the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Tracking slow-moving landslides with PlanetScope data: new perspectives on the satellite’s perspective
Bodo Bookhagen
Abstract. PlanetScope data with daily temporal and 3-m spatial resolution hold an unprecedented potential to quantify and monitor surface displacements from space. Slow-moving landslides, however, are complex and dynamic targets that alter their topography over time. This leads to orthorectification errors, resulting in inaccurate displacement estimates when images acquired from varying satellite perspectives are correlated. These errors become particularly concerning when the magnitude of orthorectification error exceeds the signal from surface displacement which is the case for many slow-moving landslides with annual velocities of 1–10 m/yr. This study provides a comprehensive assessment of orthorectification errors in PlanetScope imagery and presents effective mitigation strategies for both unrectified L1B and orthorectified L3B data. By implementing these strategies, we achieve sub-pixel accuracy, enabling the estimation of realistic and temporally coherent displacement over landslide surfaces. The improved signal-to-noise ratio results in higher-quality disparity maps, allowing for a more detailed analysis of landslide dynamics and their driving factors.
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Ariane Mueting and Bodo Bookhagen
Status: open (until 10 Oct 2023)
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CC1: 'Comment on egusphere-2023-1698', Mahmud Muhammad, 06 Sep 2023
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Excellent literature review and thorough analysis. I'd like to delve deeper into contemporary methods for determining land displacement using optical images. Often, when discussing this topic, many researchers lean towards image correlation techniques. Your paper adeptly highlighted the constraints of these methods in tracking landslide movements. However, alternative methods exist, such as the optical flow motion, which contrasts image correlation. It's proficient in detecting both minute (on a centimeter scale) and substantial (meter scale) land displacement. Our recent work, Muhammad et al., 2022, explored the principles of optical flow and its efficacy in monitoring landslide movement. We've now broadened the application scope of the optical flow algorithm to pinpoint land deformations at the centimeter level. This development has taken shape in the form of an alpha version Python package called "akhdefo-functions." I'm in the process of creating a video tutorial for it and am eager to partner and offer guidance. I'm optimistic about the community's involvement in refining these methods further.
Citation: https://doi.org/10.5194/egusphere-2023-1698-CC1 -
CC2: 'Reply on CC1', Mahmud Muhammad, 06 Sep 2023
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The below video explains well the use and differences of optical flow vs template matching/image correlation methods.
https://youtu.be/VSSyPskheaECitation: https://doi.org/10.5194/egusphere-2023-1698-CC2
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CC2: 'Reply on CC1', Mahmud Muhammad, 06 Sep 2023
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Ariane Mueting and Bodo Bookhagen
Ariane Mueting and Bodo Bookhagen
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