32 research outputs found

    Scaling the laws of thermal imaging-based whale detection

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    Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of the Atmospheric and Oceanic Technology 37(5), (2020): 807-824, doi:10.1175/JTECH-D-19-0054.1.Marine mammals are under growing pressure as anthropogenic use of the ocean increases. Ship strikes of large whales and loud underwater sound sources including air guns for marine geophysical prospecting and naval midfrequency sonar are criticized for their possible negative effects on marine mammals. Competent authorities regularly require the implementation of mitigation measures, including vessel speed reductions or shutdown of acoustic sources if marine mammals are sighted in sensitive areas or in predefined exclusion zones around a vessel. To ensure successful mitigation, reliable at-sea detection of animals is crucial. To date, ship-based marine mammal observers are the most commonly implemented detection method; however, thermal (IR) imaging–based automatic detection systems have been used in recent years. This study evaluates thermal imaging–based automatic whale detection technology for its use across different oceans. The performance of this technology is characterized with respect to environmental conditions, and an automatic detection algorithm for whale blows is presented. The technology can detect whales in polar, temperate, and subtropical ocean regimes over distances of up to several kilometers and outperforms marine mammal observers in the number of whales detected. These results show that thermal imaging technology can be used to assist in providing protection for marine mammals against ship strike and acoustic impact across the world’s oceans.This work was funded by the Office of Naval Research (ONR) under Award N000141310856, by the Environmental Studies Research Fund (ESRF; esrfunds.org) under Award 2014-03S and by the Alfred-Wegener-Institute Helmholtz Zentrum für Polar- und Meeresforschung. DPZ and OB declare competing financial interests: 1) Patent US8941728B2, DE102011114084B4: A method for automatic real-time marine mammal detection. The patent describes the ideas basic to the automatic whale detection software as used to acquire and process the data presented in this paper. 2) Licensing of the Tashtego automatic whale detection software to the manufacturer of IR sensor. The authors confirm that these competing financial interests did not alter their adherence good scientific practice. We thank P. Abgrall, J. Coffey, K. Keats, B. Mactavish, V. Moulton, and S. Penney-Belbin for data collection or IR image review. We thank S. Besaw, J. Christian, A. Coombs, P. Coombs, W. Costello, T. Elliott, E. Evans, I. Goudie, C. Jones, K. Knowles, R. Martin, A. Murphy, D. and J. Shepherd; and the staffs at the Irish Loop Express, the Myrick Wireless Interpretive Centre, the Mistaken Point Ecological Reserve, and the lighthouse keepers for logistical assistance at our remote field site. We thank D. Boutilier and B. McDonald (DFO) for assisting us in obtaining license to occupy permits for Cape Race. We thank D. Taylor (ESRF Research Manager) for his support

    Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy

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    The April 2010 Deepwater Horizon (DWH) oil spill was the largest coastal spill in U.S. history. Monitoring subsequent change in marsh plant community distributions is critical to assess ecosystem impacts and to establish future coastal management priorities. Strategically deployed airborne imaging spectrometers, like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), offer the spectral and spatial resolution needed to differentiate plant species. However, obtaining satisfactory and consistent classification accuracies over time is a major challenge, particularly in dynamic intertidal landscapes.Here, we develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem. Using field-referenced image endmembers and canonical discriminant analysis (CDA), we classified 21 AVIRIS images acquired during the fall of 2010, 2011 and 2012. Classification results were evaluated using ground surveys that were conducted contemporaneously to AVIRIS collection dates. We analyzed changes in dominant species cover from 2010 to 2012 for oiled and non-oiled shorelines.CDA discriminated dominant species with a high level of accuracy (overall accuracy=82%, kappa=0.78) and consistency over three imaging dates (overall2010=82%, overall2011=82%, overall2012=88%). Marshes dominated by Spartina alterniflora were the most spatially abundant in shoreline zones (â¤28m from shore) for all three dates (2010=79%, 2011=61%, 2012=63%), followed by Juncus roemerianus (2010=11%, 2011=19%, 2012=17%) and Distichlis spicata (2010=4%, 2011=10%, 2012=7%).Marshes that were heavily contaminated with oil exhibited variable responses from 2010 to 2012. Marsh vegetation classes converted to a subtidal, open water class along oiled and non-oiled shorelines that were similarly situated in the landscape. However, marsh loss along oil-contaminated shorelines doubled that of non-oiled shorelines. Only S. alterniflora dominated marshes were extensively degraded, losing 15% (354,604m2) cover in oiled shoreline zones, suggesting that S. alterniflora marshes may be more vulnerable to shoreline erosion following hydrocarbon stress, due to their landscape position

    IARC Monographs: 40 Years of Evaluating Carcinogenic Hazards to Humans

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    Background: Recently, the International Agency for Research on Cancer (IARC) Programme for the Evaluation of Carcinogenic Risks to Humans has been criticized for several of its evaluations, and also for the approach used to perform these evaluations. Some critics have claimed that failures of IARC Working Groups to recognize study weaknesses and biases of Working Group members have led to inappropriate classification of a number of agents as carcinogenic to humans. Objectives: The authors of this Commentary are scientists from various disciplines relevant to the identification and hazard evaluation of human carcinogens. We examined criticisms of the IARC classification process to determine the validity of these concerns. Here, we present the results of that examination, review the history of IARC evaluations, and describe how the IARC evaluations are performed. Discussion: We concluded that these recent criticisms are unconvincing. The procedures employed by IARC to assemble Working Groups of scientists from the various disciplines and the techniques followed to review the literature and perform hazard assessment of various agents provide a balanced evaluation and an appropriate indication of the weight of the evidence. Some disagreement by individual scientists to some evaluations is not evidence of process failure. The review process has been modified over time and will undoubtedly be altered in the future to improve the process. Any process can in theory be improved, and we would support continued review and improvement of the IARC processes. This does not mean, however, that the current procedures are flawed. Conclusions: The IARC Monographs have made, and continue to make, major contributions to the scientific underpinning for societal actions to improve the public’s health

    Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements

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    Vegetation structure parameters are key elements in the study of ecosystem functioning and global scale ecosystemic interactions. The detailed retrieval of many of these parameters by direct measurements is impractical due to the quantity of plant material in trees. Terrestrial LiDAR Scanners (TLSs) have been shown to hold great potential as an indirect means of estimating plant structure parameters with a high level of detail, while some studies identified a number of challenges inherent to this approach. In this study we investigate the use of a voxel-based approach to retrieve leaf area distribution of individual trees. The approach is based on the contact frequency method applied to co-registered TLS returns from two or more scanning positions. The contact frequency was computed for voxels being 10, 30, and 50 cm in size and subsequently corrected for the influence of occlusion effects, leaf inclination, the presence of non-photosynthetic material, and the laser beam size. The leaf area of voxels for which occlusion effects were too pronounced was estimated using modeled values based on the availability of light. We compared the TLS derived leaf area estimates against direct measurements, obtained by the harvesting of leaves, in a broad-leaved savanna of central Mali. The measured leaf area values of the sampled trees ranged from 30 to 530 m2, and crown LAI values between 0.8 and 7.2. The leaf area estimates lay on average 14% from the reference measurements (general bias). Our method provides vertical as well as radial distributions of leaf area in individual trees, and lends itself to the estimation of savanna vegetation structural parameters with a high level of detail.JRC.H.3-Global environment monitorin
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