scholarly journals 3-D visualisation of palaeoseismic trench stratigraphy and trench logging using terrestrial remote sensing and GPR – a multiparametric interpretation

Solid Earth ◽  
2016 ◽  
Vol 7 (2) ◽  
pp. 323-340 ◽  
Author(s):  
Sascha Schneiderwind ◽  
Jack Mason ◽  
Thomas Wiatr ◽  
Ioannis Papanikolaou ◽  
Klaus Reicherter
Keyword(s):  
Seismic Hazard ◽  
Near Infrared ◽  
Normal Faults ◽  
Data Sets ◽  
Data Set ◽  
Backscatter Signal ◽  
Multispectral Data ◽  
Sedimentary Architecture ◽  
Hazard Assessments ◽  
Multispectral Analysis

Abstract. Two normal faults on the island of Crete and mainland Greece were studied to test an innovative workflow with the goal of obtaining a more objective palaeoseismic trench log, and a 3-D view of the sedimentary architecture within the trench walls. Sedimentary feature geometries in palaeoseismic trenches are related to palaeoearthquake magnitudes which are used in seismic hazard assessments. If the geometry of these sedimentary features can be more representatively measured, seismic hazard assessments can be improved. In this study more representative measurements of sedimentary features are achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of ISO (iterative self-organising) cluster analysis of a true colour photomosaic representing the spectrum of visible light. Photomosaic acquisition disadvantages (e.g. illumination) were addressed by complementing the data set with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D interpretation of attached 2-D ground-penetrating radar (GPR) profiles collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements, and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. This manuscript combines multiparametric approaches and shows (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GPR techniques, and (ii) how a multispectral digital analysis can offer additional advantages to interpret palaeoseismic and stratigraphic data. The multispectral data sets are stored allowing unbiased input for future (re)investigations.

scholarly journals 3-D visualisation of palaeoseismic trench stratigraphy and trench logging using terrestrial remote sensing and GPR – combining techniques towards an objective multiparametric interpretation

2015 ◽  
Vol 7 (3) ◽  
pp. 2697-2733
Author(s):  
S. Schneiderwind ◽  
J. Mason ◽  
T. Wiatr ◽  
I. Papanikolaou ◽  
K. Reicherter
Keyword(s):  
Near Infrared ◽  
Earthquake Magnitude ◽  
Normal Faults ◽  
Digital Analysis ◽  
Backscatter Signal ◽  
Sedimentary Architecture ◽  
Lidar Measurements ◽  
Passive Data ◽  
Multispectral Analysis ◽  
Signal Image

Abstract. Two normal faults on the Island of Crete and mainland Greece were studied to create and test an innovative workflow to make palaeoseismic trench logging more objective, and visualise the sedimentary architecture within the trench wall in 3-D. This is achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of iso cluster analysis of a true colour photomosaic representing the spectrum of visible light. Passive data collection disadvantages (e.g. illumination) were addressed by complementing the dataset with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D-interpretation of GPR data collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. Sedimentary feature geometries related to earthquake magnitude can be used to improve the accuracy of seismic hazard assessments. Therefore, this manuscript combines multiparametric approaches and shows: (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GRP techniques, and (ii) how a multispectral digital analysis can offer additional advantages and a higher objectivity in the interpretation of palaeoseismic and stratigraphic information. The multispectral datasets are stored allowing unbiased input for future (re-)investigations.

scholarly journals HYDRA2: A Multispectral Data Analysis Toolkit for Sensors on Suomi-NPP and Other Current Satellite Platforms

2016 ◽  
Vol 97 (7) ◽  
pp. 1283-1294
Author(s):  
Tom Rink ◽  
W. Paul Menzel ◽  
Liam Gumley ◽  
Kathy Strabala
Keyword(s):  
Cross Sections ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Hyperspectral Data ◽  
Advanced Technology ◽  
Multispectral Data ◽  
Brightness Temperatures ◽  
Sensing Applications ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Multispectral Analysis

Abstract The Hyperspectral Data Viewer for Development of Research Applications, version 2 (HYDRA2), is a freeware-based multispectral analysis toolkit for satellite data that assists scientists in research and development, as well as education and training of remote sensing applications. HYDRA2 users can explore and visualize relationships between sensor measurements (brightness temperatures for infrared and reflectances for visible/near-infrared wavelengths) using spectral diagrams, cross sections, scatterplots, multiband combinations, and color enhancements on a pixel-by-pixel basis. HYDRA2 can be used with direct broadcast and archived data from sensors on board the National Oceanic and Atmospheric Administration (NOAA)/National Aeronautics and Space Administration (NASA) Suomi–National Polar-Orbiting Partnership (Suomi-NPP), NASA Aqua/Terra, European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteorological Operational (MetOp), and Chinese Fengyun-3 platforms. This paper describes HYDRA2 and presents some examples using data retrievals from the Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS), Cross-Track Infrared Sounder (CrIS), Advanced Technology Microwave Sounder (ATMS), and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments.

scholarly journals Characterization of the umbra–penumbra boundary by the vertical component of the magnetic field

2020 ◽  
Vol 638 ◽  
pp. A25
Author(s):  
P. Lindner ◽  
R. Schlichenmaier ◽  
N. Bello González
Keyword(s):  
Magnetic Field ◽  
Near Infrared ◽  
Statistical Study ◽  
Vertical Component ◽  
Fundamental Property ◽  
Field Vector ◽  
Data Sets ◽  
Data Set ◽  
Geometric Difference ◽  
The Magnetic Field

Context. The vertical component of the magnetic field was found to reach a constant value at the boundary between penumbra and umbra of stable sunspots in a recent statistical study of Hinode/SP data. This finding has profound implications as it can serve as a criterion to distinguish between fundamentally different magneto-convective modes operating in the sun. Aims. The objective of this work is to verify the existence of a constant value for the vertical component of the magnetic field (B⊥) at the boundary between umbra and penumbra from ground-based data in the near-infrared wavelengths and to determine its value for the GREGOR Infrared Spectrograph (GRIS@GREGOR) data. This is the first statistical study on the Jurčák criterion with ground-based data, and we compare it with the results from space-based data (Hinode/SP and SDO/HMI). Methods. Eleven spectropolarimetric data sets from the GRIS@GREGOR slit-spectograph containing fully-fledged stable sunspots were selected from the GRIS archive. SIR inversions including a polarimetric straylight correction are used to produce maps of the magnetic field vector using the Fe I 15648 Å and 15662 Å lines. Averages of B⊥ along the contours between penumbra and umbra are analyzed for the 11 data sets. In addition, contours at the resulting B⊥const are drawn onto maps and compared to intensity contours. The geometric difference between these contours, ΔP, is calculated for each data set. Results. Averaged over the 11 sunspots, we find a value of B⊥const = (1787 ± 100) gauss. The difference from the values previously derived from Hinode/SP and SDO/HMI data is explained by instrumental differences and by the formation characteristics of the respective lines that were used. Contours at B⊥ = B⊥const and contours calculated in intensity maps match from a visual inspection and the geometric distance ΔP was found to be on the order of 2 pixels. Furthermore, the standard deviation between different data sets of averages along umbra–penumbra contours is smaller for B⊥ than for B∥ by a factor of 2.4. Conclusions. Our results provide further support to the Jurčák criterion with the existence of an invariable value B⊥const at the umbra–penumbra boundary. This fundamental property of sunspots can act as a constraining parameter in the calibration of analysis techniques that calculate magnetic fields. It also serves as a requirement for numerical simulations to be realistic. Furthermore, it is found that the geometric difference, ΔP, between intensity contours and contours at B⊥ = B⊥const acts as an index of stability for sunspots.

scholarly journals Comparison of Individual and Integrated Inline Raman, Near-Infrared, and Mid-Infrared Spectroscopic Models to Predict the Viscosity of Micellar Liquids

2020 ◽  
Vol 74 (7) ◽  
pp. 819-831 ◽  
Author(s):  
Kiran Haroon ◽  
Ali Arafeh ◽  
Stephanie Cunliffe ◽  
Philip Martin ◽  
Thomas Rodgers ◽  
...  
Keyword(s):  
Consumer Satisfaction ◽  
Near Infrared ◽  
Process Analytical Technology ◽  
Process Efficiency ◽  
Initial Assessment ◽  
Data Sets ◽  
Spectroscopic Techniques ◽  
Signal To Noise ◽  
Data Set ◽  
Mid Infrared

In many industries, viscosity is an important quality parameter which significantly affects consumer satisfaction and process efficiency. In the personal care industry, this applies to products such as shampoo and shower gels whose complex structures are built up of micellar liquids. Measuring viscosity offline is well established using benchtop rheometers and viscometers. The difficulty lies in measuring this property directly in the process via on or inline technologies. Therefore, the aim of this work is to investigate whether proxy measurements using inline vibrational spectroscopy, e.g., near-infrared (NIR), mid-infrared (MIR), and Raman, can be used to predict the viscosity of micellar liquids. As optical techniques, they are nondestructive and easily implementable process analytical tools where each type of spectroscopy detects different molecular functionalities. Inline fiber optic coupled probes were employed; a transmission probe for NIR measurements, an attenuated total reflectance probe for MIR and a backscattering probe for Raman. Models were developed using forward interval partial least squares variable selection and log viscosity was used. For each technique, combinations of pre-processing techniques were trialed including detrending, Whittaker filters, standard normal variate, and multiple scatter correction. The results indicate that all three techniques could be applied individually to predict the viscosity of micellar liquids all showing comparable errors of prediction: NIR: 1.75 Pa s; MIR: 1.73 Pa s; and Raman: 1.57 Pa s. The Raman model showed the highest relative prediction deviation (RPD) value of 5.07, with the NIR and MIR models showing slightly lower values of 4.57 and 4.61, respectively. Data fusion was also explored to determine whether employing information from more than one data set improved the model quality. Trials involved weighting data sets based on their signal-to-noise ratio and weighting based on transmission curves (infrared data sets only). The signal-to-noise weighted NIR–MIR–Raman model showed the best performance compared with both combined and individual models with a root mean square error of cross-validation of 0.75 Pa s and an RPD of 10.62. This comparative study provides a good initial assessment of the three prospective process analytical technologies for the measurement of micellar liquid viscosity but also provides a good basis for general measurements of inline viscosity using commercially available process analytical technology. With these techniques typically being employed for compositional analysis, this work presents their capability in the measurement of viscosity—an important physical parameter, extending the applicability of these spectroscopic techniques.

scholarly journals Scientific impact of MODIS C5 calibration degradation and C6+ improvements

2014 ◽  
Vol 7 (12) ◽  
pp. 4353-4365 ◽  
Author(s):  
A. Lyapustin ◽  
Y. Wang ◽  
X. Xiong ◽  
G. Meister ◽  
S. Platnick ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Temporal Trends ◽  
Polarization Sensitivity ◽  
Data Sets ◽  
Surface Reflectance ◽  
Data Set ◽  
Cross Calibration ◽  
Calibration Approach

Abstract. The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Ångström exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and Terra–Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1% of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1–B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

Comparison of hand-held near infrared spectrophotometers for fruit dry matter assessment

2017 ◽  
Vol 25 (4) ◽  
pp. 267-277 ◽  
Author(s):  
Harpreet Kaur ◽  
Rainer Künnemeyer ◽  
Andrew McGlone
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Data Sets ◽  
Prediction Errors ◽  
Data Set ◽  
Instrument Choice ◽  
Spectroscopy Instrumentation ◽  
Portable Instruments ◽  
Mean Square Errors ◽  
Single Instrument

Comparisons are reported for developing predictive models for dry matter across a wide variety of fruits with near infrared spectroscopy instrumentation, using a number of commercially available hand-held portable instruments (NIRVANA by Integrated Spectronics, F-750 by Felix Instruments, H-100C by Sunforest and SCiO by Consumer Physics) and an in-house laboratory based instrument (Benchtop). Three intrinsic (same fruit type) and combined (all fruit types) data sets were created from two separate batches of fruit populations. The first batch (Lot I) consisted of 205 ripe fruits from three different main fruit types (apples, kiwifruit and summerfruit) and 12 distinct fruit sub-categories. The second batch (Lot II) consisted of 91 ripe fruits from two different fruit types (apples and kiwifruit) and seven distinct fruit sub-categories. The laboratory based Benchtop instrument performed the best overall with typically higher prediction r2 values (>0.92). The hand-held instruments delivered moderate to high r2 values between 0.8 and 0.95. Results obtained with the intrinsic data sets revealed typically lower root mean square errors of prediction for apples and kiwifruit (0.32% to 0.73%) and larger prediction errors for summerfruit (0.53% to 0.82%). Some large performance variations between instruments of the same type were observed suggesting caution in evaluating the relative performance of different instrument types or formats on the basis of data generated with just a single instrument and/or data set. However, performance differences between the different hand-held portable instruments, on the same data sets, were often not statistically significant ( p < 0.05). Instrument choice for any particular application will likely come down to matters not considered here, such as, for example, ease and accuracy during in-field operation and overall reliability.

scholarly journals Retrieval of Daytime Total Column Water Vapour from OLCI Measurements over Land Surfaces

2021 ◽  
Vol 13 (5) ◽  
pp. 932
Author(s):  
René Preusker ◽  
Cintia Carbajal Henken ◽  
Jürgen Fischer
Keyword(s):  
Water Vapour ◽  
Near Infrared ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Uncertainty Estimates ◽  
Resolution Imaging ◽  
Retrieval Errors ◽  
Mean Square Errors ◽  
Total Column

A new retrieval of total column water vapour (TCWV) from daytime measurements over land of the Ocean and Land Colour Instrument (OLCI) on-board the Copernicus Sentinel-3 missions is presented. The Copernicus Sentinel-3 OLCI Water Vapour product (COWa) retrieval algorithm is based on the differential absorption technique, relating TCWV to the radiance ratio of non-absorbing band and nearby water vapour absorbing band and was previously also successfully applied to other passive imagers Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS). One of the main advantages of the OLCI instrument regarding improved TCWV retrievals lies in the use of more than one absorbing band. Furthermore, the COWa retrieval algorithm is based on the full Optimal Estimation (OE) method, providing pixel-based uncertainty estimates, and transferable to other Near-Infrared (NIR) based TCWV observations. Three independent global TCWV data sets, i.e., Aerosol Robotic Network (AERONET), Atmospheric Radiation Measurement (ARM) and U.S. SuomiNet, and a German Global Navigation Satellite System (GNSS) TCWV data set, all obtained from ground-based observations, serve as reference data sets for the validation. Comparisons show an overall good agreement, with absolute biases between 0.07 and 1.31 kg/m2 and root mean square errors (RMSE) between 1.35 and 3.26 kg/m2. This is a clear improvement in comparison to the operational OLCI TCWV Level 2 product, for which the bias and RMSEs range between 1.10 and 2.55 kg/m2 and 2.08 and 3.70 kg/m2, respectively. A first evaluation of pixel-based uncertainties indicates good estimated uncertainties for lower retrieval errors, while the uncertainties seem to be overestimated for higher retrieval errors.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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