SAR Coherence in Detecting Fluvial Sediment Transport Events in Arid Environments

Abstract

Coherence change detection (CCD) is a remote sensing technique used to map phenomena that, under certain conditions, can be directly related to changes in Interferometric SAR (InSAR) coherence. Mapping the areas affected by sediment transport events in arid environments is one of the most common applications of CCD. However, the reliability of these maps remains an unsolved issue. This paper focuses on verifying that InSAR coherence is indeed able to detect all the fluvial sediment transport events that have actually mobilised sediments in arid environments by building a classification model and validating its results. The proposed methodology is tested in three study areas in Salar de Atacama, Chile, using three years of Sentinel data plus a fourth year for validation, and meteorological records of rainfall, the relative humidity of the air and snow cover. The results prove that InSAR coherence can be used to remotely detect sediment transport events related to flash floods in arid environments, that it might have a greater detection ability than meteorological records and that the perpendicular baseline does have a relevant effect on the InSAR coherence that needs to be considered. All these findings will increase the reliability of maps based on InSAR coherence. In addition, the proposed method will allow focusing the mapping tasks only on the relevant dates and, once calibrated, the classification model will enable the automatised remote detection of new events.

Evaluating the Uncertainty in Coherence-Change-Detection-Based Maps of Torrential Sediment Transport in Arid Environments

“…The geometric decorrelation depends on the perpendicular baseline and the local slope of the surface, so it is not correlated with time but its distribution in space is constant. On the other hand, the soil moisture decorrelation, in arid environments, is relevant only during and after rain-and snowfall events (for a few months at most), so it can be located in time [4,19,27]. Regarding the spatial dimension, it has been found that the quantity of precipitation only affects the duration of the soil moisture decorrelation, not its magnitude [16].…”

“…The mapping of the area affected by a torrential sediment transport event using CCD requires us to first (i) define the study area, (ii) download the SAR images of the study period (Table A1), (iii) calculate the rasters of InSAR coherence (Equation (1)), and (iv) identify the events that occurred during the study period, i.e., the dates when such events happened and the duration of the temporal soil moisture decorrelation. These steps were already carried out in a previous work [27], and the results are used here as a starting point, i.e., input data (Figure 3a). Then, with the rasters of coherence, the dates of the events, and the duration of the temporal effects related to each event, i.e., the soil moisture decorrelation, the procedure is different for each method (Figure 3):…”

“…The methods and the procedure explained in the next sections are applied to events that were already detected and located in time in a previous study [27]. In short, the events were detected based on the analysis of the histograms of the rasters of coherence between consecutive SAR images.…”

“…The pattern method follows the same idea. However, the advantage in our case is that, in our previous study [27], we determined the period affected by the…”

“…Finally, the most abundant case in the literature is still the direct correlation with other data, observations or prior knowledge [1,4,9,10,15,25,26]. However, although previous studies confirm the ability of CCD to detect surface changes [27], none of the mentioned approaches have yet resolved how to map surface changes using CCD with certainty [1]. Thus, in this paper, we propose several mapping methods for torrential sediment transport events in arid environments based on CCD, each one with a different degree of complexity and a different sensitivity to each component of the decorrelation, aiming to reduce the uncertainty in the CCD mapping by comparing their results and assessing their degree of coincidence or divergence.…”