Algae in aquatic ecosystems are an important component of biological monitoring programs for evaluating water quality, Microscopic analysis of water samples collected from lakes, streams and other bodies determines the diversity and density of algal species and provides potentially useful early warning signs of deteriorating conditions. In high densities, cyanobacteria are an undesirable component of freshwater ecosystems because they can produce hepatotoxins and neurotoxins that are ecological and public health concerns. Toxin-producing blooms may disrupt lake food webs by killing fish, birds and zooplankton and can be responsible for hypoxia conditions that follow bloom die-offs. Toxic blooms can also restrict recreation like swimming, fishing andpet-related activities. Additionally, toxins produced from blooms can pose problems for households that get their drinking water from lakes and reservoirs. The heterocyst-containing blue-green genera include Anabaena, Aphanizomenon and Gloeotrichia arefrequently found in scum samples along with the non-heterocyst genera e.g. Lyngbya, Microcystis, Oscillatoria, and Phormidium etc. Less frequently, other genera like Arthrospira may be responsible for bloom situations. Additionally, cyanobacteria bloomsare often accompanied by green algae including Botryococcus, Chiamydomonas, Cladophora, Spirogyra and diatoms associated with eutrophic conditions: Navicula, Nitzschia, Melosira and others. Several processes have been developed to treat municipal sewageincluding trickling filters, activated sludge systems and lagoon systems. Pollution algae are often encountered in water bodies by sewage from leaky septic systems, manure, and industrial processes that result in the discharge of organic wastes. This group of algae is also occasionally present in ponds and storm water retention structures where they may develop large blooms. Some of the more common pollution-tolerant genera in water bodies include: Chlamydomonas, Scenedesmus, Euglena, Phacus, Nitzschia,Oscillatoria and Pandorina etc. Laboratory microscopic analysis, which reveals the composition and density of the algal flora present in a water body, is an important component of monitoring programs and is valuable in determining diverse trophic conditions. In addition to cyanobacteria, the populations and species diversity of green algae, flagellates and diatoms, which reflect different trophic conditions, are important indicators for evaJuating water quality.
With constant advancements in remote sensing technologies resulting in higher image resolution, there is a corresponding need to be able to mine useful data and information from remote sensing images. In this paper, we study auto-encoder (SAE) and support vector machine (SVM), and to examine their sensitivity, we include additional umber of training samples using the active learning frame. We then conduct a comparative evaluation. When classifying remote sensing images, SVM can also perform better than SAE in some circumstances, and active learning schemes can be used to achieve high classification accuracy in both methods.
Landsat-7 EMT+ and ASTER GDEM images of Ejigbo area, Osun State Nigeria were processed to generate composite lineament map and lineament-intersection map. Thirty-two Schlumberger vertical electrical sounding (VES) data set were quantitatively interpreted to determine aquifer resistivities and aquifer thicknesses in the area. The lineament density ranged between 0 and 5.52 km/km~2. Thematic maps of the geology, the lineament density, the lineament-intersection density, the aquifer thickness and the aquifer resistivity of the study area were integrated to classify the area into varied groundwater potential zones. The study concluded that the groundwater potential of areas around Ejigbo, Osun state is of poor to moderate level rating.
Changes in aerosol loading affect cloud albedo and emission and Earth's radiative balance with a low level of scientific understanding. In this study, we investigate the vertical response of ice clouds to aerosols within the Indian subcontinent during monsoon season (2006–2010) based on multiple satellite observations. As a function of aerosol loading, we find that the cloud optical depth, cloud geometrical depth and ice water path decrease by 0.23 (from 0.39 to 0.16), 0.8 km (from 2.6 to 1.8 km), 5.1 g/m2 (from 7.9 to 2.8 g/m2), respectively, and that ice particles possibly decrease in size and become more spherical in shape as aerosol optical depth (AOD) increases from 0.1 to 1; these changes tend to plateau as AOD increases beyond 1. The absolute negative response between ice clouds and aerosols under moist and unstable atmospheric conditions is stronger than that under drier and stable atmospheric conditions, and vice versa. Moreover, the negative impact of smoke on ice clouds is stronger than dust and polluted dust, which is likely related to the strong absorption properties and poor ice nucleation efficiency of smoke. Aerosol impacts on ice clouds lead to a decrease in the net cloud radiative effect of 7.3 W/m2 (from 18.5 to 11.2 W/m2) as AOD increases from 0.1 to 1. This change in ice cloud properties mainly results in the decrease in downwelling LW radiation to the surface and consequently weakened radiative forcing of ice clouds during the Indian summer monsoon season.
In the present study, sequential extraction of metals from bottom sediments, the risk assessment code and Phragmites australis bioaccumulation ability were used to assess ecological risk of trace metal pollution on aquatic ecosystems. Surface bottom sediments and leaves of reed collected from agricultural and urban areas were examined for Cu, Fe, Mn, Ni and Pb contents. Results showed that the total metal content in sediment cannot be regarded as a reliable indicator of metal pollution and the risk of environment. High total contents of the metals as well as contents in individual sediments fractions were connected with high organic carbon and domination of the siltsand, claysilt or clay particles. However, the percentage of metals bound to each fraction was independent from these properties of sediment. Particular metals characterized by different behavior in the sediments: Mn had the highest percentage bound to the most labile fractions Cu and Ni had considerable high percentage in potentially bioavailable (reducible or oxidizable) fractions Fe and Pb had high percentage in the residual fraction. The risk assessment code, sequential analysis and the bioindication method showed generally consistent results: no Cu, Fe and Pb pollution and a high risk of Mn pollution in the study sites, but each one gave different detailed information.
Obtaining an up-to-date high-resolution description of land cover is a challenging task due to the high cost and labor-intensive process of human annotation through field studies. This work introduces a radically novel approach for achieving this goal by exploiting the proliferation of remote sensing satellite imagery, allowing for the up-to-date generation of global-scale land cover maps. We propose the application of multilabel classification, a powerful framework in machine learning, for inferring the complex relationships between the acquired satellite images and the spectral profiles of different types of surface materials. Introducing a drastically different approach compared to unsupervised spectral unmixing, we employ contemporary ground-collected data from the European Environment Agency to generate the label set and multispectral images from the MODIS sensor to generate the spectral features, under a supervised classification framework. To validate the merits of our approach, we present results using several state-of-the-art multilabel learning classifiers and evaluate their predictive performance with respect to the number of annotated training examples, as well as their capability to exploit examples from neighboring regions or different time instances. We also demonstrate the application of our method on hyperspectral data from the Hyperion sensor for the urban land cover estimation of New York City. Experimental results suggest that the proposed framework can achieve excellent prediction accuracy, even from a limited number of diverse training examples, surpassing state-of-the-art spectral unmixing methods.
Direction-finding SeaSonde (4.463 MHz; 5.2625 MHz) and phased-array WEllen RAdar WERA (9.33 MHz; 13.5 MHz) High-frequency radar (HFR) systems are routinely operated in Australia for scientific research, operational modeling, coastal monitoring, fisheries, and other applications. Coverage of WERA and SeaSonde HFRs in Western Australia overlap. Comparisons with subsurface currents show that both HFR types agree well with current meter records. Correlation (R), root-mean-squares differences (RMSDs), and mean bias (bias) for hourly-averaged radial currents range between R = (−0.03, 0.78), RMSD = (9.2, 30.3) cm/s, and bias = (−5.2, 5.2) cm/s for WERAs; and R = (0.1, 0.76), RMSD = (17.4, 33.6) cm/s, bias = (0.03, 0.36) cm/s for SeaSonde HFRs. Pointing errors (θ) are in the range θ = (1◦, 21◦) for SeaSonde HFRs, and θ = (3◦, 8◦) for WERA HFRs. For WERA HFR current components, comparison metrics are RU = (−0.12, 0.86), RMSDU = (12.3, 15.7) cm/s, biasU = (−5.1, −0.5) cm/s; and, RV = (0.61, 0.86), RMSDV = (15.4, 21.1) cm/s, and biasV = (−0.5, 9.6) cm/s for the zonal (u) and the meridional (v) components. Magnitude and phase angle for the vector correlation are ρ = (0.58, 0.86), φ = (−10◦, 28◦). Good match was found in a direct comparison of SeaSonde and WERA HFR currents in their overlap (ρ = (0.19, 0.59), φ = (−4◦, +54◦)). Comparison metrics at the mooring slightly decrease when SeaSonde HFR radials are combined with WERA HFR: scalar (vector) correlations for RU, V, (ρ) are in the range RU = (−0.20, 0.83), RV = (0.39, 0.79), ρ = (0.47, 0.72). When directly compared over the same grid, however, vectors from WERA HFR radials and vectors from merged SeaSonde–WERA show RU (RV) exceeding 0.9 (0.7) within the HFR grid. Despite the intrinsic differences between the two types of radars used here, findings show that different HFR genres can be successfully merged, thus increasing current mapping capability of the existing HFR networks, and minimising operational downtime, however at a likely cost of slightly decreased data quality.
The Operational Land Imager (OLI) onboard Landsat-8 is generating high-quality aquatic science products, the most critical of which is the remote sensing reflectance (Rrs), defined as the ratio of water-leaving radiance to the total downwelling irradiance just above water. The quality of the Rrs products has not, however, been extensively assessed. This manuscript provides a comprehensive evaluation of Level-1B, i.e., top of atmosphere reflectance, and Rrs products available from OLI imagery under near-ideal atmospheric conditions in moderately turbid waters. The procedure includes a) evaluations of the Rrs products at sites included in the Ocean Color component of the Aerosol Robotic Network (AERONET-OC), b) intercomparisons and cross-calibrations against other ocean color products, and c) optimizations of vicarious calibration gains across the entire OLI observing swath. Results indicate that the near-infrared and shortwave infrared (NIR-SWIR) band combinations yield the most robust and stable Rrs retrievals in moderately turbid waters. Intercomparisons against products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODISA) indicate slight across-track non-uniformities (b1%) associated with OLI scenes in the blue bands. In both product domains (TOA and Rrs), on average, the OLI products were found larger in radiometric responses in the blue channels. Following the implementation of updated vicarious calibration gains and accounting for across-track non-uniformities, matchup analyses using independent in-situ validation data con- firmed improvements in Rrs products. These findings further support high-fidelity OLI-derived aquatic science products in terms of both demonstrating a robust atmospheric correction method and providing consistent products across OLI's imaging swath.
We present a direct broadcast (DB) rapid response burned area mapping algorithm for Visible Infrared Imaging Radiometer Suite (VIIRS) data that combines products driven by the spectral signal of fire-affected areas from both emissive and reflective spectral bands. The algorithm processes VIIRS infrared M-bands (750 m) using spectral ratios of the top of atmosphere reflectance from a single satellite scene to identify pixels exhibiting surface properties consistent with burn scars. Next, this collection of candidate burn scar pixels is screened using a contextual filter based on VIIRS I-band (375 m) active fire detections (AFD) which removes erroneously classified pixels and provides burn scar detections (BSD). The AFD and BSD are then resampled to a 375m grid and reported jointly as VIIRS burned area (VBA). The accuracy of the VBA was assessed for 390 wildfires (11–114,500 ha in size) in the western United States. The spatial accuracy was assessed by comparison with a validation dataset of Monitoring Trends in Burn Severity (MTBS) burned area and incident fire perimeter polygons. The VBA temporal accuracy was evaluated using a time series of daily fire perimeter polygons derived from high resolution airborne infrared imagery. The algorithm's burned area mapping accuracy is 59%. The algorithm detected 60% of burned area on the initial day of burning and 73% within 24 h.
Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size < 0.1 m) may compensate for some low spectral resolution drawbacks. In this regard, it is shown that the post-classification majority filtering substantially improves the accuracy of all pixel sizes up to the point where the kernel size reaches the average unit size (pixel < 0.25 m). However, careful investigation as to the effect of band distribution and choice could improve the sensor suitability for the marine environment task. This in mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.