High Resolution Historical Topography: Getting More from Archival Aerial Photographs

Abstract

High resolution elevation data is a fundamental information for multiple applications in geomorphology spanning from visual analyses to modeling. 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. However, despite the increasing number of applications of SfM algorithms in the scientific literature, still little has been done in terms optimization and quality evaluation of the results. 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 a mountainous and steep area in Central Italy. The aim of the experiment consists in a quantitative evaluation of the digital surface models obtained for different scanning settings.

Structure-from-Motion-Derived Digital Surface Models from Historical Aerial Photographs: A New 3D Application for Coastal Dune Monitoring

“…One study [36] found Z error values (RMSE) varying between 0.50 to 0.91 m over two study sites and three different AP datasets. Another study [2] measured the RMSE in Z of 1.049 m, whereas [27] measured RMSE values in Z ranging between 2.11 and 1.98 m, analysing APs with resolutions of 800 and 1600 dpi over a complex topography. The uncertainty could be improved if a reliable and automatic method could be developed to remove the influence of vegetation from the DSM.…”

“…Each photograph was scanned at 1200 dpi to obtain a high pixel resolution and to simplify ground control point (GCP) positioning and the surface matching during the SfM process. According to [27], the optimal scanning resolution to apply the SfM process on historical aerial photos is between 800 and 1600 dpi. The typical black border of archive aerial photographs was removed using a batch process in Adobe Photoshop [36].…”

“…SfM techniques can also offer a new analysis of historical aerial photographs (APs). Moreover, digital surface models (DSMs), which are elevation models in a 2.5D raster format (meaning that to each pixel (XY) only a Z value is given [24]), have recently been tested using historical aerial photographs as the image source to study riverine environments [25], areas of complex topography [26,27], glacial surfaces [28][29][30], and landscape evolution [24,[30][31][32]. Products deriving from the SfM process of historical aerial photographs have also been used for archeological purposes and have been validated against DGNSS and UAV measurements [33].…”

Structure‐from‐motion photogrammetry analysis of historical aerial photography: Determining beach volumetric change over decadal scales

“…The computer vision techniques within SfM have allowed a qualitative improvement of the analysis by using much more of the information contained within the imagery to aid the orientation process, possibly unlocking large historical photogrammetric archives for morphologic analysis (Bakker and Lane, 2017). Recent examples of the application of the SfM technique using historical aerial photographs have been published for river/floodplain systems (Lane et al ., 2010; Bakker and Lane, 2017), mountainous and hilly areas (Gomez et al ., 2015; Seccaroni et al ., 2018), volcanic islands (Gomez et al ., 2015; Ishiguro et al ., 2016) and glaciers (Mölg and Bolch, 2017). However, as far as the authors know, no study has applied the technique to understand the morphodynamics of sandy beaches at decadal scale.…”

Analysis of Changes in Forest Structure using Point Clouds from Historical Aerial Photographs

“…Structure from motion (SfM) [39] and bundle adjustment [40] introduced new ways of using scanned aerial photographs [41]. The use of such detectors as SIFT (scale-invariant feature transform) [42] or SURF [43] (speeded-up robust features), their modified versions, and the Random sample consensus (RANSAC) algorithm [44] which is used to identify photograph key points, facilitates relative orienting of blocks of historical photographs [45], despite the lack of data on the interior orientation or the distortion parameters of the lens [46].…”