Deijns, A., Bevington, A., van Zadelhoff, F., de Jong, S., Geertsema, M. & Mcdougall, S. 2020. ‘Semi-automated detection of landslide timing using harmonic modelling of satellite imagery, Buckinghorse River, Canada’. International Journal of Applied Earth Observation and Geoinformation 84: 10. Elsevier. ISSN: 0303-2434. DOI: https://doi.org/10.1016/j.jag.2019.101943. URL: https://www.sciencedirect.com/science/article/pii/S0303243419303484 I.F. 4.65.
Article dans une revue scientifique / Article dans un périodique
We manually detected and mapped 66 landslides from Landsat imagery over a 33-year period from 1985 to 2017 in the Buckinghorse River region, British Columbia, Canada. We semi-automatically determined landslide timing using the cumulative difference (CD) between the normalized difference vegetation index (NDVI) and a fitted harmonic sinusoidal curve (CDNDVI). The semi-automated dating method was capable of determining the timing of 80% of the landslides using CDNDVI and 85% of the landslides after detrending CDNDVI (dCDNDVI). The CDNDVI method generally detects landslides too early and the dCDNDVI method is generally too late. Mean absolute errors (in days) are lower for the dCDNDVI (208 and 188) than the CDNDVI (227 and 267), respectively. This study, however, has many examples of extreme outliers with very large errors (>1000 days). Our method is portable to other remote regions as long as vegetation anomalies can be used as an indicator for landslide activity. We conclude that the timeseries of images available in the Landsat Archive are useful for landslide mapping, but the pixel size limits the size of the landslides that can be mapped.