Adapting sudden landslide identification product (SLIP) and detecting real-time increased precipitation (DRIP) algorithms to map rainfall-triggered landslides in Western Cameroon highlands (Central-Africa)

Mfondoum, Alfred Homère Ngandam; Nguet, Pauline Wokwenmendam; Mfondoum, Jean Valery Mefire; Tchindjang, Mesmin; Hakdaoui, Sofia; Cooper, R. W.; Gbetkom, Paul Gérard; Pénaye, Joseph; Ateba, Bekoa; Moudioh, Cyriel

doi:10.1186/s40677-021-00189-9

Cited by 6 publications

(8 citation statements)

References 64 publications

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“…In this study, all these aspects were carefully investigated and addressed to achieve the best possible landslide detection accuracy in GEE while using ready-made datasets in GEE. Comparisons with existing approaches [19,29,[31][32][33][34][38][39][40][41][42][44][45][46][47][48] demonstrated superior performance of ML-LaDeCORsat of at least 10% higher detection accuracy.…”

Section: Discussionmentioning

confidence: 92%

“…(3) steep slopes, determined from DEM, to restrict the detection process to areas with pronounced topographic inclines; and (4) s land cover mask to minimize errors of commission specifically within recognized agricultural regions. SLIP was developed for MODIS and L8 imagery, adapted by integrating the inverse NDVI to assess the soil bareness (aSLIP) [33], and improved to utilize S2 instead of L8 imagery (iSLIP) [34]. In addition, Zhang, et al [35] discussed the potential presence of "old landslides"-areas of previously triggered landslides that did not (fully) recover at the time of the new incident.…”

Section: Satellite Remote Sensing-based Landslide Detectionmentioning

confidence: 99%

“…Next, the spatial extent of sampling areas and the multi-band stacked input image were reduced by removing pixels within areas of low slopes, less than 10 degrees. Such slope filtering has been applied in many of the existing RS-based landslide detection approaches, including [29,[32][33][34]. This, however, can cause false positives (FP) for scenarios in which the landslide is triggered at steeper slopes, but its flow area reaches into areas of lower slopes, as illustrated in Figure 3.…”

Section: Sampling Strategymentioning

confidence: 99%

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Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Peters,

Liu,

Keppel

et al. 2024

Remote Sensing

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Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management.

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Section: Discussionmentioning

confidence: 92%

Section: Satellite Remote Sensing-based Landslide Detectionmentioning

confidence: 99%

Section: Sampling Strategymentioning

confidence: 99%

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Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Peters,

Liu,

Keppel

et al. 2024

Remote Sensing

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“…In the study area, most of the landslide events were recorded during and after rainfall periods, indicating that rainfall attribute is directly associated with landslide occurrence [43,45,61]. In addition, several researchers have verified that intense and continuous rainfall affects the slope stability, which consequently results in landslide occurrence [15,29,[75][76][77][78][79]. Based on their significant relationship and impact on slope stability, the selected attributes are of great importance in landslide susceptibility assessment, not only in this study area but also in other areas around the world [19,20,[80][81][82].…”

Section: Landslide Influencing Attributes (Lias)mentioning

confidence: 86%

Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

et al. 2023

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Landslide susceptibility mapping (LSM) studies provide essential information that helps various authorities in managing landslide-susceptible areas. This study aimed at applying and comparing the performance of DIvisive ANAlysis (DIANA) and RObust Clustering using linKs (ROCK) algorithms for LSM in the Baota District, China. These methods can be applied when the data has no labels and when there is insufficient inventory data. First, based on historical records, survey reports, and previous studies, 293 landslides were mapped in the study area and 7 landslide-influencing attributes were selected for modeling. Second, the methods were clustered in the study area mapping units into 469 and 476 subsets, respectively; for mapping landslide susceptibility, the subsets were classified into 5 susceptibility levels through the K-means method using landslide densities and attribute values. Then, their performances were assessed and compared using statistical metrics and the receiver operating curve (ROC). The outcomes indicated that similarity measures influenced the accuracy and the predictive power of these clustering models. In particular, when using a link-based similarity measure, the ROCK performed better with overall performance accuracy of 0.8933 and an area under the curve (AUC) of 0.875. The maps constructed from the models can be useful in landslide assessment, prevention, and mitigation strategies in the study area, especially for areas classified with higher susceptibility levels. Moreover, this comparison provides a new perspective in the selection of a considerable model for LSM in the Baota District.

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“…It is an multi-sensor approach that explores changes using four thresholds: 1) Increases in red wavelength band to signify the exposure of bare earth; 2) Variations in the Shortwave Infrared (SWIR) bands to indicate changes in soil moisture; 3) Steep slopes, determined from DEM, to restrict the detection process to areas with pronounced topographic inclines; and 4) A land-cover mask to minimize errors of commission specifically within recognized agricultural regions. SLIP had been developed for MODIS and L8 imagery, adapted by integrating the inverse NDVI to assess the soil bareness (aSLIP) [37] and improved to utilize S2 instead of L8 imagery (iSLIP) [38]. In addition, Zhang, et al [39] discussed the potential presence of "old landslides" -areas of previously triggered landslides that did not (fully) recover at the time of the new incident.…”

Section: Satellite-remote Sensing Based Landslide Detectionmentioning

confidence: 99%

Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery

Peters,

Liu,

Keppel

et al. 2024

Preprint

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Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide-disaster management. Existing Remote Sensing approaches mostly use either optical or Synthetic Aperture Radar (SAR) sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data, along with relevant spectral indices and suitable bands using machine-learning-based classification for coseismic landslide detection implemented in Google Earth Engine (GEE). The approach includes a robust and reproducible training and validation strategy. Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide iden-tification and localization, providing a detection accuracy of 87-92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proofed that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of co-seismic landslides, highlighting its potential to contribute towards more effective disaster management.

show abstract

Adapting sudden landslide identification product (SLIP) and detecting real-time increased precipitation (DRIP) algorithms to map rainfall-triggered landslides in Western Cameroon highlands (Central-Africa)

Cited by 6 publications

References 64 publications

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery

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