Application of multi-temporal high-resolution imagery and GPS in a study of the motion of a canyon rim landslide

Chadwick, John; Dorsch, Stephen; Glenn, Nancy F.; Thackray, Glenn D.; Shilling, Karen

doi:10.1016/j.isprsjprs.2005.02.001

Cited by 60 publications

(23 citation statements)

References 8 publications

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“…These common points exhibited spatial offsets representing the amount of horizontal movement over the 1990-2002 and 1998-2002 time periods (Figure 2).The results show that these points moved laterally up to 16.4 m (± 2.8 m) between 1990 and 2002, and that the same points moved up to 9.3 m (± 2.7 m) in the 1998-2002 comparison.This result indicates that while most of the landslide activity occurred after the 1998 aerial photo was acquired (which includes the period of formal BLM monitoring), significant motion occurred prior to 1998 [Chadwick et al, 2005], as previously inferred by the BLM [Ellis et al, 2004].…”

Section: The Past: Measuring Historical Landslide Movementsupporting

confidence: 72%

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Eos, Transactions, American Geophysical Union Volume 86, Number 11, 15 March 2005

2005

EoS Transactions

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Section: The Past: Measuring Historical Landslide Movementsupporting

confidence: 72%

“…Image change detection measures only two-dimensional (lateral) movement, but the GPS study measured total (three-dimensional) velocities for the landslide [Chadwick et al, 2005].…”

Section: The Present: Daily Gps Measurementsmentioning

confidence: 99%

Eos, Transactions, American Geophysical Union Volume 86, Number 11, 15 March 2005

2005

EoS Transactions

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“…Geomorphologic analysis for landslide terrains has consisted of field observation, airborne LiDAR, traditional surveying, global positioning system (GPS), aerial photograph interpretation, or a combination of these techniques ( [13][14][15][16][17][18][19][20]). Improvements made in remote sensing technology provide new opportunities to map surface models precisely and accurately at spatial resolutions and temporal frequencies necessary for landslide mapping of small failures in open areas and under most land cover conditions such as fields and sparse forests.…”

Section: Introductionmentioning

confidence: 99%

Landslide Change Detection Based on Multi-Temporal Airborne LiDAR-Derived DEMs

et al. 2018

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Abstract:Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m 2 to 27,000 m 2 in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM) quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides.

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“…Studies have indicated that land surface change detection is the process of exploring the differences between images captured at different times. With multispectral satellite images, land surface interpretations involve comparisons of multitemporal images that are 25 completely geometrically aligned (Liu et al, 2001;Chadwick et al, 2005;Chen et al, 2009;Chue et al, 2015).…”

Section: (Mhem)mentioning

confidence: 99%

Scale and spatial distribution assessment of rainfall-induced landslides along mountain roads

Tseng

Chen

Wu³

2017

Preprint

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Abstract. This study focused on landslides along mountain roads that were caused by Typhoons Nanmadol (2011) and Kong-rey (2013). Image interpretation techniques were employed to interpret satellite images captured before and after the typhoons to derive the changes in slope surfaces. The multivariate hazard evaluation method was adopted to establish a 10 landslide susceptibility assessment model. We then mapped landslide susceptibility and locations to determine the relationship between the spatial distribution of landslide areas and the natural environment along mountain roads. The results can serve as a reference for preventing and mitigating slope disasters on mountain roads.

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Application of multi-temporal high-resolution imagery and GPS in a study of the motion of a canyon rim landslide

Cited by 60 publications

References 8 publications

Eos, Transactions, American Geophysical Union Volume 86, Number 11, 15 March 2005

Eos, Transactions, American Geophysical Union Volume 86, Number 11, 15 March 2005

Landslide Change Detection Based on Multi-Temporal Airborne LiDAR-Derived DEMs

Scale and spatial distribution assessment of rainfall-induced landslides along mountain roads

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