Automatic landslide detection from remote sensing images using supervised classification methods

Danneels, Gaëlle; Pirard, Eric; Havenith, Hans-Balder

doi:10.1109/igarss.2007.4423479

Cited by 63 publications

(35 citation statements)

References 5 publications

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“…First mapping of landslides (before 2006) was also completed with Aster, SPOT, Corona and KFA-1000 imagery. Also automatic detection methods were applied to multi-spectral imagery in order to identify landslides in smaller subregions (see Danneels et al, 2007;Schlögel et al, 2011).…”

Section: Data and Methods Used For Landslide Mappingmentioning

confidence: 99%

Tien Shan Geohazards Database: Earthquakes and landslides

Havenith

Strom

Torgoev³

et al. 2015

Geomorphology

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Section: Data and Methods Used For Landslide Mappingmentioning

confidence: 99%

Tien Shan Geohazards Database: Earthquakes and landslides

Havenith

Strom

Torgoev³

et al. 2015

Geomorphology

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“…Recent developments have taken place in the use of remote sensing techniques and image analysis to evaluate the extension and impact damages of landslides (Fernández et al, 711 2005;Nichol and Wong, 2005;Danneels et al, 2007;Martha et al, 2010). Landslide inventory mapping can also greatly improve from the use of these techniques (Whitworth et al, 2003;Malamud et al, 2004;Kirschbaum et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

The 20 February 2010 Madeira Island flash-floods: VHR satellite imagery processing in support of landslide inventory and sediment budget assessment

Lira¹,

Lousada²,

Falcão³

et al. 2013

Nat. Hazards Earth Syst. Sci.

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On 20 February 2010, an extreme rainfall episode occurred on Madeira Island, which caused an exceptionally strong flash flood and several soil slip-debris flows, producing 45 confirmed deaths and 6 persons declared missing, as well as extensive material damages. In order to understand and quantify the importance of landsliding in routing sediment through mountainous drainage, such as Madeira Island's landscape, it was essential to perform extensive landslide analysis. This study describes the methodology used to semi-automatically detect the landslides, produce the landslide inventory maps and estimate the sediment volume produced during this particular event which ranged from 217 000 m³ to 344 000 m³ and 605 000 m³ to 984 000 m³ for the Funchal and Ribeira Brava basins, respectively. These results contributed to the design and implementation of measures to prevent damages caused by landslides in Madeira Island

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“…The "object-oriented" approach, however, groups image pixels into homogeneous objects, with shape, size, neighboring, and textural features in addition to spectral information (Aksoy et al, 2012). With both approaches, supervised and unsupervised classification schemes have been adopted, based on algorithms such as maximum likelihood (Nichol et al, 2005;Borghuis et al, 2007;Danneels et al, 2007), K nearest neighbor (Cheng et al, 2013;Li et al, 2013), artificial neural networks (Nichol et al, 2005;Danneels et al, 2007;Moosavi et al, 2014), random forests , or support vector machines (SVMs; Pisani et al, 2012;Van Den Eeckhaut et al, 2012;Moosavi et al, 2014). Novel object-based approaches for automated landslide mapping include the classification of different landslide types (Martha et al, 2010), identification of landslides from panchromatic imagery only through strong reliance on texture measures , or the detection and mapping of forested landslides resorting to lidar data (Van Den Eeckhaut et al, 2012).…”

Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island

Heleno

Matias

Pina

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract.A method for semiautomated landslide detection and mapping, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a support vector machine classifier and is tested using a GeoEye-1 multispectral image, sensed 3 days after a major damaging landslide event that occurred on Madeira Island (20 February 2010), and a pre-event lidar digital terrain model. The testing is developed in a 15 km 2 wide study area, where 95 % of the number of landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area, with commission errors below 26 % and omission errors below 24 %. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier, east-facing slopes.

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Automatic landslide detection from remote sensing images using supervised classification methods

Cited by 63 publications

References 5 publications

Tien Shan Geohazards Database: Earthquakes and landslides

Tien Shan Geohazards Database: Earthquakes and landslides

The 20 February 2010 Madeira Island flash-floods: VHR satellite imagery processing in support of landslide inventory and sediment budget assessment

Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island

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