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)

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