High-Resolution Landslide Susceptibility Map Generation using Machine Learning (Case Study in Pacitan, Indonesia)

The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.

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Application of a Statistical Approach to Landslide Susceptibility Map Generation in Urban Settings

IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018 - Volume 1 ◽

10.1007/978-3-319-93124-1_19 ◽

2018 ◽

pp. 155-162 ◽

Cited By ~ 1

Author(s):

Ciro Sepe ◽

Pierluigi Confuorto ◽

Anna Claudia Angrisani ◽

Diego Di Martire ◽

Mariano Di Napoli ◽

...

Keyword(s):

Landslide Susceptibility ◽

Statistical Approach ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Urban Settings ◽

Map Generation

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A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China

PLoS ONE ◽

10.1371/journal.pone.0229818 ◽

2020 ◽

Vol 15 (3) ◽

pp. e0229818

Author(s):

Xianyu Yu ◽

Huachen Gao

Keyword(s):

Spatial Scale ◽

Landslide Susceptibility ◽

Three Gorges Reservoir ◽

Three Gorges Reservoir Area ◽

Susceptibility Map ◽

Three Gorges ◽

Landslide Susceptibility Map ◽

Reservoir Area ◽

The Three Gorges

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Application Of Logistic Regression Method To Produce Landslide Susceptibility Map: A Case Study Of Tetouan Mazari, Morocco

MATEC Web of Conferences ◽

10.1051/matecconf/201814902082 ◽

2018 ◽

Vol 149 ◽

pp. 02082

Author(s):

L. Ait Brahim ◽

M. Elmoulat

Keyword(s):

Logistic Regression ◽

Land Use Planning ◽

Landslide Susceptibility ◽

Accurate Method ◽

Slope Aspect ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Logistic Regression Method ◽

Validation Set

The main purpose of this study is to use logistic regression (RL) model to map landslide susceptibility in and around the area of Tetouan Mazari in the Northern Morocco. Parameters, such as lithology, slope gradient, slope aspect, faults, drainage lines, and hillshade, were considered. Landslide susceptibility map was produced using RL method and then compared and validated. Before the modeling and validation, the observed landslides were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy of the model was measured by fitting them to a validation set of observed landslides. For validation process, the half landslides remaining was used. The final map was classified into five classes: Very High (32%), High (40%), Medium (7%), Low (7%) and Nil (15%). According to these values logistic regression was determined to be one of the most accurate method to generate landslide susceptibility map. Last but not least, logistic regression model can be used to manage and mitigate hazards related to landslides and to aid in land-use planning for the city of Tetouan‥

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ON THE USE OF SENTINEL-2 IMAGES AND HIGH RESOLUTION DTM FOR LANDSLIDE SUSCEPTIBILITY MAPPING IN A DEVELOPING URBAN SETTLEMENT (MAMAK, ANKARA, TURKEY)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w8-469-2019 ◽

2019 ◽

Vol XLII-3/W8 ◽

pp. 469-476 ◽

Cited By ~ 1

Author(s):

T. Yanar ◽

S. Kocaman ◽

C. Gokceoglu

Keyword(s):

High Resolution ◽

Urban Planning ◽

Landslide Susceptibility ◽

Earth Observation ◽

Slope Aspect ◽

Susceptibility Map ◽

Topographic Wetness Index ◽

Landslide Susceptibility Map ◽

Earth Observation Satellites ◽

Sentinel 2

Abstract. Urban planning starts with the selection of suitable sites. The main factors and components for site selection are the geological-geotechnical parameters that directly affect the natural hazards, such as landslide and flood, construction costs and the location and distribution of existing infrastructure. The presence and accuracy of up-to-date maps in planning are very important. With the increase of high resolution Earth observation satellites, the required data can be obtained with high temporal frequency and spatial availability. From these data, the base parameters for planning can be extracted with semi- or fully-automatic methods. Among the Earth observation satellites, the Sentinel-2 mission of European Space Agency (ESA) provides high resolution optical images and the data are freely available also at different processing levels such as orthorectified images. In this study, the possibility of the landslide susceptibility map production which should be one of the base maps in urban planning by using Sentinel-2 satellite images was investigated in Mamak District of Ankara City, Turkey. The land cover and land use data were produced from Sentinel-2 images by using a supervised classification method in SNAP Tool provided by ESA. The lithological definitions were received from the General Directorate of Mineral Research and Explorations. The topographical parameters such as slope, aspect, topographic wetness index, etc. were extracted from a high resolution digital terrain model (DTM) of the area. Manually extracted landslide inventory data were employed in the logistic regression method and the produced landslide susceptibility map of the study area is presented here.

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