Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring
Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Geosciences |
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| Online Access: | https://www.mdpi.com/2076-3263/13/12/371 |
| _version_ | 1797380924163227648 |
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| author | Doris Hermle Markus Keuschnig Michael Krautblatter Valentin Tertius Bickel |
| author_facet | Doris Hermle Markus Keuschnig Michael Krautblatter Valentin Tertius Bickel |
| author_sort | Doris Hermle |
| collection | DOAJ |
| description | Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems. |
| first_indexed | 2024-03-08T20:43:58Z |
| format | Article |
| id | doaj.art-95f384a839c04e69af9e3e5dc60ee5dd |
| institution | Directory Open Access Journal |
| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-03-08T20:43:58Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-95f384a839c04e69af9e3e5dc60ee5dd2023-12-22T14:11:36ZengMDPI AGGeosciences2076-32632023-12-01131237110.3390/geosciences13120371Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide MonitoringDoris Hermle0Markus Keuschnig1Michael Krautblatter2Valentin Tertius Bickel3Landslide Research Group, TUM School of Engineering and Design, Technical University of Munich, Arcisstr. 21, 80333 Munich, GermanyGeoresearch Forschungsgesellschaft mbH, 5412 Puch bei Hallein, AustriaLandslide Research Group, TUM School of Engineering and Design, Technical University of Munich, Arcisstr. 21, 80333 Munich, GermanyLaboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, 8093 Zurich, SwitzerlandAccurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems.https://www.mdpi.com/2076-3263/13/12/371digital image correlationFFTlandslide displacement monitoringslope instabilitieslandslide early warning |
| spellingShingle | Doris Hermle Markus Keuschnig Michael Krautblatter Valentin Tertius Bickel Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring Geosciences digital image correlation FFT landslide displacement monitoring slope instabilities landslide early warning |
| title | Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring |
| title_full | Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring |
| title_fullStr | Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring |
| title_full_unstemmed | Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring |
| title_short | Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring |
| title_sort | systematic quantification and assessment of digital image correlation performance for landslide monitoring |
| topic | digital image correlation FFT landslide displacement monitoring slope instabilities landslide early warning |
| url | https://www.mdpi.com/2076-3263/13/12/371 |
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