Deriving Water Fraction and Flood Maps From MODIS Images Using a Decision Tree Approach

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 4, Issue 4, 2011, Pages 814-825

  Sun, Donglian ^a   Yu, Yunyue ^b   Goldberg, Mitchell D ^b  

^a GEORGE MASON UNIVERSITY   (United States)

^b OFFICE OF RESEARCH AND APPLICATIONS   (United States)

Author keywords

Decision tree; flood; MODIS; water fraction

Indexed keywords

ACCURACY RATE; EARTH OBSERVING SYSTEMS; FLOOD MAPPING; FLOOD MAPS; HIGHER RESOLUTION; LAND COVER; LANDSAT; LINEAR MIXTURE MODELS; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; MODIS; NEW ORLEANS; REAL APPLICATIONS; REFLECTANCE DIFFERENCES; ROOT MEAN SQUARE ERRORS; SPACE AND TIME; SPLIT TEST; STANDARD DEVIATION; STANDING WATERS; SURFACE REFLECTANCE; THEMATIC MAPPERS; TRAINING DATASET; WATER FRACTION;

DECISION TREES; FLOODS; FORESTRY; GROUND PENETRATING RADAR SYSTEMS; MEAN SQUARE ERROR; RADAR IMAGING; REFLECTION; STATISTICAL TESTS; SURFACE TESTING; TIME DOMAIN ANALYSIS;

RADIOMETERS;

ACCURACY ASSESSMENT; DATA SET; EOS; ERROR ANALYSIS; FLOOD; LAND COVER; LANDSAT THEMATIC MAPPER; MAPPING; MODIS; PARAMETERIZATION; PRECISION;

DECISION THEORY; ERRORS; FLOODS; FORESTRY; RADAR; RADIOMETRY; REFLECTION; STATISTICAL ANALYSIS; WATER;

LOUISIANA; NEW ORLEANS; UNITED STATES;

EID: 83655212342     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2011.2125778     Document Type: Article

References (39)

1. L. C. Smith “Satellite remote sensing of river inundation area, stage, and discharge: A review,” Hydrol. Process., vol. 11, no. 10, pp. 1427–1439, 1997.
2. F. Bonn and R. Dixon, “Monitoring flood extent and forecasting excess runoff risk with RADARSAT-1 Data,” Natural Hazards, vol. 35, no. 3, pp. 377–393, 2005.
3. S. J. Sipple, S. K. Hamilton, and J. M. Melack “Inundation area and morphometry of lakes on the Amazon River floodplain, Brazil,” Arch. Hydrobiol., vol. 123, no. 4, pp. 385–400, 1992.
4. K. O. Pope, J. M. Rey-Benayas, and J. F. Paris “Radar remote sensing of forest and wetland ecosystems in the Central American tropics,” Remote Sens. Envion., vol. 48, pp. 205–219, 1994.
5. L. L. Hess and J. M. Melack “Mapping wetland hydrology and vegetation with synthetic aperture radar,” Int. J. Ecol. Environ. Sci., vol. 20, pp. 197–205, 1994.
6. L. L. Hess, J. M. Melack, S. Filoso, and Y. Wang “Delineation of inundated area and vegetation along the Amazon-oodplain with the SIR-C synthetic aperture radar,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 4, pp. 895–903, 1995.
7. A. Rango and V. V. Salomonson “Regional flood mapping from space,” Water Resource Research, vol. 10, no. 3, pp. 473–484, 1974.
8. M. Deutsch and F. Ruggles “Optical data processing and projected applications of the ERTS-1 imagery covering the 1973 Mississippi River Valley floods,” Water Resource Bull., vol. 10, no. 5, pp. 1023–1039, 1974.
9. D. F. McGinnis and A. Rango “Earth Resources Satellite systems for flood monitoring,” Geophys. Res. Lett., vol. 2, no. 4, pp. 132–135, 1975.
10. R. P. Gupta and S. Banerji “Monitoring of reservoir volume using Landsat data,” J. Hydrology, vol. 77, pp. 159–170, 1985.
11. J. R. Jensen, M. E. Hodgson, E. Christensen, H. E. Mackey Jr., L. R. Tinney, and R. Sharitz “Remote sensing inland wetlands: A multispectral approach,” Photogramm. Eng. Remote Sens., vol. 52, pp. 87–100, 1986.
12. F. Blasco, M. F. Bellan, and M. U. Chaudhury “Estimating the extent of floods in Bangladesh using SPOT data,” Remote Sens. Environ., vol. 39, pp. 167–178, 1992.
13. K. O. Pope, E. J. Sheffner, K. J. Linthicum, C. L. Bailey, T. M. Logan, E. S. Kasichke, K. Birney, A. R. Njogu, and C. R. Roberts “Identification of Central Kenyan Rift Valley Fever virus vector habitats with Landsat TM and evaluation of their flooding status with airbourne imaging radar,” Remote Sens. Environ., vol. 40, pp. 185–196, 1992.
14. H. Rasid and M. A. H. Pramanik “Areal extent of the 1988 flood in Bangladesh: How much did the satellite imagery show?,” Natural Hazards, vol. 8, pp. 189–200, 1993.
15. A. Ali, D. A. Quadir, and O. K. Huh “Study of river flood hydrology in Bangladesh with AVHRR data,” Int. J. Remote Sens., vol. 10, no. 12, pp. 1873–1891, 1989.
16. I. J. Barton and J. M. Bathols “Monitoring floods with AVHRR,” Remote Sens. Environ., vol. 30, pp. 89–94, 1989.
17. S. J. Gale and S. Bainbridge, “The floods in eastern Australia,” Nature, vol. 345, p. 767, 1990.
18. Y. Sheng, P. Gong, and Q. Xiao, “Quantitative dynamic flood monitoring with NOAA AVHRR,” Int. J. Remote Sens., vol. 22, no. 9, pp. 1709–1724, 2001.
19. J. P. Verdin “Remote sensing of ephemeral water bodies in western Niger,” Int. J. Remote Sens., vol. 17, pp. 733–748, 1996.
20. C. Domenikiotis, A. Loukas, and N. R. Dalezios, “The use of NOAA/AVHRR satellite data for monitoring and assessment of forest fires and floods,” Nat. Hazards Earth Syst. Sci., vol. 3, pp. 115–128, 2003.
21. C. J. Tucker “Red and photographic infrared linear combinations for monitoring vegetation,” Remote Sens. Environ., vol. 8, pp. 127–150, 1979.
22. G. M. Parmuchi, H. Karszenbaum, and P. Kandus, “Mapping wetlands using multi-temporal RADARSAT-1 data and a decision-based classifier,” Can. J. Remote Sens., vol. 28, no. 2, pp. 175–186, 2002.
23. J. R. Jensen, M. E. Hodgson, E. Christensen, H. E. Mackey Jr., L. R. Tinney, and R. Sharitz “Remote sensing inland wetlands: A multispectral approach,” Photogramm. Eng. Remote Sens., vol. 52, pp. 87–100, 1986.
24. F. Blasco, M. F. Bellan, and M. U. Chaudhury “Estimating the extent of floods in Bangladesh using SPOT data,” Remote Sens. Environ., vol. 39, pp. 167–178, 1992.
25. J. M. Melack, L. L. Hess, and S. Sippel “Remote sensing of lakes and floodplains in the amazon basin,” Remote Sens. Rev., vol. 10, pp. 127–142, 1994.
26. J. W. Han and M. Kamber, Data Mining: Concept and Techniques. San Francisco, CA: Morgan Kaufmann, 2001, p. 550.
27. M. A. Friedl and C. E. Brodley “Decision tree classification of land cover from remotely sensed data,” Remote Sens. Environ., vol. 61, no. 3, pp. 399–409, 1997.
28. M. B. Dale, P. E. R. Dale, and P. Tan, “Supervised clustering using decision trees and decision graphs: An ecological comparison,” Ecological Modeling, vol. 204, no. 1–2, pp. 70–78, 2007.
29. M. L. Carroll, C. M. DiMiceli, J. R. G. Townshend, R. A. Sohlberg, and P. Noojipady, MODIS Flood Map; 2008 US Midwest Flood, MOD_FLOOD.2008158.Midwest.Geog, 1.0 Univ. Maryland, College Park, MD, 2008.
30. M. L. Carroll, J. R. Townshend, C. M. DiMiceli, P. Noojipady, and R. A. Sohlberg, “A new global raster water mask at 250 m resolution,” Int. J. Digital Earth, vol. 2, no. 4, pp. 291–308, 2009.
31. M. Xu, P. Watanachaturapom, P. K. Varshney, and M. K. Arora, “Decision tree regression for soft classification of remote sensing data,” Remote Sens. Environ., vol. 97, no. 3, pp. 322–336, 2005.
32. R. DeFries, M. Hansen, and J. Townshend, “Global continuous fields of vegetation characteristics: A linear mixture model applied to multiyear 8 km AVHRR data,” Int. J. Remote Sens., vol. 21, pp. 1389–1414, 2000.
33. M. C. Hansen, R. S. DeFries, J. R. G. Townshend, R. Sohlberg, C. Dimiceli, and M. Carroll, “Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data,” Remote Sens. Environ., vol. 83, pp. 303–319, 2002.
34. B.-C. Gao “NDWI — A normalized difference water index for remote sensing of vegetation liquid water from space,” Remote Sens. Environ., vol. 58, pp. 257–266, 1996.
35. J. R. Quinlan, C4.5: Programs for Machine Learning. San Francisco, CA: Morgan Kaufmann, 1993, p. 316.
36. R. Kohavi, “Scaling up the accuracy of Naive-Bayes classifiers: A decision-tree hybrid,” in Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining, 1996.
37. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2004.
38. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Pacific Grove, CA: Wadsworth & Brooks/Cole, 1984.
39. W. Li, C. W. Yang, and D. L. Sun, “Mining geophysical parameters through decision-tree analysis to determine correlation with tropical cyclone development,” Comput. Geosci., vol. 35, pp. 309–316, 2009.