scholarly journals Yield estimation of the 2020 Beirut explosion using open access waveform and remote sensing data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph Pilger ◽  
Peter Gaebler ◽  
Patrick Hupe ◽  
Andre C. Kalia ◽  
Felix M. Schneider ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Shock Wave ◽  
Open Access ◽  
Remote Sensing Data ◽  
Social Relevance ◽  
Radar Remote Sensing ◽  
Site Analysis ◽  
Sensing Data ◽  
Explosive Source ◽  
The City

AbstractWe report on a multi-technique analysis using publicly available data for investigating the huge, accidental explosion that struck the city of Beirut, Lebanon, on August 4, 2020. Its devastating shock wave led to thousands of injured with more than two hundred fatalities and caused immense damage to buildings and infrastructure. Our combined analysis of seismological, hydroacoustic, infrasonic and radar remote sensing data allows us to characterize the source as well as to estimate the explosive yield. The latter is determined within 0.13 to 2 kt TNT (kilotons of trinitrotoluene). This range is plausible given the reported 2.75 kt of ammonium nitrate as explosive source. As there are strict limitations for an on-site analysis of this catastrophic explosion, our presented approach based on data from open accessible global station networks and satellite missions is of high scientific and social relevance that furthermore is transferable to other explosions.

scholarly journals Yield estimation of the 2020 Beirut explosion using open access waveform and remote sensing data

2021 ◽  
Author(s):  
Christoph Pilger ◽  
Peter Gaebler ◽  
Patrick Hupe ◽  
Andre Kalia ◽  
Felix Schneider ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Source Characterization ◽  
Social Relevance ◽  
Radar Remote Sensing ◽  
Site Analysis ◽  
Sensing Data ◽  
International Monitoring ◽  
Source Data ◽  
The City

<p>We report on a multi-technique analysis using publicly available data for investigating the huge, accidental explosion that struck the city of Beirut, Lebanon, on August 4, 2020. Its devastating shock wave led to thousands of injured with more than two hundred fatalities and caused immense damage to buildings and infrastructure. Our combined analysis of seismological, hydroacoustic, infrasonic and radar remote sensing data allows us to characterize the source as well as to estimate the explosive yield. The latter ranges between 0.8 and 1.1 kt TNT (kilotons of trinitrotoluene) equivalent and is plausible given the reported 2.75 kt of ammonium nitrate as explosive source. Data from the International Monitoring System of the CTBTO are used for infrasound array detections. Seismometer data from GEOFON and IRIS complement the source characterization based on seismic and acoustic signal recordings, which propagated in solid earth, water and air. Copernicus Sentinel data serve for radar remote sensing and damage estimation. As there are strict limitations for an on-site analysis of this catastrophic explosion, our presented approach based on openly accessible data from global station networks and satellite missions is of high scientific and social relevance that furthermore is transferable to other explosions.</p>

Investigation of the Transformation of the Plant Ecosystem in the Vicinity of the Tsemesskaya Bay in the Black Sea Based on Remote Sensing Data

2021 ◽  
Vol 25 (6) ◽  
pp. 61-67
Author(s):  
I.V. Zen’kov ◽  
Trinh Le Hung ◽  
Yu.P. Yuronen ◽  
P.M. Kondrashov ◽  
A.A. Latyntsev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Black Sea ◽  
Remote Sensing Data ◽  
Open Pit ◽  
Open Pit Mining ◽  
The Black Sea ◽  
Industrial Facilities ◽  
Logistics Center ◽  
Sensing Data ◽  
The City

A brief description of the industrial and logistics center operating in the city of Novorossiysk on the coast of the Tsemesskaya Bay in the Black Sea is presented. According to remote sensing data, the area of open pit mining of rock dumps dumped during the development of three marl deposits for use at four cement plants was determined. According to the results of satellite imagery and analytical calculations, downward trends in changes in the density of vegetation cover in territories with natural landscapes adjacent to the territory of industrial facilities located on the coast of the Tsemesskaya Bay were revealed.

scholarly journals A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

2016 ◽  
Vol 8 (1) ◽  
pp. 70 ◽  
Author(s):  
Neha Joshi ◽  
Matthias Baumann ◽  
Andrea Ehammer ◽  
Rasmus Fensholt ◽  
Kenneth Grogan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Radar Remote Sensing ◽  
Sensing Data

scholarly journals Change Detection Based Building Damage Assessment Method Using Radar Imageries with GLCM Textural Parameters

2020 ◽  
Author(s):  
Asset Akhmadiya ◽  
Nabi Nabiyev ◽  
Khuralay Moldamurat ◽  
Kanagat Dyusekeev ◽  
Sabyrzhan Atanov
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Damage Assessment ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Research Paper ◽  
Building Damage ◽  
Main Task ◽  
Radar Remote Sensing ◽  
Sensing Data

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

scholarly journals Data and techniques for studying the urban heat island effect in Johannesburg

2015 ◽  
Vol XL-7/W3 ◽  
pp. 203-206 ◽  
Author(s):  
C. H. Hardy ◽  
A. L. Nel
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Sensing Data ◽  
Island Effect ◽  
Urban Heat ◽  
The City

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg’s residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

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