The ocean remains the least observed part of our planet. This deficiency was made obvious by two recent developments in ocean governance: the emerging global movement to create massive marine protected areas (MPAs) (1) and a new commitment by the United Nations (UN) to develop a legally binding treaty to better manage high-seas biodiversity (2). Both policy goals cause us to confront whether it is meaningful to legislate change in ocean areas that we have little capacity to observe transparently. Correspondingly, there has been a surge in interest in the potential of publicly accessible data from automatic ship identification systems (AIS) to fill gaps in ocean observation. We demonstrate how AIS data can be used to empower and propel forward a new era of spatially ambitious marine governance and research. The value of AIS, however, is inextricably linked to the strength of policies by which it is backed.
Remote Sensing and GIS
Assigning uncertainty to ocean-color satellite products is a requirement to allow informed use of these data. Here, uncertainty estimates are derived using the comparison on a 12th-degree grid of coincident daily records of the remote-sensing reflectance RRSobtained with the same processing chain from three satellite missions, MERIS, MODIS and SeaWiFS. The approach is spatially resolved and produces σ, the part of the RRSuncertainty budget associated with random effects. The global average of σ decreases with wavelength from approximately 0.7–0.9 10− 3 sr− 1 at 412 nm to 0.05–0.1 10− 3 sr− 1at the red band, with uncertainties on σ evaluated as 20–30% between 412 and 555 nm, and 30–40% at 670 nm. The distribution of σ shows a restricted spatial variability and small variations with season, which makes the multi-annual global distribution of σ an estimate applicable to all retrievals of the considered missions. The comparison of σ with other uncertainty estimates derived from field data or with the support of algorithms provides a consistent picture. When translated in relative terms, and assuming a relatively low bias, the distribution of σ suggests that the objective of a 5% uncertainty is fulfilled between 412 and 490 nm for oligotrophic waters (chlorophyll-a concentration below 0.1 mg m− 3). This study also provides comparison statistics. Spectrally, the mean absolute relative difference between RRS from different missions shows a characteristic U-shape with both ends at blue and red wavelengths inversely related to the amplitude of RRS. On average and for the considered data sets, SeaWiFS RRS tend to be slightly higher than MODIS RRS, which in turn appear higher than MERIS RRS. Biases between mission-specific RRS may exhibit a seasonal dependence, particularly in the subtropical belt.
We apply a method to evaluate the strength of the evidence for deviations from uniform land change in a coastal area, in the context of Intensity Analysis. The errors in the CORINE maps at 1990 and 2006 can influence the apparent change, but the errors are unknown because error assessment of the 1990 map has never been released, while the error of the 2006 map has been checked for only some countries. The 1990 and the 2006 maps of a coastal watershed in Portugal served as the data to compute the intensities of changes among eight categories. We evaluate the sizes and types of errors that could explain deviations from uniform intensities. Errors in 2.0% of the 2006 map can explain all apparent deviations from uniform gains. Errors in 1.5% of the 1990 map can explain all apparent deviations from uniform losses. Errors in less than 0.7% of the 1990 map can explain all apparent deviations from uniform transitions to each gaining category. We analyse the strength of the evidence for deviations from uniform intensities in light of historical processes of change. Historical processes can explain some transitions that the data show, while the hypothesised errors in the data are the explanation for other transitions that are not consistent with known processes. Inconsistent transitions are an indication of the misclassification errors that could propagate to other land cover change applications, as in the assessment of hydrological processes.
To forecast marine disease outbreaks as oceans warm requires new environmental surveillance tools. We describe an iterative process for developing these tools that combines research, development and deployment for suitable systems. The first step is to identify candidate host–pathogen systems. The 24 candidate systems we identified include sponges, corals, oysters, crustaceans, sea stars, fishes and sea grasses (among others). To illustrate the other steps, we present a case study of epizootic shell disease (ESD) in the American lobster. Increasing prevalence of ESD is a contributing factor to lobster fishery collapse in southern New England (SNE), raising concerns that disease prevalence will increase in the northern Gulf of Maine under climate change. The lowest maximum bottom temperature associated with ESD prevalence in SNE is 12°C. Our seasonal outlook for 2015 and long-term projections show bottom temperatures greater than or equal to 12°C may occur in this and coming years in the coastal bays of Maine. The tools presented will allow managers to target efforts to monitor the effects of ESD on fishery sustainability and will be iteratively refined. The approach and case example highlight that temperature-based surveillance tools can inform research, monitoring and management of emerging and continuing marine disease threats.
Current bathymetric models for the South China Sea (SCS) are largely based on predicted depths from gravity and sparse single-beam echo-sounder measurements. Such models lack high-resolution coastlines and shallow-water bottom features around atolls and islands. This study refines the gravity field of the SCS using sea surface heights from measurements of satellite altimeter Geosat/GM, ERS-1/GM, Jason-1/GM and the original Cryosat-2. A new one-minute gravity anomaly grid is determined. The modeled gravity anomalies show a 6-mgal RMS discrepancy with shipborne measurements in shallow waters. An altimeter-only bathymetric model is derived from the new gravity grid by the gravity-geological method that uses the latest global and regional models of the ocean depth and marine gravity as a priori knowledge. The new model outperforms current SCS bathymetric models and is accurate to 100 m, based on comparison with multi-beam depth measurements. Optical images from IKONOS-2, QuickBird-2, GeoEye-1, WorldView-1-2 and -3, are rectified and digitized to derive the zero (coastline) and 20-m depth contours (reef lines) around 44 atolls, which are integrated with the altimeter-only depths, giving significantly improved accuracies and spatial resolutions in modeled depths. The improvement percentages of coastlines by the satellite imagery range from 50% to 97% at 41 of the 44 atolls. We establish a webpage for free access to the optical and depth images, and the depth and gravity grids. We will continue to update satellite images, altimeter-derived gravity grids and bathymetric models over major atolls of the SCS.
This 2015 edition of the Future Trends report recognises that the most significant changes in the geospatial industry will come not through a single technology, but rather from linking multiple technologies and policies. The first part of the report, which has been produced through a global consensus process, focuses on the new and emerging trends; these are explored through a series of themes covering one or more topics. The second half of the report incorporates, where relevant, changes that have occurred in the trends identified in the first edition.
Due to increased global urbanisation, it is expected that more focus will be placed on urban environments. The integration of smart technologies and efficient governance models will increase and the mantra of ‘doing more for less’ is more relevant than ever before. The emerging trends of Smart Cities and the Internet of Things, coupled with of smart resource management and interoperable services, will lead to a focus on citizen services, better land management, and the sustainability of resources.
Aquatic biogeochemical models are vital tools in understanding and predicting human impacts on water clarity. In this paper, we develop a spectrally-resolved optical model that produces remote-sensing reflectance as a function of depth-resolved biogeochemical model properties such as phytoplankton biomass, suspended sediment concentrations and benthic reflectance. We compare simulated remote-sensing reflectance from a 4 km resolution coupled hydrodynamic, optical, sediment and biogeochemical model configured for the Great Barrier Reef with observed remote-sensing reflectance from the MODIS sensor at the 8 ocean colour bands. The optical model is sufficiently accurate to capture the remote-sensing reflectance that would arise from a specific biogeochemical state. Thus the mismatch between simulated and observed remote-sensing reflectance provides an excellent metric for model assessment of the coupled biogeochemical model. Finally, we combine simulated remote-sensing reflectance in a red/green/blue colour model to produce simulated true colour images during the passage of Tropical Cyclone Yasi in February 2011.
This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in the last decade, consists in planning the paths of gliders to minimize a specific criterion pertinent to the phenomenon under investigation. Different criteria (e.g., A, G, or E optimality), used in geosciences to obtain an optimum design, lead to different sampling strategies. In particular, the A criterion produces paths for the gliders that minimize the overall level of uncertainty over the area of interest. However, there are commonly operative situations in which the marine scientists may prefer not to minimize the overall uncertainty of a certain area, but instead they may be interested in achieving an acceptable uncertainty sufficient for the scientific or operational needs of the mission. We propose and discuss here an approach named sampling on-demand that explicitly addresses this need. In our approach the user provides an objective map, setting both the amount and the geographic distribution of the uncertainty to be achieved after assimilating the information gathered by the fleet. A novel optimality criterion, called A η , is proposed and the resulting minimization problem is solved by using a Simulated Annealing based optimizer that takes into account the constraints imposed by the glider navigation features, the desired geometry of the paths and the problems of reachability caused by ocean currents. This planning strategy has been implemented in a Matlab toolbox called SoDDS (Sampling on-Demand and Decision Support). The tool is able to automatically download the ocean fields data from MyOcean repository and also provides graphical user interfaces to ease the input process of mission parameters and targets. The results obtained by running SoDDS on three different scenarios are provided and show that SoDDS, which is currently used at NATO STO Centre for Maritime Research and Experimentation (CMRE), can represent a step forward towards a systematic mission planning of glider fleets, dramatically reducing the efforts of glider operators.
In 2014, the United States National Oceanic and Atmospheric Administration (NOAA) utilized unique partnerships with the National Aeronautics and Space Administration (NASA), and the US Coast Guard for the first comparative testing of two unmanned aircraft systems (UAS): the Ikhana(an MQ-9 Predator B) and a Puma All-Environment (Puma AE). A multidisciplinary team of scientists developed missions to explore the application of the two platforms to maritime surveillance and marine resource monitoring and assessment. Testing was conducted in the Papahānaumokuākea Marine National Monument, a marine protected area in the Northwest Hawaiian Islands. Nearly 30 h of footage were collected by the test platforms, containing imagery of marine mammals, sea turtles, seabirds, marine debris, and coastal habitat. Both platforms proved capable of collecting usable data, although imagery collected using the Puma was determined to be more useful for resource monitoring purposes. Lessons learned included the need for increased camera resolution, co-location of mission scientists and UAS operators, the influence of weather on the quality of imagery collected, post-processing resource demands, and the need for pre-planning of mission targets and approach to maximize efficiency.
Assessing and mapping impacts or pressures of human activities on coastal region help managers and policy makers have insight about the degree and magnitude of human influence, thus important in decision making of management practice, e.g. rationalize spatial distribution or management of human industry. In this research, a methodology for assessing and mapping pressures of a variety of human activities was described. The intensity of each activity at its source was obtained based on a stepwise logic decision process. Then the pressure of each human activity on the coastal area was calculated based on weighted distance from the activity by spatial analysis tools in Geographic Information System (GIS). Considering different types and pressures of activities, a weighting factor for each activity was determined through analytic hierarchy process (AHP). Finally maps of multiple pressures were combined into a single cumulative human impact on coastal regions. The methodology was applied to Jiaozhou Bay and results showed that human activities impact almost every part of the bay. Spatially, the pressure intensity in the east and northeast part were the highest due to a variety of intensive human activities surrounding this part. Pressures caused by sewage/riverine discharge, anchor ground and urbanization were relatively high among all the human pressures. Coastal management authorities should bear in mind that population and industry in the east and northeast coast of Jiaozhou Bay may already be saturated and call for more rationalized distribution of human activities for the whole Jiaozhou Bay.