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)

Background NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This double method uses Landsat 8 satellite images and daily rainfall data for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods The SLIP algorithm is adapted, by integrating the inverse Normalized Difference Vegetation Index (NDVI) to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948–2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate landslide occurrence to rainfall, with the first known event in Cameroon as starting point, and using the Cox model. Results From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime analysis for a more flexibility in observation and prediction thresholding.

LANDSLIDE DETECTION IN CENTRAL AMERICA USING THE DIFFERENTIAL BARE SOIL INDEX

The increasing availability of EO data from the Copernicus program through its Sentinel satellites of the medium spatial and spectral resolution has generated new applications for risk management and disaster management. The recent growth in the intensity and number of hurricanes and earthquakes has demanded an increase in the monitoring of landslides. It is necessary to monitor large areas at a detailed level, which has previously been unsatisfactory due to its reliance on the interpretation of aerial photographs and the cost of high-resolution images.Using the differential Bare Soil Index for optical imagery interpretation in combination with cloud-computing in Google Earth Engine is a novel approach. Applying this method on a recent landslide event in Oaxaca in Mexico around 62% of the landslides were detected automatically, however, there is a big potential for improvement. Including NDVI values and considering images with a higher spatial resolution could contribute to the enhancement of landslide detection, as the majority of missed events have a size smaller than half a pixel. Landslide detection in Google Earth Engine has become a promising approach for big data processing and landslide inventory creation.

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroon highlands (Central-Africa)

Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Characteristics and emergency mitigation of the 2018 Laochang landslide in Tianquan County, Sichuan Province, China

This paper describes a recent landslide event, which occurred at Liucheng village in Tianquan County, Sichuan Province, China, on July 15, 2018. The Laochang landslide described in this research is an outstanding and valuable reference for understanding the characteristics of such kind of landslides that are geologically similar to the landslide. The deformation characteristics of the landslide are investigated based on field investigations, drilled boreholes, and exploratory trenches. The 225 residents of 64 households living on the flat platform were threatened by the landslide. Therefore, to guarantee the safety of human life and property becomes the primary emergency task. The anti-sliding piles were taken to stabilize the landslide and mitigate impacts caused by the landslide. Based on the analysis of the monitoring data, the effectiveness of anti-sliding piles is evaluated. The results indicate that the anti-sliding piles are effective in increasing the stability of the landslide, and this work can provide a reference for similar slope engineering projects.

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model.Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96 % . Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99% , between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and the intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlating the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – The outcome of the SLIP adapted model is the Landslide Hazard Zonation (LHZ) map, with an overall accuracy of 96%. Further, the outcome of the DRIP adapted model states that the probability of rainfall-triggered landslides is 99.99%, for 6/9 ranges of probability between June and October. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Monsoon-driven landslide dynamics in Nepal – the complex mass movement system in the Muktinath Valley

<p>The Nepalese Himalaya is affected by a major rift valley, the Thakkhola half graben (THG). Along this fault-bounded basin, the Kali Gandaki (KG) flows from the Tibetan plateau southwards to the Dhaulagiri and Annapurna massifs, where it forms the deepest gorge on earth. The THG has been filled with up to 1 km thick Plio- and Pleistocene sediments, underlain by clay shales of the Jurassic Spiti Formation that are strongly water swellable and prone to landslides. These pre-conditions led to a series of large and complex landslides, particularly along the eastern flank of the THG, with strong effects on infrastructure and the local population. One of these landslide systems (c. 15 km²) is located in the semi-arid Muktinath Valley, a tributary basin of the KG (c. 92.5 km²).  Water as most important driver of the system is provided by precipitation mainly during the summer monsoon (annual rainfall: ~ 350 mm), snowmelt and irrigation.</p><p>Against this background, we aim i) to better understand regional-scale landslide systems (spatial pattern, drivers/controls), ii) to establish a long-term monitoring of local-scale landsliding in the Muktinath Valley, and iii) to share our findings with local communities to support the development of mitigation strategies.</p><p>Reconstruction of landslide dynamics over the past 30 years is based on local information (interviews), field observations (damaged buildings and walls), geomorphological mapping and multi-temporal (ortho-) photo analyses (WorldView, Pleiades). Since 2018, annual UAV surveying is applied.</p><p>Results include a geomorphological map of the area focusing on landslide related processes and landforms, indicators of recent landslide activity, hydrologic characteristics and irrigation infrastructure, as well as the distribution of Spiti shale outcrops. Surrounding the presently most active landslide, we observed an average displacement of c. 20 cm/a since 1988 with an increasing trend towards present (30 - 50 cm/a since 2011). In the center of the most active landslide significantly higher displacements of up to 15 m have been detected since 2011, which corresponds to an average of about 2 m/a. The landslide monitoring based on UAV surveying, structure-from-motion processing and different approaches of high-resolution topographic change and error modelling (DEM resolution: 2.6 - 4.3 cm) shows massive change between April 2018 and March 2019 (gain: 33395 ± 5489 m³; loss: 50276 ± 10781 m³), accompanied by a total sediment export of 16881 ± 12098 m³ to the Jhong River. Detailed orthophotos (resolution: 1.29 - 2.15 cm) provide valuable supplementary information not only on recent landslide propagation and dynamics but also with regard to future threatened areas (opening cracks). Boosted landslide activity in 2018 is associated to the strong monsoon that heavily impacted in the larger region as well (debris flows, flash floods, multiple bank collapses): In August 2018 Muktinath recorded the highest monthly rainfall since 1978 (172 mm, DHM Nepal). </p><p>The research is located at the interface between humans and the environment. The "symbiosis" of the local population and the landslide system is unique - and enables to deconstruct various interacting landslide processes driven and modified by climate (change) and human impact.</p>