scholarly journals Damage Proxy Map of the Beirut Explosion on 4th of August 2020 as Observed from the Copernicus Sensors

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6382
Author(s):  
Athos Agapiou
Keyword(s):  
Open Access ◽  
Satellite Data ◽  
Optical Sensors ◽  
Principal Component ◽  
Optical Data ◽  
Radar Images ◽  
Medium Resolution ◽  
Current Article ◽  
Radar Satellite ◽  
Blast Zone

On the 4th of August 2020, a massive explosion occurred in the harbor area of Beirut, Lebanon, killing more than 100 people and damaging numerous buildings in its proximity. The current article aims to showcase how open access and freely distributed satellite data, such as those of the Copernicus radar and optical sensors, can deliver a damage proxy map of this devastating event. Sentinel-1 radar images acquired just prior (the 24th of July 2020) and after the event (5th of August 2020) were processed and analyzed, indicating areas with significant changes of the VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) backscattering signal. In addition, an Interferometric Synthetic Aperture Radar (InSAR) analysis was performed for both descending (31st of July 2020 and 6th of August 2020) and ascending (29th of July 2020 and 10th of August 2020) orbits of Sentinel-1 images, indicating relative small ground displacements in the area near the harbor. Moreover, low coherence for these images is mapped around the blast zone. The current study uses the Hybrid Pluggable Processing Pipeline (HyP3) cloud-based system provided by the Alaska Satellite Facility (ASF) for the processing of the radar datasets. In addition, medium-resolution Sentinel-2 optical data were used to support thorough visual inspection and Principal Component Analysis (PCA) the damage in the area. While the overall findings are well aligned with other official reports found on the World Wide Web, which were mainly delivered by international space agencies, those reports were generated after the processing of either optical or radar datasets. In contrast, the current communication showcases how both optical and radar satellite data can be parallel used to map other devastating events. The use of open access and freely distributed Sentinel mission data was found very promising for delivering damage proxies maps after devastating events worldwide.

scholarly journals SHORT PERIOD CHANGE DETECTION OF SPERCHIOS LOWER DELTA AREA USING SPACE RADAR IMAGES

2018 ◽  
Vol 36 (2) ◽  
pp. 919 ◽  
Author(s):  
Em. Psomiadis ◽  
G. Migiros ◽  
Is. Parcharidis ◽  
S. Poulos
Keyword(s):  
Principal Component ◽  
Hydrological Regime ◽  
Period Change ◽  
Optical Data ◽  
Delta Area ◽  
Principal Component Transform ◽  
Radar Images ◽  
Short Period ◽  
Landsat 7 ◽  
Changing Patterns

Being highly dynamic by nature, due to their changing hydrological regime and to the encroachment of urbanization, industrialization and changing patterns in agriculture, reliable and timely information of coastal areas is a prerequisite for their effective management. The aim of this paper is to assess the use of ERS-2 SAR satellite data to detect short period changes in the case of the R. Sperchios coastal area that is located at the eastern part of the Maliakos Gulf (near the middle of the east coast of the Greek mainland). A Landsat 7 (ΕΤΜ+) image served as a reference for the interpretation of the ERS images. In order to highlight and detect the changes occurred in the study area two methods were applied. The first method is based on the creation of a Temporal Differentiate Image, consisted of the three ERS-2 images (Figure 1). The second method concerns the implementation of Principal Component Transform (PCT) on the three multitemporal scenes. The final images derived from the two different methods were compared and evaluated. Both methods didn't show any significant change along the coastline. PCT method illustrates more clearly the seasonal changes of crops in the lower delta area. Eventually, radar technology gave the opportunity to discriminate shallow areas, which does not appear in satellite optical data. Concurrently, the effect of wind direction was investigated.

The research study in the convergence of the results of processing radar images Sentinel-1A with field observations of agricultural sites

2019 ◽  
Vol 950 (8) ◽  
pp. 52-58
Author(s):  
D.V. Mozer ◽  
Е.L. Levin ◽  
A.K. Satbergenova
Keyword(s):  
Satellite Remote Sensing ◽  
Satellite Images ◽  
Research Study ◽  
High Accuracy ◽  
Agricultural Fields ◽  
Field Observations ◽  
Radar Images ◽  
The Earth ◽  
Radar Satellite ◽  
Modern Technologies

The manuscript discusses how to monitor the condition of seedlings on agricultural fields planted with winter wheat, fodder maize and areas of fir forest located in the Freudenstadt district of Baden-Wuerttemberg in Germany. To solve the range of agricultural problems , they often use modern technologies such as satellite remote sensing of the Earth. The paper displays the monitoring results of the Sentinel-1A radar satellites scenes, as well as visual spectrum imagery of field observations are presented when leaving directly to terrain segments. The processing deployed data chain, consisting of 11 Sentinel-1A scenes acquired in the timefrane from March to November 2018. Specifically, the SNAP Sentinel Toolboxes software was used to process the radar satellite images Sentinel-1А, the. Based on the the research outcomes the Committee of Agriculture of the Freudenstadt district is able to predict the yield amount with high accuracy due to good data convergence. According to the study, the following three important problems can be resolved by means of Sentinel-1A imagery

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

scholarly journals Structural and optical properties of amorphous Si–Ge–Te thin films prepared by combinatorial sputtering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. Mihai ◽  
F. Sava ◽  
I. D. Simandan ◽  
A. C. Galca ◽  
I. Burducea ◽  
...  
Keyword(s):  
Optical Properties ◽  
Data Storage ◽  
Optical Sensors ◽  
Material Selection ◽  
Functional Materials ◽  
Stability Study ◽  
Optical Data Storage ◽  
Optical Data ◽  
Structural And Optical Properties ◽  
Topological Constraint

AbstractThe lack of order in amorphous chalcogenides offers them novel properties but also adds increased challenges in the discovery and design of advanced functional materials. The amorphous compositions in the Si–Ge–Te system are of interest for many applications such as optical data storage, optical sensors and Ovonic threshold switches. But an extended exploration of this system is still missing. In this study, magnetron co-sputtering is used for the combinatorial synthesis of thin film libraries, outside the glass formation domain. Compositional, structural and optical properties are investigated and discussed in the framework of topological constraint theory. The materials in the library are classified as stressed-rigid amorphous networks. The bandgap is heavily influenced by the Te content while the near-IR refractive index dependence on Ge concentration shows a minimum, which could be exploited in applications. A transition from a disordered to a more ordered amorphous network at 60 at% Te, is observed. The thermal stability study shows that the formed crystalline phases are dictated by the concentration of Ge and Te. New amorphous compositions in the Si–Ge–Te system were found and their properties explored, thus enabling an informed and rapid material selection and design for applications.

scholarly journals High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2123 ◽  
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Tao Yu ◽  
Xingfa Gu ◽  
Qiyue Liu
Keyword(s):  
Soil Moisture ◽  
High Precision ◽  
Satellite Data ◽  
Solution Method ◽  
Pearson Correlation ◽  
Optimal Solution ◽  
Background Knowledge ◽  
Optical Data ◽  
Inversion Method ◽  
Synthetic Aperture Radar Data

This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

scholarly journals Vulnerability Assessment of Buildings due to Land Subsidence Using InSAR Data in the Ancient Historical City of Pistoia (Italy)

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2749 ◽  
Author(s):  
Pablo Ezquerro ◽  
Matteo Del Soldato ◽  
Lorenzo Solari ◽  
Roberto Tomás ◽  
Federico Raspini ◽  
...  
Keyword(s):  
Fragility Curves ◽  
Central Italy ◽  
Sar Interferometry ◽  
Ground Subsidence ◽  
Public And Private ◽  
Radar Images ◽  
Spatial Deformation ◽  
Damage Probability ◽  
Medium Resolution ◽  
Vertical Displacements

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources.

scholarly journals Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

2019 ◽  
Vol 11 (20) ◽  
pp. 2389 ◽  
Author(s):  
Deodato Tapete ◽  
Francesca Cigna
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Google Earth ◽  
Optical Data ◽  
Mena Region ◽  
Processing Methods ◽  
Optical Imagery

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

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