Abstract
Abstract. Landscapes have been photographed dozens of times at scales ca. 1/25,000 and better since World War II. Scans are distributed freely online (e.g. remonterletemps.ign.fr). In parallel, Structure-from-Motion (SFM) software made photogrammetric processing easy to non-specialists. Yet puzzling questions crop up to use both: (i) Can raw scans be used as is? (ii) Can Ground Control Points (GCP) and checkpoints be safely collected from a web portal? (iii) How many parameters are sufficient for camera interior orientation? (iv) Are single flight camera networks sufficient to constrain camera models compared to multiple flights? (v) Are photogrammetric Digital Surface Models (DSM) fit for quantifying landslide activity? Processing of scanned black-and-white 1/27,000 photographs from IGN flown in May 1978 over Cirque de Salazie in La Réunion Island answer these questions. We find that raw scanned photographs need translation, rotation and cropping to match the camera reference frame. GCP and Check point coordinates collected on geoportail.gouv.fr with assumed accuracy of 10 m, achieved ca. 7 m accurate SFM registration. The optimal camera model uses only 4 parameters: f, cx, cy and K1. Compared to a 2015 lidar Digital Terrain Model (DTM), the 0.66 m/pixel DSM of 1978 has a median deviation of −1.39 m ± 3.34 m (Median Absolute Deviation) which is comparable to GCP quality. Elevation difference more importantly reveals, for the first time, the 37 years and 13 cyclones cumulated landslides pattern on Cirque de Salazie. Photographic archives hold decades-long 3D history. SFM is a game changer for landslide risk mitigation planning.
Highlights
Structure-from-Motion (SFM) is a method widely applied in geoscience to produce topographic data of a landscape from digital photos (Eltner et al, 2016)
We ask and answer a series of practical questions: (i) Can raw photo scans be readily used without preprocessing? (ii) Can Ground Control Points (GCP) be collected effectively on a webmapping portal? (iii) How many parameters are sufficient to parametrize the camera interior orientation? (iv) Is minimal stereoscopic coverage sufficient to constrain the camera model compared to more redundant multiple flight configuration? (v) Are photogrammetric Digital Surface Models fit for quantifying landsliding?
We focus our analysis on the 6 aerial surveys of May 1978 (Table 1), as they predate the occurrence of cyclone Hyacinthe of January 1980
Summary
Structure-from-Motion (SFM) is a method widely applied in geoscience to produce topographic data of a landscape from digital photos (Eltner et al, 2016). This SFM paradigm, does not fit archive aerial surveys, with their weak geometry, all parallel nadir-looking axes with minimal stereoscopy (60% overlap along-track and 30% across-track) The reason for this weak geometry is that large format photogrammetric film was expensive to purchase and develop, and that stereo interpretation was done by human operators one pair of photos at a time. - Camera resolution and/or digitization resolution: a low resolution limits small ground feature recognition and cross-photograph matching This may generate false fixes that create spurious elevation artefacts (Gomez et al, 2015; Riquelme et al, 2019). The tropical island of La Réunion is characterized by elevations reaching 3069 m above sea level encroached by clouds in a somewhat unpredictable fashion This adverse meteorological phenomenon opportunistically turns into a SFM advantage since several aerial survey flights are sometimes necessary to patch together a cloudless coverage for the whole of the island. We ask and answer a series of practical questions: (i) Can raw photo scans be readily used without preprocessing? (ii) Can GCP be collected effectively on a webmapping portal? (iii) How many parameters are sufficient to parametrize the camera interior orientation? (iv) Is minimal stereoscopic coverage (single flight) sufficient to constrain the camera model compared to more redundant multiple flight configuration? (v) Are photogrammetric Digital Surface Models fit for quantifying landsliding?
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