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Suzan Pantaroto de Vasconcellos Elaine Crespim Georgiana Feitosa da Cruz Diego Barbosa Senatore Karen Christina Marques Simioni Eugênio Vaz dos Santos Neto Anita Jocelyne Marsaioli Valéria Maia de Oliveira 《Organic Geochemistry》2009,40(5):574-588
The detection of microorganisms with potential for biodeterioration and biodegradation in petroleum fields is of great relevance, since these organisms may be related to a decrease in petroleum quality in the reservoirs or damage in the production facilities. In this sense, petroleum formation water and oil samples were collected from the Campos Basin, Brazil, with the aim of isolating microorganisms and evaluating their ability to degrade distinct classes of hydrocarbon biomarkers (9,10-dihydrophenanthrene, phytane, nonadecanoic acid and 5α-cholestane). Twenty eight bacterial isolates were recovered and identified by sequencing their 16S rRNA genes. Biodegradation assays revealed that bacterial metabolism of hydrocarbons occurred through reactions based on oxidation, carbon–carbon bond cleavage and generation of new bonds or by the physical incorporation of hydrocarbons into microbial cell walls. Based on the biodegradation results, selective PCR-based systems were developed for direct detection in petroleum samples of bacterial groups of interest, namely Bacillus spp., Micrococcus spp., Achromobacter xylosoxidans, Dietzia spp. and Bacillus pumilus. Primer sets targeting 16S rRNA genes were designed and their specificity was confirmed in silico (i.e. computational analysis) and in PCR reactions using DNA from reference strains as positive and negative controls. Total DNA from oil was purified and the amplification tests revealed the presence of the target bacteria in the samples, unraveling a significant potential for petroleum deterioration in the reservoirs sampled, once proper conditions are present for hydrocarbon degradation. The application of molecular methods for rapid detection of specific microorganisms in environmental samples would be valuable as a supporting tool for the evaluation of oil quality in production reservoirs. 相似文献
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Distribution and sources of aliphatic hydrocarbons in surface sediments of Sergipe River estuarine system 总被引：1，自引：0，他引：1
The assessment of aliphatic hydrocarbons was performed in the Sergipe River estuarine system, northeastern Brazil. Aliphatic hydrocarbons concentration ranged from 9.9 ug g?1 up to 30.8 ug g?1 of dry sediment. The carbon preference index (CPI, based on nC?? to nC?? range), indicated predominance of petrogenic input in two of the sites analyzed (P4 and P5). The unresolved complex mixture (UCM) was found to be present in seven of the nine sites sampled (except for P4 and P5). Overall, the results of this work suggest that there is a mix of organic matter sources to the sediment. Although the coast of Sergipe has an intense off shore petroleum exploration and the Sergipe River crosses the entire city of Aracaju, the capital city of Sergipe, non-significant anthropogenic fingerprint was assessed. 相似文献
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By the consumption of algae, parrotfishes open space for young coral settlement and growth, thus playing a central role on the maintenance of coral reefs. However, juvenile parrotfish ecology is often overlooked due to the difficulty discerning species during this phase. Herein, we present the first attempt to investigate changes in habitat use and diet that happen to juveniles of the Redeye parrotfish Sparisoma axillare, focusing on four zones within an algal‐dominated reef: the macroalgal beds, back reef, reef flat, and fore reef. Smaller S. axillare juveniles (<5 cm) preferred to inhabit the macroalgal beds and the reef flat, whereas juveniles larger than 5 cm were more abundant in the back and fore reefs due to distinct post‐settlement habitat conditions. Aggressive interactions with the territorial damselfish Stegastes fuscus were the primary driving factor of juvenile distribution and feeding rates. Attack rates increased with juvenile size and the lowest bite rates were observed in zones with higher densities of territorial damselfish. In previous studies, the persistence of parrotfish recruits in habitats dominated by damselfish was reduced, but newly settled parrotfish occurred more densely within the damselfish domain by behaving as a cryptic reef fish. As these juveniles grew, their bite rates increased, a change associated with a shift from cryptic to roving behavior. Feeding preferences were determined by substrate cover, where juveniles fed on available food sources in each habitat. Juveniles relied on jointed calcareous algae in habitats dominated by these algae, a pattern not observed for thick leathery algae. Filamentous algae were the preferred food for smaller fish; for individuals greater than 10 cm, a higher ingestion of sand was observed. Most studies evaluating the functional role of parrotfish do not consider species feeding preferences. However, the potential for a species to turn an impacted reef back to a coral‐dominated phase is influenced by their food selection, which is dependent on the algal species composition. 相似文献
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C. Sothe C. M. De Almeida M. B. Schimalski L. E. C. La Rosa J. D. B. Castro R. Q. Feitosa 《地理信息系统科学与遥感》2020,57(3):369-394
ABSTRACT The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced classification methods. This study investigated the following topics concerning the classification of 16 tree species in two subtropical forest fragments of Southern Brazil: i) the potential integration of UAV-borne hyperspectral images with 3D information derived from their photogrammetric point cloud (PPC); ii) the performance of two machine learning methods (support vector machine – SVM and random forest – RF) when employing different datasets at a pixel and individual tree crown (ITC) levels; iii) the potential of two methods for dealing with the imbalanced sample set problem: a new weighted SVM (wSVM) approach, which attributes different weights to each sample and class, and a deep learning classifier (convolutional neural network – CNN), associated with a previous step to balance the sample set; and finally, iv) the potential of this last classifier for tree species classification as compared to the above mentioned machine learning methods. Results showed that the inclusion of the PPC features to the hyperspectral data provided a great accuracy increase in tree species classification results when conventional machine learning methods were applied, between 13 and 17% depending on the classifier and the study area characteristics. When using the PPC features and the canopy height model (CHM), associated with the majority vote (MV) rule, the SVM, wSVM and RF classifiers reached accuracies similar to the CNN, which outperformed these classifiers for both areas when considering the pixel-based classifications (overall accuracy of 84.4% in Area 1, and 74.95% in Area 2). The CNN was between 22% and 26% more accurate than the SVM and RF when only the hyperspectral bands were employed. The wSVM provided a slight increase in accuracy not only for some lesser represented classes, but also some major classes in Area 2. While conventional machine learning methods are faster, they demonstrated to be less stable to changes in datasets, depending on prior segmentation and hand-engineered features to reach similar accuracies to those attained by the CNN. To date, CNNs have been barely explored for the classification of tree species, and CNN-based classifications in the literature have not dealt with hyperspectral data specifically focusing on tropical environments. This paper thus presents innovative strategies for classifying tree species in subtropical forest areas at a refined legend level, integrating UAV-borne 2D hyperspectral and 3D photogrammetric data and relying on both deep and conventional machine learning approaches. 相似文献
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Raul Q. Feitosa Gilson A.O.P. Costa Guilherme L.A. Mota Kian Pakzad Maria C.O. Costa 《ISPRS Journal of Photogrammetry and Remote Sensing》2009,64(2):159-170
This paper proposes a new fuzzy cascade multitemporal classification method based on Fuzzy Markov Chains. This method differs from prior fuzzy multitemporal approaches proposed thus far, as the method does not require the knowledge of the true class at an earlier date; instead it uses the attributes of the image object being classified at the earlier date. This method combines the fuzzy, non-temporal, classification of a geographical region at two points in time to provide a single unified result. A transformation law based on class transition possibilities projects the earlier classification onto the later date before combining both results. Performance analysis was conducted upon a sequence of three LANDSAT images from the central region of Brazil using a Genetic Algorithm to estimate transition possibilities. The analysis showed that the increase in performance is highly dependent on whether or not a significant correlation exists between the temporal data sets, as well as on the accuracy of the monotemporal classifier at the earlier date. While the monotemporal approach used in the experiments attained an average class accuracy of approximately 55%, the multitemporal scheme achieved between 65% and 95%. Similar results in terms of overall accuracy were also observed. Furthermore, compared to two alternative cascade multitemporal classification approaches, the proposed method consistently showed better results. 相似文献
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GPS Solutions - In addition to traditional tide gauges, the ground-based global navigation satellite system reflectometry (GNSS-R) that utilizes signal-to-noise ratio data from a single GNSS... 相似文献
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