Open AccessThis article is
- freely available
Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy
Research Institute for Geo-Hydrological Protection, National Research Council, I-06128 Perugia, Italy
Umbria Region Functional Centre, I-006121 Foligno (PG), Italy
Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, A-1040 Vienna, Austria
* Author to whom correspondence should be addressed.
Received: 9 March 2012; in revised form: 27 April 2012 / Accepted: 28 April 2012 / Published: 4 May 2012
Abstract: Predicting the spatial and temporal occurrence of rainfall triggered landslides represents an important scientific and operational issue due to the high threat that they pose to human life and property. This study investigates the relationship between rainfall, soil moisture conditions and landslide movement by using recorded movements of a rock slope located in central Italy, the Torgiovannetto landslide. This landslide is a very large rock slide, threatening county and state roads. Data acquired by a network of extensometers and a meteorological station clearly indicate that the movements of the unstable wedge, first detected in 2003, are still proceeding and the alternate phases of quiescence and reactivation are associated with rainfall patterns. By using a multiple linear regression approach, the opening of the tension cracks (as recorded by the extensometers) as a function of rainfall and soil moisture conditions prior the occurrence of rainfall, are predicted for the period 2007–2009. Specifically, soil moisture indicators are obtained through the Soil Water Index, SWI, a product derived by the Advanced SCATterometer (ASCAT) on board the MetOp (Meteorological Operational) satellite and by an Antecedent Precipitation Index, API. Results indicate that the regression performance (in terms of correlation coefficient, r) significantly enhances if an indicator of the soil moisture conditions is included. Specifically, r is equal to 0.40 when only rainfall is used as a predictor variable and increases to r = 0.68 and r = 0.85 if the API and the SWI are used respectively. Therefore, the coarse spatial resolution (25 km) of satellite data notwithstanding, the ASCAT SWI is found to be very useful for the prediction of landslide movements on a local scale. These findings, although valid for a specific area, present new opportunities for the effective use of satellite-derived soil moisture estimates to improve landslide forecasting.
Keywords: soil moisture; shallow landslides; remote sensing; ASCAT
Citations to this Article
Cite This Article
MDPI and ACS Style
Brocca, L.; Ponziani, F.; Moramarco, T.; Melone, F.; Berni, N.; Wagner, W. Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy. Remote Sens. 2012, 4, 1232-1244.
Brocca L, Ponziani F, Moramarco T, Melone F, Berni N, Wagner W. Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy. Remote Sensing. 2012; 4(5):1232-1244.
Brocca, Luca; Ponziani, Francesco; Moramarco, Tommaso; Melone, Florisa; Berni, Nicola; Wagner, Wolfgang. 2012. "Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy." Remote Sens. 4, no. 5: 1232-1244.