TY - JOUR
T1 - Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring
AU - Hermle, Doris
AU - Keuschnig, Markus
AU - Krautblatter, Michael
AU - Bickel, Valentin Tertius
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - FFT
KW - digital image correlation
KW - landslide displacement monitoring
KW - landslide early warning
KW - slope instabilities
UR - http://www.scopus.com/inward/record.url?scp=85180674293&partnerID=8YFLogxK
U2 - 10.3390/geosciences13120371
DO - 10.3390/geosciences13120371
M3 - Article
AN - SCOPUS:85180674293
SN - 2076-3263
VL - 13
JO - Geosciences (Switzerland)
JF - Geosciences (Switzerland)
IS - 12
M1 - 371
ER -