Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method

Li, Zhongbin; Shi, Wenzhong; Myint, Soe W.; Lü, Ping; Wang, Qunming

doi:10.1016/j.rse.2016.01.003

Cited by 119 publications

(97 citation statements)

References 91 publications

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“…Booth et al, 2009;Borghuis et al, 2007;Li et al, 2016), and some methods are even capable of identifying landslides through cloud cover (Kimura and Yamaguchi, 2000). Such techniques can accurately map landslide locations much faster than traditional manual mapping, potentially reducing the time required to map sufficient numbers of landslides.…”

Section: Potential Speed Of Applicationmentioning

confidence: 99%

Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support

Robinson

Rosser

Densmore

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Current methods to identify coseismic landslides immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Issues with cloud cover, data collection and processing, and manual landslide identification mean even the most rapid mapping exercises are often incomplete when the emergency response ends. In this study, we demonstrate how traditional empirical methods for modelling the total distribution and relative intensity (in terms of point density) of coseismic landsliding can be successfully undertaken in the hours and days immediately after an earthquake, allowing the results to effectively inform stakeholders during the response. The method uses fuzzy logic in a GIS (Geographic Information Systems) to quickly assess and identify the location-specific relationships between predisposing factors and landslide occurrence during the earthquake, based on small initial samples of identified landslides. We show that this approach can accurately model both the spatial pattern and the number density of landsliding from the event based on just several hundred mapped landslides, provided they have sufficiently wide spatial coverage, improving upon previous methods. This suggests that systematic high-fidelity mapping of landslides following an earthquake is not necessary for informing rapid modelling attempts. Instead, mapping should focus on rapid sampling from the entire affected area to generate results that can inform the modelling. This method is therefore suited to conditions in which imagery is affected by partial cloud cover or in which the total number of landslides is so large that mapping requires significant time to complete. The method therefore has the potential to provide a quick assessment of landslide hazard after an earthquake and may therefore inform emergency operations more effectively compared to current practice.

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Section: Potential Speed Of Applicationmentioning

confidence: 99%

Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support

Robinson

Rosser

Densmore

et al. 2017

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The current major issues of landslide identification with OOAs include the selection of training samples, determination of threshold values, and choice of diagnostic features and a semantic network for classification [15]. Previous approaches for the recognition of landslides were mainly derived from knowledge that was developed by experts for the detection of landslides [14,15,28]. In our study, we quantitatively analyzed the spectral, textural, and morphometric properties of loess landslides and topographic characteristics.…”

Section: Discussionmentioning

confidence: 99%

Loess Landslide Inventory Map Based on GF-1 Satellite Imagery

Sun

Tian

et al. 2017

Remote Sensing

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Rainfall-induced landslides are a major threat in the hilly and gully regions of the Loess Plateau. Landslide mapping via field investigations is challenging and impractical in this complex region because of its numerous gullies. In this paper, an algorithm based on an object-oriented method (OOA) has been developed to recognize loess landslides by combining spectral, textural, and morphometric information with auxiliary topographic parameters based on high-resolution multispectral satellite data (GF-1, 2 m) and a high-precision DEM (5 m). The quality percentage (QP) values were all greater than 0.80, and the kappa indices were all higher than 0.85, indicating good landslide detection with the proposed approach. We quantitatively analyze the spectral, textural, morphometric, and topographic properties of loess landslides. The normalized difference vegetation index (NDVI) is useful for discriminating landslides from vegetation cover and water areas. Morphometric parameters, such as elongation and roundness, can potentially improve the recognition capacity and facilitate the identification of roads. The combination of spectral properties in near-infrared regions, the textural variance from a grey level co-occurrence matrix (GLCM), and topographic elevation data can be used to effectively discriminate terraces and buildings. Furthermore, loess flows are separated from landslides based on topographic position data. This approach shows great potential for quickly producing accurate results for loess landslides that are induced by extreme rainfall events in the hilly and gully regions of the Loess Plateau, which will help decision makers improve landslide risk assessment, reduce the risk from landslide hazards and facilitate the application of more reliable disaster management strategies.

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“…Land cover change detection (LCCD) plays an increasingly important role in various practical applications, such as urban land resource management, urban expansion, and damage assessment [1][2][3][4][5][6][7][8][9][10]. With the development of remote sensing techniques, obtaining remote sensing images over a certain area at different times is convenient.…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images

Shi

Zhou

et al. 2017

Remote Sensing

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Abstract:Change detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining parameters is time-consuming. Furthermore, numerous methods are data-dependent. Therefore, their degree of automation should be improved significantly. Three techniques, which consist of a semi-automatic change detection system, are proposed for LCCD to overcome the abovementioned drawbacks. The three techniques are as follows: (1) change magnitude image (CMI) noise reduction is based on Gaussian filter (GF), which is coupled with OTSU for reducing CMI noise automatically using an iterative optimization strategy; (2) a method based on histogram curve fitting is suggested to predict the threshold range for parameter determination; and (3) a modified region growing algorithm is built for iteratively constructing the final change detection map. The detection accuracies of the proposed system are investigated through four experiments with different bi-temporal image scenes. Compared with several widely used change detection methods, the proposed system can be applied to detect land cover change with high accuracy and flexibility. This work is an attempt to provide a change detection system that is compatible with remote sensing images with high and median-low spatial resolution.

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Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method

Cited by 119 publications

References 91 publications

Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support

Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support

Loess Landslide Inventory Map Based on GF-1 Satellite Imagery

Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images

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