Tomohito Asaka

Simultaneous observation data of GB-SAR/PiSAR to detect flooding in an urban area

We analyzed simultaneous observation data with ground-based synthetic aperture radar (GB-SAR) and airborne SAR (PiSAR) over a flood test site at which a simple house was constructed in a field. The PiSAR under flood condition was 0.9 to 3.4 dB higher than that under nonflood condition. GB-SAR gives high spatial resolution as we could identify a single scattering component and a double bounce component from the house. GB-SAR showed that the difference between the flooding and nonflooding conditions came from the double bounce scattering. We also confirm that the entropy is a sensitive parameter in the eigenvalue decomposition parameters, if the scattering process is dominated by the double bounce scattering. We conclude that and entropy are a good parameter to be used to detect flooding, not only in agricultural and forest regions, but also in urban areas. We also conclude that GB-SAR is a powerful tool to supplement satellite and airborne observation, which has a relatively low spatial resolution.

Quantitative analysis of relative geolocation accuracy of the TerraSAR-X enhanced ellipsoid corrected product

Abstract. High-resolution commercial synthetic aperture radar (SAR) satellites with resolutions of several meters have recently been used for effective disaster monitoring. One study reported the earthquake’s displacement using the pixel matching method with both pre- and postevent TerraSAR-X data, with a validated accuracy of ∼30  cm at global navigation satellite system (GNSS) Earth observation network (GEONET) reference points. However, it is insufficient to determine the accuracy using analysis of only a couple of data points per orbit. In addition, the errors were not reported because the number of data samples was too small to discuss the statistics. In order to better understand displacement accuracy, we analyzed displacement features using the pixel matching method to evaluate the relative geolocation accuracies of the TerraSAR-X product. First, we used fast Fourier transform oversampling 16 times to develop the pixel matching method for estimating the displacement at the subpixel level using the TerraSAR-X StripMap dataset. Second, we applied this methodology to 20 pairs of images from the Tokyo metropolitan area and calculated the displacement for each image pair. Third, we conducted spatial and temporal analyses in order to understand the displacement features. Finally, we evaluated the displacement accuracy by comparison with GEONET and solid earth tide data as a reference.

Evaluation of Atmospheric Effects on Interferograms Using DEM Errors of Fixed Ground Points

High-resolution synthetic aperture radar (SAR) data are widely used for disaster monitoring. To extract damaged areas automatically, it is essential to understand the relationships among the sensor specifications, acquisition conditions, and land cover. Our previous studies developed a method for estimating the phase noise of interferograms using several pairs of TerraSAR-X series (TerraSAR-X and TanDEM-X) datasets. Atmospheric disturbance data are also necessary to interpret the interferograms; therefore, the purpose of this study is to estimate the atmospheric effects by focusing on the difference in digital elevation model (DEM) errors between repeat-pass (two interferometric SAR images acquired at different times) and single-pass (two interferometric SAR images acquired simultaneously) interferometry. Single-pass DEM errors are reduced due to the lack of temporal decorrelation and atmospheric disturbances. At a study site in the city of Tsukuba, a quantitative analysis of DEM errors at fixed ground objects shows that the atmospheric effects are estimated to contribute 75% to 80% of the total phase noise in interferograms.

Estimation of phase noise by time series analysis of ALOS-2 PALSAR-2 data

PALSAR-2 on ALOS-2 developed by JAXA, is a follow-on L-band SAR sensor from PALSAR on ALOS. The mission objectives defined by JAXA include contribution to disaster monitoring. To establish the methodology to acquire the damaged area for disaster monitoring, we need to improve our understanding of the relationships between the sensor parameters (e.g., coherence and errors of the interferogram) and specification. This study conducted InSAR analysis using several pairs of PALSAR-2 data of the Tsukuba study site, and quantitatively analyzed the coherence and errors of the DEM of flat targets. In addition, we developed a method to evaluate the phase noise and revealed that the phase noise of PALSAR-2 was about 2% (half) that of the noise of PALSAR.

Detection of damaged buildings using GeoEye-1 imagery and airborne lidar data: a case study on the 2011 Tohoku earthquake

This paper presents a methodology that utilizes high-resolution optical satellite imagery, specifically GeoEye-1, and airborne lidar data to detect disaster-related damaged buildings in order to conduct a case study on the 2011 Tohoku earthquake. The methodology is based on change detection algorithms used in the field of image processing for remote sensing. Specifically, we examine the use of the image algebra change detection algorithm. This algorithm identifies the amount of change between two rectified images by band rationing or image differencing. On the other hand, it seems that the results calculated are different depending on the calculation method used because the data type of satellite data is different from that of the airborne lidar data. In this research, we propose three methods for creating a dataset used to detect damaged buildings: the Difference method, the Ratio method, and the Normalized Difference method, which are simply referred to as the D-method, R-method, and ND-method, respectively. The D-method is based on the difference in the value of the post-event imagery compared to that of the pre-event imagery. The R-method is based on the quotient of dividing the value of the pre-event imagery by that of the post-event imagery. The ND-method uses a calculation formula that is similar to that used by the Normalized Difference Vegetation Index (NDVI). The experimental results indicate that the dataset created using the ND-method has a higher sensitivity in the detection of damaged buildings than that of other methods.

Change detection method for landslide area using RGB color composite image of SRTM and ALOS/PALSAR InSAR DEM: A case study of the Iwate-Miyagi Nairiku Earthquake in 2008

The aim of this study is to advocate a change detection method for coseismic landsliding using RGB color composite image assigned pre-earthquake DEM (SRTM-3) and post-earthquake DEM (ALOS/PALSAR InSAR DEM). In this study, we applied our proposed method to landslide areas occurred by the Iwate-Miyagi Nairiku Earthquake in 2008. As a result, we found out that the area of sliding length and sliding width over 200m where is detected in the RGB color composite image using SRTM-3 and ALOS/PALSAR InSAR DEM. In conclusion, we showed that it is effective in using PALSAR data to analyze topographical changes in mountainous area.

Landsat analysis for evaluating spatiotemporal changes of regional-scale landscapes

This paper evaluates spatiotemporal changes of regional-scale landscapes using Landsat data, which are able to acquire large amount of data since 1972. The data provides high-quality imagery for many different applications such as land cover mapping and change detection. In this study Landsat TM images on November 1985 and October 1997 were used to evaluate quantitative landscape changes of the Chubu and Kinki regions in Japan. Land classification images were analyzed using simple landscape metrics and sequential analysis method. Consequently, we identified that the urban area grew spatially and aggregated around an existing urban core area. This study demonstrates that remote satellite images provide the desired information such as urban planning and regional planning for a spatiotemporal assessment of various regional-scale landscape changes. Keywords-Landsat; landscapes; landscape metrics; spatiotemporal