Emergency response to landslide using GNSS measurements and UAV

Abstract

Landslide monitoring can be performed using many different methods: Classical geotechnical measurements like inclinometer, topographical survey measurements with total stations or GNSS sensors and photogrammetric techniques using airphotos or high resolution satellite images. However all these methods are expensive or difficult to be developed immediately after the landslide triggering. In contrast airborne technology and especially the use of Unmanned Aerial Vehicles (UAVs) make response to landslide disaster easier as UAVs can be launched quickly in dangerous terrains and send data about the sliding areas to responders on the ground either as RGB images or as videos. In addition, the emergency response to landslide is critical for the further monitoring. For proper displacement identification all the above mentioned monitoring methods need a high resolution and a very accurate representation of the relief. The ideal solution for the accurate and quick mapping of a landslide is the combined use of UAV’s photogrammetry and GNSS measurements. UAVs have started their development as expensive toys but they currently became a very valuable tool in large scale mapping of sliding areas. The purpose of this work is to demonstrate an effective solution for the initial landslide mapping immediately after the occurrence of the phenomenon and the possibility of the periodical assessment of the landslide. Three different landslide cases from Greece are presented in the current study. All three landslides have different characteristics: occurred in different geomorphologic environments, triggered by different causes and had different geologic bedrock. In all three cases we performed detailed GNSS measurements of the landslide area, we generated orthophotos as well as Digital Surface Models (DSMs) at an accuracy of less than ±10 cm. Slide direction and velocity, mass balances as well as protection and mitigation measurements can be derived from the application of the UAVs. Those data in addition are accurate, cost- and time-effective.