Using High-Resolution Satellite Images for Post-Earthquake Building Damage Assessment: A Study following the 26 January 2001 Gujarat Earthquake

2004 ◽  
Vol 20 (1) ◽  
pp. 145-169 ◽  
Author(s):  
Keiko Saito ◽  
Robin J. S. Spence ◽  
Christopher Going ◽  
Michael Markus
Keyword(s):  
Damage Assessment ◽  
Satellite Images ◽  
Satellite Image ◽  
Building Damage ◽  
Large Area ◽  
Rapid Visual Screening ◽  
High Resolution Satellite Images ◽  
Collapsed Buildings ◽  
Optical Satellite Images ◽  
The City

Newly available optical satellite images with 1-m ground resolution such as IKONOS mean that rapid postdisaster damage assessment might be made over large areas. Such surveys could be of great value to emergency management and post-event recovery operations and have particular promise for earthquake areas, where damage distribution is often very uneven. In this paper three satellite images taken before and after the 26 January 2001 Gujarat earthquake were studied for damage assessment purposes. The images comprised a post-earthquake cover of the city of Bhuj, which was close to the epicenter, and pre- and post-earthquake cover of the city Ahmedabad. The assessment data was then compared with damage surveys actually made on-site. Three separate experiments were conducted. In the first, the satellite image of Bhuj was compared with detailed ground photos of 28 severely damaged buildings taken at about the same time as the satellite image, to investigate the levels and types of damage that can and cannot be identified. In the second experiment, the whole city center of Bhuj was damage mapped using only the satellite image. This was subsequently compared with a map produced from a building-by-building damage survey. In the third experiment, pre- and post-earthquake images for a large area of Ahmedabad were compared and totally collapsed buildings were identified. These sites were subsequently visited to confirm the accuracy of the observations. The experiment results indicate that rapid visual screening can identify areas of heavy damage and individual collapsed buildings, even when comparative cover does not exist. The need to develop a tool with direct application to support emergency response is discussed.

Visual Damage Assessment using High-Resolution Satellite Images following the 2003 Bam, Iran, Earthquake

2005 ◽  
Vol 21 (1_suppl) ◽  
pp. 309-318 ◽  
Author(s):  
Keiko Saito ◽  
Robin Spence ◽  
Terence A. de C Foley
Keyword(s):  
High Resolution ◽  
Damage Assessment ◽  
Satellite Images ◽  
Building Damage ◽  
Absolute Difference ◽  
Visual Interpretation ◽  
Damage Distribution ◽  
Visual Damage ◽  
High Resolution Satellite Images ◽  
Damage Map

Visual interpretation of the building damage distribution in Bam, Iran, caused by the earthquake on 26 December 2003 has been carried out using pre- and post-earthquake QuickBird panchromatic high-resolution satellite images to produce a damage map. Two experienced interpreters carried out the assessments, and their results were compared to analyze the reasons for discrepancies likely to occur from interpretations by different interpreters. The first damage interpretation was carried out on the post-earthquake image, whereas the second interpretation compared the pre- and post-earthquake images. The analysis revealed that when using only the post-earthquake image, interpreters tend to underestimate the levels of damage, since both interpreters assigned higher damage levels when the pre- and post-earthquake image were compared than when only using the post-earthquake image. The absolute difference in the damage levels the two interpreters assigned in the post-only assessment and pre-and post-event comparison assessment remained the same.

scholarly journals TOWARDS POST-DISASTER DEBRIS IDENTIFICATION FOR PRECISE DAMAGE AND RECOVERY ASSESSMENTS FROM UAV AND SATELLITE IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 297-302 ◽  
Author(s):  
S. Ghaffarian ◽  
N. Kerle
Keyword(s):  
Remote Sensing ◽  
Disaster Response ◽  
Damage Assessment ◽  
Satellite Images ◽  
Satellite Image ◽  
Removal Time ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Uav Images ◽  
Disaster Debris

<p><strong>Abstract.</strong> Often disasters cause structural damages and produce rubble and debris, depending on their magnitude and type. The initial disaster response activity is evaluation of the damages, i.e. creation of a detailed damage estimation for different object types throughout the affected area. First responders and government stakeholders require the damage information to plan rescue operations and later on to guide the recovery process. Remote sensing, due to its agile data acquisition capability, synoptic coverage and low cost, has long been used as a vital tool to collect information after a disaster and conduct damage assessment. To detect damages from remote sensing imagery (both UAV and satellite images) structural rubble/debris has been employed as a proxy to detect damaged buildings/areas. However, disaster debris often includes vegetation, sediments and relocated personal property in addition to structural rubble, i.e. items that are wind- or waterborne and not necessarily associated with the closest building. Traditionally, land cover classification-based damage detection has been categorizing debris as damaged areas. However, in particular in waterborne disaster such as tsunamis or storm surges, vast areas end up being debris covered, effectively hindering actual building damage to be detected, and leading to an overestimation of damaged area. Therefore, to perform a precise damage assessment, and consequently recovery assessment that relies on a clear damage benchmark, it is crucial to separate actual structural rubble from ephemeral debris. In this study two approaches were investigated for two types of data (i.e., UAV images, and multi-temporal satellite images). To do so, three textural analysis, i.e., Gabor filters, Local Binary Pattern (LBP), and Histogram of the Oriented Gradients (HOG), were implemented on mosaic UAV images, and the relation between debris type and their time of removal was investigated using very high-resolution satellite images. The results showed that the HOG features, among other texture features, have the potential to be used for debris identification. In addition, multi-temporal satellite image analysis showed that debris removal time needs to be investigated using daily images, because the removal time of debris may change based on the type of disaster and its location.</p>

Detection of Building Damage Areas of the 2006 Central Java, Indonesia, Earthquake through Digital Analysis of Optical Satellite Images

2013 ◽  
Vol 29 (2) ◽  
pp. 453-473 ◽  
Author(s):  
Hiroyuki Miura ◽  
Saburoh Midorikawa ◽  
Norman Kerle
Keyword(s):  
Image Analysis ◽  
Satellite Images ◽  
Building Damage ◽  
Bare Ground ◽  
Central Java ◽  
Gis Data ◽  
Before And After ◽  
Collapsed Buildings ◽  
Optical Satellite Images ◽  
The Relationship

In order to evaluate the capability of building damage detection from optical satellite images, a procedure for digital image analysis is examined and applied to images captured before and after the 2006 Central Java, Indonesia, earthquake. In the image analysis, the pixels of the images are classified into vegetation, bare ground, and built-up areas. The damage areas are detected by the differential of the digital numbers in the built-up areas. The estimated damage distribution is validated by comparing it with the GIS data on building damage obtained from a field survey. The results show that the severely damaged areas were well detected by the analysis. In the densely vegetated areas, however, the damage was underestimated because many of the buildings were obscured by trees. For assessing quantitative damage information, the relationship between the number of collapsed buildings and the areas detected by the image analysis is evaluated.

scholarly journals Comparison between satellite image analysis and site data for monitoring Trail Road landfill site

2021 ◽  
Author(s):  
Ramona Mirtorabi
Keyword(s):  
Image Analysis ◽  
Surface Temperature ◽  
Satellite Images ◽  
Satellite Image ◽  
Monitoring Program ◽  
Landfill Site ◽  
Site Monitoring ◽  
Landfill Sites ◽  
Satellite Image Analysis ◽  
The City

Human life affects the environment in different ways; therefore monitoring human's actions is very important to safeguarding the environment. Studying the human impact on nature is essential to protecting our environment from contaminations. Landfill sites are one of the most influential structures upon nature. Landfills pose a potential danger to the surrounding environment. Therefore they must be supervised for long periods of time to determine their impact. Monitoring the effects of the landfill sites on the surrounding area over a period of time is a useful tool to analyze and understand its effect on the environment. This research work presents a study which uses data analyzed from satellite images for the monitoring of landfill sites. The data collected from satellite images is compared with the data collected from ground measurements. The main goal of this research is to verify the usefulness of remote sensing as a tool for landfill site monitoring. The ground measurement data used in this study is from yearly reports of a monitoring program by the City of Ottawa that are collect by Dillon Limited. The satellite images used are Landsat satellite images downloaded from the U.S. Geological Survey and Earth Resources, and analyzed by ERDAS IMAGINE and ArcMap software. The images are taken from four years: May 1992, August 1998, October 2000, and September 2001. The images are analyzed in terms of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Results from the LST and NDVI value of different years are compared with the results of monitoring program [sic] that has been conducted for the City of Ottawa. Preliminary data analysis of the satellite images reveals that the surface temperature of the landfill site is always higher than the immediate surrounding areas. Any significant changes in LST and NDVI value, especially in the surrounding vegetation areas, are regarded as suspect sites which may be influenced by the development of the landfill site. The result of the comparison between testing and sampling at monitoring wells with satellite image analysis confirms the areas that are more contaminated. The polluted areas show the same locations from both analyses. However, changes at LST and NDVI value analysis could imply the pollution movement earlier than the traditional site sampling monitoring method. These results show the possibility of combining the ground sampling system and satellite images analysis to improve landfill site monitoring.

scholarly journals Developing fragility functions for the areas affected by the 2009 Samoa earthquake and tsunami

2014 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
H. Gokon ◽  
S. Koshimura ◽  
K. Imai ◽  
M. Matsuoka ◽  
Y. Namegaya ◽  
...  
Keyword(s):  
Satellite Images ◽  
Hydrodynamic Force ◽  
Deep Ocean ◽  
Building Damage ◽  
Fragility Functions ◽  
Tsunami Propagation ◽  
Tsunami Inundation ◽  
Field Surveys ◽  
High Resolution Satellite Images ◽  
Tsunami Fragility

Abstract. Fragility functions in terms of flow depth, flow velocity and hydrodynamic force are developed to evaluate structural vulnerability in the areas affected by the 2009 Samoa earthquake and tsunami. First, numerical simulations of tsunami propagation and inundation are conducted to reproduce the features of tsunami inundation. To validate the results, flow depths measured in field surveys and waveforms measured by Deep-ocean Assessment and Reporting of Tsunamis (DART) gauges are utilized. Next, building damage is investigated by manually detecting changes between pre- and post-tsunami high-resolution satellite images. Finally, the data related to tsunami features and building damage are integrated using GIS, and tsunami fragility functions are developed based on the statistical analyses.

scholarly journals ALGORITHM AND APPLICATION OF GCP-INDEPENDENT BLOCK ADJUSTMENT FOR SUPER LARGE-SCALE DOMESTIC HIGH RESOLUTION OPTICAL SATELLITE IMAGERY

2018 ◽  
Vol XLII-3 ◽  
pp. 1607-1615
Author(s):  
Y. S. Sun ◽  
L. Zhang ◽  
B. Xu ◽  
Y. Zhang
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Optimization Method ◽  
Adjustment Process ◽  
Large Area ◽  
Rank Defect ◽  
And Performance

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image &amp;ndash; GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10&amp;thinsp;m and 6&amp;thinsp;m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

scholarly journals STUDY ON MOSAIC AND UNIFORM COLOR METHOD OF SATELLITE IMAGE FUSION IN LARGE SREA

2018 ◽  
Vol XLII-3 ◽  
pp. 1099-1102
Author(s):  
S. Liu ◽  
H. Li ◽  
X. Wang ◽  
L. Guo ◽  
R. Wang
Keyword(s):  
Information Entropy ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Texture Features ◽  
Color Difference ◽  
Large Area ◽  
Color Mapping ◽  
Multi Temporal ◽  
Mosaic Images

Due to the improvement of satellite radiometric resolution and the color difference for multi-temporal satellite remote sensing images and the large amount of satellite image data, how to complete the mosaic and uniform color process of satellite images is always an important problem in image processing. First of all using the bundle uniform color method and least squares mosaic method of GXL and the dodging function, the uniform transition of color and brightness can be realized in large area and multi-temporal satellite images. Secondly, using Color Mapping software to color mosaic images of 16bit to mosaic images of 8bit based on uniform color method with low resolution reference images. At last, qualitative and quantitative analytical methods are used respectively to analyse and evaluate satellite image after mosaic and uniformity coloring. The test reflects the correlation of mosaic images before and after coloring is higher than 95&amp;thinsp;% and image information entropy increases, texture features are enhanced which have been proved by calculation of quantitative indexes such as correlation coefficient and information entropy. Satellite image mosaic and color processing in large area has been well implemented.

scholarly journals Comparison between satellite image analysis and site data for monitoring Trail Road landfill site

2021 ◽  
Author(s):  
Ramona Mirtorabi
Keyword(s):  
Image Analysis ◽  
Surface Temperature ◽  
Satellite Images ◽  
Satellite Image ◽  
Monitoring Program ◽  
Landfill Site ◽  
Site Monitoring ◽  
Landfill Sites ◽  
Satellite Image Analysis ◽  
The City

Human life affects the environment in different ways; therefore monitoring human's actions is very important to safeguarding the environment. Studying the human impact on nature is essential to protecting our environment from contaminations. Landfill sites are one of the most influential structures upon nature. Landfills pose a potential danger to the surrounding environment. Therefore they must be supervised for long periods of time to determine their impact. Monitoring the effects of the landfill sites on the surrounding area over a period of time is a useful tool to analyze and understand its effect on the environment. This research work presents a study which uses data analyzed from satellite images for the monitoring of landfill sites. The data collected from satellite images is compared with the data collected from ground measurements. The main goal of this research is to verify the usefulness of remote sensing as a tool for landfill site monitoring. The ground measurement data used in this study is from yearly reports of a monitoring program by the City of Ottawa that are collect by Dillon Limited. The satellite images used are Landsat satellite images downloaded from the U.S. Geological Survey and Earth Resources, and analyzed by ERDAS IMAGINE and ArcMap software. The images are taken from four years: May 1992, August 1998, October 2000, and September 2001. The images are analyzed in terms of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Results from the LST and NDVI value of different years are compared with the results of monitoring program [sic] that has been conducted for the City of Ottawa. Preliminary data analysis of the satellite images reveals that the surface temperature of the landfill site is always higher than the immediate surrounding areas. Any significant changes in LST and NDVI value, especially in the surrounding vegetation areas, are regarded as suspect sites which may be influenced by the development of the landfill site. The result of the comparison between testing and sampling at monitoring wells with satellite image analysis confirms the areas that are more contaminated. The polluted areas show the same locations from both analyses. However, changes at LST and NDVI value analysis could imply the pollution movement earlier than the traditional site sampling monitoring method. These results show the possibility of combining the ground sampling system and satellite images analysis to improve landfill site monitoring.

scholarly journals REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE

2016 ◽  
Vol XLI-B7 ◽  
pp. 249-255
Author(s):  
Warinthorn Kiadtikornthaweeyot ◽  
Adrian R. L. Tatnall
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Of Interest ◽  
Satellite Imaging ◽  
Proposed Model ◽  
High Resolution Satellite Images ◽  
Image Area ◽  
Interest Detection

High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.

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