It is useful to utilize paper-based archive data of various kinds, in order to analyze the low frequency phenomena such as debris flow disaster by volcanic activity. SfM-MVS (Structure from Motion-Multiple View Stereo), which is able to create a 3 D shape by means of multiple-view photos, has been increasingly used in the field of geomorphology and disaster mitigation. In this study, we reconstruct Digital Surface Model (DSM) through SfM-MVS by using the archived time-series paper-based aerial photographs of volcanic activity of Mt Unzen from 1990. In our results, the DSM difference of more than about 6 m is evaluated to be significant by error analysis. In order to decrease these error, though it is difficult to match the same Ground Control Points (GCPs), which are necessary to transform the geographic coordinate system, on these archived photographs between different periods because of topographic change etc., it is useful to set the GCPs by using more accurate location such as LiDAR data or different idea for minimizing error propagation.

Debris flows simulations are useful for evacuation planning but they are not applied widely because the initial setting such as debris flow scale and landform affect the results but setting conditions seems to be complicated. In this study, we focused on DEM (Digital Elevation Model) landform data with 5 m mesh provided from Geospatial Information Authority of Japan (GSI) and DEM data acquired from LiDAR with high resolution, which are widely used in Japan. We conducted simulations on housing area in Hiroshima which have experienced debris flows events in 2014 and in 2018. We also conducted simulation on farming area which has not experienced debris flow recently but designated as hazard area from local government. For disaster cases, results using LiDAR DEM data and also considering the houses can describe the influence area and high risk area around the valley exit, and also can describe locally dangerous area and debris flow moving on roads. For farming area, LiDAR DEM can describe the field area deposition comparing with GSI DEM, but didn't show remarkable difference for considering houses. To consider detailed evacuation planning such as safe evacuation routs and shelters, applying high resolution LiDAR DEM data and considering houses for housing area will be useful.

If volcanic ash from explosive eruptions accumulates on a slope, debris and mud flows may occur owing to subsequent light rainfall before the accumulation. Mitigating damage from such debris and mud flows for residents and properties in downstream areas necessitates the immediate identification of affected areas and thickness distribution of volcanic ash to enable emergency structural countermeasures and evacuation of residents. Coherence analysis using satellite SAR (synthetic aperture radar) imageries is a useful method to detect areas of volcanic ash accumulation, because it can safely observe wide areas without the influence of volcanic smoke and even during bad weather and at night. Previous studies of coherence analysis have estimated the area of ash fall, but they have not quantitatively estimated the thickness of the fallen ash. In this study, we analyze the relationship between estimated volcanic ash fall areas obtained by coherence analysis using two or three ALOS-2 SAR imageries before and after the eruptions and the thickness of fallen ash measured through field surveys after Mt. Ontake (2014) and Mt. Aso (2016) eruptions. The results of this study were as follows. 1) The deeper depth of accumulated volcanic ash measured through field surveys corresponded to lower coherence value. 2) The coherence value for coherence analysis using two imageries from 0.7 to 0.6 was suitable for the detection of areas where volcanic ash accumulation exceeded 1 cm. 3) Approximately 0.2 of the coherence difference value for coherence analysis using three imageries was suitable to detect areas where volcanic ash accumulation exceeded 2 cm. 4) The coherence analyses using two and three imageries with short imagery capture intervals were found to be better than these with longer imagery capture intervals to estimate volcanic ash fall areas.

Many collapses on Mt. Fuji are caused by the development of cracks. Crack investigation of high mountain areas is generally conducted by field surveys using handheld digital cameras and tape measures. Although we use light equipments to keep safe field surveys, it is not easy to conduct crack investigations all over Mt. Fuji. Therefore, we have proposed a methodology to automated crack detection with multi-view stereo using multiple aerial images. First, we verified that our pixel-by-pixel triplet matching can achieve high success rates for point cloud generation after spike noise rejection. Second, we confirmed that our methodology can extract cracks on mountain surfaces from aerial triplet images and can draw cracks with the edge-tracking algorithm. We also confirmed that our crack drawing processing can

In this study, we conducted a volcanic ash fall experiment using a drone for the purpose of accurately estimating and predicting the distribution of volcanic ash depositing on the ground based on information obtained by observation of volcanic eruptions using radar. We dropped volcanic ash up to several millimeters from any altitude by self-made faller, and we measured the velocity and aspect ratio of falling volcanic ash by 2 DVD. It is possible to change the substance, the particle size, and the falling height in the fall experiment, so it has the advantage of being able to obtain the desired data quickly. We would like to carry out experiments on volcanic ash with large particle size and to obtain information that can make effective use of radar observations.

Large and powerful Typhoon Hagibis (1919) made landfall on the Izu Peninsula on 12 th October 2019, and it brought widespread and record-breaking torrential rain across Japan, especially in Eastern Japan. Emergency warning was issued in Tokyo and 12 prefectures, and many slope failures and debris flows were caused. It marked the largest number of sediment disaster occurrences by a typhoon since 1982 that the statistics have been started to be recorded. 952 sediment disasters were caused by the typhoon (as of 24 th December 2019), and 16 people were killed and one person is missing. In Tohoku region, most disasters happened in Iwate, Miyagi, and Fukushima prefectures. From 1 : 00 am, 12 th October to 12 : 00 am, 13 th October (two days) total precipitation (observed by AMeDAS of Japan Meteorological Agency) was 594 mm in Hippo, Marumori-machi, Miyagi prefecture, and 466.5 mm in Fudai, Iwate prefecture. From the above, Japan Society of Erosion Control Engineering organized “Emergency investigation team for the sediment disasters in Tohoku region caused by Typhoon Hagibis 2019” and performed the investigation three times.

The number of sediment disasters caused by Typhoon Hagibis in October 2019 reached 952 as of December 24, 2019, which is the largest number of typhoon-related sediment disasters since 1982. The sediment disasters caused by this typhoon are distributed over a wide area, mainly in East Japan. In this report, the authors describe three landslides that occurred in Sagamihara, Tomioka, and Chiba. The common feature of these three landslides is that all of them has high fluidity, which seemed to result in the serious damage. It is thought that all the three have a deep relationship with the Kanto loam area consisting of volcanic ash layers. An assessment of rainfall amount that triggered the disasters by return period showed that the largest was Sagamihara, then Chiba, and finally Tomioka. Looking at the volume of landslide, the largest was about 15,000 m³ in Sagamihara, followed by about 3200 m³ in Tomioka, and 250 m³ in Chiba. In addition, the time delay, which is defined as the time from the point when the Soil Water Index (SWI) exceeds 2-year return period value to the time when the slope failure occurs, is about 12 hours in Sagamihara, about 8 hours in Tomioka, and about 2 hours in Chiba, respectively. The 2-year return period of SWI is obtained by applying extreme value analysis to the values of fixed duration between 1981 and 2010. And the result that the larger landslides require the longer time until the landslide occurrences is in harmony with the previous study. These results suggest that it is possible to evaluate an evacuation time according to the scale of landslides in the same standard time scale.