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.
2023
Abstract
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Abstract
Information on tree height-growth dynamics is essential for optimizing forest management and wood procurement. Although methods to derive information on height-growth information from multi-temporal laser scanning data already exist, there is no method to derive such information from data acquired at a single point in time. Drone laser scanning data (unmanned aerial vehicles, UAV-LS) allows for the efficient collection of very dense point clouds, creating new opportunities to measure tree and branch architecture. In this study, we examine if it is possible to measure the vertical positions of branch whorls, which correspond to nodes, and thus can in turn be used to trace the height growth of individual trees. We propose a method to measure the vertical positions of whorls based on a single-acquisition of UAV-LS data coupled with deep-learning techniques. First, single-tree point clouds were converted into 2D image projections, and a YOLOv5 (you-only-look-once) convolutional neural network was trained to detect whorls based on a sample of manually annotated images. Second, the trained whorl detector was applied to a set of 39 trees that were destructively sampled after the UAV-LS data acquisition. The detected whorls were then used to estimate tree-, plot- and stand-level height-growth trajectories. The results indicated that 70 per cent (i.e. precision) of the measured whorls were correctly detected and that 63 per cent (i.e. recall) of the detected whorls were true whorls. These results translated into an overall root-mean-squared error and Bias of 8 and −5 cm for the estimated mean annual height increment. The method’s performance was consistent throughout the height of the trees and independent of tree size. As a use case, we demonstrate the possibility of developing a height-age curve, such as those that could be used for forecasting site productivity. Overall, this study provides proof of concept for new methods to analyse dense aerial point clouds based on image-based deep-learning techniques and demonstrates the potential for deriving useful analytics for forest management purposes at operationally-relevant spatial-scales.
Abstract
The identification of individual tree logs along the wood procurement chain is a coveted goal within the forest industry. The tracing of logs from the sawmill back to the forest would support the legal and sustainable sourcing of wood, as well as increase the resource efficiency and value of harvested timber. In this work, using a dataset of thousands of Scots pine (Pinus sylvestris L.) log end images displaying varying perspectives, lighting, and aging effects, we develop and assess log identification methods based on deep convolutional neural networks. The estimated rank-1 accuracy of our final model on an independent test set of 99 logs is 84 and 91% when allowing for random rotations of the log ends and when keeping each log at approximately fixed orientation, respectively. We estimate the scaling of these methods up to a template pool size of 493 logs, which reveals a weak dependence of accuracy on pool size for logs at fixed orientation. The deep learning approach gives superior results to a classical local binary pattern method, and appears feasible in practice, assuming that pre-filtering of the log database can be leveraged depending on the use case and properties of the queried log image. We make our dataset publicly available.
Authors
Yi Zhang Yijing Feng Zhonghao Ren Runguo Zuo Tianhui Zhang Yeqing Li Yajing Wang Zhiyang Liu Ziyan Sun Yongming Han Lu Feng Mortaza Aghbashlo Meisam Tabatabaei Junting PanAbstract
The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R2 values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3–0.5 mm and the specific surface area was approximately 290 m2/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.
Authors
Ravindu Panditharathne Miyuru Gunathilake Imiya M. Chathuranika Upaka Rathnayake Mukand S. Babel Manoj K. JhaAbstract
Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen’s slope test, Pettitt’s test, indicators of hydrological alteration (IHA), and Pearson’s correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt’s test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB.
Authors
Espen Rimstad Dean Basic Åsa Maria Olofsdotter Espmark Thomas Fraser Snorre Gulla Johan Johansen Tor Atle Mo Ingrid Olesen Rolf-Erik Olsen Erik Georg Bø-Granquist Sokratis Ptochos Amin Sayyari Bjørnar YtrehusAbstract
Background and terms of reference Farmed Atlantic salmon (Salmo salar) that escape into the wild could interbreed with native fish, posing a potential risk to the genetic diversity of wild Atlantic salmon populations. The Atlantic salmon in aquaculture are diploid, meaning the fish has two sets of chromosomes. To mitigate the genetic impact on wild populations, the concept of producing sterile triploid farmed Atlantic salmon has been suggested as a solution. However, it is important to ensure that the utilization of triploids in commercial farming aligns with the regulations set forth in the Norwegian Animal Welfare Act. The Norwegian Food Safety Authority (NFSA) requested the Norwegian Scientific Committee for Food and Environment (VKM) to do an assessment about health- and welfare consequences in triploid Atlantic salmon under commercial farming conditions, as compared to diploid counterparts. VKM was also requested to describe the underlying physiological mechanisms concerning consequences of triploidy as well as address potential measures to reduce the negative impacts on the health and welfare of the fish. Methods A working group consisting of members with expertise in salmonid biology, aquaculture systems, veterinary medicine, fish health and welfare, virology, bacteriology, parasitology, breeding and genetics has drafted this opinion. To answer the Terms of Reference as mandated by the NFSA, the authors addressed fish health and welfare as a unified concept in this report. Two external experts have reviewed and provided their opinion before it was assessed and approved by the VKM’s Panel on Animal Health and welfare. The literature used in this work was peer-reviewed studies retrieved from a search in four databases as well as non peer-reviewed reports. Selection of studies was conducted independently by two members in the working group and based on predefined inclusion and exclusion criteria. Conclusions Under commercial farming conditions, triploid Atlantic salmon are often found to have lower standards of health and welfare compared to diploids. For example, field and experimental studies have found triploids to be more prone to skeletal and heart deformities, and cataracts, while field studies suggest that under commercial farming conditions they cope less well with handling and are more susceptible to skin ulcers. However, research has indicated that some of the effects of triploidy can be mitigated through specialized diets or environmental adjustments. There is a noticeable tendency across farm studies and experimental trials for triploid salmon to be equal or larger in size at the end of freshwater phase, but equal or smaller in size at the end of the seawater phase. Most publications conclude that within what is considered the optimal temperature range of diploids, oxygen consumption rate, oxygen binding capacity, and aerobic swimming capacity do not significantly differ between triploid and diploid Atlantic salmon. However, findings from experimental trials suggest a lower optimal temperature range for triploids, and data consistent across studies indicate that triploids possess lower tolerance to hypoxia at elevated temperatures. Triploid Atlantic salmon are less robust to higher water temperatures than diploid, and have other nutritional needs than diploids, especially regarding phosphorus, and histidine. There are few studies on the susceptibility of triploid salmon to infectious agents and diseases. Field observations indicate that triploid fish are more susceptible to primary infectious salmon anaemia (ISA) outbreaks than diploids under commercial farming conditions at the level of the farm, and at cage level within farms that experience an ISA outbreak. A higher susceptibility to the ISA virus would potentially affect not only the health and welfare of the triploid fish at the farm with an outbreak but may potentially spread to other farms. .............
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
Livestock husbandry has raised enormous environmental concerns around the world, including water quality issues. Yet there is a need to document long-term water quality trends in livestock-intensive regions and reveal the drivers for the trends based on detailed catchment monitoring. Here, we assessed the concentration and load trends of dissolved reactive phosphorus (DRP) in streamwater of a livestock-intensive catchment in southwestern Norway, based on continuous flow measurements and flow-proportional composite water sampling. Precipitation and catchment-level soil P balance were monitored to examine the drivers. At the field level, moreover, the relationship between soil P balance and soil test P (measured using the ammonium lactate extraction method, P-AL) was assessed. Results showed that on average of 20 years 95 % of the P was applied to the catchment during March–August, when 40 % of annual precipitation and 25 % of annual discharge occurred. The low runoff helped reduce P loss following P applications. However, flow-weighted annual mean DRP concentration significantly increased with increasingly cumulative soil P surplus (R2 = 0.55, p = 0.0002). With a mean annual P surplus of 8.8 kg ha−1, the annual mean DRP concentration (range: 49–140 μg L−1; mean: 80 μg L−1) and annual DRP load (range: 0.35–1.46 kg ha−1; mean: 0.65 kg ha−1) significantly increased over the 20-year monitoring period (p = 0.001 and 0.0003, respectively). At the field level, P-AL concentrations were positively correlated with soil P balances (R2 = 0.48, p < 0.0001), confirming the long-term impact of P balances on the risks of P loss. The study highlights the predominant role of long-term P balances in affecting DRP loss in livestock-intensive regions through the effect on soil test P.