2.1. Study Area
The study area spans the east coast of the Attica region, including the affected settlements of Neos Voutzas, Mati, Neos Pontos, Agia Varvara, Kokkino Limanaki, Kioupi Skoufeika, and Peukonas, as shown in
Figure 3. The area is characterized as a mixed-area consisting of low-rise buildings, low vegetation, and bare land. The size of the burned area reached 13,000 acres approximately, where half of them residential, which is the main area of interest (AOI) of this research.
The topography of the area creates an intense network of streams starting from the hilly area in the west and ending up in the seaside Mati settlement in the east by crossing the rest of the settlements. This fact adds a higher pressure to the ground deformation phenomena, especially during intense rainstorms. Apart from the devastating fire event, an additional reason for choosing to study this area arises from its character as a recreational or second-housing area that is predominantly used during summertime along with its reputation as a seaside resort attracting many tourists every year.
According to Corine 2018, the land use in the study area primarily consists of a discontinuous urban fabric intertwined with complex cultivation patterns. In the northern region, the urban fabric aligns itself with a coniferous forest, while in the western part, it interfaces with sparsely vegetated areas. However, following the occurrence of a fire, the post-fire land use has transformed into a combination of discontinuous urban fabric and barren land.
From a geological perspective, the fourth-generation sediments are loosely consolidated and composed of mixed phases as follows: clay silts, sands, pebbles, gravels, and variable-sized pebbles with fluctuating proportions, as shown in
Figure 4.
These deposits are found in low-lying areas, valleys, and streams and originate from the weathering and erosion of older formations with diverse compositions. They often exhibit significant thickness, reaching several hundreds of meters, and show frequent and rapid changes in lithological composition and grain size distribution both horizontally and vertically within the formation.
They are characterized by moderate to high hydraulic conductivity and typically give rise to aquifers with a high potential and significant fluctuations. Due to their extensive surface distribution, many settlements across the country have developed on these deposits, often facing geotechnical issues such as settlement and soil displacement. They are susceptible to erosion and washing. Their physical and mechanical characteristics vary depending on the individual lithological composition and grain size distribution. Additionally, their behavior is influenced by factors beyond the aforementioned, including the deposit thickness and slope of the terrain (especially during dynamic loads).
They exhibit rapid lateral variations in lithological composition, leading to strong heterogeneity in the mechanical behavior of the formation on a larger scale.
2.3. Method
Starting with the data preparation, it is required to subset the area of interest, update the orbit files for appropriate image coregistration, and estimate the coherence of all possible connections in the images’ space. Adopting a multi-master approach, the full graph connectivity was chosen and modified based on the coherence threshold of 0.35, aiming to minimize the processing time and keep the graph connected, as shown in
Figure 5. The rationale behind the selection of this specific threshold was to identify the highest possible value for optimal connection minimization while also ensuring graph connectivity. The derived value of 0.35 proved to be the most suitable choice.
Indeed, the number of connections decreased from 6671 to 2572 for the ascending data and from 6329 to 1648 for the descending data. This shows that the most complex method is not always the most suitable for every application and study area. In our case, the excessive number of connections is unnecessary and modifying it via the selected coherence threshold appears to be a suitable approach with the additional benefit of saving processing time. Having defined the connection graph of the images, the preliminary analysis must be carried out while including the reflectivity map and amplitude dispersion index (1-sigma/mu) calculation.
For the preliminary geocoding procedure, a clearly visible pixel in the SAR images was selected as a ground control point (GCP) to geocode the dataset. The transfer of the external DEM in the SAR coordinates’ system is mandatory to complete the preprocessing stage.
Since we are dealing with Permanent Scatterers (PS) and Distributed Scatterers (DS) points, it is desirable to enhance the DS points’ Signal to Noise Ratio (SNR), improving the estimation of their parameters. To achieve it, a space adaptive filter is applied to extract the Statistically Homogeneous Pixels (SHP) only. To evaluate the likeness of the adjusting pixels, the amplitude time series was compared according to the Anderson–Darling statistical test [
46], aiming to identify the reasonable clusters of similar pixels. The search window size, significance level, and connectivity were set at 25 × 7 (rg × az) for the ascending data and at 23 × 7 (rg × az) for the descending data, with 0.95 and 8 pixels, respectively. The dimensions of the search window used correspond to approximately a hectare, assuming this will yield a reliable estimation of the coherence values.
Next, the interferograms and spatial coherence were calculated and filtered using the multi-temporal adaptive mask.
Figure 6 illustrates the reflectivity map, amplitude dispersion index, multi-temporal adaptive mask, and spatial coherence products. Contrasting the reflectivity map and adaptive mask cluster size, it becomes evident that the pixels with low amplitude values, likely corresponding to the DS points, tend to form larger clusters. Conversely, the pixels with high amplitude values, possibly indicative of the PS points, exhibit minimal changes or remain unchanged.
As the preprocessing was completed, PSI analysis was followed. A first set of Permanent Scatterer Candidate (PSC) points was selected based on the amplitude dispersion index (1-sigma/mu) plus the spatial coherence threshold of 1.5, assuming that this set is stable enough in time to estimate the atmospheric phase screen (APS). The triangulation network between the selected points, as shown in
Figure 7 was implemented and the parameters as the deformation trend, residual height, and temporal coherence were computed in relation to a reference point. The reference point is a permanent GNSS reference station; therefore, it is considered to be quite stable. The PSC points present quite dense spatial distribution and high coherence connections that is desirable for the APS estimation. It must be noted that, during the APS calculation, images with low coherence and significant “phase jumps” in the calculated atmosphere were discarded from the time series data.
After estimating and evaluating APS, it is intriguing to explore the additional PSC points. To accomplish this, a second set of points was chosen utilizing the same indicator as mentioned earlier, but with a relaxed threshold. A reasonable value of 1.0 was employed to apply the multi-temporal PSI analysis. One more difference in this stage is the adaption of spatial coherence as a weight during the processing, aiming to maximize the number of measuring points. Since the parameters were calculated, the measuring points that showed temporal coherence equal to or higher than 0.8 were selected as the stable points to reliably estimate the deformation trend.
After extracting and assessing the consistency of the two velocity fields obtained from the ascending and descending datasets, two vector files representing the LOS deformation trend were created for the post-processing stage.
The deformation values were converted into absolute values and classified based on the conservation criticality index classes and the relative PS velocity range, as shown in
Table 2 [
47].
Inverse Distance Weighted (IDW) interpolation was implemented to identify and map the candidate deformation zones, setting a search radius distance of 60 m that selected based on a spatial autocorrelation via distance analysis using Morans I statistic, as shown in
Figure 8.
Since the research deals with absolute deformation values, the point features from the ascending and descending orbits were merged for interpolation implementation.
Furthermore, the burn severity (dNBR) was calculated with the use of two Sentinel-2 images before and after the fire event. The severity is the outcome of the subtraction between the pre-fire and post-fire Normalized Burn Ratio (NBR), as described in Equation (1), and is classified based on the proposed table of the United States Geological Survey (USGS), as shown in
Table 3 [
48].
where NIR is the near infrared and SWIR is the short wave infrared.
Burn severity is used in developing emergency rehabilitation and restoration post-fire plans. It can be applied to estimate not only the soil burn severity, but also the likelihood of future downstream impacts due to flooding, landslides, and soil erosion.
Our proposed method for the combination of the deformation trend and burn severity is based on the reclassification of the two datasets on a scale from 1 to 5 and in Equation (2). The proposed equation will preserve the outcome in the same scale as the reclassified datasets (from 1 to 5). The initial component of the equation aims to delineate the spatially correlated regions within the two datasets by categorizing each zone with a hazard level ranging from 1 (low) to 5 (high). The subsequent segment of the equation functions as a weighting factor, particularly in cases where one dataset exhibits a maximum value of 5, while the other displays a minimum value of 1. A smaller numerical value corresponds to the reduced impact on the final outcome and vice versa. The addition of integer 1 serves to eliminate integer zero from the resulting decimal number, facilitating multiplication between the two equation components.
with
Hi (hazard index) as the outcome,
as the reclassified deformation trend,
as the reclassified burn severity, and
NAD as the normalized absolute difference between
dt and
bs, as shown in Equation (3).
Equation (2) generates a new information layer about the hazard level and is classified into four classes (A to D) using the equal interval classification method, as shown in
Table 4. The statistics of the result are presented in
Table 5.
The minimum and maximum values pertain to the result of multiplying the two components of the equation and correspond to the hazard level category. The mean and standard deviation values suggest that the study area is largely classified as having a low hazard level.