Day 4

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Paper title Geohazard Assessment by PhotoMonitoring: IRIS a new powerful tool for analysis
  1. Antonio Cosentino “Sapienza” University of Rome Speaker
  2. Michele Gaeta NHAZCA S.r.l.
  3. Alessandro Brunetti NHAZCA S.r.l.
  4. Paolo Mazzanti “Sapienza” University of Rome Speaker
Form of presentation Poster
  • D1. Managing Risks
    • D1.01 Satellite EO for Geohazard Risks
Abstract text Introduction
The paper presents the results obtained from Digital Image Correlation (DIC) analyses carried out with the intention of mapping the hazards and geological risks potentially impacting a large infrastructure project in Africa. Specifically, the processing was carried out with the aim of quantifying and understanding the direction and direction of migration of dune fields. Unstable sandy elements such as dunes can cause various problems for infrastructures. The analysis was performed by IRIS, an innovative software developed by NHAZCA S.r.l., Startup of Sapienza University of Rome, designed for PhotoMonitoring applications. The analysis was carried out by using Open Source satellite Multispectral images, provided by the ESA Sentinel constellation ( Sentinel 2). PhotoMonitoring is a new monitoring solution that exploits the widespread use of optical/multispectral sensors around the world to obtain information about changes or displacements in the terrain, making it an ideal tool for studying and monitoring surface deformation processes in the context of land and structure control. PhotoMonitoring is based on the concept of "digital image processing", i.e. the manipulation of digital images to obtain data and information. Analyses can be carried out on datasets of images acquired from the same type of platform, on the same area of interest, at different times, and can be conducted using specific algorithms that allow the evaluation of any variation in radiometric characteristics (Change Detection) and/or the displacement occurred in the time interval covered by the acquisition of images (Digital Image Correlation). Through these applications it is possible to study the evolution and significant changes of the observed scenario, therefore, when applied to Earth Observation they allow to better map geological and hydrogeological hazards, understanding the evolution and causes of the processes in progress. Different digital approaches can be used to analyze and manipulate available images and different types of information can be extracted depending on the type of image processing chosen as shown by [1]. Basically, digital image processing techniques are based on extracting information about changes in the terrain by comparing different types of images (e.g. satellite, aerial or terrestrial images) collected at different times over the same area and scene. [2].

Material and Methods
DIC (Digital Image Correlation) is an optical-numerical measurement technique capable of providing full-field 2D surface displacements or deformations of any type of object. The deformations are calculated by comparing and processing co-registered digital images of the surface of the same "object" collected before and after the deformation event [2]. Digital Image Correlation (DIC) allows to quantitatively evaluate the displacement and deformations occurred between two images acquired at different times by analyzing the different pixel blocks and allowing to obtain a resolution that can go up to 1/10 of a pixel (Fig.1).

This technique is affected by environmental effects caused by different atmospheric and lighting conditions, different temperatures and problems inherent in the camera's viewing geometry. Using high resolution, accurately positioned and aligned imagery, it is possible through DIC to identify differences, deformations and changes in the observed scenario with high accuracy. Recently, several authors have presented interesting results derived from the application of DIC analysis with satellite imagery for landslide displacement monitoring [3,5] [6].
The analysis was carried out on three contiguous areas and involved the use of 3 different pairs of images for a total area of approximately 30.000 Square kilometers. In particular, the analysis was carried out on Sentinel-2 images, with a Pansharpened resolution of 10 x 10 m, taken over a period of one year from July 2020 to July 2021.
The IRIS software allows Digital Image Correlation (DIC) analyses to be carried out using different types of algorithms. In this case the analysis was carried out using the Phase Correlation (PC) algorithm [7] which is based on a frequency domain representation of the data, usually calculated through fast Fourier transforms, with a floating window of 16 pixels (Fig.2).

Result and Discussion
The results obtained are displacement maps representing the position of the main dune fields and the magnitude (depicted according to a metric color scale) and direction (represented by arrows) of dune migration during the studied period. In particular, two large corridors characterized by strong southward dune movements were identified. For the northernmost corridor, the analyses allowed the assessment of an average displacement rate of about 80 m per year, with peaks of displacement up to 100 m. For the southern corridor, on the other hand, lower displacement rates were measured, averaging about 50 m per year. The analyses also showed a good correlation between the direction of displacement and the dominant wind direction for these areas (Fig. 3).

The PhotoMonitoring analysis presented in this paper allowed to map the presence of dune fields and to quantify their annual displacement rate. This analysis carried out on Open Source Sentinel-2 images and with a new generation software, IRIS, developed by NHAZCA S.r.l., Startup of Sapienza University of Rome, allowed to identify and map some geological risks for a strategic infrastructure in the planning phase. The results obtained allow us to fully understand the potential of Earth Observation techniques, and more specifically of IRIS and satellite Photomonitoring, now a reliable and versatile tool that allows the monitoring and study of the impact of Geohazards and geological risks such as Earthquakes, Landslides, Floods (Fig.4) and through data from different sensors (Optical, Radar, Laser).

[1] Ekstrom, M. P. (2012). Digital image processing techniques (Vol. 2). Academic Press.

[2] Caporossi, P., Mazzanti, P., & Bozzano, F. (2018). Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, southern Italy): results from a multi-dataset investigation. ISPRS International Journal of Geo-Information, 7(9), 372.

[3] Bontemps, N., Lacroix, P., & Doin, M. P. (2018). Inversion of deformation fields time-series from optical images, and application to the long term kinematics of slow-moving landslides in Peru. Remote sensing of environment, 210, 144-158.

[4] Pham, M. Q., Lacroix, P., & Doin, M. P. (2018). Sparsity optimization method for slow-moving landslides detection in satellite image time-series. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 2133-2144.

[5] Lacroix, P., Araujo, G., Hollingsworth, J., & Taipe, E. (2019). Self‐Entrainment Motion of a Slow‐Moving Landslide Inferred From Landsat‐8 Time Series. Journal of Geophysical Research: Earth Surface, 124(5), 1201-1216.

[6] Mazzanti, P., Caporossi, P., & Muzi, R. (2020). Sliding time master digital image correlation analyses of cubesat images for landslide monitoring: The Rattlesnake Hills landslide (USA). Remote Sensing, 12(4), 592.

[7] Tong, X., Ye, Z., Xu, Y., Gao, S., Xie, H., Du, Q., ... & Stilla, U. (2019). Image registration with Fourier-based image correlation: A comprehensive review of developments and applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10), 4062-4081.