An event such as prolonged rainfall, an earthquake, or rapid snowmelt may cause many landslides. Typically, most of these landslides will be small in size, some will be medium in size and a few will be very large. Knowing the statistics of landslide size (LStats) is important for landslide hazard and vulnerability modelling, for risk assessment, and for landscape and erosion modelling.
LAMPRE has developed software to determine the statistics of landslide size (LStats). The software can be used anywhere information on the size of the landslides is available. This information can be obtained from a geomorphological landslide inventory , an event landslide inventory, or a seasonal or multitemporal inventory in a Geographic Information System (GIS). The software is most appropriate for analysis of low mobility landslides, and should be used with caution when examining rock falls or debris flows.
LStats can be prepared or updated when a new landslide inventory is prepared after a landslide-triggering event, for example, an intense rainstorm, a rapid snowmelt event, or an earthquake.
Civil Protection authorities use LStats to anticipate the sizes of the landslides caused by an intense or prolonged rainfall, an earthquake, or a rapid snowmelt event.
Planning & development authorities use LStats to anticipate the size of the landslides expected in a territory.
Transportation authorities & utility managers use LStats to evaluate the potential vulnerability to event landslides of transportation or utility network.
Agricultural & forest agencies use LStats to evaluate the potential vulnerability of crops and forests to event landslides.
Scientists use LStats for erosional studies and landscape modelling.
LAMPRE prepares LStats from any landslide map available in digital format for which the size (area, volume) of the individual landslides is known, or can be calculated in a GIS. Analyses can be performed as soon as landslide information is available, and takes minutes to hours. The quality of the LStats depends on the quality and completeness of the landslide inventory.
Malamud et al. (2004) doi: 10.1002/esp.1064
Brunetti et al. (2009) doi: 10.5194/npg-16-179-2009
Rossi et al. (2012) doi: 10.13140/2.1.3280.0969