To evaluate the performance of ocean-colour retrievals of total chlorophyll-a concentration requires direct comparison with concomitant and co-located in situ data. For global comparisons, these in situ match-ups should be ideally representative of the distribution of total chlorophyll-a concentration in the global ocean. The oligotrophic gyres constitute the majority of oceanic water, yet are under-sampled due to their inaccessibility and under-represented in global in situ databases. The Atlantic Meridional Transect (AMT) is one of only a few programmes that consistently sample oligotrophic waters. In this paper, we used a spectrophotometer on two AMT cruises (AMT19 and AMT22) to continuously measure absorption by particles in the water of the ship's flow-through system. From these optical data continuous total chlorophyll-a concentrations were estimated with high precision and accuracy along each cruise and used to evaluate the performance of ocean-colour algorithms. We conducted the evaluation using level 3 binned ocean-colour products, and used the high spatial and temporal resolution of the underway system to maximise the number of match-ups on each cruise. Statistical comparisons show a significant improvement in the performance of satellite chlorophyll algorithms over previous studies, with root mean square errors on average less than half (~ 0.16 in log10 space) that reported previously using global datasets (~ 0.34 in log10space). This improved performance is likely due to the use of continuous absorption-based chlorophyll estimates, that are highly accurate, sample spatial scales more comparable with satellite pixels, and minimise human errors. Previous comparisons might have reported higher errors due to regional biases in datasets and methodological inconsistencies between investigators. Furthermore, our comparison showed an underestimate in satellite chlorophyll at low concentrations in 2012 (AMT22), likely due to a small bias in satellite remote-sensing reflectance data. Our results highlight the benefits of using underway spectrophotometric systems for evaluating satellite ocean-colour data and underline the importance of maintaining in situ observatories that sample the oligotrophic gyres.
Remote Sensing and GIS
There is ongoing interest to develop remote sensing methods for mapping and monitoring the spatial distribution and biomass of mangroves. In this study, we develop a suite of methods to evaluate the combination of Landsat-8, ALOS PALSAR, and SRTM data for mapping spatial distribution of mangrove composition, canopy height, and aboveground biomass in the wide intertidal zones and coastal plains of Mimika district, Papua, Indonesia. Image segmentation followed by visual interpretation of composite PALSAR images was used to delineate mangrove areas, whereas a flexible statistical rule based classification of spectral signatures from Landsat-8 images was used to classify mangrove associations. The overall accuracy of land cover classification was 94.38% with a kappa coefficient of 0.94 when validated with field inventory data and Google Earth images. Mangrove height and aboveground biomass were mapped using the SRTM DEM, which were calibrated with field-measured data via quantile regression models. There was a strong correlation between the SRTM DEM and the 0.98 quantile of field canopy heights (H.98), which was used to represent the tallest trees in each of 196 10 m radius subplots (r = 0.84 and R2 = 0.804). Model performance was evaluated through 10,000 bootstrapped simulations, producing a mean absolute error (MAE) of 3.0 m for canopy height estimation over 30 m pixels of SRTM data. Quantile regression revealed a relatively strong non-linear relationship between the SRTM derived canopy height model and aboveground biomass measured in 0.5 ha mangrove inventory plots (n = 33, R2 = 0.46). The model results produced estimates of mean standing biomass of 237.52 ± 98.2 Mg/ha in short canopy (Avicennia/Sonneratia) stands to 353.52 ± 98.43 Mg/ha in mature tall canopy (Rhizophora) dominated forest. The model estimates of mangrove biomass were within 90% confidence intervals of area-weighted biomass derived from field measurements. When validated at the landscape scale, the difference between modeled and measured aboveground mangrove biomass was 3.48% with MAE of 105.75 Mg/ha. These results indicate that the approaches developed here are reliable for mapping and monitoring mangrove composition, height, and biomass over large areas of Indonesia.
Physical, chemical and biological characteristics of seawaters are primary descriptors for understanding environmental patterns and improving maritime spatial planning for potential aquaculture uses. By analyzing these descriptors in spatial and temporal dimensions, it is possible to characterize the potential productivity performances of different locations for specific aquaculture species. We developed a toolbox that, starting from the actual competing uses of the maritime space, aims at: (a) identifying sites with conditions feasible for aquaculture fish growth (feasibility scenario); and (b) assessing their different productivity performances in terms of potential fish harvest (suitability scenario). The toolbox is being designed in the Mediterranean, northern Adriatic Sea, but because of its modularity/multi-stage process, it can be easily adapted to other areas, or scaled to larger areas. The toolbox, representing a pre-operational Copernicus downstreaming service that integrates data and products from different sources (in situ, Earth Observation and modeling), is innovative because it is based more on parameters relevant for fish vitality than on those oriented to farm functioning. Stakeholders and farmers involved in the maritime spatial planning can use resulting scenarios for decision-making and market-trading processes.
Oceanic shelf sea fronts have significant effects on local dynamics, ecology and climate. An assessment of the impact of climate change on frontal positions and frontal gradients requires reliable reference data and the possibility to monitor oceanic fronts. Therefore, the development of algorithms which automatically detect frontal positions from Earth Observation (EO) data is an important tool to analyse long EO time series, i.e. to process big data volumes. The development of GRADHIST was driven by the need to generate a climatology for North Sea fronts. GRADHIST is a new algorithm for the detection and mapping of oceanic fronts, which is based on a combination and refinement of the gradient algorithm of Canny (1986) and the histogram algorithm of Cayula and Cornillon (1992). GRADHIST preserves the main principles of both algorithms and can be applied to various ocean parameters as well as to different sensors with very little effort. GRADHIST was validated and tested using both synthetic and real data and applied to sea surface temperature and ocean colour parameters retrieved from satellite data; i.e. data from MODIS (Moderate Resolution Imaging Spectroradiometer), MERIS (MEdium Resolution Imaging Spectrometer), AVHRR (Advanced Very High Resolution Radiometer) and AATSR (Advanced Along-Track Scanning Radiometer). Selected results and statistical analysis of a new North Sea climatology for oceanic fronts are presented and discussed.
A framework is proposed for utilizing remote sensing and ground-truthing field data to map benthic habitats in the State of Qatar, with potential application across the Arabian Gulf. Ideally the methodology can be applied to optimize the efficiency and effectiveness of mapping the nearshore environment to identify sensitive habitats, monitor for change, and assist in management decisions. The framework is applied to a case study for northeastern Qatar with a key focus on identifying high sensitivity coral habitat. The study helps confirm the presence of known coral and provides detail on a region in the area of interest where corals have not been previously mapped. Challenges for the remote sensing methodology associated with natural heterogeneity of the physical and biological environment are addressed. Recommendations on the application of this approach to coastal environmental risk assessment and management planning are discussed as well as future opportunities for improvement of the framework.
With increasing demands for ocean color (OC) products with improved accuracy and well characterized, per-retrieval uncertainty budgets, it is vital to decompose overall estimated errors into their primary components. Amongst various contributing elements (e.g., instrument calibration, atmospheric correction, inversion algorithms) in the uncertainty of an OC observation, less attention has been paid to uncertainties associated with spatial sampling. In this paper, we simulate MODIS (aboard both Aqua and Terra) and VIIRS OC products using 30 m resolution OC products derived from the Operational Land Imager (OLI) aboard Landsat-8, to examine impacts of spatial sampling on both cross-sensor product intercomparisons and in-situ validations of Rrsproducts in coastal waters. Various OLI OC products representing different productivity levels and in-water spatial features were scanned for one full orbital-repeat cycle of each ocean color satellite. While some view-angle dependent differences in simulated Aqua-MODIS and VIIRS were observed, the average uncertainties (absolute) in product intercomparisons (due to differences in spatial sampling) at regional scales are found to be 1.8%, 1.9%, 2.4%, 4.3%, 2.7%, 1.8%, and 4% for the Rrs(443), Rrs(482), Rrs(561), Rrs(655), Chla, Kd(482), and bbp(655) products, respectively. It is also found that, depending on in-water spatial variability and the sensor's footprint size, the errors for an in-situ validation station in coastal areas can reach as high as ± 18%. We conclude that a) expected biases induced by the spatial sampling in product intercomparisons are mitigated when products are averaged over at least 7 km × 7 km areas, b) VIIRS observations, with improved consistency in cross-track spatial sampling, yield more precise calibration/validation statistics than that of MODIS, and c) use of a single pixel centered on in-situ coastal stations provides an optimal sampling size for validation efforts. These findings will have implications for enhancing our understanding of uncertainties in ocean color retrievals and for planning of future ocean color missions and the associated calibration/validation exercises.
Remote species classification using fisheries acoustic techniques in coral reef ecosystems remains one of the greatest hurdles in developing informative metrics and indicators required for ecosystem management. We reviewed long-term marine ecosystem acoustic surveys that have been carried out in the US Caribbean covering various coral reef habitat types and evaluated metrics that may be helpful in classifying multifrequency acoustic signatures of fish aggregations to taxonomic groups. We found that the energetic properties across frequencies, in particular the mean and the maximum volume backscattering coefficient, provided the majority of the discriminating power in separating schools and aggregations into distinct groups. To a lesser extent, school shape and geometry helped isolate a distinctive group of reef fishes based on shoaling behaviour. Schools and aggregations were clustered into five distinct groups. The use of underwater video surveys from a Remote Operating Vehicle (ROV) conducted in the proximity of the acoustic observations allowed us to associate the clusters with broad categories of species groups such as large predators, including fishery important species to small forage fishes. The remote classification methods described here are an important step toward improving marine ecosystem acoustics for the study and management of coral reef fish communities.
The seabed can be classified using data from vertical, split-beam echosounders. This was demonstrated recently using a model parameterized with acoustic estimates of slope, roughness, normal-incidence backscattering strength, and variation of backscattering strength by frequency and incidence angle. These seabed classifications were interpreted and validated using published surficial geology maps, but the acoustic data indicated greater spatial variability. Here, classifications of sediment grain or feature size are ascribed to areas ∼10 m2. First, images of the seabed in the study area are ascribed, based on per cent coverage, to seven primary classes ranging from mud through high-relief rock, and 25 primary–secondary classes. Then, a refined seabed classifier, based on the acoustic model parameters is trained, using a nearest-neighbours algorithm, on a subset of the class data. The classifier accurately predicts 96% of the primary classes, and 93% of the primary–secondary classes from an independent data subset. These methods should be useful for characterizing, mapping, and quantifying potential seabed habitat domains of demersal fish and benthic invertebrates.
- Data sets on wetlands required for the representation of aquatic ecosystem biodiversity and systematic wetland conservation planning are typically not available or are inadequate, particularly at country-wide scale, which hinders conservation planning. The improvement in hierarchical classification systems and increased availability of broad-scale data sets offers new opportunities to overcome these limitations.
- This study demonstrates replicable methods for classifying wetland ecosystem types and condition country-wide using broad-scale data sets in data-scarce countries.
- A country-wide data set, compiled primarily using remote sensing techniques, was combined with regional and landscape-setting data sets to reflect the ecological and geomorphic biodiversity of wetlands. Geographical Information Systems (GIS) were employed to model wetland types, disturbance indices and identify priority wetlands through threatened faunal species associations using existing data. Accuracy of the national data was assessed through a congruency with two local data sets.
- Most of the 1 680 306 ha of inland wetlands were classified as Natural (80%), of which the majority were located on Valley Floors (68%). However, the national data were found only to represent 54% of wetlands mapped at a local scale, and comparison with local data showed inaccuracies in the types and condition classifications.
- Problems regarding spatial data quality and scale are discussed and suggestions for improvement are provided. The desktop classification steps can be reproduced easily for other data-scarce countries. Data sets on freshwater ecosystems can assist in raising awareness and influence policy at a national scale.
Oceana, in partnership with Google and SkyTruth, has developed Global Fishing Watch, a public, web- based technology platform that will track global fishing activity and be used to improve transparency and traceability in the world’s fishing industry. It will allow scientists to study the interactions between fishing and ocean processes, help governments better manage fish stocks and enforce policies aimed at rebuilding their fisheries, and provide citizens, NGOs and activists with the information they need to hold governments and fisheries management organizations accountable for responsible fisheries management practices. Oceana and its partners released the Global Fishing Watch prototype in late 2014, and are now developing a version for public release.