High resolution elevation data is a fundamental information for multiple applications in geomorphology, spanning from visual analyses (e.g., mapping) to modeling. For example, estimation of short-term erosion rates, quantitative geomorphic analysis, land-shaping processes modeling, landslide identification and mapping, river dynamics studies, among others, rely on good quality elevation data. Nowadays, gathering of high-quality elevation data relies on multiple sensors and technologies which can be mounted on terrestrial, aerial and satellite platforms. In the last years, the Structure from Motion (SfM) algorithms have made possible the acquisition of high and very-high resolution elevation data from optical images acquired with high overlapping rates at virtually no cost. Such a feature made it possible to exploit remote sensing archival imagery to build historical topographic information with unprecedented detail.
Despite the increasing number of applications of SfM algorithms in the scientific literature, however, still little has been done in terms of evaluation of the quality of the resulting elevation data, and of the best acquisition mode (i.e. scanning resolution and color depth) to get the most from such archival imagery. Moreover a large number of those application are based on proprietary commercial software and undisclosed algorithms which could make the experiments not reproducible and replicable.
We have applied the SfM algorithm developed in the photogrammetric open source software MicMac to six black and white aerial photographs taken in 1954 at 1:33.000 in in a mountainous and steep area in Central Italy, where, the 30th October 2016, a seismic sequence triggered, among the others, a large disrupted rock slide that partially dammed a river and blocked a road. The aim of the experiment consists in a quantitative evaluation of the digital surface models obtained for different scanning resolutions, along with the time needed for the computation. Elevation data were quantitatively compared to GPS RTK measurements, and results indicate planimetric and altimetric accuracies smaller than 1m at the calibration ground control points.
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here a link to a web-based visualization of one dense point cloud produced in our work.
Desktop (obtained subsampling 400g8 model from original 24M points to 5M points).
Mobile-friendly (obtained subsampling 400g8 model from original 24M points to 0.5M points).
here a link to one dense point cloud we created in our work:
Desktop (obtained subsampling 400g8 model from 15M to 5M of points)
Mobile friendly (obtained subsampling 400g8 model from 15M to 0.5M of points)