Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

Photogrammetric Engineering and Remote Sensing

Volumn 74, Issue 2, 2008, Pages 247-264

  Chastain Jr , Robert A ^a,b   Struckhoff, Matthew A ^c   He, Hong S ^b   Larsen, David R ^b  

^a Fort Lewis Directorate of Public Works   (United States)

^b UNIVERSITY OF MISSOURI   (United States)

^c Jackson Field Research Station   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); REMOTE SENSING; STATISTICAL METHODS; TOPOGRAPHY; VEGETATION;

DATA SOURCES; NATIONAL VEGETATION CLASSIFICATION SYSTEM; PHOTOINTERPRETATION METHODS; VEGETATION COMMUNITY MAP;

MAPPING;

COMMUNITY COMPOSITION; DISTURBANCE; LAND COVER; MAPPING; REMOTE SENSING; SPECTRAL REFLECTANCE; STATISTICAL ANALYSIS; TOPOGRAPHY; VEGETATION TYPE;

MISSOURI; NORTH AMERICA; UNITED STATES;

EID: 39049156975     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.74.2.247     Document Type: Article

References (55)

1. Allen, T.R., 2000. Topographic normalization of Landsat Thematic Mapper data in three mountain environments, Geocarto International, 15(2):13-19.
2. Austin, M.P., and T.M. Smith, 1989. A new model for the continuum concept, Vegetatio, 83:35-47.
3. Austin, M.P., and P.C. Heyligers, 1989. Vegetation survey design for conservation: Gradsect sampling of forests in north-eastern New South Wales, Biological Conservation, 50,13-32.
4. Avers, P.E., D.T. Cleland, W.H. McNab, M.E. Jensen, R.G. Bailey, T. King, C.B. Goudey, and W.E. Russell, 1994. The National Hierarchical Framework of Ecological Units, ECOMAP, USDA Forest Service, Washington, D.C.
5. Beers, T.W., P.E. Dress, and L.C. Wensel, 1966. Aspect transformation in site productivity research, Journal of Forestry, 64:691-692.
6. Boutin, C., and B. Jobin, 1998. Intensity of agricultural practices and effects on adjacent habitats, Ecological Applications, 8(2):544-557.
7. Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone, 1984. Classification and Regression Trees, Chapman and Hall, New York.
8. Carleer, A., and E. Wolff, 2005. Exploitation of very high-resolution satellite data for tree species identification, Photogrammetric Engineering & Remote Sensing, 70(1):135-140.
9. Chander, G., and B. Markham, 2003. Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges, IEEE Transactions on Geoscience and Remote Sensing, 41(11):2674-2677.
10. Chastain, R.A., M.A. Struckhoff, K.W. Grabner, E.D. Stroh, H. He, D.R. Larsen, T.A. Nigh, and J. Drake, 2006. Mapping Vegetation Communities in Ozark National Scenic Riverways-Final Technical Report to the National Park Service, U.S. Geological Survey Open-File Report 2006-1354, 90 p., plus appendices.
11. Chen, D., and D. Stow, 2003. Strategies for integrating information from multiple spatial resolutions into land-use/land-cover classification routines, Photogrammetric Engineering & Remote Sensing, 69(11):1279-1287.
12. Clark, M.L., D.A. Roberts, and D.B. Clark, 2005. Hyperspectral discrimination of tropical tree species at leaf to crown scales, Remote Sensing of Environment, 96(3-4):375-398.
13. Crist, E.P., and R.J. Kauth, 1986. The tasseled cap de-mystified, Photogrammetric Engineering & Remote Sensing, 8(2):81-86.
14. Cunningham, B., and C. Hauser, 1992. The decline of the Missouri Ozark forest between 1880 and 1920, (A.R. Journet, and H.G. Spratt, editors), Proceedings of Towards a Vision for Missouri's Public Forests, 27-28 March. Southeast Missouri State University, Cape Girardeau, Missouri, pp. 14-19.
15. De Colstoun, E.C.B., M.H. Story, C. Thompson, K. Commiso, T.G. Smith, and J.R. Irons, 2003. National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier, Remote Sensing of Environment, 85(3):316-327.
16. Federal Geographic Data Committee, 1996. National Vegetation Classification Standard, Version 2-Working Draft, FGDC-STD-005, Version 2, FGDC Secretariat, USGS, Reston, Virginia, 62 p.
17. Frank, T.D., 1988. Mapping dominant vegetation communities in the Colorado Rocky Mountain front range with Landsat Thematic Mapper and digital terrain data, Photogrammetric Engineering & Remote Sensing, 54(12):1727-1734.
18. Franklin, J., 1995. Predictive vegetation mapping: Geographic modelling of biospatial patterns in relation to environmental gradients, Progress in Physical Geography, 19(4):474-499.
19. Gardner, R.H., and D.L. Urban, 2003. Model validation and testing: Past lessons, present concerns, future prospects, (C.D. Canham, J.C. Cole, and W.K. Lauenroth, editors), The Role of Models in Ecosystem Science, Princeton University Press, Princeton, New Jersey.
20. Guyette, R.P., and B.E. Cutter, 1991. Tree ring analysis of fire history in a post oak savanna in the Missouri Ozarks, Natural Areas Journal, 11:93-99.
21. Hansen, M., R. Dubayah, and R. DeFries, 1996. Classification trees: An alternative to traditional land cover classifiers, International Journal of Remote Sensing, 17(5):1075-1081.
22. Hansen, M.C., R. DeFries, J.R.G. Townshend, and R. Sohlberg, 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach, International Journal of Remote Sensing, 21:1331-1364.
23. Hay, G.J., K.O. Niemann, and G.F. McLean, 1996. An object-specific image-texture analysis of H-resolution forest imagery, Remote Sensing of Environment, 55:108-122.
24. Huang, C., B. Wylie, C. Homer, L. Yang, and G. Zylstra, 2002. Derivation of a tasseled cap transformation based on Landsat 7 at-satellite reflectance, International Journal of Remote Sensing, 23(8):1741-1748.
25. Huang, T., Z. Liu, Y. Pan, and Y. Zhang, 1997. Land-cover survey in northeast China using remote sensing and GIS, Chinese Geographical Science, 8(3):264-270.
26. Irish, R.R., 2000. Landsat-7 Science Data User's Handbook, Chapter 11: Data Products, Report 430-15-01-003-0, National Aeronautics and Space Administration, URL: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/ chapter11/chapter11.htm (last date accessed: 07 November 2007).
27. Ivits, E., B. Koch, T. Blaschke, M. Jochum, and P. Adler, 2005. Landscape structure assessment with image grey-values and object-based classification at three spatial scales, International Journal of Remote Sensing, 26(14):2975-2993.
28. Jacobson, R.B., and K.B. Gran, 1999. Gravel sediment routing from widespread, low-intensity landscape disturbance, Current River basin, Missouri, Earth Surface and Processes and Landforms, 24:897-917.
29. Jensen, J.R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall, Upper Saddle River, New Jersey, 316 p.
30. Joy, S.M., R.M. Reich, and R.T. Reynolds, 2003. A non-parametric, supervised classification of vegetation types on the Kaibob National Forest using decision trees, International Journal of Remote Sensing, 24(9):1835-1852.
31. Karimi, Y., S.O. Prasher, H. McNairn, R.B. Bonnell, P. Dutilleul, and R.K. Goel, 2005. Classification accuracy of discriminant analysis, artificial neural networks, and decision trees for weed and nitrogen stress detection, Transactions of the ASAE, 48(3):1261-1268.
32. Klecka, W.R., 1980. Discriminant Analysis, Sage University Paper Series on Quantitative Applications in the Social Sciences, 07-019, Beverly Hills, California, Sage Publications.
33. Lawrence, R., R. Hurst, T. Weaver, and R. Aspinall, 2006. Mapping prairie pothole communities with multitemporal Ikonos satellite imagery, Photogrammetric Engineering & Remote Sensing, 72(2):169-174.
34. Lillesand, T.M., and R.W. Keifer, 1994. Remote Sensing and Image Analysis, Third edition, Wiley, New York.
35. Markham, B.L., and J.L. Barker, 1986. Landsat MSS and TM Post-calibration Dynamic Ranges, Exo-atmospheric Reflectance, and At-satellite Temperatures, Landsat Technical Notes, EOSAT, URL: http://ltpwww.gsfc.nasa. gov/IAS/handbook/pdfs/L5_cal_document.pdf (last date accessed: 07 November 2007).
36. Mason, S.J., and N.E. Graham, 2002. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation, Quarterly Journal of the Royal Meteorological Society, 128:2145-2166.
37. Meyer, P., K.I. Itten, T. Kellenberger, S. Sandmeier, and R. Sandmeier, 1993. Radiometric corrections of topographically induced effects on Landsat data in an alpine environment, ISPRS Journal of Photogrammetry and Remote Sensing, 48(4):17-28.
38. NatureServe, 2006. International Ecological Classification Standard: Terrestrial Ecological Classifications, NatureServe Central Databases Arlington, Virginia, URL: www.natureserve.org (last date accessed: 07 November 2007).
39. Nigh, T., C. Buck, K. Grabner, J. Kabrick, and D. Meinert, 2000. An Ecological Classification System for the Current River Hills, Working Draft, Missouri Department of Conservation, Jefferson City, Missouri.
40. Nupp, T.E., and R.K. Swihart, 2000. Landscape-level correlates of small-mammal assemblages in forest fragments of farmland, Journal of Mammology, 81(2):512-526.
41. Ohmann, J.L., and M.J. Gregory, 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, USA, Canadian Journal of Forest Research, 32(4):725-741.
42. Parker, A.J., 1982. The topographic relative moisture index: An approach to soil-moisture assessment in mountain terrain, Physical Geography, 3(2):160-168.
43. Pontius, R.G., and L.C. Schneider, 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA, Agriculture, Ecosystems, and Environment, 85:239-248.
44. SAS Institute, Inc., 2004. Statistical Analysis Software version 8, The SAS Institute, Inc., Cary, North Carolina.
45. Smith, A., 2001. Delineation of Glade Complexes in the Southeast Missouri Ozarks Using Multi-temporal Multi-spectral Satellite Imagery, M.S. thesis, Southern Illinois University.
46. Strahler, A.H., C.E. Woodcock, and J.A. Smith, 1986. On the nature of models in remote sensing, Remote Sensing of Environment, 20(2):121-139.
47. The Nature Conservancy, 1994. Field Methods for Vegetation Mapping, Report to U.S. Department of Interior, The Nature Conservancy, Arlington, Virginia and Environmental Systems Research Institute, Redlands, California, URL: http://biology.usgs.gov/npsveg/field-methods/index.html (last date accessed: 07 November 2007).
48. The Nature Conservancy Ozarks Ecoregional Assessment Team, 2003. Ozarks Ecoregional and Conservation Assessment, Minneapolis, Minnesota, TNC Midwestern Resource Office, 48 p.
49. Trietz, P., and P. Howarth, 2000. High spatial resolution remote sensing data for forest ecosystem classification: An examination of spatial scale, Remote Sensing of Environment, 72(2):268-289.
50. USGS-NPS, 2006. USGS - NPD Vegetation Mapping Program, URL: http://biology.usgs.gov/npsveg/ (last date accessed: 07 November 2007).
51. Venables, W.N., and B.D. Ripley, 1994. Modern Applied Statistics with S-Plus, Springer Verlag, New York.
52. Wang, L., W.P. Sousa, and P. Gong, 2004. Integration of object-based and pixel-based classification for mapping mangroves with Ikonos imagery, International Journal of Remote Sensing, 25(24):5655-5668.
53. Warner, T.A., J.Y. Lee, and J.B. McGraw, 1998. Delineation and identification of individual trees in the eastern deciduous forest, Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry (D.A. Hill and D.G. Leckie, editors), 10-12 February, Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, British Columbia, pp. 81-91.
54. Wolter, P.T., D.J. Mladenoff, G.E. Host, and T.R. Crow, 1995. Improved forest classification in the Northern Lake States using multi-temporal Landsat imagery, Photogrammetric Engineering & Remote Sensing, 61(9):1129-1143.
55. Wulder, M.A., R.J. Hall, N.C. Coops, and S.E. Franklin, 2004. High spatial resolution remotely sensed data for ecosystem characterization, Bioscience, 54(6):511-521.