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.
2018
Abstract
Degradation of organic chemicals in natural soils depends on oxidation-reduction conditions. To protect our groundwater resources we need to understand the degradation processes under anaerobic conditions. Available iron and manganese oxides are used as electron acceptors for anaerobic degradation and are reduced to the dissolved form of metallic cations in pore water. To monitor this process is a challenge, because anaerobic conditions are difficult to sample directly without introducing oxygen. A few studies have shown an impact of iron reduction on spectral induced polarisation (SIP) signature, often associated with bacterial growth. Our objective is to study the impact of iron and manganese oxide dissolution, caused by degradation of an organic compound, with spectral induced polarisation signatures. Twenty-six vertical columns (30 cm high, inner diameter 4.6 cm) were filled with a sand rich in oxides (manganese and iron) with a static water table in the middle. In half of the columns, a 2 cm high contaminated layer was installed just above the water table. As the contaminant degrades, the initial oxygen is consumed and anaerobic conditions form Every three days over a period of one month, spectral induced polarisation (twenty frequencies between 5mHz and 10 kHz) data were collected on six columns: three contaminated replicates and three control replicates. Chemical analysis was done on twenty columns assigned for destructive water sampling, ten contaminated columns and ten control. The results show an increase of the real conductivity associated with the degradation processes, independent of frequency. Compared with the pore water electrical conductivity in the saturated zone, the real conductivity measurement revealed the formation of surface conductivity before iron was released in the pore water. In parallel, we also observed an evolution of the imaginary conductivity in both saturated and unsaturated zones at frequencies below 1 Hz. Overall, the anaerobic reduction of iron and manganese oxide during the organic degradation increased both the conductive and polarisation component of the complex conductivity.
Abstract
The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km2 at 18 km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric–vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.
Abstract
Inspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR) data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep learning approach. Our investigation concludes with the proposition of some novel deep learning architectures for generating detailed land resource maps by employing a semantic segmentation approach. The contribution of our work is threefold. (1) First, we implement the multiclass version of the intersection-over-union (IoU) loss function that contributes to handling highly imbalanced datasets and preventing overfitting. (2) Thereafter, we propose a novel deep learning architecture integrating the deep atrous network architecture with the stochastic depth approach for speeding up the learning process, and impose a regularization effect. (3) Finally, we introduce an early fusion deep layer that combines image-based and LiDAR-derived features. In a benchmark study carried out using the Follo 2014 LiDAR data and the NIBIO AR5 land resources dataset, we compare our proposals to other deep learning architectures. A quantitative comparison shows that our best proposal provides more than 5% relative improvement in terms of mean intersection-over-union over the atrous network, providing a basis for a more frequent and improved use of LiDAR data for automatic land cover segmentation.
Abstract
The extent of land lease is increasing in many countries, including Norway. This paper develops a von Thünen type model of optimal land plots to lease from a farm’s center. For a single farm setting the optimality principle is that land is leased as long as the expected marginal value of leasing the land is greater than or equal to the expected marginal costs of leasing the land. The single farm model setting captures land lease at the extensive margin, i.e., under absence of competition for leasing land. Land lease at the intensive margin, i.e., when there is competition for leasing farm fields, is more interesting. We distinguish between two cases. In the first case, continued farm operations do not depend on being able to lease more land. Then we show that optimal land lease results when the expected profits for each farm of leasing its least profitable field is equal among farms competing for the same farm field. This also corresponds to an economically efficient allocation of leased land. Our second case at the intensive margin is more complicated. Here, farm survival depends on attracting acreage of leased land to allow for investment in cost saving technology. We show that the resulting allocation of leased land corresponds to the solution of a game involving bidding for land to prevent other farmers from getting land, which in turn leads to farmer exit and therefore increases the future supply of land available at the land lease market. In the first round of the game, winners of the land lease auction pays more for the leased land than they would have done in absence preventive bidding. The model framework is applicable for other settings where locking out competitors are parts of agents’ strategy space. Key words: von Thünen, non-cooperative game theory, auctions with preventive bidding. JEL classification: C72, D44, L13
Abstract
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Authors
Signe Kynding Borgen Gry Alfredsen Johannes Breidenbach Lise Dalsgaard Gunnhild Søgaard Aaron SmithAbstract
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Authors
Eveliina KallioniemiAbstract
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Authors
Wei He Weimin Ju Christopher R. Schwalm Sebastian Sippel Xiaocui Wu Qiaoning He Lian Song Chunhua Zhang Jing Li Stephen Sitch Nicolas Viovy Pierre Friedlingstein Atul K. JainAbstract
Recently, severe droughts that occurred in North America are likely to have impacted its terrestrial carbon sink. However, process‐based understanding of how meteorological conditions prior to the onset of drought, for instance warm or cold springs, affect drought‐induced carbon cycle effects remains scarce. Here we assess and compare the response of terrestrial carbon fluxes to summer droughts in 2011 and 2012 characterized by contrasting spring conditions. The analysis is based on a comprehensive ensemble of carbon cycle models, including FLUXCOM, TRENDY v5, SiBCASA, CarbonTracker Europe, and CarbonTracker, and emerging Earth observations. In 2011, large reductions of net ecosystem production (NEP; −0.24 ± 0.17 Pg C/year) are due to decreased gross primary production (−0.17 ± 0.18 Pg C/year) and slightly increased ecosystem respiration (+0.07 ± 0.17 Pg C/year). Conversely, in 2012, NEP reductions (−0.17 ± 0.25 Pg C/year) are attributed to a larger increase of ecosystem respiration (+0.48 ± 0.27 Pg C/year) than gross primary production (+0.31 ± 0.29 Pg C/year), induced predominantly by an extra warmer spring prior to summer drought. Two temperate ecoregions crops/agriculture and the grass/shrubs contribute largest to these reductions and also dominate the interannual variations of NEP during 2007–2014. Moreover, the warming spring compensated largely the negative carbon anomaly due to summer drought, consistent with earlier studies; however, the compensation occurred only in some specific ecoregions. Overall, our analysis offers a refined view on recent carbon cycle variability and extremes in North America. It corroborates earlier results but also highlights differences with respect to ecoregion‐specific carbon cycle responses to drought and heat.
Abstract
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Authors
Jonathan Rizzi Clara Antón Fernández Paulo Jorge de Almeida Borges Ryan Bright Rasmus AstrupAbstract
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