Surface reconstruction and landslide displacement measurements with Pléiades satellite images

Stumpf, André; Malet, Jean‐Philippe; Allemand, Pascal; Ulrich, Patrice

doi:10.1016/j.isprsjprs.2014.05.008

Cited by 122 publications

(104 citation statements)

References 37 publications

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“…For the generation of surface models and the orthorectification of all images commercial software (Imagine Photogrammetry) was used in a cloud-based environment (SaaS) following the approach described in (Stumpf et al 2014b Rosu et al 2015) is used. The algorithm follows a coarse-to-fine hierarchical scheme and sub-pixel precision is achieved through sinc-interpolation of the original image.…”

Section: Methods and Datamentioning

confidence: 99%

“…Pléiades). Sub-pixel image correlation of optical satellite images has been demonstrated to be a valuable tool for measuring surface deformation such as co-seismic slip (Leprince et al 2007), glacier flow (Heid and Kääb 2012) or landslides (Delacourt et al 2004;Stumpf et al 2014b). Due to co-registration errors and environmental changes between image acquisitions (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Towards Time-Series Processing of VHR Satellite Images for Surface Deformation Detecton and Measurements

Stumpf

Delacourt

Malet

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission III, WG III/5KEY WORDS: deformation measurement, image correlation, high-performance computing, satellite remote sensing ABSTRACT:The increasing fleet of VHR optical satellites (e.g. Pléiades, Spot 6/7, WorldView-3) offers new opportunities for the monitoring of surface deformation resulting from gravitational (e.g. glaciers, landslides) or tectonic forces (coseismic slip). Image correlation techniques have been developed and successfully employed in many geoscientific studies to quantify horizontal surface deformation at sub-pixel precision. The analysis of time-series, however, has received less attention in this context and there is still a lack of techniques that fully exploit archived image time-series and the increasing flux of incoming data. This study targets the development of an image correlation processing chain that relies on multiple pair-wise matching to exploit the redundancy of deformation measurements recorded at different view angles and over multiple time steps. The proposed processing chain is based on a hierarchical image correlation scheme that readily uses parallel processing. Since pair-wise matching can be performed independently the distribution of individual tasks is straightforward and yields to significant reductions of the overall runtime scaling with the available HPC infrastructure. We find that it is more convenient to implement experimental analytical tasks in a high-level programming language (i.e. R) and explore the use of parallel programming to compensate for performance bottlenecks of the interpreted language. Preliminary comparisons against maps from domain expert suggest that the proposed methodology is suitable to eliminate false detections and, thereby, enhances the reliability of correlation-based detections of surface deformation.

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Section: Methods and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Time-Series Processing of VHR Satellite Images for Surface Deformation Detecton and Measurements

Stumpf

Delacourt

Malet

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It should also be noted that orthorectification DEMs (or DEMs for topographic phase removal within SAR interferometry) are by necessity outdated unless acquired simultaneously with image acquisition (Stumpf et al, 2014). Any orthorectification, no matter how accurate in space, is therefore temporally corrupted by the fact that the ground is a moving target, always changing in time.…”

Section: The Planet Cubesat Constellationmentioning

confidence: 99%

Coseismic displacements of the 14 November 2016 <i>M</i><sub>w</sub> 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation

Kääb

Altena

Mascaro

2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Satellite measurements of coseismic displacements are typically based on synthetic aperture radar (SAR) interferometry or amplitude tracking, or based on optical data such as from Landsat, Sentinel-2, SPOT, ASTER, very high-resolution satellites, or air photos. Here, we evaluate a new class of optical satellite images for this purpose – data from cubesats. More specific, we investigate the PlanetScope cubesat constellation for horizontal surface displacements by the 14 November 2016 Mw 7.8 Kaikoura, New Zealand, earthquake. Single PlanetScope scenes are 2–4 m-resolution visible and near-infrared frame images of approximately 20–30 km  ×  9–15 km in size, acquired in continuous sequence along an orbit of approximately 375–475 km height. From single scenes or mosaics from before and after the earthquake, we observe surface displacements of up to almost 10 m and estimate matching accuracies from PlanetScope data between ±0.25 and ±0.7 pixels (∼ ±0.75 to ±2.0 m), depending on time interval and image product type. Thereby, the most optimistic accuracy estimate of ±0.25 pixels might actually be typical for the final, sun-synchronous, and near-polar-orbit PlanetScope constellation when unrectified data are used for matching. This accuracy, the daily revisit anticipated for the PlanetScope constellation for the entire land surface of Earth, and a number of other features, together offer new possibilities for investigating coseismic and other Earth surface displacements and managing related hazards and disasters, and complement existing SAR and optical methods. For comparison and for a better regional overview we also match the coseismic displacements by the 2016 Kaikoura earthquake using Landsat 8 and Sentinel-2 data.

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“…A vertical precision of glacier DEMs in high mountains with an SD between 0.51 and 1.26 m is achieved with the aid of Pléiades stereo image pairs (Berthier et al, 2014). For more inclined structures and vegetation ±3 m has been determined (Stumpf et al, 2014).…”

Section: Satellitementioning

confidence: 99%

Detailed and Highly Accurate 3d Models of High Mountain Areas by the Macs-Himalaya Aerial Camera Platform

Brauchle

Hein

Berger

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Remote sensing in areas with extreme altitude differences is particularly challenging. In high mountain areas specifically, steep slopes result in reduced ground pixel resolution and degraded quality in the DEM. Exceptionally high brightness differences can in part no longer be imaged by the sensors. Nevertheless, detailed information about mountainous regions is highly relevant: time and again glacier lake outburst floods (GLOFs) and debris avalanches claim dozens of victims. Glaciers are sensitive to climate change and must be carefully monitored. Very detailed and accurate 3D maps provide a basic tool for the analysis of natural hazards and the monitoring of glacier surfaces in high mountain areas. There is a gap here, because the desired accuracies are often not achieved. It is for this reason that the DLR Institute of Optical Sensor Systems has developed a new aerial camera, the MACS-Himalaya. The measuring unit comprises four camera modules with an overall aperture angle of 116° perpendicular to the direction of flight. A High Dynamic Range (HDR) mode was introduced so that within a scene, bright areas such as sun-flooded snow and dark areas such as shaded stone can be imaged. In 2014, a measuring survey was performed on the Nepalese side of the Himalayas. The remote sensing system was carried by a Stemme S10 motor glider. Amongst other targets, the Seti Valley, Kali-Gandaki Valley and the Mt. Everest/Khumbu Region were imaged at heights up to 9,200 m. Products such as dense point clouds, DSMs and true orthomosaics with a ground pixel resolution of up to 15 cm were produced. Special challenges and gaps in the investigation of high mountain areas, approaches for resolution of these problems, the camera system and the state of evaluation are presented with examples.

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Surface reconstruction and landslide displacement measurements with Pléiades satellite images

Cited by 122 publications

References 37 publications

Towards Time-Series Processing of VHR Satellite Images for Surface Deformation Detecton and Measurements

Towards Time-Series Processing of VHR Satellite Images for Surface Deformation Detecton and Measurements

Coseismic displacements of the 14 November 2016 <i>M</i><sub>w</sub> 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation

Detailed and Highly Accurate 3d Models of High Mountain Areas by the Macs-Himalaya Aerial Camera Platform

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Surface reconstruction and landslide displacement measurements with Pléiades satellite images

Cited by 122 publications

References 37 publications

Towards Time-Series Processing of VHR Satellite Images for Surface Deformation Detecton and Measurements

Towards Time-Series Processing of VHR Satellite Images for Surface Deformation Detecton and Measurements

Coseismic displacements of the 14 November 2016 &lt;i&gt;M&lt;/i&gt;&lt;sub&gt;w&lt;/sub&gt; 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation

Detailed and Highly Accurate 3d Models of High Mountain Areas by the Macs-Himalaya Aerial Camera Platform

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Coseismic displacements of the 14 November 2016 <i>M</i><sub>w</sub> 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation