Lidar And Hyperspectral Data Integration For Landslide Monitoring: The Test Case Of Valoria Landslide

In the framework of the WISELAND project, funded by MIUR, we tested the integration between Lidar and hyperspectral methodologies in the Valoria landslide (Modena province, Italy), a high risk area with vulnerable elements, subjected to periodic and abrupt reactivations. Multitemporal Lidar Digital Terrain Models (DTMs) allowed the calculation of a differential surface, highlighting absolute height variations, recognizing the main landslide components and identifying depletion and accumulation zones. Hyperspectral data helped in the landslide terrain roughness characterization, performing the Principal Component Analysis (PCA) and correlating the results with Flatness and Organization geomorphometric parameters derived from Lidar DTM

Use of multitemporal airborne LiDAR surveys to analyse post-failure behaviour of earthslides

Two helicopter-borne LiDAR surveys were conducted in August 2004 and May 2005 on a 2 km2earth flow that was totally reactivated in late winter 2004. Shaded view maps and differentialanalysis of terrain models from the two surveys allowed residual movements, as well as ruptureand accumulation features over the slope, to be assessed and mapped for the period between 2004and 2005. In particular, it has been made evident that residual movements involved about 20% ofthe whole landslide area. Retrogression of the crown zones, with a depletion estimated in theorder of maximum 20 m, was coupled with a more than 10 m advancement of the deeptranslational slide affecting earth and rock materials in the source area, that resulted in anapparent uplift of more than 15 m. Down slope, the upper accumulation lobe sector was loweredby about 10 m due to depletion and also by the progressive decrease in water content.This analysis proved the usefulness of LiDAR surveys for analysing post-failure behaviour ofthis type of mass movements

The application of remote-sensing techniques to monitor CO2-storage sites for surface leakage : method development and testing at Latera (Italy) where naturally produced CO2 is leaking to the atmosphere

Two airborne remote-sensing flights were conducted above a geothermal field in central Italy (the Latera caldera) where deep, naturally produced CO2 is migrating to surface along faults and leaking to the atmosphere at spatially restricted gas vents. The goal of these surveys was to understand if it is possible to locate CO2 leaking from a CO2 geological storage site through the application of indirect remote-sensing methods that primarily measure plant stress and subsequent ground-based verification using near-surface gas geochemistry techniques. The overall success rate obtained by integrating six different datasets was 39%, although some individual techniques, such as one NDVI survey, achieved a 47% success rate. While the work did discover some vents that were previously unknown, it also failed to locate five vents that are known to exist and, perhaps, other unknown vents. Future work will focus on understanding the various causes of false positives, automation of preliminary data interpretation, and the direct hyperspectral measurement of atmospheric CO2 produced by these natural seeps

Innovative integrated airborne and wireless systems for landslide monitoring

Landslides are a widespread phenomenon over the Italian territory and economical losses due to this hazard are impressive (an average of 2 billion of euros per year in the last 50 years). In the framework of the WISELAND research project (Integrated Airborne and Wireless Sensor Network systems for Landslide Monitoring) funded by the Italian Government, we are testing new monitoring devices devoted to control large landslides at different degrees of activity. Integrated monitoring tools with a strong innovative character are being explored, in particular ground-based wireless sensor networks combined with airborne laser-scanning and hyperspectral surveys.A wireless sensor network (WSN) consists of a set of low cost micro-computers capable to measure physical parameters and to communicate between them. Such a technique allows landslides remote monitoring, measuring spatially distributed parameters and recognizing deformation patterns. Ground-based sensor networks can be effectively integrated with grid-based data measured by the use of airborne techniques. The Light Detection and Ranging (Lidar) technology is used primarily to densely map wide areas, even in presence of a thick vegetation coverage, to retrieve high resolution Digital Terrain Models (DTMs); DTMs are fundamental in monitoring and describing landslide movements. Hyperspectral sensors are capable to measure parameters such as soil moisture content, vegetation coverage and surface roughness, that can be correlated with slope movements.In the first year of the project we tested and validated these monitoring tools on two large earthflows, which are representative of the widespread slope instability in the Northern Apennine: the Silla landslide (Bologna Province, Italy) and the Valoria landslide (Modena Province, Italy). Although characterised by different geological settings and evolution stages, both landslides are associated to a high degree of risk because of the presence of vulnerable elements and their tendency to periodic and abrupt reactivations.Periodic airborne surveys were performed in Valoria site in different periods, in order to monitor the surface displacement of the slopes. Multitemporal Lidar DTMs allowed the calculation of a differential surface, therefore highlighting absolute height variations and recognizing the main landslide components. Hyperspectral data helped in the landslide characterization; for instance the analysis of PCA components are also correlated with results coming from DTM analysis and this has been evidenced to be a proper system to identify depletion and accumulation zones.A prototype wireless sensor network was installed at Silla landslide in July 2009. The network consists of four nodes (located in the upper part of the landslide) configured with static routing table which forward packets (one data every 15 minutes) to a master node connected to a laptop. Parallel to this test, a new node hardware platform, more shaped for low power – high range data transmission in outdoor conditions has been developed and it is now ready to be deployed in the field

Utilizzo dei sistemi di telerilevamento per il monitoraggio di fenomeni franosi: il progetto WISELAND

I fenomeni franosi sono ampiamente diffusi in tutto il territorio italiano e sono responsabili di ingenti perdite economiche, stimate nell’ordine dei 2 miliardi di euro l’anno negli ultimi 50 anni. Lo scopo del progetto WISELAND, afferente al Programma di Ricerca Scientifica di Rilevante Interesse Nazionale (PRIN) 2007, finanziato dal Ministero dell’Università e della Ricerca (MIUR), è la creazione di un sistema integrato ed innovativo di sensori wireless ed airborne per il monitoraggio e il controllo di fenomeni franosi a cinematica lenta. La sperimentazione di tale sistema è stata condotta su una frana per scorrimento-colata, tipica dell’Appennino: la frana di Valoria, in provincia di Modena. Nel corso del 2009 e 2010 sono stati condotti diversi rilievi aerotrasportati con sensoristica Lidar e iperspettrale. I primi hanno consentito di ricostruire accurati Modelli Digitali del Terreno (DTM), dalla cui analisi si è stati in grado di caratterizzare le componenti principali di una frana. Inoltre, tramite un’analisi differenziale e geomorfometrica dei DTM (relativi a rilievi effettuati in passato mediante la stessa metodologia), si sono individuati e monitorati gli spostamenti avvenuti in un certo arco temporale. I rilievi iperspettrali hanno contributo anch’essi alla caratterizzazione dei fenomeni franosi; in particolare si è dimostrato che i risultati ottenuti dalla Principal Component Analysis (PCA) sono correlabili con la rugosità del terreno ed il dinamismo di frana e consentono di metterne chiaramente in risalto le zone di accumulo e deplezione. Oltre ai datasets telerilevati, si sono utilizzati dei dati provenienti da una rete sperimentale di sensori wireless (WSN) installata nell’ambito del progetto, che ha permesso di rilevare gli spostamenti differenziali della frana in modo efficace e poco costoso. L’utilizzo combinato della metodologia Lidar, iperspettrale e di reti asensori wireless (WSN) si è dimostrata, pertanto, un valido strumento in grado di monitorareperiodicamente un fenomeno franoso. La potenzialità dei risultati aumenta con l’interpretazionecongiunta dei datasets. Il monitoraggio con queste metedologie può essere inoltre facilmenteprogrammato sia temporalmente che spazialmente, e questo garantisce una specificitàdell’informazione attualmente non garantita da altre metodologie operative

Using Lidar to define roughness fingerprint and displacement of earth flows

When confronted with earth \ufb02ows, \ufb01eld surveyors base their assessment of the state of activity of the landslide by performing (often unconsciously) a subjective, expert-based estimate of small scale topographic roughness. The general assumption is that the higher is the movement, the higher is terrain roughness. De\ufb01ning the roughness \ufb01ngerprint of dormant and active earth \ufb02ows using Lidar data from local surveys, could contribute, in perspective, to develop routines for semi automatic detection of landslides from large regional datasets, and for evaluating their degree of activity. Differential DEMs are only capable of detecting elevation changes related to depletion or accumulation, but not movements. Image correlation techniques might be used on Lidar data to estimate 3D displacement vectors, an information that cannot be retrieved by simple DEM subtraction. The validation of this approach could provide an added value to the use of multitemporal Lidar on active earth \ufb02ows. In this research we considered dormant and active earth \ufb02ows for which multitemporal local-scale Lidar surveys were available. Surveys contained an average of 4 to 6 pt/sqm classi\ufb01ed as ground. Several different roughness calculation methods (Slope Curvature Kernel 3x3, RMS height, RMS deviation, Hurst exponent, Flatness and Organization strength)were applied to 0.2 m rasters obtained by point-cloud interpolation. Results showd that the difference between active and dormant landslides is evident in most cases and that long-time dormant landslides are less easily discernable from stable areas than from active ones. However, results also demonstrated that roughness is highly variable within the same landslide, that the correlation with the measured rates of movement is not always straightforward and that different roughness estimate methods can provide partly contradictory results. Multitemporal Lidar surveys have been also analysed to generate displacement maps.. The use of Image Correlation techniques was found to be rather complex, raising many problems concerning point cloud densities, lidar-noise, subpixel correlation, change of morphology patterns, judging of miss-correlations. However, when compared with displacement vectors obtained by supervised analysis of shaded reliefs or by independent monitoring methods, preliminary results obtained so far are somehow promising. The activities reported in this abstract are part of research project WISELAND (Integrated Airborne and Wireless Sensor Network systems for Landslide Monitoring) funded by the Italian Ministry of Research (2007-2009) and Marie Curie Project \u201cMOUNTAIN RISKS\u201d funded by the EU (2006-2010

LIDAR And Hyperspectral Data Integration For Landslide Monitoring: The Test Case Of Valoria Landslide

In the framework of the WISELAND project, funded by MIUR, we tested the integration between LiDAR and hyperspectral methodologies in the Valoria landslide (Modena province, Italy), a high risk area with vulnerable elements, subjected to periodic and abrupt reactivations. Multitemporal LiDAR Digital Terrain Models (DTMs) allowed the calculation of a differential surface, highlighting absolute height variations, recognizing the main landslide components and identifying depletion and accumulation zones. Hyperspectral data helped in the landslide terrain roughness characterization, performing the Principal Component Analysis (PCA) and correlating the results with Flatness and Organization geomorphometric parameters derived from LiDAR DTM