Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system

Oh, Hyun-Joo; Lee, Saro

doi:10.1007/s12665-010-0579-2

Cited by 69 publications

(17 citation statements)

References 60 publications

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“…For this, the logistic regression analysis result in modeling rock-fall susceptibility in the study area was evaluated by calculating the success curve rate (SCR) and the percentage of known rock-falls in the various susceptibility categories. For the verification by the SCR method, the area under the SCR curve (AUC) model was used (e.g., Lee and Sambath 2006;Lee and Pradhan 2006;Oh et al 2009;Pradhan andLee 2009, 2010;Nandi and Shakoor 2009;Pradhan 2010;Oh and Lee 2011). To obtain the SCR, the calculated rock-fall susceptibility index values of all pixels in the study area were ranked in a descending order.…”

Section: Susceptibility Map and Its Reliabilitymentioning

confidence: 99%

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

et al. 2012

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This study describes the application of logistic regression to rock-fall susceptibility mapping along 11 km of a mountainous road on the Salavat Abad saddle, in southwest Kurdistan, Iran. To determine the factors influencing rock-falls, data layers of slope degree, slope aspect, slope curvature, elevation, distance to road, distance to fault, lithology, and land use were analyzed by logistic regression analysis. The results are shown as rock-fall susceptibility maps. A spatial database, which included 68 sites (34 rock-fall point cells with value of 1 and 34 no rock-fall point cells with value of 0) was developed and analyzed using a Geographic Information System, GIS. The results are shown as four classes of rock-fall susceptibility. In this study, distance to fault, lithology, slope curvature, slope degree, and distance to road were found to be the most important factors affecting rock-fall. It was concluded that about 76 % of the study area can be classified as having moderate and high susceptibility classes. Rock-fall point cells were used to verify results of the rock-fall susceptibility map using success curve rate and the area under the curve. The verification results showed that the area under the curve for rock-fall susceptibility map is 77.57 %. The results from this study demonstrated that the use of a logistic regression model within a GIS framework is useful and suitable for rock-fall susceptibility mapping. The rock-fall susceptibility map can be used to reduce susceptibility associated with rock-fall.

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Section: Susceptibility Map and Its Reliabilitymentioning

confidence: 99%

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

et al. 2012

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“…This is despite the fact that DEMs such as those from SRTM and ASTER are being used as major sources of topographic information for many applications including hydrological analysis and simulations (e.g., Jaranilla-Sanchez et al, 2011;Santillan et al, 2011;Sarmiento et al, 2012;Clutario and David, 2014;Chen and Senarath, 2014), flood modelling and hazard mapping (e.g., Abon et al, 2011;Ignacio and Henry, 2013), geological hazard analysis (e.g., Lagmay et al, 2012), and landslide mapping characterization (e.g., Evans et al, 2006;Oh and Lee, 2011). The quality and accuracy of the DEMs used and their suitability for these applications were not adequately assessed.…”

Section: Introductionmentioning

confidence: 99%

Vertical Accuracy Assessment of 30-M Resolution Alos, Aster, and SRTM Global Dems Over Northeastern Mindanao, Philippines

Santillan¹,

Makinano-Santillan²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The ALOS World 3D -30m (AW3D30), ASTER Global DEM Version 2 (GDEM2), and SRTM-30m are Digital Elevation Models (DEMs) that have been made available to the general public free of charge. An important feature of these DEMs is their unprecedented horizontal resolution of 30-m and almost global coverage. The very recent release of these DEMs, particularly AW3D30 and SRTM30m, calls for opportunities for the conduct of localized assessment of the DEM's quality and accuracy to verify their suitability for a wide range of applications in hydrology, geomorphology, archaelogy, and many others. In this study, we conducted a vertical accuracy assessment of these DEMs by comparing the elevation of 274 control points scattered over various sites in northeastern Mindanao, Philippines. The elevations of these control points (referred to the Mean Sea Level, MSL) were obtained through 3rd order differential levelling using a high precision digital level, and their horizontal positions measured using a global positioning system (GPS) receiver. These control points are representative of five (5) land-cover classes namely brushland (45 points), built-up (32), cultivated areas (97), dense vegetation (74), and grassland (26). Results showed that AW3D30 has the lowest Root Mean Square Error (RMSE) of 5.68 m, followed by SRTM-30m (RMSE = 8.28 m), and ASTER GDEM2 (RMSE = 11.98 m). While all the three DEMs overestimated the true ground elevations, the mean and standard deviations of the differences in elevations were found to be lower in AW3D30 compared to SRTM-30m and ASTER GDEM2. The superiority of AW3D30 over the other two DEMS was also found to be consistent even under different landcover types, with AW3D30's RMSEs ranging from 4.29 m (built-up) to 6.75 m (dense vegetation). For SRTM-30m, the RMSE ranges from 5.91 m (built-up) to 10.42 m (brushland); for ASTER GDEM2, the RMSE ranges from 9.27 m (brushland) to 14.88 m (dense vegetation). The results of the vertical accuracy assessment suggest that the AW3D30 is more accurate than SRTM-30m and ASTER GDEM2, at least for the areas considered in this study. On the other hand, the tendencies of the three DEMs to overestimate true ground elevation can be considered an important finding that users of the DEMs in the Philippines should be aware of, and must be considered into decisions regarding use of these data products in various applications.

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“…According to the geological hazard, these models have been used to analyze landslide susceptibility mapping in many recent studies [17][18][19][20][21][22][23][24][25][26][27]. In this study, the fuzzy algebra model was used to integrate ground subsidence hazard maps, withthis model being used most frequently in analysis landslide susceptibility mapping [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Ensemble of ground subsidence hazard maps using fuzzy logic

Park

Lee

Lee³

2014

Open Geosciences

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Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping groundsubsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

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Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system

Cited by 69 publications

References 60 publications

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

Vertical Accuracy Assessment of 30-M Resolution Alos, Aster, and SRTM Global Dems Over Northeastern Mindanao, Philippines

Ensemble of ground subsidence hazard maps using fuzzy logic

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