Robust Automated Image Co-Registration of Optical  Multi-Sensor Time Series Data: Database Generation  for Multi-Temporal Landslide Detection

Behling, Robert; Roessner, Sigrid; Segl, Karl; Kleinschmit, Birgit; Kaufmann, Hermann

doi:10.3390/rs6032572

Cited by 49 publications

(36 citation statements)

References 35 publications

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“…This dataset was obtained using an automated object-oriented landslide mapping approach that utilizes multi-temporal satellite-based imagery acquired by different optical sensors (Landsat E(TM), SPOT 1-5, ASTER, IRS-1C LISS III, and RapidEye) between 1986 and 2016 [1][2][3]21]. The resulting landslide dataset is composed of 1846 polygons.…”

Section: Landslide Inventorymentioning

confidence: 99%

“…However, such a task requires analyzing large amounts of remote sensing data, which can only be accomplished using automated methods. We have developed such a method for the automated object-based detection of landslide occurrences using multi-sensor time series of optical satellite images [1,2]. This method is based on the analysis of normalized difference vegetation index (NDVI) trajectories [3] and has been successfully applied in this study area [2].…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, we derived two landslide inventories: (i) a systematic multi-temporal inventory generated by automated landslide detection from time series of satellite remote sensing data and (ii) a historical inventory prepared by an expert based on the visual interpretation of high-resolution satellite remote sensing data as well as incorporating already existing knowledge on landslide occurrences. Detailed descriptions of the methodologies used for the derivation of these two inventories can be found in [1,2,16,17].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

et al. 2017

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Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan.

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Section: Landslide Inventorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

et al. 2017

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“…Image registration in modern photogrammetry approaches (Behling et al, 2014, Zitová et al, 2003 integrates computer vision techniques for automated processing workflows and often it involves the so called Structure from Motion (SfM) (Westoby et al, 2012). SfM consists of few steps such as the feature extraction and matching, the concatenation of the images and their final refinement in BBA (Bundle Block Adjustment).…”

Section: Methods Overviewmentioning

confidence: 99%

Co-Registration of Multitemporal Uav Image Datasets for Monitoring Applications: A New Approach

Aicardi

Nyapwere²,

Nex

et al. 2016

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

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Commission ICWG I/VbKEY WORDS: Multi-temporal, UAV, image-based approach, image registration, photogrammetry ABSTRACT:In the last years we have witnessed a rapid development of UAVs (Unmanned Aerial Vehicles), especially for image collection. One of the advantages is the possibility to perform high resolution and repeated flights in a cheap way to detect changes over time. Thus, dynamic scenes can be monitored acquiring image blocks in different epochs in a flexible way. Anyway, most of UAVs are not able to provide accurate direct geo-referencing information, so image blocks from different epochs still need to be co-registered to efficiently detect changes. This task is mostly completed using GCPs (Ground Control Points), although this approach is time consuming as manual intervention is needed. This paper aims at investigating new techniques to automate the co-registration of image blocks without the use of GCPs, just relying on an image based co-registration (IBCR) approach. The image alignment is initially performed on a reference (anchor) epoch and the registration of the following (slave) epochs is performed including some (anchor) images from the reference epoch with fixed external orientation parameters. This allows constraining the Bundle Block Adjustment of the slave epoch to be consistent with the reference one. The study involved the use of 10 multi-temporal image block over a large building construction site, and spanning a time frame of 2 years. Different tests have been performed for the reference image choice with a manual approach and then evaluating the reached accuracy. The performed tests on the chosen test site have shown that the accuracy of the proposed methodology provides results comparable to the common GCPs registration approach.

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“…All RapidEye datasets were pre-processed, i.e., atmospherically-corrected using ATCOR for ERDAS IMAGINE, resampled to a pixel size of 6.5 m, image-to-image co-registered using an in-house algorithm [43], and clipped to various extents (e.g., single field, field composite). The resulting RapidEye subsets, correspondingly-derived NDVI images (including descriptive statistics) became the basis for further image analysis.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

et al. 2015

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This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multifield landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soilpattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area.

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Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

Cited by 49 publications

References 35 publications

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

Co-Registration of Multitemporal Uav Image Datasets for Monitoring Applications: A New Approach

Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

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