Governance hurdles for expansion of low trophic mariculture production in Sweden.

The study examines the governance of low trophic species mariculture (LTM) using Sweden as a case study. LTM, involving species such as seaweeds and mollusks, offers ecosystem services and nutritious foods. Despite its potential to contribute to blue growth and Sustainable Development Goals, LTM development in the EU and OECD countries has stagnated. A framework for mapping governance elements (institutions, structures, and processes) and analyzing governance objective (effective, equitable, responsive, and robust) was combined with surveys addressed to the private entrepreneurs in the sector. Analysis reveals ineffective institutions due to lack of updated legislation and guidance, resulting in ambiguous interpretations. Governance structures include multiple decision-making bodies without a clear coordination agency. Licensing processes were lengthy and costly for the private entrepreneurs, and the outcomes were uncertain. To support Sweden's blue bioeconomy, LTM governance requires policy integration, clearer direction, coordinated decision-making, and mechanisms for conflict resolution and learning.

Relationship between reflectance and degree of polarization in the VNIR-SWIR: A case study on art paintings with polarimetric reflectance imaging spectroscopy.

We study the relationship between reflectance and the degree of linear polarization of radiation that bounces off the surface of an unvarnished oil painting. We design a VNIR-SWIR (400 nm to 2500 nm) polarimetric reflectance imaging spectroscopy setup that deploys unpolarized light and allows us to estimate the Stokes vector at the pixel level. We observe a strong negative correlation between the S0 component of the Stokes vector (which can be used to represent the reflectance) and the degree of linear polarization in the visible interval (average -0.81), while the correlation is weaker and varying in the infrared range (average -0.50 in the NIR range between 780 and 1500 nm, and average -0.87 in the SWIR range between 1500 and 2500 nm). By tackling the problem with multi-resolution image analysis, we observe a dependence of the correlation on the local complexity of the surface. Indeed, we observe a general trend that strengthens the negative correlation for the effect of artificial flattening provoked by low image resolutions.

Raw Spectral Filter Array Imaging for Scene Recognition.

Scene recognition is the task of identifying the environment shown in an image. Spectral filter array cameras allow for fast capture of multispectral images. Scene recognition in multispectral images is usually performed after demosaicing the raw image. Along with adding latency, this makes the classification algorithm limited by the artifacts produced by the demosaicing process. This work explores scene recognition performed on raw spectral filter array images using convolutional neural networks. For this purpose, a new raw image dataset is collected for scene recognition with a spectral filter array camera. The classification is performed using a model constructed based on the pretrained Places-CNN. This model utilizes all nine channels of spectral information in the images. A label mapping scheme is also applied to classify the new dataset. Experiments are conducted with different pre-processing steps applied on the raw images and the results are compared. Higher-resolution images are found to perform better even if they contain mosaic patterns.

Improving RGB illuminant estimation exploiting spectral average radiance.

We introduce a method that enhances RGB color constancy accuracy by combining neural network and k-means clustering techniques. Our approach stands out from previous works because we combine multispectral and color information together to estimate illuminants. Furthermore, we investigate the combination of the illuminant estimation in the RGB color and in the spectral domains, as a strategy to provide a refined estimation in the RGB color domain. Our investigation can be divided into three main points: (1) identify the spatial resolution for sampling the input image in terms of RGB color and spectral information that brings the highest performance; (2) determine whether it is more effective to predict the illuminant in the spectral or in the RGB color domain, and finally, (3) assuming that the illuminant is in fact predicted in the spectral domain, investigate if it is better to have a loss function defined in the RGB color or spectral domain. Experimental results are carried out on NUS: a standard dataset of multispectral radiance images with an annotated spectral global illuminant. Among the several considered options, the best results are obtained with a model trained to predict the illuminant in the spectral domain using an RGB color loss function. In terms of comparison with the state of the art, this solution improves the recovery angular error metric by 66% compared to the best tested spectral method, and by 41% compared to the best tested RGB method.

Life Cycle Assessment of a large commercial kelp farm in Shandong, China.

The environmental benefits of seaweed cultivation have gained a lot of attention, both in policy strategies and by private companies. Sustainability evaluations of seaweed farming have however focused on a very small part of global production of seaweed - on European cultivations at research and pilot-scales although Asia stands for 99 % of global production with China alone producing 60 %. In this study, we use Life Cycle Assessment (LCA) to evaluate the environmental performance of a 400-hectare Chinese kelp farm with a yearly harvest of 60,000 tons. Primary data from the farm was used to assess impacts up until harvest for the functional unit of 1 ton of fresh-weight kelp. Included in the LCA were impact on climate change, acidification terrestrial and marine eutrophication, and use of land water and energy. In addition, we calculated nutrient uptake. Further, we extracted inventory data of four published LCA studies of farmed kelp and recalculated environmental impacts, applying the same background data and method choices with the aim to compare the effects of scale and cultivation system. The results of the hotspot analysis showed that the plastic ropes and buoys dominated impacts on climate change, freshwater and marine eutrophication, and energy consumption. Consequently, the most effective improvement action was recycling after use. The yearly harvest of the Chinese farm was 1000-4000 times larger than previously evaluated farms compared. Results suggest that streamlined and mature production in the large-scale Chinese kelp farm led to lower electricity and fuel consumption compared to small-scale production, thus placing the Chinese farm with a climate impact of 57.5 kg CO2 eq. per ton fresh-weight kelp on the lower end when comparing the carbon footprint. There was a large variation in carbon footprints, which implies that the kelp cultivation sector has considerable room for optimization.

Measuring the Optical Properties of Highly Diffuse Materials.

Measuring the optical properties of highly diffuse materials is a challenge as it could be related to the white colour or an oversaturation of pixels in the acquisition system. We used a spatially resolved method and adapted a nonlinear trust-region algorithm to the fit Farrell diffusion theory model. We established an inversion method to estimate two optical properties of a material through a single reflectance measurement: the absorption and the reduced scattering coefficient. We demonstrate the validity of our method by comparing results obtained on milk samples, with a good fitting and a retrieval of linear correlations with the fat content, given by R2 scores over 0.94 with low p-values. The values of absorption coefficients retrieved vary between 1 × 10-3 and 8 × 10-3 mm-1, whilst the values of the scattering coefficients obtained from our method are between 3 and 8 mm-1 depending on the percentage of fat in the milk sample, and under the assumption of the anisotropy factor g>0.8. We also measured and analyzed the results on white paint and paper, although the paper results were difficult to relate to indicators. Thus, the method designed works for highly diffuse isotropic materials.

Impact of Exposure and Illumination on Texture Classification Based on Raw Spectral Filter Array Images.

Spectral Filter Array cameras provide a fast and portable solution for spectral imaging. Texture classification from images captured with such a camera usually happens after a demosaicing process, which makes the classification performance rely on the quality of the demosaicing. This work investigates texture classification methods applied directly to the raw image. We trained a Convolutional Neural Network and compared its classification performance to the Local Binary Pattern method. The experiment is based on real SFA images of the objects of the HyTexiLa database and not on simulated data as are often used. We also investigate the role of integration time and illumination on the performance of the classification methods. The Convolutional Neural Network outperforms other texture classification methods even with a small amount of training data. Additionally, we demonstrated the model's ability to adapt and scale for different environmental conditions such as illumination and exposure compared to other methods. In order to explain these results, we analyze the extracted features of our method and show the ability of the model to recognize different shapes, patterns, and marks in different textures.

Logistic splicing correction for VNIR-SWIR reflectance imaging spectroscopy.

In the field of spectroscopy, a splicing correction is a process by which two spectra captured with different sensors in adjacent or overlapping electromagnetic spectrum ranges are smoothly connected. In our study, we extend this concept to the case of reflectance imaging spectroscopy in the visible-near-infrared (VNIR) and short-wave infrared (SWIR), accounting for additional sources of noise that arise at the pixel level. The proposed approach exploits the adaptive fitting of a logistic function to compute correcting coefficients that harmonize the two spectral sets. This short Letter addresses usage conditions and compares results against the existing state of the art.

Comparative life cycle assessment of conventional and novel microalgae production systems and environmental impact mitigation in urban-industrial symbiosis.

The versatility of microalgae biomass as candidates for various products and bioremediation needs motivates interests towards design and implementation of novel microalgae bioreactors. Conventional open-reactors are reliant on large quantities of sunlight and space while yields are constrained by outdoor environment conditions. Conversely, closed-reactor systems like bubble columns reduces these constrains on microalgae growth while occupying far less space at the expense of high energy demands, notably from lighting systems. A novel patented closed reactor design has recently been proposed that improves the bubble column concept with an efficient and effective lighting system. The present study uses Life Cycle Assessment approach to compare the environmental performance of conventional reactors and the proposed internally luminated novel closed reactor design, expressing impacts per kg biostimulant for the Scenedesmus almeriensis harvest from such units. All performance data was collected from a pilot facility in Almeria, Spain. Urban-industrial symbiosis scenarios are also portrayed in the study using wastewater and incinerator flue gas. Results show that under synthetic nutrient and carbon inputs in Spanish pilot operations, the cumulative energy demand for the novel photobioreactors is similar to conventional vertically-stacked horizon bioreactors but are substantially more demanding than conventional open reactors. However, when leveraging renewable energy sources and the photosynthesis process to consume wastestreams in urban-industrial symbiosis scenarios, the novel photobioreactor was able to achieve up to 80 % improvements in several impact categories e.g. eutrophication and climate change. Impact mitigation credits per kg dwt biomass across all energy scenarios in symbiosis amount to ≈1.8 kg CO2eq and ≈0.09 kg PO4 eq. This highlights that such closed and internally illuminated photobioreactors can be competitive with conventional reactors, and have potential to harness photosynthesis to reduce environmental burdens in an urban-industrial symbiosis setting. Possible economies of scale and the associated potential gains in efficiencies are further discussed.

Image quality and perception: introduction.

This feature issue focuses on image quality and perception, including image and video quality, subjective and objective quality, and enhancement. The feature issue contains papers on several important topics, such as contrast discrimination, analysis of color imaging in cameras, image quality assessment, and more. The papers represent different important aspects in image quality and perception, contributing to the advancement of the field.

Modification and evaluation of the peripheral contrast sensitivity function models.

We propose a series of modifications to the Barten contrast sensitivity function model for peripheral vision based on anatomical and psychophysical studies. These modifications result in a luminance pattern detection model that could quantitatively describe the extent of veridical pattern resolution and the aliasing zone. We evaluated our model against psychophysical measurements in peripheral vision. Our numerical assessment shows that the modified Barten leads to lower estimate errors than its original version.

A System for Autonomous Seaweed Farm Inspection with an Underwater Robot.

This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup.

Pyrolysis of engineered beach-cast seaweed: Performances and life cycle assessment.

The blooming of beach-cast seaweed has caused environmental degradation in some coastal regions. Therefore, a proper treating and utilizing method of beach-cast seaweed is demanded. This study investigated the potential of producing power or biofuel from pyrolysis of beach-cast seaweed and the effect of the ash-washing process. First, the raw and washed beach-cast seaweeds (RS and WS) were prepared. Thereafter, thermogravimetric analysis (TG), bench-scale pyrolysis experiment, process simulation, and life cycle assessment (LCA) were conducted. The TG results showed that the activation energies of thermal decomposition of the main organic contents of RS and WS were 44.23 and 58.45 kJ/mol, respectively. Three peak temperatures of 400, 500, and 600 °C were used in the bench-scale pyrolysis experiments of WS. The 600 °C case yielded the most desirable gas and liquid products. The bench-scale pyrolysis experiment of RS was conducted at 600 °C as well. Also, an LCA was conducted based on the simulation result of 600 °C pyrolysis of WS. The further process simulation and LCA results show that compare to producing liquid biofuel and syngas, a process designed for electricity production is most favored. It was estimated that treating 1 ton of dry WS can result in a negative cumulative energy demand of -2.98 GJ and carbon emissions of -790.89 kg CO2 equivalence.

Cost-benefit analysis of beach-cast harvest: Closing land-marine nutrient loops in the Baltic Sea region.

Harvesting beach-cast can help mitigate marine eutrophication by closing land-marine nutrient loops and provide a blue biomass raw material for the bioeconomy. Cost-benefit analysis was applied to harvest activities during 2009-2018 on the island of Gotland in the Baltic Sea, highlighting benefits such as nutrient removal from the marine system and improved recreational opportunities as well as costs of using inputs necessary for harvest. The results indicate that the activities entailed a net gain to society, lending substance to continued funding for harvests on Gotland and assessments of upscaling of harvest activities to other areas in Sweden and elsewhere. The lessons learnt from the considerable harvest experience on Gotland should be utilized for developing concrete guidelines for carrying out sustainable harvest practice, paying due attention to local conditions but also to what can be generalized to a wider national and international context.

Stakeholder participation in sustainability assessment of non-wicked problems: The case of a future seaweed industry in Sweden.

Acceptance by, and cooperation with relevant stakeholders in developing new sustainability initiatives when they are generally perceived as positive, is one of the keys for successful implementation of such new sustainability initiatives later on. It is remarkable, however, that ample literature exists about involving stakeholders in research projects focusing on problems with diverging views (controversy) around facts and values (wicked problems), but there is very little literature addressing whether and how to involve relevant stakeholders in case of initiatives where diverging norms and values do not play a (substantial) role, like in sustainability assessment for a future seaweed industry. This perspectives paper addresses that gap, and explores how to design such sustainability assessment, illustrated by how stakeholder interaction influenced the assessment and its results for a future seaweed industry in Sweden, followed by a discussion whether and how a similar approach may benefit sustainability assessment of other non-wicked sustainability initiatives.

Translucency perception: A review.

Translucency is an optical and a perceptual phenomenon that characterizes subsurface light transport through objects and materials. Translucency as an optical property of a material relates to the radiative transfer inside and through this medium, and translucency as a perceptual phenomenon describes the visual sensation experienced by humans when observing a given material under given conditions. The knowledge about the visual mechanisms of the translucency perception remains limited. Accurate prediction of the appearance of the translucent objects can have a significant commercial impact in the fields such as three-dimensional printing. However, little is known how the optical properties of a material relate to a perception evoked in humans. This article overviews the knowledge status about the visual perception of translucency and highlights the applications of the translucency perception research. Furthermore, this review summarizes current knowledge gaps, fundamental challenges and existing ambiguities with a goal to facilitate translucency perception research in the future.

Comparison of Imaging Models for Spectral Unmixing in Oil Painting.

The radiation captured in spectral imaging depends on both the complex light-matter interaction and the integration of the radiant light by the imaging system. In order to obtain material-specific information, it is important to define and invert an imaging process that takes into account both aspects. In this article, we investigate the use of several mixing models and evaluate their performances in the study of oil paintings. We propose an evaluation protocol, based on different features, i.e., spectral reconstruction, pigment mapping, and concentration estimation, which allows investigating the different properties of those mixing models in the context of spectral imaging. We conduct our experiment on oil-painted mockup samples of mixtures and show that models based on subtractive mixing perform the best for those materials.

Correlations in Joint Spectral and Polarization Imaging.

Recent imaging techniques enable the joint capture of spectral and polarization image data. In order to permit the design of computational imaging techniques and future processing of this information, it is interesting to describe the related image statistics. In particular, in this article, we present observations for different correlations between spectropolarimetric channels. The analysis is performed on several publicly available databases that are unified for joint processing. We perform global investigation and analysis on several specific clusters of materials or reflection types. We observe that polarization channels generally have more inter-channel correlation than the spectral channels.

Socioeconomic prospects of a seaweed bioeconomy in Sweden.

Seaweed cultivation is a large industry worldwide, but production in Europe is small compared to production in Asian countries. In the EU, the motivations for seaweed farming may be seen from two perspectives; one being economic growth through biomass production and the other being the provisioning of ecosystem services such as mitigating eutrophication. In this paper, we assess the economic potential of large-scale cultivation of kelp, Saccharina latissima, along the Swedish west coast, including the value of externalities. The findings suggest that seaweed farming has the potential of becoming a profitable industry in Sweden. Furthermore, large-scale seaweed farming can sequester a significant share of annual anthropogenic nitrogen and phosphorus inflows to the basins of the Swedish west coast (8% of N and 60% of P). Concerning the valuation of externalities, positive values generated from sequestration of nitrogen and phosphorus are potentially counteracted by negative values from interference with recreational values. Despite the large N and P uptake, the socioeconomic value of this sequestration is only a minor share of the potential financial value from biomass production. This suggests that e.g. payment schemes for nutrient uptake based on the socioeconomic values generated is not likely to be a tipping point for the industry. Additionally, seaweed cultivation is not a cost-efficient measure in itself to remove nutrients. Policy should thus be oriented towards industry development, as the market potential of the biomass will be the driver that may unlock these bioremediation opportunities.

An Evaluation Framework for Spectral Filter Array Cameras to Optimize Skin Diagnosis.

Comparing and selecting an adequate spectral filter array (SFA) camera is application-specific and usually requires extensive prior measurements. An evaluation framework for SFA cameras is proposed and three cameras are tested in the context of skin analysis. The proposed framework does not require application-specific measurements and spectral sensitivities together with the number of bands are the main focus. An optical model of skin is used to generate a specialized training set to improve spectral reconstruction. The quantitative comparison of the cameras is based on reconstruction of measured skin spectra, colorimetric accuracy, and oxygenation level estimation differences. Specific spectral sensitivity shapes influence the results directly and a 9-channel camera performed best regarding the spectral reconstruction metrics. Sensitivities at key wavelengths influence the performance of oxygenation level estimation the strongest. The proposed framework allows to compare spectral filter array cameras and can guide their application-specific development.