Classifying and mapping wildfire severity: A comparison of methods

Photogrammetric Engineering and Remote Sensing

Volumn 71, Issue 11, 2005, Pages 1311-1320

  Brewer, C Kenneth ^a   Winne, J Chris ^b   Redmond, Roland L ^b   Opitz, David W ^b   Mangrich, Mark V ^b  

^a USDA FOREST SERVICE   (United States)

^b UNIVERSITY OF MONTANA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ARTIFICIAL INTELLIGENCE; DATA REDUCTION; IMAGE ANALYSIS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

IMAGE DIFFERENCING; POST-FIRE IMAGERY; PRE-FIRE IMAGERY; WILDFIRE SEVERITY;

FIRE PROTECTION;

ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; IMAGE PROCESSING; LANDSAT THEMATIC MAPPER; MAPPING METHOD; REMOTE SENSING; WILDFIRE;

EID: 27644571545     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.71.11.1311     Document Type: Article

References (59)

1. Abuelgasim, A.A., W.D. Ross, S. Gopal, C.E. Woodcock, 1999. Change detection using adaptive fuzzy neural networks: environmental damage assessment after the Gulf War, Remote Sensing of Environment, 70:208-223.
2. Bain, W., 2000. Comparison of Three Machine Learning Algorithms for Automated Feature Extraction from Digital Images, M.S. thesis, University of Montana, Missoula, Montana.
3. Blackwell, B., M.C. Feller, and R. Trowbridge, 1992. Conversion of dense lodgepole pine stands in west-central British Columbia into young lodgepole pine plantations using prescribed fire, 1. Biomass consumption during burning treatments, Canadian Journal of Forest Resources, 22:572-581.
4. Blackwell, B., M.C. Feller, and R. Trowbridge, 1995. Conversion of dense lodgepole pine stands in west-central British Columbia into young lodgepole pine plantations using prescribed fire, 2. Effects of burning treatments on tree seedling establishment, Canadian Journal of Forest Resources, 25:175-183.
5. Bobbe, T., M. Finco, B. Quayle, K. Lannom, R. Sohlberg, and A. Parsons, 2001. Field Measurements for the Training and Validation of Burn Severity Maps from Spaceborne Remotely Sensed Imagery, 2001-2B Joint Fire Science Approved Project, Project Number: 01B-2-1-01, URL: http://www.fs.fed.us/eng/rsac/baer/ jfs.html (last date accessed: 03 August 2005).
6. Brewer, D., K. Brewer, L. Queen, 2000. Monitoring change in forest ecosystems: Satellite remote sensing or aerial detection surveys and stand records, Remote Sensing and Geospatial Technologies for the New Millennium, Proceedings of the Eighth Forest Service Remote Sensing Conference (Jerry Dean Greer, editor), American Society for Photogrammetry and Remote Sensing, unpaginated CD-ROM.
7. Brown, J.K., S.F. Arno, L.S. Bradshaw, J.P. Menakis, 1995. Comparing the Selway-Bitterroot fire program with presettlement fires, Proceedings of the Symposium on Fire in Wilderness and Park Management, 1993 March 30-April 1; Missoula, MT, General Technical Report INT-GTR-320 (James K. Brown, Robert W. Mutch, Charles W. Spoon, and Ronald H. Wakimoto, Technical Coordinators), Ogden, Utah, U.S. Department of Agriculture, Forest Service, Intermountain Research Station, pp. 48-54.
8. Caselles, V., and M.J. L. Garcia, 1989. An alternative simple approach to estimate atmospheric correction in multitemporal studies, International Journal of Remote Sensing, 10: 1127-1134.
9. Congalton, R.G., and K. Green, 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Lewis Publishers, ISBN 0-939970-73-2, Washington D.C., 137 p.
10. Coppin, P.R., and M.E. Bauer, 1994'. Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features, IEEE Transactions on Geoscience and Remote Sensing, 32:918-927.
11. Coppin, P., I. Jonckheere, K. Nackaerts, and B. Muys, 2004. Digital Change detection in environmental monitoring: a review, International Journal of Remote Sensing, 25:1565-1596.
12. Coppin, P., K. Nackaerts, L. Queen, and K. Brewer, 2001. Operational monitoring of green biomass change for forest management, Photogrammetric Engineering & Remote Sensing, 67(5):603-611.
13. Dai, X.L., and S. Khorram, 1999. The effects of image mis-registration on the accuracy of remotely sensed change detection, IEEE Transactions on Geoscience and Remote Sensing, 36:1566-1577.
14. Edwards, G., 1990. Image segmentation, cartographic information and knowledge-based reasoning: getting the mixture right, Proceedings of the IGARSS '90' Symposium, University of Maryland, College Park, Maryland.
15. ERDAS, 1997. The ERDAS Field Guide, Fourth edition, ERDAS, Inc., Atlanta, Georgia, 656 p.
16. Franklin, S.E., R.H. Waring, R.W. McCreight, W.B. Cohen, and M. Fiorella, 1995. Aerial and satellite sensor detection and classification of Western spruce budworm defoliation in a subalpine forest?, Canadian Journal of Remote Sensing, 21(3): 299-308.
17. Fung, T., and E. LeDrew, 1987. Application of principal components analysis for change detection, Photogrammetric Engineering & Remote Sensing, 53:1649-1658.
18. Fung, T., and E. LeDrew, 1988. The determination of optimal threshold levels for change detection using various accuracy indices, Photogrammetric Engineering & Remote Sensing, 54:1449-1454.
19. Gmelin, M., and K. Brewer, 2002. Operational change detection-based fire severity mapping using Landsat TM data, Rapid Delivery of Remote Sensing Products, Proceedings of the Ninth Forest Service Remote Sensing Conference (Jerry Dean Greer, editor), American Society for Photogrammetry and Remote Sensing, unpaginated CD-ROM.
20. Gopal, S., and C.E. Woodcock, 1999. Artificial neural networks for detecting forest change, Information Processing for Remote Sensing (C.H. Chen, editor), Singapore World Scientific Publishing, pp. 225-236.
21. Hashem, M., El-Khattib, M. Nabil, El-Mowelhi, and H. Hetoh, 1996. Monitoring land cover of the desert fringes of the eastern Nile delta, Egypt, Proceedings of the IGARSS'96 Symposium, Lincoln, Nebraska, 3:1756-1758.
22. Helms, J.A. (editor), 1998. The Dictionary of Forestry, The Society of American Foresters, ISBN 0-939970-73-2, Bethesda, Maryland, 210 p.
23. Hogda, K.A., H. Tommervik, I. Solheim, and I. Lauknes, 1995. Mapping of air pollution effects on the vegetation cover in the Kikenes-Nikel area using remote sensing, Proceedings of the IGARSS'95 Symposium, Florence, Italy, 2:1249-1251.
24. Hutto, R.L., 1995. The importance of intense crown fires to some bird species in Rocky Mountain coniferous forests, Proceedings: Symposium on Fire in Wilderness and Park Management (James K. Brown, Robert W. Mutch, Charles W. Spoon, and Ronald H. Wakimoto, technical coordinators), USDA Forest Service General Technical Report, INT-GTR-320, Ogden, Utah.
25. Jensen, J.R., 1981. Urban change detection mapping using Landsat digital data, The American Cartographer, 8:127-147.
26. Keifer, M.B., and P.M. Stanzler, 1995. Fire effects monitoring in Sequoia and King's Canyon National Parks, Proceedings: Symposium on Fire in Wilderness and Park Management (James K. Brown, Robert W. Mutch, Charles W. Spoon, and Ronald H. Wakimoto, technical coordinators), USDA Forest Service General Technical Report, INT-GTR-320, Ogden, Utah.
27. Key, C.H., Z. Zhu, D. Ohlen, S. Howard, R. McKinley, and N. Benson, 2002. The normalized burn ratio and relationships to burn severity: Ecology, remote sensing and implementation, Rapid Delivery of Remote Sensing Products, Proceedings of the Ninth Forest Service Remote Sensing Conference, (Jerry Dean Greer, editor), American Society for Photogrammetry and Remote Sensing, unpaginated CD-ROM.
28. Lillesand, T.M., and R.W. Kiefer, 1987. Remote Sensing and Image Interpretation, Second edition, John Wiley and Sons, New York, New York, 721 p.
29. Lopez-Garcia, M.J., and V. Caselles, 1991. Mapping Burns and Natural Reforestation Using Thematic Mapper Data, Geocarto International, 6:31-37.
30. Lu, D., P. Mausel, E. Brondizio, and E. Moran, 2003. Change detection techniques, International Journal of Remote Sensing, 25:2365-2407.
31. Lund, H.G., 1983. Change: now you see it-now you don't!, Proceedings of the International Conference on Renewable Resource Inventories for Monitoring Changes and Trends, Oregon State University, Corvallis, pp. 211-213.
32. Lyon, J.G., D. Yuang, R.S. Lunetta, and C.D. Elvidge, 1998. A change detection experiment using vegetation indices, Photogrammetric Engineering & Remote Sensing, 64(2):143-150.
33. Mahlke, J., 1996. Characterization of Oklahoma reservoir wetlands for preliminary change detection mapping using IRS-1B satellite imagery, Proceedings of the IGARSS'96 Symposium, Burnham Yates Conference Center, Lincoln, Nebraska, 3:1769-1771.
34. Mangold, R.D., 1995. Sustainable development: the Forest Service's approach, Journal of Forestry, November, pp. 25-28.
35. Mangrich, M.V., 2001. Hierarchical Feature Extraction, M.S. thesis, University of Montana, Missoula, Montana.
36. Mattikalli, N.M., 1994. An integrated geographical information system's approach to land cover change assessment, Proceedings of the IGARSS'94 Symposium, California Institute of Technology, Pasadena, California, 2:1204-1206.
37. Milne, A.K., 1988. Change direction analysis: an approach detecting forest changes with Landsat imagery: a review of methodology, Proceedings of the IGARSS'88 Symposium, Edinburgh, Scotland, pp. 541-544.
38. Mitchell, T., 1997. Machine Learning, MIT Press, Cambridge, Massachusetts.
39. Nemani, R., L.L. Pierce, S.W. Running, and L.S. Band, 1993. Forest ecosystem processes at the watershed scale: sensitivity to remotely sensed lead-area index estimates, International Journal of Remote Sensing, 14:2519-2534.
40. Ngai, F.M., and J.J. Curlander, 1994. Model-based feature classification and change detection, Proceedings of the IGARSS'94 Symposium, California Institute of Technology, Pasadena, California, 4:2531-2533.
41. Opitz, D.W., 2002. The use of spatial context in image understanding, Remote sensing and rapid delivery of remote sensing products, Proceedings of the Ninth Forest Service Remote Sensing Conference (Jerry Dean Greer, editor), American Society for Photogrammetry and Remote Sensing, unpaginated CD-ROM.
42. Opitz, D.W., and R. Machlin, 1999. Popular ensemble methods: an empirical study, Journal of Artificial Intelligence Research, 11:169-198.
43. Orlemann, A., 2002. BAER Imagery Support, USDA Forest Service Engineering Field Notes.
44. Park, A.B., R.A. Houghton, G.M. Hicks, and C.J. Peterson, 1983. Multitemporal change detection techniques for the identification and monitoring of forest disturbances, Proceedings of the 17th International Symposium on Remote Sensing of the Environment, Ann Arbor, Minnesota, pp. 77-97.
45. Parra, G.A., M. Mouchot, and C. Mouchot, 1996. A multitemporal land-cover change analysis tool using change vector and principal components analysis, Proceedings of the IGARSS'96 Symposium, Burnham Yates Conference Center, Lincoln, Nebraska, 3:1753-1755.
46. Redmond, R.L., M.M. Hart, J.C. Winne, W.A. Williams, P.C. Thornton, Z. Ma, C.M. Tobalske, M.M. Thornton, K.P. McLaughlin, T.P. Tady, F.B. Fisher, and S.W. Running, 1998. The Montana Gap Analysis Project: Final Report (unpublished), Montana Cooperative Wildlife Research Unit, The University of Montana, Missoula, 136 p.
47. Riordan, C.J., 1981. Change detection for resource inventories using digital remote sensing data, Proceedings of the Workshop on InPlace Resource Inventories: Principles and Practices, University of Maine, Orono, Maine, pp. 278-283.
48. Robichaud, P.R., and T.A. Waldtrop, 1994. A comparison of surface runoff and sediment yields from low- and high-severity site preparation burns, Water Resources Bulletin, 30(1):27-34.
49. Rumelhart, D., G. Hinton, and R. Williams, 1986. Learning internal representations by error propagation, Parallel Distributed Processing: Exploration in the Microstructure of cognition, Volume 1: Foundations, MIT Press, Cambridge, Massachusetts, pp. 135-143.
50. Sloggett, D., C. Gurney, and W. Newton, 1994. An automated change detection system, Proceedings of the IGARSS'94 Symposium, California Institute of Technology, Pasadena, California, 2:875-877.
51. Solheim, I., K.A., Hogda, and H. Tommervik, 1995. Detection of abnormal vegetation change in the Monchegorsk, Russia, area, Proceedings of the IGARSS'95 Symposium, Florence, Italy, 1:105-107.
52. Song, C., C.E. Woodcock, K.C. Seto, M.P. Lenney, and S.A. Macomber, 2001. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?, Remote Sensing of Environment, 75:230-244.
53. U.S. Department of Agriculture, Forest Service, 2001. A Preliminary Assessment of the Extent and Effects of the 2000 Fires, Intermountain and Northern Regions, U.S. Department of Agriculture, Forest Service, 87 p.
54. Vogelmann, J.E., T. Sohl, and S.M. Howard, 1998. Regional characterization of land cover using multiple sources, Photogrammetric Engineering & Remote Sensing, 64:45-57.
55. Vogelmann, J.E., and J.D. Wickham, 2000. Implementation strategy for production of national land cover data (NLCD) from the Landsat 7 Thematic Mapper Satellite, EPA 600/R-00/051, Washington, D.C., U.S. Environmental Protection Agency, Office of Research and Development.
56. Vogelmann, J.E., S.M. Howard, L. Yang, C.R. Larson, B.K. Wylie, and N. Van Driel, 2001. Completion of the 1990s national land cover data set for the conterminous United States from landsat thematic mapper data and ancillary data sources, Photogrammetric Engineering & Remote Sensing, 67(6):650-662.
57. Wallace, J.F., and H. Campbell, 1989. Analysis of remotely sensed data, Remote Sensing of Biosphere Functioning, Springer Verlag, New York, pp. 297-304.
58. Wilson, R.C. 1960. Manual of Photographic Interpretation (Chapter 7), American Society of Photogrammetry, Washington, D.C., 868 p.
59. Yin, Z., and T.H.L. Williams, 1997. Obtaining spatial and temporal vegetation data from Landsat MSS and AVHRR/NOAA satellite images for a hydrologic model, Photogrammetric Engineering & Remote Sensing, 63(1):69-78.