[show abstract][hide abstract] ABSTRACT: Remote sensing has recently become a powerful tool for water quality
monitoring. Satellite-borne remote sensing information about water
quality indicators of inland water bodies has limited availability. Due
to cloudy weather or satellite instrument unavailability, time periods
without remote sensing scenes may vary from several hours to several
weeks. This is often unacceptable for operational end users. To overcome
this problem we propose an idea of assimilating satellite observations
into hydrodynamic model. In our study, we use MODIS-retrieved total
suspended matter (TSM) distribution across the lake, processed with the
Modular Inversion and Processing System (MIP). This scalar value is a
vertically integrated quantity and has to be converted to information
about TSM distribution in modeled water column. Besides TSM
concentration, also optical penetration depth (z90) can be derived from
MODIS satellite images. This quantity sets the lower integration
boundary for integrated TSM concentration from the whole water column.
Based on field studies with CTD profiler, satellite borne integrated TSM
and z90, we reconstruct a TSM profile structure. Reconstructed TSM
profiles are systematically (as far as MODIS acquisition is possible)
assimilated to hydrodynamic model. Assimilation is based on weighted
average between modeled (from Delft3d) and reconstructed (from satellite
observation) TSM profiles. Averaging weights come from pixel quality
information which is retrieved from satellite image during atmospheric
correction. As a study site, Lake Constance has been chosen. Thanks to
its large dimensions, it was possible to monitor it using the Moderate
Resolution Imaging Spectroradiometer (MODIS). Another advantage of this
study site is that in spring Lake Constance experiences also the mixing
of inflowing, highly turbid, cold Alpine Rhine water and transparent
warmer lake water. This phenomenon is highly related to hydrodynamic
(wind induced and density-driven) currents and can be monitored with
satellite-borne remote sensing. For hydrodynamic modeling, a three
dimensional Reynolds averaged Navier-Stokes hydrodynamic model (Delft3D)
was used. Inflowing glacier particles were assumed to be a passive
tracer, transported by advection according to simulated water currents.
Despite strong physical background hydrodynamic models still encounter
problems with results quality. Together with data from boundary
conditions (ex. wind shear stress, sun heat flux), models assimilate
also errors and uncertainties which make model results unreliable for
longer prediction time. This idea of data fusion from remote sensing and
hydrodynamic modeling leads to more reliable hydrodynamic transport
model results and more frequent data availability (in compare to pure
remote sensing). This research shows new possibilities in overcoming
hydrodynamic transport model uncertainty constraints and new application
field for remote sensing water quality products.
[show abstract][hide abstract] ABSTRACT: Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km(2) of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km(2)). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas.
PLoS ONE 01/2013; 8(7):e70105. · 3.73 Impact Factor
[show abstract][hide abstract] ABSTRACT: We applied remote sensing techniques using WorldView-2 images with high spatial resolution and field verification to map the bathymetry and benthic habitats of the Puerto Morelos Reef National Park in the Mexican Caribbean. These images were processed using the standardized physics-based data processing of EOMAP´s Modular Inversion and Processing System (MIP). To generate a detailed benthic habitat map we developed a two level-classification scheme based on biological and geomorphologic characteristics. These images showed high effectiveness for mapping benthic habitat. Further improvements will emphasize and focus on the selection of regional specific main spectral components and the automation of the radiometric fine tuning. The main contribution is the generation of a detailed benthic habitat map representing functional classification by combining both maps. These studies serve for the management of marine areas in Mexico and could be extended to the entire Mesoamerican Reef System.
Geoscience and Remote Sensing Symposium (IGARSS), München; 07/2012
[show abstract][hide abstract] ABSTRACT: A method to obtain underwater topography for coastal areas using state-of-the-art remote sensing data and techniques worldwide is presented. The data from the new Synthetic Aperture Radar (SAR) satellite TerraSAR-X with high resolution up to 1 m are used to render the ocean waves. As bathymetry is reflected by long swell wave refraction governed by underwater structures in shallow areas, it can be derived using the dispersion relation from observed swell properties. To complete the bathymetric maps, optical satellite data of the QuickBird satellite are fused to map extreme shallow waters, e.g., in near-coast areas. The algorithms for bathymetry estimation from optical and SAR data are combined and integrated in order to cover different depth domains. Both techniques make use of different physical phenomena and mathematical treatment. The optical methods based on sunlight reflection analysis provide depths in shallow water up to 20 m in preferably calm weather conditions. The depth estimation from SAR is based on the observation of long waves and covers the areas between about 70- and 10-m water depths depending on sea state and acquisition quality. The depths in the range of 20 m up to 10 m represent the domain where the synergy of data from both sources arises. Thus, the results derived from SAR and optical sensors complement each other. In this study, a bathymetry map near Rottnest Island, Australia, is derived. QuickBird satellite optical data and radar data from TerraSAR-X have been used. The depths estimated are aligned on two different grids. The first one is a uniform rectangular mesh with a horizontal resolution of 150 m, which corresponds to an average swell wavelength observed in the 10 × 10-km SAR image acquired. The second mesh has a resolution of 150 m for depths up to 20 m (deeper domain covered by SAR-based technique) and 2.4 m resolution for the shallow domain imaged by an optical sensor. This new technique provides a platform for mapping of coastal bathymetry over a broad area on a scale that is relevant to marine planners, managers, and offshore industry.
[show abstract][hide abstract] ABSTRACT: Multispectral satellite data (WordView-2, IKONOS, QuickBird) are used to map bathymetry and spectral sea floor classes in a range of coastal areas. The standardized physics-based data processing integrates MODIS satellite data for the radiometric intercalibration and estimates of turbidity. This process includes corrections for sunglitter, the adjacency and the atmospheric effect. The water depth is calculated iteratively in combination with the spectral unmixing of the respective bottom reflectance on base of the subsurface reflectance. The final step of the processing classifies the bottom reflectance due to the spectral signature of different bottom types and biota using a specific cluster and classification approach. The comparison with in situ data at different sites worldwide proves the approach, but also emphasizes the necessity of radiometric well calibrated satellite data.
[show abstract][hide abstract] ABSTRACT: The Applied Remote Sensing Cluster at the German Aerospace Center DLR has long lasting experiences with air- and spaceborne acquisition and processing of hyperspectral image data. Jointly with the German Space Operations Center it is responsible for the establishment of the ground segment of the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program) which is planned to be launched in 2013. The primary goal of EnMAP is to quantify and analyze diagnostic parameters describing key processes on the Earth’s surface. Extensive calibration and validation activities are foreseen during the planned five years of operations to ensure high quality data products, which include radiometric, geometric and atmospheric correction. This paper focuses on the automatic processing chain, as well as the calibration and quality control activities for the generation of standard EnMAP products.
[show abstract][hide abstract] ABSTRACT: The German Aerospace Center DLR – namely the Applied Remote Sensing Cluster CAF and the German Space Operations Center GSOC – is responsible for the establishment of the ground segment of the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program). The Applied Remote Sensing Cluster has long lasting experiences with air- and spaceborne acquisition, processing, and analysis of hyperspectral image data. This paper mainly addresses the concept of the operational and automatic processing chain and the calibration / data quality to generate high quality data products.
[show abstract][hide abstract] ABSTRACT: Semi-analytical remote sensing applications for eutrophic waters are not applicable to oligo- and mesotrophic lakes in the perialpine area, since they are insensitive to chlorophyll concentration variations between 1 and 10 mg/m3. The neural network based Case-2-Regional algorithm for MERIS was developed to fill this gap, along with the ICOL adjacency effect correction algorithm. The algorithms are applied to a collection of 239 satellite images from 2003–2008, and the results are compared to experimental and official water quality data collected in six perialpine lakes in the same period. It is shown that remote sensing estimates can provide an adequate supplementary data source to in situ data series of the top 5 m water layer, provided that a sufficient number of matchups for a site specific maximum temporal offset are available.
[show abstract][hide abstract] ABSTRACT: Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared. Another type of approach which is becoming very popular in atmospheric correction over water is based on the simultaneous retrieval of atmospheric and water parameters through the inversion of coupled atmospheric and bio-optical water models. Both types of approaches may lead to substantial errors over optically-complex water bodies, such as case II waters, in which a wide range of temporal and spatial variations in the concentration of water constituents is expected. This causes the water reflectance in the near-infrared to be non-negligible, and that the water reflectance response under extreme values of the water constituents cannot be described by the assumed bio-optical models. As an alternative to these methods, the SCAPE-M atmospheric processor is proposed in this paper for the automatic atmospheric correction of ENVISAT/MERIS data over inland waters. A-priori assumptions on the water composition and its spectral response are avoided by SCAPE-M by calculating reflectance of close-to-land water pixels through spatial extension of atmospheric parameters derived over neighboring land pixels. This approach is supported by the results obtained from the validation of SCAPE-M over a number of European inland water validation sites which is presented in this work. MERIS-derived aerosol optical thickness, water reflectance and water pigments are compared to in-situ data acquired concurrently to MERIS images in 20 validation match-ups. SCAPE-M has also been compared to specific processors designed for the retrieval of lake water constituents from MERIS data. The performance of SCAPE-M to reproduce ground-based measurements under a range of water types and the ability of MERIS data to monitor chlorophyll-a and phycocyanin pigments using semiempirical algorithms after SCAPE-M processing are discussed. It has been found that SCAPE-M is able to provide high accurate water reflectance over turbid waters, outperforming models based on site-specific bio-optical models, although problems of SCAPE-M to cope with clear waters in some cases have also been identified.
[show abstract][hide abstract] ABSTRACT: Earth observation sensors collect valuable data of aquatic systems, which are further used for the retrieval of concentrations of water constituents such as suspended matter and phytoplankton. Different sensors deliver data with various spatial and temporal resolution ranging from 1 day to approximately a month in time and from 1 km to 3 m in space, high spatial resolution being connected with low temporal resolution and vice versa. In order to have detailed information on both spatial distribution of water constituents and their temporal variability, that is especially important for small aquatic objects like rivers and lakes, it is necessary to combine the data from several sensors. This creates certain problems as also spectral and radiometric resolutions of the sensors can be different. The use of the modular image processing system MIP for the integration of data from different sensor is advantageous as it ensures standardized product outputs for a variety of satellite sensors such as MERIS, MODIS, SPOT, IKONOS, RapidEye. The processing chain of this system is automatically adapted to the sensor parameters as well as to the region specific inherent optical properties (SIOP) of the water basin. Various worldwide applications and time series for lakes, rivers and coastal areas are demonstrated and discussed.
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009; 08/2009
[show abstract][hide abstract] ABSTRACT: The applicability of MERIS data for the retrieval of water constituent concentrations in oligo- to mesotrophic perialpine lakes is demonstrated by means of two different algorithms. The C2R algorithm is an easily applicable neural network processor which is bound to MERIS data. The MIP algorithm is a complex, coupled inversion program, which can be used with a variety of remote sensing sensors, but requires extensive parameterization. Both algorithms were applied with the ICOL adjacency effect correction. C2R's potential for automatic processing of large data quantities is validated in a time series study with water quality monitoring data. Individual C2R image products are then compared to MIP, which will allow for an extended comparison with new APEX imaging spectrometry data in the near future.
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009; 08/2009
[show abstract][hide abstract] ABSTRACT: A physically based water constituent retrieval algorithm is used for the automatic processing of MERIS level 1B full resolution data. The algorithm requires several input variables for individual optimization with different sensors (i. e. channel calibration and weighting), aquatic regions (i. e. specific inherent optical properties) or atmospheric conditions (i. e. Aerosol models). The optical properties are derived from optical in situ measurements during concurrent MERIS data acquisition on 20 April 2007. Remaining parameters are iteratively optimized for best performance with 21 MERIS datasets of Lake Constance in the years 2003-2005, and validated with 11 datasets in 2006. Operational water quality sampling measurements acquired by local authorities serve as reference.
[show abstract][hide abstract] ABSTRACT: The objective of the ESA funded project “Development of MERIS Lake Water Algorithms” (January 2007 – June 2008) was to develop and validate a plug-in module for the BEAM toolbox that allows the retrieval of water quality parameters in lake waters from MERIS imagery. For this purpose, new algorithms were developed, based on the optical properties of lakes and atmospheric aerosols from different areas of Europe. The validation campaigns were carried out in eleven lakes in Finland, Germany and Spain. A summary of the main validation results is presented.
[show abstract][hide abstract] ABSTRACT: A physically based algorithm is used for automatic processing of MERIS level 1B full resolution data. The algorithm is originally used with input variables for optimization with different sensors (i.e. channel recalibration and weighting), aquatic regions (i.e. specific inherent optical properties) or atmospheric conditions (i.e. aerosol models). For operational use, however, a lake-specific parameterization is required, representing an approximation of the spatio-temporal variation in atmospheric and hydrooptic conditions, and accounting for sensor properties. The algorithm performs atmospheric correction with a LUT for at-sensor radiance, and a downhill simplex inversion of chl-a, sm and y from subsurface irradiance reflectance. These outputs are enhanced by a selective filter, which makes use of the retrieval residuals. Regular chl-a sampling measurements by the Lake’s protection authority coinciding with MERIS acquisitions were used for parameterization, training and validation.
[show abstract][hide abstract] ABSTRACT: General contamination of heavy metals in the environment is a major global concern, which has provoked the emergence of phytoremediation technologies for cleaning aquatic environment. Heavy metals are released into the environment from a wide range of natural and anthropogenic sources. Macrophytes are known as good indicators of heavy metal contamination in aquatic ecosystems and they act as biological filters by accumulating heavy metals from the surrounding environments. Concentrations of heavy metals such as Hg, Cd, Co, Cu, Mo, Ni, Pb, Tl and Zn were measured in macrophytes and water samples from the mouth of five rivers namely; Gavaraget, Argichi, Makenis, Masrik each of them meeting the Lake Sevan, Armenia. The collected plants were Batrachium rionii, Myosotis palustris, Lythrum salicaria, Scrophularia alata, Calamagrostis epigeios, Lepidium latifolium, Glyceria plicata, Veronica anagallis-aquatica, Butomus umbellatus, Sparganium erectum. The highest concentration of Ni (5.5 mg/kg) was observed in Glyceria plicata whereas concentrations (mg/kg) of all other metals were highest (Hg, 0.02; Cd, 0.46; Co, 3; Cu, 18.9; Pb, 6.9; Tl, 0.13 and Zn, 113) in Batrachium rionii. Range and trend in concentrations of Co (<0.5µg/l), Cd (<0.5µg/l), Tl (<0.1µg/l) and Hg (<0.3µg/l) in water samples were similar at all the sites. Occurrence of heavy metals was much higher in macrophytes and water from Gavaraget and Masrik than that of the Argichi and Makenis due to the discharge of sewage into the river Gavaraget and industrial wastewaters into the river Masrik. The fact that the concentrations of different heavy metals in these macrophytes were far higher than in their respective water column indicates to their role in the biogeochemical cycles of heavy metals. This study aimed at understanding the importance of macrophytes in accumulation of heavy metals and suggesting remedial measures for the preservation and restoration of the lake ecosystem.