvesting activity under the given conditions.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
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.
Sammendrag
Accurately positioned single-tree data obtained from a cut-to-length harvester were used as training harvester plot data for k-nearest neighbor (k-nn) stem diameter distribution modelling applying airborne laser scanning (ALS) information as predictor variables. Part of the same harvester data were also used for stand-level validation where the validation units were stands including all the harvester plots on a systematic grid located within each individual stand. In the validation all harvester plots within a stand and also the neighboring stands located closer than 200 m were excluded from the training data when predicting for plots of a particular stand. We further compared different training harvester plot sizes, namely 200 m2, 400 m2, 900 m2 and 1600 m2. Due to this setup the number of considered stands and the areas within the stands varied between the different harvester plot sizes. Our data were from final fellings in Akershus County in Norway and consisted of altogether 47 stands dominated by Norway spruce. We also had ALS data from the area. We concentrated on estimating characteristics of Norway spruce but due to the k-nn approach, species-wise estimates and stand totals as a sum over species were considered as well. The results showed that in the most accurate cases stand-level merchantable total volume could be estimated with RMSE values smaller than 9% of the mean. This value can be considered as highly accurate. Also the fit of the stem diameter distribution assessed by a variant of Reynold’s error index showed values smaller than 0.2 which are superior to those found in the previous studies. The differences between harvester plot sizes were generally small, showing most accurate results for the training harvester plot sizes 200 m2 and 400 m2.