1^st International Conference on Natural Hazards and Disaster Management June 1-3, 2017 Osaka, Japan

Tadashi NAKASU is an Academic researcher/Lecturer at College of Population Studies, Chulalongkorn University in Thailand. He had been working at NIED (National Research Institute for Earth Science and Disaster Prevention) as a principal research fellow and ICHARM (International Centre for Water Hazard and Risk Management), PWRI (Public Works Research Institute) as a research specialist in Japan for a decade. He has conducted many disaster field surveys such as Indian Ocean Tsunami (2004), Hurricane Katrina (2005), Typhoon Ondoy and Pepeng (2009), Great East Japan Earthquake and Tsunami (2011), and Chao Phraya River Flood (2011). He also conducted abundant disaster management research around the globe. He had been a project leader of the Working Group of Hydrology, the Typhoon Committee (WMO and UN/ESCAP) for nearly 3 years. He was also a visiting researcher at JICA (Japan International Cooperation Agency) and an adjunct instructor at several universities in Japan.

In March 2015, the Sendai Framework for Disaster Risk Reduction mentioned “build back better” as one of the four priorities for action in the Third UN World Conference on Disaster Risk Reduction (UNISDR 2015). Disaster recovery has been of great concern in the disaster risk reduction field(Qurantelli, E.L. 1989; Tierney and Nigg 1993). However, disaster recovery theories have not attracted much attention from researchers to date (Etkin 2015). In addition, disaster risk due to demographic changes, especially a declining population and aging society, also has not been well recognized. This paper explains how disaster recovery theories and demographic risks are significant in the examination of the recovery from a disaster in order to build a resilient society. The two disaster recovery theories are used, along with several case studies, to investigate disaster recovery processes with declining populations and aging societies. One is Haas et al. (E.J., R.W., and M.J. 1977) and Bates et al. (Bates 1982; Bates and Peacock 1989, 2008), who argue “the recovery process follows or facilitates the trend which the affected communities originally had before the disasters.” The other is Hirose (1982), who indicates that “the disaster recovery process depends on three major ingredients, the physical scale of the hazard, aid from outside, and community strength (Hirose 1982).” After the examining these viewpoints, the paper investigates disaster recovery processes of municipalities with declining populations and aging societies severely affected by the 1993 Southwest-off Hokkaido Earthquake and the 2011 Great East Japan Earthquake and Tsunami disaster. Through analyzing the disaster recovery process, this paper attempts to answer the following questions: Could it be said that disaster recovery reflects pre-disaster situations and how do we use this thought for the disaster management? What is a better way to consider disaster resiliency, especially, ‘build back better’, for our sustainable future?

Makiko KONDO obtained her Doctor of Philosophy in Nursing from Osaka Prefecture University in 2007. Currently, she is an associate professor at Okayama University’s Graduate School of Health Sciences, where she teaches undergraduate, postgraduate, and doctoral nursing courses. Her field of expertise is qualitative studies, and her research themes involve caring for parents with cancer and their children, grief care, clinical nursing competency, and extreme cases of Hansen’s disease.

Statement of the Problem: The Great East Japan Earthquake in 2011 caused not only several casualties but also isolated several towns that did not receive aid rapidly due to widespread destruction. We must ensure communities are self-contained in order to maintain autonomy and independence after disasters since Nankai megathrust earthquakes are expected to occur in the near future. This study aims at creating a resilience model for disaster-stricken communities by employing ancient wisdom. Methodology & Theoretical Orientation: We utilized Strauss & Corbin’s Grounded Theory approach and created an essential pillar from ancient wisdom, garnering required support from official reports and newspapers about recent disasters. First, we extracted an essential pillar from literature on Hansen’s disease survivors, who overcame severe poverty during World War II by creating a superior community in an isolated island. Second, we obtained an essential pillar from ancient documents regarding disasters and famines during the Edo period or earlier. Findings: According to Hansen’s disease survivors, the essential pillars for creating a superior community are: 1) developing safety strategies through self-sufficiency, 2) promoting mental strength to overcome hardship, 3) assembling wise people to overcome hardship, 4) formulating self-defense organization, 5) building systems for protecting weaker individuals and assisting each other, 6) creating strategies for receiving external aid, and 7) evolving uncomplicated survival strategies. Meanwhile, the essential pillars from ancient wisdom include: a) developing strategies for future generations, b) employing places or festivals that residents value to begin reconstruction after disasters, c) supporting families debilitated by death, d) fostering spirituality and philosophy to endure loss, and e) receiving assistance from the government. Conclusion & Significance: Predecessors’ wisdom provides valuable suggestions for creating a strong community to cope with disasters. We will continue to garner required support.

Rasoul Kharazmi was born in Kerman, Iran, in 1986. He received the Bsc degree in natural resources from the University of Zabol, Zabol, Iran, in 2009, and Msc degrees in Information system and technology from the Moscow state university of geodesy and cartography (MIIGAiK), Russia, in JUN 2014. At the moment he is PhD candidate in Geoinformatics in St. Petersburg State University (SPBU) from SEP 2014.\r\n.\r\n

Actual issue of remote sensing data analysis is to design highly automated algorithms. In general case, full automation cannot be achieved due to complexity of most computational and analysis techniques. Nevertheless, automatic computations can be implemented in some special cases (e.g., when producing some standardised analytic products). In our study, we investigate the capabilities of automatic analysis of the land cover change in Eastern Iran, where the dry lands presented in significant amount and high risk of desertification exists. The study has a regional scale. We used automatic unsupervised classification technique to detect and monitor the desertification at the Sistan Basin in Iran. Currently we have designed the automatic computational algorithm and evaluated its accuracy. We used the Landsat satellite imagery of years 1990s, 2000s, and 2010s. To produce the benchmark maps of the land cover, we conducted the supervised classification with training samples, basing on our previous field observations on this area. In order to adopt unsupervised classification technique to the study context (i.e., to the features of arid lands), we used the Soil Adjusted Vegetation Index (SAVI) maps. These maps were used as the additional input data for unsupervised classification. As a result, the unsupervised classification produced three basic classes of land cover (vegetation cover, bare land and water bodies) in automatic mode, with appropriate accuracy. Produced land cover maps show the intensive decrease of vegetated areas, loss of water, and arid lands increase during the study period.