Software to facilitate remote sensing data access for disease early warning systems

Abstract. Construction of intensity-duration (ID) curves and early warning systems for landslides (EWSL) are hampered by the paucity of temporal and spatial archival data. We developed methodologies that could be used for the construction of an ID curve that could be used for the construction of an EWSL over the Faifa Mountains in the Red Sea Hills. The developed methodologies relies on temporal, readily available, archival Google Earth and Sentinel-1 imagery, precipitation measurements, and limited field data. These methodologies accurately distinguished landslide-producing storms from non–landslide producing ones and identified the locations of these landslides with an accuracy of 60 %.

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Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-2753-2021 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2753-2772

Author(s):

Doris Hermle ◽

Markus Keuschnig ◽

Ingo Hartmeyer ◽

Robert Delleske ◽

Michael Krautblatter

Keyword(s):

Remote Sensing ◽

Early Warning ◽

Lead Time ◽

Early Warning Systems ◽

Image Correlation ◽

Optical Data ◽

Optical Remote Sensing ◽

Warning Systems ◽

Conceptual Approach ◽

Landslide Early Warning

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

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Brief communication: The role of geophysical imaging in local landslide early warning systems

10.5194/nhess-2021-225 ◽

2021 ◽

Author(s):

Jim Scott Whiteley ◽

Arnaud Watlet ◽

Jonathan Michael Kendall ◽

Jonathan Edward Chambers

Keyword(s):

Remote Sensing ◽

Early Warning ◽

Slope Failure ◽

Time Lapse ◽

Early Warning Systems ◽

Warning Systems ◽

Geophysical Monitoring ◽

Landslide Early Warning ◽

Geophysical Imaging

Abstract. We summarise the contribution of geophysical imaging to local landslide early warning systems (LoLEWS), highlighting how LoLEWS design and monitoring components benefit from the enhanced spatial and temporal resolutions of time-lapse geophysical imaging. In addition, we discuss how with appropriate laboratory-based petrophysical transforms, these geophysical data can be crucial for future slope failure forecasting and modelling, linking other methods of remote sensing and intrusive monitoring across different scales. We conclude that in light of ever increasing spatiotemporal resolutions of data acquisition, geophysical monitoring should be a more widely considered technology in the toolbox of methods available to stakeholders operating LoLEWS.

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Brief communication: The role of geophysical imaging in local landslide early warning systems

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-3863-2021 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3863-3871

Author(s):

Jim S. Whiteley ◽

Arnaud Watlet ◽

J. Michael Kendall ◽

Jonathan E. Chambers

Keyword(s):

Remote Sensing ◽

Early Warning ◽

Slope Failure ◽

Time Lapse ◽

Early Warning Systems ◽

Warning Systems ◽

Geophysical Monitoring ◽

Landslide Early Warning ◽

Geophysical Imaging

Abstract. We summarise the contribution of geophysical imaging to local landslide early warning systems (LoLEWS), highlighting how the design and monitoring components of LoLEWS benefit from the enhanced spatial and temporal resolutions of time-lapse geophysical imaging. In addition, we discuss how with appropriate laboratory-based petrophysical transforms, geophysical data can be crucial for future slope failure forecasting and modelling, linking other methods of remote sensing and intrusive monitoring across different scales. We conclude that in light of ever-increasing spatiotemporal resolutions of data acquisition, geophysical monitoring should be a more widely considered technology in the toolbox of methods available to stakeholders operating LoLEWS.

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Streaming Remote Sensing Data Processing for the Future Smart Cities

International Journal of Distributed Systems and Technologies ◽

10.4018/ijdst.2016010101 ◽

2016 ◽

Vol 7 (1) ◽

pp. 1-14 ◽

Cited By ~ 4

Author(s):

Xihuang Sun ◽

Peng Liu ◽

Yan Ma ◽

Dingsheng Liu ◽

Yechao Sun

Keyword(s):

Remote Sensing ◽

Data Processing ◽

Smart Cities ◽

Remote Sensing Data ◽

Data Access ◽

Research Area ◽

Data Streaming ◽

Real Time Processing ◽

Sensing Data ◽

Programming Interfaces

The explosion of data and the increase in processing complexity, together with the increasing needs of real-time processing and concurrent data access, make remote sensing data streaming processing a wide research area to study. This paper introduces current situation of remote sensing data processing and how timely remote sensing data processing can help build future smart cities. Current research on remote sensing data streaming is also introduced where the three typical and open-source stream processing frameworks are introduced. This paper also discusses some design concerns for remote sensing data streaming processing systems, such as data model and transmission, system model, programming interfaces, storage management, availability, etc. Finally, this research specifically addresses some of the challenges of remote sensing data streaming processing, such as scalability, fault tolerance, consistency, load balancing and throughput.

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