Advancing satellite photogrammetric mapping of snow depth in high alpine terrain: a comparative study between Pléiades PHR and Neo

Abstract ID: 3.9119 | Accepted as Talk | Talk/Oral | TBA | TBA

Pascal Sirguey (0)
Miller, Aubrey (1), Redpath, Todd (2)
Pascal Sirguey (1)
Miller, Aubrey (1), Redpath, Todd (2)

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(1) University of Otago, 310 Castle Street, Dunedin, 9016, Aotearoa/New Zealand
(2) Interpine Group Ltd, 99 Sala Street, Rotorua, 3010, Aotearoa/New Zealand

(1) University of Otago, 310 Castle Street, Dunedin, 9016, Aotearoa/New Zealand
(2) Interpine Group Ltd, 99 Sala Street, Rotorua, 3010, Aotearoa/New Zealand

Categories: Cryo- & Hydrosphere, Monitoring, Remote Sensing, Resources, Water Resources
Keywords: Satellite Photogrammetric Mapping, Snow Depth, Pléiades, Change Detection

Categories: Cryo- & Hydrosphere, Monitoring, Remote Sensing, Resources, Water Resources
Keywords: Satellite Photogrammetric Mapping, Snow Depth, Pléiades, Change Detection

The content was (partly) adapted by AI
Content (partly) adapted by AI

Mapping snow depth in complex alpine terrain with satellite photogrammetry challenges the limits of 3D-Change Detection (3D-CD), particularly in steep topography and low-contrast conditions. This study evaluates and compares Pléiades PHR and the next-generation Pléiades Neo (PNEO) for snow depth mapping over Kawarau/The Remarkables in Aotearoa/New Zealand.

Tri-stereo acquisitions from PHR and PNEO during winter/spring 2022, combined with snow-free lidar (2016) and PHR (2020) datasets, establish a benchmark for sensor performance. GNSS campaigns provided photogrammetric control and validation, including an in-situ GNSS snow depth survey on and off ski trails, nearly coinciding with the PHR snow-on acquisition. The processing workflow applied an innovative bundle-block adjustment method that automatically propagated absolute georeferencing through cross-matching virtual ground control points (vGCP) from the snow-off photogrammetric model. This approach ensured sub-pixel alignment and enabled repeatable, coherent mapping of snow depth distribution with minimal convolution with the challenging topography.

The results confirm PHR’s capability for snow depth mapping with sub-meter accuracy and demonstrates PNEO’s improved spatial resolution and ability to capture finer-scale snow distribution patterns with greater precision. By validating our cross-matching technique, this study also establishes a repeatable and automated workflow for satellite photogrammetric snow depth mapping in complex terrain.

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