An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Abstract

Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/ sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre-and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre-and postevent images and attributed to landslide classes.

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

“…Very few previous landslide studies have integrated OBIA and SVMs (Van den Eeckhaut et al, 2012;Moosavi et al, 2014), and we confirm that this is a robust and efficient approach, able to detect 95 % of the number of landslides scars present in the validation areas. Also, to our knowledge, very few previous studies have used OBIA to tackle the problem of automated mapping of landslide source and run-out areas in optical images: Holbling et al (2015) have recently tested such an approach in northern Taiwan, but their reference data set excluded debris flows or other sediment transport areas. In contrast, Mondini et al (2011aMondini et al ( , 2013 developed semiautomated pixel-based approaches to map landslide source and run-out areas, and our OBIA results compare well with theirs in terms of match to reference data.…”

“…Ideally, pre-event and post-event images should be acquired at the same time of the year and with similar view angle and solar illumination, but this is often not feasible (Guzzetti et al, 2012). Semiautomated approaches to landslide mapping can be classed, according to the type of image element used, as "pixel based" (e.g., Chang et al, 2007;Yang and Chen, 2010;Chini et al, 2011;Cheng et al, 2013;Mondini et al, 2013Mondini et al, , 2011a or "object based" (e.g., Aksoy et al, 2012;Holbling et al, 2012Holbling et al, , 2015Lacroix et al, 2013;Lahousse et al, 2011;Lu et al, 2011;Martha et al, 2010Martha et al, , 2011Martha et al, , 2013Stumpf et al, 2011Stumpf et al, , 2014Van Den Eeckhaut et al, 2012). When applied to very high spatial resolution images, pixel-based methods often exhibit a "salt and pepper" appearance (Van Westen et al, 2008;Guzzetti et al, 2012) which requires image post-processing.…”

“…Mondini et al (2011aMondini et al ( , 2013 developed semiautomated pixel-based approaches to map landslides into source and run-out areas (defined as the union of transport and depositional areas), using a single post-event image and pre-and post-event image changes, respectively. Recently, Holbling et al (2015) developed an object-based approach for semiautomated landslide change detection, with the ability to separate landslide sources from debris flow/sediment transport areas. Van Den Eeckhaut et al (2012) also separate landslide source area and run-out but, in contrast to the previous authors, they use lidar data instead of optical imagery.…”

Classificação Semiautomática De Cicatrizes De Movimentos De Massa Por Meio De Técnicas De Detecção De Mudanças

“…Em âmbito internacional, é possível observar esforços para o desenvolvimento de metodologias que buscam subsidiar e automatizar o mapeamento de cicatrizes de movimentos de massa (MOINE et al, 2009;MONDINI et al, 2011;GUZZETTI et al, 2012;BLASCHKE e FEIZIZADEH, 2014;HÖLBLING et al, 2015). Dentre estes, o uso de técnicas de detecção de mudanças e algoritmos de classifi cação de imagens tem se mostrado efi ciente para a detecção de cicatrizes (GONG et al, 2008;MONDINI et al, 2011;HÖLBLING et al, 2015), embora ainda seja incipiente no país.…”

“…Dentre estes, o uso de técnicas de detecção de mudanças e algoritmos de classifi cação de imagens tem se mostrado efi ciente para a detecção de cicatrizes (GONG et al, 2008;MONDINI et al, 2011;HÖLBLING et al, 2015), embora ainda seja incipiente no país.…”