New Insights for Understanding the Structural Deformation Style of the Strike-Slip Regime along the Wadi Shueib and Amman-Hallabat Structures in Jordan Based on Remote Sensing Data Analysis

This paper presents new findings that contribute to the understanding of the deformational style of the Wadi Shueib Structure (WSS) and the Amman-Halabat Structure (AHS) and their relationship with the regional tectonic regime of the Dead Sea Transform Fault (DSTF). Our research utilized Landsat-8 OLI imagery for the automatic extraction of lineaments, and our lineament mapping was facilitated by processing and digital image enhancement using principal component analysis (PCA). Our data revealed a relatively higher density of lineaments along the extension of the major faults of the WSS and AHS. However, a relatively lower density of lineaments was shown in areas covered by recent deposits. Two major lineament trends were observed (NNE-SSW and NW-SE) in addition to a minor one (NE-SW), and most of these lineaments are parallel to the orientation of the WSS and AHS. We offer the supposition that the DSTF has merged into the major faults of the WSS and AHS. We further suppose that these faults were reactivated as a restraining bend composed of active strike-slip fault branches that developed due to the NNW-SSE-trending Dead Sea transpressional stress field. Depending on the relationship between the direction of the WSF and AHF strands and the regional tectonic displacement along the DSTF, thrust components are present on faults with horsetail geometry, and these movements are accompanied by folding and uplifting. Thus, the major faults of the WSS and AHS represent a contractional horsetail geometry with associated folding and thrusting deformation.

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Development of the Statistical Model for Monitoring Salinization in the Mekong Delta of Vietnam Using Remote Sensing Data and In-Situ Measurements

Proceedings ◽

10.3390/iecg_2018-05362 ◽

2018 ◽

Vol 2 (10) ◽

pp. 565

Author(s):

Nguyen Nguyen Vu ◽

Le Van Trung ◽

Tran Thi Van

Keyword(s):

Statistical Model ◽

Mekong Delta ◽

Remote Sensing Data ◽

Principal Component ◽

Measured Data ◽

In Situ Measurements ◽

Landsat 8 ◽

Spectral Bands ◽

Component Band

This article presents the methodology for developing a statistical model for monitoring salinity intrusion in the Mekong Delta based on the integration of satellite imagery and in-situ measurements. We used Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat- 8 OLI and TIRS) satellite data to establish the relationship between the planetary reflectance and the ground measured data in the dry season during 2014. The three spectral bands (blue, green, red) and the principal component band were used to obtain the most suitable models. The selected model showed a good correlation with the exponential function of the principal component band and the ground measured data (R2 > 0.8). Simulation of the salinity distribution along the river shows the intrusion of a 4 g/L salt boundary from the estuary to the inner field of more than 50 km. The developed model will be an active contribution, providing managers with adaptation and response solutions suitable for intrusion in the estuary as well as the inner field of the Mekong Delta.

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Computation of Temporal Decline to a Vanished Island A Case Study Zalzal Koh

International Journal of Innovations in Science and Technology ◽

10.33411/ijist/2019010409 ◽

2019 ◽

Author(s):

Ali Asghar ◽

Muhammad Ayaz Ahmad ◽

Memona Zafar ◽

Shazma Saman ◽

Muhammad Awais Arshad ◽

...

Keyword(s):

Water Waves ◽

Dead Sea ◽

Strike Slip ◽

Landsat 8 ◽

Reduced Pressure ◽

Richter Scale ◽

Pressure Water ◽

Spatio Temporal ◽

The Impact

A massive tremor stuck Baluchistan Pakistan on September 24, 2013 with a magnitude of 7.7 recorded on Richter scale. The epicenter was Awaran Baluchistan which directly affected about 300,000 people leaving about 386 causalities. The impact of this earthquake was so much large that it created a new landmass which was named as Zalzala Koh later. It was the result of strike slip faulting at a depth of 15km. The new born island was full of rich minerals, gases and dead sea animals. This island was 60 feet high, 100 feet wide and 250 feet long. The classification results of Landsat 8 show that the island completely disappeared in 2019 after 6 six years of its birth. First the volume of this island decreases due to reduced pressure of internal gases and secondly, the high-pressure water waves vanished it completely. Satellite imagery proved efficient for spatio-temporal monitoring of various landuse classes.

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A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

Remote Sensing ◽

10.3390/rs12193123 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3123

Author(s):

Étienne Clabaut ◽

Myriam Lemelin ◽

Mickaël Germain ◽

Marie-Claude Williamson ◽

Éloïse Brassard

Keyword(s):

Deep Learning ◽

Canadian Arctic ◽

Remote Sensing Data ◽

Principal Component ◽

Ore Deposits ◽

Great Promise ◽

Landsat 8 ◽

Significant Difference ◽

Band Ratioing ◽

Elevation Model

Gossans are surficial deposits that form in host bedrock by the alteration of sulphides by acidic and oxidizing fluids. These deposits are typically a few meters to kilometers in size and they constitute important vectors to buried ore deposits. Hundreds of gossans have been mapped by field geologists in sparsely vegetated areas of the Canadian Arctic. However, due to Canada’s vast northern landmass, it is highly probable that many existing occurrences have been missed. In contrast, a variety of remote sensing data has been acquired in recent years, allowing for a broader survey of gossans from orbit. These include band ratioing or methods based on principal component analysis. Spectrally, the 809 gossans used in this study show no significant difference from randomly placed points on the Landsat 8 imageries. To overcome this major issue, we propose a deep learning method based on convolutional neural networks and relying on geo big data (Landsat-8, Arctic digital elevation model lithological maps) that can be used for the detection of gossans. Its application in different regions in the Canadian Arctic shows great promise, with precisions reaching 77%. This first order approach could provide a useful precursor tool to identify gossans prior to more detailed surveys using hyperspectral imaging.

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Evaluating salinity intrusion in estuaries using remote sensing data integrated in-situ observation

Science & Technology Development Journal - Science of The Earth & Environment ◽

10.32508/stdjsee.v2i2.493 ◽

2019 ◽

Vol 2 (2) ◽

pp. 50-56

Author(s):

Vu Nguyen Nguyen ◽

Trung Van Le ◽

Van Thi Tran

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Principal Component ◽

Crop Productivity ◽

Dry Season ◽

In Situ Observation ◽

Landsat 8 ◽

Saline Intrusion ◽

Salinity Intrusion ◽

Actual Observation

Saline intrusion reduces crop productivity, causes land degradation, decreases water quality, and severely affects agricultural production, the environment as well as livelihoods. Under the evolution of climate change and human activities from the upstream of the Mekong, the downstream areas of Dinh An and Cung Hau estuaries in Tra Vinh province are also significantly affected by saline intrusion from the East Sea. This article presents the integrated solution of remote sensing and GIS in monitoring and mapping salinity intrusion. The data used are Landsat 8 satellite images combined with salinity water monitoring data collected from actual observation stations during the dry season. Analysis showed that there was a statistically significant correlation between the observed salinity value of the water and the pixel value of the first principal component image. Simulation of spatial distribution from the study indicates that saline intrusion is now entering the interior with a distance from the estuary to about 30 - 48 km depending on the time of the dry season. The results of this study will assist managers in planning food safety strategies at the risk of saline intrusion.

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The Method of Detection of Clay Minerals and Iron Oxide Based on Landsat Multispectral Images (as Exemplified in the Territory of Thai Nguyen Province, Vietnam)

Mining science and technology ◽

10.17073/2500-0632-2019-1-65-75 ◽

2019 ◽

Vol 4 (1) ◽

pp. 65-75

Author(s):

Le Hung Trinh ◽

V. R. Zabloskii

Keyword(s):

Iron Oxide ◽

Clay Minerals ◽

Spectral Characteristics ◽

Remote Sensing Data ◽

Principal Component ◽

Mineral Deposits ◽

Landsat 8 ◽

Multispectral Images ◽

Landsat 8 Oli ◽

The World

Landsat multispectral images have been successfully used for discovering some mineral deposits in different regions of the world. Some minerals, including clay minerals and iron oxide, can be detected by multispectral surveys due to their spectral characteristics. This paper presents the results of the application of principal component analysis and Crosta technique for detecting accumulations of clay minerals and iron oxide based on a Landsat 8 Oli multispectral image of Thai Nguyen Province, north of Vietnam. The obtained results have demonstrated the feasibility and suitability of prompt detecting mineral deposits based on the remote sensing data. The image processing methods and facilities tested in this study can be used to create maps of distribution of clay minerals and iron oxide for effective and expedient prospecting and exploration for minerals.

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SPECTRAL SIGNATURE IDENTIFICATION AND SURFACE AREA ESTIMATION FOR FRUIT CROPS GROWN IN TIBA REGION OF WESTERN NILE DELTA OF EGYPT BY USING LANDSAT-8 SATELLITE REMOTE SENSING DATA

Journal of Plant Production ◽

10.21608/jpp.2015.52120 ◽

2015 ◽

Vol 6 (12) ◽

pp. 1917-1940

Author(s):

D. El-Ansary

Keyword(s):

Remote Sensing ◽

Surface Area ◽

Satellite Remote Sensing ◽

Nile Delta ◽

Remote Sensing Data ◽

Spectral Signature ◽

Landsat 8 ◽

Fruit Crops ◽

Area Estimation ◽

Nile Delta Of Egypt

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Optimized Method for Mapping Inorganic Pigments by Means of Multispectral Imaging Combined with Hyperspectral Spectroscopy for the Study of Vincenzo Pasqualoni’s Wall Painting at the Basilica of S. Nicola in Carcere in Rome

Minerals ◽

10.3390/min11080839 ◽

2021 ◽

Vol 11 (8) ◽

pp. 839

Author(s):

Lucilla Pronti ◽

Giuseppe Capobianco ◽

Margherita Vendittelli ◽

Anna Candida Felici ◽

Silvia Serranti ◽

...

Keyword(s):

Multispectral Imaging ◽

Imaging System ◽

Principal Component ◽

Reflectance Spectroscopy ◽

Wall Painting ◽

High Spectral Resolution ◽

Spectral Difference ◽

Color Palette ◽

A Minor ◽

Nir Reflectance Spectroscopy

Multispectral imaging is a preliminary screening technique for the study of paintings. Although it permits the identification of several mineral pigments by their spectral behavior, it is considered less performing concerning hyperspectral imaging, since a limited number of wavelengths are selected. In this work, we propose an optimized method to map the distribution of the mineral pigments used by Vincenzo Pasqualoni for his wall painting placed at the Basilica of S. Nicola in Carcere in Rome, combining UV/VIS/NIR reflectance spectroscopy and multispectral imaging. The first method (UV/VIS/NIR reflectance spectroscopy) allowed us to characterize pigment layers with a high spectral resolution; the second method (UV/VIS/NIR multispectral imaging) permitted the evaluation of the pigment distribution by utilizing a restricted number of wavelengths. Combining the results obtained from both devices was possible to obtain a distribution map of a pictorial layer with a high accuracy level of pigment recognition. The method involved the joint use of point-by-point hyperspectral spectroscopy and Principal Component Analysis (PCA) to identify the pigments in the color palette and evaluate the possibility to discriminate all the pigments recognized, using a minor number of wavelengths acquired through the multispectral imaging system. Finally, the distribution and the spectral difference of the different pigments recognized in the multispectral images, (in this case: red ochre, yellow ochre, orpiment, cobalt blue-based pigments, ultramarine and chrome green) were shown through PCA false-color images.

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Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020058 ◽

2021 ◽

Vol 10 (2) ◽

pp. 58

Author(s):

Muhammad Fawad Akbar Khan ◽

Khan Muhammad ◽

Shahid Bashir ◽

Shahab Ud Din ◽

Muhammad Hanif

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Learning Algorithms ◽

Remote Sensing Data ◽

Kappa Coefficient ◽

Machine Learning Algorithms ◽

Landsat 8 ◽

Sensing Data ◽

Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

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Seismic Energy Release from Intra-Basin Sources along the Dead Sea Transform and Its Influence on Regional Ground Motions

Bulletin of the Seismological Society of America ◽

10.1785/0120200215 ◽

2020 ◽

Author(s):

Roey Shimony ◽

Zohar Gvirtzman ◽

Michael Tsesarsky

Keyword(s):

Ground Motions ◽

Seismic Energy ◽

Sedimentary Basins ◽

Seismic Wave Propagation ◽

Dead Sea ◽

Strike Slip ◽

Dead Sea Transform ◽

Bay Area ◽

The Dead ◽

Surrounding Areas

ABSTRACT The Dead Sea Transform (DST) dominates the seismicity of Israel and neighboring countries. Whereas the instrumental catalog of Israel (1986–2017) contains mainly M<5 events, the preinstrumental catalog lists 14 M 7 or stronger events on the DST, during the past two millennia. Global Positioning System measurements show that the slip deficit in northern Israel today is equivalent to M>7 earthquake. This situation highlights the possibility that a strong earthquake may strike north Israel in the near future, raising the importance of ground-motion prediction. Deep and narrow strike-slip basins accompany the DST. Here, we study ground motions produced by intrabasin seismic sources, to understand the basin effect on regional ground motions. We model seismic-wave propagation in 3D, focusing on scenarios of Mw 6 earthquakes, rupturing different active branches of the DST. The geological model includes the major structures in northern Israel: the strike-slip basins along the DST, the sedimentary basins accompanying the Carmel fault zone, and the densely populated and industrialized Zevulun Valley (Haifa Bay area). We show that regional ground motions are determined by source–path coupling effects in the strike-slip basins, before waves propagate into the surrounding areas. In particular, ground motions are determined by the location of the rupture nucleation within the basin, the near-rupture lithology, and the basin’s local structure. When the rupture is located in the crystalline basement or along material bridges connecting opposite sides of the fault, ground motions behave predictably, decaying due to geometrical spreading and locally amplified atop sedimentary basins. By contrast, if rupture nucleates or propagates into shallow sedimentary units of the DST strike-slip basins, ground motions are amplified within, before propagating outside. Repeated reflections from the basin walls result in a “resonant chamber” effect, leading to stronger regional ground motions with prolonged durations.

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