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.
Remote Sensing and GIS
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.
Accurate estimates of fishing effort are necessary in order to assess interactions with the wider ecosystem and for defining and implementing appropriate management. In intertidal and inshore fisheries in which vessel monitoring systems (VMS) or logbook programmes may not be implemented, quantifying the distribution and intensity of fishing can be difficult. The most obvious effects of bottom-contact fishing are often physical changes to the habitat, such as scarring of the sediment following dredging or trawling. We explored the potential of applying remote sensing techniques to aerial imagery collected by an unmanned aerial vehicle, or drone, in an area of intertidal mud flat (0.52 km2) in Poole Harbour, UK, where shellfish dredging is widely carried out and conflicts between commercial fishing interests and the conservation of internationally important shorebird populations are a concern. Image classification and image texture analysis were performed on imagery collected during the open dredge season in November 2015, in order to calculate measures of fishing intensity across three areas of the harbour subject to different management measures. We found a significant correlation between results of the image texture analysis and official sightings records collected during the dredging season, indicating that this method most accurately quantified dredging disturbance. The relationship between shorebird densities and food intake rates and the results of this analysis method were then investigated to assess the potential for using remotely sensed measures of fishing effort to assess responses of overwintering shorebird populations to intertidal shellfish dredging. Our work highlights the application of such methods, providing a low-cost tool for quantifying fishing effort and predicting wildlife conflicts.
As petroleum development and other activities move further north, the potential for oil spills in ice-covered waters is of great concern. As a tool for contingency planning and forecasting during response, oil spill models play a key role. With the development of new, high-resolution coupled ice-ocean models, better predictions of sea ice are becoming available. We have updated the OSCAR oil spill model to use sea-ice velocity and coverage fields from coupled ice-ocean models to improve simulation of oil fate and transport in ice-covered waters.
We describe the implementation of oil transport in the presence of ice, and demonstrate the improvement by considering three case studies. We find clear improvement when taking ice velocity from a coupled ice-ocean model into account, compared to a heuristic model that uses surface current and wind velocity. The difference is found to be especially important in a response situation near the marginal ice zone.
The geometric accuracy of tens of millions of scenes of medium-resolution remote sensing (RS) images collected in the past 45 years has been systematically evaluated for land scenes, but the accuracy of ocean scenes is poorly known due to the lack of ground control points (GCPs). In this study, the locations of offshore platforms are first derived from time-series of Landsat-8 OLI images, and are then used as offshore reference points to systematically assess the geometric performance of RS images covering offshore oil/gas development areas. An inventory of 16,131 offshore platforms at the global scale is established, and then a novel method using the position-invariant characteristic of offshore platforms and the coherent characteristic of the geometric shift among tie-points (i.e. between sensed points from to-be-assessed images and the corresponding OLI-derived reference points) is developed for assessing the geometric accuracy of Landsat and other RS images. The method has been applied to 112,935 Landsat scenes (~1.87% of the entire archive) over oceans. The results indicate an optimal performance of Landsat OLI images (both pre-collection and Collection-1) but a less reliable performance of Landsat TM/ETM+ L1TP images. Approximately 50% of TM L1GS and ETM+ L1GT images have at least 2 pixels of geometric error. The new reference points inventory and the developed method were also applied to many other low-resolution and finer-resolution imagery (e.g. VIIRS Night-fire product, Terra/Aqua MODIS active fire product, ENVISAT ASAR, ALOS-1 PALSAR, Sentinel-1 SAR, Sentinel-2 MSI, the National Agriculture Imagery Program (NAIP) aerial images, and images from several Chinese satellites), and a quantitative description of the geometric accuracy of these sensors is also presented. The findings suggest that the new offshore reference point inventory is probably useful to help establish more robust offshore GCPs for U.S. Geological Survey (USGS) GCP library and further improve the ongoing USGS Global GCP improvement plan and European Space Agency Global Reference Image plan.
Coral reefs are among the most biodiverse ecosystems on Earth in large part owing to their unique three-dimensional (3D) structure, which provides niches for a variety of species. Metrics of structural complexity have been shown to correlate with the abundance and diversity of fish and other marine organisms, but they are imperfect representations of a surface that can oversimplify key structural elements and bias discoveries. Moreover, they require researchers to make relatively uninformed guesses about the features and spatial scales relevant to species of interest. This paper introduces a machine-learning method for automating inferences about fish abundance from reef 3D models. It demonstrates the capacity of a convolutional neural network (ConvNet) to learn ecological patterns that are extremely subtle, if not invisible, to the human eye. It is the first time in the literature that no a priori assumptions are made about the bathymetry–fish relationship.
Seagrass meadows are one of the most important coastal habitats across the globe. These are mainly constituted by the marine plants of the genus Posidonia and Thalassia. In the Mediterranean Sea, Posidonia oceanica is the dominant endemic plant that affects physical, biogeochemical, and biological processes. The decline in the spatial distribution has been attributed to excessive anthropic pressures and other large-scale environmental changes. The monitoring of the spatial distribution requires an update and accurate seagrass meadows delineation, i.e. meadow edge marking with a replicable method. The present study aims to present an approach to support the coastal marine habitat mapping, under the scheme of the Natura 2000 network using very high resolution Earth observation data and to prove that satellite images can be used for the mapping of the deep limits of the seagrass meadows. Pixel-based classification and object-oriented image analysis have been implemented for the image classification. Pixel-based Support Vector Machines and object-based Nearest Neighbor classifiers provided the best results with an overall accuracy of more than 90%, while deep limits have been successfully identified and separated from the deep waters.
The aim of this research was to propose and evaluate a methodological approach to integration and spatial data analysis in order to generate information towards a participatory site selection for bivalve marine aquaculture in the Baía Sul, Florianópolis, Santa Catarina, Brazil. For this purpose, the Baía Sul was investigated considering an ecosystem approach for aquaculture leading to an assessment of its potential for marine aquaculture. The planning of the aquaculture parks was made through a participatory process to incorporate both environmental carrying capacity and social carrying capacity. Experts and modellers developed a GIS model to assess the potential for marine aquaculture in Baía Sul. Continuous (unclassified) maps were used to provide spatial information about the variation of the potential for marine aquaculture in the Baía Sul. The maps were used to plan 53 aquaculture parks over the Baía Sul. The site selection of the parks was made in six public hearings attended by 403 stakeholders from 38 institutions representing different sectors with diverse interests in coastal zone. The results showed that although the Baía Sul is suitable for the growth of bivalve molluscs, some hydrodynamic characteristics and the influence of urbanization constitute a sanitary risk for the activity. Experts, modellers and stakeholders had a different perception about the importance of criteria in the aquaculture parks site selection. While the experts and modellers considered the environmental criteria as the most important aspect to locate the aquaculture parks, the stakeholders took into account mainly the logistics. The final result of the aquaculture parks location, approved by the Brazilian Ministry of Fisheries and Aquaculture (MPA), adopted the site selection by the stakeholders, providing aquaculture parks in areas with sanitary risk for the bivalve cultivation. The main advantage of the adopted assessment strategy was to identify the divergence between experts, modellers and the stakeholders and the distance that still exist between scientist and decision makers in Brazil.