Terrain visibility can affect landslide data collection

Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, or landslide susceptibility, hazard, and risk evaluation. Their quality can depend on the data and the methods with which they were produced. The poor visibility of the territory to investigate offered by the point of observation from which landslides are interpreted is not frequently considered as a source of error in manually produced inventories. In this work, we present an approach to relate visibility and spatial distribution of the information collected in field work based inventories and inventories obtained through interpretation of satellite images.
We first used the r.survey tool and a digital elevation model to model and classify the visibility of the territory explored by field work based inventories. Furthermore, we assumed uniform visibility for inventories obtained through interpretation of satellite images.
Then, we measured the landslide density in the different visibility classes of the field based inventories. Last, we simulated visibility classes for the image based inventories using the road net of the area as virtual observation points, and we measured the relative landslide density.
We applied this approach to four inventories: one was produced by photo-interpretation, another one concerns to a regional multi-temporal database and the other 2 were done by direct field-mapping.
Our results show that 1) the density of the information is strongly related to the visibility in inventories obtained through field work, where landslides are abundant in high visibility classes but rarely reported in low visibility classes; and 2) the density of information is almost constant in inventories obtained by photo-interpretation of images, but they suffer from a marked under representation of small landslides in areas with potentially high visibility, e.g. close to roads. We maintain that the proposed procedure can be useful to evaluate the quality of landslide inventories and drive their correct use.