r.survey: a tool for calculating visibility of variable-size objects based on orientation

Abstract

Identification of terrain surface features can be done using approaches such as visual observation or remote sensing image processing. Accurate detection of survey targets at the ground level primarily depends on human visual acuity or sensor resolution, and then on acquisition geometry (i.e. the relative position and orientation between the surveyor and the terrain). Further, the delimitation of the observer's viewshed boundary or of the sensor's ground footprint is sometimes insufficient to ensure that all enclosed targets can be correctly detected. Size and orientation can hamper ground target visibility. In this paper we describe a new release of r.survey, an open-source spatial analysis tool for terrain survey assessment. This tool offers the necessary information to assess how terrain morphology is perceived by observers and/or sensors by means of three basic visibility metrics: 3D distance, view angle, and solid angle. It is also fully customizable, allowing single or multiple observation points, ground or aerial point of view, and size setting of the observed target, making it useful for many different purposes.

Terrain visibility impact on the preparation of landslide inventories: some practical cases

“…We calculated SA maps using r.survey (Bornaetxea and Marchesini, 2021). r.survey is an open source spatial analysis tool useful to assess how the terrain morphology is perceived by an observer located at a defined observation point, or a group of points.…”

“…Size, distance and orientation determine the visibility of an observed object (like a landslide) (Bornaetxea and Marchesini, 2021;Domingo-Santos et al, 2011). In the case of field mapping, surveyors often follow roads and observe different portions of the territory from different observation points.…”

“…We further simulated the visibility of landslides included in the GSI Historic Sat and Spot 2020 inventories from roads. By setting a visibility threshold of 400 square minutes (which is slightly larger than a person's maximum visual acuity (Bornaetxea and Marchesini, 2021;Healey and Sawant, 2012), we calculated the SLD values for the landslides potentially visible from roads (Fig. 10).…”

“…Inventories can be compiled in several ways (Guzzetti et al, 2012) and exploiting different sources of data. In the case of non-automatic (or 'manual') methods which include visual interpretation of remote sensing images, direct field investigation and reorganisation of data inherited from historical archives a good visibility of the territory can become a key factor (Bornaetxea and Marchesini, 2021;Steger et al, 2021).…”

Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education

“…GRASS GIS, an open‐source software, has an extensive geospatial toolset including support for multi‐temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. Because of its long history and large contributing community, numerous advanced analytical and modeling tools have been implemented and continue to be implemented (Bornaetxea & Marchesini, 2022; Cimburova & Blumentrath, 2022). GRASS GIS also has a well‐documented and extensively used Python API, making it well‐suited for geospatial programming.…”