Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

Space-borne study of seasonal, multi-year, and decadal phytoplankton dynamics in the Bay of Biscay

Seasonal and inter-annual variations in phytoplankton community abundance in the Bay of Biscay are studied. Preliminarily processed by the National Aeronautics and Space Administration (NASA) to yield normalized water-leaving radiance and the top-of-the-atmosphere solar radiance, Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Coastal Zone Color Scanner (CZCS) data are further supplied to our dedicated retrieval algorithms to infer the sought for parameters. By applying the National Oceanic and Atmospheric Administration's (NOAA's) Advanced Very High Resolution Radiometer (AVHRR) data, the surface reflection coefficient in the only band in the visible spectrum is derived and employed for analysis. Decadal bridged time series of variations of diatom-dominated phytoplankton and green dinoflagellate Lepidodinium chlorophorum within the shelf zone and the coccolithophore Emiliania huxleyi in the pelagic area of the Bay are documented and analysed in terms of impacts of some biogeochemical and geophysical forcing factors.

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.

Macroscale factors affecting diatom abundance: a synergistic use of Continuous Plankton Recorder and satellite remote sensing data

Diatoms exist in almost every aquatic regime; they are responsible for 20% of global carbon fixation and 25% of global primary production, and are regarded as a key food for copepods, which are subsequently consumed by larger predators such as fish and marine mammals. A decreasing abundance and a vulnerability to climatic change in the North Atlantic Ocean have been reported in the literature. In the present work, a data matrix composed of concurrent satellite remote sensing and Continuous Plankton Recorder (CPR) in situ measurements was collated for the same spatial and temporal coverage in the Northeast Atlantic. Artificial neural networks (ANNs) were applied to recognize and learn the complex non-monotonic and non-linear relationships between diatom abundance and spatiotemporal environmental factors. Because of their ability to mimic non-linear systems, ANNs proved far more effective in modelling the diatom distribution in the marine ecosystem. The results of this study reveal that diatoms have a regular seasonal cycle, with their abundance most strongly influenced by sea surface temperature (SST) and light intensity. The models indicate that extreme positive SSTs decrease diatom abundances regardless of other climatic conditions. These results provide information on the ecology of diatoms that may advance our understanding of the potential response of diatoms to climatic change.

Interference with the germination and growth ofUlvazoospores by quorum-sensing molecules fromUlva-associated epiphytic bacteria

Ulva zoospores preferentially settle on N-acylhomoserine lactone (AHL) producing marine bacterial biofilms. To investigate whether AHL signal molecules also affect the success and rate of zoospore germination in addition to zoospore attraction, the epiphytic bacteria associated with mature Ulva linza were characterized and bacterial isolates representative of this community tested for the ability to produce AHLs. Two of these AHL-producing isolates, Sulfitobacter spp. 376 and Shewanella spp. 79, were transformed with plasmids expressing the Bacillus spp. AHL lactonase gene aiiA to generate AHL-deficient variants. The germination and growth of U. linza zoospores was studied in the presence of these AHL-deficient strains and their AHL-producing counterparts. This revealed that the AHLs produced by Sulfitobacter spp. and Shewanella spp. or the bacterial products they regulate have a negative impact on both zoospore germination and the early growth of the Ulva germling. Further experiments with Escherichia coli biofilms expressing recombinant AHL synthases and synthetic AHLs provide data to demonstrate that zoospores germinated and grown in the absence of AHLs were significantly longer than those germinated in the presence of AHLs. These results reveal an additional role for AHLs per se in the interactive relationships between marine bacteria and Ulva zoospores.

Global assessment of ocean carbon export by combining satellite observations and food-web models

The export of organic carbon from the surface ocean by sinking particles is an important, yet highly uncertain, component of the global carbon cycle. Here we introduce a mechanistic assessment of the global ocean carbon export using satellite observations, including determinations of net primary production and the slope of the particle size spectrum, to drive a food-web model that estimates the production of sinking zooplankton feces and algal aggregates comprising the sinking particle flux at the base of the euphotic zone. The synthesis of observations and models reveals fundamentally different and ecologically consistent regional-scale patterns in export and export efficiency not found in previous global carbon export assessments. The model reproduces regional-scale particle export field observations and predicts a climatological mean global carbon export from the euphotic zone of ~6 Pg C yr−1. Global export estimates show small variation (typically < 10%) to factor of 2 changes in model parameter values. The model is also robust to the choices of the satellite data products used and enables interannual changes to be quantified. The present synthesis of observations and models provides a path for quantifying the ocean's biological pump.