criteria for the optimal selection of remote sensing optical images to map event landslides
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2018
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Abstract
Landslides leave discernible signs on the land surface, most of which can be
captured in remote sensing images. Trained geomorphologists analyse remote
sensing images and map landslides through heuristic interpretation of
photographic and morphological characteristics. Despite a wide use of remote
sensing images for landslide mapping, no attempt to evaluate how the image
characteristics influence landslide identification and mapping exists. This
paper presents an experiment to determine the effects of optical image
characteristics, such as spatial resolution, spectral content and image type
(monoscopic or stereoscopic), on landslide mapping. We considered eight maps
of the same landslide in central Italy: (i) six maps obtained through expert
heuristic visual interpretation of remote sensing images, (ii) one map
through a reconnaissance field survey, and (iii) one map obtained through a
real-time kinematic (RTK) differential global positioning system (dGPS)
survey, which served as a benchmark. The eight maps were compared pairwise
and to a benchmark. The mismatch between each map pair was quantified by
the error index, E. Results show that the map closest to the benchmark
delineation of the landslide was obtained using the higher resolution image,
where the landslide signature was primarily photographical (in the landslide
source and transport area). Conversely, where the landslide signature was
mainly morphological (in the landslide deposit) the best mapping result was
obtained using the stereoscopic images. Albeit conducted on a single
landslide, the experiment results are general, and provide useful
information to decide on the optimal imagery for the production of event,
seasonal and multi-temporal landslide inventory maps.
| Reference Key |
fiorucci2018naturalcriteria
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|---|---|
| Authors | ;F. Fiorucci;D. Giordan;M. Santangelo;F. Dutto;M. Rossi;F. Guzzetti |
| Journal | anziam journal |
| Year | 2018 |
| DOI |
10.5194/nhess-18-405-2018
|
| URL | |
| Keywords |
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