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Crowdsourcing photograph locations for debris flow hot spot mapping

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Abstract

This study shows the feasibility of obtaining hazardous hot spot information on landslide and debris flow from crowdsourced data. Historical hazard or disaster photographs were voluntarily uploaded by the public to a Web photograph album. A total of 2245 hazard photographs from 1973 to 2015 were crowdsourced, and each photograph was tagged with geographical coordinates. After the removal of outliers, 96% of the photograph points were found within the 4 km potential debris flow buffer of existing databases, and none was found along the steep slopes with a mean of 14°. The photograph hot spot analysis using local Moran’s I or G i * was identified statistically significant without subjective judgment. The DBSCAN model was also used to detect hot spot clusters effectively. The model parameters were nearly automatically generated on the basis of the count plot and the nearest neighbor distance graph. The results of these approaches were generally consistent with the hazardous hot spot maps and strongly related to central and southern Taiwan from the crowdsourced photograph data. Results reveal that the hot spot areas are found in areas with faults and near the potentially weak and fractured rocky regions. The majority of the landslides occur near the fault line because the strong ground motions triggered by an earthquake propagated along the fault rupture plane. Hot spot mapping using crowdsourced data can be used to estimate where debris flow will frequently occur and show how large the debris flow will be. Potentially hazardous areas can be effectively determined by the hot spot analysis of crowdsourced data.

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References

  • Anselin L (1995) Local indicators of spatial association LISA. Geogr Anal 27(2):93–115

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1):15–31

    Article  Google Scholar 

  • Batty M, Hudson-Smith A, Milton R, Crooks A (2010) Map mashups, Web 2.0 and the GIS revolution. Ann GIS 16(1):1–13

    Article  Google Scholar 

  • Boulos MNK, Resch B, Crowley DN, Breslin JG, Sohn G, Burtner R, Pike WA, Jezierski E, Chuang KYS (2011) Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int J Health Geogr 10(1):67

    Article  Google Scholar 

  • Chang K-T, Chiang S-H, Hsu M-L (2007) Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression. Geomorphology 89(3–4):335–347

    Article  Google Scholar 

  • Chu HJ (2014) Spatiotemporal analysis of vegetation index after typhoons in the mountainous watershed. Int J Appl Earth Obs Geoinf 28:20–27

    Article  Google Scholar 

  • Chu HJ, Lin YP, Huang YL, Wang YC (2009) Detecting the land-cover changes induced by large-physical disturbances using landscape metrics, spatial sampling,simulation and spatial analysis. Sensors 9:6670–6700

    Article  Google Scholar 

  • Chu HJ, Lin YP, Chang TK (2011) Spatial autocorrelation analysis of soil pollution data in central Taiwan. In: 2011 International conference on computational science and its applications (ICCSA), pp 219–222. IEEE

  • Crooks A, Croitoru A, Stefanidis A, Radzikowski J (2013) # Earthquake: Twitter as a distributed sensor system. Trans GIS 17(1):124–147

    Article  Google Scholar 

  • Crooks A, Pfoser D, Jenkins A, Croitoru A, Stefanidis A, Smith D, Karagiorgou S, Efentakis A, Lamprianidis G (2015) Crowdsourcing urban form and function. Int J Geogr Inf Sci 29(5):720–741

    Article  Google Scholar 

  • Dadson SJ, Hovius N, Chen H, Dade WB, Hsieh M-L, Willett SD, Hu J-C, Horng M-J, Chen M-C, Stark CP, Lague D, Lin J-C (2003) Links between erosion, runoff variability and seismicity in the Taiwan orogen. Nature 426(6967):648–651

    Article  Google Scholar 

  • Dai FC, Lee CF (2001) Frequency–volume relation and prediction of rainfall-induced landslides. Eng Geol 59(3):253–266

    Article  Google Scholar 

  • de Albuquerque JP, Herfort B, Brenning A, Zipf A (2015) A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. Int J Geogr Inf Sci 29(4):667–689

    Article  Google Scholar 

  • Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis E, Han J, Fayyad UM (eds) Proceedings of the second international conference on knowledge discovery and data mining (KDD-96), pp 226–231. AAAI Press

  • Getis A, Ord JK (1996) Local spatial statistics: an overview. In: Longley P, Batty M (eds) Spatial analysis: modeling in GIS environment. Wiley, New York, pp 261–278

    Google Scholar 

  • Glade T (1998) Establishing the frequency and magnitude of landslide-triggering rainstorm events in New Zealand. Environ Geol 35(2):160–174

    Article  Google Scholar 

  • Glade T, Crozier M, Smith P (2000) Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “Antecedent Daily Rainfall Model”. Pure Appl Geophys 157(6–8):1059. http://www.springerlink.com/index/8L3DQ4YMU6M4W4C3.pdf

  • Goodchild MF (2009) Geographic information systems and science: today and tomorrow. Ann GIS 15(1):3–9

    Article  Google Scholar 

  • Goodchild MF, Glennon JA (2010) Crowdsourcing geographic information for disaster response: a research frontier. Int J Digit Earth 3:231–241

    Article  Google Scholar 

  • Guthrie RH, Evans SG (2004a) Analysis of landslide frequencies and characteristics in a natural system, coastal British Columbia. Earth Surf Proc Land 29(11):1321–1339

    Article  Google Scholar 

  • Guthrie RH, Evans SG (2004b) Magnitude and frequency of landslides triggered by a storm event, Loughborough Inlet, British Columbia. Nat Hazards Earth Syst Sci 4(3):475–483

    Article  Google Scholar 

  • Guzzetti F (2000) Landslide fatalities and the evaluation of landslide risk in Italy. Eng Geol 58(2):89–107

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1):272–299

    Article  Google Scholar 

  • Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorol Atmos Phys 98(3):239–267

    Article  Google Scholar 

  • Guzzetti F, Peruccacci S, Rossi M, Stark CP (2008) The rainfall intensity–duration control of shallow landslides and debris flows: an update. Landslides 5(1):3–17

    Article  Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112(1):42–66

    Article  Google Scholar 

  • Hong Y, Adler R, Huffman G (2007) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43(2):245–256

    Article  Google Scholar 

  • Huang J-C, Milliman JD, Lee T-Y, Chen Y-C, Lee J-F, Liu C-C, Lin J-C, Kao S-J (2017) Terrain attributes of earthquake- and rainstorm-induced landslides in orogenic mountain Belt,Taiwan. Earth Surf Process Landf. https://doi.org/10.1002/esp.4112

    Google Scholar 

  • Jaedicke C, Van Den Eeckhaut M, Nadim F, Hervás J, Kalsnes B, Vangelsten BV, Smith JT, Tofani V, Ciurean R, Winter WG, Sverdrup-Thygeson K (2014) Identification of landslide hazard and risk ‘hotspots’ in Europe. Bull Eng Geol Environ 73(2):325–339

    Google Scholar 

  • Kerle N, Hoffman RR (2013) Collaborative damage mapping for emergency response: the role of Cognitive Systems Engineering. Nat Hazards Earth Syst Sci 13:97–113

    Article  Google Scholar 

  • Khazai B, Sitar N (2004) Evaluation of factors controlling earthquake-induced landslides caused by Chi-Chi earthquake and comparison with the Northridge and Loma Prieta events. Eng Geol 71(1–2):79–95

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113

    Article  Google Scholar 

  • Lin YP, Chu HJ, Wang CL, Yu HH, Wang YC (2009) Remote sensing data with the conditional latin hypercube sampling and geostatistical approach to delineate landscape changes induced by large chronological physical disturbances. Sensors 9(1):148–174

    Article  Google Scholar 

  • Lu P, Stumpf A, Kerle N, Casagli N (2011) Object-oriented change detection for landslide rapid mapping. IEEE Geosci Remote Sens Lett 8(4):701–705

    Article  Google Scholar 

  • Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Process Landf 29(6):687–711

    Article  Google Scholar 

  • Mondini AC, Guzzetti F, Reichenbach P, Rossi M, Cardinali M, Ardizzone F (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens Environ 115(7):1743–1757

    Article  Google Scholar 

  • Nichol J, Wong MS (2005) Satellite remote sensing for detailed landslide inventories using change detection and image fusion. Int J Remote Sens 26(9):1913–1926

    Article  Google Scholar 

  • Ord JK, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal 27(4):286–306

    Article  Google Scholar 

  • Patwary M, Palsetia D, Agrawal A, Liao WK, Manne F, Choudhary A (2012) A new scalable parallel DBSCAN algorithm using the disjoint-set data structure. In: 2012 International conference for high performance computing, networking, storage and analysis (SC), pp 1–11. IEEE

  • Peeters A, Zude M, Käthner J, Ünlü M, Kanber R, Hetzroni A, Gebbers R, Ben-Gal A (2015) Getis–Ord’s hot-and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Comput Electron Agric 111:140–150

    Article  Google Scholar 

  • Peruccacci S, Brunetti MT, Gariano SL, Melillo M, Rossi M, Guzzetti F (2017) Rainfall thresholds for possible landslide occurrence in Italy. Geomorphology 290:39–57

    Article  Google Scholar 

  • Pirasteh S, Li J (2016) Landslides investigations from geoinformatics perspective: quality, challenges, and recommendations. Geomat Nat Hazards Risk 1–18. https://doi.org/10.1080/19475705.2016.1238850

  • Pultar E, Raubal M, Cova TJ, Goodchild MF (2009) Dynamic GIS case studies: wildfire evacuation and volunteered geographic information. Trans GIS 13(s1):85–104

    Article  Google Scholar 

  • Rossi M, Witt A, Guzzetti F, Malamud BD, Peruccacci S (2010) Analysis of historical landslide time series in the Emilia-Romagna region, northern Italy. Earth Surf Process Landf 35(10):1123–1137

    Article  Google Scholar 

  • Samia J, Temme A, Bregt A, Wallinga J, Guzzetti F, Ardizzone F, Rossi M (2017) Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory. Landslides 14(2):547–558

    Article  Google Scholar 

  • Savvaidis PD (2003) Existing landslide monitoring systems and techniques. In Proceedings of the conference from stars to earth and culture, in honor of the memory of Professor Alexandros Tsioumis, The Aristotle University of Thessaloniki. Thessaloniki, Greece, pp 242–258

  • See L, Comber A, Salk C, Fritz S, van der Velde M, Perger C, Schill C, McCallum I, Kraxner F, Obersteiner M (2013) Comparing the quality of crowdsourced data contributed by expert and non-experts. PLoS ONE 8(7):e69958

    Article  Google Scholar 

  • Shou KJ, Hong CY, Wu CC, Hsu HY, Fei LY, Lee JF, Wei CY (2011) Spatial and temporal analysis of landslides in Central Taiwan after 1999 Chi-Chi earthquake. Eng Geol 123(1):122–128

    Article  Google Scholar 

  • Tran TN, Drab K, Daszykowski M (2013) Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometr Intell Lab Syst 120:92–96

    Article  Google Scholar 

  • van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3):112–131

    Article  Google Scholar 

  • Walstra J, Chandler JH, Dixon N, Dijkstra TA (2007a) Aerial photography and digital photogrammetry for landslide monitoring. Geol Soc Lond Spec Publ 283(1):53–63

    Article  Google Scholar 

  • Walstra J, Dixon N, Chandler JH (2007b) Historical aerial photographs for landslide assessment: two case histories. Q J Eng Geol Hydrogeol 40(4):315–332

    Article  Google Scholar 

  • Wan Z, Hong Y, Khan S, Gourley J, Flamig Z, Kirschbaum D, Tang G (2014) A cloud-based global flood disaster community cyber-infrastructure: development and demonstration. Environ Model Softw 58:86–94

    Article  Google Scholar 

  • Yin HY, Lee CY, Jan CD (2015) A web-based decision support system for debris flow disaster management in Taiwan. In Engineering geology for society and territory, vol 3, pp 109–113. Springer, Cham

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Acknowledgments

We are grateful for open data support from SWCB and enhancing the quality of the paper from the editors and anonymous reviewers.

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Authors and Affiliations

  1. Department of Geomatics, National Cheng Kung University, No. 1, University Road, Tainan City, 701, Taiwan

    Hone-Jay Chu

  2. Department of Geography, National Changhua University of Education, No. 1, Jin-De Road, Changhua City, Taiwan

    Yi-Chin Chen

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  1. Hone-Jay Chu
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Correspondence to Hone-Jay Chu.

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Chu, HJ., Chen, YC. Crowdsourcing photograph locations for debris flow hot spot mapping. Nat Hazards 90, 1259–1276 (2018). https://doi.org/10.1007/s11069-017-3098-6

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