Very-High Resolution Stereoscopic Satellite Images for Landslide Mapping

Ardizzone, Francesca; Fiorucci, Federica; Santangelo, Michele; Cardinali, Mauro; Mondini, Alessandro Cesare; Rossi, Mauro; Reichenbach, Paola; Guzzetti, Fausto

doi:10.1007/978-3-642-31325-7_12

Cited by 28 publications

(26 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5b and 6 reveals that, when compared to the benchmark (Map A), the reconnaissance field mapping (Map B) exhibited the largest mapping error of all the performed pairwise comparisons, with E = 0.45 in the source and transportation area, E = 0.67 in the landslide deposit, and E = 0.55 for the entire landslide. Our results are similar to the results of tests performed to compare fieldbased landslide maps against GPS-based surveys of single landslides (Santangelo et al, 2010), the visual interpretation of very-high resolution stereoscopic satellite images (Ardizzone et al, 2013), or the semi-automatic processing of monoscopic satellite images , and confirm the inherent difficulty in preparing accurate landslide maps in the field, unless the mapping is supported by a GPS survey or a similar technology.…”

Section: Discussionsupporting

confidence: 81%

“…Mapping of individual landslides can be executed using the same techniques and tools commonly used by geomorphologists to prepare landslide inventory maps. Such techniques and tools include (a) field survey (Santangelo et al, 2010), (b) heuristic visual interpretation of monoscopic or stereoscopic aerial or satellite images (Brardinoni et al, 2003;Fiorucci et al, 2011;Ardizzone et al, 2013), (c) lidar-derived images (Ardizzone et al, 2007;Van Den Eeckhaut et al, 2007;Haneberg et al, 2009;Giordan et al, 2013;Razak et al, 2013;Niculita et al, 2016, Petschko et al, 2016, (d) ultra-high-resolution images acquired by unmanned aerial vehicles (UAVs; Niethammer et al, 2010;Giordan et al, 2015a, b;Torrero et al, 2015;Turner et al, 2015). Heuristic visual mapping of landslide features is based on the systematic analysis of photographic characteristics such as colour, tone, mottling, texture, shape, and morphological characteristics such as size, curvature, concavity and convexity (Pike, 1988).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Criteria for the optimal selection of remote sensing optical images to map event landslides

Fiorucci

Giordan

Santangelo

et al. 2018

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.

show abstract

Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Criteria for the optimal selection of remote sensing optical images to map event landslides

Fiorucci

Giordan

Santangelo

et al. 2018

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The identification and mapping of landslides can be organized into landslide event maps. Landslide event mapping is a wellknown activity obtained through field surveys (Yoon et al, 2012;Santangelo et al, 2010), visual interpretation of aerial or satellite images (Brardinoni et al, 2003;Ardizzone et al, 2013) and combined analysis of lidar DTM and images (Van Den Eeckhaut et al, 2007;Haneberg et al, 2008;Giordan et al, 2013;Razak et al, 2013;Niculiţa, 2016). The use of RPASs for the identification and mapping of a landslide has been described by several authors (Niethammer et al, , 2010(Niethammer et al, , 2011Rau et al, 2011;Carvajal et al, 2011;Travelletti et al, 2012;Torrero et al, 2015;Casagli et al, 2017).…”

Section: Landslide Recognitionmentioning

confidence: 99%

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Giordan

Hayakawa

Nex

et al. 2018

Nat. Hazards Earth Syst. Sci.

140

View full text Add to dashboard Cite

Abstract. The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazards. IntroductionIn the last three decades, the number of natural disasters showed a positive trend with an increase in the number of affected populations. Disasters not only affected the poor and characteristically more vulnerable countries but also those thought to be better protected. The Annual Disaster Statistical Review describes recent impacts of natural disasters on the population and reports 342 naturally triggered disasters in 2016 (Guha-Sapir et al., 2017). This is less than the annual average disaster frequency observed from 2006 to 2015 (376.4 events). However, natural disasters are still responsible for a high number of casualties (8733 death). In the period 2006-2015, the average number of causalities caused annually by natural disasters is 69 827. In 2016, hydrological disasters (177) had the largest share in natural disaster occurrence (51.8 %), followed by meteorological disasters (96; 28.1 %), climatological disasters (38; 11.1 %) and geophysical disasters (31; 9.1 %) (Guha-Sapir et al., 2017). To face these disasters, one of the most important solutions is the use of systems able to provide an adequate level of information for correctly understanding these events and their evolution. In this context, surveying and monitoring natural hazards gained importance. In particular, during the emergency phase it is very important to evaluate and control the phenomenon of evolution, preferably operating in near real time or real time, and consequently, use this information for a better risk assessment scenario. The available acquired data must be processed rapidly to support the emergency services and decision makers.Recently, the use of remote sensing (satellite and airborne platform) in the field of natural hazards and disasters has become common, also supported by the increase in geospatial technologies and the ability to provide and process up-to-date imagery (Joyce et al., 2009;Tarolli, 2014). Remotely sensed data play an integral role in predicting hazard events such as floods and landslides, subsidence events and other ground instabilities. Because of their acquisition mode and capabilPublished by Copern...

show abstract

“…The inventory was originally prepared at 1:10,000 scale through the visual interpretation of five sets of stereoscopic aerial photographs taken unsystematically in the period 1941-1997 at scales ranging from 1:13,000 to 1:33,000. The landslide inventory was continued in the period from 1999 to December 2005 through field surveys carried out after periods of prolonged rainfall and in March and May 2010 using stereo satellite images (Ardizzone et al 2013).…”

Section: Study Area and Datamentioning

confidence: 99%

“…For this, we will use a rich multi-temporal landslide inventory from the Collazzone area in central Umbria, Italy (Ardizzone et al 2013;Galli et al 2008;Guzzetti et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory

Samia¹,

Temme

Bregt³

et al. 2016

Landslides

Self Cite

143

109

View full text Add to dashboard Cite

Landslides are a major category of natural disasters, causing loss of lives, livelihoods and property. The critical roles played by triggering (such as extreme rainfall and earthquakes), and intrinsic factors (such as slope steepness, soil properties and lithology) have previously successfully been recognized and quantified using a variety of qualitative, quantitative and hybrid methods in a wide range of study sites. However, available data typically do not allow to investigate the effect that earlier landslides have on intrinsic factors and hence on follow-up landslides. Therefore, existing methods cannot account for the potentially complex susceptibility changes caused by landslide events. In this study, we used a substantially different alternative approach to shed light on the potential effect of earlier landslides using a multitemporal dataset of landslide occurrence containing 17 time slices. Spatial overlap and the time interval between landslides play key roles in our work. We quantified the degree to which landslides preferentially occur in locations where landslides occurred previously, how long such an effect is noticeable, and how landslides are spatially associated over time. We also investigated whether overlap with previous landslides causes differences in landslide geometric properties. We found that overlap among landslides demonstrates a clear legacy effect (path dependency) that has influence on the landslide affected area. Landslides appear to cause greater susceptibility for follow-up landslides over a period of about 10 years. Follow-up landslides are on average larger and rounder than landslides that do not follow earlier slides. The effect of earlier landslides on follow-up landslides has implications for understanding of the landslides evolution and the assessment of landslide susceptibility.

show abstract

Very-High Resolution Stereoscopic Satellite Images for Landslide Mapping

Cited by 28 publications

References 16 publications

Criteria for the optimal selection of remote sensing optical images to map event landslides

Criteria for the optimal selection of remote sensing optical images to map event landslides

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory

Contact Info

Product

Resources

About