Close-Range Sensing of Alpine Glaciers

Glacial processes can have a strong impact on human activities in terms of hazards and freshwater supply. Therefore, scientific observation is fundamental to understand their current state and possible evolution. To achieve this aim, various monitoring systems have been developed in the last decades to monitor different geophysical and geochemical properties. In this manuscript, we describe examples of close-range monitoring sensors to measure the glacier dynamics: (i) terrestrial interferometric radar, (ii) monoscopic time-lapse camera, (iii) total station, (iv) laser scanner, (v) ground-penetrating radar and (vi) structure form motion. We present the monitoring applications in the Planpincieux and Grandes Jorasses glaciers, which are located in the touristic area of the Italian side of the Mont Blanc massif. In recent years, the Planpincieux-Grandes Jorasses complex has become an open-air research laboratory of glacial monitoring techniques. Many close-range surveys have been conducted in this environment and a permanent network of monitoring systems that measures glacier surface deformation is presently active

Integrated ground-based remote sensing sensors for glaciological monitoring

Glaciological processes, such as glacier break-offs, snow/ice avalanches, can threaten the population, urban areas and infrastructures. For their potential hazard, it is necessary to study their activity in order to understand their possible geophysical dynamics evolution and to develop strategies of preventive alert and mitigation actions. Therefore, the first step of this observation-alert-mitigation framework is the monitoring of such phenomena. One of the most relevant parameters to be investigated is the surface deformation, as it provides a direct measurement of the process activity. The gravitational slope processes are common in mountain environment and hence they are often placed in harsh and remote areas. Therefore, a practical approach for their monitoring is the adoption of remote-sensing apparatuses, which allow at not accessing into possible perilous investigated areas, with consequent reduction of human resources and risks. The remote sensing systems can be classified into two main categories: i) systems installed on aerospace platforms and ii) ground-based sensors. The first group provides data at global scale and the recent free availability of satellite constellations such as Sentinel 1 e Sentinel 2 is making their use widely studied. However, such systems suffer limitation of low temporal resolution, with revisiting time of days or weeks. The data availability of specific and localised areas is not always guaranteed and/or it can require high financial costs (e.g., airborne surveys, private satellite constellations). Moreover, complex geometries, typical of gravitational processes located in mountain areas, can affect the data acquisition. By contrast, ground-based apparatuses are able to acquire data in environments with complex geometry. Furthermore, they can often operate in continuous, therefore providing data with high spatio-temporal resolution. In general, a single monitoring system is able to measure specific parameters that can partially describe the state and the evolution of the investigated phenomenon. Therefore, a common approach consists in adopt different sensors and to collect separately their measurements to obtain a more comprehensive outline of the process. However, the independent analysis of the data of each sensor might not be sufficient to exploit all the available information. Rather, merging the different data in a coupled model can provide more informative results. Moreover, the data coupling allows at exploiting the qualities and potentialities of each sensor and to minimise their limits. However, the realisation of an integrated system requires an accurate assessment of the instrument capacities. Therefore, the first step of such realisation involves the characterisation of the monitoring devices. Moreover, the development of specific and innovative processing techniques might be necessary to optimise the coupling process. The methodologies developed for processing data of single and coupled sensors can be applied to practical case studies where the monitoring of gravitational slope phenomena can yield results about their present geophysical state, their dynamics and their possible evolution. This text presents a collection of articles published in international scientific journals. The papers represent the work conducted during the PhD whose focus was the development of methodologies for coupling data collected by different sensors to monitor glaciological processes

Comparison of Digital Image Correlation Methods and the Impact of Noise in Geoscience Applications

Digital image correlation (DIC) is a commonly-adopted technique in geoscience and natural hazard studies to measure the surface deformation of various geophysical phenomena. In the last decades, several different correlation functions have been developed. Additionally, some authors have proposed applying DIC to other image representations, such as image gradients or orientation. Many works have shown the reliability of specific methods, but they have been rarely compared. In particular, a formal analysis of the impact of different sources of noise is missing. Using synthetic images, we analysed 15 different combinations of correlation functions and image representations and we investigated their performances with respect to the presence of 13 noise sources. Besides, we evaluated the influence of the size of the correlation template. We conducted the analysis also on terrestrial photographs of the Planpincieux Glacier (Italy) and Sentinel 2B images of the Bodélé Depression (Chad). We observed that frequency-based methods are in general less robust against noise, in particular against blurring and speckling, and they tend to underestimate the displacement value. Zero-mean normalised cross-correlation applied to image intensity showed high-quality results. However, it suffers variations of the shadow pattern. Finally, we developed an original similarity function (DOT) that proved to be quite resistant to every noise source

Ku Band Terrestrial Radar Observations by Means of Circular Polarized Antennas

This paper reports some experimental results obtained by means of a commercial apparatus used by many researchers and users, where a pair of novel and specifically developed circular polarized antennas, designed to operate with Ku band-terrestrial radar interferometers, are used alternatively to the most conventional linear vertical polarized horns provided by the manufacturer of the apparatus. These radar acquisitions have been carried out to investigate for the first time the potential of circular polarization (CP) configurations for terrestrial radar interferometers (TRI) applications, aiming at improving monitoring of landslides, mines, and semi-urban areas. The study tries to evaluate whether the circular polarization response of natural and man-made targets can improve the interpretation of the radar images, with respect to the standard approach used in terrestrial radar interferometry, usually carried out in co-polar vertical polarization. The goal is to investigate how different polarization combinations, in terrestrial radar interferometry, affect the coherence and amplitude dispersion of natural media, potentially improving the identification of stable scatter

Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

In Earth Science, image cross-correlation (ICC) can be used to identify the evolution of active processes. However, this technology can be ineffective, because it is sometimes difficult to visualize certain phenomena, and surface roughness can cause shadows. In such instances, manual image selection is required to select images that are suitably illuminated, and in which visibility is adequate. This impedes the development of an autonomous system applied to ICC in monitoring applications. In this paper, the uncertainty introduced by the presence of shadows is quantitatively analysed, and a method suitable for ICC applications is proposed: The method automatically selects images, and is based on a supervised classification of images using the support vector machine. According to visual and illumination conditions, the images are divided into three classes: (i) No visibility, (ii) direct illumination and (iii) diffuse illumination. Images belonging to the diffuse illumination class are used in cross-correlation processing. Finally, an operative procedure is presented for applying the automated ICC processing chain in geoscience monitoring applications

Remote Sensing Analysis of Geologic Hazards

In recent decades, classical survey techniques (i [...

4D surface kinematics monitoring through terrestrial radar interferometry and image cross-correlation coupling

Complex gravitational phenomena can require terrestrial remote sensing solutions for monitoring their possible evolution, especially when in situ installations are not possible. This study merges terrestrial radar interferometry (TRI) and image cross-correlation (ICC) processing, which can detect complementary motion components, to obtain a 3-dimensional system able to measure the actual surface motion field of a pre-defined target. The coupling can be carried out on data acquired from different installations of the devices, and by applying specific transformations of the related coordinate systems. The data georeferencing is a critical issue that affects the correct spatial correspondence of the data and a new approach for georeferencing radar data is proposed. The result is a spatio-temporal (3 + 1-dimensional) high-resolution representation of the surface kinematics. The presented method has been tested for the measurement of the Planpicieux glacier surface kinematics (NW of Italy). The error analysis revealed a millimeter accuracy and precision of the measurement and a georeferencing uncertainty of a few metres

Monitoring Alpine glacier surface deformations with GB-SAR

ABSTRACTWe present methods and results from interferometric data processing of a long-lasting survey campaign monitoring the Planpincieux glacier, located on the Italian side of the Mont Blanc, using a ground-based synthetic aperture radar (GB-SAR). Monitoring a European Alpine glacier during the winter, when the meteorological conditions are highly variable, presents some difficulties in radar data interpretation. The main issues to tackle in interferometric processing are unwrapping errors and high amplitude dispersion (DA), mainly due to the high velocity and dielectric heterogeneity of the backscattering surface. To improve the reliability of the results, a coherence-driven pixel-selection criterion for identifying the glacier area and a simple approach to reduce possible unwrapping errors in interferograms with low coherence are here proposed. The development of a new 2D polynomial regression model, as a function of elevation, for atmospheric phase screen (APS) estimation is also discussed. A comparison with the results obtained with a vision-based approach gave showed good agreement

Multisource data of glacier kinematics

surface kinematics data of the Planpincieux and Grandes Jorasses glaciers (Mont Blanc area), collected by robotised total station, time-lapse camera and terrestrial interferometric rada