## 6 Discussion  
By taking advantage of a large dataset of pre- and post-failure images of the 3 December 2013 Montescaglioso landslide, an extensive sensitivity analysis using DIC has been performed. In addition to the evaluation of the landslide displacement pattern, this work provided insights into the reliability and accuracy of the DIC methodology for landslide investigation and monitoring purposes. For most of the analyses, the percentage of the landslide area with a good correlation (i.e., where reliable deformation values can be derived) exceeded 70% of the available pixels, thereby providing a comprehensive overview of the landslide deformation field. The landslide deformation field was quite consistent among the different datasets and the different data processing techniques used in this paper, thus demonstrating the reliability of the measurement technique. Furthermore, by selecting a correlation threshold of 0.9, the results are similar to those available in the literature [38; 39; 41; 42; 43; 55; 73; 74; 75; 76], as well as the evidence inferred from field inspections. Figure 16 and Table 5 show the comparative results between the analyses performed using the COSI-Corr and GOM Correlate software for the same dataset. An analysis of the corresponding pixels (Table 5) showed that the average discrepancy was less than 0.2 pixels, with the differences ranging from 0.07 to 0.54 pixels. Figure 15: DIC displacement map retrieved from the HR (high-resolution) optical orthophoto with COSI-Corr software. The white arrows represent the displacement vector field direction of the landslide (red polygon), which is consistent with the other displacement maps. Grey coloring corresponds to areas with loss of correspondence between the pre- and post-failure. deviation of the background noise value, a window (i.e., background noise interval) corresponding to an overall percentage of pixels equal to 30% was calculated for each curve. Focusing only on the two end members, LANDSAT 8 OLI-TIRS is characterized by a displacement accuracy of approximately 1/10 of a pixel, whereas the shaded DTM dataset is characterized by a displacement accuracy of approximately 8/10 of a pixel (Figure 17). However, if the different pixel sizes of the two datasets (i.e., 15 m for LANDSAT OLI-TIRS and 1 m for the shaded DTM) are accounted for, better accuracy is achieved for the DTM dataset (i.e., 0.8 m) than for the LANDSAT OLI-TIRS dataset (i.e., 1.3 m) (Figure 18). For the 30% reliability windows, high variability between the different datasets can be observed, and is likely caused by the different image resolutions; i.e., geometric, radiometric and spectral [85]. For example, by only focusing on the two end members, we can see that the orthophoto dataset is characterized by a reliability window of approximately 0.05 m, whereas the LANDSAT OLI-TIRS dataset is characterized by a reliability window of 0.9 m (Figure 18). Figure 17: Diagram showing the background noise (in pixels) histograms for each dataset considered using COSI-Corr software. Each curve represents the percentage of pixels (on the y-axis) characterized by different values of background noise/displacement accuracy (on the x-axis) for each dataset. For each curve, the most likely value of displacement accuracy (i.e., the one with the highest percentage of pixels) and the reliability interval above 30% of the measured background noise are shown. Figure 18: Diagram showing the background noise (in meters) histograms for each dataset considered using COSI-Corr software. Each curve represents the percentage of pixels (on the y-axis) characterized by different values of background noise/displacement accuracy (on the x-axis) for each dataset. For each curve, the most likely value of the displacement accuracy (i.e., the one with the highest percentage of pixels) and the reliability interval above 30% of the measured background noise are shown.