PSInSAR ＆ Landslide その2

こちらは地すべり分布図と PSInSAR の組み合わせ。
前者はDEM＋RF。その精度を高めるのに後者を使用。

Andrea Ciampalini et al. (2016)
Landslide susceptibility map refinement using PSInSAR data
https://www.sciencedirect.com/science/article/pii/S0034425716302759

Landslide Susceptibility Map
The landslide susceptibility map (LSM) of Messina Province was produced by implementing a Random Forests (RF) algorithm in Matlab (Catani et al., 2013).
To avoid subjectivity in the choice of explanatory variables, several parameters were considered among the DEM-derived products: Aspect, Planar Curvature, Profile Curvature, Curvature s.s., Elevation, Flow Accumulation, Topographic Wetness Index (TWI), Log Flow Accumulation and Slope.
The choice of parameters depends on the map unit resolution (MUR) (Catani et al., 2013). Among the possible parameters, we used those suggested for a map unit resolution of 100 m.
Using this MUR, the expected Area Under the Curve (AUC) value is 0.81. A training set was created by randomly selecting 10% of the landslide database.
The DEM had a 20 by 20 m spatial resolution. In the LSM, the average value inside a 100 by 100 m cell was calculated.
Each pixel was classified using four susceptibility classes:
(i) low to null (0–0.3)
(ii) moderate (0.3–0.55)
(iii) high (0.55–0.75)
(iv) very high (0.75–1)

PSInSAR
SqueeSAR: TRE ALTAMIRA, detection of millimetre surface displacements, improving previous PSInSAR algorithm.
Xband COSMO-SkyMed (CSK) in Stripmap mode (40 × 40 km in the range and azimuth directions and 3 × 3 m ground resolution) in both ascending and descending geometry

Integration
The VSlope intervals were determined based on the standard deviation (σ = 7 mm/year) of VSlope for the whole PSI data set after merging the ascending and descending data sets. The matrix in Table 3 shows the correction factors for each considered case. The velocity intervals were determined to increase the susceptibility degree from level 1 to 4. For example, an area characterized by a susceptibility degree of 1 will increase by 1 degree if its VSlope falls within 1 σ to 2 σ of the stability threshold (7–14 in this case), 2 degrees if the VSlope is between 2 σ and 3 σ and so on. The higher the ground deformation velocity, the higher the susceptibility degree. A cell characterized by the maximum susceptibility degree cannot increase further.

Discussion
To improve the PS/DS detection, L-band SAR sensors can be used because their wavelength (30–15 cm) is more suitable to investigate densely vegetated areas.
Both the landslide susceptibility map and the ground deformation velocity map can be used in different ways to forecast landslide occurrence in a selected area. Combining them may improve the reliability associated with predicting these types of phenomena, particularly for slow moving landslides