La Comunidad geospacial y el acceso a datos oceanográficos

Nowadays, Oceanographic and Geospatial communities are closely related worlds. The problem is that they follow parallel paths in data storage, distributions, modelling and data analyzing. This situation produces different data model implementations for the same features. While Geospatial information systems have 2 or 3 dimensions, the Oceanographic models uses multidimensional parameters like temperature, salinity, streams, ocean colour... This implies significant differences between data models of both communities, and leads to difficulties in dataset analysis for both sciences. These troubles affect directly to the Mediterranean Institute for Advanced Studies ( IMEDEA (CSIC-UIB)). Researchers from this Institute perform intensive processing with data from oceanographic facilities like CTDs, moorings, gliders… and geospatial data collected related to the integrated management of coastal zones. In this paper, we present an approach solution based on THREDDS (Thematic Real-time Environmental Distributed Data Services). THREDDS allows data access through the standard geospatial data protocol Web Coverage Service, inside the European project (European Coastal Sea Operational Observing and Forecasting system). The goal of ECOOP is to consolidate, integrate and further develop existing European coastal and regional seas operational observing and forecasting systems into an integrated pan- European system targeted at detecting environmental and climate changes

The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data

The absorption spectra of phytoplankton in the visible domain hold implicit information on the phytoplankton community structure. Here we use this information to retrieve quantitative information on phytoplankton size structure by developing a novel method to compute the exponent of an assumed power-law for their particle-size spectrum. This quantity, in combination with total chlorophyll-a concentration, can be used to estimate the fractional concentration of chlorophyll in any arbitrarily-defined size class of phytoplankton. We further define and derive expressions for two distinct measures of cell size of mixed populations, namely, the average spherical diameter of a bio-optically equivalent homogeneous population of cells of equal size, and the average equivalent spherical diameter of a population of cells that follow a power-law particle-size distribution. The method relies on measurements of two quantities of a phytoplankton sample: the concentration of chlorophyll-a, which is an operational index of phytoplankton biomass, and the total absorption coefficient of phytoplankton in the red peak of visible spectrum at 676 nm. A sensitivity analysis confirms that the relative errors in the estimates of the exponent of particle size spectra are reasonably low. The exponents of phytoplankton size spectra, estimated for a large set of in situ data from a variety of oceanic environments (~ 2400 samples), are within a reasonable range; and the estimated fractions of chlorophyll in pico-, nano- and micro-phytoplankton are generally consistent with those obtained by an independent, indirect method based on diagnostic pigments determined using high-performance liquid chromatography. The estimates of cell size for in situ samples dominated by different phytoplankton types (diatoms, prymnesiophytes, Prochlorococcus, other cyanobacteria and green algae) yield nominal sizes consistent with the taxonomic classification. To estimate the same quantities from satellite-derived ocean-colour data, we combine our method with algorithms for obtaining inherent optical properties from remote sensing. The spatial distribution of the size-spectrum exponent and the chlorophyll fractions of pico-, nano- and micro-phytoplankton estimated from satellite remote sensing are in agreement with the current understanding of the biogeography of phytoplankton functional types in the global oceans. This study contributes to our understanding of the distribution and time evolution of phytoplankton size structure in the global oceans.

Modelling the time-evolution of phytoplankton size spectra from satellite remote sensing

A dynamic size-structured model is developed for phytoplankton and nutrients in the oceanic mixed layer and applied to extract phytoplankton biomass at discrete size fractions from remotely sensed, ocean-colour data. General relationships between cell size and biophysical processes (such as sinking, grazing, and primary production) of phytoplankton were included in the model through a bottom–up approach. Time-dependent, mixed-layer depth was used as a forcing variable, and a sequential data-assimilation scheme was implemented to derive model trajectories. From a given time-series, the method produces estimates of size-structured biomass at every observation, so estimates seasonal succession of individual phytoplankton size, derived here from remote sensing for the first time. From these estimates, normalized phytoplankton biomass size spectra over a period of 9 years were calculated for one location in the North Atlantic. Further analysis demonstrated that strong relationships exist between the seasonal trends of the estimated size spectra and the mixed-layer depth, nutrient biomass, and total chlorophyll. The results contain useful information on the time-dependent biomass flux in the pelagic ecosystem.

Retrieval of phytoplankton size from bio-optical measurements: theory and applications

The absorption coefficient of a substance distributed as discrete particles in suspension is less than that of the same material dissolved uniformly in a medium—a phenomenon commonly referred to as the flattening effect. The decrease in the absorption coefficient owing to flattening effect depends on the concentration of the absorbing pigment inside the particle, the specific absorption coefficient of the pigment within the particle, and on the diameter of the particle, if the particles are assumed to be spherical. For phytoplankton cells in the ocean, with diameters ranging from less than 1 µm to more than 100 µm, the flattening effect is variable, and sometimes pronounced, as has been well documented in the literature. Here, we demonstrate how the in vivo absorption coefficient of phytoplankton cells per unit concentration of its major pigment, chlorophyll a, can be used to determine the average cell size of the phytoplankton population. Sensitivity analyses are carried out to evaluate the errors in the estimated diameter owing to potential errors in the model assumptions. Cell sizes computed for field samples using the model are compared qualitatively with indirect estimates of size classes derived from high performance liquid chromatography data. Also, the results are compared quantitatively against measurements of cell size in laboratory cultures. The method developed is easy-to-apply as an operational tool for in situ observations, and has the potential for application to remote sensing of ocean colour data.

The phenology of phytoplankton blooms: ecosystems indicators from remote sensing

Remote sensing offers many advantages in the development of ecosystem indicators for the pelagic zone of the ocean. Particularly suitable in this context are the indicators arising from time series that can be constructed from remotely sensed data. For example, using ocean-colour radiometry, the phenology of phytoplankton blooms can be assessed. Metrics defined in this way show promise as informative indicators for the entire pelagic ecosystem. A simple phytoplankton–substrate model, with forcing dependent on latitude and day number is used to explore the qualitative features of bloom phenology for comparison with the results observed in a suite of 10-year time series of chlorophyll concentration, as assessed by remote sensing, from the Northwest Atlantic Ocean. The model reveals features of the dynamics that might otherwise have been overlooked in evaluation of the observational data.

Selecting algorithms for Earth observation of climate within the European Space Agency Climate Change initiative: introduction to a special issue

This special issue is focused on the assessment of algorithms for the observation of Earth’s climate from environ- mental satellites. Climate data records derived by remote sensing are increasingly a key source of insight into the workings of and changes in Earth’s climate system. Producers of data sets must devote considerable effort and expertise to maximise the true climate signals in their products and minimise effects of data processing choices and changing sensors. A key choice is the selection of algorithm(s) for classification and/or retrieval of the climate variable. Within the European Space Agency Climate Change Initiative, science teams undertook systematic assessment of algorithms for a range of essential climate variables. The papers in the special issue report some of these exercises (for ocean colour, aerosol, ozone, greenhouse gases, clouds, soil moisture, sea surface temper- ature and glaciers). The contributions show that assessment exercises must be designed with care, considering issues such as the relative importance of different aspects of data quality (accuracy, precision, stability, sensitivity, coverage, etc.), the availability and degree of independence of validation data and the limitations of validation in characterising some important aspects of data (such as long-term stability or spatial coherence). As well as re- quiring a significant investment of expertise and effort, systematic comparisons are found to be highly valuable. They reveal the relative strengths and weaknesses of different algorithmic approaches under different observa- tional contexts, and help ensure that scientific conclusions drawn from climate data records are not influenced by observational artifacts, but are robust.

Retrospective application of remote sensing to the Tasmanian lakeland

This thesis describes the research undertaken for a degree of Master of Science in a retrospective study of airborne remotely sensed data registered in 1990 and 1993, and field captured data of aquatic humus concentrations for ~ 45 lakes in Tasmania. The aim was to investigate and describe the relationship between the remotely sensed data and the field data and to test the hypothesis that the remotely sensed data would establish further evidence of a limnological corridor of change running north-west to south- east. The airborne remotely sensed data consisted of data captured by the CSIRO Ocean Colour Scanner (OCS) and a newly developed Canadian scanner, a compact airborne spectrographic imager (CASI). The thesis investigates the relationship between the two kinds of data sources. The remotely sensed data was collected with the OCS scanner in 1990 (during one day) and with both the OCS and the CASI in 1993 (during three days). The OCS scanner registers data in 9 wavelength bands between 380 nm and 960 nm with a 10-20 nm bandwidth, and the CASI in 288 wavelength bands between 379.57 nm and 893.5 nm (ie. spectral mode) with a spectral resolution of 2.5 nm. The remotely sensed data were extracted from the original tapes with the help of the CSIRO and supplied software and digital sample areas (band value means) for each lake were subsequently extracted for data manipulation and statistical analysis. Field data was captured concurrently with the remotely sensed data in 1993 by lake hopping using a light aircraft with floats. The field data used for analysis with the remotely sensed data were the laboratory determined g440 values from the 1993 water samples collated with g440 values determined from earlier years. No spectro-radiometric data of the lakes, data of incoming irradiance or ancillary climatic data were captured during the remote sensing missions. The sections of the background chapter in the thesis provide a background to the research both in regards to remote sensing of water quality and the relationship between remotely sensed spectral data and water quality parameters, as well as a description of the Tasmanian lakes flown. The lakes were divided into four groups based on results from previous studies and optical parameters, especially aquatic humus concentrations as measured from field captured data. The four groups consist of the ‘green” clear water lakes mostly situated on the Central Plateau, the ‘brown” highly dystrophic lakes in western Tasmania, the ‘corridor” lakes situated along a corridor of change lying approximately between the two lines denoting the Jurassic edge and 1200 mm isohyet, and the ‘eastern, turbid” lakes make up the fourth group. The analytical part of the research work was mostly concerned with manipulating and analysing the CASI data because of its higher spectral resolution. The research explores methods to apply corrections to this data to reduce the disturbing effects of varying illumination and atmospheric conditions. Three different methods were attempted. In the first method two different standardisation formulas are applied to the data as well as ‘day correction” factors calculated from data from one of the lakes, Lake Rolleston, which had data captured for all three days of the remote sensing operations. The standardisation formulas were also applied to the OCS data. In second method an attempt to reduce the effects of the atmosphere was performed using spectro-radiometric captured in 1988 for one of the lakes flown, Great Lake. All the lake sample data were time normalised using general irradiance data obtained from the University of Tasmania and the sky portion as calculated from Great Lake upwelling irradiance data was then subtracted. The last method involved using two different band ratios to eliminate atmospheric effects. Statistical analysis was applied to the data resulting from the three methods to try to describe the relationship between the remotely sensed data and the field captured data. Discriminant analysis, cluster analysis and factor analysis using principal component analysis (pea) were applied to the remotely sensed data and the field data. The factor scores resulting from the pca were regressed against the field collated data of g440 as were the values resulting from last method. The results from the statistical analysis of the data from the first method show that the lakes group well (100%) against the predetermined groups using discriminant analysis applied to the remotely sensed CASI data. Most variance in the data are contained in the first factor resulting from pca regardless of data manipulation method. Regression of the factor scores against g440 field data show a strong non- linear relationship and a one-sided linear regression test is therefore considered an inappropriate analysis method to describe the dataset relationships. The research has shown that with the available data, correction and analysis methods, and within the scope of the Masters study, it was not possible to establish the relationships between the remotely sensed data and the field measured parameters as hoped. The main reason for this was the failure to retrieve remotely sensed lake signatures adequately corrected for atmospheric noise for comparison with the field data. This in turn is a result of the lack of detailed ancillary information needed to apply available established methods for noise reduction - to apply these methods we require field spectroradiometric measurements and environmental information of the varying conditions both within the study area and within the time frame of capture of the remotely sensed data.

Relationship between long-term environmental fluctuations and diving effort of female Australian fur seals

For predators foraging within spatially and temporally heterogeneous marine ecosystems, environmental fluctuations can alter prey availability. Using the proportion of time spent diving and foraging trip duration as proxies of foraging effort, a multi-year dataset was used to assess the response of 58 female Australian fur seals Arctocephalus pusillus doriferus to interannual environmental fluctuations. Multiple environmental indices (remotely sensed ocean colour data and numerical weather predictions) were assessed for their influence on inter-annual variations in the proportion of time spent diving and trip duration. Model averaging revealed strong evidence for relationships between 4 indices and the proportion of time spent diving. There was a positive relationship with effort and 2 yr-lagged spring sea-surface temperature, current winter zonal wind and southern oscillation index, while a negative relationship was found with 2 yr-lagged spring zonal wind. Additionally, a positive relationship was found between foraging trip duration and 1 yr-lagged spring surface chlorophyll a. These results suggest that environmental fluctuations may influence prey availability by affecting the survival and recruitment of prey at the larval and post-larval phases while also affecting current distribution of adult prey.

An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing

Satellite remote sensing of ocean colour is the only method currently available for synoptically measuring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical and ecological methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this study, several of these techniques were evaluated against in situ observations to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). The techniques are applied to a 10-year ocean-colour data series from the SeaWiFS satellite sensor and compared with in situ data (6504 samples) from a variety of locations in the global ocean. Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. Detection of microplankton and picoplankton were generally better than detection of nanoplankton. Abundance-based approaches were shown to provide better spatial retrieval of PSCs. Individual model performance varied according to PSC, input satellite data sources and in situ validation data types. Uncertainty in the comparison procedure and data sources was considered. Improved availability of in situ observations would aid ongoing research in this field. (C) 2010 Elsevier B.V. All rights reserved.

Climate-induced interannual variability of marine primary and export production in three global coupled climate carbon cycle models

Fully coupled climate carbon cycle models are sophisticated tools that are used to predict future climate change and its impact on the land and ocean carbon cycles. These models should be able to adequately represent natural variability, requiring model validation by observations. The present study focuses on the ocean carbon cycle component, in particular the spatial and temporal variability in net primary productivity (PP) and export production (EP) of particulate organic carbon (POC). Results from three coupled climate carbon cycle models (IPSL, MPIM, NCAR) are compared with observation-based estimates derived from satellite measurements of ocean colour and results from inverse modelling (data assimilation). Satellite observations of ocean colour have shown that temporal variability of PP on the global scale is largely dominated by the permanently stratified, low-latitude ocean (Behrenfeld et al., 2006) with stronger stratification (higher sea surface temperature; SST) being associated with negative PP anomalies. Results from all three coupled models confirm the role of the low-latitude, permanently stratified ocean for anomalies in globally integrated PP, but only one model (IPSL) also reproduces the inverse relationship between stratification (SST) and PP. An adequate representation of iron and macronutrient co-limitation of phytoplankton growth in the tropical ocean has shown to be the crucial mechanism determining the capability of the models to reproduce observed interactions between climate and PP.