scholarly journals Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images

2021 ◽  
Vol 13 (4) ◽  
pp. 639
Author(s):  
Wen Liu ◽  
Kiho Fujii ◽  
Yoshihisa Maruyama ◽  
Fumio Yamazaki
Keyword(s):  
Change Detection ◽  
Field Survey ◽  
Geospatial Information ◽  
Recall Accuracy ◽  
Average Value ◽  
Inundation Maps ◽  
Multi Temporal ◽  
Intensity Images ◽  
Inundation Map ◽  
Flooded Areas

Typhoon Hagibis passed through Japan on October 12, 2019, bringing heavy rainfall over half of Japan. Twelve banks of seven state-managed rivers collapsed, flooding a wide area. Quick and accurate damage proximity maps are helpful for emergency responses and relief activities after such disasters. In this study, we propose a quick analysis procedure to estimate inundations due to Typhoon Hagibis using multi-temporal Sentinel-1 SAR intensity images. The study area was Ibaraki Prefecture, Japan, including two flooded state-managed rivers, Naka and Kuji. First, the completely flooded areas were detected by two traditional methods, the change detection and the thresholding methods. By comparing the results in a part of the affected area with our field survey, the change detection was adopted due to its higher recall accuracy. Then, a new index combining the average value and the standard deviation of the differences was proposed for extracting partially flooded built-up areas. Finally, inundation maps were created by merging the completely and partially flooded areas. The final inundation map was evaluated via comparison with the flooding boundary produced by the Geospatial Information Authority (GSI) and the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan. As a result, 74% of the inundated areas were able to be identified successfully using the proposed quick procedure.

scholarly journals Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

2018 ◽  
Vol 18 (7) ◽  
pp. 1905-1918 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Heavy Rain ◽  
Geospatial Information ◽  
Torrential Rain ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Intensity Images ◽  
Elevation Model

Abstract. Torrential rain triggered by two typhoons hit the Kanto and Tohoku regions of Japan from 9 to 11 September 2015. Due to the record-breaking amount of rainfall, several riverbanks were overflowed and destroyed, causing floods over wide areas. The PALSAR-2 sensor on board the ALOS-2 satellite engaged in emergency observations of the affected areas during and after the heavy rain. Two pre-event and three co-event PALSAR-2 images were employed in this study to extract flooded areas in the city of Joso, Ibaraki Prefecture. The backscattering coefficient of the river water was investigated first using the PALSAR-2 intensity images and a land-cover map with a 10 m resolution. The inundation areas were then extracted by setting threshold values for backscattering from water surfaces in the three temporal synthetic aperture radar (SAR) images. The extracted results were modified by considering the land cover and a digital elevation model (DEM). Next, the inundated built-up urban areas were extracted from the changes in SAR backscattering. The results were finally compared with those from visual inspections of airborne imagery by the Geospatial Information Authority of Japan (GSI), and more than 85 % of the maximum inundation areas were extracted successfully.

scholarly journals Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

2018 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Heavy Rain ◽  
Geospatial Information ◽  
Torrential Rain ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Intensity Images ◽  
Elevation Model

Abstract. Torrential rain triggered by two typhoons hit the Kanto and Tohoku regions of Japan from September 9 to 11, 2015. Due to the record-breaking amount of rainfall, several river banks were overflowed and destroyed, causing floods over wide areas. The PALSAR-2 sensor onboard the ALOS-2 satellite engaged in emergency observations of the affected areas during and after the heavy rain. Two pre-event and three co-event PALSAR-2 images were employed in this study to extract flooded areas in Joso city, Ibaraki prefecture. The backscattering coefficient of the river water was investigated first using the PALSAR-2 intensity images and a land-cover map with a 10-m resolution. The inundation areas were then extracted by setting threshold values for backscattering from water surfaces in the three temporal Synthetic Aperture Radar (SAR) images. The extracted results were modified by considering the land-cover and a digital elevation model (DEM). Next, the inundated built-up urban areas were extracted from the changes in SAR backscattering. The results were finally compared with those from visual inspections of airborne imagery by the Geospatial Information Authority of Japan (GSI), and they showed a good level of agreement.

scholarly journals AUTOMATIC URBAN ILLEGAL BUILDING DETECTION USING MULTI-TEMPORAL SATELLITE IMAGES AND GEOSPATIAL INFORMATION SYSTEMS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 387-393 ◽  
Author(s):  
N. Khalili Moghadam ◽  
M. R. Delavar ◽  
P. Hanachee
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Building Detection ◽  
Existing Buildings ◽  
Geospatial Information ◽  
Processing Step ◽  
Multi Temporal ◽  
Under Construction ◽  
Processing Techniques ◽  
The City

With the unprecedented growth of urban population and urban development, we are faced with the growing trend of illegal building (IB) construction. Field visit, as the currently used method of IB detection, is time and man power consuming, in addition to its high cost. Therefore, an automatic IB detection is required. Acquiring multi-temporal satellite images and using image processing techniques for automatic change detection is one of the optimum methods which can be used in IB monitoring. In this research an automatic method of IB detection has been proposed. Two-temporal panchromatic satellite images of IRS-P5 of the study area in a part of Tehran, the city map and an updated spatial database of existing buildings were used to detect the suspected IBs. In the pre-processing step, the images were geometrically and radiometrically corrected. In the next step, the changed pixels were detected using K-means clustering technique because of its quickness and less user’s intervention required. Then, all the changed pixels of each building were identified and the change percentage of each building with the standard threshold of changes was compared to detect the buildings which are under construction. Finally, the IBs were detected by checking the municipality database. The unmatched constructed buildings with municipal database will be field checked to identify the IBs. The results show that out of 343 buildings appeared in the images; only 19 buildings were detected as under construction and three of them as unlicensed buildings. Furthermore, the overall accuracies of 83%, 79% and 75% were obtained for K-means change detection, detection of under construction buildings and IBs detection, respectively.

scholarly journals A SAR INTENSITY IMAGES CHANGE DETECTION METHOD BASED ON FUSION DIFFERENCE DETECTOR AND STATISTICAL PROPERTIES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 439-443 ◽  
Author(s):  
B. Cui ◽  
Y. Zhang ◽  
L. Yan ◽  
X. Cai
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Hypothesis Test ◽  
Threshold Selection ◽  
Sar Images ◽  
Difference Map ◽  
Multi Temporal ◽  
Intensity Images ◽  
Log Ratio ◽  
Change Threshold

Detecting the land cover changes is an important application of multi-temporal synthetic aperture radar (SAR) images. This study puts forward a novel SAR change detection method which has two-steps: change detector construction and change threshold selection. For change detector construction, considering the SAR intensity images follow the gamma distribution, the conditional probabilities of the binary hypothesis test are provided, then the log likelihood ratio (LLR) combined with the log ratio (LR) to construct a detector which can enhance the degree of change to calculate the diversity degree convenient between the two images; for change threshold selection, owing to the characteristic that the curve about the ratio value of adjacent grey-level (GL) values in normalized difference map, the normalized difference map can be segmented in three parts by two thresholds selected which correspond to the regions of unchanged, backscatter enhanced and weakened separately. And as this, the change areas can be also determined simultaneously. The experimental results on different areas and sensors indicate that the proposed algorithm is effective and feasible.

scholarly journals A Novel Method of Unsupervised Change Detection Using Multi-Temporal PolSAR Images

2017 ◽  
Vol 9 (11) ◽  
pp. 1135 ◽  
Author(s):  
Wensong Liu ◽  
Jie Yang ◽  
Jinqi Zhao ◽  
Le Yang
Keyword(s):  
Change Detection ◽  
Multi Temporal ◽  
Novel Method

scholarly journals Multi-temporal Satellite Images Change Detection Algorithm Based on NSCT

2011 ◽  
Vol 24 ◽  
pp. 252-256 ◽  
Author(s):  
Wei Cui ◽  
Zhenhong Jia ◽  
Xizhong Qin ◽  
Jie Yang ◽  
Yingjie Hu
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Multi Temporal
Sign in / Sign up

Export Citation Format

Share Document