Sliding Time Master Digital Image Correlation Analyses of CubeSat Images for landslide Monitoring: The Rattlesnake Hills Landslide (USA)

Abstract

Landslide monitoring is a global challenge that can take strong advantage from opportunities offered by Earth Observation (EO). The increasing availability of constellations of small satellites (e.g., CubeSats) is allowing the collection of satellite images at an incredible revisit time (daily) and good spatial resolution. Furthermore, this trend is expected to grow rapidly in the next few years. In order to explore the potential of using a long stack of images for improving the measurement of ground displacement, we developed a new procedure called STMDA (Slide Time Master Digital image correlation Analyses) that we applied to one year long stack of PlanetScope images for back analyzing the displacement pattern of the Rattlesnake Hills landslide occurred between the 2017 and 2018 in the Washington State (USA). Displacement maps and time-series of displacement of different portions of the landslide was derived, measuring velocity up to 0.5 m/week, i.e., very similar to velocities available in literature. Furthermore, STMDA showed also a good potential in denoising the time-series of displacement at the whole scale with respect to the application of standard DIC methods, thus providing displacement precision up to 0.01 pixels.The most common optical satellite missions (e.g., QuickBird, SPOT, LANDSAT, Sentinel-2, WorldView, Pléiades, just to mention few of them) are characterized by spatial resolution ranging between 0.3 and 30 m and a revisit time of some days [25][26][27], therefore, they are not fully adequate to detect landslides at a spatial and temporal scale suitable for continuous monitoring. However, as stated in [4,28], the fusion of images collected by different optical satellite sensors has certainly increased the opportunities for cloud-free surface investigation, thus allowing also a general improvement of the temporal resolution.In the last few years, EO has been affected by an astonishing sensor and platform development and improvement, thanks to small satellites. According to [27,29,30], among the main features of these satellites, properly named CubeSats, there are: (i) the general small size and weight (a single-unit of CubeSat normally measures 10 × 10 × 11 cm and typically weights less than 1.5 kg), (ii) high geometric resolution (~3-5 m/pixel) and (iii) daily/near-daily revisit time. CubeSats represent a cost-effective EO strategy, allowing unique chances for a wide variety of application fields [8,31].Planet Labs Inc. has recently made available a large constellation (> 130 units) of optical 3-U CubeSats (10 × 10 × 30 cm), also called PlanetScope or, more commonly, "Doves" [27,30,[32][33][34]. With an average orbit height of about 475 km a.s.l., the main CCD array sensor is able to collect images of 7000 × 2000 pixels, with a footprint of approximately 24 × 8 km 2 . Each Doves collects one image/second along his orbit with a slight overlapping between consecutive scenes. In this way, taking advantage of the whole constellation, images at a daily sampling rate are achievable, thus allowing to ...

Integration of Digital Image Correlation of Sentinel-2 Data and Continuous GNSS for Long-Term Slope Movements Monitoring in Moderately Rapid Landslides

“…In the case of the Ca' Lita landslide, DIC can be applied to monitor rapid accelerations that cannot be resolved by interferometry based techniques, while SAR interferometry can be used to monitor slow movements phases. For future developments, the application of redundant NCC algorithms [25,30] to the Sentinel-2 stack used in this work would be of great interest in order to measure the accuracy improvement that could be achieved by using these techniques. Future research will also focus on the error estimation of DIC applied to Landsat-7 scenes (15 m, panchromatic band) in order to verify the feasibility of analysing historical datasets and following the evolution of large landslides along decades.…”

“…Among remote sensing techniques, Digital Image Correlation (DIC) algorithms are quite versatile, since they can be used with various kinds of multitemporal georeferenced datasets. This class of algorithms (also known as Offset Tracking-OT), is capable of retrieving velocity fields in an Area Of Interest (AOI) by using input data ranging from high-resolution slope angle maps derived from Airborne Lidar [22], to medium resolution Landsat-7 or Landsat-8 [23], high resolution satellite images [24,25] and X-band Synthetic Aperture Radar amplitude images [26]. In principle, the minimum sensed displacement corresponds to the spatial resolution of the dataset, but this can be upgraded to subpixel resolution by redundant processing [24] or by pre-processing [27].…”

“…Mazzanti at al. [25] report sub-pixel accuracies using a DIC methodology based on redundant correlations with multiple master images, and declare precisions up to 1% of the pixel size. Stumpf et al [30] also reports accuracies in between 1/9th and 1/20th of a pixel by using a two-step method that calibrates 1st grade DIC displ residuals against ground measurements using High Performance Computing (HPC) infrastructures.…”

“…Moreover, the presence of snow cover also inhibits the application of DIC algorithms, making the identification of movements during snow-cover or snow melt phases quite problematic. Such issue could be tackled by using satellite constellation with sub metric resolution and up to 12 h revisit time (i.e., "Planet Scope" from "planet" company) [25]. An alternative solution to reduce the influence of cloud cover could be represented by the use of the SAR amplitude scenes.…”

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

“…This limitation has been partially overcome through the implementation of specific low-resolution approaches [14][15][16]. The use of optical images to track Earth's surface displacement is a potential alternative to SAR interferometry that has no limitations in terms of maximum detectable velocity and has the potential to be used in a number of deformation scenarios [17][18][19][20][21]. Similar to SAR imagery, there are major limitations, where optical images are not suitable for fast mapping and fast process understanding, because of their relevant cost especially for very large areas and the time necessary to access the data.…”