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.
2021
Authors
Johannes Breidenbach Janis Ivanovs Annika Kangas Thomas Nord-Larsen Mats Nilsson Rasmus AstrupAbstract
Policy measures and management decisions aimed at enhancing the role of forests in mitigating climate change require reliable estimates of carbon (C)-stock dynamics in greenhouse gas inventories (GHGIs). The aim of this study was to assemble design-based estimators to provide estimates relevant for GHGIs using National Forest Inventory (NFI) data. We improve basic expansion (BE) estimators of living-biomass C-stock loss using only field data, by leveraging with remote sensing auxiliary data in model-assisted (MA) estimators. Our case studies from Norway, Sweden, Denmark, and Latvia covered an area of >70 Mha. Landsat-based forest cover loss (FCL) and one-time wall-to-wall airborne laser scanning (ALS) served as auxiliary data. ALS provided information on the C stock before a potential disturbance indicated by FCL. The use of FCL in MA estimators resulted in considerable efficiency gains, which in most cases were further increased by adding ALS. A doubling of efficiency was possible for national estimates and even larger efficiencies were observed at the subnational level. Average annual estimates were considerably more precise than pooled estimates of the NFI data from all years at once. The combination of remotely sensed and NFI field data yields reliable estimators, which is not necessarily the case when using remotely sensed data without reference observations.
Abstract
There is a need for mapping of forest areas with young stands under regeneration in Norway, as a basis for conducting tending, or precommercial thinning (PCT), whenever necessary. The main objective of this article is to show the potential of multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) data for characterization and detection of forest stands under regeneration. We identify the most powerful radar and optical features for discrimination of forest stands under regeneration versus other forest stands. A number of optical and radar features derived from multitemporal S-1 and S-2 data were used for the class separability and cross-correlation analysis. The analysis was performed on forest resource maps consisting of the forest development classes and age in two study sites from south-eastern Norway. Important features were used to train the classical random forest (RF) classification algorithm. A comparative study of performance of the algorithm was used in three cases: I) using only S-1 features, II) using only S-2 optical bands, and III) using combination of S-1 and S-2 features. RF classification results pointed to increased class discrimination when using S-1 and S-2 data in relation to S-1 or S-2 data only. The study shows that forest stands under regeneration in the height interval for PCT can be detected with a detection rate of 91% and F-1 score of 73.2% in case III as most accurate, while tree density and broadleaf fraction could be estimated with coefficient of determination ( R2 ) of about 0.70 and 0.80, respectively.
Abstract
No abstract has been registered
Abstract
Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed-effects (PPM), generalized linear-mixed (GLM), and k nearest-neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95–1.47 and 0.96–1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%.
Abstract
Field-based monitoring of deer food availability and browsing on recruiting forest trees is a necessary but labour-intensive task. We explored how such estimates from a low-resolution multipurpose national forest inventory (NFI) (plot density 0.3 km−2) corresponded with estimates from local inventories that specifically and in greater detail monitor the availability of deer food and browsing intensity (LFI) (plot density 2–3 km−2). We used NFI and LFI data from 16 moose Alces alces ranges (mean area 276 ± SE 69 km2) in southern Norway. Only the height segment 30–130 cm of browsable trees could be obtained from the NFI data, while moose can browse trees from 30 to 300 cm in height. According to the LFI, the browse species did not have similar proportions of their browsable stems below 130 cm. Using only the stems from heights of 30–130 cm overestimated the availability of RAS (rowan, aspen and sallow) relative to birch (silver birch and downy birch) and Scots pine. The browsable biomass per stem of each species also varied between ranges, which introduces uncertainty to the food availability estimates that are based on stems only. Nevertheless, the NFI density of stems at 30–130 cm heights can be a useful index for species-specific comparisons of browse availability across ranges, because the variations between ranges in stem densities outweighed the biomass variations per stem. The NFI and LFI estimates of the species-specific densities of stems at 30–130 cm heights were significantly related and close to isometric (1:1), especially for RAS and pine. We did not find strong relationships between NFI and LFI in the browsing intensity (i.e. proportion of shoots that were browsed during the winter). The explained variation was only 11% (R2) for RAS (p = 0.281) and 32% for pine (p = 0.028). This was likely due to the small sample sizes of browsed trees in the NFI and methodological differences between the NFI and LFI in how browsing intensity is estimated. Conclusions Using data from national forest inventories can be an efficient but low-resolution way to monitor browse availability for deer, provided that the monitoring includes the full range of tree heights reachable for the deer (e.g., 30–300 cm for moose). It is also a prerequisite that the number of NFI plots is sufficient to cover the spatial variability of the area. Regarding browsing intensities, adjustments in both the NFI and LFI approaches are needed to make the two monitoring schemes more comparable.
Authors
Teresa Gómez de la Bárcena Lise Dalsgaard Line Tau Strand Christian Wilhelm Mohr Knut Bjørkelo Rune Eriksen Gunnhild SøgaardAbstract
Denne publikasjonen presenterer en ny metodikk for estimering av endringer i lageret av jordkarbon som følge av arealbruksendringer på mineraljord. Metodikken er utviklet for bruk i den nasjonale rapporteringen av arealbrukssektoren under FNs klimakonvensjon. Metodikken baserer seg på den enkleste tilnærming i følge IPCC sine retningslinjer, en såkaldt Tier 1. Tier 1 metodikken baseres i stor grad på standardverdier fra retningslinjene (IPCC default), men trenger en kopling mot nasjonal arealinformasjon. Denne koplingen beskrives i rapporten. Metodikken tar utgangspunkt i standardverdier for lageret av jordkarbon (SOCREF). Disse er basert på jordtype-grupperinger og klimasone som stammer fra en verdensdekkende jorddatabase. Endringer i jordkarbon etter arealbruksendring estimeres ved hjelp av SOCREF i kombinasjon med et sett faktorer (også standardverdier) som er arealbruksavhengige. Metodikken legger til grunn at endringer i jordkarbon skjer lineært over 20 år (ifølge 2006 IPCC Guidelines). Grunnleggende informasjon for å kunne kople standardverdier mot arealer på en konsistent måte er stort sett manglende for Norge på nasjonal skala. Rapporten gir derfor detaljert informasjon om de datakildene som har vært brukt til å kunne definere hvilke standariserte verdier som tilhører et bestemt areal i overgang....
Abstract
No abstract has been registered
Abstract
No abstract has been registered
2020
Authors
Johannes Breidenbach Lars T. Waser Misganu Debella-Gilo Johannes Schumacher Johannes Rahlf Marius Hauglin Stefano Puliti Rasmus AstrupAbstract
No abstract has been registered
Authors
Johannes Breidenbach Lars T. Waser Misganu Debella-Gilo Johannes Schumacher Johannes Rahlf Marius Hauglin Stefano Puliti Rasmus AstrupAbstract
Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) plot data for modelling and subsequent mapping of spruce-, pine-, and deciduous-dominated forest in Norway at a 16 m × 16 m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42 000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by ∼10%. The produced map was subsequently used to generate model-assisted (MA) and poststratified (PS) estimates of species-specific forest area. At the national level, efficiencies of the estimates increased by 20% to 50% for MA and up to 90% for PS. Greater minimum numbers of observations constrained the use of PS. For MA estimates of municipalities, efficiencies improved by up to a factor of 8 but were sometimes also less than 1. PS estimates were always equally as or more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.