Phytoplankton carbon fixation chlorophyll-biomass and diagnostic pigments in the Atlantic Ocean

We have made daily measurements of phytoplankton pigments, size-fractionated (<2 and >2-μm) carbon fixation and chlorophyll-a concentration during four Atlantic Meridional Transect (AMT) cruises in 2003–04. Surface rates of carbon fixation ranged from <0.2-mmol C m−3 d−1 in the subtropical gyres to 0.2–0.5-mmol C m−3 d−1 in the tropical equatorial Atlantic. Significant intercruise variability was restricted to the subtropical gyres, with higher chlorophyll-a concentrations and carbon fixation in the subsurface chlorophyll maximum during spring in either hemisphere. In surface waters, although picoplankton (<2-μm) represented the dominant fraction in terms of both carbon fixation (50–70%) and chlorophyll-a (80–90%), nanoplankton (>2-μm) contributions to total carbon fixation (30–50%) were higher than to total chlorophyll-a (10–20%). However, in the subsurface chlorophyll maximum picoplankton dominated both carbon fixation (70–90%) and chlorophyll-a (70–90%). Thus, in surface waters chlorophyll-normalised carbon fixation was 2–3 times higher for nanoplankton and differences in picoplankton and nanoplankton carbon to chlorophyll-a ratios may lead to either higher or similar growth rates. These low chlorophyll-normalised carbon fixation rates for picoplankton may also reflect losses of fixed carbon (cell leakage or respiration), decreases in photosynthetic efficiency, grazing losses during the incubations, or some combination of all these. Comparison of nitrate concentrations in the subsurface chlorophyll maximum with estimates of those required to support the observed rates of carbon fixation (assuming Redfield stoichiometry) indicate that primary production in the chlorophyll maximum may be light rather than nutrient limited.

Identification of a diagnostic marker to detect freshwater cyanophages of filamentous cyanobacteria

Cyanophages are viruses that infect the cyanobacteria, globally important photosynthetic microorganisms. Cyanophages are considered significant components of microbial communities, playing major roles in influencing host community diversity and primary productivity, terminating cyanobacterial water blooms, and influencing biogeochemical cycles. Cyanophages are ubiquitous in both marine and freshwater systems; however, the majority of molecular research has been biased toward the study of marine cyanophages. In this study, a diagnostic probe was developed to detect freshwater cyanophages in natural waters. Oligonucleotide PCR-based primers were designed to specifically amplify the major capsid protein gene from previously characterized freshwater cyanomyoviruses that are infectious to the filamentous, nitrogen-fixing cyanobacterial genera Anabaena and Nostoc. The primers were also successful in yielding PCR products from mixed virus communities concentrated from water samples collected from freshwater lakes in the United Kingdom. The probes are thought to provide a useful tool for the investigation of cyanophage diversity in freshwater environments.

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.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.