scholarly journals Convective phenomena forecasting based on output data of numerical models available in the Hydrometeorological Centre of the Republic of Belarus

2017 ◽  
pp. 57-64
Author(s):  
M. I. Prokharenya
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Numerical Models ◽  
Calculation Methods ◽  
Output Data ◽  
Model Calculations ◽  
Convective Processes ◽  
The Republic ◽  
Radar Satellite ◽  
Selection Of

In the article you can read about the methods used for forecasting convective processes by means of output products of numerical models with various spatial resolution. It presents the methods for forecasting convective phenomena applied in the Hydrometeorological Centre of the Republic of Belarus. The state of the atmosphere affected by intensive convection over the territory of the Republic of Belarus on July 13, 2016 is analyzed. The categorial evaluation of the thunderstorm forecasting methods by G.D. Reshetov and I.A. Slavin is specified with the respective results presented. The article analyzes the forecast of convective phenomena conducted with the help of the non-hydrostatic regional model WRF-ARW. Use of convective instability indices and calculation methods can facilitate convective phenomena forecasting. The advantage of their use consists in possibility of their application within the areas not covered by aerological sounding. However, selection of indicators of instability and methods of thunderstorm and other dangerous phenomena forecasting depends on features of an area under study and this requires further research. To ensure more accurate convective phenomena forecasting it is necessary to consider radar, satellite and aerological observations when conducting numerical model calculations. The results of the research showed that convective phenomena forecasting requires use of models with a high spatial resolution.

scholarly journals Bathymetry Time Series Using High Spatial Resolution Satellite Images

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 531 ◽  
Author(s):  
Manuel Erena ◽  
José A. Domínguez ◽  
Joaquín F. Atenza ◽  
Sandra García-Galiano ◽  
Juan Soria ◽  
...  
Keyword(s):  
High Precision ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Environmental Variability ◽  
High Spatial Resolution ◽  
Numerical Models ◽  
Three Dimensional ◽  
Satellite System ◽  
Precision Data ◽  
Mar Menor

The use of the new generation of remote sensors, such as echo sounders and Global Navigation Satellite System (GNSS) receivers with differential correction installed in a drone, allows the acquisition of high-precision data in areas of shallow water, as in the case of the channel of the Encañizadas in the Mar Menor lagoon. This high precision information is the first step to develop the methodology to monitor the bathymetry of the Mar Menor channels. The use of high spatial resolution satellite images is the solution for monitoring many hydrological changes and it is the basis of the three-dimensional (3D) numerical models used to study transport over time, environmental variability, and water ecosystem complexity.

Acoustic Microscopy for Imaging and Characterization

MRS Bulletin ◽  
1996 ◽  
Vol 21 (10) ◽  
pp. 30-35 ◽  
Author(s):  
Andrew Briggs ◽  
Oleg Kolosov
Keyword(s):  
Nondestructive Testing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Surface Coatings ◽  
Acoustic Microscopy ◽  
Minimum Size ◽  
Structure And Properties ◽  
Acoustic Microscope ◽  
Object A ◽  
Selection Of

Acoustic microscopy is useful for characterizing with high spatial resolution the elastic structure and properties of an object. A range of techniques is now available for doing this, which enables the user to select the method and instrument that is most appropriate for a particular requirement. For imaging the interior of structures such as electronic-component packaging, an acoustic microscope operating at a relatively modest frequency can provide advanced nondestructive testing. For characterizing surface coatings and layers that may be only a fraction of a micrometer thick, higher frequency quantitative techniques are needed. For a given application, three questions should be asked at the outset: (1) What depth of material do I wish to include in my inspection? (2) Do I wish to image structures and/or defects, or do I wish to characterize elastic properties? (3) What is the minimum size of a defect or inhomogeneity that I wish to resolve or characterize (at a given depth) during my inspection? Selection of the appropriate technique will depend on the answers.

scholarly journals Selection of informative features to highlight areas of waste disposal on high spatial resolution satellite imagery

2019 ◽  
pp. 29-44
Author(s):  
A. B. Murynin ◽  
A. A. Richter ◽  
M. A. Shakhramanyan
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Waste Disposal ◽  
High Spatial Resolution ◽  
Ground Truth ◽  
Space Images ◽  
Radar Images ◽  
High Resolution Images ◽  
Interpretive Signs ◽  
Selection Of

The paper deals with the problem of integrated interpretation of waste disposal facilities according to satellite imagery and ground truth monitoring, features of space images of landfills from various points of view: texture analysis, statistical properties, fractal analysis, color features, and the possibility of using machine learning methods. The main visual interpretive signs of landfills on optical and radar images of high spatial resolution are given. The fractal dimension of landfills was calculated for high resolution images using two models.

scholarly journals Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7935
Author(s):  
Shuang Hao ◽  
Yuhuan Cui ◽  
Jie Wang
Keyword(s):  
Image Segmentation ◽  
Image Classification ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Selection Method ◽  
Scale Selection ◽  
Average Area ◽  
Optimal Segmentation ◽  
Selection Of ◽  
Segmentation Parameters

High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.

scholarly journals Geometry Aware Evaluation of Handcrafted Superpixel-Based Features and Convolutional Neural Networks for Land Cover Mapping Using Satellite Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 513
Author(s):  
Dawa Derksen ◽  
Jordi Inglada ◽  
Julien Michel
Keyword(s):  
Feature Extraction ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Land Cover Mapping ◽  
Geometric Accuracy ◽  
Training Set ◽  
Contextual Features ◽  
Thematic Accuracy ◽  
Selection Of

In land cover mapping at a high spatial resolution, pixel values alone are not always sufficient to recognize the more complex classes. Contextual features (computed with a sliding kernel or other kind of spatial support) can be discriminating for certain land cover classes, for example, different levels of urban density, or classes containing heterogeneous pixels, such as orchards and vineyards. However, the reference data used for training the supervised classifier are almost always sparsely labeled, in other words, not every pixel of the training area is labeled. This makes the selection of an appropriate contextual classification method for land cover mapping problematic. Indeed, the current state-of-the art contextual classification model, the Deep Convolutional Neural Network (D-CNN), encounters issues when the geometry of the desired output is absent from the training set. Data-driven methods like D-CNN rely heavily on the availability of extensive training labels to learn both the feature extraction and classification steps. With a sparse training set, sharp corners are rounded, and thin elongated elements may be either thickened, or entirely lost. Alternatively, there are several methods based on the manual selection of contextual features in a chosen neighborhood, guided by the knowledge of the data and past experience from similar problems. Such approaches should not be as sensitive to sparsely labeled data, as they do not rely on any training data for feature extraction. This paper presents a new process for including contextual information in an image classification scheme: the Histogram Of Auto Context Classes in Superpixels (HACCS), which involves classifying an image using the local class histograms as contextual features. These histograms are calculated within superpixels of different sizes in order to provide a multi-scale characterization of the neighborhood, while preserving the geometry of the image objects. This method is evaluated on two data sets presenting different spatial, temporal, and spectral resolutions, and each case is compared with a D-CNN in terms of class accuracy, but also of the quality of the geometry in the produced map. Experiments on the Sentinel-2 time series show that HACCS provides equivalent thematic accuracy compared to the D-CNN, while exhibiting a higher degree of geometric accuracy. On very high spatial resolution imagery (SPOT-7), the D-CNN provides significantly stronger thematic accuracy, but this comes at the cost of a lower level of geometric accuracy.

scholarly journals Object-oriented feature selection of high spatial resolution images using an improved Relief algorithm

2013 ◽  
Vol 58 (3-4) ◽  
pp. 619-626 ◽  
Author(s):  
Jianhua Jia ◽  
Ning Yang ◽  
Chao Zhang ◽  
Anzhi Yue ◽  
Jianyu Yang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Object Oriented ◽  
Relief Algorithm ◽  
Selection Of

Using Rapid Repeat SAR Interferometry to improve Hydrodynamic Models of flood propagation in Coastal Wetlands 

2021 ◽  
Author(s):  
Sergio Fagherazzi ◽  
Xiaohe Zhang ◽  
Cathleen Jones ◽  
Talib Oliver-Cabrera ◽  
Marc Simard
Keyword(s):  
Water Level ◽  
Spatial Resolution ◽  
Coastal Wetlands ◽  
High Spatial Resolution ◽  
Numerical Models ◽  
Water Levels ◽  
Sar Interferometry ◽  
Model Parameters ◽  
Water Level Change ◽  
Level Change

<p>The propagation of tides and riverine floodwater in coastal wetlands is controlled by subtle topographic differences and a thick vegetation canopy. A precise quantification of fluxes of water, sediments and nutrients is crucial to determine the resilience and vulnerability of coastal wetlands to sea level rise. High-resolution numerical models have been used in recent years to simulate fluxes across wetlands. However, these models are based on sparse field data that can lead to unreliable results. Here, we utilize high spatial-resolution, rapid repeat interferometric data from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to provide a synoptic measurement of sub-canopy water-level change resulting from tide propagation into wetlands.  These data are used to constrain crucial model parameters and improve the performance and realism of simulations of the Wax Lake wetlands in coastal Louisiana (USA). A sensitivity analysis shows that the boundary condition of river discharge should be calibrated first, followed by iterative correction of terrain elevation. The calibration of bed friction becomes important only with the boundary and topography calibrated. With the model parameters calibrated, the overall Nash-Sutcliffe model efficiency for water-level change increases from 0.15 to 0.53 with the RMSE reduced by 26%. More importantly, constraining model simulations with UAVSAR observations drives iterative modifications of the original Digital Terrain Model. In areas with dense wetland grasses, the LiDAR signal is unable to reach the soil surface, but the L-band UAVSAR instrument detects changes in water levels that can be used to infer the true ground elevation. The high spatial resolution and repeat-acquisition frequency (minutes to hours) observations provided by UAVSAR represent a groundbreaking opportunity for a deeper understanding of the complex hydrodynamics of coastal wetlands.</p>

High spatial resolution AEM observations of yttrium segregation in chromia scales grown on Co-45%Cr

1986 ◽  
Vol 44 ◽  
pp. 518-519
Author(s):  
K. Przybylski ◽  
A. J. Garratt-Reed ◽  
G. J. Yurek
Keyword(s):  
Spatial Resolution ◽  
Outer Surface ◽  
Maximum Concentration ◽  
High Spatial Resolution ◽  
Reactive Elements ◽  
Chromia Scales

The addition of so-called “reactive” elements such as yttrium to alloys is known to enhance the protective nature of Cr2O3 or Al2O3 scales. However, the mechanism by which this enhancement is achieved remains unclear. An A.E.M. study has been performed of scales grown at 1000°C for 25 hr. in pure O2 on Co-45%Cr implanted at 70 keV with 2x1016 atoms/cm2 of yttrium. In the unoxidized alloys it was calculated that the maximum concentration of Y was 13.9 wt% at a depth of about 17 nm. SIMS results showed that in the scale the yttrium remained near the outer surface.

Integration of Core-Edge Spectroscopy Methods for the Study of Polymers

1996 ◽  
Vol 54 ◽  
pp. 154-155
Author(s):  
E. G. Rightor
Keyword(s):  
Excited State ◽  
Polymer Blends ◽  
Gas Phase ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Electronic States ◽  
Core Electron ◽  
Bulk Solids ◽  
Development Application

Core edge spectroscopy methods are versatile tools for investigating a wide variety of materials. They can be used to probe the electronic states of materials in bulk solids, on surfaces, or in the gas phase. This family of methods involves promoting an inner shell (core) electron to an excited state and recording either the primary excitation or secondary decay of the excited state. The techniques are complimentary and have different strengths and limitations for studying challenging aspects of materials. The need to identify components in polymers or polymer blends at high spatial resolution has driven development, application, and integration of results from several of these methods.

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