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Remote Sensing 2013, 5(11), 5488-5492; doi:10.3390/rs5115488
The relevant impact  that landslide geo-hazards may have on society in terms of human lives and economic losses, has resulted in great efforts to develop sustainable solutions to deal with their prediction and mitigation. To date, several aspects have been investigated involving geological and geo-statistical analysis, geotechnical modeling, design of effective mitigation and protection structures, and sensor development .
The impressive progress of remote sensing techniques has given a great contribution to research on landslides during the last two decades. According to the classification given in , applications of remote sensing can be grouped into three main categories: recognition and mapping, monitoring, spatial analysis and hazard prediction. Moreover, several studies reported in the literature demonstrate that in real applications different aspects should be closely integrated. Recognition may well provide precise information to help monitoring, and both are expected to furnish useful data for modeling and prediction.
The second important consideration is related to the impact that research on remote sensing techniques for landslide investigations may have on the practices of public bodies and institutions dealing with civil protection and management of natural hazards. Several scientific studies have focused on coping with high-risk situations and provided useful information to help mitigation or prediction of different landslide phenomena. Like global navigation satellite systems (GNSS), engineering geodesy techniques and geotechnical sensors, satellite and ground-based remote sensing have become the routine solutions adopted for assessment and monitoring of such geo-hazards.
This Special Issue is aimed at collecting papers describing how remote sensing for landslide investigations can be put into practice. Fourteen articles have been accepted for publication, reporting on different experiences carried out in several areas of the world (Austria [4,5], Brazil , China [7,8], Hungary , Iran [10,11], Iraq , Italy [9,12–14], Peru , Poland , Spain , Switzerland [9,16], and Taiwan ). Three Feature Papers [4,9,14] are included from recognized authors at the request of the Guest Editor.
The problem of construction and application of landslide inventories  is mainly discussed in four papers. Mapping of shallow landslides on steep Alpine grasslands is the subject of paper . In , a landslide inventory derived from high-resolution satellite imagery is used to study the effects of landslides on the drainage network. In , an inventory for the analysis of earthquake-induced landslides is created by using an object-oriented technique  applied with high-resolution satellite imagery . This group of papers shows how images from spaceborne and airborne platforms can be used for landslide recognition and mapping at different scales. The use of unmanned aerial vehicles (UAV) is addressed as a promising tool to gather high-resolution images and to cover areas that are difficult to access from the ground. In , the geomorphological information collected from photo-interpretation is integrated with the outcomes of deformation analyses coming from Differential-InSAR (D-InSAR) and Persistent Scatterer-InSAR (PS-InSAR) techniques which are based on the processing of multiple SAR images.
InSAR techniques have also been widely used for the analysis of existing landslides and for monitoring surface displacements over time. In the former case, results of these analyses can help redefine the landslide borders and better understand the state of activity, as well as the deformation pattern and its relationship with the causative reasons of instability. In the latter case, the availability of continuous up-to-date SAR images can be used to detect changes on the slopes. This application is particularly important because of the growing capability of new spaceborne SAR sensors, featuring higher resolution and shorter revisiting time with respect to the first generation satellites. From the papers included in the Special Issue, it is possible to understand where the major limitations of advanced InSAR techniques are, and where future research efforts should be focused. The vegetation coverage on most mountain slopes may prevent the acquisition of coherent SAR images over time, this, however, works quite efficiently in the case of buildings and rocks . Some suggested processing improvements have been published recently to extend the application of PS-InSAR to those areas with lower coherence [20,21]. The integration of InSAR techniques with other in situ sensors (like GNSS, geodetic and geotechnical sensors) is also important for validation and for completing data acquisition in low coherence regions [10,16]. Moreover, the integration of InSAR processing outputs with underground observations from local sensors allows the establishment of a link between surface and subsurface displacements . A second relevant problem related to the use of InSAR, is the need of combining data from different orbits to achieve a more complete description of the spatial displacement components, as addressed in [8,14]. Outcomes of an FP7 research project also showed how large archives of C-Band SAR data have been exploited in different European regions for the analysis of subsidence and landslide phenomena .
The second group of papers concerning monitoring applications is based on the use of laser scanning techniques . In , the comparison of multi-temporal terrestrial laser scanning data integrated with airborne photogrammetry allows to detect the velocity of surface displacements on the slopes of an Italian volcano. This paper demonstrates how useful multi-source remote sensing data is for the characterization of steep slopes where geodetic benchmarks cannot be installed. Similarly, in  integrated data from terrestrial and airborne laser scanning are exploited to study an active landslide in Austria. Here a 3-D range-flow algorithm derived from computer vision applications has been originally applied to track 3-D displacements on a mountain slope.
The remaining papers can be classified into the category of spatial analysis and hazard prediction . In , the combination of spatial data and two simulation models allows to map hazards in the occurrence of both shallow landslides and debris-flows. In , different aspects are highlighted. Susceptibility mapping based on an artificial neural network is adopted to locate failure-prone slopes in the 2008 Wenchuan earthquake area in China. Topography, river and road distributions turn out to be the most important factors for the localization of such landslides. A monitoring sensor network designed for deployment in a specific site has proven to be particularly critical because of the high geo-hazard risk. In , the problem of correcting the rainfall intensity on the basis of topographic data is addressed, being a crucial parameter in many models used for landslide prediction. These papers highlight how different approaches for modeling landslide behavior strictly depend on the quality of rheological, geotechnical, and geomorphological input parameters. On the one hand, remote sensing techniques have the capability to improve this knowledge, especially for aspects related to topography, land cover and surface displacement. Further, a great effort is required for collecting more information from the field. A better integration between modeling and Earth observations from different platforms is the key-point to ensure future research on landslides will be more effective in real applications.
A further general conclusion is evident from most of the papers. Successful solutions to cope with landslide prediction need to be based on data and sensor integration due to the absence of a technique which can be efficiently applied in all situations. In particular, the dream of investigating landslides with the sole use of remotely sensed data is quite utopic. However, many results of research activities described here (and from previous literature) confirmed that remote sensing techniques may provide a remarkable contribution to this field, but they need to be validated and complemented by in situ data. Remote sensing may provide an overview of problems over wide areas and help localize specific high-risk sites. These can be thoroughly investigated by deploying local sensor networks able to gather real-time and more extensive observations . In a successive stage, remote sensing techniques are expected to provide wide-coverage data to be integrated with in situ observations in the modeling process. In this direction, terrestrial remote sensors (laser scanning, ground-based InSAR, image-based techniques) have demonstrated suitability because of the higher resolution and the opportunity to focus on specific problems.
Finally, I would like to thank all people who have contributed to this Special Issue: all the authors of submitted papers, the reviewers who gave valuable support, and the Remote Sensing Editorial Office for conducting a high-quality review and editing process for all the published papers. I hope that this Special Issue will further promote research on the application of remote sensing in landslide investigations, and that it will foster the process of technology and knowledge transfer from the scientific community to the institutions in charge of handling geo-hazards. Such an exchange will be beneficial to the sustainable development of society as a whole.
- Schuster, R.L. Socioeconomic Significance of Landslides. In Landslides: Investigation and Mitigation; Turner, A.K., Schuster, R.L., Eds.;. Transportation Research Board Special Report 247; National Academy Press: Washington, DC, USA, 1996; pp. 12–35.
- Gokceoglu, K.; Sezer, E. A statistical assessment on international landslide literature (1945–2008). Landslides 2009, 6, 345–351, doi:10.1007/s10346-009-0166-3.
- Metternicht, G.; Hurni, L.; Gogu, R. Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sens. Environ 2005, 98, 284–303, doi:10.1016/j.rse.2005.08.004.
- Ghuffar, S.; Székely, B.; Roncat, A.; Pfeifer, N. Landslide displacement monitoring using 3D range flow on airborne and terrestrial LiDAR data. Remote Sens 2013, 5, 2720–2745, doi:10.3390/rs5062720.
- Wiegand, C.; Rutzinger, M.; Heinrich, K.; Geitner, C. Automated extraction of shallow erosion areas based on multi-temporal ortho-imagery. Remote Sens 2013, 5, 2292–2307, doi:10.3390/rs5052292.
- Gomes, R.A.T.; Guimarães, R.F.; de Carvalho Júnior, O.A.; Fernandes, N.F.; do Amaral Júnior, E.V. Combining spatial models for shallow landslides and debris-flows prediction. Remote Sens 2013, 5, 2219–2237, doi:10.3390/rs5052219.
- Qiao, G.; Lu, P.; Scaioni, M.; Xu, S.; Tong, X.; Feng, T.; Wu, H.; Chen, W.; Tian, Y.; Wang, W.; Li, R. Landslide investigation with remote sensing and sensor network: From susceptibility mapping and scaled-down simulation towards in situ sensor network design. Remote Sens 2013, 5, 4319–4346, doi:10.3390/rs5094319.
- Tantianuparp, P.; Shi, X.; Zhang, L.; Balz, T.; Liao, M. Characterization of landslide deformations in Three Gorges area using multiple InSAR data Stacks. Remote Sens 2013, 5, 2704–2719, doi:10.3390/rs5062704.
- Del Ventisette, C.; Ciampalini, A.; Manunta, M.; Calò, F.; Paglia, L.; Ardizzone, F.; Mondini, A.C.; Reichenbach, P.; Mateos, R.M.; Bianchini, S.; et al. Exploitation of Large Archives of ERS and ENVISAT C-Band SAR Data to Characterize Ground Deformations. Remote Sens 2013, 5, 3896–3917, doi:10.3390/rs5083896.
- Akbarimehr, M.; Motagh, M.; Haghshenas-Haghighi, M. Slope stability assessment of the Sarcheshmeh Landslide, Northeast Iran, Investigated using InSAR and GPS observations. Remote Sens 2013, 5, 3681–3700, doi:10.3390/rs5083681.
- Othman, A.A.; Gloaguen, R. River Courses Affected by Landslides and Implications for Hazard Assessment: A High Resolution Remote Sensing Case Study in NE Iraq–W Iran. Remote Sens 2013, 5, 1024–1044, doi:10.3390/rs5031024.
- Frattini, P.; Crosta, G.B.; Allievi, J. Damage to buildings in large slope rock instabilities monitored with the PSInSAR™ technique. Remote Sens 2013, 5, 4753–4773, doi:10.3390/rs5104753.
- Pesci, A.; Teza, G.; Casula, G.; Fabris, M.; Bonforte, A. Remote sensing and geodetic measurements for volcanic slope monitoring: Surface variations measured at Northern Flank of La Fossa Cone (Vulcano Island, Italy). Remote Sens 2013, 5, 2238–2256.
- Tofani, V.; Raspini, F.; Catani, F.; Casagli, N. Persistent Scatterer Interferometry (PSI) technique for landslide characterization and monitoring. Remote Sens 2013, 5, 1045–1065, doi:10.3390/rs5031045.
- Lacroix, P.; Zavala, B.; Berthier, E.; Audin, L. Supervised method of landslide inventory using panchromatic SPOT5 images and application to the earthquake-triggered landslides of Pisco (Peru, 2007, Mw8.0). Remote Sens 2013, 5, 2590–2616, doi:10.3390/rs5062590.
- Strozzi, T.; Ambrosi, C.; Raetzo, H. Interpretation of aerial photographs and satellite SAR interferometry for the inventory of landslides. Remote Sens 2013, 5, 2554–2570, doi:10.3390/rs5052554.
- Liu, J.-K.; Shih, P.T. Topographic correction of wind-driven rainfall for landslide analysis in Central Taiwan with validation from aerial and satellite optical images. Remote Sens 2013, 5, 2571–2589, doi:10.3390/rs5062571.
- Guzzetti, F.; Mondini, A.C.; Cardinali, M.; Fiorucci, F.; Santangelo, M.; Chang, K.-T. Landslide inventory maps: New tools for an old problem. Earth-Sci. Rev 2012, 112, 42–66, doi:10.1016/j.earscirev.2012.02.001.
- Lu, P.; Stumpf, A.; Kerle, N.; Casagli, N. Object-oriented change detection for landslide rapid mapping. IEEE Geosci. Remote Sens. Lett 2011, 8, 701–705, doi:10.1109/LGRS.2010.2101045.
- Ferretti, A.; Fumagalli, A.; Novali, F.; Prati, C.; Rocca, F.; Rucci, A. A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Trans. Geosci. Remote Sens 2011, 49, 3460–3470, doi:10.1109/TGRS.2011.2124465.
- Perissin, D.; Wang, T. Repeat-pass SAR interferometry with partially coherent targets. IEEE Trans. Geosci. Remote Sens 2012, 50, 271–280.
- Jaboyedoff, M.; Oppikofer, T.; Abellán, A.; Derron, M.H.; Loye, A.; Metzger, R.; Pedrazzini, A. Use of LIDAR in landslide investigations: A review. Nat. Hazards 2012, 61, 1–24, doi:10.1007/s11069-011-9991-5.
- Van Westen, C.J.; Castellanos, E.; Kuriakose, S.K. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Eng. Geol 2008, 102, 112–131, doi:10.1016/j.enggeo.2008.03.010.
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