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) - Alfred Homère Ngandam Mfondoum; Pauline Wokwenmendam Nguet; Jean Valery Mefire Mfondoum; Mesmin Tchindjang; Sofia Hakdaoui; Ryan Cooper; Paul Gérard Gbetkom; Joseph Penaye; Ateba Bekoa; Cyriel Moudioh (Geoenvironmental Disasters, 2021)
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 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.