A conceptual approach on optimising lead time for the forecasting of landslides using remote sensing systems

2020 ◽  
Author(s):  
Markus Keuschnig ◽  
Doris Hermle ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Lead Time ◽  
Image Correlation ◽  
Multispectral Images ◽  
Spatiotemporal Resolution ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Image Collection ◽  
Selection Of

<p>New remote sensing systems offer an increased spatiotemporal resolution and accuracy. These systems  increase the chance of snow- and cloud-free multispectral images to detect and monitor landslides for early warning issues. Various studies showed the applicability of multispectral remote sensing systems for landslide detection and monitoring. However, a systemic evaluation of the remote sensing systems especially in respect to early warning is still missing. In this study we present a new conceptional approach to evaluate the capability of different systems for early warning issues based on a well suited case study located in the Hohe Tauern Range, Austria.     </p><p>The Sattelkar is a highly dynamic west-facing deglaciated high-alpine cirque in the Großvenedigergruppe, Austria. The abundant rock debris exhibits high movement rates and showed massively enhanced landslide activity after ongoing heavy precipitation in 08/2014, resulting in a 170.000 m³ debris flow event. We estimated time demands for three successive steps consisting of (i) image collection, (ii) processing with motion delineation and (iii) the final evaluation. Digital image correlation, an established tool in landslide remote-sensing research, was used to derive displacement patterns and assess the capabilities of the multispectral images in terms of spatiotemporal resolution and data quality. For our study we used Sentinel-2, RapidEye and PlanetScope images and compared their deduced motion patterns and rates to those from accurate UAV data as well as manually digitized boulder tracks (≥10 m in diameter).</p><p>Within a reasonable amount of processing time, some satellite data revealed similar clustered motions identifiable in the UAV images. However, our analysis also showed identification limitations due positional inaccuracy, image errors and spatiotemporal resolution of the data. On that account, certain processing steps reduce the forecasting window and as a result the lead time, i. e. the remaining time to take action. We postulate that remote sensing data has the ability to support landslide monitoring, but the pre-selection of usable and sound data is essential as it directly influences the forecasting window. Sound knowledge of its different application possibilities enhances overall steps of image collection, processing and final analysis. The critical selection of which data source is best can lead to faster response times for landslide events. This increases the forecasting window, hence the time to take action until a landslide occurs.</p>

Potential of multisensor assessment using digital image correlation for landslide detection and monitoring

2020 ◽  
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Digital Image Correlation ◽  
Digital Image ◽  
Image Correlation ◽  
Positional Accuracy ◽  
Sensing Systems ◽  
Rapid Changes ◽  
Remote Sensing Systems ◽  
Uav Images ◽  
Sentinel 2

<p>With the combination of diverse remote sensing data, one can estimate the detection capabilities of gravitational mass movement dynamics and behaviour. Recent multispectral satellite sensors such as Sentinel-2, RapidEye and PlanetScope offer unprecedented spatiotemporal resolutions, hence reducing data gaps of alpine meteorological constraints. In addition to this data, very high resolution and accurate UAV images cover a broad range of spatial resolutions. The strengths of these remote sensing systems allow the data compilation of vast, difficult and dangerous to access mountain areas. However, the limitations of the spatiotemporal resolution for (i) pre-event landslide detection, (ii) monitoring of already known mass movements and (iii) the capability to measure rapid changes (e.g.  accelerations) for warnings have not been examined extensively. Thus, there is an important need to understand the potential of multispectral images to detect, monitor, and identify rapid changes prior to landslide events to increase the forecasting window.</p><p>Digital image correlation (DIC), as indispensable tool to measure surface displacements, aids in estimating the fitness of different remote sensing images. Here, we present first results of motion delineation by DIC of the Sattelkar, a high-alpine, deglaciated and debris-laden cirque in the Obersulzbach-valley, Austria. We used comprehensive knowledge of the study site to thoroughly understand DIC motion clusters for verification purposes. We then compared three different DIC software tools, COSI-Corr, DIC‑FFT and IMCORR. They revealed similar results for the three satellite systems in terms of hot spot areas as well as noise. Our findings show large motion inaccuracies for Sentinel-2, RapidEye and PlanetScope images due to spatial resolution, poor image co-registration and changing data quality. In contrast, displacement patterns from the three UAV images (7/2018, 7/2019, 9/2019) demonstrate good positional accuracy as well as data usability for this approach. The inherited noise results from decorrelation due to high velocities suggest using an increased temporal image acquisition for further evaluation.</p><p>Reliable, precise results for landslide detection, their ongoing monitoring and the measurement capability for significant changes are necessary for targeted investigations, precautionary measures and the start of the forecasting window. Multispectral UAV images of high positional accuracy and quality are able to provide dependable relative displacement velocities and have the capability to serve as a reliable tool. On the contrary, satellite images showed delusive results, and we recommend reconsidering their deployment in future applications. The knowledge of the most suitable data in terms of accuracy and processing speed is crucial for landslide identification, monitoring and acceleration threshold detection. At present, our prelimiary findings show the capability to detect and monitor relative and mainly slow changes. The detection of rapid changes lacks due to the accuracy, resolution and revisit time of the investigated remote sensing systems.</p>

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 Multi-threshold object selection in images of remote sensing systems

2019 ◽  
Vol 29 (2) ◽  
pp. 76-88 ◽  
Author(s):  
V. Yu. Volkov ◽  
M. I. Bogachev ◽  
O. A. Markelov
Keyword(s):  
Remote Sensing ◽  
Geometric Features ◽  
Scale Invariant ◽  
Object Selection ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Efficiency Of Selection ◽  
Threshold Setting ◽  
Threshold Processing ◽  
Selection Of

The aim of the work is to increase the efficiency of selection of objects of different nature in digital monochrome images formed in remote sensing systems. For this purpose, algorithms for the formation of features of objects with respect to which boundary values are specified are introduced into the structure of multi-threshold processing. New schemes of multi-threshold processing and selection of objects of interest with threshold setting based on selection results are proposed. Algorithms of multi-threshold selection of objects by area and other scale-invariant geometric features, such as the elongation coefficient of the perimeter of the object and the elongation coefficient of the main axis of the describing ellipse, are obtained and tested. The binarization threshold is set for each of the selected objects based on the extremum of the applied geometric criterion. The new invariant geometric features used are different for round and elongated objects and provide independence of characteristics with changes in the image scale. Results of processing of typical models of images, and also results of selection of objects on the real television and infrared images showing efficiency of the proposed selection method are presented.

scholarly journals Challenging the timely prediction of landside early warning systems with multispectral remote sensing: a novel conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWS) were not met until recently. We introduce a novel conceptual approach for comprehensive lead time assessment and optimisation for LEWS. We analysed time to warning as a sequence; (i) time to collect, (ii) to process and (iii) to evaluate relevant optical data. The difference between 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 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 UAS and PlanetScope totals 31 h/21 h and is comprised of (i) time to 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 lead time for reactive measures. We show optical remote sensing data can support LEWS with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWS.

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