Next Article in Journal
Taking Advantage of the ESA G-POD Service to Study Ground Deformation Processes in High Mountain Areas: A Valle d’Aosta Case Study, Northern Italy
Next Article in Special Issue
Spatial Correlation of Satellite-Derived PM2.5 with Hospital Admissions for Respiratory Diseases
Previous Article in Journal
Abrupt Change in Forest Height along a Tropical Elevation Gradient Detected Using Airborne Lidar
Previous Article in Special Issue
Spectral Discrimination of Vegetation Classes in Ice-Free Areas of Antarctica
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(10), 866; doi:10.3390/rs8100866

Guidance Index for Shallow Landslide Hazard Analysis

1
Department of Earth and Atmospheric Science, Graduate Center, The City University of New York, New York, NY 10016, USA
2
Department of Civil Engineering, The City College of New York, New York, NY 10031, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Yuei-An Liou, Chyi-Tyi Lee, Yuriy Kuleshov, Jean-Pierre Barriot, Chung-Ru Ho, Richard Gloaguen and Prasad S. Thenkabail
Received: 9 August 2016 / Revised: 29 September 2016 / Accepted: 14 October 2016 / Published: 20 October 2016
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
View Full-Text   |   Download PDF [4159 KB, uploaded 20 October 2016]   |  

Abstract

Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness. View Full-Text
Keywords: shallow landslides; root-soil moisture; SMAP; GPM; logistic regression shallow landslides; root-soil moisture; SMAP; GPM; logistic regression
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Avalon Cullen, C.; Al-Suhili, R.; Khanbilvardi, R. Guidance Index for Shallow Landslide Hazard Analysis. Remote Sens. 2016, 8, 866.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top