A computational method for quantifying morphological variation in scleractinian corals

Morphological variation in marine sessile organisms is frequently related to environmental factors. Quantifying such variation is relevant in a range of ecological studies. For example, analyzing the growth form of fossil organisms may indicate the state of the physical environment in which the organism lived. A quantitative morphological comparison is important in studies where marine sessile organisms are transplanted from one environment to another. This study presents a method for the quantitative analysis of three-dimensional (3D) images of scleractinian corals obtained with X-ray Computed Tomography scanning techniques. The advantage of Computed Tomography scanning is that a full 3D image of a complex branching object, including internal structures, can be obtained with a very high precision. There are several complications in the analysis of this data set. In the analysis of a complex branching object, landmark-based methods usually do not work and different approaches are required where various artifacts (for example cavities, holes in the skeleton, scanning artifacts, etc.) in the data set have to be removed before the analysis. A method is presented, which is based on the construction of a medial axis and a combination of image-processing techniques for the analysis of a 3D image of a complex branching object where the complications mentioned above can be overcome. The method is tested on a range of 3D images of samples of the branching scleractinian coral Madracis mirabilis collected at different depths. It is demonstrated that the morphological variation of these samples can be quantified, and that biologically relevant morphological characteristics, like branch-spacing and surface/volume ratios, can be computed. Electronic supplementary material The online version of this article (doi:10.1007/s00338-007-0270-6) contains supplementary material, which is available to authorized users

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Towards an end-to-end analysis and prediction system for weather, climate, and marine applications in the Red Sea

Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 102(1), (2021): E99-E122, https://doi.org/10.1175/BAMS-D-19-0005.1.The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.The development of the Red Sea modeling system is being supported by the Virtual Red Sea Initiative and the Competitive Research Grants (CRG) program from the Office of Sponsored Research at KAUST, Saudi Aramco Company through the Saudi ARAMCO Marine Environmental Center at KAUST, and by funds from KAEC, NEOM, and RSP through Beacon Development Company at KAUST

Towards an end-to-end analysis and prediction system for weather, climate, and Marine applications in the Red Sea

AbstractThe Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.</jats:p

A protocol for the large‐scale analysis of reefs using Structure from Motion photogrammetry

1. Substrate complexity is an essential metric of reef health and a strong predictor of several ecological processes connected to the reef, including disturbance, resilience, and associated community abundance and diversity. / 2. Underwater Structure from Motion (SfM) photogrammetry has been growing rapidly in use over the last 5 years due to advances in computing power, reduced costs of underwater digital cameras and a push for reproducible data. This has led to the adaptation of an originally terrestrial survey technique into the marine realm, which can now be applied at the habitat scale. / 3. This technique allows researchers to make detailed 3D reconstructions of reef surfaces for morphometric analysis of reef physical structure and perform large‐scale image‐mosaic mapping. SfM is useful for both reef‐scale and colony‐scale assessments, where visual or acoustic methods are impractical or not sufficiently detailed. / 4. Here we provide a protocol for the collection, analysis and display of 3D reef data, focussing on large‐scale habitat assessments of coral reefs using primarily open‐source software. We further suggest applications for other underwater environments and scales of assessment, and hope this standardized protocol will help researchers apply this technology and inspire new avenues of ecological research

Atlas of Ocean Wealth

The Atlas of Ocean Wealth is the largest collection to date of information about the economic, social and cultural values of coastal and marine habitats from all over the world. It is a synthesis of innovative science, led by The Nature Conservancy (TNC), with many partners around the world. Through these efforts, they've gathered vast new datasets from both traditional and less likely sources.The work includes more than 35 novel and critically important maps that show how nature's value to people varies widely from place to place. They also illustrate nature's potential. These maps show that one can accurately quantify the value of marine resources. Further, by enumerating such values, one can encourage their protection or enhancement for the benefit of people all around the world. In summary, it clearly articulates not just that we need nature, but how much we need it, and where

Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation

Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Climate Adaptation and Resilience Across Scales

Climate Adaptation and Resilience Across Scales provides professionals with guidance on adapting the built environment to a changing climate. This edited volume brings together practitioners and researchers to discuss climate-related resilience from the building to the city scale. This book highlights North American cases that deal with issues such as climate projections, public health, adaptive capacity of vulnerable populations, and design interventions for floodplains, making the content applicable to many locations around the world. The contributors in this book discuss topics ranging from how built environment professionals respond to a changing climate, to how the building stock may need to adapt to climate change, to how resilience is currently being addressed in the design, construction, and operations communities. The purpose of this book is to provide a better understanding of climate change impacts, vulnerability, and resilience across scales of the built environment. Architects, urban designers, planners, landscape architects, and engineers will find this a useful resource for adapting buildings and cities to a changing climate