Evaluating Pléiades Neo capabilities for deriving rock glacier velocity

• Repeated Pléiades Neo images can be used to effectively monitor RGV in remote areas. • Multitemporal block adjustment provides robust co-registration of images at a sub-pixel level. • Surface velocity errors can achieve one-third of the native pixel level. • Tristereo Pléiades Neo images provide accurate topography reconstruction from a single acquisition. The Pléiades Neo (PNEO) constellation, deployed in 2021 and conceived for both civilian and military applications, can be utilised to monitor ground deformation in remote periglacial environments due to its exceptionally high spatial resolution and tasking capabilities. In this study, we demonstrate the potential of utilising PNEO to derive Rock Glacier Velocity (RGV) with exceptionally high detail from repeated image acquisitions. We processed tristereo images acquired in February 2023 and March 2024 over an area of 50 km 2 in the Dry Andes (Chile), resulting in one high-resolution digital elevation model and three orthorectified images for each acquisition. By using a feature tracking algorithm, we were able to gather the uncertainty on different combinations of the tristereo images over a stable area. Our results indicate a high consistency in the velocity vectors with mean RGV values between 0.90 to 0.25 m/year. The high granularity of the derived flow field means that yearly RGV monitoring in remote regions using high resolution optical images from the PNEO sensor is now a viable option.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2026-03-02
DOI
https://doi.org/10.1016/j.jag.2026.105207
Primary Topic
Cryospheric studies and observations
Type
article
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article

Evaluating Pléiades Neo capabilities for deriving rock glacier velocity

David Farías-Barahona, Cécile Pellet, Sebastián Vivero
International Journal of Applied Earth Observation and Geoinformation
Cryospheric studies and observations
article

Evaluating Pléiades Neo capabilities for deriving rock glacier velocity

David Farías-Barahona, Cécile Pellet, Sebastián Vivero
article en

Abstract

• Repeated Pléiades Neo images can be used to effectively monitor RGV in remote areas. • Multitemporal block adjustment provides robust co-registration of images at a sub-pixel level. • Surface velocity errors can achieve one-third of the native pixel level. • Tristereo Pléiades Neo images provide accurate topography reconstruction from a single acquisition. The Pléiades Neo (PNEO) constellation, deployed in 2021 and conceived for both civilian and military applications, can be utilised to monitor ground deformation in remote periglacial environments due to its exceptionally high spatial resolution and tasking capabilities. In this study, we demonstrate the potential of utilising PNEO to derive Rock Glacier Velocity (RGV) with exceptionally high detail from repeated image acquisitions. We processed tristereo images acquired in February 2023 and March 2024 over an area of 50 km 2 in the Dry Andes (Chile), resulting in one high-resolution digital elevation model and three orthorectified images for each acquisition. By using a feature tracking algorithm, we were able to gather the uncertainty on different combinations of the tristereo images over a stable area. Our results indicate a high consistency in the velocity vectors with mean RGV values between 0.90 to 0.25 m/year. The high granularity of the derived flow field means that yearly RGV monitoring in remote regions using high resolution optical images from the PNEO sensor is now a viable option.

International Journal of Applied Earth Observation and GeoinformationVol. 148
University of Fribourg (CH), University of Concepción (CL), École Polytechnique Fédérale de Lausanne (CH)
Climate action
Openalex Percentile: Top 54%
Cryospheric studies and observations
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Evaluating Pléiades Neo capabilities for deriving rock glacier velocity — David Farías-Barahona, Cécile Pellet, et al. · International Journal of Applied Earth Observation and Geoinformation (2026) | TGRS Research Map | TGRS