Image classification allows to profess a great number of images and identify specific objects within these images...
Remote Sensing and GIS
We present thorough this review the developments in the field, point out their current limitations, and outline its timelines and unique potential. In order to do so we introduce the methods used in each of the advances in the application of deep learning (DL) to coral research that took place between the years: 2016–2018. DL has unique capability of streamlining the description, analysis, and monitoring of coral reefs, saving time, and obtaining higher reliability and accuracy compared with error-prone human performance. Coral reefs are the most diverse and complex of marine ecosystems, undergoing a severe decline worldwide resulting from the adverse synergistic influences of global climate change, ocean acidification, and seawater warming, exacerbated by anthropogenic eutrophication and pollution. DL is an extension of some of the concepts originating from machine learning that join several multilayered neural networks. Machine learning refers to algorithms that automatically detect patterns in data. In the case of corals these data are underwater photographic images. Based on “learned” patterns, such programs can recognize new images. The novelty of DL is in the use of state-of-art computerized image analyses technologies, and its fully automated methodology of dealing with large data sets of images. Automated Image recognition refers to technologies that identify and detect objects or attributes in a digital video or image automatically. Image recognition classifies data into selected categories out of many. We show that Neural Network methods are already reliable in distinguishing corals from other benthos and non-coral organisms. Automated recognition of live coral cover is a powerful indicator of reef response to slow and transient changes in the environment. Improving automated recognition of coral species, DL methods already recognize decline of coral diversity due to natural and anthropogenic stressors. Diversity indicators can document the effectiveness of reef bioremediation initiatives. We explored the current applications of deep learning for corals and benthic image classiﬁcation by discussing the most recent studies conducted by researchers. We review the developments in the field, point out their current limitations, and outline their timelines and unique potential. We also discussed a few future research directions in the ﬁelds of deep learning. Future needs are the age detection of single species, in order to track trends in their population recruitment, decline, and recovery. Fine resolution, at the polyp level, is still to be developed, in order to allow separation of species with similar macroscopic features. That refinement of DL will allow such comparisons and their analyses. We conclude that the usefulness of future, more refined automatic identification will allow reef comparison, and tracking long term changes in species diversity. The hitherto unused addition of intraspecific coral color parameters, will add the inclusion of physiological coral responses to environmental conditions and change thereof. The core aim of this review was to underscore the strength and reliability of the DL approach for documenting coral reef features based on an evaluation of the currently available published uses of this method. We expect that this review will encourage researchers from computer vision and marine societies to collaborate on similar long-term joint ventures.
Underwater gliders have become widely used in the last decade. This has led to a proliferation of data and the concomitant development of tools to process the data. These tools are focused primarily on converting the data from its raw form to more accessible formats and often rely on proprietary programing languages. This has left a gap in the processing of glider data for academics, who often need to perform secondary quality control (QC), calibrate, correct, interpolate and visualize data. Here, we present GliderTools, an open-source Python package that addresses these needs of the glider user community. The tool is designed to change the focus from the processing to the data. GliderTools does not aim to replace existing software that converts raw data and performs automatic first-order QC. In this paper, we present a set of tools, that includes secondary cleaning and calibration, calibration procedures for bottle samples, fluorescence quenching correction, photosynthetically available radiation (PAR) corrections and data interpolation in the vertical and horizontal dimensions. Many of these processes have been described in several other studies, but do not exist in a collated package designed for underwater glider data. Importantly, we provide potential users with guidelines on how these tools are used so that they can be easily and rapidly accessible to a wide range of users that span the student to the experienced researcher. We recognize that this package may not be all-encompassing for every user and we thus welcome community contributions and promote GliderTools as a community-driven project for scientists.
Tomorrow’s smart lakes and oceans will be able to, among other things, predict changes in the water environment and produce information critical to proper management and planning. Smart Lake Erie – a proof of concept – will integrate data from distributed sensors using resilient networks to feed adaptive, predictive analytics that define and perhaps even perform necessary management actions. This paper presents the Smart Lake Erie pilot as a series of steps that include convening innovation competitions, engaging stakeholders, securing the core observation system, and designing then building a sustainable early warning system for harmful algal blooms. The pilot’s data platform will show what is needed to serve new data contributors, service providers, stakeholders and consumers of the data and information service paradigm. Lessons learned from the early implementation of the pilot will be applicable to the overall Great Lakes region, other regional associations within the U.S. Integrated Ocean Observing System (IOOS), and the Global Ocean Observing System (GOOS).
Multidisciplinary ocean observing activities provide critical ocean information to satisfy ever-changing socioeconomic needs and require coordinated implementation. The upper oxycline (transition between high and low oxygen waters) is fundamentally important for the ecosystem structure and can be a useful proxy for multiple observing objectives connected to eastern boundary systems (EBSs) that neighbor oxygen minimum zones (OMZs). The variability of the oxycline and its impact on the ecosystem (VOICE) initiative demonstrates how societal benefits drive the need for integration and optimization of biological, biogeochemical, and physical components of regional ocean observing related to EBS. In liaison with the Global Ocean Oxygen Network, VOICE creates a roadmap toward observation-model syntheses for a comprehensive understanding of selected oxycline-dependent objectives. Local to global effects, such as habitat compression or deoxygenation trends, prompt for comprehensive observing of the oxycline on various space and time scales, and for an increased awareness of its impact on ecosystem services. Building on the Framework for Ocean Observing (FOO), we present a first readiness level assessment for ocean observing of the oxycline in EBS. This was to determine current ocean observing design and future needs in EBS regions (e.g., the California Current System, the Equatorial Eastern Pacific off Ecuador, the Peru–Chile Current system, the Northern Benguela off Namibia, etc.) building on the FOO strategy. We choose regional champions to assess the ocean observing design elements proposed in the FOO, namely, requirement processes, coordination of observational elements, and data management and information products and the related best practices. The readiness level for the FOO elements was derived for each EBS through a similar and very general ad hoc questionnaire. Despite some weaknesses in the questionnaire design and its completion, an assessment was achievable. We found that fisheries and ecosystem management are a societal requirement for all regions, but maturity levels of observational elements and data management and information products differ substantially. Identification of relevant stakeholders, developing strategies for readiness level improvements, and building and sustaining infrastructure capacity to implement these strategies are fundamental milestones for the VOICE initiative over the next 2–5 years and beyond.
Sea-surface temperature (SST) was one of the first ocean variables to be studied from earth observation satellites. Pioneering images from infrared scanning radiometers revealed the complexity of the surface temperature fields, but these were derived from radiance measurements at orbital heights and included the effects of the intervening atmosphere. Corrections for the effects of the atmosphere to make quantitative estimates of the SST became possible when radiometers with multiple infrared channels were deployed in 1979. At the same time, imaging microwave radiometers with SST capabilities were also flown. Since then, SST has been derived from infrared and microwave radiometers on polar orbiting satellites and from infrared radiometers on geostationary spacecraft. As the performances of satellite radiometers and SST retrieval algorithms improved, accurate, global, high resolution, frequently sampled SST fields became fundamental to many research and operational activities. Here we provide an overview of the physics of the derivation of SST and the history of the development of satellite instruments over half a century. As demonstrated accuracies increased, they stimulated scientific research into the oceans, the coupled ocean-atmosphere system and the climate. We provide brief overviews of the development of some applications, including the feasibility of generating Climate Data Records. We summarize the important role of the Group for High Resolution SST (GHRSST) in providing a forum for scientists and operational practitioners to discuss problems and results, and to help coordinate activities world-wide, including alignment of data formatting and protocols and research. The challenges of burgeoning data volumes, data distribution and analysis have benefited from simultaneous progress in computing power, high capacity storage, and communications over the Internet, so we summarize the development and current capabilities of data archives. We conclude with an outlook of developments anticipated in the next decade or so.
Seagrass meadows play a key ecological role as nursery and feeding grounds for multiple fish species. Underwater Visual Census (UVC) has been historically used as the non-extractive method to characterize seagrass fish communities, however, less intrusive methodologies such as Remote Underwater Video (RUV) are gaining interest and could be particularly useful for seagrass habitats, where juvenile fish camouflage among the vegetation and could easily hide or flee from divers. Here we compared the performance of UVC and RUV methodologies in assessing the fish communities of two seagrass meadows with low and high canopy density. We found that RUV detected more species and fish individuals than UVC, particularly on the habitat with higher seagrass density, which sheltered more juveniles, especially herbivorous, and adult piscivorous of commercial importance, evidencing significant differences in energy flow from macrophytes to predators between seagrass habitats, and also differences in the ecosystem services they can provide. Considering the ongoing worldwide degradation of seagrass ecosystems, our results strongly suggest that fish surveys using RUV in ecologic and fisheries programs would render more accurate information and would be more adequate to inform the conservation planning of seagrass meadows around the world.
Harmful Algal Blooms (HABs) are of global concern, as their presence is often associated with socio-economic and environmental issues including impacts on public health, aquaculture and fisheries. Therefore, monitoring the occurrence and succession of HABs is fundamental for managing coastal regions around the world. Yet, due to the lack of adequate in situmeasurements, the detection of HABs in coastal marine ecosystems remains challenging. Sensors on-board satellite platforms have sampled the Earth synoptically for decades, offering an alternative, cost-effective approach to routinely detect and monitor phytoplankton. The Red Sea, a large marine ecosystem characterised by extensive coral reefs, high levels of biodiversity and endemism, and a growing aquaculture industry, is one such region where knowledge of HABs is limited. Here, using high-resolution satellite remote sensing observations (1km, MODIS-Aqua) and a second-order derivative approach, in conjunction with available in situ datasets, we investigate for the first time the capability of a remote sensing model to detect and monitor HABs in the Red Sea. The model is able to successfully detect and generate maps of HABs associated with different phytoplankton functional types, matching concurrent in situdata remarkably well. We also acknowledge the limitations of using a remote-sensing based approach and show that regardless of a HAB’s spatial coverage, the model is only capable of detecting the presence of a HAB when the Chl-a concentrations exceed a minimum value of ~ 1 mg m-3. Despite the difficulties in detecting HABs at lower concentrations, and identifying species toxicity levels (only possible through in situ measurements), the proposed method has the potential to map the reported spatial distribution of several HAB species over the last two decades. Such information is essential for the regional economy (i.e., aquaculture, fisheries & tourism), and will support the management and sustainability of the Red Sea’s coastal economic zone.
Barrier islands are dynamic ecosystems that change gradually from coastal processes, including currents and tides, and rapidly from episodic events, such as storms. These islands provide many important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. Habitat maps, developed by scientists, provide a critical tool for monitoring changes to these dynamic ecosystems. Barrier island monitoring often requires custom habitat maps due to several factors, including island size and the classification of unique geomorphology-based habitats, such as beach, dune, and barrier flats. In this study, we reviewed barrier-island-specific habitat mapping efforts and highlighted common habitat class types, source data, and mapping approaches. We also developed a framework for mapping geomorphology-based barrier island habitats using a rule-based, geographic object-based image analysis approach, which included the use of field data, tide data, high-resolution orthophotography, and lidar data. This framework integrates several barrier island mapping advancements with regard to the use of landscape position information for automated dune extraction and the use of Monte Carlo analyses for the treatment of elevation uncertainty for elevation-dependent habitats. Specifically, we used the uncertainty analyses to refine automated dune delineation based on elevation relative to extreme storm water levels and to increase the accuracy of intertidal and supratidal/upland habitat delineation. We found that dune extraction results were enhanced when elevation relative to storm water levels and visual interpretation were also applied. This framework could also be applied to beach–dune systems found along a mainland.
Global elasmobranch populations have declined dramatically over the past 50 years, and continued research into the drivers of their habitats and distributions is vital for improved conservation and management. How environmental factors influence elasmobranch behavior, habitat use, and movement patterns is still relatively poorly understood, in part because of the scale over which many of these animals roam and the remote nature of the marine ecosystems they inhabit. In the last decade there has been an explosion of satellite remote sensing (SRS) technologies that can cover these vast spatial scales for the marine environment. Consequentially, SRS presents an opportunity to analyze important environmental drivers in elasmobranch ecology and to aid management decisions for the conservation of declining populations. A systematic literature review was undertaken to synthesize the current use of SRS environmental data in elasmobranch research. In addition, to facilitate the use of SRS in this field moving forward, we have compiled a list of popular SRS data sources and sensors for common environmental variables in marine science. Our review of 71 papers (55 published in the last 10 years) identified ten SRS-derived environmental variables that have been used in elasmobranch studies, from a range of satellite sensors and data sources. Sea surface temperature and ocean productivity were the most frequently used variables. Articles primarily analyzed variables individually or in pairs, with few studies looking at a suite of interacting variables. Here, we present a summary of the current state of knowledge on the application of SRS, current gaps and limitations, and discuss some of the potential future directions in which we envisage this field developing. Threatened elasmobranch populations inhabit some of the world’s most remote marine ecosystems. With often global coverage, SRS presents an opportunity to analyze the important environmental drivers of elasmobranch ecology to aid management decisions for the conservation of declining and threatened populations.