Assessing coupled human-flood interactions using LiDAR geostatistics and neighbourhood analyses

References

• Armstrong S, Lazarus E. 2019. Reconstructing patterns of coastal risk in space and time along the US Atlantic coast, 1970-2016. Nat Hazards Earth Syst Sci. 19(11):2497–2511. doi:10.5194/nhess-19-2497-2019.
   Web of Science ®Google Scholar
• Brasington J, Smart R. 2003. Close range digital photogrammetric analysis of experimental drainage basin evolution. Earth Surf Processes Landf. 28(3):231–247. doi:10.1002/esp.480.
   Web of Science ®Google Scholar
• Breen MJ, Kebede AS, König CS. 2022. The safe development paradox in flood risk management: a critical review. Sustainability. 14(24):16955. doi:10.3390/su142416955.
   Web of Science ®Google Scholar
• Brock P, Purkis J. 2009. The emerging role of lidar remote sensing in coastal research and resource management. J Coast Res. 10053:1–5. doi:10.2112/SI53-001.1.
   Google Scholar
• Burby R. 2006. Hurricane Katrina and the paradoxes of government disaster policy: bringing about wise governmental decisions for hazardous areas. Ann Am Acad Polit Soc Sci. 604(1):171–191. doi:10.1177/0002716205284676.
   Web of Science ®Google Scholar
• Burton C, Cutter S. 2008. Levee failures and social vulnerability in the Sacramento-San Joaquin Delta Area, California. Nat Hazards Rev. 9(3):136–149. doi:10.1061/(ASCE)1527-6988(2008)9:3(136).
   Google Scholar
• Chang H, Huang K, Wu C. 2006. Determination of sample size in using central limit theorem for Weibull distribution. Int J Inf Manage Sci. 17(3):31–46.
   Google Scholar
• Chen L, Lin L. 2010. Detection of building changes from aerial images and light detection and ranging (LIDAR) data. J Appl Remote Sens. 4(1):041870. doi:10.1117/1.3525560.
   Google Scholar
• Di Baldassarre G, Kooy M, Kemerink J, Brandimarte L. 2013b. Towards understanding the dynamic behaviour of floodplains as human-water systems. Hydrol Earth Syst Sci. 17(8):3235–3244. doi:10.5194/hess-17-3235-2013.
   Web of Science ®Google Scholar
• Di Baldassarre G, Viglione A, Carr G, Kuil L, Salinas J, Blöschl G. 2013a. Socio-hydrology: conceptualising human-flood interactions. Hydrol Earth Syst Sci. 17(8):3295–3303. doi:10.5194/hess-17-3295-2013.
   Web of Science ®Google Scholar
• Erener A, Sarp G, Karaca M. 2020. An approach to urban building height and floor estimation by using LiDAR data. Arab J Geosci. 13(19):1005. doi:10.1007/s12517-020-06006-1.
   Web of Science ®Google Scholar
• Eriksen S, Schipper L, Scoville-Simonds M, Vincent K, Adam H, Brooks N, Harding B, Khatri D, Lenaerts L, Liverman D, et al. 2021. Adaptation interventions and their effect on vulnerability in developing countries: help, hindrance or irrelevance? World Dev. 141:105383. doi:10.1016/j.worlddev.2020.105383.
   Web of Science ®Google Scholar
• Fekete A, Sandholz S. 2021. Here comes the flood, but not failure? Lessons to learn after the heavy rain and pluvial floods in Germany 2021. Water. 13(21):3016. doi:10.3390/w13213016.
   Web of Science ®Google Scholar
• Gesch D. 2009. Analysis of lidar elevation data for improved identification and delineation of lands vulnerable to sea-level rise. J Coast Res. 10053:49–58. doi:10.2112/SI53-006.1.
   Google Scholar
• Haer T, Husby T, Botzen W, Aerts J. 2020. The safe development paradox: an agent-based model for flood risk under climate change in the European Union. Global Environ Change. 60:102009. doi:10.1016/j.gloenvcha.2019.102009.
   Web of Science ®Google Scholar
• Hallegatte S, Green C, Nicholls R, Corfee-Morlot J. 2013. Future flood losses in major coastal cities. Nature Clim Change. 3(9):802–806. doi:10.1038/nclimate1979.
   Web of Science ®Google Scholar
• Heris M, Foks N, Bagstad K, Troy A, Ancona Z. 2020. A rasterized building footprint dataset for the United States. Sci Data. 7(1):207. doi:10.1038/s41597-020-0542-3.
   PubMed Web of Science ®Google Scholar
• Jiao S, Li W, Wen J. 2022. Spatiotemporal changes of manufacturing firms in the flood-prone Yangtze Delta. Environmental Hazards. 21(4):334–360. doi:10.1080/17477891.2021.1988502.
   Google Scholar
• Kates R, Colten C, Laska S, Leatherman S. 2006. Reconstruction of New Orleans after Hurricane Katrina: a research perspective. Proc Natl Acad Sci USA. 103(40):14653–14660. doi:10.1073/pnas.0605726103.
   PubMed Web of Science ®Google Scholar
• King R. 2005. Hurricane Katrina: insurance losses and national capacities for financing disaster risk. Government and Finance Division.
   Google Scholar
• Leger C, Balch C, Essex S. 2016. Understanding the planning challenges of Brownfield development in coastal urban areas of England. Plann Pract Res. 31(2):119–131. doi:10.1080/02697459.2016.1146428.
   Web of Science ®Google Scholar
• Lin Y, Cheng Y, Zhou T, Ravi R, Hasheminasab S, Flatt J, Troy C, Habib A. 2019. Evaluation of UAV LiDAR for mapping coastal environments. Remote Sens. 11(24):2893. doi:10.3390/rs11242893.
   Google Scholar
• Ludy J, Kondolf G. 2012. Flood risk perception in lands “protected” by 100-year levees. Nat Hazards. 61(2):829–842. doi:10.1007/s11069-011-0072-6.
   Web of Science ®Google Scholar
• MacMahon A, Smith K, Lawrence G. 2015. Connecting resilience, food security and climate change: lessons from flooding in Queensland, Australia. J Environ Stud Sci. 5(3):378–391. doi:10.1007/s13412-015-0278-0.
   Google Scholar
• MacManus K, Balk D, Engin H, McGranahan G, Inman R. 2021. Estimating population and urban areas at risk of coastal hazards, 1990–2015: how data choices matter. Earth Syst Sci Data. 13(12):5747–5801. doi:10.5194/essd-13-5747-2021.
   Web of Science ®Google Scholar
• Magnan A, Schipper E, Burkett M, Bharwani S, Burton I, Eriksen S, Gemenne F, Schaar J, Ziervogel G. 2016. Addressing the risk of maladaptation to climate change. WIREs Clim Change. 7(5):646–665. doi:10.1002/wcc.409.
   Google Scholar
• Mascha E, Vetter T. 2018. Significance, errors, power, and sample size: the blocking and tackling of statistics. Anesthia Analg. 126(2):691–698. doi:10.1213/ANE.0000000000002741.
   PubMed Web of Science ®Google Scholar
• Montz B, Tobin G. 2008. Livin’ large with levees: lessons learned and lost. Nat Hazards Rev. 9(3):150–157. doi:10.1061/(ASCE)1527-6988(2008)9:3(150).
   Google Scholar
• Palmer R. 2018. History of coastal engineering in Great Britain. Hist Heritage Coast Eng. 6:214–274. doi:10.1061/9780784401965.006.
   Google Scholar
• Priestnall G, Jaafar J, Duncan A. 2000. Extracting urban features from LiDAR digital surface models. Comput Environ Urban Syst. 24(2):65–78. doi:10.1016/S0198-9715(99)00047-2.
   Google Scholar
• Rumson A, Garcia A, Hallet S. 2020. The role of data within coastal resilience assessments: an East Anglia, UK, case study. Ocean Coast Manage. 185:105004. doi:10.1016/j.ocecoaman.2019.105004.
   Web of Science ®Google Scholar
• Schmid K, Hadley B, Wijekoon N. 2011. Vertical accuracy and use of topographic LIDAR data in coastal marshes. J Coast Res. 275(6A):116–132. doi:10.2112/JCOASTRES-D-10-00188.1.
   Google Scholar
• Sharma M, Paige G, Miller S. 2010. DEM development from ground-based LiDAR data: a method to remove non-surface objects. Remote Sens. 2(11):2629–2642. doi:10.3390/rs2112629.
   Google Scholar
• Siegert M. 2015. Sea level change. Grantham Institute.
   Google Scholar
• Tobin G. 1995. The Levee love affair: a stormy relationship? J Am Water Resour Assoc. 31:359–367. doi:10.1111/j.1752-1688.1995.tb04025.x.
   Google Scholar
• Tsutsui K, Rokugawa S, Nakagawa H, Miyazaki S, Cheng C, Shiraishi T, Yang S. 2007. Detection and volume estimation of large-scale landslides based on elevation-change analysis using DEMs extracted from high-resolution satellite stereo imagery. IEEE. 45(6):1681–1696. doi:10.1109/TGRS.2007.895209.
   Google Scholar
• Wheaton J, Brasington J, Darby S, Sear D. 2009. Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surf Processes Landf. 35(2):136–156. doi:10.1002/esp.1886.
   Web of Science ®Google Scholar
• White G. 1945. Human adjustment to floods. Chicago: University of Chicago.
   Google Scholar
• Yan W, Shaker A, El-Ashmawy N. 2015. Urban land cover classification using airborne LiDAR data: a review. Remote Sens Environ. 158:295–310. doi:10.1016/j.rse.2014.11.001.
   Web of Science ®Google Scholar
• Zhou K, Lindenbergh R, Gorte B, Zlatanova S. 2020. LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data. ISPRS J Photogramm Remote Sens. 162:200–213. doi:10.1016/j.isprsjprs.2020.02.005.
   Web of Science ®Google Scholar