Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series

Pham, Mai Quyen; Lacroix, Pascal; Doin, Marie‐Pierre

doi:10.1109/tgrs.2018.2871550

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…For instance, NDVI time-series were used for adaptive change calculation, allowing the separation between permanently non-vegetated and post-event landslide areas in different geographic settings (Behling et al 2014;Golovko et al 2017). To monitor the geomorphological process of slow-movement landslides, time-series based optical imagery approaches have also been developed for retrieving surface displacements (Pham et al 2018;Stumpf et al 2017). Synthetic Aperture Radar interferometry (InSAR), known for its ability in dealing with atmospheric noise and monitoring deformation, was also found to perform better using time-series analysis of landslides (Hooper et al 2012;Dong et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Chen

Prishchepov

Fensholt

et al. 2019

Remote Sensing of Environment

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Monitoring long-term landslide activity is of importance for risk assessment and land management. Daytime airborne drones or very high-resolution optical satellites are often used to create landslide maps. However, such imagery comes at a high cost, making long-term risk analysis cost-prohibitive. Despite the widespread use of open-access 30m Landsat imagery, their utility for landslide detection is often limited due to low classification accuracy. One of the major challenges is to separate landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km 2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), with multi-seasonal daytime optical Landsat time-series, and digital elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We employed a non-parametric machine-learning classifier, random forest, to classify the satellite imagery. The classifier was trained with data from three years (2005, 2010, and 2015), and was validated with an independent reference sample from twelve years. Our results demonstrated that combining nighttime light data and multi-seasonal imagery significantly improved the classification (p<0.001), compared to conventional methods based on single-season optical imagery. The results confirmed that the developed classification model enabled mapping of landslides across Taiwan over a long period with annual overall accuracy varying between 96% and 97%, user's and producer's accuracies between 73% and 86%. Spatiotemporal analysis of the landslide inventories from 1998 to 2017 revealed different temporal patterns of landslide activities, showing those areas where landslides were persistent and other areas where landslides tended to reoccur after vegetation regrowth. In sum, we provide a robust method to detect long-term landslide activities based on freely available satellite imagery, which can be applied elsewhere. Our mapping effort of landslide spatiotemporal patterns is expected to be of high importance in developing effective landslide remediation strategies.

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Section: Introductionmentioning

confidence: 99%

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Chen

Prishchepov

Fensholt

et al. 2019

Remote Sensing of Environment

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“…The Colca river erodes the lacustrine deposits and trigger landslides that can then be remobilized by the seasonal rainfall infiltration (Zerathe et al., 2016) and M5+ earthquakes (Bontemps et al., 2020). Many rapid and slow‐moving (m/yr) landslides were detected in the area, based on the comparison of DEMs (Zerathe et al., 2016) and the correlation of diachronic optical images (Bontemps et al., 2018; Lacroix et al., 2015; Pham et al., 2018).…”

Section: Study Sitementioning

confidence: 99%

Precursory Motion and Time‐Of‐Failure Prediction of the Achoma Landslide, Peru, From High Frequency PlanetScope Satellites

Lacroix,

Huanca,

Albinez

et al. 2023

Geophysical Research Letters

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Landslide time‐of‐failure prediction is crucial in natural hazards, often requiring precise measurements from in situ instruments. This instrumentation is not always possible, and remote‐sensing techniques have been questioned for detecting precursors and predicting landslides. Here, based on high frequency acquisitions of the PlanetScope satellite constellation, we study the kinematics of a large landslide located in Peru that failed in June 2020. We show that the landslide underwent a progressive acceleration in the 3 months before its failure, reaching at most 8 m of total displacement. The high frequency of satellite revisit allows us to apply the popular Fukuzono method for landslide time‐of‐failure prediction, with sufficient confidence for faster moving areas of the landslide. These results open new opportunities for landslide precursors detection from space, but also show the probable seldom applicability of the optical satellites for landslide time‐of‐failure prediction.

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“…In the last years, new image processing tools were developed and examples of landslide displacement monitoring, based on optical satellite images are available [7,12,[18][19][20][21]. However, only few authors attempted to derive time-series of displacement using long stack of satellite optical images, thus demonstrating the potential of such an approach to derive time of series of displacement along the plane normal to the line of sight with a sub-pixel accuracy [6,20,[22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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

Caporossi

Muzi

2020

Remote Sensing

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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 ...

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Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series

Cited by 9 publications

References 36 publications

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Precursory Motion and Time‐Of‐Failure Prediction of the Achoma Landslide, Peru, From High Frequency PlanetScope Satellites

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

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