In this paper, we evaluate the potential accuracy for the volcanic ash coverage using interferometric coherence of the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2”). In order to detect the disaster affected area from interferometric coherence, we require pre-disaster and co-disaster pairs. That is, we require at least two interferometric archives taken before the disaster in addition to one archive after the disaster. The ideal multi-temporal analysis is performed with of course, highest resolution pairs. However, we do not always have enough number of the archive especially in the early stage of the satellite mission. Here, we use not only Stripmap but also ScanSAR mode archive in order to achieve enough number of pairs. The combination of the Stripmap and ScanSAR archives will help to increase the interferometric pair while it suffers the spatial resolution and coherency. Especially for ALOS-2 PALSAR-2 data, it has not been evaluated the potential accuracy of such Stripmap-ScanSAR interferometry method. In this paper, we use the volcanic eruption event in May 2015 at Kuchinoerabu-jima Island, Kagoshima prefecture, Japan for the case study. We evaluated the proposed method with the truth data which was achieved by manual classification using aerial photography. Experimental results showed that the proposed method marked approximately 91% overall accuracy with 0.64 Kappa coefficients to detect the dense volcanic ash coverage.
In March 2011, an earthquake off the Pacific coast of Tohoku caused an accident at the Fukushima Daiichi Nuclear Power Plant (FDNPP). This discharged a large amount of radioactive material into the environment. After the accident, a Planned Evacuation Zone based on a macro-scale dose rate distribution map, which was generated from airborne (aircraft) survey observation, was delineated and implemented on April 22, 2011. Airborne survey played a major role in providing information for developing an appropriate response to this emergency.
It has been five years since the accident occurred and the focus must now shift toward environmental recovery and resumption of normal life. Thus, new monitoring methods are required to reveal the micro-scale dose rate distribution, including that in the forest area. We used an Unmanned Aerial Vehicle (UAV), which can fly at altitudes lower than an aircraft, to establish monitoring techniques and methods that could be applied to micro-scale dose rate distribution monitoring and mapping. We found that the dose rate distribution map from the UAV, flown at altitudes of 30 m and 60 m, compared favorably with maps generated from ground-based survey observations. However, in the forest area, the dose rate values observed in the UAV survey were lower than those observed in the ground-based survey due to shielding by trees.
Surveys of dose rate using UAVs that can fly at low altitudes enable the monitoring of micro-scale dose rate distribution in places that are inaccessible to ground-based surveys. Further, the resulting micro-scale dose rate distribution maps provide important base information that assists in decision making of residents who return to the area.
Data from the Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2), an L-band SAR on board the Advanced Land Observing Satellite-2 (ALOS-2), were used to detect landslides caused by the 2016 Kumamoto earthquakes. Differences in α angle and HH-VV coherence obtained before and after the disaster were used. Some landslides occurred in a forested area before the disasters were detected by these parameters. Misidentifications were prominently observed. A forest mask was applied to the image taken before the disaster, and it was confirmed that the forest mask worked well to reduce misidentification of landslide areas. Detection rates are estimated to be 63%. The main reasons for failures of detection were radar shadow, and landslide areas consisting of either a vacant piece of land or land with low vegetation before the disaster.
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