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Article

A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia

1
CUNY-Remote Sensing Earth System Institute (CUNY-CREST Institute), The City University of New York, New York, NY 10453, USA
2
Department of Civil Engineering, The City College of New York, New York, NY 10031, USA
3
Departamento de Geociencias y Medio Ambiente, Universidad Nacional de Colombia, Sede Medellín 050034, Colombia
*
Author to whom correspondence should be addressed.
Academic Editor: Francesca Ardizzone
Remote Sens. 2022, 14(9), 2239; https://doi.org/10.3390/rs14092239
Received: 25 April 2022 / Revised: 3 May 2022 / Accepted: 4 May 2022 / Published: 7 May 2022
(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)
Despite great advances in remote sensing technologies, accurate satellite information is sometimes challenged in tropical regions where dense vegetation prevents the instruments from retrieving reliable readings. In this work, we introduce a satellite-based landslide rainfall threshold for the country of Colombia by studying 4 years of rainfall measurements from The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for 346 rainfall-triggered landslide events (the dataset). We isolate the two successive rainy/dry periods leading to each landslide to create variables that simulate the dynamics of antecedent wetness and dryness. We test the performance of the derived variables (Rainfall Period 1 (PR1), Rainfall Sum 1 (RS1), Rainfall Period 2 (PR2), Rainfall Sum 2 (RS2), and Dry Period (DT)) in a logistic regression that includes three (3) static parameters (Soil Type (ST), Landcover (LC), and Slope angle). Results from the logistic model describe the influence of each variable in landslide occurrence with an accuracy of 73%. Subsequently, we use these dynamic variables to model a landslide threshold that, in the absence of satellite antecedent soil moisture data, helps describe the interactions between the dynamic variables and the slope angle. We name it the Landslide Triggering Factor—LTF. Subsequently, with a training dataset (65%) and one for testing (35%) we evaluate the LTF threshold performance and compare it to the well-known event duration (E-D) threshold. Results demonstrate that The LTF performs better than the E-D threshold for the training and testing datasets at 71% and 81% respectively. View Full-Text
Keywords: rainfall-triggered landslides; tropics; statistical analysis; CHIRPS rainfall-triggered landslides; tropics; statistical analysis; CHIRPS
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MDPI and ACS Style

Cullen, C.A.; Al Suhili, R.; Aristizabal, E. A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia. Remote Sens. 2022, 14, 2239. https://doi.org/10.3390/rs14092239

AMA Style

Cullen CA, Al Suhili R, Aristizabal E. A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia. Remote Sensing. 2022; 14(9):2239. https://doi.org/10.3390/rs14092239

Chicago/Turabian Style

Cullen, Cheila Avalon, Rafea Al Suhili, and Edier Aristizabal. 2022. "A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia" Remote Sensing 14, no. 9: 2239. https://doi.org/10.3390/rs14092239

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