Remote Sensing for Assessing Landslides and Associated Hazards
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geochemistry and Petrology,Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s10712-020-09609-1.pdf
Reference237 articles.
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3. Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
4. Anders NS, Seijmonsbergen AC, Bouten W (2011) Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens Environ 115(12):2976–2985. https://doi.org/10.1016/j.rse.2011.05.007
5. Arabameri A, Pradhan B, Rezaei K, Lee CW (2019) Assessment of landslide susceptibility using statistical-and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sens 11(9):999. https://doi.org/10.3390/rs11090999
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