The heavy rain occurred in Hiroshima city on 20 August 2014. Then, debris flows and shallow slides were induced by the heavy rain. Rapid damage assessment after natural disasters is crucial for initiating effective emergency response actions. Synthetic Aperture Radar (SAR) has a great potential due primarily to its all-weather day-and- night imaging capabilities. In this study, we examined an extraction of damaged area caused by debris flows using three COSMO-SkyMed images. The extraction methods are interferometric coherence, intensity correlation and normalized difference sigma nought index (NDSI) calculated from COSMO-SkyMed image pair. In this study, we investigated the applicability of the methods for extraction of damaged area caused by debris flows. The single look complex data of COSMO-SkyMed were co-registered each other for calculating interferometric coherence. The interferometric coherence images were ortho-rectified using 10 m gridded Digital Elevation Model (DEM) generated by the Geospatial Information Authority of Japan (GSI). The intensity correlation and NDSI were calculated from ortho-rectified images. For damaged area extraction, we investigated the mean and standard deviation of interferometric coherence, intensity correlation and NDSI using pre- and co-disaster image pairs. The mean values derived from the three methods in damaged area almost increased between pre- and co-disaster images. As a result, NDSI in damaged area indicated good separation between pre- and co-disaster images. In conclusion, NDSI showed good capability for extraction of damaged area caused by the debris flows at a rapid disaster response phase.
The 2016 Kumamoto earthquake sequence started with a Mj (Japan Meteorological Agency magnitude) 6.5 event on 14 April, and culminated in a Mj 7.3 event on 16 April. Rapid damage assessment after natural disasters is crucial to fast crisis response. Synthetic aperture radar (SAR) interferometric analysis has a great potential due primarily to its phase difference. In this study, we demonstrated the applicability of interferometric coherence from SAR images for detection of building damages and liquefactions caused by the earthquake. Three ALOS-2/PALSAR-2 data acquired on 30 November, 2015, 7 March, 2016 and 18 April, 2016 were used. The interferometric coherence was calculated within several moving window pixels from pre- and post-disaster PALSAR-2 images. For damage assessment, normalized difference (ND) of interferometric coherence was calculated using pre- and co-disaster SAR image pairs. For validation, we adopted a map of estimated number of collapsed buildings calculated by the National research Institute for Earth science and Disaster resilience, Japan (NIED). The estimation map shows seven ranking of collapsed buildings within 250 m × 250 m area. The averaged ND of interferometric coherence indicates the trend of increase with corresponding to the increase of the estimated number of collapsed buildings. In addition, ND of interferometric coherence image showed liquefaction as a high value in urban areas. The distribution pattern was in good agreement with a liquefaction map referred to literatures. These results indicate the possibility of rapid damage mapping after the earthquake for fast crisis response using SAR interferometric coherence.
The heavy rain induced by the 12th typhoon caused landslide disaster at Kii Peninsula in the middle part of Japan. We
propose a quick response method for landslide disaster mapping using very high resolution (VHR) satellite imageries.
Especially, Synthetic Aperture Radar (SAR) is effective because it has the capability of all weather and day/night
observation. In this study, multi-temporal COSMO-SkyMed imageries were used to detect the landslide areas. It was
difficult to detect the landslide areas using only backscatter change pattern derived from pre- and post-disaster COSMOSkyMed
imageries. Thus, the authors adopted a correlation analysis which the moving window was selected for the
correlation coefficient calculation. Low value of the correlation coefficient reflects land cover change between pre- and
post-disaster imageries. This analysis is effective for the detection of landslides using SAR data. The detected landslide
areas were compared with the area detected by EROS-B high resolution optical image. In addition, we have developed
3D viewing system for geospatial visualizing of the damaged area using these satellite image data with digital elevation
model. The 3D viewing system has the performance of geographic measurement with respect to elevation height, area
and volume calculation, and cross section drawing including landscape viewing and image layer construction using a
mobile personal computer with interactive operation. As the result, it was verified that a quick response for the detection
of landslide disaster at the initial stage could be effectively performed using optical and SAR very high resolution
satellite data by means of 3D viewing system.
Rice crop is one of the most important agricultural products in Asia. It is necessary to monitor rice in wide area for the
food control and the adjustment of food. This study focuses on the validation for monitoring of rice crop growth and
extraction of rice-planted area using the German TerraSAR-X (X-band), ENVISAT-1/ASAR (C-band) and
ALOS/PALSAR (L-band). TerraSAR-X is an advanced satellite which is able to observe 1m resolution with single
polarization (HH or VV) or 2m resolution with dual polarization (HH/VV) in SpotLight mode. Also ASAR and
PALSAR have single and dual polarization mode. Multi-temporal SAR data of each satellite are processed and analyzed
to investigate temporal change of SAR backscattering coefficient of rice-planted area during the rice growing cycle with
different wavelength, polarization and resolution in the test site of Hiroshima, Japan. Ground truth data are measured
simultaneously with satellite observation such as height of plant, vegetation cover and Leaf Area Index (LAI)
corresponding to SAR observation, and also the correlation between SAR backscattering coefficient and those
parameters of rice crop growing were analyzed. SAR backscatter shows the significant change in early stage of rice
growing cycle. Therefore, rice-planted area extraction is conducted with multi-temporal SAR data based on a
classification technique using maximum likelihood method (MLC). In conclusion, rice crop growth and rice-planted area
extraction can be successfully monitored using multi-temporal and multi-wavelength SAR data.
The Hiroshima Institute of Technology (HIT) manages direct downlinks for microwave and optical earth observation satellite data in Japan. This study focuses on validating rice monitoring using ground truth data from ENIVISAT-1/ASAR, such as the height of rice crop, vegetation cover, and leaf area index in test sites in the Hiroshima district in Japan. ENVISAT-1/ASAR data can monitor the rice-crop growing cycle using alternating polarization (AP) mode images. However, ASAR data is influenced by several parameters, such as land-cover structure, and the direction and alignment of rice fields in the test sites. To investigate these parameters, in this study the validation was combined with microwave image data and ground truth data for rice-crop fields. Multitemporal, multidirection (descending and ascending), and multiangle ASAR AP-mode images were used to investigate the rice-crop growing cycle. Finally, the extraction of rice-planted areas was attempted using multitemporal ASAR AP mode data, such as VV/VH and HH/HV. This study clarifies that the estimated rice-planted area agrees with the existing statistical data for areas within the rice field. In addition, HH/HV is more effective than VV/VH in extracting the rice-planted area.
This study aims to establish a practical image analysis method for the use of middle-scale resolution images acquired by
the multi-spectral sensors such as Landsat-7/ETM+, Terra/ASTER and ALOS/AVNIR-2 as the complementary data
sources of higher resolution images such as Quickbird for the purpose of environmental monitoring of wide-range areas.
For this purpose, an image analysis based on mixture is investigated as one of the effective approaches. As the
information target, we selected vegetation cover rate (VCR) in urban area because it is one of the important
environmental factors to affect urban environment issue such as heat island phenomena.
In order to realize easy and efficient computation for estimating the mixture rate of vegetation categories, the linear
mixture model using two main categories including vegetation and non-vegetation, is applied in combination with the
least square estimation of multi-regressive coefficients for vegetation cover rate (VCR) and non-vegetation cover rate
(non-VCR) with several bands data by multi-spectral sensors. In addition, two sub-categories for both of vegetation and
non-vegetation categories are considered to specify representative pixel values as correct as possible, that is, trees and
grasses for vegetation, and buildings and bare-soils for non-vegetation respectively, and their optical mixture rates are
estimated as well as the mixture rate of vegetation and non-vegetation categories. For this purpose, an iterative procedure
is adopted, in which each mixture rate of two sub-categories for vegetation and non-vegetation is varied by ten percent
steps and the least square estimation is applied with all combinations of mixture rates of sub-categories for vegetation
The experiments for VCR extraction were conducted in the test site of Hiroshima-city and by using multi-spectral data
acquired by Landsat-7/ETM+, Terra/ASTER, and ALOS/AVNIR-2. The accuracy for VCR extraction was evaluated
based on the comparison with the VCRs obtained by means of pixel-wise vegetation/non-vegetation classification of a
Quickbird multi-spectral image. The result shows that the number of bands is one of the important parameters in general.
However, it was verified that the combination of wavelength regions is more important than the number of bands. The
result of this study suggests that the combination of wavelength regions is essential in middle-resolution multi-spectral
images for vegetation cover rate estimation based on mixture analyses.
Hiroshima Institute of Technology (HIT) is operating the direct down-links of microwave and optical earth observation
satellite data in Japan. This study focuses on the validation for rice crop monitoring using microwave remotely sensed
image data acquired by ENIVISAT-1 referring to ground truth data such as height of rice crop, vegetation cover rate and
leaf area index in the test sites of Hiroshima district, the western part of Japan.
ENVISAT-1/ASAR data has the capabilities for the monitoring of the rice crop growing cycle by using alternating cross
polarization mode images. However, ASAR data is influenced by several parameters such as land cover structure,
direction and alignment of rice crop fields in the test sites. In this study, the validation was carried out to be combined
with microwave image data and ground truth data regarding rice crop fields to investigate the above parameters. Multi-temporal,
multi-direction (descending and ascending) and multi-angle ASAR alternating cross polarization mode images
were used to investigate during the rice crop growing cycle. On the other hand, LANDSAT-7/ETM+ data were used to
detect land cover structure, direction and alignment of rice crop fields corresponding to the backscatter of ASAR.
Finally, the extraction of rice planted area was attempted by using multi-temporal ASAR AP mode data such as VV/VH
and HH/HV. As the result of this study, it is clear that the estimated rice planted area coincides with the existing
statistical data for area of the rice crop field. In addition, HH/HV is more effective than VV/VH in the rice planted area
With the objective of developing accurate global image data and to find the effects of difference of observation time, several reliable global cloud free data sets of Terra MODIS and Aqua MODIS will be developed utilizing personal computers. Out of 36 bands seven bands (Band 1 through 7) with similar spectral features to those of Landsat-7 ETM+ are selected, since these bands cover the most important spectra to derive land cover features. And then, some statistical information of the developed data sets and NDVI computed from the data sets were investigated. From the analysis, it was clarified that the means and standard deviations of band 2, 3, 4 and 6 of Aqua MODIS data are more slightly larger than those of Terra MODIS data although the values of band 5 and 7 of Aqua MODIS are more slightly smaller than those of Terra MODIS data. And it was also found that NDVI of Aqua MODIS data computed from band 2 (NIR) and band 1 (R) are also higher (approximately from 0.03 to 0.05 at the range of NDVI) than those of Terra NDVI because of the above mentioned reasons.