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Landslides are one of the most dangerous and disastrous geological hazard worldwide, posing threats to human life, infrastructures and to the natural environment. Consequently, monitoring active landslides is crucial in order to reduce the risk of damages and casualties. With this aim, this work proposes a way to compute landslide displacements thr...
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... Initially applied to SAR imagery, these methods have been extended to a variety of satellite remote sensing datasets, including SAR, optical, and LiDAR imagery. They have proven effective in refining georeferencing accuracy [35][36][37] and have been employed in diverse applications such as quantifying landslide deformation [38], glacier surface velocity extraction [39], and estimating sea-ice flow [40]. Optical flow co-registration has only been tested on satellite based datasets and, for co-registration of high-resolution pushbroom imagery taken from UAS platforms, this technique is appealing due to its ability to account for local displacements whilst also being computationally efficient [33]. ...
Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring.
... To validate our approach to a realistic scenario, we evaluated it on optical satellite images, especially from the ESA's Sentinel-2 mission. The adopted landslide case study is the Ruinon landslide in Northern Italy, which was active in the period 2019-2021 and thoroughly studied by using ground-, air-and spaceborne means (Del Ventisette et al., 2012, Carlà et al., 2021, Amici et al., 2022, Yordanov et al., 2023. This application offers a more challenging and practical setting, as Sentinel-2 (SE2) images have a much lower spatial resolution (10 m/pix) compared to the submillimetre level of the simulator setup. ...
... Moreover, external factors such as cloud cover, vegetation seasonality and uncontrolled image acquisition can introduce more variability and complexity in the data. This case study follows the already defined SE2 yearly application scheme used by (Amici et al., 2022) for the application of local Maximum Cross-Correlation (MCC) analysis, i.e. the applicability of LK dense flow for displacement estimation was tested on one image per year for the period 2015 to 2020. ...
... The number of wraps was kept the same, two, as the experimental setups. Overall, the results depict accurate estimated displacement fields and movement directions which are in line with the ones reported in previous studies (Amici et al., 2022). The estimated magnitudes range from a few meters to more than 20 meters between two acquisitions. ...
Landslides are natural hazards that can cause severe damage and loss of life. Optical cameras are a low-cost and high-resolution alternative among many monitoring systems, as their size and capabilities can vary, allowing for flexible implementation and location. Computer vision is a branch of artificial intelligence that can analyze and understand optical images, using techniques such as optical flow, image correlation and machine learning. The application of such techniques can estimate the motion vectors, displacement fields, providing valuable information for landslide detection, monitoring and prediction. However, computer vision also faces some challenges such as illumination changes, occlusions, image quality, and computational complexity. In this work, a computer vision approach based on Lucas-Kanade optical dense flow was applied to estimate the motion vectors between consecutive images obtained during landslide simulations in a laboratory environment. The approach is applied to two experiments that vary in their illumination and setup parameters to test its applicability. We also discuss the application of this methodology to images from Sentinel-2 satellite optical sensors for landslide monitoring in real-world scenarios.
... Aside from the GBInSAR, the landslide has been monitored since 1997 through a manual and automatic geotechnical network [33]. In addition to the data from permanent ground stations during the last two decades, the Ruinon landslide was also monitored through spaceborne optical and radar missions [36,37], which recorded the most disastrous reactivations during the summers of 2016, 2018 and 2019. During the year 2021, no significant critical activities on the landslide body were reported [38]. ...
... By changing the spatial and intensity scale, the approach succeeded in precisely estimating velocity vectors through the epochs of the most severe reactivations of the central part of the landslide body, which obviously cannot be considered for estimating volumetric changes and more abrupt land cover changes (e.g., loss of forested areas). Some of our results derived from the 2019 surveys (Figure 10a-c) are consistent with the results obtained from another approach we implemented for estimating Ruinon reactivations through Sentinel-2 imagery [37]. Furthermore, comparable outputs for the Ruinon landslide are reported by Carla et al. [36], with the difference that their work utilized a GBInSAR monitoring technique. ...
Many techniques are available for estimating landslide surface displacements, whether from the ground, air- or spaceborne. In recent years, Unmanned Areal Vehicles have also been applied in the domain of landslide hazards, and have been able to provide high resolution and precise datasets for better understanding and predicting landslide movements and mitigating their impacts. In this study, we propose an approach for monitoring and detecting landslide surface movements using a low-cost lightweight consumer-grade UAV setup and a Red Relief Image Map (a topographic visualization technique) to normalize the input datasets and mitigate unfavourable illumination conditions that may affect the further implementation of Lucas–Kanade optical flow for the final displacement estimation. The effectiveness of the proposed approach in this study was demonstrated by applying it to the Ruinon landslide, Northern Italy, using the products of surveys carried out in the period 2019–2021. Our results show that the combination of different techniques can accurately and effectively estimate landslide movements over time and at different magnitudes, from a few centimetres to more than several tens of meters. The method applied is shown to be very computationally efficient while yielding precise outputs. At the same time, the use of only free and open-source software allows its straightforward adaptation and modification for other case studies. The approach can potentially be used for monitoring and studying landslide behaviour in areas where no permanent monitoring solutions are present.
PlanetScope data with daily temporal and 3 m spatial resolution hold an unprecedented potential to quantify and monitor surface displacements from space. Slow-moving landslides, however, are complex and dynamic targets that alter their topography over time. This leads to orthorectification errors, resulting in inaccurate displacement estimates when images acquired from varying satellite perspectives are correlated. These errors become particularly concerning when the magnitude of orthorectification error exceeds the signal from surface displacement, which is the case for many slow-moving landslides with annual velocities of 1–40 m yr-1. This study provides a comprehensive assessment of orthorectification errors in PlanetScope imagery and presents effective mitigation strategies for both unrectified Level 1B (L1B) and orthorectified Level 3B (L3B) data. By implementing these strategies, we achieve sub-pixel accuracy, enabling the estimation of realistic and temporally coherent displacement over landslide surfaces. The improved signal-to-noise ratio results in higher-quality displacement maps, allowing a more detailed analysis of landslide dynamics and their driving factors.
The precise and prompt categorization of land cover types holds significant importance in the realm of land resource planning and management, as well as in risk reduction. The utilization of hyperspectral satellite imagery, such as the imagery delivered by PRISMA, plays a vital role in analyzing environmental changes. Even though PRISMA products are distributed at Preprocessing Level 2D (radiometrically and geometrically calibrated), the images may exhibit registration errors on the order of a few hundred meters. Therefore, co-registration is a crucial preprocessing step before their utilization. This study utilized a local co-registration method based on the optical flow estimation technique to co-register the PRISMA images using Sentinel-2/Landsat 8–9 as references. The results showed that a careful selection of an appropriate reference image holds immense importance in the co-registration process, and the closer the acquisition time of the reference image is to the acquisition time of the image to be co-registered, the higher the quality of the co-registration results. By integrating cutting-edge machine learning techniques, the proposed co-registration approach further enhances the usability and accuracy of PRISMA products for land cover classification, and makes them a valuable source of information for applications in land management and thematic hazard studies, including scenarios such as flood monitoring and landslide analysis.
