Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation

An increasing number of satellite platforms provide daily images of the Earth’s surface that can be used in quantitative monitoring applications. However, their cost and the need for specific processing software make such products not often suitable for rapid mapping and deformation tracking. Google Earth images have been used in a number of mapping applications and, due to their free and rapid accessibility, they have contributed to partially overcome this issue. However, their potential in Earth’s surface displacement tracking has not yet been explored. In this paper, that aspect is analyzed providing a specific procedure and related MATLAB™ code to derive displacement field maps using digital image correlation of successive Google Earth images. The suitability of the procedure and the potential of such images are demonstrated here through their application to two relevant case histories, namely the Slumgullion landslide in Colorado and the Miage debris-covered glacier in Italy. Result validation suggests the effectiveness of the proposed procedure in deriving Earth’s surface displacement data from Google Earth images

Evaluation of the Use of Sub-Pixel Offset Tracking Techniques to Monitor Landslides in Densely Vegetated Steeply Sloped Areas

Sub-Pixel Offset Tracking (sPOT) is applied to derive high-resolution centimetre-level landslide rates in the Three Gorges Region of China using TerraSAR-X Hi-resolution Spotlight (TSX HS) space-borne SAR images. These results contrast sharply with previous use of conventional differential Interferometric Synthetic Aperture Radar (DInSAR) techniques in areas with steep slopes, dense vegetation and large variability in water vapour which indicated around 12% phase coherent coverage. By contrast, sPOT is capable of measuring two dimensional deformation of large gradient over steeply sloped areas covered in dense vegetation. Previous applications of sPOT in this region relies on corner reflectors (CRs), (high coherence features) to obtain reliable measurements. However, CRs are expensive and difficult to install, especially in remote areas; and other potential high coherence features comparable with CRs are very few and outside the landslide boundary. The resultant sub-pixel level deformation field can be statistically analysed to yield multi-modal maps of deformation regions. This approach is shown to have a significant impact when compared with previous offset tracking measurements of landslide deformation, as it is demonstrated that sPOT can be applied even in densely vegetated terrain without relying on high-contrast surface features or requiring any de-noising process

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

A Robust SAR Speckle Tracking Workflow for Measuring and Interpreting the 3D Surface Displacement of Landslides

We present a workflow for investigating large, slow-moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post-processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive the easting, northing, and vertical components of the displacement vectors across the rock slope for two five-year windows, 2010–2015 and 2015–2020. Then, we perform post-processing in a GIS environment to derive displacement magnitude, trend, and plunge maps of the landslide area. Finally, we compare the ST-derived displacement data with structural lineament maps and profiles extracted from the ALS dataset. Relying on remotely sensed data, we estimate that the thickness of the slide mass is more than 100 m and displacements occur through a combination of slumping at the toe and planar sliding in the central and upper slope. Our approach provides information and interpretations that can assist in optimizing and planning fieldwork activities and site investigations at landslides in remote locations

A robust sar speckle tracking workflow for measuring and interpreting the 3d surface displacement of landslides

We present a workflow for investigating large, slow‐moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post‐processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive the easting, northing, and vertical components of the displacement vectors across the rock slope for two five‐year windows, 2010–2015 and 2015–2020. Then, we perform post‐processing in a GIS environment to derive displacement magnitude, trend, and plunge maps of the landslide area. Finally, we compare the ST‐derived displacement data with structural lineament maps and profiles extracted from the ALS dataset. Relying on remotely sensed data, we estimate that the thickness of the slide mass is more than 100 m and displacements occur through a combination of slumping at the toe and planar sliding in the central and upper slope. Our approach provides information and interpretations that can assist in optimizing and planning fieldwork activities and site investigations at landslides in remote locations

Entering the Era of Earth Observation-Based Landslide Warning Systems: A novel and exciting framework

Landslide early warning remains a grand challenge due to the high human cost of catastrophic landslides globally and the difficulty of identifying a diverse range of landslide triggering factors. There have been only a very limited number of success stories to date. However, recent advances in earth observation (EO) from ground, aircraft and space have dramatically improved our ability to detect and monitor active landslides and a growing body of geotechnical theory suggests that prefailure behavior can provide clues to the location and timing of impending catastrophic failures. In this paper, we use two recent landslides in China as case studies, to demonstrate that (i) satellite radar observations can be used to detect deformation precursors to catastrophic landslide occurrence, and (ii) early warning can be achieved with real-time in-situ observations. A novel and exciting framework is then proposed to employ EO technologies to build an operational landslide early warning system.This work was supported by the National Natural Science Foundation of China under grants 41801391, 41874005, and 41929001; the National Science Fund for Outstanding Young Scholars of China under grant 41622206; the Fund for International Cooperation under grant NSFCRCUK_NERC; Resilience to Earthquake-Induced Landslide Risk in China under grant 41661134010; the open fund of State Key Laboratory of Geodesy and Earth’s Dynamics (SKLGED2018-5-3-E); Sichuan Science and Technology Plan Project under grant 2019YJ0404; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project under grant SKLGP2018Z019; the Spanish Ministry of Economy and Competitiveness, the State Agency of Research, and the European Funds for Regional Development under projects TEC2017-85244-C2-1-P and TIN2014-55413-C2-2-P; and the Spanish Ministry of Education, Culture, and Sport under project PRX17/00439. This work was also partially supported by the U.K. Natural Environment Research Council through the Center for the Observation and Modeling of Earthquakes, Volcanoes, and Tectonics under come30001 and the Looking Inside the Continents From Space and Community Earthquake Disaster Risk Reduction in China projects under NE/K010794/1 and NE/N012151/1, respectively, and by the European Space Agency through the ESA-MOST DRAGON-4 project (32244 [4]). Roland Bürgmann acknowledges support by the NASA Earth Surface and Interior focus area

Characterizing slope instability kinematics by integrating multi-sensor satellite remote sensing observations

Over the past few decades, the occurrence and intensity of geological hazards, such as landslides, have substantially risen due to various factors, including global climate change, seismic events, rapid urbanization and other anthropogenic activities. Landslide disasters pose a significant risk in both urban and rural areas, resulting in fatalities, infrastructure damages, and economic losses. Nevertheless, conventional ground-based monitoring techniques are often costly, time-consuming, and require considerable resources. Moreover, some landslide incidents occur in remote or hazardous locations, making ground-based observation and field investigation challenging or even impossible. Fortunately, the advancements in spaceborne remote sensing technology have led to the availability of large-scale and high-quality imagery, which can be utilized for various landslide-related applications, including identification, monitoring, analysis, and prediction. This efficient and cost-effective technology allows for remote monitoring and assessment of landslide risks and can significantly contribute to disaster management and mitigation efforts. Consequently, spaceborne remote sensing techniques have become vital for geohazard management in many countries, benefiting society by providing reliable downstream services. However, substantial effort is required to ensure that such benefits are provided. For establishing long-term data archives and reliable analyses, it is essential to maintain consistent and continued use of multi-sensor spaceborne remote sensing techniques. This will enable a more thorough understanding of the physical mechanisms responsible for slope instabilities, leading to better decision-making and development of effective mitigation strategies. Ultimately, this can reduce the impact of landslide hazards on the general public. The present dissertation contributes to this effort from the following perspectives: 1. To obtain a comprehensive understanding of spaceborne remote sensing techniques for landslide monitoring, we integrated multi-sensor methods to monitor the entire life cycle of landslide dynamics. We aimed to comprehend the landslide evolution under complex cascading events by utilizing various spaceborne remote sensing techniques, e.g., the precursory deformation before catastrophic failure, co-failure procedures, and post-failure evolution of slope instability. 2. To address the discrepancies between spaceborne optical and radar imagery, we present a methodology that models four-dimensional (4D) post-failure landslide kinematics using a decaying mathematical model. This approach enables us to represent the stress relaxation for the landslide body dynamics after failure. By employing this methodology, we can overcome the weaknesses of the individual sensor in spaceborne optical and radar imaging. 3. We assessed the effectiveness of a newly designed small dihedral corner reflector for landslide monitoring. The reflector is compatible with both ascending and descending satellite orbits, while it is also suitable for applications with both high-resolution and medium-resolution satellite imagery. Furthermore, although its echoes are not as strong as those of conventional reflectors, the cost of the newly designed reflectors is reduced, with more manageable installation and maintenance. To overcome this limitation, we propose a specific selection strategy based on a probability model to identify the reflectors in satellite images

Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data

Publisher's version (útgefin grein)Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hítardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hítará river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that-without further post-processing-the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery.This research has been supported by the Austrian Science Fund (FWF) through the project MORPH (Mapping, monitoring and modelling the spatio-temporal dynamics of land surface morphology; FWF-P29461-N29) and the Doctoral Collage GIScience (DKW1237-N23), as well as by the Austrian Academy of Sciences (?AW) through the project RiCoLa (Detection and analysis of landslide-induced river course changes and lake formation).Peer Reviewe