CAPITULO II: ASPECTOS TÉCNICOS DEL ESTUDIO
6.1 CONSIDERACIONES GENERALES
The radar data for the Palermo province consist of 74 COSMO-SkyMed Stripmap images (38 ascending and 36 descending) with 40x40 Km swath width and 3x3 m geometrical resolution. These cover the region of interest between November 2008 and October 2011, with ~40° look angle at the centre of the scene, and a nominal revisiting time of 16 days.
The ISBAS algorithm has been chosen for the A-DInSAR processing, because it increases the radar targets in non-urban areas (e.g., agricultural terrain, forests, vegetated terrain, water bodies or wetlands). Non-urban areas are extensively diffuse in the three selected sites where they represent, on average, over 60% of the surface. Consequently the absence or poor coverage of potential radar points does not allow for the use of conventional SBAS techniques. The low radar targets coverage is shown in the Target Suitability Map - TSM (fig. 4.43; Cigna et al., 2013).
TSM for X-band sensor shows highest predicted densities of radar targets (up to 8000 /km2) over the urban areas of Piana degli Albanesi (fig. 4.43a), Bolognetta (fig. 4.43b) and Marineo (fig. 4.43c), while the lowest densities (< 5-20/km2) correspond to water bodies (Piana degli Albanesi and Scanzano lakes).
118
Figure 4.43 - Target Suitability Map of X-band data for the different land cover classes. Piana degli Albanesi (a), Marineo (b) and Ventimiglia di Sicilia areas (c).
In particular the Ventimiglia di Sicilia area is the most critical, because over 70% of the territory is characterized by a low-density of targets (fig. 4.43c). The ISBAS approach, differently from the standard A-DInSAR methodologies, overcomes the limitations due to land cover to guarantee an almost fully coverage in the regions of interest.
To obtain such a result a γ threshold of 0.4 was used to discriminate good from low coherence pixels and an interferogram threshold of 160 and 140 has been set for the 306 ascending and 298 descending interferograms, respectively (Table 4.11; Novellino et al., 2014b). Therefore, the radar targets takes into consideration intermittently coherence pixels that are added to the continuously coherent pixels (Cigna et al., 2014).
119
Table 4.11 – Main ISBAS data processing parameters of CSK data for Sicily.
The square root of the number of interferograms adopted for each pixel is mathematically given by the Standard Error (SE) and expressed in the ‘cohcount’ image.
The cohcount image shows how non-urban areas always account for fewer interferograms than urban areas (fig. 4.44) and this effect is particularly evident for higher γ values (fig. 4.45).With an average SE of ±1 mm/yr in the ISBAS target measurements, the class of stable points has been identified between ±2mm/yr.
Spatial baseline threshold 500 m Temporal baseline threshold 2 years Multilooking factor (azimuth x range) 5 x 5
Coherence threshold 0.4
Number of interferograms 306asc – 298desc Interferograms threshold 140/160
120 0 50 100 150 200 250 300 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 n o f in te rf er og ra m s w h e re t he p ix el is c o he re nt ϒ threshold CSK, coherence vscohcount water terrain rocks urban area Poli. (water) Poli. (terrain ) Poli. (rocks) Poli. (urban area)
Figure 4.44 – Example of how γ and p affect the target coverage in the Piana degli Albanesi area: average coherence maps from all 306 interferograms and a γ threshold of 0.4 (a); average coherence maps from 160 interferograms and a γ threshold of 0.4 (b); frequency histogram of a and b (c); average coherence maps from all 306 interferograms and a γ threshold of 0.65 (d); average coherence maps from 160 interferograms and a γ threshold of 0.65 (e); frequency histogram of d and e (f).
Figure 4.45 – Example of how the land cover affects the cohcount value in the CSK images: the higher the γ value threshold, the smaller the number of interferograms where the pixel is coherent. The non-urban areas are more affected by the γ value threshold as corroborated by the higher tilt angle of the fitting curve.
Three kinds of parameters have been taken into account to verify the possible exploitation and reliability of ISBAS data: (i) a threshold for the minimum numbers of radar targets inside a landslide,
121
(ii) the evaluation of the velocity along the slope dip direction and (iii) the comparison with conventional landslide mapping techniques, whose result will be illustrated in section 4.4.5.
As concern the minimum number of radar targets inside a landslide considered as sufficient for the inclusion in the updating landslide process, this value ranges from 5, for a landslide area <0.05 Km2, to 20, for a landslide area >1 Km2. Indeed, analyses established on considering single or few targets may not be indicative of a landslide process but more likely due to a single object (e.g. building settlement).
Through the 2,729,188 ISBAS points identified within the three study areas between ascending and descending geometries, 371,360 coincide with landsliding areas and <1% for the 368 phenomena surveyed do not have a sufficient number of radar targets.
In addition, the reliability of ISBAS results (both ascending and descending) is also geometrically implemented by considering the velocity along the slope dip direction; this parameter should always be positive implying that it follows the force of gravity. Therefore the LOS deformation data (VLOS) has
been converted, following the GIS-procedure of Cigna et al. (2012), to the steepest slope direction (VSLOPE) for each landslide, using the equations [8 and 9]:
[8]
[9]
Where the ρ value represents the conversion factor related to the projection of the LOS to slope values, and E, N, and Z are the directional cosines of the LOS and the slope vectors in the east, north, and zenith directions, respectively.
The azimuth (α) and slope (β) angles of VSLOPE were evaluated, as the average of the aspects and slopes
of the 10 m DEM cells used to estimate the directional cosines of the slope through the equations [10, 11 and 12]:
122
[11]
[12] An example of the VSLOPE evaluation is given in Figure 4.46.
Figure 4.46 - 3D view of the updated landslide mapping in the Marineo village, captured from a ~45° SW angle. Heights of the DEM are amplified by a factor of 2. Velocity vectors of the landslide are shown with arrows; they refer to descending ISBAS data. Landslide location is labelled as Hotspot 1in Figure 4.56. A sudden collapse of a 50-m wide section of this flow occurred in the foot area on the 26th of February 2015.
123