Using multiresolution and multitemporal satellite data for post-disaster landslide inventory in the Republic of Serbia

Đurić, Dragana; Mladenović, Ana; Pešić-Georgiadis, Milica; Marjanović, Miloš; Abolmasov, Biljana

doi:10.1007/s10346-017-0847-2

Cited by 44 publications

(14 citation statements)

References 47 publications

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“…Thousands of landslides can be triggered by seismic shaking. These coseismic landslides generate large amounts of debris, part of which will reach large streams and form landslide-dammed lakes that impound large volumes of water and sediments (Fan et al, 2012(Fan et al, , 2017bTang et al, 2018). More debris will deposit, with marginal stability, high on the slopes and in low-order channels (Meunier et al, 2008;Gorum et al, 2011;Kargel et al, 2016;Fan et al, 2018a, b).…”

Section: Earthquake-induced Enhanced Landslidingmentioning

confidence: 99%

Two multi-temporal datasets that track the enhanced landsliding after the 2008 Wenchuan earthquake

Fan¹,

Scaringi²,

Domènech³

et al. 2019

Earth Syst. Sci. Data

110

105

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Abstract. We release two datasets that track the enhanced landsliding induced by the 2008 Mw 7.9 Wenchuan earthquake over a portion of the Longmen Mountains, at the eastern margin of the Tibetan Plateau (Sichuan, China). The first dataset is a geo-referenced multi-temporal polygon-based inventory of pre- and coseismic landslides, post-seismic remobilisations of coseismic landslide debris and post-seismic landslides (new failures). It covers 471 km2 in the earthquake's epicentral area, from 2005 to 2018. The second dataset records the debris flows that occurred from 2008 to 2017 in a larger area (∼17 000 km2), together with information on their triggering rainfall as recorded by a network of rain gauges. For some well-monitored events, we provide more detailed data on rainfall, discharge, flow depth and density. The datasets can be used to analyse, on various scales, the patterns of landsliding caused by the earthquake. They can be compared to inventories of landslides triggered by past or new earthquakes or by other triggers to reveal common or distinctive controlling factors. To our knowledge, no other inventories that track the temporal evolution of earthquake-induced mass wasting have been made freely available thus far. Our datasets can be accessed from https://doi.org/10.5281/zenodo.1405489. We also encourage other researchers to share their datasets to facilitate research on post-seismic geological hazards.

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Section: Earthquake-induced Enhanced Landslidingmentioning

confidence: 99%

Two multi-temporal datasets that track the enhanced landsliding after the 2008 Wenchuan earthquake

Fan¹,

Scaringi²,

Domènech³

et al. 2019

Earth Syst. Sci. Data

110

105

View full text Add to dashboard Cite

show abstract

“…When the obtained results were examined, although landslide mapping results were obtained with high accuracy from all developed models, higher accuracy values were obtained in classifier models using post-event datasets ( Table 5). These results show that while pre-event and post-event images are useful to create or update the landslide inventory maps (Yang and Chen, 2010;Ðurić et al, 2017), post-event images without the need for any archive images in crisis management are sufficient in detecting the locations where the dense landslide events occurred. In the comparison of dataset size in this study, it was observed that increasing the dataset size slightly increased the mapping accuracy ( Table 5).…”

Section: Discussionmentioning

confidence: 87%

Investigation of the Effect of the Dataset Size and Type in the Earthquake-Triggered Landslides Mapping: A Case Study for the 2018 Hokkaido Iburu Landslides

Çömert

2021

Front. Earth Sci.

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Rapid mapping of landslides that occur after an earthquake is important for rapid crisis management. In this study, experimental research was conducted on the size of the model area and the data types used in developing classifiers for the supervised classification approaches used in rapid landslide mapping. The Hokkaido Iburu earthquake zone that occurred on September 6, 2018, was selected as the study area. PlanetScope pre-event and post-event images and ALOS-PALSAR Digital Elevation Model (DEM) were used in the analysis processes. In this context, five model areas with different sizes and one test area were determined. Object-based image analysis (OBIA) was used as a landslide mapping approach. Random Forest classifier, which is a supervised classification algorithm, was performed in the mapping of image objects produced by the segmentation stage of OBIA. Two different data sets were created for landslide mapping: change-based dataset and post-event dataset. The change-based dataset is generated from change data such as the difference of normalized difference vegetation index (δNDVI), change detection Image (CDI), princiable component analysis (PCA), and Independent component analysis (ICA) which are used in change detection applications. The post-event dataset was created from data generated from post-event image bands. When the obtained results were examined, higher accuracy results were obtained with the post-event dataset. Increasing the size of the model area, in other words, increasing the training data slightly increases the accuracy of landslide mapping. However, a model area that represents the region to be mapped in small sizes to make rapid decisions provides a 94% F-measure accuracy for earthquake-triggered landslide detection.

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“…We propose another option that works well for satellite-based images, which is to stabilize the Census transform by combination with a sum of absolute differences (SAD) cost function. SAD is normalized by the bit depth of the images, that is, 2 16 for Pléiades. Normalized cross-correlation yields more blurring at 3D breaklines than Census transform such that it is not applied (cf.…”

Section: Image Matchingmentioning

confidence: 99%

“…Mapping, in general, can be described as the process of generating 3D maps of a region of interest on the Earth's surface consisting of 3D coordinates linked with the spectral information that was observed by the underlying sensor. Exemplary applications in the domain of remote sensing are, for instance, city modeling [1][2][3], forest assessment and biomass estimation [4][5][6], change detection [7,8], land cover and land use classification [9,10], carbon reporting [11], farm land monitoring [12], glacier observation [13,14], disaster damage mapping [15,16] and mapping in general [17,18]. To allow semantic analysis those applications need mapping products in form of digital surface models (DSM), digital terrain models (DTM), their difference, that is, normalized digital surface models (nDSM) and the according multi-spectral ortho-rectified images.…”

Section: Introductionmentioning

confidence: 99%

Mapping with Pléiades—End-to-End Workflow

2019

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In this work, we introduce an end-to-end workflow for very high-resolution satellite-based mapping, building the basis for important 3D mapping products: (1) digital surface model, (2) digital terrain model, (3) normalized digital surface model and (4) ortho-rectified image mosaic. In particular, we describe all underlying principles for satellite-based 3D mapping and propose methods that extract these products from multi-view stereo satellite imagery. Our workflow is demonstrated for the Pléiades satellite constellation, however, the applied building blocks are more general and thus also applicable for different setups. Besides introducing the overall end-to-end workflow, we need also to tackle single building blocks: optimization of sensor models represented by rational polynomials, epipolar rectification, image matching, spatial point intersection, data fusion, digital terrain model derivation, ortho rectification and ortho mosaicing. For each of these steps, extensions to the state-of-the-art are proposed and discussed in detail. In addition, a novel approach for terrain model generation is introduced. The second aim of the study is a detailed assessment of the resulting output products. Thus, a variety of data sets showing different acquisition scenarios are gathered, allover comprising 24 Pléiades images. First, the accuracies of the 2D and 3D geo-location are analyzed. Second, surface and terrain models are evaluated, including a critical look on the underlying error metrics and discussing the differences of single stereo, tri-stereo and multi-view data sets. Overall, 3D accuracies in the range of 0.2 to 0.3 m in planimetry and 0.2 to 0.4 m in height are achieved w.r.t. ground control points. Retrieved surface models show normalized median absolute deviations around 0.9 m in comparison to reference LiDAR data. Multi-view stereo outperforms single stereo in terms of accuracy and completeness of the resulting surface models.then results in 3D coordinates (i.e., a 3D point cloud) that is resampled into an intermediate DSM. Second, those DSMs are fused and post-processed yielding the final DSM. Within this task, the characteristics of the underlying sensor system is exploited, like availability of image triplets in case of Pléiadesand including high-level processing techniques for the given input. The applicability of high-level processing options, like the semi-global matching (SGM) technique, is investigated, enhanced and resulting achievements are illustrated and validated. 3.In the third task, one DTM and one nDSM are generated solely based on the DSM produced in the previous task. The main steps are ground point filtering, detecting points in the given DSM which are located on bare earth, hole filling to interpolate non-ground information and post-processing mainly for outlier removal. In this way, we can (a) filter man-made structures and vegetation and (b) retrieve vegetation or building heights, finally yielding the DTM and nDSM models. While this is a well-established procedure for airborne light dete...

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Using multiresolution and multitemporal satellite data for post-disaster landslide inventory in the Republic of Serbia

Cited by 44 publications

References 47 publications

Two multi-temporal datasets that track the enhanced landsliding after the 2008 Wenchuan earthquake

Two multi-temporal datasets that track the enhanced landsliding after the 2008 Wenchuan earthquake

Investigation of the Effect of the Dataset Size and Type in the Earthquake-Triggered Landslides Mapping: A Case Study for the 2018 Hokkaido Iburu Landslides

Mapping with Pléiades—End-to-End Workflow

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