The Influence of External Digital Elevation Models on PS-InSAR and SBAS Results: Implications for the Analysis of Deformation Signals Caused by Slow Moving Landslides in the Northern Apennines (Italy)

Bayer, Benedikt; Schmidt, D. A.; Simoni, Alessandro

doi:10.1109/tgrs.2017.2648885

Cited by 32 publications

(16 citation statements)

References 27 publications

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“…DEM errors often exist in differential interferograms, and will inevitably result in errors in the final deformation results. According to the height-to-phase formula [63], the topographic phase is proportional to the perpendicular baseline B ⊥ between two SAR images, and the DEM error ∆Z can be estimated with following equation:…”

Section: Dem Error Correctionmentioning

confidence: 99%

Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China

et al. 2018

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The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique.

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Section: Dem Error Correctionmentioning

confidence: 99%

Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China

et al. 2018

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“…So as land use is the most influencing factor to the occurrence of landslides [24]. An analysis of the effect of DEM on InSAR processing to estimate terrain deformations showed that DEM only had a significant impact on InSAR displacement but not on InSAR coherence, such as also is highlighted in Bayer et al [25].…”

Section: Discussionmentioning

confidence: 83%

“…All of the variables generated (25) in this research by remote sensing techniques were overlapped and cut into a common sub-zone and then combined into a multidimensional image. Here are found the classification variables.…”

Section: Model To the Detection Of Landslidesmentioning

confidence: 99%

The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection at a Regional Scale: The Case in Southeastern Colombia

Muñoz¹,

Murillo-Feo²

2021

Slope Engineering

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This landslide detection research applied remote sensing techniques. Morphometry to derive both DEM terrain parameters and land use variables. SAR interferometry (InSAR) for showing that InSAR coherence and InSAR displacement obtained with SRTM DEM 30 m resolution were strongly related to landslides. InSAR coherence values from 0.43 to 0.66 had a high association with landslides. PS-InSAR allowed to estimate terrain velocities in the satellite line-of-sight (LOS) in the range − 10 to 10 mm/year concerning extremely slow landslide displacement rates. SAR polarimetry (PolSAR) was used over L-band UAVSAR quad-pol data, obtaining the scattering mechanism of volume and surface retrodispersion more associated with landslides. The optical remote sensing with a multitemporal approach for change detection by multi-year Landsat (5, 7 and 8)-NDVI, showed that NDVI related to landslides had values between 0.42 and 0.72. All the information was combined into a multidimensional grid product and crossed with training data containing a Colombian Geologic Service (CGS) landslide inventory. A detection model was implemented using the Random Forest supervised method relating the training sample of landslides with multidimensional explanatory variables. A test sample with a proportion of 70:30 allowed to find the accuracy of detection of about 70.8% for slides type.

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“…The accuracy of ArcticDEM not only enables mapping of coeruptive deposition but also enhances estimates of coeruptive ground deformation from InSAR, which are partially dependent on the quality of the DEM used in InSAR processing (e.g., Bayer et al, 2017; Du et al, 2017; Samsonov, 2010). A more accurate DEM can improve the deformation estimates in two ways.…”

Section: Data Processingmentioning

confidence: 99%

Characterization of the 2008 Phreatomagmatic Eruption of Okmok From ArcticDEM and InSAR: Deposition, Erosion, and Deformation

et al. 2020

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Okmok volcano, one of the most active volcanoes in the Aleutian Islands, erupted between 12 July and 19 August 2008 and produced a new tephra cone (Ahmanilix) up to 253 m high. A coeruptive deposit thickness map and the posteruptive elevation change rate map are generated for this phreatomagmatic eruption using the new, high-resolution (2 m) digital elevation model (DEM) time series provided by ArcticDEM. The total dense rock equivalent (DRE) volume over the proximal deposit field classified from the elevation change map is estimated as 0.147 ± 0.001 km 3 DRE (bulk volume of 0.453 ± 0.003 km 3). Combined with the distal deposit volume derived from field measurements, the total DRE is 0.25 km 3 (bulk volume of 0.76 km 3). The estimated erosion rate is up to −15 ± 3 m/year along the flanks of Ahmanilix, and the redeposition rate is about 1 ± 0.3 m/year at the base and 6 ± 1 m/year within the center of Ahmanilix. The surface elevation change also reveals a large ground surface collapse that disrupted the old Cone D lava bench, with a total collapse area of 0.15 km 2 and a volume of (7.1 ± 0.4) × 10 −3 km 3. We also produce a coeruptive deformation map from satellite interferometric synthetic aperture radar (InSAR) using topography from both ArcticDEM and the Shuttle Radar Topography Mission (SRTM) and compare the results. Finally, based on historical eruption volumes, the magma supply rate at Okmok is estimated as (1.77 ± 0.1) × 10 −3 km 3 /year.

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The Influence of External Digital Elevation Models on PS-InSAR and SBAS Results: Implications for the Analysis of Deformation Signals Caused by Slow Moving Landslides in the Northern Apennines (Italy)

Cited by 32 publications

References 27 publications

Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China

Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China

The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection at a Regional Scale: The Case in Southeastern Colombia

Characterization of the 2008 Phreatomagmatic Eruption of Okmok From ArcticDEM and InSAR: Deposition, Erosion, and Deformation

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