Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

Plank, Simon; Twele, André; Martinis, Sandro

doi:10.3390/rs8040307

Cited by 90 publications

(89 citation statements)

References 51 publications

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“…In the landslide, terrain slope averages 11 • , and is steeper (12 • ) in the source and transportation area than in the deposition area (9 • ). The landslide signature (Pike, 1988) is different in the different parts of the landslide. In the source and transportation area the signature is predominantly photographical (radiometric), whereas in the landslide deposit it is mainly morphological (topographic).…”

Section: The Assignano Landslidementioning

confidence: 99%

“…Accurate detection of individual landslides has different scopes, including landslide mapping (Di Maio and Vassallo, 2011;Manconi et al, 2014;Plank et al, 2016), landslide hazard analysis and risk assessment , to support the installation of landslide monitoring systems (Tarchi et al, 2003;Teza et al, 2007;Monserrat and Crosetto, 2008;Giordan et al, 2013), and for landslide geotechnical characterization and modelling (Gokceoglu et al, 2005;Rosi et al, 2013). Mapping of individual landslides can be executed using the same techniques and tools commonly used by geomorphologists to prepare landslide inventory maps.…”

Section: Introductionmentioning

confidence: 99%

“…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). The mentioned photographic and morphological characteristics encompass all the possible landslide features that can be used for the (visual) image interpretation.…”

Section: Introductionmentioning

confidence: 99%

“…This was expected, as the stereoscopic vision allowed us to better capture the 3-D terrain features typical of a landslide (Pike, 1988), including curvature, convexity and concavity. Conversely, visual examination of the FCC images resulted in more accurate maps than the corresponding TC images in the landslide source and transport area, where the signature of the landslide was primarily photographic.…”

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confidence: 99%

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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.

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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.

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Section: The Assignano Landslidementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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.

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“…Watanabe et al [18] use airborne L-band fully polarimetric SAR to detect landsliding areas induced by Typhoon Wipha on 16 October 2013 on Izu Oshima Island (Japan). Plank et al [19] propose a fast procedure for mapping landslides based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event very high resolution (VHR) polarimetric SAR data.…”

Section: Landslide Hazardsmentioning

confidence: 99%

Earth Observations for Geohazards: Present and Future Challenges

Tomás

2017

Remote Sensing

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Earth Observations (EO) encompasses different types of sensors (e.g., Synthetic Aperture Radar, Laser Imaging Detection and Ranging, Optical and multispectral) and platforms (e.g., satellites, aircraft, and Unmanned Aerial Vehicles) and enables us to monitor and model geohazards over regions at different scales in which ground observations may not be possible due to physical and/or political constraints. EO can provide high spatial, temporal and spectral resolution, stereo-mapping and all-weather-imaging capabilities, but not by a single satellite at a time. Improved satellite and sensor technologies, increased frequency of satellite measurements, and easier access and interpretation of EO data have all contributed to the increased demand for satellite EO data. EO, combined with complementary terrestrial observations and with physical models, have been widely used to monitor geohazards, revolutionizing our understanding of how the Earth system works. This Special Issue presents a collection of scientific contributions focusing on innovative EO methods and applications for monitoring and modeling geohazards, consisting of four Sections: (1) earthquake hazards; (2) landslide hazards; (3) land subsidence hazards; and (4) new EO techniques and services.

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Integration of a Graph Network for Landslide Detection in Space-borne Earth Observation

Luck¹,

Hajnsek

2022

Preprint

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Landslides pose a significant threat to society and infrastructure and their occurrence is projected to increase in many regions under the effect of climate change. There is an urgent demand for reliable monitoring of this natural hazard. The combination of spaceborne remote sensing data with state-of-the-art machine learning algorithms offers valuable tools for landslide detection in remote areas. However, a key limitation for the detection lies in the scale factor, especially for methods relying on pixel neighbourhoods. This study presents an innovative methodology which combines a spatial graph with SAR and multi-spectral change products. The graph integrates the flow direction based on the topography into the neighbourhood determination. This unique neighbourhood allows for the preservation of the unique shape and signature of an individual landslide. This paper compares the proposed graph neighbourhood to a common square window approach. Therefore, a RFC is trained with neighbourhood statistics from both approaches and applied to landslides of varying extent. A research area in New Zealand's West Coast region is selected due to the continuous evolution of a single landslide over multiple events. The graph approach shows promising results, particularly for small-scale events which are successfully detected while being missed by the common window approach. Using the graph neighbourhood, we can even detect the smallest visible extent of the landslide at 2-3 pixels (30-45m) width. The main limitation of the proposed approach lies in the quality of the input data. Future work will focus on the improvement of the Sentinel-1 and Sentinel-2 pre-processing.

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Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

Cited by 90 publications

References 51 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

Earth Observations for Geohazards: Present and Future Challenges

Integration of a Graph Network for Landslide Detection in Space-borne Earth Observation

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