Brockmann Consult

Geesthacht, Germany

Brockmann Consult

Geesthacht, Germany
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Philipson P.,Brockmann Geomatics | Eriksso K.,Brockmann Geomatics | Stelzer K.,Brockmann Consult
Measuring and Modeling of Multi-Scale Interactions in the Marine Environment - IEEE/OES Baltic International Symposium 2014, BALTIC 2014 | Year: 2014

According to the Water Framework Directive (WFD), all lakes larger than 50 ha (0,5 km2) should be monitored on a regular basis. In Sweden, chlorophyll a is one of the parameters used to classify the phytoplankton status of a lake. The possibility to use satellite based information (ENVISAT-MERIS) to measure and monitor the water quality status of Lake Bolmen and surrounding smaller lakes (>2 km2) has been investigated. The work was focused on chlorophyll a and data from 83 lakes in the investigated region has been analysed. In general, chlorophyll a (chl a) levels between 0-10 ug/l prevail in these lakes, but also more extreme levels of chla around 50 ug/l exist. Data from 5 years (2007-2011) has been analysed together with existing field data from approximately 20 lakes. The results indicate that good chl a estimates could be generated for lakes larger than 2-3 km2. This means that not all lakes required by the WFD are possible to monitor using MERIS, and future Sentinel-3-OLCI data, but that a significant contribution to the present and future monitoring program should be possible by adding earth observation data. © 2014 IEEE.


Palmer S.C.J.,Hungarian Academy of Sciences | Palmer S.C.J.,University of Leicester | Odermatt D.,Brockmann Consult | Hunter P.D.,University of Stirling | And 4 more authors.
Remote Sensing of Environment | Year: 2015

Phytoplankton biomass is important to monitor in lakes due to its influence on water quality and lake productivity. Phytoplankton has also been identified as sensitive to environmental change, with shifts in the seasonality of blooms, or phenology, resulting from changing temperature and nutrient conditions. A satellite remote sensing approach to retrieving and mapping freshwater phytoplankton phenology is demonstrated here in application to Lake Balaton, Hungary. Chlorophyll- a (chl- a) concentration mapping using Medium Resolution Imaging Spectrometer (MERIS) allows new insights into such spatiotemporal dynamics for Lake Balaton as bloom start, peak and end timing, duration, maximum chl- a concentrations, spatial extent, rates of increase and decrease, and bloom chl- a concentration integral. TIMESAT software is used to extract and map these phenology metrics. Three approaches to time series smoothing are compared and mapped metrics are evaluated in comparison with phenology metrics of in situ chl- a. The high degree of both spatial and temporal variability is highlighted and discussed, as are methodological limitations and correlation between phenology metrics. Both the feasibility of and novel insights permitted through such phenology mapping are demonstrated, and priority topics for future research are suggested. © 2014.


Weiss M.,French National Institute for Agricultural Research | Baret F.,French National Institute for Agricultural Research | Block T.,Brockmann Consult | Koetz B.,Earth Observation Directorate | And 12 more authors.
Remote Sensing | Year: 2014

The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts. © 2014 by the authors.


Kwiatkowska E.J.,EUMETSAT | Ruddick K.,Royal Belgian Institute Of Natural Sciences | Ramon D.,HYGEOS | Vanhellemont Q.,Royal Belgian Institute Of Natural Sciences | And 3 more authors.
Ocean Science | Year: 2016

Ocean colour applications from medium-resolution polar-orbiting satellite sensors have now matured and evolved into operational services. These applications are enabled by the Sentinel-3 OLCI space sensors of the European Earth Observation Copernicus programme and the VIIRS sensors of the US Joint Polar Satellite System programme. Key drivers for the Copernicus ocean colour services are the national obligations of the EU member states to report on the quality of marine, coastal and inland waters for the EU Water Framework Directive and Marine Strategy Framework Directive. Further applications include CO2 sequestration, carbon cycle and climate, fisheries and aquaculture management, near-real-time alerting to harmful algae blooms, environmental monitoring and forecasting, and assessment of sediment transport in coastal waters. Ocean colour data from polar-orbiting satellite platforms, however, suffer from fractional coverage, primarily due to clouds, and inadequate resolution of quickly varying processes. Ocean colour remote sensing from geostationary platforms can provide significant improvements in coverage and sampling frequency and support new applications and services. EUMETSAT's SEVIRI instrument on the geostationary Meteosat Second Generation platforms (MSG) is not designed to meet ocean colour mission requirements, however, it has been demonstrated to provide valuable contribution, particularly in combination with dedicated ocean colour polar observations. This paper describes the ongoing effort to develop operational ocean colour water turbidity and related products and user services from SEVIRI. SEVIRI's multi-temporal capabilities can benefit users requiring improved local-area coverage and frequent diurnal observations. A survey of user requirements and a study of technical capabilities and limitations of the SEVIRI instruments are the basis for this development and are described in this paper. The products will support monitoring of sediment transport, water clarity, and tidal dynamics by providing hourly coverage and long-term time series of the diurnal observations. Further products and services are anticipated from EUMETSAT's FCI instruments on Meteosat Third Generation satellites (MTG), including potential chlorophyll a products. © Author(s) 2016.


Muller D.,Helmholtz Center Geesthacht | Krasemann H.,Helmholtz Center Geesthacht | Brewin R.J.W.,Plymouth Marine Laboratory | Brockmann C.,Brockmann Consult | And 12 more authors.
Remote Sensing of Environment | Year: 2015

The Ocean Colour Climate Change Initiative intends to provide a long-term time series of ocean colour data and investigate the detectable climate impact. A reliable and stable atmospheric correction procedure is the basis for ocean colour products of the necessary high quality. In order to guarantee an objective selection from a set of four atmospheric correction processors, the common validation strategy of comparisons between in-situ and satellite-derived water leaving reflectance spectra, is extended by a ranking system. In principle, the statistical parameters such as root mean square error, bias, etc. and measures of goodness of fit, are transformed into relative scores, which evaluate the relationship of quality dependent on the algorithms under study. The sensitivity of these scores to the selected database has been assessed by a bootstrapping exercise, which allows identification of the uncertainty in the scoring results. Although the presented methodology is intended to be used in an algorithm selection process, this paper focusses on the scope of the methodology rather than the properties of the individual processors. © 2015 Elsevier Inc.


Brewin R.J.W.,Plymouth Marine Laboratory | Brewin R.J.W.,National Center for Earth Observation | Sathyendranath S.,Plymouth Marine Laboratory | Sathyendranath S.,National Center for Earth Observation | And 23 more authors.
Remote Sensing of Environment | Year: 2015

Satellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situ Rrs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489. nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443. nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies. © 2013 Elsevier Inc.


Breitbach G.,Helmholtz Center Geesthacht | Krasemann H.,Helmholtz Center Geesthacht | Behr D.,Helmholtz Center Geesthacht | Beringer S.,Helmholtz Center Geesthacht | And 3 more authors.
Ocean Science | Year: 2016

The coastal observation system COSYNA aims to describe the physical and biogeochemical state of a regional coastal system. The COSYNA data management is the link between observations, model results and data usage. The challenge for the COSYNA data management CODM1 is the integration of diverse data sources in terms of parameters, dimensionality and observation methods to gain a comprehensive view of the observations. This is achieved by describing the data using metadata in a generic way and by making all gathered data available for different analyses and visualisations in an interrelated way, independent of data dimensionality. Different parameter names for the same observed property are mapped to the corresponding CF2 standard name (Eaton et al., 2010) leading to standardised and comparable metadata. These metadata together with standardised web services are the base for the data portal. The URLs of these web services are also stored within the metadata as direct data access URLs, e.g. a map such as a GetMap request. © Author(s) 2016.


Gade M.,University of Hamburg | Melchionna S.,University of Hamburg | Stelzer K.,Brockmann Consult | Kohlus J.,LKN
Estuarine, Coastal and Shelf Science | Year: 2014

We demonstrate that Synthetic Apertur Radar (SAR) data have great potential to improve an existing monitoring system based on optical data for intertidal flats and to complement the classification of sediments, macrophytes, and mussels in the German Wadden Sea. Multi-satellite SAR data acquired at different radar bands (L, C, and X band, from ALOS PALSAR, from ERS SAR, Radarsat-2 and ENVISAT ASAR, and from TerraSAR-X, respectively) were used to investigate whether they can be jointly used for crude sediment classification on dry-fallen intertidal flats and for detecting benthic fauna such as blue mussel or oyster beds. In this respect, we show that both multi-satellite and multi-temporal analyses provide valuable input for the routine monitoring of exposed intertidal flats on the German North Sea coast, the latter already improving the identification of the spatial extent of mussel (oyster) beds. In addition, we demonstrate that high-resolution SAR is capable of detecting residuals of historical land use in areas that were lost to the sea during major storm surges in the 14th and 17th centuries. © 2014 Elsevier Ltd.


Bicheron P.,French National Center for Space Studies | Amberg V.,MAGELLIUM | Bourg L.,ACRI ST | Petit D.,MAGELLIUM | And 8 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2011

The GlobCover project has developed a service dedicated to the generation of multiyear global land cover maps at 300-m spatial resolution using as its main source of data the full-resolution full-swath (300 m) data (FRS) acquired by the MERIS sensor on-board the ENVISAT satellite. As multiple single daily orbits have to be combined in one single data set, an accurate relative and absolute geolocation of GlobCover orthorectified products is required and needs to be assessed. We describe in this paper the main steps of the orthorectification pre-processing chain as well as the validation methodology and geometric performance assessments. Final results are very satisfactory with an absolute geolocation error of 77-m rms and a relative geolocation error of 51-m rms. © 2011 IEEE.


Radoux J.,Catholic University of Louvain | Lamarche C.,Catholic University of Louvain | Van Bogaert E.,Catholic University of Louvain | Bontemps S.,Catholic University of Louvain | And 2 more authors.
Remote Sensing | Year: 2014

Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable for climate modeling based on Earth observation by satellite sensors. The challenge is to generate a set of successive maps that are both accurate and consistent over time. To do so, operational methods for the automated classification of optical images are investigated. The proposed approach consists of a locally trained classification using an automated selection of training samples from existing, but outdated land cover information. Combinations of local extraction (based on spatial criteria) and self-cleaning of training samples (based on spectral criteria) are quantitatively assessed. Two large study areas, one in Eurasia and the other in South America, are considered. The proposed morphological cleaning of the training samples leads to higher accuracies than the statistical outlier removal in the spectral domain. An optimal neighborhood has been identified for the local sample extraction. The results are coherent for the two test areas, showing an improvement of the overall accuracy compared with the original reference datasets and a significant reduction of macroscopic errors. More importantly, the proposed method partly controls the reliability of existing land cover maps as sources of training samples for supervised classification. © 2014 by the authors.

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