Extraction of Inundation Areas Due to the July 2018 Western Japan Torrential Rain Event Using Multi-Temporal ALOS-2 Images

2019 ◽  
Vol 14 (3) ◽  
pp. 445-455 ◽  
Author(s):  
Wen Liu ◽  
◽  
Fumio Yamazaki ◽  
Yoshihisa Maruyama
Keyword(s):  
Aerial Photographs ◽  
Rain Event ◽  
Visual Interpretation ◽  
Web Based ◽  
Inundation Area ◽  
Survey Report ◽  
Multi Temporal ◽  
Western Japan ◽  
Intensity Images ◽  
Okayama Prefecture

A series of heavy rainfalls hit the western half of Japan from June 28 to July 8, 2018. Increased river water overflowed and destroyed river banks, causing flooding over vast areas. In this study, two pre-event and one co-event ALOS-2 PALSAR-2 images were used to extract inundation areas in Kurashiki and Okayama Cities, Okayama Prefecture, Japan. First, water regions were extracted by threshold values from three-temporal intensity images. The increased water regions in July 2018 were obtained as inundation. Inundated built-up areas were identified by the increase of backscattering intensity. Differences between the pre-and co-event coherence values were calculated. The area with decreased coherence was extracted as a possible inundation area. The results of a field survey conducted on July 16, 2018 were used to estimate the optimal parameters for the extraction. Finally, the results from the intensity and coherence images were verified by making comparisons between a web-based questionnaire survey report and the visual interpretation of aerial photographs.

scholarly journals EXTRACTION OF FLOODED AREAS DUE THE 2015 KANTO-TOHOKU HEAVY RAINFALL IN JAPAN USING PALSAR-2 IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 179-183
Author(s):  
F. Yamazaki ◽  
W. Liu
Keyword(s):  
River Water ◽  
Urban Areas ◽  
Heavy Rainfall ◽  
Threshold Value ◽  
Aerial Photographs ◽  
Topographic Map ◽  
Visual Interpretation ◽  
Inundation Area ◽  
Colour Composite ◽  
Flooded Areas

Triggered by two typhoons, heavy rainfall hit Kanto and Tohoku regions of Japan from September 9 to 11, 2015. Increased river water by the continuous rainfall overflowed and destroyed several river banks and caused damaging floods in wide areas. PALSAR-2 onboard ALOS-2 satellite carried out emergency observation for the impacted areas during and after the heavy rainfall. In this study, two pre-event and four co- and post-event PALSAR-2 images were used to extract the inundation area in Joso city, Ibaraki prefecture. First, using the pre-event SAR intensity image and a detailed topographic map, the backscattering coefficient of river water was investigated. Then the flooded areas were extracted by a common threshold value of backscatter for water bodies in the six temporal images. The colour composite of the sigma naught values was also made to visualize pixels that had been converted from ground to water. Finally, the extracted results were compared with those from the visual interpretation of aerial photographs and field survey reports. This comparison revealed that the accuracy of the flood extraction was fairly good for agricultural lands and non-urban land uses. But for built-up urban areas, it was not easy to extract water body since radar illumination did to reach the ground (water) surface.

scholarly journals Distribution and Characteristics of Slope Movements in the Southern Part of Hiroshima Prefecture Caused by the Heavy Rain in Western Japan in July 2018

2019 ◽  
Vol 14 (6) ◽  
pp. 894-902
Author(s):  
Hideaki Goto ◽  
Yasuhiro Kumahara ◽  
Shoichiro Uchiyama ◽  
Yoshiya Iwasa ◽  
Tomoru Yamanaka ◽  
...  
Keyword(s):  
Heavy Rain ◽  
Slope Movement ◽  
Aerial Photographs ◽  
Large Area ◽  
Geospatial Information ◽  
Mountain Ranges ◽  
Slope Movements ◽  
Western Japan ◽  
Hiroshima Prefecture ◽  
Okayama Prefecture

Record-breaking heavy rainfall in July 2018 caused an extremely large number of slope movements over a broad area of western Japan. We mapped the distribution of slope movements in the southern part of Hiroshima Prefecture through an interpretation of aerial photographs that were acquired after the rainfall by the Geospatial Information Authority of Japan, and counted a total of 8,497 slope-movement starting points. The widespread distribution of slope movements – from Etajima City of Hiroshima Prefecture to Kasaoka City of Okayama Prefecture – suggests that the heavy rain affected a very large area. The starting points of debris flow during this disaster were commonly close to the crest of mountain ranges. We compared the distribution of slope movements to the 24-hr rainfall accumulation during the heaviest rainfall event to clarify the factors that caused regional difference in slope-movement distribution. We found the area of highest density of the slope movements was consistent with the area receiving a cumulative rainfall of >250 mm. This observation indicated that the position of slope-movement starting points was not related to differences in geology.

scholarly journals EXTRACTION OF FLOODED AREAS DUE THE 2015 KANTO-TOHOKU HEAVY RAINFALL IN JAPAN USING PALSAR-2 IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 179-183 ◽  
Author(s):  
F. Yamazaki ◽  
W. Liu
Keyword(s):  
River Water ◽  
Urban Areas ◽  
Heavy Rainfall ◽  
Threshold Value ◽  
Aerial Photographs ◽  
Topographic Map ◽  
Visual Interpretation ◽  
Inundation Area ◽  
Colour Composite ◽  
Flooded Areas

Triggered by two typhoons, heavy rainfall hit Kanto and Tohoku regions of Japan from September 9 to 11, 2015. Increased river water by the continuous rainfall overflowed and destroyed several river banks and caused damaging floods in wide areas. PALSAR-2 onboard ALOS-2 satellite carried out emergency observation for the impacted areas during and after the heavy rainfall. In this study, two pre-event and four co- and post-event PALSAR-2 images were used to extract the inundation area in Joso city, Ibaraki prefecture. First, using the pre-event SAR intensity image and a detailed topographic map, the backscattering coefficient of river water was investigated. Then the flooded areas were extracted by a common threshold value of backscatter for water bodies in the six temporal images. The colour composite of the sigma naught values was also made to visualize pixels that had been converted from ground to water. Finally, the extracted results were compared with those from the visual interpretation of aerial photographs and field survey reports. This comparison revealed that the accuracy of the flood extraction was fairly good for agricultural lands and non-urban land uses. But for built-up urban areas, it was not easy to extract water body since radar illumination did to reach the ground (water) surface.

scholarly journals An approach to reduce mapping errors in the production of landslide inventory maps

2015 ◽  
Vol 15 (9) ◽  
pp. 2111-2126 ◽  
Author(s):  
M. Santangelo ◽  
I. Marchesini ◽  
F. Bucci ◽  
M. Cardinali ◽  
F. Fiorucci ◽  
...  
Keyword(s):  
Mapping Method ◽  
Landslide Inventory ◽  
Aerial Photographs ◽  
Positional Error ◽  
Visual Interpretation ◽  
Gis Technology ◽  
Digital Database ◽  
Different Types ◽  
Landslide Mapping ◽  
Transfer Method

Abstract. Landslide inventory maps (LIMs) show where landslides have occurred in an area, and provide information useful to different types of landslide studies, including susceptibility and hazard modelling and validation, risk assessment, erosion analyses, and to evaluate relationships between landslides and geological settings. Despite recent technological advancements, visual interpretation of aerial photographs (API) remains the most common method to prepare LIMs. In this work, we present a new semi-automatic procedure that makes use of GIS technology for the digitization of landslide data obtained through API. To test the procedure, and to compare it to a consolidated landslide mapping method, we prepared two LIMs starting from the same set of landslide API data, which were digitized (a) manually adopting a consolidated visual transfer method, and (b) adopting our new semi-automatic procedure. Results indicate that the new semi-automatic procedure (a) increases the interpreter's overall efficiency by a factor of 2, (b) reduces significantly the subjectivity introduced by the visual (manual) transfer of the landslide information to the digital database, resulting in more accurate LIMs. With the new procedure, the landslide positional error decreases with increasing landslide size, following a power-law. We expect that our work will help adopt standards for transferring landslide information from the aerial photographs to a digital landslide map, contributing to the production of accurate landslide maps.

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.

Space-borne terrain deformation monitoring for large infrastructure projects

2021 ◽  
Author(s):  
Regula Frauenfelder ◽  
Malte Vöge ◽  
Sean E. Salazar ◽  
Carsten Hauser
Keyword(s):  
High Resolution ◽  
Geodetic Survey ◽  
Development Project ◽  
Deformation Monitoring ◽  
Cost Driver ◽  
Development Projects ◽  
Infrastructure Projects ◽  
Visual Interpretation ◽  
Web Based ◽  
Railway Line

<p>Ground settlement and associated deformation of existing infrastructure is a major risk in urban development projects. Project owners have a responsibility to document and manage settlement records before, during and after construction works. Traditionally, land surveying (e.g. leveling and total station) techniques have been the state-of-practice to provide settlement monitoring data. However, in big infrastructure projects, conventional geodetic data acquisition is a major cost driver. Modern space-borne radar interferometry (InSAR) provides the opportunity to drastically increase the number of monitored locations, while at the same time reducing expenses for traditional geodetic survey work. Furthermore, the method allows for highly effective monitoring during all phases of a project.</p><p>The application of InSAR technology is demonstrated for three large development projects near Oslo, the capital of Norway. Showcase examples include a new highway development project and two railway line upgrade projects. In two of the cases, InSAR monitoring was performed by exploiting very high resolution TerraSAR-X data (ca. 1.5 x 1.5 m spatial ground resolution), and in one case, using high resolution Radarsat-2 data (ca. 7 x 7 m spatial ground resolution). A combined area of 127 km<sup>2</sup> was monitored for all three projects, yielding a total of roughly 800,000 measurement points on the ground. Achieved measurement point density based on the TerraSAR-X data was around 37,000 points per km<sup>2</sup>, while density based on the Radarsat-2 data resulted in approximately 6,000 points per km<sup>2</sup> in built-up areas. Both data resolutions offer millimetric deformation precision, with surfaces of buildings and infrastructure providing the best signal reflection and phase coherence, resulting in high-quality results. In all cases, the interferometric time series analyses were communicated to the end users through a web-based map portal, enabling simple visual interpretation of the results, as well as integration with the settlement records of the project.</p>

Improving the Efficiency of Image Interpretation Using Ground Truth Terrestrial Photographs

2019 ◽  
pp. 1098-1128
Author(s):  
Gennady Gienko ◽  
Michael Govorov
Keyword(s):  
Data Collection ◽  
Image Interpretation ◽  
Ground Truth ◽  
Remotely Sensed ◽  
Natural Sciences ◽  
Aerial Photographs ◽  
Field Observations ◽  
Web Based ◽  
Remotely Sensed Imagery ◽  
The Social

Researchers worldwide use remotely sensed imagery in their projects, in both the social and natural sciences. However, users often encounter difficulties working with satellite images and aerial photographs, as image interpretation requires specific experience and skills. The best way to acquire these skills is to go into the field, identify your location in an overhead image, observe the landscape, and find corresponding features in the overhead image. In many cases, personal observations could be substituted by using terrestrial photographs taken from the ground with conventional cameras. This chapter discusses the value of terrestrial photographs as a substitute for field observations, elaborates on issues of data collection, and presents results of experimental estimation of the effectiveness of the use of terrestrial ground truth photographs for interpretation of remotely sensed imagery. The chapter introduces the concept of GeoTruth – a web-based collaborative framework for collection, storing and distribution of ground truth terrestrial photographs and corresponding metadata.

Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake

2020 ◽  
Vol 36 (3) ◽  
pp. 1166-1187 ◽  
Author(s):  
Shohei Naito ◽  
Hiromitsu Tomozawa ◽  
Yuji Mori ◽  
Takeshi Nagata ◽  
Naokazu Monma ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Aerial Photographs ◽  
Learning Models ◽  
Visual Interpretation ◽  
Damage Classification ◽  
Kumamoto Earthquake ◽  
Machine Learning Models

This article presents a method for detecting damaged buildings in the event of an earthquake using machine learning models and aerial photographs. We initially created training data for machine learning models using aerial photographs captured around the town of Mashiki immediately after the main shock of the 2016 Kumamoto earthquake. All buildings are classified into one of the four damage levels by visual interpretation. Subsequently, two damage discrimination models are developed: a bag-of-visual-words model and a model based on a convolutional neural network. Results are compared and validated in terms of accuracy, revealing that the latter model is preferable. Moreover, for the convolutional neural network model, the target areas are expanded and the recalls of damage classification at the four levels range approximately from 66% to 81%.

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