All Title Author
Keywords Abstract


Implications of Classification of Methodological Decisions in Flooding Analysis from Hurricane Katrina

DOI: 10.3390/rs4123877

Keywords: classification process, change detection, flooding assessment, Hurricane Katrina, racial distribution

Full-Text   Cite this paper   Add to My Lib

Abstract:

Recent climatic patterns indicate that extreme weather events will increase in frequency and magnitude. Remote sensing offers unique advantages for large-scale monitoring. In this research, Landsat 5 remotely sensed imagery was used to assess flooding caused by Hurricane Katrina, one of the worst natural disasters in the US over the past decades. The objective of our work is to assess whether decisions associated with the classification process, such as location of reference data and algorithm choice, affected flooding results and subsequent analysis using census data. Maximum Likelihood (ML) and Back Propagation Neural Network (NN) were the tested algorithms, the former reflecting a simple and popular classifier, and the latter an advanced but complex method. Flooding estimations were almost identical within the reference sample area, 124.4 km2 for the ML classifier and 123.7 km2 for the NN classifier. However, large discrepancies were found outside the reference sample area with the ML predicting 462.5 km2 and the NN identifying 797.2 km2 as flooded, almost twice the amount. Further investigation took place to evaluate the influence of the classification method to a social study, namely the racial characteristics of flooded areas. Using Census 2000 data, our study area was segmented in census tracts. Results indicated a strong positive correlation between concentration of African Americans and proportional residential flooding. Pairwise T-Tests also verified that flooding among different African American concentrations was statistically different. There were no significant differences between the ML and NN methods in the results interpretation, which is mostly attributed to the significant geographic overlap between reference sample area and the examined census tracts. This study suggests that emergency responders should exercise significant caution in their decision making when using classification products from undersampled geographic areas in terms of classification reference data.

References

[1]  Kamal, M.M.; Yang, R.; Qu, J.J. Multivariate analysis of MODerate Resolution Imaging Spectroradiometer (MODIS) aerosol retrievals and the Statistical Hurricane Intensity Prediction Scheme (SHIPS) parameters for Atlantic hurricanes. Remote Sens 2012, 4, 2846–2865.
[2]  O?tir, K.; Veljanovski, T.; Podobnikar, T.; Stan?i?, Z. Application of satellite remote sensing in natural hazard management: The mount mangart landslide case study. Int. J. Remote Sens 2003, 24, 3983–4002.
[3]  Sedano, F.; Kempeneers, P.; Strobl, P.; McInerney, D.; San Miguel, J. Increasing spatial detail of burned scar maps using IRS-AWiFS data for Mediterranean Europe. Remote Sens 2012, 4, 726–744.
[4]  Van der Sande, C.J.; de Jong, S.M.; de Roo, A.P.J. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. Int. J. Appl. Earth Obs. Geoinf 2003, 4, 217–229.
[5]  Lu, D.; Mausel, P.; Brondízio, E.; Moran, E. Change detection techniques. Int. J. Remote Sens 2004, 25, 2365–2407.
[6]  Knabb, R.D.; Rhome, J.R.; Brown, D.P. Tropical Cyclone Report Hurricane Katrina 23–30 August 2005; National Hurricane Center: Miami, FL, USA, 2011.
[7]  Jonkman, S.N.; Maaskant, B.; Boyd, E.; Levitan, M.L. Loss of life caused by the flooding of New Orleans after hurricane Katrina: Analysis of the relationship between flood characteristics and mortality. Risk Analysis 2009, 29, 676–698.
[8]  Blake, E.S.; Landseam, C.W.; Gibney, E.J. The Deadliest, Costliest, and Most Intense United States Tropical Cyclones from 1851 to 2010 (and Other Frequently Requested Hurricane Facts). NOAA Technical Memorandum NWS NHC-6; National Hurricane Center: Miami, FL, USA, 2011.
[9]  Elliott, J.R.; Pais, J. Race, class, and Hurricane Katrina: Social differences in human responses to disaster. Social Sci. Res 2006, 35, 295–321.
[10]  Lavelle, K.; Feagin, J. Hurricane Katrina: The race and class debate. Mon. Review 2006, 58, 52–66.
[11]  Sharkey, P. Survival and death in New Orleans: An empirical look at the human impact of Katrina. J. Black Stud 2007, 37, 482–501.
[12]  Fussell, E.; Sastry, N.; VanLandingham, M. Race, socioeconomic status, and return migration to New Orleans after Hurricane Katrina. Population Environ 2010, 31, 20–42.
[13]  White, I.K.; Philpot, T.S.; Wylie, K.; McGowen, E. Feeling the pain of my people: Hurricane Katrina, racial inequality, and the psyche of black America. J. Black Stud 2007, 37, 523–538.
[14]  Sankey, T.T. Regional assessment of aspen change and spatial variability on decadal time scales. Remote Sens 2009, 1, 896–914.
[15]  Cansler, C.A.; McKenzie, D. How robust are burn severity indices when applied in a new region? Evaluation of alternate field-based and remote-sensing methods. Remote Sens 2012, 4, 456–483.
[16]  Vassilakis, E. Remote sensing of environmental change in the Antirio Deltaic Fan Region, Western Greece. Remote Sens 2010, 2, 2547–2560.
[17]  Li, P.; Xu, H. Land-cover change detection using one-class support vector machine. Photogramm. Eng. Remote Sensing 2010, 76, 255–263.
[18]  Dewan, A.M.; Yamaguchi, Y. Land use and land cover change in greater dhaka, bangladesh: Using remote sensing to promote sustainable urbanization. Appl. Geogr 2009, 29, 390–401.
[19]  Ahlqvist, O. Extending post-classification change detection using semantic similarity metrics to overcome class heterogeneity: A study of 1992 and 2001 U.S. national land cover database changes. Remote Sens. Environ 2008, 112, 1226–1241.
[20]  Abd El-Kawy, O.R.; R?d, J.K.; Ismail, H.A.; Suliman, A.S. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Appl. Geogr 2011, 31, 483–494.
[21]  Yuan, F.; Sawaya, K.E.; Loeffelholz, B.C.; Bauer, M.E. Land cover classification and change analysis of the twin cities (Minnesota) metropolitan area by multitemporal landsat remote sensing. Remote Sens. Environ 2005, 98, 317–328.
[22]  Wang, F.; Xu, Y.J. Comparison of remote sensing change detection techniques for assessing hurricane damage to forests. Environ. Monit. Assess 2010, 162, 311–326.
[23]  Mas, J.F. Monitoring land-cover changes: A comparison of change detection techniques. Int. J. Remote Sens 1999, 20, 139–152.
[24]  Strahler, A.H. The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sens. Environ 1980, 10, 135–163.
[25]  Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning representations by back-propagating errors. Nature 1986, 323, 533–536.
[26]  US Federal Register Official Web Portal. Urban Area Criteria for Census 2000, 2002, Available online: https://www.federalregister.gov/articles/2002/03/15/0186/urban-area-criteria-for-census-2000#h-19 (accessed on 12 September 2012).

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

微信:OALib Journal