Elementos del dolo

As well as the wavelength, there are several other acquisition parameters that vary between SAR datasets that impact their applicability to landslide studies. One obvious parameter is the spatial resolution of the acquired data, which determines the smallest landslide that is likely to be detectable in the data. Another is the Swath width, which describes the coverage in the range direction. Adjacent scenes in the azimuth direction can be merged, but as scenes adjacent in the range dir- ection are acquired at different times and with differing viewing geometries, they have to be processed as separate tracks. To map landslides triggered over a large area, a wide swath width is advantageous as it allows all the landslide information to be extracted from data acquired at the same time and under the same condi- tions. Often satellite systems are capable of acquiring data in different modes, with a trade-off between swath width and spatial resolution. For example, the ALOS-2 satellite can acquire SAR data at a spatial resolution of 3 × 1 m with a swath width of 25 km up to a resolution of 60 m with a swath width of 490 km (JAXA, 2020). Smaller landslides could be detected at the first extreme, but a much larger area could be covered with the second. In this study, I use Sentinel-1 data with a spatial resolution of 2.7-3.5 × 22 m and a swath width of 250 km, and ALOS-2 data acquired at a spatial resolution of 3-10 m and swath width 30-70 km.

2.3

SAR Data Processing

SAR data requires more complex processing than optical satellite imagery before they can be used in landslide detection. I used the GAMMA SAR processing software package to process the the SAR data used in this study. For Sentinel-1, I used the LiCSAR processing software, which uses GAMMA tools to process SAR data (Lazeck`y et al., 2020). Processing SAR data also requires a DEM, for which I used the 1-arc-second Shuttle Radar Topography Mission (SRTM) DEM of Farr et al. (2007).

The Sentinel-1 data I used in this study was acquired in ’Interferometric Wide’ mode, and so every track is made up of three subswaths, which are in turn made up of a series of "bursts". Each burst can be considered as a separate SLC image. As the bursts overlap, and are acquired within a very short timeframe (within 10s of seconds), the bursts which cover the desired area can be stitched together into a complete SLC image. LiCSAR does this automatically (Lazeck`y et al., 2020). For the ALOS-2 data used here, this processing step was not necessary, and from here on, unless otherwise stated, the processing steps are the same for both ALOS-2 and Sentinel-1 data.

2.3. SAR Data Processing

The SAR methods I use in this thesis require a time series of SAR images spanning the earthquake. One of these is selected as the ’primary’ image, against which all other images are referenced. The first step in processing is to multi-look this image. Multi-looking is a spatial averaging step which increases the signal strength at the expense of the spatial resolution. In particular, the aim of multi-looking is to reduce noise due to ’speckle’, an effect of many scatterers of varying characteristics within a resolution cell being recorded as a single pixel (Figure 2.4; Moreira et al., 2013). As my purpose in this study was to detect landslides, the extent of the multi-looking was fairly limited in order to preserve the spatial resolution. For Sentinel-1, I multi-looked the Sentinel-1 data used in this study by five in range and one in azimuth, and the ALOS-2 data by 5 in both range and azimuth. The geometry of this primary Multi-looked Intensity (MLI) raster is then used as the reference geometry throughout the processing.

The next step is to generate a look-up table, which describes how the 2D image in the radar coordinate system reprojects to the geographic coordinate system. This is a two step process. An initial lookup table is calculated from the primary MLI and the DEM. This is then used to simulate an MLI, and the offset between this and the real MLI are used to refine the lookup table and improve its accuracy. This look-up table can then be used to convert any data in radar coordinates, for example, an interferogram, coherence map or MLI amplitude map, into geographic coordinates, or vice versa e.g. to generate a DEM in the radar coordinate system, which is necessary for SAR processing.

The next step is geometric coregistration. All SAR images used in any joint ana- lysis must have the same geometry as the primary MLI. Therefore the remaining ’secondary’ SLCs are resampled onto the geometry of the primary SLC. For every secondary SLC, initial offset estimates between the primary and secondary SLC are calculated based on orbit information and the DEM and are used to generate an initial co-registered secondary SLC. The offsets are then re-estimated based on cross correlation between the primary SLC and the initial co-registered secondary SLC, and a final co-registered secondary SLC is generated based on these estim- ates (Lazeck`y et al., 2020). In the case of Sentinel-1 data, a further coregistration refinement step is carried out based on spectral diversity (Wegnüller et al., 2016). The co-registered secondary SLCs are then multi-looked in the same way as the primary SLC.

From any pair of these multi-looked SLCs, which now all have the same geometry, an interferogram can then be calculated as the difference in phase between the two images. However, there are other effects which need to be accounted for before this can be used to map ground deformation. From Equation 2.5, ∆φ is dependent on