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Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information

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Abstract

A series of earthquakes occurred in Kumamoto, Japan, in April 2016, which caused numerous landslides. In this study, high-resolution pre-event and post-event optical imagery, plus bi-temporal Synthetic Aperture Radar (SAR) data are paired with geospatial data to train a pixel-based machine learning classification algorithm using logistic regression to identify landslides occurred because of the Kumamoto earthquakes. The geospatial data used include a categorical variable (surficial geology), and six continuous variables including elevation, slope, aspect, curvature, annual precipitation, and landslide probability derived by the USGS preferred geospatial model which incorporates ground shaking in the input parameters. Grayscale index change and vegetation index change are also calculated from the optical imagery and used as input variables, in addition to temporal differences in HH (horizontally transmitted and horizontally received polarization) and HV (horizontally transmitted and vertically received polarization) amplitudes of SAR data. A detailed human-drawn landslide occurrence inventory was used as ground-truth for model development and testing. The selection of optimal features was done using a supervised feature ranking method based on the Receiver Operating Characteristic (ROC) curve. To weigh the benefit of combining different types of imagery, temporal change information and geospatial environmental indicators for landslide mapping after earthquakes, five different combinations of features were tested, and the results showed that adding data of selected geospatial parameters (landslide probability, slope, curvature, precipitation, and geology) plus selected change indices (grayscale index change, vegetation index change, and HV amplitude difference of SAR data) to the imagery (post event optical) lead to the highest classification accuracy of 86.5% on class-balanced independent testing data. A comparative analysis was conducted to evaluate the performance of the proposed method with five other commonly used machine learning classification methods, and the results have shown the superiority of the logistic regression method, followed by support vector machines.

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Acknowledgements

The support of National Geospatial Intelligence Agency (NGIA) for this research through the NGIA Academic Research Program Grant #HM0476-20-1-0006 (NGIA NURI Project: Benchmark data development to classify damage for natural disaster relief efforts) is greatly appreciated. The authors are also thankful for the support of U.S. Geological Survey (USGS) through research grant #G22AS00006–Proposal 2022-0047 (Innovative data-driven frameworks for geospatial ground failure models). The authors would also like to acknowledge the GSI, JAXA, Geological Survey of Japan and NIED organization of Japan for publishing their GIS datasets of landslide inventories, aerial imagery, and satellite SAR datasets related to the Kumamoto 2016 earthquake, plus the geology map of the study area. All other organizations which provided data resources are greatly appreciated, including DigitalGlobe (MAXAR) for the satellite imagery used in this study, NASA/METI and their collaborators for providing the digital elevation products, USGS for the Ground Failure products (https://earthquake.usgs.gov/data/ground-failure/), and WorldClim database (https://www.worldclim.org/data/index.html) for the historical precipitation data.

Funding

The support of National Geospatial Intelligence Agency (NGIA) for this research through the NGIA Academic Research Program Grant #HM0476-20–1-0006 (NGIA NURI Project: Benchmark data development to classify damage for natural disaster relief efforts) is greatly appreciated. The authors are also thankful for the support of U.S. Geological Survey (USGS) through research grant #G22AS00006 – Proposal 2022–0047 (Innovative data-driven frameworks for geospatial ground failure models).

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Authors and Affiliations

  1. Geohazards Research Lab, Department of Civil and Environmental Engineering, School of Engineering, Tufts University, Medford, MA, 02155, USA

    Adel Asadi, Laurie G. Baise & Babak Moaveni

  2. Center for Remote Sensing, Department of Earth and Environment, Boston University, Boston, MA, 02215, USA

    Magaly Koch & Yusupujiang Aimaiti

  3. Geological and Mining Engineering and Sciences Department, Michigan Technological University, Houghton, MI, 49931, USA

    Snehamoy Chatterjee

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  1. Adel Asadi
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  6. Yusupujiang Aimaiti
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Contributions

AA contributed to Dataset preparation, literature review, framework design, methodology development, computer programming, model validation, manuscript writing, visualizations. LGB contributed to Conceptualization and idea development, model review, manuscript writing, manuscript review, project management, supervision. MK contributed to Dataset preparation, model review, manuscript review, supervision. BM contributed to Model review, model evaluation, manuscript review, supervision. SC contributed to Model review, methodology development, manuscript review, supervision. YA contributed to Dataset preparation, methodology development, manuscript review.

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Correspondence to Adel Asadi.

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Asadi, A., Baise, L.G., Koch, M. et al. Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information. Nat Hazards 120, 5163–5200 (2024). https://doi.org/10.1007/s11069-023-06399-8

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