Intense geomorphic processes in mountain areas modify landscape topography, requiring detailed and frequent surveys in order to characterize and study the evolution of its properties. In the last two decades, high-resolution (HR) topography has provided new opportunities in the Earth Sciences. These have benefit from important developments in surveying techniques, methods, sensors and platforms. Between these, the use of Structure from Motion (SfM) photogrammetry has become a wide use method to acquire HR topography data and orthomosaics at variable scales. SfM photogrammetry has revolutionised the possibility to collect multi-temporal HR data in rugged or inaccessible environments like the observed debris-flow catchments. However, appropriate workflows incorporating survey planning, data acquisition, post-processing, and error and uncertainty assessment are required, especially when multi-temporal surveys are compared to study topographic changes through time. In this paper we present a general workflow to acquire multi-temporal SfM (4D-SfM) HR topographic data, to develop multi-temporal DEMs, to assess data accuracy and precision, and to study topographic changes through time in a complex and rugged environments considering data uncertainties in those estimates. The workflow is applied in a reach of the debris-flow channel of the Moscardo Torrent (Eastern Italian Alps), developing strategies for streamlining of data acquisition as an integrated approach of image taken from the ground and UAV to reproduce in detail each topographic surface. Eight photogrammetric surveys were carried out between December 2015 and August 2017. In this time interval, five debris flows occurred and the objective is to demonstrate the applicability of the workflow to study geomorphic effects of debris flows and check dams functionality in such mountain environments. The surveying and data processing procedure described in the workflow permitted a close control on all the steps of the analysis, and helped to identify and minimize potential sources of error in the multi-temporal SfM data.
