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See detailReconstruction of missing data in satellite data sets using DINEOF with constraints to reduce spurious high-frequency variations in the temporal EOFs
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Sirjacobs, Damien ULg et al

Conference (2009)

DINEOF (Data Interpolating Empirical Orthogonal Functions) is a method to reconstruct missing data in geophysical data sets, such as gaps originated by the presence of clouds in infrared satellite sensors ... [more ▼]

DINEOF (Data Interpolating Empirical Orthogonal Functions) is a method to reconstruct missing data in geophysical data sets, such as gaps originated by the presence of clouds in infrared satellite sensors. Based on Empirical Orthogonal Functions (EOFs), DINEOF uses an iterative procedure to calculate the missing values. DINEOF has been compared to Optimal Interpolation, showing that more accurate results are achieved, with up to 30 times less computational time (tests made with sea surface temperature of the Adriatic Sea, and validated with in situ data). Another advantage of this technology is that there is no need for a priori knowledge of the reconstructed data set statistics (such as covariance or correlation length). The technique can be applied to a broad range of data (physical, biological, chemical), and to a variety of platforms (satellite data, in situ data...). Given the nature of the EOFs, it is not necessary that data sets are regularly distributed in time. Irregularly distributed data sets, however, may lead to discontinuities in the temporal EOFs calculated from them, and these discontinuities can affect in turn the quality of the DINEOF reconstruction. In satellite data, some images can present a large amount of cloud cover, and only a few pixels with valid data. EOF projection to such images can also lead to discontinuities in the temporal modes, as there might be an over-fitting to the scarce information present in those images. After briefly describe DINEOF and its applications, we present a study aiming to reduce these discontinuities by including a time constraint to the covariance matrix used in the EOF decomposition. The approach is tested with sea surface temperature data of the Black Sea, and the results are compared to independent data. [less ▲]

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See detailEnhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Sirjacobs, Damien ULg et al

in Ocean Science (2009), 5(4), 475-485

DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface ... [more ▼]

DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The approach is tested in a three years sea surface temperature data set over the Black Sea. The effect of the filter in the temporal EOFs is presented, as well as some examples of the improvement achieved with the filtering in the SST reconstruction, both compared to the DINEOF approach without filtering. [less ▲]

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See detailUsing Diva on large datasets: applications and tips
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Lenartz, Fabian et al

Scientific conference (2008, October 16)

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See detailReconstruction of Missing Satellite Total Suspended Matter Data over the Southern North Sea and English Channel using Empirical Orthogonal Function Decomposition of Satellite Imagery and Hydrodynamical Modelling
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2008, October)

Optical remote sensing data archives generally have many gaps caused by clouds or other retrieval problems. However, for the light forcing of ecosystem models continuous fields are required. For ... [more ▼]

Optical remote sensing data archives generally have many gaps caused by clouds or other retrieval problems. However, for the light forcing of ecosystem models continuous fields are required. For parameters exhibiting strong spatial and temporal correlations for regions of similar dynamics or from day to day, the missing data can be estimated by use of statistical techniques. In this context, the Data Interpolation with Empirical Orthogonal Functions (DINEOF) method is used for reconstruction of complete space-time information for surface total suspended matter (TSM) and chlorophyll a from a 5-year archive of MODIS and MERIS products over the Southern North Sea and English Channel. The DINEOF univariate methodology has been previously demonstrated for Mediterranean sea surface temperature data (Alvera-Azcarate et al., 2005, Beckers et al., 2006). Alvera-Azcarate et al (2007) showed that SST reconstructions could be improved by using a multivariate approach in which SST, chlorophyll and wind fields were taken into account together for the analyses. Here, TSM images will be used in combination with information from the COHERENS hydrodynamical model to provide a complete and continuous estimate of surface TSM for the Southern North Sea throughout the period 2003-2005. In addition to the remotely sensed TSM, the DINEOF multivariate analysis will consider wind fields, depth integrated currents, surface elevations and bottom stresses. Reconstucted images are compared with the original incomplete images. Validation of the method is achieved by estimation of information removed from the training data by exclusion of entire images and by addition of artificial clouds. The data reconstruction technique has further applications in the processing and quality control of optical remote sensing data. Perspectives will be outlined for improving the quality control of retrieved parameters and for the improvement of retrievals by adding statistical information to the conventional spectral processing. References: Alvera-Azcarate, A., Barth, A., Rixen, M., and Beckers, J.-M.: Reconstruction of incomplete oceanographic data sets using Empirical Orthogonal Functions. Application to the Adriatic Sea, Ocean Modelling, 9, 325–346, 2005. Alvera-Azcarate, A., Barth, A., Beckers, J. M., and Weisberg, R. H.: Multivariate Reconstruction of Missing Data in Sea Surface Temperature, Chlorophyll and Wind Satellite Fields, Journal of Geophysical Research, 112, C03008, doi:10.1029/2006JC003660, 2007. Beckers J.-M., A. Barth & A. Alvera-Azcarate, DINEOF reconstruction of clouded images including error maps. Application to the Sea-Surface Temperature around Corsican Island, Ocean Sciences, 2: 183–199, 2006. [less ▲]

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See detailRemote Sensing of Suspended Particulate Matter in Turbid Waters: State of the Art and Future Perspectives.
Ruddick, Kevin; Nechad, Bouchra; Park, Youngje et al

Conference (2008, October)

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See detailUnderwater Imagery, a Measuring Tool to Extend the Spatio-Temporal Understanding of Benthic Organisms Dynamics: Case Study of Codium elisabethae in the Azores.
Sirjacobs, Damien ULg; Tempera, Fernando; Cardigos, Frederico et al

Poster (2008, October)

Benthic habitat mapping studies have been increasingly exploiting the use of underwater images to collect information on substrate nature and biological coverage. Concurrently, research has been ongoing ... [more ▼]

Benthic habitat mapping studies have been increasingly exploiting the use of underwater images to collect information on substrate nature and biological coverage. Concurrently, research has been ongoing to develop methods that use the imagery collected to conduct regular quantitative monitoring studies of biological resources distributed over large areas. This study provides the first multi-annual monitoring information on the dynamics of a benthic macroalgae population derived from underwater imagery collected by scuba divers in the Monte da Guia Site of Community Importance /Natura 2000 network (Faial isl. Azores, NE Atlantic). The green alga Codium elisabethae - a long-living green alga that represents a potential good indicator of coastal environmental change - was chosen for the study. The analyses focus on using the underwater imagery to quantify seasonal fluctuations of density, percentage cover, biomass, growth rate and primary production of the species. Two study sites were investigated: one was located in a sheltered no-go reserve exhibiting a dense C. elisabethae population, and the other in a location experiencing more exposed conditions and holding a sparser population. Between August 2003 and November 2005, fifteen (15) photo coverages were collected by scuba-divers. Subsequent processing consisted of producing image mosaics and using automated and interactive change detection methods that recognized, measured and counted individuals present in photos of fixed quadrats and yielded dynamical parameters such as population structures, growth, recruitment, and mortality. Chi-square tests of image-derived estimates and in situ measurements confirmed the validity of a centimeter precision estimation of population structure for individuals above 4 cm diameter. Important variability of population structure and density were observed at small spatial scales. Population density showed a sharp reduction in autumn 2003 and did not show a full recovery in spring and summer 2004. During the following year, population of the protected site maintained density and biomass, while at the exposed site population density dropped. The production of information based on observations of thousands of individuals is mandatory in biological population statistics. The presented imagery approach made it possible, avoiding the need to collect all the measurements and quantitative information during time-constrained SCUBA diving operations. [less ▲]

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See detailCloud filling of total suspended matter, chlorophyll and sea surface temperature remote sensing products by the Data Interpolation with Empirical Orthogonal Functions methodology, application to the BELCOLOUR-1 database
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

in ESA Special Publication SP666 (2008, September)

Space-time filling of the gaps in satellite data archives is an important step for the improvement of various marine ecosystem studies. The Data Interpolation with Empirical Orthogonal Functions ... [more ▼]

Space-time filling of the gaps in satellite data archives is an important step for the improvement of various marine ecosystem studies. The Data Interpolation with Empirical Orthogonal Functions methodology (DINEOF) allows calculating missing data in geophysical datasets without requiring a priori knowledge about statistics of the full data set [1]. It was successfully applied to SST reconstructions as in [1] and [2]. Here, the DINEOF reconstruction method is applied to surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) data over the Southern North Sea and English Channel obtained from the BELCOLOUR archive. 1. Beckers, J.-M. and Rixen, M. (2003). EOF Calculations and Data Filling from Incomplete Oceanographic Datasets. Journal of Atmospheric and Oceanic Technology, 20:18391856. 2. Alvera-Azcárate, A., Barth, A., Rixen, M. and Beckers, J.-M. (2005). Reconstruction of incomplete oceanographic data sets using Empirical Orthogonal Functions. Application to the Adriatic Sea surface temperature. Ocean Modelling, 9:325–346. [less ▲]

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See detailThree-dimensional analysis of oceanographic data with the software DIVA
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Machín, Francisco et al

Poster (2008, April 13)

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See detailDIVA-4.2.1: presentation of the new features
Troupin, Charles ULg; Machín, Francisco; Ouberdous, Mohamed ULg et al

Poster (2008, April 03)

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See detailImplementation of hydrostatic constraint in the software DIVA: Theory and applications
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Rixen, Michel et al

Poster (2008, March 31)

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See detailREconstruction of COLOUR scenes
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2008, February 12)

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See detailDINEOF univariate reconstruction of missing satellite data from the North Sea Belcolour-1 database.
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2008)

The Belcolour-1 database holds more than 4 years of uniformly resampled MERIS chlorophyll (CHL), total suspended matter (TSM), MODIS-AQUA CHL, TSM and sea surface temperature (SST) over the North Sea. A ... [more ▼]

The Belcolour-1 database holds more than 4 years of uniformly resampled MERIS chlorophyll (CHL), total suspended matter (TSM), MODIS-AQUA CHL, TSM and sea surface temperature (SST) over the North Sea. A first step of the RECOLOUR* project consists in the univariate reconstruction of missing data with the DINEOF method (Data Interpolating Empirical Orthogonal Functions). In particular, the DINEOF treatment of MERIS CHL and TSM images available for the year 2003 allowed an efficient synthesis of the coherent modes of variability existing at the scale of the whole North Sea. For both parameters, 4 modes were retained by general cross validation as an optimum for the reconstruction of missing data. For CHL, the first spatial mode shows the high influence of coastal nutrients outputs (mainly continental estuaries and diffused coastal sources) and the lower concentration in the well stratified central and northern part of the North Sea compared to the southern bight and the eastern English Channel. The spatial trends described by the first mode are permanent features during the year, although slightly enhanced during the summer and reduced during winter. The second spatial mode correspond to the main algal blooming events (spring and autumn) with increased concentrations in the whole southern bight of the north sea, around the Isle of Wight and in frontal alike structure north-west from Denmark. The third Eofs describes well the dynamics of an early phytoplankton bloom occurring in march along the Norwegian coast, where a strong stratification induced by an output of cold water from Baltic Sea provides good light conditions to phytoplankton. Concerning TSM, the first spatial mode shows the dominant influence of large estuaries and of resuspension from shallow coastal sedimental plains. The patterns suggest a general transport of sediments from south-east England up to the northern Dutch coastal waters, as a clear distinction between the stratified northern part and the well mixed and charged southern and German bights. Although these trends are permanent during the year, the range of the spatial variations are slightly reduced during the summer, following the reduction of resuspension, of total sediment outputed by rivers and of advection along continental coasts. The second mode shows a clear seasonal signal. The winter influence of the second spatial mode can be understood as general sediment enrichment due to higher resuspension, but a clear influence of intense winter terrestrial water outflows leading to lower sediment concentration in the plumes then in the surrounding waters. This is clear for the Elbe river discharge, the whole natural part of the Wadden Sea and the Seine river plume. The Scheelde and Thames rather seems to be just neutralizing the seasonal TSM resuspension signal. The Rhine river discharge seems to make exception as no influence is detected in the second spatial mode. During summer, the contribution of the second EOF is reversed with a general reduction of suspended matter concentration in most part of the area but some local sediment enrichment at specific river discharges. Original MERIS CHL and TSM images were filled and reconstructions were produced at a daily interval based on a linear interpolation of the temporal modes. From this, weekly averages could be calculated at stations such as the turbidity maximum of the Scheelde river plume, showing the onset of the spring bloom co-occurring with a period characterised both by the TSM seasonal reduction and by important TSM temporal variability. [less ▲]

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See detailUsing monovariate and multivariate EOFs to reconstruct missing data with DINEOF
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Sirjacobs, Damien ULg et al

Conference (2008)

DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based method to reconstruct missing data in geophysical data sets. DINEOF can be used to reconstruct monovariate data sets (as sea ... [more ▼]

DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based method to reconstruct missing data in geophysical data sets. DINEOF can be used to reconstruct monovariate data sets (as sea surface temperature (SST), chlorophyll, etc), and multivariate data sets with little increase in complexity. For multivariate reconstructions, extended EOFs are used, which take into account the interrelationships between related variables to infer data at missing locations. Spatial maps of the standard deviation of the reconstruction error can be also calculated. In the past, DINEOF has been compared to Optimal Interpolation (OI) techniques for the Adriatic Sea SST. The results showed that DINEOF was faster than OI, making it very suitable for operational applications. DINEOF was also more accurate when compared to in situ data. Another advantage of DINEOF is that there is no need for a priori knowledge of the statistics of the reconstructed data set (such as covariance or correlation length), thus reducing the subjectivity of the analysis. DINEOF has been successfully used to reconstruct a large variety of domains over the world ocean, mostly at the regional scale. In addition to an overview of the technique's capabilities, limitations and future developments, recent work aimed to improve the quality of the reconstructions at the global and local scales will be presented. [less ▲]

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See detailThree-dimensional analysis of oceanographic data with the software DIVA
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Rixen, Michel et al

in Geophysical Research Abstracts (2008)

In oceanography, the process of gridding data is frequently used for various purposes, e.g. initialization of hydrodynamic models, or graphical representation of sparse data. DIVA (Data-Interpolating ... [more ▼]

In oceanography, the process of gridding data is frequently used for various purposes, e.g. initialization of hydrodynamic models, or graphical representation of sparse data. DIVA (Data-Interpolating Variational Analysis) is designed to perform such gridding tasks. It has the advantage of taking into account the intrinsic nature of oceanographic data, i.e. uncertainty in in situ measurements and anisotropy due to advection and irregular coastlines and topography. Three-dimensional reconstruction of temperature and salinity fields is achieved by stacking horizontal layers where independent analysis with DIVA are performed. Nevertheless, analysis in regions void of data may result in the presence of static instabilities between two or more consecutive layers. The method implemented in DIVA to remove such kinds of instabilities is the object of the present work. It consists of adding pseudo-data from one layer to the upper adjacent layer in order to create stable stratification in the vicinity of instabilities. Two approaches for assigning values to the pseudo data are tested: the first is called the mixing approach and aims at simulating a mixing process between two layers; the second is called the minimal perturbation, as it strives to minimise the perturbations inthe pseudo-data. A realistic application using temperature and salinity profiles in the North Atlantic is carried out and the results are compared with World Ocean Atlas climatologies. [less ▲]

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