Knut Bjørkelo

Chief Engineer

(+47) 974 72 653
knut.bjorkelo@nibio.no

Place
Ås R9

Visiting address
Raveien 9, 1430 Ås

Abstract

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

Planning sustainable use of land resources and environmental monitoring benefit from accurate and detailed forest information. The basis of accurate forest information is data on the spatial extent of forests. In Norway land resource maps have been carefully created by field visits and aerial image interpretation for over four decades with periodic updating. However, due to prioritization of agricultural and built-up areas, and high requirements with respect to the map accuracy, forest areas and outfields have not been frequently updated. Consequently, in some part of the country, the map has not been updated since its first creation in the 1960s. The Sentinel-2 satellite acquires images with high spatial and temporal resolution which provides opportunities for creating cloud-free mosaic images over areas that are often covered with clouds. Here, we combine object-based image analysis with machine learning methods in an automated framework to map forest area in Sentinel-2 mosaic images. The images are segmented using the eCogntionTM software. Training data are collected automatically from the existing land resource map and filtered using height and greenness information so that the training samples certainly represent their respective classes. Two machine learning algorithms, namely Random Forest (RF) and the Multilayer Perceptron Neural Network (MLP), are then trained and validated before mapping forest area. The effects of including and excluding some features on the classification accuracy is investigated. The results show that the method produces forest cover map at very high accuracy (up to 97%). The MLP performs better than the RF algorithm both in classification accuracy and in robustness against inclusion and exclusion of features.

Abstract

Cultivated organic soils account for ~7% of Norway’s agricultural land area, and they are estimated to be a significant source of greenhouse gas (GHG) emissions. The project ‘Climate smart management practices on Norwegian organic soils’ (MYR), commissioned by the Research Council of Norway (decision no. 281109), aims to evaluate GHG (e.g. carbon dioxide, methane and nitrous oxide) emissions and impacts on biomass productivity from three land use types (cultivated, abandoned and restored) on organic soils. At the cultivated sites, impacts of drainage depth and management intensity will be measured. We established experimental sites in Norway covering a broad range of climate and management regimes, which will produce observational data in high spatiotemporal resolution during 2019-2022. Using state-of-the-art modelling techniques, MYR aims to predict the potential GHG mitigation under different scenarios (e.g. different water table depth, management practices and climate pattern). Four models (BASGRA, DNDC, Coup and ECOSSE) will be further developed according to the physical/chemical properties of peat soil and then used independently in simulating biogeochemical processes and biomass dynamics in the different land uses. Robust parameterization schemes for each model to improve the predictive accuracy will be derived from a new dataset collected from multiple experimental sites in the Nordic region. Thereafter, the models will be used in the regional simulation to present the spatial heterogeneity in large scale. Eventually, a multi-model ensemble prediction will be carried out to provide scenario analyses by 2030 and 2050. By integrating experimental results and modelling, the project aims at generating useful information for recommendations on environment-friendly use of Norwegian peatlands.

Abstract

This paper describes the development and utility of the Norwegian forest resources map (SR16). SR16 is developed using photogrammetric point cloud data with ground plots from the Norwegian National Forest Inventory (NFI). First, an existing forest mask was updated with object-based image analysis methods. Evaluation against the NFI forest definitions showed Cohen's kappa of 0.80 and accuracy of 0.91 in the lowlands and a kappa of 0.73 and an accuracy of 0.96 in the mountains. Within the updated forest mask, a 16×16 m raster map was developed with Lorey's height, volume, biomass, and tree species as attributes (SR16-raster). All attributes were predicted with generalized linear models that explained about 70% of the observed variation and had relative RMSEs of about 50%. SR16-raster was segmented into stand-like polygons that are relatively homogenous in respect to tree species, volume, site index, and Lorey's height (SR16-vector). When SR16 was utilized in a combination with the NFI plots and a model-assisted estimator, the precision was on average 2–3 times higher than estimates based on field data only. In conclusion, SR16 is useful for improved estimates from the Norwegian NFI at various scales. The mapped products may be useful as additional information in Forest Management Inventories.

Abstract

Cultivated organic soils account for ∼7% of Norway’s agricultural land area, and they are estimated to be a significant source of greenhouse gas (GHG) emissions. The project ‘Climate smart management practices on Norwegian organic soils’ (MYR), commissioned by the Research Council of Norway (decision no. 281109), aims to evaluate GHG (e.g. carbon dioxide, methane and nitrous oxide) emissions and impacts on biomass productivity from three land use types (cultivated, abandoned and restored) on organic soils. At the cultivated sites, impacts of drainage depth and management intensity will be measured. We established experimental sites in Norway covering a broad range of climate and management regimes, which will produce observational data in high spatiotemporal resolution during 2019-2021. Using state-of-the-art modelling techniques, MYR aims to predict the potential GHG mitigation under different scenarios. Four models (BASGRA, DNDC, Coup and ECOSSE) will be further developed according to the soil properties, and then used independently in simulating biogeochemical processes and biomass dynamics in the different land uses. Robust parameterization schemes for each model will be based in the observational data from the project for both soil and crop combinations. Eventually, a multi-model ensemble prediction will be carried out to provide scenario analyses by 2030 and 2050. By integrating experimental results and modelling, the project aims at generating useful information for recommendations on environment-friendly use of Norwegian peatlands.

Abstract

Cultivated organic soils (7-8% of Norway’s agricultural land area) are economically important sources for forage production in some regions in Norway, but they are also ‘hot spots’ for greenhouse gas (GHG) emissions. The project ‘Climate smart management practices on Norwegian organic soils’ (MYR; funded by the Research Council of Norway, decision no. 281109) will evaluate how water table management and the intensity of other management practices (i.e. tillage and fertilization intensity) affects both GHG emissions and forage’s quality & production. The overall aim of MYR is to generate useful information for recommendations on climate-friendly management of Norwegian peatlands for both policy makers and farmers. For this project, we established two experimental sites on Norwegian peatlands for grass cultivation, of which one in Northern (subarctic, continental climate) and another in Southern (temperate, coastal climate) Norway. Both sites have a water table level (WTL) gradient ranging from low to high. In order to explore the effects of management practices, controlled trials with different fertilization strategies and tillage intensity will be conducted at these sites with WTL gradients considered. Meanwhile, GHG emissions (including carbon dioxide, methane and nitrous oxide), crop-related observations (e.g. phenology, production), and hydrological conditions (e.g. soil moisture, WTL dynamics) will be monitored with high spatiotemporal resolution along the WTL gradients during 2019-2021. Besides, MYR aims at predicting potential GHG mitigation under different scenarios by using state-of-the-art modelling techniques. Four models (BASGRA, Coup, DNDC and ECOSSE), with strengths in predicting grass growth, hydrological processes, soil nitrification-denitrification and carbon decomposition, respectively, will be further developed according to the soil properties. Then these models will be used independently to simulate biogeochemical and agroecological processes in our experimental fields. Robust parameterization schemes will be based on the observational data for both soil and crop combinations. Eventually, a multi-model ensemble prediction will be carried out to provide scenario analyses by 2030 and 2050. We will couple these process-based models with optimization algorithm to explore the potential reduction in GHG emissions with consideration of production sustenance, and upscale our assessment to regional level.

To document

Abstract

Forestry in coastal Norway has traditionally been a marginal activity with a low annual harvest rate. However, the region is now faced with large areas of spruce plantations that will reach harvest maturity within the next 25 years. Due to the poor infrastructure in the region, the current challenge is to harvest the maturing spruce plantations at an acceptable cost. Hence, there is considerable interest both from the forest sector and politicians to invest in infrastructure that can provide the basis for profitable forest sector development in coastal Norway. This paper presents a mathematical optimization model for timber transportation from stump to industry. The main decision variables are location of quays, upgrade of public road links, the length of new forest roads, and when the investments should happen. The main objective is to provide decision support for prioritization of infrastructure investments. The optimization model is combined with a dynamical forest resource model, providing details on available volumes and costs. A case study for coastal Norway is presented and solved to optimality. The instance includes 10 counties comprising more than 200 municipalities with forest resources, 53 possible new quays for timber export and 916 public road links that also can be upgraded. Compared with a no investment case, the optimal solution improved the objective by 23%. The study shows that consistent, informative and good analyses can be performed to evaluate trade-offs, prioritization, time and order of investment, and cost saving potentials of infrastructure investments in the forest industry. The solution seems reasonable based on present infrastructure and state of the forest.

Abstract

Et overordnet samfunnsmål er å sikre en bærekraftig bruk og forvaltning av Norges arealressurser. Det krever en kontinuerlig leveranse av pålitelig og oppdatert informasjon til beslutningstakere. For å være i stand til å levere denne informasjonen, produserer Norsk institutt for skog og landskap blant annet arealressursstatistikk for alle kommuner i Norge. Statistikken produseres også på fylkesnivå og for hele landet. Arealtallene hentes ut fra en kombinasjon av ulike nasjonale datasett i ulike målestokker sammen med tolkning av satellittbilder. Gjennom en omklassifisering beregnes statistikk for visse landressursklasser som dyrka jord, beite, skog basert på produktivitet, ferskvann, snø og isbre, snaumark og bebygd område. Skog og landskap har de siste par årene brukt åpen kildekode. Hele produksjonslinje utføres ved hjelp av slik programvare. Resultatene lagres i XML-filer som legges ut på internett. Produksjonen krever behandling av flere databaser med nasjonal dekning og må håndtere geometriske operasjoner effektivt og uten feil. Den åpne kildekodeløsningen er pålitelig, stabil og rask.