Table 1.

Landslide-related studies using various sensors on airborne platforms.

Data type	Site characteristic	Method	Finding	Reference
•RGB images	•Forest	•Landslide detection	•The results has high accuracy and the effectiveness of predicting new potential landslides is demonstrated. •The results showed that the landslide inventory suggested a potentially important supplement to field mapping.	[27,28,[30], [31], [32], [33],35]
•RGB + Multi-spectral images	•Forest	•Landslide detection	•Continuous expansion of landslide scarps and movement of landslide head along the slope.	[29,34]
•RGB 3-D point cloud	•Hill •Cliff	•Landslide detection •Topographic change	•The slope portions being prone to failure and involved area and volume were calculated. •Understanding sediment provenance and transport. •Erosion and deposition of soils were detected.	[[36], [37], [38], [39]]
•LiDAR point cloud	•Forest	•Topographic change	•The sediment discharge was highly correlated with the flow length.	[40]
•LiDAR point cloud	•Forest	•Topographic data	•Landslide vulnerability and risk assessment.	[41]
•LiDAR point cloud	•Hill	•Topographic change	•Landslide area and volume were calculated from multiple LiDAR data. •Based on the data, they found the rupture of the retaining wall.	[42]
•LiDAR point cloud	•Cliff	•Topographic data	•Stability analysis of a blocky rock mass slope.	[43]
•Thermal infrared image	•Cliff	•Thermal change of permafrost slopes	•Thermal effect caused by engineering structures (i.e., highway, railway, electric tower, etc.) could increase the ground surface temperature and cause the underlying permafrost foundation to thaw.	[44]