The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 679–684, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-679-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 679–684, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-679-2021

  29 Jun 2021

29 Jun 2021

LANDSLIDE DETECTION IN CENTRAL AMERICA USING THE DIFFERENTIAL BARE SOIL INDEX

A. Ariza1, N. A. Davila2, H. Kemper1, and G. Kemper3 A. Ariza et al.
  • 1UN-SPIDER, Bonn, Germany
  • 2UNAM, Mexico City, Mexico
  • 3GGS GmbH Speyer, Germany

Keywords: Landslides, Differential Bare Soil Index, Object-based detection, Disaster Management

Abstract. The increasing availability of EO data from the Copernicus program through its Sentinel satellites of the medium spatial and spectral resolution has generated new applications for risk management and disaster management. The recent growth in the intensity and number of hurricanes and earthquakes has demanded an increase in the monitoring of landslides. It is necessary to monitor large areas at a detailed level, which has previously been unsatisfactory due to its reliance on the interpretation of aerial photographs and the cost of high-resolution images.

Using the differential Bare Soil Index for optical imagery interpretation in combination with cloud-computing in Google Earth Engine is a novel approach. Applying this method on a recent landslide event in Oaxaca in Mexico around 62% of the landslides were detected automatically, however, there is a big potential for improvement. Including NDVI values and considering images with a higher spatial resolution could contribute to the enhancement of landslide detection, as the majority of missed events have a size smaller than half a pixel. Landslide detection in Google Earth Engine has become a promising approach for big data processing and landslide inventory creation.