Abstract. On 8 August 2009, the extreme rainfall of Typhoon Morakot triggered enormous landslides in mountainous regions of southern Taiwan, causing catastrophic infrastructure and property damages and human casualties. A comprehensive evaluation of the landslides is essential for the post-disaster reconstruction and should be helpful for future hazard mitigation. This paper presents a systematic approach to utilize multi-temporal satellite images and other geo-spatial data for the post-disaster assessment of landslides on a regional scale. Rigorous orthorectification and radiometric correction procedures were applied to the satellite images. Landslides were identified with NDVI filtering, change detection analysis and interactive post-analysis editing to produce an accurate landslide map. Spatial analysis was performed to obtain statistical characteristics of the identified landslides and their relationship with topographical factors. A total of 9333 landslides (22 590 ha) was detected from change detection analysis of satellite images. Most of the detected landslides are smaller than 10 ha. Less than 5% of them are larger than 10 ha but together they constitute more than 45% of the total landslide area. Spatial analysis of the detected landslides indicates that most of them have average elevations between 500 m to 2000 m and with average slope gradients between 20° and 40°. In addition, a particularly devastating landslide whose debris flow destroyed a riverside village was examined in depth for detailed investigation. The volume of this slide is estimated to be more than 2.6 million m3 with an average depth of 40 m.
Abstract. While disaster studies researchers usually view risk as a function of hazard, exposure, and vulnerability, few studies have systematically examined the relationships among the various physical and socioeconomic determinants underlying disasters, and fewer have done so through seismic risk analysis. In the context of the 1999 Chi-Chi earthquake in Taiwan, this study constructs three statistical models to test different determinants that affect disaster fatality at the village level, including seismic hazard, exposure of population and fragile buildings, and demographic and socioeconomic vulnerability. The Poisson regression model is used to estimate the impact of these factors on fatalities. Research results indicate that although all of the determinants have an impact on seismic fatality, some indicators of vulnerability, such as gender ratio, percentages of young and aged population, income and its standard deviation, are the important determinants deteriorating seismic risk. These findings have strong social implications for policy interventions to mitigate such disasters.
ANPCyT, Argentina ; YerPhI, Armenia ; ARC, Australia ; BMWFW, Austria ; FWF, Austria ; ANAS, Azerbaijan ; SSTC, Belarus ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; NSERC, Canada ; NRC, Canada ; CFI, Canada ; CERN ; CONICYT, Chile ; CAS, China ; MOST, China ; NSFC, China ; COLCIENCIAS, Colombia ; MSMT CR, Czech Republic ; MPO CR, Czech Republic ; VSC CR, Czech Republic ; DNRF, Denmark ; DNSRC, Denmark ; IN2P3-CNRS, CEA-DRF/IRFU, France ; SRNSFG, Georgia ; BMBF, Germany ; HGF, Germany ; MPG, Germany ; GSRT, Greece ; RGC, Hong Kong SAR, China ; ISF, Israel ; Benoziyo Center, Israel ; INFN, Italy ; MEXT, Japan ; JSPS, Japan ; CNRST, Morocco ; NWO, Netherlands ; RCN, Norway ; MNiSW, Poland ; NCN, Poland ; FCT, Portugal ; MNE/IFA, Romania ; MES of Russia, Russian Federation ; NRC KI, Russian Federation ; JINR ; MESTD, Serbia ; MSSR, Slovakia ; ARRS, Slovenia ; MIZS, Slovenia ; DST/NRF, South Africa ; MINECO, Spain ; SRC, Sweden ; Wallenberg Foundation, Sweden ; SERI, Switzerland ; SNSF, Switzerland ; Canton of Bern, Switzerland ; MOST, Taiwan ; TAEK, Turkey ; STFC, United Kingdom ; DOE, United States of America ; NSF, United States of America ; BCKDF, Canada ; CANARIE, Canada ; CRC, Canada ; Compute Canada, Canada ; COST, European Union ; ERC, European Union ; ERDF, European Union ; Horizon 2020, European Union ; Marie Sk lodowska-Curie Actions, European Union ; Investissements d' Avenir Labex and Idex, ANR, France ; DFG, Germany ; AvH Foundation, Germany ; Greek NSRF, Greece ; BSF-NSF, Israel ; GIF, Israel ; CERCA Programme Generalitat de Catalunya, Spain ; Royal Society, United Kingdom ; Leverhulme Trust, United Kingdom ; BMBWF (Austria) ; FWF (Austria) ; FNRS (Belgium) ; FWO (Belgium) ; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; FAPERGS (Brazil) ; MES (Bulgaria) ; CAS (China) ; MoST (China) ; NSFC (China) ; COLCIENCIAS (Colombia) ; MSES (Croatia) ; CSF (Croatia) ; RPF (Cyprus) ; SENESCYT (Ecuador) ; MoER (Estonia) ; ERC IUT (Estonia) ; ERDF (Estonia) ; Academy of Finland (Finland) ; MEC (Finland) ; HIP (Finland) ; CEA (France) ; CNRS/IN2P3 (France) ; BMBF (Germany) ; DFG (Germany) ; HGF (Germany) ; GSRT (Greece) ; NKFIA (Hungary) ; DAE (India) ; DST (India) ; IPM (Iran) ; SFI (Ireland) ; INFN (Italy) ; MSIP (Republic of Korea) ; NRF (Republic of Korea) ; MES (Latvia) ; LAS (Lithuania) ; MOE (Malaysia) ; UM (Malaysia) ; BUAP (Mexico) ; CINVESTAV (Mexico) ; CONACYT (Mexico) ; LNS (Mexico) ; SEP (Mexico) ; UASLP-FAI (Mexico) ; MOS (Montenegro) ; MBIE (New Zealand) ; PAEC (Pakistan) ; MSHE (Poland) ; NSC (Poland) ; FCT (Portugal) ; JINR (Dubna) ; MON (Russia) ; RosAtom (Russia) ; RAS (Russia) ; RFBR (Russia) ; NRC KI (Russia) ; MESTD (Serbia) ; SEIDI (Spain) ; CPAN (Spain) ; PCTI (Spain) ; FEDER (Spain) ; MOSTR (Sri Lanka) ; MST (Taipei) ; ThEPCenter (Thailand) ; IPST (Thailand) ; STAR (Thailand) ; NSTDA (Thailand) ; TAEK (Turkey) ; NASU (Ukraine) ; SFFR (Ukraine) ; STFC (United Kingdom ; DOE (U.S.A.) ; NSF (U.S.A.) ; Marie-Curie programme ; Horizon 2020 Grant (European Union) ; Leventis Foundation ; A.P. Sloan Foundation ; Alexander von Humboldt Foundation ; Belgian Federal Science Policy Office ; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; F.R.S.-FNRS (Belgium) ; Beijing Municipal Science & Technology Commission ; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic ; Hungarian Academy of Sciences (Hungary) ; New National Excellence Program UNKP (Hungary) ; Council of Science and Industrial Research, India ; HOMING PLUS programme of the Foundation for Polish Science ; European Union, Regional Development Fund ; Mobility Plus programme of the Ministry of Science and Higher Education ; National Science Center (Poland) ; National Priorities Research Program by Qatar National Research Fund ; Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu ; Programa Severo Ochoa del Principado de Asturias ; EU-ESF ; Greek NSRF ; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand) ; Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; Welch Foundation ; Weston Havens Foundation (U.S.A.) ; Canton of Geneva, Switzerland ; Herakleitos programme ; Thales programme ; Aristeia programme ; European Research Council (European Union) ; Horizon 2020 Grant (European Union): 675440 ; FWO (Belgium): 30820817 ; Beijing Municipal Science & Technology Commission: Z181100004218003 ; NKFIA (Hungary): 123842 ; NKFIA (Hungary): 123959 ; NKFIA (Hungary): 124845 ; NKFIA (Hungary): 124850 ; NKFIA (Hungary): 125105 ; National Science Center (Poland): Harmonia 2014/14/M/ST2/00428 ; National Science Center (Poland): Opus 2014/13/B/ST2/02543 ; National Science Center (Poland): 2014/15/B/ST2/03998 ; National Science Center (Poland): 2015/19/B/ST2/02861 ; National Science Center (Poland): Sonata-bis 2012/07/E/ST2/01406 ; Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu: MDM-2015-0509 ; Welch Foundation: C-1845 ; This paper presents the combinations of single-top-quark production cross-section measurements by the ATLAS and CMS Collaborations, using data from LHC proton-proton collisions at = 7 and 8 TeV corresponding to integrated luminosities of 1.17 to 5.1 fb(-1) at = 7 TeV and 12.2 to 20.3 fb(-1) at = 8 TeV. These combinations are performed per centre-of-mass energy and for each production mode: t-channel, tW, and s-channel. The combined t-channel cross-sections are 67.5 +/- 5.7 pb and 87.7 +/- 5.8 pb at = 7 and 8 TeV respectively. The combined tW cross-sections are 16.3 +/- 4.1 pb and 23.1 +/- 3.6 pb at = 7 and 8 TeV respectively. For the s-channel cross-section, the combination yields 4.9 +/- 1.4 pb at = 8 TeV. The square of the magnitude of the CKM matrix element V-tb multiplied by a form factor f(LV) is determined for each production mode and centre-of-mass energy, using the ratio of the measured cross-section to its theoretical prediction. It is assumed that the top-quark-related CKM matrix elements obey the relation |V-td|, |V-ts| « |V-tb|. All the |f(LV)V(tb)|(2) determinations, extracted from individual ratios at = 7 and 8 TeV, are combined, resulting in |f(LV)V(tb)| = 1.02 +/- 0.04 (meas.) +/- 0.02 (theo.). All combined measurements are consistent with their corresponding Standard Model predictions.