Semi-automatic orientation and monoplotting of oblique images using the moniQue QGIS plugin: A case study of the Pasterze glacier in Austria

Abstract

Monoplotting is a common technique for documenting landscape changes from a single image by intersecting image rays with a given elevation model. However, estimating the unknown camera parameters based on manually identified ground control points is time-consuming and hence prevents the large-scale documentation of landscape changes from single images. Furthermore, existing monoplotting solutions do not estimate the uncertainty of monoplotted points, which is critical for subsequent spatial analysis. To address these limitations, we developed moniQue, a QGIS plugin that enables semi-automatic image orientation via rendered 3D scenes and monoplotting uncertainty estimation. We validated moniQue using 31 historical oblique images of the Pasterze glacier in Austria, comparing various feature point descriptors and geometric verification methods. Our results show that for the best-performing descriptor, the semi-automatic orientation approach achieves results comparable to manual alignment (positional accuracy within 50m, angular deviation below 0.5°, and average focal length difference within 15%) in 25% of the images. In all other cases, the results serve as sufficient initialization for subsequent manual orientation. To demonstrate the uncertainty estimation, we monoplotted the glacier outline from 16 images, revealing that the average planimetric (x, y) uncertainty of the monoplotted points is 2–3 times greater than the vertical uncertainty (∼[jls-end-space/]1.5m). By quantifying these uncertainties, the accuracy of monoplotted features can be rigorously evaluated. This case study demonstrates moniQue’s potential for the efficient processing of large image collections and the thorough integration of monoplotting results into spatial analyses.