Object-Oriented Change Detection for Landslide Rapid Mapping

Lü, Ping; Stumpf, André; Kerle, N.; Casagli, Nicola

doi:10.1109/lgrs.2010.2101045

Cited by 242 publications

(154 citation statements)

References 23 publications

Supporting

Mentioning

138

Contrasting

Unclassified

Order By: Relevance

“…Therefore, automatic or semi-automatic detection methods have been developed in the past years (e.g., [15][16][17][18][19][20][21][22][23]). These methods are either based on the supervised or unsupervised classification of one satellite image (e.g., [18,24]), or on the detection of new landslides in a pair of images acquired at different dates [20,21]. Detection is either based on the comparison of individual pixels or on the analysis of objects extracted from the images.…”

Section: Introductionmentioning

confidence: 99%

“…Supervised methods use training data to estimate the parameters of the image segmentation in object oriented algorithms. It allows to increase this accuracy with errors of commission between 12% and 27%, similar to omission errors [20,21,23]. These 3 latter methods showing better accuracy used Very High Resolution (VHR, ≤1 m) sensors, whose past archives have small coverage on the globe.…”

Section: Introductionmentioning

confidence: 99%

“…The recent launch of DLR TerrarSar-X and Tandem-X missions (among others) improves the availablity of high resolution radar images, even if past archives are still limited. Most recent methods use VHR data, either Quickbird (0.6 m) or Ikonos data (0.4 m) (e.g., [21,23]). The compensation of such very high resolution instruments is that their images cover only a limited area.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Supervised Method of Landslide Inventory Using Panchromatic SPOT5 Images and Application to the Earthquake-Triggered Landslides of Pisco (Peru, 2007, Mw8.0)

et al. 2013

View full text Add to dashboard Cite

Earthquake is one of the dominant triggering factors of landslides. Given the wide areas covered by mega earthquake-triggered landslides, their inventory requires development of automatic or semi-automatic methods applied to satellite imagery. A detection method is here proposed for this purpose, to fit with simple datasets; SPOT5 panchromatic images of 5 m resolution coupled with a freely and globally available DEM. The method takes advantage of multi-temporal images to detect changes based on radiometric variations after precise coregistration/orthorectification. Removal of false alarms is then undertaken using shape, orientation and radiometric properties of connected pixels defining objects. 80% of the landslides and 93% of the landslide area are detected indicating small omission errors but 50% of false alarms remain. They are removed using expert based analysis of the inventory. The method is applied to realize the first comprehensive inventory of landslides triggered by the Pisco earthquake (Peru, 15/08/2007, Mw 8.0) over an area of 27,000 km 2 . 866 landslides larger than 100 m 2 are detected covering a total area of 1.29 km 2 . The area/number distribution follows a power-law with an exponent of 1.63, showing a very particular regime of triggering in this arid environment compared to other areas in the world. This specific triggering can be explained by the little soil cover in the coastal and forearc regions of Peru.Analysis of this database finally shows a major control of the topography (both orientation and inclination) on the repartition of the Pisco-triggered landslides.Remote Sens. 2013, 5 2591

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Supervised Method of Landslide Inventory Using Panchromatic SPOT5 Images and Application to the Earthquake-Triggered Landslides of Pisco (Peru, 2007, Mw8.0)

et al. 2013

View full text Add to dashboard Cite

show abstract

“…communicate or interpret. Roche et al, 2013; ascertainment of total spatial extent and relative intensity Poiani et al, 2016 Lu et al, 2011;.…”

Section: Empirically Modeledmentioning

confidence: 99%

Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

Williams

Rosser

Kincey

et al. 2018

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 M w 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.

show abstract

“…The segmentation was performed using five scale parameters (5, 7, 8, 9 and 10), with the same shape (0.3) and compactness (0.5) factors. Then, the data were parameterized according to the specific requirements for mapping landslides and incorporated into a multi-scale optimization routine (Stumpf et al, 2011). In this way we aimed to integrate the spectral, spatial, and morphometric characteristics of landslides.…”

Section: Object Based Landlide Deleiantionmentioning

confidence: 99%

Assessing uncertainties associated with digital elevation models for object based landslide delination

Feizizadeh¹,

Blaschke²

2016

GEOBIA 2016: Solutions and Synergies

View full text Add to dashboard Cite

ABSTRACT:Digital elevation models (DEMs) are representations of topography with inherent errors that constitute uncertainty. DEMs data are often used in object based analyses without quantifying the effects of these errors. The main objective of this research is to establish a semi-automated object-based image analysis (OBIA) methodology for modelling uncertainty associated with DEMs when applied for locating landslides. In order to assess the uncertainty of DEMs, the Monte Carlo Simulation methodology was employed for evaluation of the effects of uncertainty on elevation and derived topographic parameters. The effect of DEM error is investigated, using stochastic conditional simulation to generate multiple equally likely representations of an actual terrain surface. Accordingly, distributional measures including accuracy surfaces, spatial autocorrelation indices, and variograms were also employed to quantify the magnitude and spatial pattern of the uncertainty. The semi-automated object based rule-sets for landslide delineation were developed based on two approaches a): without uncertainty assessing of DEMs and b): under applying uncertainty assessing of DEMs to examine the probable and possible uncertainties in delineating the landslides and measuring the improved accuracy after minimizing these associated uncertainties. The results of two approaches were validated using a landslide inventory database and very accurate GPS dataset. Results indicated very significant improvement in accuracy of results (> 28 %) when employing DEMs under uncertainty assessment. This research demonstrates how application of this methodology can address DEMs uncertainty, contributing to more responsible use of elevation and derived topographic parameters, and ultimately results obtained from their use.

show abstract

Object-Oriented Change Detection for Landslide Rapid Mapping

Cited by 242 publications

References 23 publications

Supervised Method of Landslide Inventory Using Panchromatic SPOT5 Images and Application to the Earthquake-Triggered Landslides of Pisco (Peru, 2007, Mw8.0)

Supervised Method of Landslide Inventory Using Panchromatic SPOT5 Images and Application to the Earthquake-Triggered Landslides of Pisco (Peru, 2007, Mw8.0)

Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

Assessing uncertainties associated with digital elevation models for object based landslide delination

Contact Info

Product

Resources

About