Understanding how bleaching severity varies across space and among and within taxa helps predict changes in community composition due to climate change and informs conservation efforts. Photogrammetry offers a non-invasive and time effective method for quantifying attributes of thousands of coral colonies across large, environmentally diverse reef areas. This approach circumvents the limitations of traditional survey methods, where detailed tracking of individual colonies comes at the expense of large sampling areas and sample sizes. Using photogrammetry, we measured colony size and scored bleaching severity of > 5000 colonies of 13 taxa across 26 sites (> 7400 m2 of reef) during a mild bleaching event in the central Great Barrier Reef (GBR) in 2022. We quantified the relationship between bleaching severity and key biological and environmental factors: colony size, taxonomic identity, degree-heating weeks (DHWs), water velocity, various measures of reef structural complexity, depth, and distance to coast. Our results show that bleaching probability decreased with increasing colony size for most taxa, contradicting the current understanding of size-dependent bleaching. Counter to conventional thinking, tabular Acropora spp. presented very low levels of bleaching in 2022 despite being among the most severely bleached taxa during the bleaching event in 1998, suggesting possible adaptation in the last two decades. Our results show a high level of idiosyncrasy in environmental gradients of bleaching severity. For instance, the effect of depth on was taxon-dependent and the effect of wave velocity differed between inshore and offshore reefs. Our results challenge prevailing paradigms around the role of colony size and environment in regulating bleaching susceptibility, suggesting that refugia are not universal but instead depend on specific environment-taxonomic combinations and taxon-specific colony sizes.

Abstract Underwater photogrammetry is routinely used to monitor large areas of complex and heterogeneous ecosystems, such as coral reefs. However, deriving data on benthic components (i.e. sand, rubble, coral and algae) from photogrammetry products has remained challenging due to the highly time‐consuming process of manual data extraction. We developed a machine learning approach to quantify benthic community composition in coral reefs from orthomosaics, which requires no manual delineation of benthic components for training or implementation. The current study presents RapidBenthos, an automated workflow that segments and classifies large‐area images. Our pipeline (1) uses a pre‐trained segmentation model, eliminating the need for manually generated fine‐scale segmented training data, and (2) classifies the resulting segments from multiple views using the underlying survey images, allowing for classification to fine taxonomic levels. Within a test photomosaic built from a coral reef area of 40 m−2, the model automatically detected 43 different benthic classes. Validation resulted in an overall classification accuracy of 0.96 and a segmentation accuracy of 0.87, when compared to a manually digitised replica. The RapidBenthos workflow was 195 times faster than manual segmentation and classification. Additional validation of 524 Acropora coral colonies from 11 additional test plots resulted in a segmentation accuracy of 0.92 and classification accuracy of 0.88 to the coarser ‘Acropora’ group. RapidBenthos has the capability to extract an unprecedented level of data from photomosaics of coral reefs or other complex environments, allowing to sustainably scale photogrammetric monitoring technique both in replicate and survey extent, which consequently can lead to new research questions and more informed ecosystem management.

The renewable energy transition is a priority for many researchers, policy makers, and political leaders because it is projected to stop the dependence of economic growth on increasing fossil fuel use and thus curtail climate change. This study examines how expert judgments affect development decisions to enable the renewable energy transition. Geospatial Multi-Criteria Decision Analyses (MCDA) are frequently used to select offshore wind energy (OWE) sites, however, they are often weak and/or often rely on limited judgement. The Analytical Hierarchy Process is used here with 25 diverse experts to assess the variability in priorities for OWE siting criteria. A geospatial MCDA is implemented using experts' individual priorities, aggregated weights and Monte Carlo simulations. Case study results reveal large variations in expert opinions and bias strongly affecting MCDAs weighted by single decision-makers. A group-decision approach is proposed to strengthen consent for OWE, underpinning the renewable energy transition.

Close-range underwater photogrammetry, hereafter referred to as photogrammetry, is rapidly emerging as a new standard in measuring and monitoring coral reefs due to its potential to record colony- and habitat-scale metrics in two and three dimensions at sub-centimetre scales. Despite the recent popularisation of photogrammetry, a comprehensive assessment of its applications to coral reefs seascape ecology has not yet been conducted. We systematically reviewed 125 publications on coral reef photogrammetry to assess: 1) its global trends and use; 2) how benthic community data is extracted from imagery; 3) the range of metrics derived and their ecological applications; and 4) key limitations of the approach. Results indicate that development and application of photogrammetry to coral reef ecology has accelerated rapidly in the last 15 years. In total, 55 metrics derived from photogrammetry, grouped in 10 categories, have been used to inform ecological studies on benthic assemblage, habitat structural complexity, and ecosystem condition and trajectory. The high level of effort required to quantify benthic assemblages was identified as a primary workflow bottleneck. We highlight the versatility of photogrammetry to study and monitor coral reef ecosystems and its capacity to quantify benthic community dynamics, habitat, and trajectories, which are vital to inform coral reef conservation and restoration.

The renewable energy transition is a priority for many researchers, policy makers, and political leaders because it is projected to stop the dependence of economic growth on increasing fossil fuel use and thus curtail climate change. This study examines how expert judgments affect development decisions to enable the renewable energy transition. Geospatial Multi-Criteria Decision Analyses (MCDA) are frequently used to select offshore wind energy (OWE) sites, however, they are often weak and/or often rely on limited judgement. The Analytical Hierarchy Process is used here with 25 diverse experts to assess the variability in priorities for OWE siting criteria. A geospatial MCDA is implemented using experts’ individual priorities, aggregated weights and Monte Carlo simulations. Case study results reveal large variations in expert opinions and bias strongly affecting MCDAs weighted by single decision-makers. A group-decision approach is proposed to strengthen consent for OWE projects, underpinning the renewable energy transition.

Offshore wind energy (OWE) is offering an increasingly important contribution to low-carbon energy production to offset anthropogenic global warming due to technological advances that increase the viability of this relatively new industry. These attributes highlight the importance of research being done in this field; however, direct assessments of the research underpinning OWE developments are rare. This systematic review provides a direct assessment of OWE research trends by examining tactics and data employed by a common research tool: Geographic Information Systems (GIS). Clarivate Analytics and Elsevier databases were searched, providing 2668 results that were assessed statistically. The results of the review and meta-analyses highlight several trends, including: (1) <40% of the reviewed studies considered marine spatial planning aspects and <20% mentioned requirements for environmental impact assessments; (2) the maximum viable water depth for fixed-bottom foundations increases steadily over time, ostensibly driven by improved OWE infrastructure development, however this trend is less clear with newer floating technologies; (3) the spatial resolutions of data vary drastically between studies with no relationships in time or between locations; (4) site selection analyses are typified by the most frequent and significant deviations from overall trends in both water depths and spatial resolutions; (5) the number of GIS parameters assessed ranged from 2 to 14 and all studies using >11 parameters employed prescriptive research strategies. These findings allow assessments of overall and research-specific trends that enable suggestions to improve future research compatibilities with disparate study types and actual technology.

Offshore wind energy (OWE) is offering an increasingly important contribution to low-carbon energy production to offset anthropogenic global warming due to technological advances that increase the viability of this relatively new industry. These attributes highlight the importance of research being done in this field; however, direct assessments of the research underpinning OWE developments are rare. This systematic review provides a direct assessment of OWE research trends by examining tactics and data employed by a common research tool: Geographic Information Systems (GIS). Clarivate Analytics and Elsevier databases were searched, providing 2668 results that were assessed statistically. The results of the review and meta-analyses highlight several trends, including: (1) <40% of the reviewed studies considered marine spatial planning aspects and <20% mentioned requirements for environmental impact assessments; (2) the maximum viable water depth for fixed-bottom foundations increases steadily over time, ostensibly driven by improved OWE infrastructure development, however this trend is less clear with newer floating technologies; (3) the spatial resolutions of data vary drastically between studies with no relationships in time or between locations; (4) site selection analyses are typified by the most frequent and significant deviations from overall trends in both water depths and spatial resolutions; (5) the number of GIS parameters assessed ranged from 2 to 14 and all studies using >11 parameters employed prescriptive research strategies. These findings allow assessments of overall and research-specific trends that enable suggestions to improve future research compatibilities with disparate study types and actual technology.

In this paper, surface wind speed and average wind power derived from Sentinel-1 Synthetic Aperture Radar Level 2 Ocean (OCN) product were validated against four weather buoys and three coastal weather stations around Ireland. A total of 1544 match-up points was obtained over a 2-year period running from May 2017 to May 2019. The match-up comparison showed that the satellite data underestimated the wind speed compared to in situ devices, with an average bias of 0.4 m s−1, which decreased linearly as a function of average wind speed. Long-term statistics using all the available data, while assuming a Weibull law for the wind speed, were also produced and resulted in a significant reduction of the bias. Additionally, the average wind power was found to be consistent with in situ data, resulting in an error of 10 % and 5 % for weather buoys and coastal stations, respectively. These results show that the Sentinel-1 Level 2 OCN product can be used to estimate the wind resource distribution, even in coastal areas. Maps of the average and seasonal wind speed and wind power illustrated that the error was spatially dependent, which should be taken into consideration when working with Sentinel-1 Synthetic Aperture Radar data.

The offshore wind industry has seen unprecedented growth over the last few years. In line with this growth, there has been a push towards more exposed sites, farther from shore, in deeper water with consequent increased investor risk. There is therefore a growing need for accurate, reliable, met-ocean data to identify suitable sites, and from which to base preliminary design and investment decisions. This study investigates the potential of hyper-temporal satellite remote sensing Advanced Scatterometer (ASCAT) data in generating information necessary for the optimal site selection of offshore renewable energy infrastructure, and hence providing a cost-effective alternative to traditional techniques, such as in situ data from public or private entities and modelled data. Five years of the ASCAT 12.5 km wind product were validated against in situ weather buoys and showed a strong correlation with a Pearson coefficient of 0.95, when the in situ measurements were extrapolated with the log law. Temporal variations depicted by the ASCAT wind data followed the same inter-seasonal and intra-annual variations as the in situ measurements. A small diurnal bias of 0.12 m s−1 was observed between the descending swath (10:00 to 12:00) and the ascending swath (20:30 to 22:30), indicating that Ireland’s offshore wind speeds are slightly stronger in the daytime, especially in the nearshore areas. Seasonal maps showed that the highest spatial variability in offshore wind speeds are exhibited in winter and summer. The mean wind speed extrapolated at 80 m above sea level showed that Ireland’s mean offshore wind speeds at hub height ranged between 9.6 m s−1 and 12.3 m s−1. To best represent the offshore wind resource and its spatial distribution, an operational frequency map and a maximum yield frequency map were produced based on the ASCAT wind product in an offshore zone between 20 km and 200 km from the coast. The operational frequency indicates the percentage of time during which the observed local wind speed is between cut-in (3 m/s) and cut-out (25 m/s) for a standard turbine. The operational frequency map shows that the frequency of the wind speed within the cut-in and cut-off range of wind turbines was between 92.4% and 97.2%, while the maximum yield frequency map showed that between 40.6% and 59.5% of the wind speed frequency was included in the wind turbine rated power range. The results showed that the hyper-temporal ASCAT 12.5 km wind speed product (five consecutive years, two observations daily per satellite, two satellites) is representative of wind speeds measured by in situ measurements in Irish waters, and that its ability to depict temporal and spatial variability can assist in the decision-making process for offshore wind farm site selection in Ireland.