Purpose: Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024). Methods and Results: Two inversion approaches – look-up table (LUT) and artificial neural network (ANN) were applied to PROSAIL simulations. UAV–PROSAIL–ANN outperformed LUT-based inversion and SRS observations, achieving the highest accuracy for LAI (R2 = 0.81–0.95; RMSE = 0.27–0.77 m2/m2), followed by CCC (R2 = 0.58–0.94; RMSE < 60 μg/cm2), while LCC remained less accurately estimated (R2 = 0.26–0.78; RMSE < 16 μg/cm2). Across sensors and methods, retrieval accuracy decreased in the order of LAI, CCC, and LCC, reflecting the stronger spectral control of canopy structure compared to biochemical traits. Conclusions: The UAV–PROSAIL–ANN framework effectively captured spatial and temporal variability in crop traits, producing canopy-scale maps consistent with field observations. These results demonstrate the robustness and scalability of hybrid PROSAIL–ANN inversion for high-latitude crop monitoring, while highlighting current limitations in biochemical trait retrieval using multispectral data

Ship-bridge allision risk assessments often address either probability or consequence; integrations of both in a unified methodology are rare. This paper fills that gap by introducing Ship Traffic Allision Probability using Monte Carlo Simulations – consequence (STAPS-cons), a methodology where a mid-fidelity simulation methodology developed for probability assessment is used together with the results of Finite Element Analysis (FEA) simulations to include consequence assessments. Using Automatic Information System (AIS) data and the proposed methodology, a potential bridge over Norway’s Bjørnafjorden is analysed in a case study to determine the risk of structural collapse. The case study also illustrates how the risk is affected by different input parameters, both related to probability and consequence estimations. Five scenarios were analysed in this case study; three passed and two failed the Norwegian criterion for the probability of structural collapse, which is less frequent than 10−4 per year. A 20 % increase in the duration of a ship’s miss of turning point notably raised both the allision frequencies and the necessary levels of energy the bridge must withstand. In another scenario, where a less stiff bridge structure was analysed, the demand on the global structure decreased. In conclusion, the STAPS-cons methodology represents a significant advancement in the field of ship-bridge allision risk assessment. By integrating probability and consequence assessments, this methodology offers a more robust and comprehensive tool for managing the risks associated with increased shipping traffic and bridge construction in navigational waters.

Charging needs of leisure boats – possibilities to use analysis of AIS data The project FRITIDSLADD developed methods for using data from AIS-B, e.g. segmentation algorithms. The starting point was that analysis of AIS-B data would provide opportunities to include smaller boats in AIS analyses. It was investigated how AIS-B can be used to map recreational boats' usage patterns. This report describes the work that has been carried out. AIS-B data has been made available for analysis. Application was tested with analysis of usage patterns in terms of speed, mileage and length of stay in port for two ports on the west coast. The report also describes several challenges related to data quality regarding AIS-B. Overall, transmitter power, refresh rate, antenna height and geography mean that data from Class B is less reliable than for Class A. For analysis of traffic patterns, this means that you cannot always rely on the accuracy of positions for a particular boat. At the end of the report, suggestions are made for further in-depth study.

The advances in civil engineering with novel bridge designs between islands and across fjords with long spans, increasing ship traffic density and larger ships in coastal areas, have resulted in an increased frequency of ship-bridge allision accidents worldwide. It is thus essential to have reliable models and methods for engineers to create safe designs of these new bridges to simulate and analyse early pro-active mitigation measures. This study presents a new ship traffic allision probabilistic simulation mid fidelity model (STAPS), which includes a ship's manoeuvrability and motion physics and uses the Monte Carlo simulation method in the probabilistic calculations. It is compared with the low fidelity model IWRAP Mk2, which is used to analyse the risk of ship allisions with structures. Two case studies with ship-allision scenarios are presented to compare how the model fidelity levels of STAPS and IWRAP Mk2 affect the calculated probability levels of ship-bridge allision events. On a general level, the results show that IWRAP Mk2 overestimates the accident probability, for example IWRAP Mk2 predicts a 4.5 times higher probability of allisions compared to STAPS in the base case, and that the failure's duration and route layouts significantly influence both models. The study concludes that IWRAP Mk2 is preferred in the early phase of bridge design and STAPS is preferred in later stages.

This paper studies the probability of active navigational error events for use in ship-bridge allision risk analysis. To estimate the probability of these kinds of events, accident databases, incident reports and AIS data were studied; the case studies herein cover 6 years and 15 bridges in Scandinavia. The main findings of this paper show that there is great variation in the probability of ship-bridge allision due to active navigational errors, and it is not recommended to use the currently common practice of 2% uniform distribution of the number of ship passages on all bridges. Another important finding is that the probability of a ship striking a bridge due to the error type Wrong Course at a Turning point is not uniform along the length of the bridge, but is only likely to occur in a cone formation from the last turning point.

This paper studies the probability of active navigational error events for use in ship–bridge allision risk analysis. To estimate the probability of these kinds of events, accident databases, incident reports and AIS data were studied; the case studies herein cover 6 years and 15 bridges in Scandinavia. The main findings of this paper show that there is great variation in the probability of ship–bridge allision due to active navigational errors, and it is not recommended to use the currently common practice of 2% uniform distribution of the number of ship passages on all bridges. Another important finding is that the probability of a ship striking a bridge due to the error type Wrong Course at a Turning point is not uniform along the length of the bridge, but is only likely to occur in a cone formation from the last turning point.

Risk assessment is important both while planning to build new infrastructure and for maintenance of existing structures. The probability of ship-structure allisions could be estimated with IWRAP Mk II, which is a commonly used software for estimating maritime risks. However, the research coverage of the software is limited with regards to groundings and allisions. The aim of this study is to verify how IWRAP Mk II estimates the accident probability. To perform this verification a separate tool is constructed, OMRAT, based on the same theory as IWRAP Mk II. The aim is also to highlight the sensitivity of different parameters in these types of models. It is concluded that IWRAP Mk II estimates the probability of allisions and groundings with the same equations. Another conclusion is that some parameters have a linear effect on the accident probabilities and other parameters are also influenced by the model layout.

Risk assessment is important both while planning to build new infrastructure and for maintenance of existing structures. The probability of ship-structure allisions could be estimated with IWRAP Mk II, which is a commonly used software for estimating maritime risks. However, the research coverage of the software is limited with regards to groundings and allisions. The aim of this study is to verify how IWRAP Mk II estimates the accident probability. To perform this verification a separate tool is constructed, OMRAT, based on the same theory as IWRAP Mk II. The aim is also to highlight the sensitivity of different parameters in these types of models. It is concluded that IWRAP Mk II estimates the probability of allisions and groundings with the same equations. Another conclusion is that some parameters have a linear effect on the accident probabilities and other parameters are also influenced by the model layout.

In the transition to sustainable shipping, Liquified Natural Gas (LNG), is proposed to play a role, reducing emissions of sulphur and nitrogen oxides, and particulate matter. However, LNG is a fossil fuel and there is an ongoing discussion regarding the extent of methane slip from ships operating on LNG, challenging the assumptions of LNG as a sustainable solution. Here we show another aspect to consider in the environmental assessment of shipping; LNG feeder vessels may spend as much as 25% of their time at sea just running the ship to ensure the pressure in the tanks are not exceeded, i.e., run time not directly attributed to the shipment of gas from one port or ship, to another. In other words, the economic incentives are currently allowing for roughly 32% increase of the ships’ operational emissions and discharges and increased navigational risks. Most coastal areas are heavily affected by anthropogenic activities and e.g., in the Baltic Sea there is consensus among the HELCOM member states that the input of nutrient and hazardous substances must be reduced. Even if the LNG feeder vessels are currently few, the possibility to reduce their environmental impact by 32% should be an attractive opportunity for future policy measures and investigation of technological solutions of the problem. 

The study presents a methodology that uses AIS data and a ship manoeuvring simulator to simulate and analyse marine traffic schemes with regard to risks for accidents. An event identification method is presented, which is needed for the accident scenario part of the methodology. This is based on AIS data, where the Great Belt VTS area was used to verify the methodology. Three events that could result in ship-bridge allisions were modelled and simulated in the simulator: drifting ship, sharp turning ship and miss of turning point. The Monte Carlo method was used to perform large number of simulator runs, including a parameter sensitivity analysis. The probability of a ship allision against the Great Belt Bridge was calculated to be 0.007. Analysis of the ship-bridge allision cases was shown to be dominated by the event drifting ship. This event has a relatively low kinetic energy at the impact, and the expected allision energy for a 1,000-year allision corresponds to a 178 m tanker with 57,870 DWT and ship speed 14.6 knots. Finally, this study presents a mitigation analysis, which shows how the probability of allisions can be reduced by reducing the ship speed or altering the traffic separation scheme. © 2021 The Authors