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.
Floods are natural hazards that occur in most countries in the world. According to the special report on “Managing the risks of extreme events and disasters” from the Intergovernmental Panel on Climate Change (IPCC), increasing flood risks are a concern due to global warming (Field et al., 2012). Floods can be classified as river (fluvial) floods, flash floods, urban floods, pluvial floods, sewer floods, coastal floods and glacial lake outburst floods. Floods can also be categorized by their durations: flash floods, rapid onset floods and slow onset floods (Dolan, 1995). Flash floods are the most lethal and are usually caused by heavy rainfall, tropical storms or dam failures. The water rushes quickly over land, flooding houses and destroying roads (Jonkman, 2005; Haynes et al., 2009). Rapid onset floods last for relatively longer periods of 1 or 2 days. Slow onset floods may last for weeks or even months. This type of flood occurs almost every year in Thailand. In 2011, the worst flooding of the last 5 decades hit Thailand from 5 August to 9 January, lasting 158 days (Gale and Saunder, 2013; Nakmuenwai et al., 2017).
Remote sensing using satellite imagery is effective for repeatedly observing broad areas on the Earth's surface. There are two categories of remote sensing based on sensor type: passive (mainly optical and thermal) and active (mainly radar). Optical sensors only work in daytime and cannot observe objects under cloud-cover conditions. Radar sensors such as synthetic aperture radars (SARs) can avoid this problem. SAR systems have been used widely in disaster situations such as earthquakes, volcanic eruptions, tsunamis, typhoons and floods (Weissel et al., 2004; Feng et al., 2013; Liu et al., 2013; Dumitru et al., 2015; Yulianto et al., 2015). Klemas (2015) and Lin et al. (2016) summarized recent research on flood assessments using optical and SAR sensors. Because microwaves exhibit specular reflections against a smooth water surface, water regions in a SAR image show low backscattering intensity. SAR images are effective for extracting inundation areas. Several methods, both pixel- and object-based, have been proposed to extract inundation zones from SAR images (Martinis et al., 2009, 2013; Hoque et al., 2011; Manjusree et al., 2012; Pulvirenti et al., 2014; Kundu et al., 2015; Nakmuenwai et al., 2017). Thresholding is a common and effective pixel-based approach. Since backscattering of a water surface depends on many factors such as acquisition conditions of SAR images and their environments, its value is highly variable. It is difficult to judge the most suitable value objectively without additional information. Automated thresholding methods using the gray-level histogram have been introduced to overcome this issue (Fan and Lei, 2012; Martinis et al., 2009, 2013; Pulvirenti et al., 2011; Nakmuenwai et al., 2017). The global threshold value was merged from several local threshold values, which were obtained from the multimodal histograms of sub-areas. However, this approach is time-consuming when the study area is large. In addition, sufficient contrast was necessary for automated thresholding. Giustarini et al. (2013) found the previous proposed methods were difficult to apply in urban areas containing radar shadow and layover. They proposed a method based on image differencing to detect floodwater inside urban areas. Mason et al. (2009, 2012) used a SAR simulator and lidar data to estimate inundated buildings. Interferometric coherence was also used to extract floods in either rural or urban areas, but the acquisitions of temporal and spatial baselines were strict (Nico et al., 2000; Chini et al., 2012; Pulvirenti et al., 2016). All of these researchers used SAR images taken by X and C bands with short wavelengths, which were sensitive to separate water and non-water regions. Flood mapping using L-band satellite images was employed in a few studies (Zhang and Wang, 2003; Allan et al., 2012; Yulianto et al., 2015).
Limited by long revisit cycles, satellite images have been used more for post-flood analysis than for monitoring floods (Jain et al., 2005). ALOS-2 was launched on 24 May 2014 and is a follow-up satellite of the ALOS program. It carries the PALSAR-2 enhanced high-resolution SAR sensor. Owing to the right- and left-looking function of the PALSAR-2 sensor, the observation repetition frequency is improved. It is now possible to monitor affected areas shortly after a disaster strikes (JAXA, 2017a). PALSAR-2 images have been used to detect damage following the 2015 earthquake in Gorkha, Nepal (Watanabe et al., 2016), and to detect pyroclastic ash coverage on Kuchinoerabu Island, Japan (Hara et al., 2017; Natsuaki et al., 2017). The 2015 Kanto and Tohoku torrential rain was the first flood event that occurred on a large scale in Japan after the ALOS-2 was launched. PALSAR-2 performed emergency observations of the impacted areas during and after the heavy rain (Natsuaki et al., 2016; Kwak et al., 2017; Rimba and Miura, 2017).
In this paper, five pre- and co-event PALSAR-2 images are employed to monitor the changes in the inundation areas in the city of Joso, Ibaraki Prefecture, Japan. The images were used in a previous study to extract the inundations (Yamazaki and Liu, 2016). In the study, one threshold value of backscattering intensity was investigated using the pre-event water regions and the pre-event PALSAR-2 images, and it was applied to all co-event images. In addition, the obtained results were only verified via visual comparison. In this study, the method of the inundation extraction is improved by introducing land-cover information and elevation data. The flooded urban areas are also extracted using the intensity difference between the pre- and co-event images. The obtained results are verified quantitatively via comparison with those from visual inspections of airborne imagery.
Affected by two typhoons, torrential rain hit the Kanto and Tohoku regions of Japan from 9 to 11 September 2015, and destructive floods were caused in many places. A linear heavy-rain cloud was generated on 9 September and moved slowly from Kanto to Tohoku. It remained in the upstream region of the Kinugawa River for several hours, as shown in Fig. 1a (CEReS, 2015). The maximum cumulative rainfall exceeded 600 mm in the Kanto region and 500 mm in the Tohoku region, which are record-breaking volumes in those parts of Japan. Due to the rising water levels, collapsed banks and overflows were reported for 85 rivers (Cabinet Office, Government of Japan, 2016).
The city of Joso is located approximately 50 km to the northwest of Tokyo, as shown in Fig. 1b. In the figure, the study area is depicted by the orange rectangle, where the Kinugawa and Kokai rivers flow from north to south. The locations of the two rivers are shown in Fig. 2. Due to the heavy rainfall, the water volume of the Kinugawa River increased rapidly in the city of Joso in the early morning of 10 September 2015. An overflow of the riverbank in the Wakamiyado district (yellow square I in Fig. 3) was reported by the city government at 07:40. The floodwater flowed through the city from north to south. A riverbank failure finally occurred in the Misaka district (yellow square II in Fig. 3) at 12:50 in the afternoon, and floodwaters quickly covered almost the entire area between the two rivers.
Table 1 shows the observational conditions for the five PALSAR-2 images
used in this study. The radar incidence angles are almost the same,
39.7
Acquisition conditions for the five PALSAR-2 images used in this study, which were taken from three different paths (A, B and C).
Backscattering coefficient (sigma naught) images for the five temporal PALSAR-2 images after preprocessing.
ENVI SARscape software was used, and several preprocessing steps were
applied. A multi-look process with two looks was applied in the range and
azimuth directions to improve the quality of the SAR images and maintain the
resolution as much as possible; the subsequent azimuth resolution was 4.2 m,
and the slant range resolution was 2.9 m. A 5 m digital elevation model
(GSI, 2017a) was employed to project the data onto a WGS84 reference
ellipsoid with a pixel size of 2.5 m. Radiometric calibration was carried
out to convert the amplitude data into backscattering coefficient (sigma
naught) values (JAXA, 2017a). An enhanced Lee filter with
5
The 5 m digital elevation model (DEM) is shown in Fig. 2a. It was created
from lidar data with standard deviations of less than 1.0 m in the vertical
direction and 0.3 m in geolocation (GSI, 2017a). Most of the target area is
flat, with an elevation difference of less than 20 m, especially for the city of Joso, which is located between the rivers. The altitude gradually decreases
from upstream (north) to downstream (south). A land-cover map was introduced
to understand the surface conditions in the inundated area. The land-cover
maps were produced by Hashimoto et al. (2014), who used multi-temporal
optical satellite data and were published by JAXA (2017b). The land surface
was classified into 10 classes: water, urban, rice paddy, crop, grass,
deciduous broad-leaved tree, deciduous needle-leaved tree, evergreen
broad-leaved tree, evergreen needle-leaved tree and bare land. The land-cover
map (version 16.09) for the target area is shown in Fig. 2b. The classes of
deciduous broad-leaved and needle-leaved trees were merged into one deciduous
tree class, and a similar merge was applied to the evergreen tree classes. In
addition to the Kinugawa and Kokai rivers, Sanuma lake, which is located in
the north, is classified as permanent water. Most of the study area is
covered by rice paddies and crop fields. Between the rivers, three large
settlement areas exist, and they were classified as urban. The inundation map
produced by the Geospatial Information Authority of Japan (GSI) (GSI, 2015)
is shown in Fig. 2c. It was made by visual interpretations of
multi-temporal aerial photographs. The 40 km
Field photos taken on 26 October 2015, in the Wakamiyado
district at location I
Comparison of the backscattering coefficient values within the water and non-water references from the PALSAR-2 images taken from different paths.
A field survey was carried out on 26 October 2015, 1 month after the heavy rainfall. The route of our survey is shown in Fig. 4a, overlapped on Google Earth. Recovery work was ongoing, and several roads were still closed. The overflow location (I) in the Wakamiyado district and the collapsed bank location (II) in the Misaka district were primarily investigated. The pre- and co-event aerial photographs, which were taken by GSI (GSI, 2017b), are shown in Fig. 4b, c. The pre-event images were taken in 2007, and the co-event images were taken on 11 September 2015, after the overflow and bank collapse occurred. There were two groups of solar panels in the Wakamiyado district, which were located next to the Kinugawa River. A part of the natural levee in this area, as shown in the pre-event aerial photo, was excavated in March 2015 to set up solar panels. A large amount of water flowed from the Kinugawa River and washed away the solar panels. The residential areas and farmlands in the city of Joso were widely flooded, which can be observed clearly in the co-event aerial photo. During the field survey, the removal of the flooded solar panels was underway. Many panels were still scattered on the farmland, as shown in Fig. 5a. The temporary bank, which was built up using concrete blocks and sand bags for emergency restoration, can be seen in the first photo. The width of the collapsed bank in the Misaka district was 20 m at first and gradually expanded to a final width of 200 m. Many wooden houses behind the bank were washed away. Approximately one-third of the area of the city of Joso was inundated by the flood. As part of the emergency recovery work, temporary foot protection blocks were installed on 16 September. Additional reinforcement work using a steel sheet pile was performed outside the provisional bank after the main reinforcement work was finished on 19 September. The double wall cofferdam with the sheet pile (left) and the filling bank (right) can be identified in the left photo in Fig. 5b. Due to the rapid flow from the Kinugawa River, the telegraph poles tilted, and the road was blocked. The damaged houses could still be seen in October during our field survey.
The color composites of the preprocessed SAR intensity images for these two locations are shown in Fig. 4d. The top image shows the Wakamiyado district; the pre-event image taken on 13 August is shown in green and blue, and the co-event image on 10 September is shown in red. The cyan pixels represent the decrease in backscatter, which also indicates the flooded area. As the water level rose in the Kinugawa River, the island in the river and the dry riverbed were underwater. The overflow location can be confirmed on the right side of the river. The bottom image shows the Misaka district; the pre-event image taken on 31 July is shown in green and blue, and the co-event image on 11 September is in red. Due to the bank collapse, the houses were washed away and show a decrease in backscatter. Thus, the location can be confirmed easily from the cyan color.
Profiles of the backscattering coefficients for the water
and non-water references; the threshold values were obtained by the optimal
solution with best accuracy and by the proposed method (mean
In this study, the threshold value for water was investigated automatically
using reference areas. The white and black polygons shown in the enlargement
of land cover of Fig. 2b were used as water and non-water references,
respectively. The references were selected according to the aerial photos and
the land-cover map. Water references over a total area of 0.24 km
According to Fig. 6, the threshold value of water was set from
Statistical features were introduced to calculate the threshold value in a
simple manner. Because the backscattering intensity of the water references
is more stable than that of the non-water regions, the mean and SD values of
the water references were used to investigate the threshold value.
Furthermore, the water references were commonly available using the GIS
database. The results of combinations using the mean and SD values
(mean
Flowchart for the extraction of inundation areas.
The extraction of inundation areas was conducted in three steps. First, the water regions were extracted from each SAR image. The land-cover map and the extracted results from the pre-event images were used to improve the results from the co-event images. The inundated urban areas were then detected by the difference in the backscattering coefficient (sigma naught) values between the pre- and co-event SAR images. Finally, the 5 m DEM was applied to modify the extracted inundation area. A flowchart of the current approach is shown in Fig. 7.
First, the water regions in the two pre-event PALSAR-2 images were extracted
using the threshold values proposed in the previous section. Extracted pixel
groups that were smaller than 0.01 km
Land covers extracted using the proposed threshold values from the two pre-event PALSAR-2 images and the extracted water regions from the three co-event images after applying land-cover masks (corresponding to the low backscatter non-water regions).
The water regions during and after the heavy rainfall were also extracted from the three SAR images taken in September 2015 using the proposed threshold values. After removing noise from the results, the land-cover masks were applied. For the 10 September image, the path B mask was applied to remove the extracted crop, grass and bare land areas. The path A mask was applied to the 11 and 13 September images. Although the image from 13 September was taken from a different path, the backscattering characteristics were similar to the image taken from path A owing to the same heading angle but opposite range direction. The final results are shown in Fig. 8 (the three images on the right), for which most of the water commission errors due to smooth land cover were reduced successfully. The extracted water regions in the co-event images were mainly water and rice paddy land cover classes.
Due to the rise in water level on 10 September, the width of the Kinugawa
River doubled from that of July and August. The high-water riverbeds covered
by grasses and crops were extracted as being water regions due to inundation. A total
area of 27.1 km
Different from other land covers, the backscattering intensity of urban areas
still showed high values after inundation owing to multiple reflections from
buildings and water surfaces (Mason et al., 2010, 2012; Kwak et al., 2017). Thus, it
is difficult to extract the inundated urban areas using the proposed simple
water threshold values. In this study, backscatter differences were added to
extract inundated urban areas. The differences in backscattering intensity
for paths A and B were obtained and are shown in Fig. 9a. The mean and SD
values for the urban areas within the non-water references were obtained. The
mean value of the difference in backscatter for path A was 0.10 dB, whereas
the SD value was 1.28 dB. For path B, the mean value was 0.82 dB, and the
SD value was 1.72 dB. The threshold value for the inundated urban areas was
set using the same method as for water, i.e., by using the mean and SD values
(mean
The extracted water regions, except the Kinugawa and Kokai rivers on 10, 11, and 13 September, and the extracted urban areas with increased backscatter on 10 and 11 September were considered to be the area of inundation. To verify this result, the inundation map shown in Fig. 2c was introduced as the truth data. The truth data focused on the plain in the city of Joso between the rivers; the extracted results within the black dotted frame of Fig. 9a were enlarged and are shown in Fig. 8b for comparison with the truth data.
In Fig. 9b, the extracted inundation area is primarily paddy fields and
urban land cover. According to the GSI, a total area of 40 km
Final extracted results for the inundation of the plain between the Kinugawa and the Kokai rivers and their accuracies in comparison with the reference data produced by GSI.
The comparison of the extracted inundation and the truth data within the estimated affected area is shown in Table 3. The producer accuracy for the result on 11 September was 65.6 %, whereas the user accuracy was 92.2 %. The low producer accuracy was caused by the inundated urban areas and roads that could not be detected by either low or increased backscatter. Drainage work was also considered to be a reason for the low producer accuracy. The O.A. was 68.7 %, and the kappa coefficient was 0.33. The producer accuracy for the result for 13 September was 61.0 %, and the user accuracy was 87.0 %, both of which were lower than those obtained for 11 September. However, the O.A. was 81.3 %, and the kappa coefficient was 0.58, which were both higher than the results for 11 September.
In the previous study (Yamazaki and Liu, 2016), the inundation areas in the
three co-event PALSAR images were extracted using one threshold value of
The 5 m DEM was introduced to improve the inundation extraction result and estimate the inundation depth on 11 September. The elevation between the Kinugawa and Kokai rivers, which is shown by the black arrows in Figs. 3a and 9b, is shown in Fig. 10a. The blue arrow represents the initial extracted result for 11 September. It shows that the low-altitude plain area between the rivers was extracted as being under inundation. The inundation height on the Kinugawa riverside was 50.4 m, whereas that on the Kokai riverside was 50.2 m. Considering the flow of water, the inundation heights on the east and west sides should be almost equal. Therefore, the flooded range was modified to match the higher inundation height. The modified inundation is shown by the red arrow. The inundation depth was calculated by subtracting the altitude of the ground surface from the inundation height.
This modification was carried out from north to south, which is the rivers'
transverse direction. To remove the influence of bad DEM values, a low pass
filter with a 3
Close-up of the color composite of the SAR image for the red square
in Fig. 9b
Comparison of results by the automated thresholding methods: Otsu
An enlargement of the area surrounding City Hall is shown in Fig. 11. According to the aerial photo in (b), this area was still flooded on 11 September 2015. The increase in the backscattering intensity in the SAR image could be confirmed from the color composite in (a), especially in the parking lot, which was caused by the multiple reflections of the vehicles and the water. Water marks were observed in the field survey, as shown in the ground photo in (c) taken in front of City Hall. According to the water marks, the maximum inundation height was approximately 1.2 m, and the sustained water level was approximately 0.6 m, which shows good agreement with the estimated inundation height of 57 cm by our analysis.
To verify the effectiveness of our results, a comparison with the previous studies for the same event was carried out. Natsuaki et al. (2016) proposed a combination of coherence and amplitude values to detect affected areas using two pre-event images and one co-event PALSAR-2 image taken on 12 September 2015. Inundation was extracted by the decrease of coherence and a low backscatter intensity. Kwak et al. (2017) extracted the floods on 11 September from a pair of pre-event and co-event PALSAR-2 images taken on 31 July and 11 September 2015, which were also used in our study. Flooded rice paddies were extracted by the differences of intensity, whereas flooded urban areas were extracted by the correlation coefficient. These two studies extracted both the inundated rice paddies and urban areas using only SAR images. The producer accuracy in the study of Natsuaki et al. (2016) was 75 %, a little higher than our results before the improvement using DEM. However, the O.A. was 52 % since some areas could not be evaluated due to low pre-event coherence values. Our method using only the backscatter intensity could be applied to the whole study area. The accuracy in the study of Kwak et al. (2017) was not indicated. By visual comparison, their results extracted more inundated areas with more commission errors. Many agriculture fields outside the inundation were extracted as being false alarms.
Rimba and Miura (2017) compared three common methods: unsupervised and supervised classifications, a threshold method using the same SAR pair of Kwak et al. (2017) and a 5 m DEM. The scheme of threshold method showed the best result, which extracted water regions from the pre- and co-event images, respectively, similar to our proposed approach. The inundation was obtained by the change of the extracted water regions. However, their scheme would not work when the rice paddies were inundated in a pre-event image. Our method could overcome this problem by applying the land cover map. The inundated urban areas were not extracted in the Rimba and Miura's research.
These three previous studies for this event were all based on change
detection, which needs more than one pre- and post-event SAR pair. The
threshold values used in these studies were defined by training samples, the
same as our proposed method. Several common automated thresholding algorithms
were applied to the PALSAR-2 image on 11 September to compare with our
results (Kapur et al., 1985; Ridler and Calvard, 1978; Kittler and
Illingworth, 1986; Otsu, 1979). Most of the automated algorithms extracted
the inundation excessively. Figure 12 shows the comparison of two best
results by the Otsu (1979) and minimum error thresholding algorithms (Kittler
and Illingworth, 1986) and our thresholding result. In this enlarged region,
the O.A. for the three results was calculated. Our results using
the threshold value
Although both the pre- and co-event SAR images were used in this study, the extraction of water region was carried out for each image. When a pre-event image is not available, an inundation map can still be created by the thresholding method. Inundated urban areas can be extracted by the change detection; however, a pre-event image taken in the same path is necessary. A land-cover map was introduced to define the threshold values and to reduce commission errors in a smooth surface due to a longer L-band wavelength. In the thresholding approach, the land cover could be replaced by visual interpretation. Without a land cover map, commission errors in the inundation extraction would decrease the accuracy. The 5 m DEM was used for improving the inundation map and for estimating the inundation depth. In an emergency response phase, our method could still obtain a reasonable result, with the O.A. higher than 70 %. Thus, our method is still valid if a land-cover map and detailed DEM data are not available. However, the accuracy of the obtained inundation map would decrease.
In this study, the flood situation in the city of Joso, Ibaraki Prefecture, Japan, caused by heavy rainfall in September 2015, was monitored using five pre- and co-event ALOS-2 PALSAR-2 intensity images. The threshold value for water extraction was discussed using the pre-event images and a 10 m land-cover map. The water regions on 10, 11 and 13 September, after the heavy rainfall, were extracted using the proposed threshold values. The land-cover map was applied to reduce the commission errors caused by smooth ground surfaces (crop, grass and bare land). In addition, the differences in backscattering intensities were introduced to extract inundated urban areas, which showed high backscatter even in the inundation period. The result for 10 September shows that the inundation in the Wakamiyado district due to the bank overflow could be extracted correctly. The expansion of the inundation area after the bank collapse was observed by the results for 11 and 13 September. When compared against the truth data produced by GSI, most of the inundated area on the plain between the Kinugawa and Kokai rivers was extracted successfully, with an overall accuracy exceeding 60 %.
The extracted results were improved by adding the 5 m resolution digital
elevation model (DEM). The inundation area extracted for 11 September was
modified and expanded to 9.0 km
Based on our results, the proposed thresholding method using water references is capable of extracting water regions from single SAR images. However, inundated urban areas could not be detected using this method. Although this problem was overcome in this study by introducing backscatter differences, the method will be difficult to apply to events without pre-event images. The modification using the DEM showed promising results, but several inundated urban areas and roads could still not be extracted. These omission errors were caused by high backscatter from the parts higher than the inundation surface. In this study, the 10 m land-cover map and the 5 m high-resolution DEM were introduced to improve the inundation extraction results. Without that additional information, the extraction accuracy might be decreased. In the future, the changes in gradient in the along-river direction will be considered to improve accuracy when extracting inundation areas and depths.
The ALOS-2 PALSAR-2 data used in this study are owned by the Japan Aerospace Exploration Agency (JAXA) and were provided through the ALOS-2 research program (RA4, PI No. 1503) and the image analysis working group for large-scale disasters of JAXA.
This research was conducted by WL under the supervision of FY. All authors reviewed the article.
The authors declare that they have no conflict of interest.
This work was partially supported by JSPS KAKENHI, grant number 17H2066, Japan. Edited by: Kai Schröter Reviewed by: Guy J.-P. Schumann and one anonymous referee