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271.
We show how traditional instrument quality control trending can be augmented by the use of a physical instrument model. The ESO VLT archive contains a detailed record of instrument diagnostics and calibration parameters while ESO quality control monitors changes in critical parameters. The physical model allows changes in positions, orientations and other physical properties of a spectrograph to be determined from standard wavelength calibration exposures via an optimisation process that seeks the physical model parameters that best reproduce the calibration features in the data. We introduce physical model parameters to the quality control monitoring. When applying this technique to archived calibration exposures, we find that the results are sensitive to the combination of parameters open to the optimisation process. Therefore we determine the most favourable set of physical parameters to optimise for each arm. We then show correlations between several physical parameters and instrument temperature sensor readings and epoch. In addition we find clear discontinuities in some physical parameter values that correspond to known maintenance events.  相似文献   
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Stochastic Environmental Research and Risk Assessment - A robust estimator is proposed for the parameters that characterize the linear regression problem. It is based on the notion of shrinkages,...  相似文献   
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Fuel type mapping of the wildland-urban interface (WUI) in support of fire spread simulation modelling should include both natural and urban features. The objective of this study was to evaluate the utility of (1) Light Detection and Ranging (LiDAR) structural data, (2) ortho-image data and (3) a combination of both as input to an object-based classification approach for mapping fuels within two WUI areas in San Diego, California. A separability analysis was utilized to determine the surface topographical and spectral layers most influential for discriminating WUI fuels. An accuracy assessment revealed that the combination of LiDAR and ortho-image data inputs substantially increased classification accuracy by 20–30% and achieved overall accuracies?>?80%. Results from the study provide knowledge on how reliable fuel types within the WUI can be mapped using high-resolution LiDAR and ortho-image data while presenting new insights into fuel type mapping.  相似文献   
275.
The decapod taxocoenosis living in shallow muddy bottoms with the green algae Caulerpa prolifera was studied monthly between February 1994 and January 1996 in the Inner Bay of Cádiz (SW Spain). More than 32,000 specimens belonging to 35 species were collected. Six species were dominant (representing the 85.8% of the total number of specimens), but the structure of the taxocoenosis was regulated by the Hippolyte species, Sicyonia carinata, Palaemon adspersus and Liocarcinus arcuatus. There was no significant qualitative difference between years. There was no clear change in the dominance of groups of species during the year, as happened in the outer Bay. This is probably due to the sheltered character of the area and the more stable and dense vegetal cover, but some seasonal differences were found.The benthic characteristics of the Inner Bay of Cádiz, such as shallow soft bottoms of fine and muddy sediments and the presence of macrophytes (seagrasses and seaweeds) might be key factors influencing the composition and structure of the general and seasonal decapod assemblage. In spite of human impacts on the bay (e.g. aquaculture activities, sewage), the values of the diversity, equitability and richness indexes appeared stable over time (higher than those found in outer adjacent areas) and no significant differences between years were found.  相似文献   
276.
  总被引:1,自引:0,他引:1  
This paper presents results related to the occurrence and distribution of estrogens along the Brazilian coast. Three mangrove areas were chosen to evaluate the presence of estrogens in surface sediments of mangrove forests. The presence of estrogens was observed in all studied sites. 17-α-Ethinylestradiol (EE2), a synthetic estrogen, was the most common and has been found in higher concentration (0.45-129.78 ng/g) compared to 17-β-estradiol (E1) and estrone (E2) (both being natural estrogens). The concentrations of E1 and E2 ranged from 0.02 to 49.27 ng/g and 0.03 to 39.77 ng/g, respectively. Theoretically, under anaerobic conditions EE2 can be reduced to E1 even in environments such as sediments of mangrove forests, which are essentially anaerobic. Even if the concentrations of estrogens seem to be insignificant in some samples, the effects remain uncertain.  相似文献   
277.
The filtration process of Sabella spallanzanii Gmelin on bacterial community was studied in a coastal area of the Northern Ionian Sea (Mediterranean Sea) at three sites, S. Vito, Lido Gandoli and Lido Silvana, where some specimens of S. spallanzanii were transplanted. Analyses were performed both on water and worm samples. A total of six microbial groups were examined: culturable heterotrophic bacteria, total culturable bacteria at 37 degrees C, culturable vibrios, total and fecal coliforms and fecal streptococci. The bacterial densities were usually orders of magnitude higher in the worm homogenates than in the corresponding seawater and the highest values were observed in August. The ability of S. spallanzanii to accumulate the microbial pollution indicators suggests this species can be employed as a bioindicator for monitoring water quality. Moreover, the accumulation capability of S. spallanzanii for specific micro-organisms provides a potential role in sewage bioremediation.  相似文献   
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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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