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Susceptibility maps are essential to identify the potential loss of areas affected by these geological processes, minimizing its impact (Das, Sahoo, van Westen, Stein, & Hack, Citation2010). Landslides are one of the greatest natural threats to human endeavors, as they often cause human and economic losses, as well as property damage, and elevated costs in infrastructure maintenance (Shahabi, Khezri, Ahmad, & Hashim, Citation2014). This result was contrasted with a spatial prediction model of debris flow, explaining the high frequency of avalanches. This model’s performance was tested with a ROC curve over a sample, with 20% of landslide database resulting in an Area Under the Curve of 0,55. The binary logistic regression showed the propensity of the area to be adversely affected by landslides. The resulting landslide susceptibility mapping took dependent (a mask with unstable-stable cells from an existing landslide inventory) and independent variables (selected morphometric ones).

We got a Kappa coincidence index of about 30%. Then, the Principal Component Analysis selected variables with low collinearity, and we classified twelve landforms using fuzzy k-means algorithm, which was compared to a geomorphological map by using the multinomial logistic regression method in R software. The PALSAR_RTC_hi data was selected for having the best accuracy of heights and was used to derivate terrain parameters at SAGA software. From a morphometric stance, the first step was to evaluate the quality of DEM sources by comparison to control points obtained by static-mode GPS. Thus for the study site, using ICESat-2 ATL06 products, ALOS PALSAR RTC HR DEM is found more suitable than TanDEM-X 90m openly accessible datasets for any kind of application.This study demonstrated the potential of methods derived from geomorphometry and regression models to evaluate landslide susceptibility in a study area located in southern Colombia. Whereas the ME, MAE, and RMSE for ALOS PALSAR RTC HR DEM were found as 0.20m, 9.50m, and 13.88m respectively. Considering higher accuracy ICESat-2 values for the difficult terrain as a reference, the mean error (ME), mean absolute error (MAE), and RMSE for TanDEM-X were found as 0.26m, 12.92m, and 17.4m respectively. The standard deviation representing terrain ruggedness using ICESat-2 elevation values is found as 432.06m. The results of Track ID: 325, show that the range of elevations in ICESat-2 elevation values in the study area is from 3409.75m to 5976.31m. The visualization of the region in the Google earth and OpenAltimetry 3D viewer depicts that the mountain slopes are very steep indicating rugged terrain difficult to access and challenging for construction of transport facilities. The analysis of pre-processed 19,755 ICESat-2 footprints (out of 20,948 footprints) was done with ALOS PALSAR RTC HR (12.5m) and TanDEM-X (90m) datasets. Preprocessing of datasets was done for selecting ICESat-2 footprints (Track ID: 325, 1270, 828, 386) at locations of high-quality datasets for analysis. The ICESat-2 elevation data sets were compared with the openly accessible DEM datasets namely, ALOS PALSAR RTC HR (12.5m) and TanDEM-X (90m) at ICESat-2 footprint locations. The ICESat-2 datasets are used in this study for the visualization and investigation of the complex Himalayan terrain in the parts of the Kinnaur district and surroundings, which are prone to landslides due to the geology of the region as observed during the recent landslide events. Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) with Advanced Topographic Laser Altimeter System (ATLAS) instrument was launched by NASA on September 15, 2018, to measure the elevation of Earth’s surface using laser wavelength of 532 nm and pulse repetition frequency of 10kHz giving footprint of approximately 70cm on the ground.

Spaceborne sensors are now providing invaluable datasets for the Earth’s surface studies.