scholarly journals Sentinel-1 SLC Preprocessing Workflow for Polarimetric Applications: A Generic Practice for Generating Dual-pol Covariance Matrix Elements in SNAP S-1 Toolbox

2019 ◽  
Author(s):  
Dipankar Mandal ◽  
Divya Sekhar Vaka ◽  
Narayana Rao Bhogapurapu ◽  
V. S. K. Vanama ◽  
Vineet Kumar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Covariance Matrix ◽  
Natural Hazards ◽  
Urban Growth ◽  
Data Preprocessing ◽  
Earth Observation ◽  
Matrix Elements ◽  
Vegetation Monitoring ◽  
Radar Remote Sensing ◽  
Application Fields

Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community.

scholarly journals Remote Sensing Based Methodology for the Quick Update of Population Exposed to Natural Hazards

2020 ◽  
Author(s):  
Giorgio Boni ◽  
Silvia De Angeli ◽  
Angela Celeste Taramasso ◽  
Giorgio Roth
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Developing Countries ◽  
Natural Hazards ◽  
Urban Growth ◽  
Flood Hazard ◽  
Reliability Test ◽  
Refresh Rate ◽  
Good Agreement

The assessment of the number of people exposed to natural hazards, especially in countries with strong urban growth, is difficult to be updated at the same rate as land use develops. This paper presents a remote sensing based procedure for quick updating the assessment of the population exposed to natural hazards. A relationship between satellite nightlights intensity and urbanization density from global available cartography is first assessed when all data are available. This can be used to extrapolate urbanization data at different time steps, updating exposure each time new nightlights intensity maps are available. As reliability test for the proposed methodology, the number of people exposed to riverine flood in Italy is assessed, deriving a probabilistic relationship between DMSP nightlights intensity and urbanization density from GUF database for the year 2011. People exposed to riverine flood are assessed crossing the population distributed on the derived urbanization density with flood hazard zones provided by ISPRA. The validation on reliable exposures derived from ISTAT data shows good agreement. The possibility to update exposure maps with higher refresh rate makes this approach particularly suitable for applications in developing countries, where exposure may change at sub-yearly scale.

scholarly journals Modification of Freeman-Durden Decomposition to Eliminate the Negative Power Problem

2020 ◽  
Author(s):  
Asset Akhmadiya ◽  
Khuralay Moldamurat ◽  
Nabi Nabiyev
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
New Method ◽  
Matrix Elements ◽  
Negative Power ◽  
Radar Remote Sensing ◽  
Qinghai Province ◽  
Sensing Data ◽  
Comparison Results ◽  
Power Problem

A new method in which completely eliminated the negative value of the scattering power is proposed in this paper. Primarily, here are presented the Freeman-Durden decomposition of the scattering powers, which are computed by using the coherency matrix elements with rotation and without rotation. Secondly, this paper investigates the reasons of the occurrence of negative values of the scattering powers. Then using the modification like in algorithm proposed by Yamaguchi for G4U, it was applied for the algorithm of Freeman-Durden decomposition to eliminate negative values. In this research, Radarsat-2 radar remote sensing data were used, which are acquired for study area Yushu County, Qinghai province, China. At the end, the comparison results of Yamaguchi G4U and a modified Freeman-Durden decompositions were presented.

scholarly journals Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time

2018 ◽  
Vol 10 (9) ◽  
pp. 1378 ◽  
Author(s):  
Daniele Ehrlich ◽  
Michele Melchiorri ◽  
Aneta Florczyk ◽  
Martino Pesaresi ◽  
Thomas Kemper ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Population Density ◽  
Risk Reduction ◽  
Natural Hazards ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Earth Observation ◽  
Human Settlement ◽  
Global Population ◽  
Over Time

Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction.

scholarly journals Summary of Agricultural Application of Radar Remote Sensing

2020 ◽  
Vol 9 (1) ◽  
pp. 18
Author(s):  
Sicheng Li
Keyword(s):  
Remote Sensing ◽  
Research Literature ◽  
Growth Monitoring ◽  
Radar Remote Sensing ◽  
Agricultural Application ◽  
Farmland Soil ◽  
Shape Structure ◽  
Penetration Ability ◽  
Application Fields ◽  
Application Requirements

<p>Radar remote sensing has the ability of all-day and all-weather monitoring, has certain penetration ability to vegetation, and is sensitive to the shape, structure and dielectric constant of vegetation scatterers. These characteristics make it have great potential in agricultural application. Firstly, this paper introduces the application fields of radar remote sensing in agriculture, and summarizes the current research literature in many fields, such as crop identification and classification, farmland soil moisture inversion, crop growth monitoring and so on. Then, the application status and research achievements of radar scatterometer and various SAR features (including SAR backscattering features, polarization features, interference features and tomography features) in various fields of agriculture are described respectively. Finally, the problems and reasons existing in the current research are summarized according to the agricultural application requirements and the development of SAR technology, and the future development is prospected.</p><p align="justify"> </p>

scholarly journals Remote Sensing-Based Methodology for the Quick Update of the Assessment of the Population Exposed to Natural Hazards

2020 ◽  
Vol 12 (23) ◽  
pp. 3943
Author(s):  
Giorgio Boni ◽  
Silvia De Angeli ◽  
Angela Celeste Taramasso ◽  
Giorgio Roth
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Developing Countries ◽  
Natural Hazards ◽  
Urban Growth ◽  
Time Scale ◽  
Flood Hazard ◽  
Population Densities ◽  
Refresh Rate ◽  
Good Agreement

The assessment of the number of people exposed to natural hazards, especially in countries with strong urban growth, is difficult to be updated at the same rate as land use develops. This paper presents a remote sensing-based procedure for quickly updating the assessment of the population exposed to natural hazards. A relationship between satellite nightlights intensity and urbanization density from global available cartography is first assessed when all data are available. This is used to extrapolate urbanization data at different time steps, updating exposure each time new nightlights intensity maps are available. To test the reliability of the proposed methodology, the number of people exposed to riverine flood in Italy is assessed, deriving a probabilistic relationship between DMSP nightlights intensity and urbanization density from the GUF database for the year 2011. People exposed to riverine flood are assessed crossing the population distributed on the derived urbanization density with flood hazard zones provided by ISPRA. The validation against reliable exposures derived from ISTAT data shows good agreement. The possibility to update exposure maps with a higher refresh rate makes this approach particularly suitable for applications in developing countries, where urbanization and population densities may change at a sub-yearly time scale.

Analysis of grid operation state based on improved maximum eigenvalue sample covariance matrix algorithm

2021 ◽  
pp. 095965182110012
Author(s):  
Dinghui Wu ◽  
Juan Zhang ◽  
Bo Wang ◽  
Tinglong Pan
Keyword(s):  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Poor Performance ◽  
Sample Covariance Matrix ◽  
Maximum Eigenvalue ◽  
Signal To Noise ◽  
Matrix Algorithm ◽  
Sample Covariance ◽  
Noise Ratio ◽  
Application Fields

Traditional static threshold–based state analysis methods can be applied to specific signal-to-noise ratio situations but may present poor performance in the presence of large sizes and complexity of power system. In this article, an improved maximum eigenvalue sample covariance matrix algorithm is proposed, where a Marchenko–Pastur law–based dynamic threshold is introduced by taking all the eigenvalues exceeding the supremum into account for different signal-to-noise ratio situations, to improve the calculation efficiency and widen the application fields of existing methods. The comparison analysis based on IEEE 39-Bus system shows that the proposed algorithm outperforms the existing solutions in terms of calculation speed, anti-interference ability, and universality to different signal-to-noise ratio situations.

scholarly journals Prediction of crop biophysical variables with panel data techniques and radar remote sensing imagery

2021 ◽  
Vol 205 ◽  
pp. 76-92
Author(s):  
Clara Simón de Blas ◽  
Rubén Valcarce-Diñeiro ◽  
Ana E. Sipols ◽  
Nilda Sánchez Martín ◽  
Benjamín Arias-Pérez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Panel Data ◽  
Radar Remote Sensing ◽  
Remote Sensing Imagery

Overview of NASDA's earth observation remote sensing

2002 ◽  
Vol 29 (11) ◽  
pp. 1619-1624
Author(s):  
T. Igarashi
Keyword(s):  
Remote Sensing ◽  
Earth Observation

Multi-scale synthetic aperture radar remote sensing for archaeological prospection in Han Hangu Pass, Xin'an China

2016 ◽  
Vol 8 (1) ◽  
pp. 38-47 ◽  
Author(s):  
Fulong Chen ◽  
Aihui Jiang ◽  
Panpan Tang ◽  
Ruixia Yang ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Remote Sensing ◽  
Archaeological Prospection ◽  
Multi Scale ◽  
Aperture Radar
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