scholarly journals Extraction of Tsunami Damaged Areas Due to the 2010 Chile Earthquake Using Optical and SAR Data of ALOS

2018 ◽  
Vol 2 (2) ◽  
pp. 66
Author(s):  
Ni Made Pertiwi Jaya ◽  
Fusanori Miura
Keyword(s):  
Remote Sensing ◽  
Damage Detection ◽  
Future Study ◽  
Large Scale ◽  
Coastal Areas ◽  
Processing Method ◽  
Alos Palsar ◽  
Inundation Area ◽  
Chile Earthquake ◽  
Good Agreement

Information about damage areas is important due to the large-scale disasters worldwide. In the last decade, both optical and SAR remote sensing were applied in many disaster researches, such as tsunami damage detection. In this study, the ALOS AVNIR-2 and PALSAR images are used to extract the damaged areas caused by the 2010 Chile earthquake. In the processing of ALOS/AVNIR-2, the inundation area was estimated based on the NDVI calculation and classification. Furthermore, damaged areas of the ALOS/PALSAR are extracted by integrating the AVNIR-2 image for water mask and the DEM image for elevation mask. The damaged area result of AVNIR-2 is 8.91 Km2 and for the PALSAR is 8.72 Km2 that is along the coastal areas. The image results showed a good agreement and corresponding area according to the institutional map of the inundation area. Future study in another area is needed in order to strengthen the processing method.

scholarly journals ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR

2016 ◽  
Vol III-8 ◽  
pp. 35-42
Author(s):  
Pertiwi Jaya Ni Made ◽  
Fusanori Miura ◽  
A. Besse Rimba
Keyword(s):  
Large Scale ◽  
Aerial Photographs ◽  
Alos Palsar ◽  
Aster Gdem ◽  
Tsunami Damage ◽  
Before And After ◽  
Chile Earthquake ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Inundation Map

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km<sup>2</sup>. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

scholarly journals Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

2019 ◽  
Vol 11 (6) ◽  
pp. 658 ◽  
Author(s):  
James Shepherd ◽  
Pete Bunting ◽  
John Dymond
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Ground Cover ◽  
Spectral Signature ◽  
Spectral Variation ◽  
Alos Palsar ◽  
Mapping Unit ◽  
Minimum Mapping Unit ◽  
Gis Software

Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, many approaches are not scalable for national mapping programmes due to limits in the size of images that can be processed. Therefore, we present a scalable segmentation algorithm, which is seeded using k-means and provides support for a minimum mapping unit through an innovative iterative elimination process. The algorithm has also been demonstrated for the segmentation of time series datasets capturing both the intra-image variation and change regions. The quality of the segmentation results was assessed by comparison with reference segments along with statistics on the inter- and intra-segment spectral variation. The technique is computationally scalable and is being actively used within the national land cover mapping programme for New Zealand. Additionally, 30-m continental mosaics of Landsat and ALOS-PALSAR have been segmented for Australia in support of national forest height and cover mapping. The algorithm has also been made freely available within the open source Remote Sensing and GIS software Library (RSGISLib).

A simple projection moiré system to measure displacements of aircraft structures

2007 ◽  
Vol 42 (6) ◽  
pp. 477-495 ◽  
Author(s):  
U Galietti ◽  
K Genovese ◽  
L Lamberti ◽  
D Posa
Keyword(s):  
Analytical Model ◽  
Large Scale ◽  
Industrial Applications ◽  
Processing Method ◽  
Simple Analytical Model ◽  
Aerodynamic Loads ◽  
Out Of Plane ◽  
Plane Displacement ◽  
Good Agreement ◽  
Projection Moire

This work presents a simple projection moiré system (PMS) to measure displacements of large-scale aeronautical components. The system includes standard optics, uses a standard fringe-processing method, and relies on a simple analytical model to recover topographic information. An extensive calibration campaign, based on design of experiments, is conducted in order to find the best analytical model for retrieving the out-of-plane displacement field from the moiré pattern and to find the optimal combination of parameters involved in the measurement system. In order to check the suitability of the present PMS device for practical industrial applications, distortions induced by aerodynamic loads on a landing-light glazing of an Airbus A340 are measured. Experimental results are in good agreement with other measurements carried out independently.

scholarly journals ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR

2016 ◽  
Vol III-8 ◽  
pp. 35-42 ◽  
Author(s):  
Pertiwi Jaya Ni Made ◽  
Fusanori Miura ◽  
A. Besse Rimba
Keyword(s):  
Large Scale ◽  
Aerial Photographs ◽  
Alos Palsar ◽  
Aster Gdem ◽  
Tsunami Damage ◽  
Before And After ◽  
Chile Earthquake ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Inundation Map

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km<sup>2</sup>. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

scholarly journals Inundation Analysis of the Dike Breach of the Chikuma River Taking Drainage Process and House Damage into Consideration

2021 ◽  
Vol 16 (3) ◽  
pp. 343-350
Author(s):  
Makoto Takeda ◽  
Daisuke Sato ◽  
Kenji Kawaike ◽  
Masashi Toyota ◽  
◽  
...  
Keyword(s):  
Water Depth ◽  
Large Scale ◽  
Heavy Rain ◽  
Left Bank ◽  
Analysis Model ◽  
River Flooding ◽  
Inundation Area ◽  
Water Behavior ◽  
Model Improvement ◽  
Good Agreement

Heavy rain and river flooding due to Typhoon No. 19 in October 2019 led to overflow and a dike breach on the left bank of the Chikuma River that caused large-scale inundation damage in Nagano City, Japan. To devise countermeasures, an inundation analysis model is an important tool. In this study, an inundation analysis model was developed to examine the inundation water behavior. The calculated inundation water depth and inundation area showed good agreement with the observed inundation water depth and the inundated area, confirming the validity of the analysis model. In addition, temporal changes of the inundation state were calculated considering the drainage process. However, the sewerage system, waterway, and drainage pump car were not taken into consideration in this analysis, and future issues for model improvement were also revealed. In addition, an analysis model with a 2 m grid was developed in the dike breach site, and the inundation water flow on roads and the fluid force around houses were obtained after taking into consideration the effect of houses. In paticular, the calculated value of the specific force exerted on damaged houses was very high. Moreover, it was proposed that house hazard should be evaluated while taking into consideration the loss of houses around the dike.

scholarly journals A Literature Review on the Development of Remote Sensing in Damage Detection of Civil Structures

2021 ◽  
pp. 39-56
Author(s):  
Khaled Ghaedi ◽  
Meisam Gordan ◽  
Zubaidah Ismail ◽  
Huzaifa Hashim ◽  
Marieh Talebkhah
Keyword(s):  
Remote Sensing ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Engineering ◽  
Large Scale ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Fast Response ◽  
Wide Field ◽  
Comparative Review

Remote sensing technologies have a direct impact on gaining structural damage information due to their powerful flexibilities, such as wide field of view, non-contact, low cost, and fast response capacities. It is because remote sensing is often applied to monitor near-real-time damage for large-scale events. Therefore, diverse types of remote sensing data became available and various methods have been designed and reported for structural damage assessment. In this line, a number of remote sensing procedures have been proposed to develop an extensive range of temporal, spectral, and spatial parameters. In this study, a comparative review is conducted in order to present the applied remote sensing-based damage detection approaches in buildings and bridges. It should be noted that the survey is supported by an extensive list for up-to-date references. Based on this review, it can be concluded that remote sensing has widely attracted attentions in different structural engineering fields due to its ability in providing fast response in terms of continuous monitoring for large areas after a natural hazard.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Global Scale ◽  
Area Network ◽  
Biophysical Characterization ◽  
Challenges And Opportunities ◽  
Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

scholarly journals Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

2021 ◽  
Vol 13 (11) ◽  
pp. 2220
Author(s):  
Yanbing Bai ◽  
Wenqi Wu ◽  
Zhengxin Yang ◽  
Jinze Yu ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Flood Events ◽  
Test Set ◽  
Source Data ◽  
Flood Disasters ◽  
Temporary Water ◽  
Sentinel 2

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

Sign in / Sign up

Export Citation Format

Share Document