scholarly journals A Remote Sensing–based Intensity-Duration Curve, Faifa Mountains, Saudi Arabia

2018 ◽  
Author(s):  
Sita Karki ◽  
Mohamed Sultan ◽  
Saleh A. Al-Sefry ◽  
Hassan M. Alharbi ◽  
Mustafa Kemal Emil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Early Warning ◽  
Field Data ◽  
Early Warning Systems ◽  
Google Earth ◽  
Archival Data ◽  
Warning Systems ◽  
Temporal And Spatial ◽  
Red Sea Hills

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 %.

scholarly journals A remote-sensing-based intensity–duration threshold, Faifa Mountains, Saudi Arabia

2019 ◽  
Vol 19 (6) ◽  
pp. 1235-1249 ◽  
Author(s):  
Sita Karki ◽  
Mohamed Sultan ◽  
Saleh Alsefry ◽  
Hassan Alharbi ◽  
Mustafa Kemal Emil ◽  
...  
Keyword(s):  
Field Data ◽  
Mass Movement ◽  
Google Earth ◽  
Archival Data ◽  
Backscatter Coefficient ◽  
Duration Threshold ◽  
Radar Imagery ◽  
Temporal And Spatial ◽  
Precipitation Events ◽  
Red Sea Hills

Abstract. Construction of intensity–duration (ID) thresholds and early-warning and nowcasting systems for landslides (EWNSLs) are hampered by the paucity of temporal and spatial archival data. This work represents significant steps towards the development of a prototype EWNSL to forecast and nowcast landslides over the Faifa Mountains in the Red Sea Hills. The developed methodologies rely on readily available, temporal, archival Google Earth and Sentinel-1A imagery, precipitation measurements, and limited field data to construct an ID threshold for Faifa. The adopted procedures entail the generation of an ID threshold to identify the intensity and duration of precipitation events that cause landslides in the Faifa Mountains, and the generation of pixel-based ID curves to identify locations where movement is likely to occur. Spectral and morphologic variations in temporal Google Earth imagery following precipitation events were used to identify landslide-producing storms and generate the Faifa ID threshold (I =4.89D−0.65). Backscatter coefficient variations in radar imagery were used to generate pixel-based ID curves and identify locations where mass movement is likely to occur following landslide-producing storms. These methodologies accurately distinguished landslide-producing storms from non-landslide-producing ones and identified the locations of these landslides with an accuracy of 60 %.

Dengue early warning systems using environmental remote sensing data

2020 ◽  
Author(s):  
Zahidur Rahman ◽  
Leonid Roytman ◽  
Abdelhamid Kadik ◽  
Dilara A. Rosy ◽  
Pradipta Nandi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Remote Sensing Data ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Sensing Data ◽  
Environmental Remote Sensing

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

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.

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

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.

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

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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