GEOBIA vegetation mapping in Great Smoky Mountains National Park with spectral and non-spectral ancillary information

Photogrammetric Engineering and Remote Sensing

Volumn 76, Issue 2, 2010, Pages 137-149

  Kim, Minho ^a   Madden, Marguerite ^a   Xu, Bo ^a,b  

^a UNIVERSITY OF GEORGIA   (United States)

^b CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LANDFORMS; MAPPING; VEGETATION;

AERIAL PHOTOGRAPHS; GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS; GREAT SMOKY MOUNTAINS NATIONAL PARK; OVERALL ACCURACIES; VEGETATION CLASS; VEGETATION CLASSIFICATION; VEGETATION MAPPING; VERY-HIGH SPATIAL RESOLUTIONS;

PHOTOMAPPING;

ACCURACY ASSESSMENT; AERIAL PHOTOGRAPHY; IKONOS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; MOUNTAIN REGION; NATIONAL PARK; SEGMENTATION; SPATIAL RESOLUTION; VEGETATION MAPPING; VEGETATION STRUCTURE;

GREAT SMOKY MOUNTAINS NATIONAL PARK; UNITED STATES;

EID: 77649138136     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.76.2.137     Document Type: Conference Paper

References (70)

1. Addink, E.A., S.M. de Jong, and E.J. Pebesma, 2007. The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery, Photogramemtric Engineering & Remote Sensing, 72(8):905-912.
2. Blaschke, T., S. Lang, E. Lorup, J. Strobl, and P. Zeil, 2000. Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications, Environmental Information for Planning, Politics and the Public (A. Cremers and K. Greve, editors), Metropolis Verlag, Marburg, pp. 555-570.
3. Blaschke, T., 2003. Object-based contextual image classification built on image segmentation, Proceedings of the 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 27-28 October, Washington D.C., pp. 113-119.
4. Boyd, D.S., and F.M. Danson, 2005. Satellite remote sensing for forest resources: Three decades of research development, Progress in Physical Geography, 29(1):1-26.
5. Burnett, C., and T. Blaschke, 2003. A multi-scale segmentation/object relationship modeling methodology for landscape analysis, Ecological Modelling, 168:233-249.
6. Burrough, P.A., J.P. Wilson, P.F.M. Gaans, and A.J. Hansen, 2001. Fuzzy k-means classification of topo-climatic data as an aid to forest mapping in the Greater Yellowstone Area, USA, Landscape Ecology, 36:523-546.
7. Carleer, A., and E. Wolff, 2004. Exploitation of very high resolution satellite data for tree species identification, Photogrammetric Engineering & Remote Sensing, 70(1):135-140.
8. Castilla, G., G.J. Hay, and J.R. Ruiz, 2008. Size-controlled region merging (SCRM): An automated delineation tool for assisted photointerpretation, Photogrammetric Engineering & Remote Sensing, 74(4):409-419.
9. Chastain, R.A., A.S. Matthew, H.S. He, and D.R. Larsen, 2008. Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA, Photogrammetric Engineering & Remote Sensing, 74(2):247-264.
10. Clausi, D.A., 2002. An analysis of co-occurrence texture statistics as a function of gray level quantization, Canadian Journal of Remote Sensing, 1:45-62.
11. Congalton, R.G., and K. Green, 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC/Lewis Press, Boca Raton, Florida, 137 p.
12. Colwell, R.N., 1950, New technique for interpreting aerial color photography, Journal of Forestry, 48:204-205.
13. Corbane, C., D. Raclot, F. Jacob, J. Albergel, and P. Andrieux, 2008. Remote sensing of soil surface characteristics from a multiscale classification approach, Catena, 75(3):308-318.
14. Debeir, O., I.V. den Steen, P. Latinne, P.V. Ham, and E. Wolff, 2002. Textural and contextual land-cover classification using single and multiple classifier systems, Photogrammetric Engineering & Remote Sensing, 68(6):597-605.
15. Domaç, A., and M.L. Süzen, 2006. Integration of environmental variables with satellite images in regional scale vegetation classification, International Journal of Remote Sensing, 27(7):1329-1350.
16. Dorren, L.K.A., B. Maier, and A.C. Seijmonsbergen, 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification, Forest Ecology and Management, 183:31-46.
17. Dragut, L., and T. Blaschke, 2006. Automated classification of landform elements using object-based image analysis, Geomorphology, 81:330-344.
18. Federal Geographic Data Committee (FGDC), 2008. National Vegetation Classification Standard, version 2, FGDC-STD-005-2008, Vegetation Subcommittee, Federal Geographic Data Committee, FGDC Secretariat, U.S. Geological Survey, Reston, Virginia, 119 p.
19. st OBIA Conference, 04-05 July, Salzburg, Austria (International Society for Photogrammetry and Remote Sensing), unpaginated CD-ROM.
20. Ferro, C.J.S., and T.A. Warner, 2002. Scale and texture in digital image classification, Photogrammetric Engineering & Remote Sensing, 68(1):51-63.
21. Florinsky, I.V., and G.A. Kurykova, 1996. Influence of topography on some vegetation cover properties, Catena, 27:123-141.
22. Foster, D., G. Motzkin, J. O'Keefe, E. Boose, D. Orwig, J. Fuller, and B. Hall, 2004. The environmental and human history of New England, Forests in Time: The Environmental Consequences of 1000 Years of Change in New England (D. Foster and J.D. Aber, editors), Yale University, pp. 43-100.
23. Hall, O., and G.J. Hay, 2003. A multiscale object-specific approach to digital change detection, International Journal of Applied Earth Observation and Geoinformation, 4(4):311-327.
24. Hall, O., G.J. Hay, A. Bouchard, and D.J. Marceau, 2004. Detecting dominant landscape objects through multiple scales: An integration of object-specific methods and watershed segmentation, Landscape Ecology, 19:59-76.
25. 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.
26. Hay, G.J., D.J. Marceau, A. Bouchard, and P. Dube, 2001. A multi-scale framework for landscape analysis: Object-specific upscaling, Landscape Ecology, 16:471-490.
27. Hay, G.J., T. Blaschke, D.J. Marceau, and A. Bouchard, 2003. A comparison of three image-object methods for the multiscale analysis of landscape structure, ISPRS Journal of Photogrammetry and Remote Sensing, 57:327-345.
28. Hay, G.J., G. Castilla, M.A. Wulder, and J.R. Ruiz, 2005. An automated object-based approach for the multiscale image segmentation of forest scenes, International Journal of Applied Earth Observation and Geoinformation, 7:339-359.
29. st Century Conference, University of Calgary, Calgary, Alberta, Canada, 05-08 August, ISPRS Archives, XXXVIII-4/C1, 373 p.
30. Hay, G.J., and G. Castilla, 2008. Geographic object-based image analysis (GEOBIA): A new name for a new discipline, Object-Based Image Analysis - Spatial Concepts for Knowledge-driven Remote Sensing Applications (T. Blaschke, S. Lang, and G.J. Hay, editors), Springer-Verlag, Berlin, pp. 75-89.
31. Heller, R.C., and J.J. Ulliman, 1983. Forest resources assessments, Manual of Remote Sensing, (R.N. Colwell, editor), Vol II, Second edition, American Society of Photogrammetry, Falls Church, Virginia, pp. 2229-2324.
32. Heyman, O., G.G. Gaston, A.J. Kimerling, and J.T. Campbell, 2003. A per-segment approach to improving aspen mapping from high-resolution remote sensing imagery, Journal of Forestry, 101(4):29-33.
33. Hoffer, R.M., S.C. Noyer, and R.P. Mroczynski, 1978. A comparison of Landsat and forest survey estimates of forest cover, Proceedings of the Fall Technical Meeting of the American Society of Photogrammetry, Albuquerque, New Mexico, pp. 221-231.
34. Jackson, P., 2004. Notes on the overstory vegetation classification system for Great Smoky Mountains National Park, Digital Vegetation Maps for the Great Smoky Mountains National Park (by M. Madden, R. Welch, T. Jordan, P. Jackson, R. Seavey, and J. Seavey), Final Report to the U.S. Dept. of Interior, National Park Service, 1443-CA-5460-98-019, Center for Remote Sensing and Mapping Science, The University of Georgia, Athens, Georgia, Attachment C, 30 p.
35. Jenkins, M., 2007. Thematic Accuracy Assessment: Great Smoky Mountains National Park Vegetation Map, National Park Service, Great Smoky Mountains National Park, Gatlinburg, Tennessee, 26 p.
36. Jensen, J.R., J.E. Estes, and L.R. Tinney, 1978. Evaluation of high altitude photography and Landsat imagery for digital crop identification, Photogrammetric Engineering & Remote Sensing, 44(6):723-733.
37. Johansen, K., and S. Phinn, 2006. Mapping structural parameters and species composition of riparian vegetation using Ikonos and Landsat ETM+ data in Australian tropical Savannahs, Photogrammetric Engineering & Remote Sensing, 2(1):71-80.
38. Jordan, T.R., 2002. Softcopy Photogrammetric Techniques for Mapping Mountainous Terrain: Great Smoky Mountains National Park, Ph.D. Dissertation, Department of Geography, The University of Georgia, Athens, Georgia, 193 p.
39. Kaiser, J., 1999. Great Smokies species census under way, Science, 284(5421):1747-1748.
40. Kayitakire, F., C. Hamel, and P. Defourny, 2006. Retrieving forest structure variables based on image texture analysis and Ikonos-2 imagery, Remote Sensing of Environment, 102:390-401.
41. Kim, M., M. Madden, and T. Warner, 2008. Estimation of optimal image object size for the segmentation of forest stands with multispectral Ikonos imagery, Object-based Image Analysis - Spatial Concepts for Knowledge-driven Remote Sensing Applications (T. Blaschke, S. Lang, and G.J. Hay, editors), Springer-Verlag, Berlin, pp. 291-307.
42. Kim, M., M. Madden, and T. Warner, 2009. Forest type mapping using object-specific texture measures from multispectral Ikonos imagery: Segmentation quality and image classification issues, Photogrammetric Engineering & Remote Sensing, 75(7):819-830.
43. Kim, M., J.B. Holt, C.Y. Ku, and M. Madden, in press. GEOBIA building extraction of Mae La refugee camp, Thailand: Step-wise multi-scale feature classification approach, Landscape Analysis using Geospatial Tools: Community to the Globe (M. Madden and E. Allen, editors), Springer-Verlag, New York, in press.
44. Lu, D., and Q. Weng, 2007. Survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 28(5):823-870.
45. Madden, M., 2004. Visualization and analysis of vegetation patterns in National Parks of the southeastern United States, Proceedings of Challenges in Geospatial Analysis, Integration and Visualization II (J. Schiewe, M. Hahn, M. Madden, and M. Sester, editors), International Society for Photogrammetry and Remote Sensing Commission IV Joint Workshop, Stuttgart, Germany, pp. 143-146.
46. Madden, M., T. Jordan, M. Kim, H. Allen, and B. Xu, 2009. Integrating remote sensing and GIS: From overlays to GEOBIA and geovisualization, Manual of Geographic Information Systems (M. Madden, editor-in-chief), American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, pp. 701-720.
47. Madden, M., R. Welch, T. Jordan, P. Jackson, R. Seavey, and J. Seavey, 2004. Digital Vegetation Maps for the Great Smoky Mountains National Park, Final Report to the U.S. Department of Interior, National Park Service, 1443-CA-5460-98-019, Center for Remote Sensing and Mapping Science, The University of Georgia, Athens, Georgia, 112 p.
48. Marceau, D.J., P.J. Howarth, J.M.M. Dubois, and D.J. Gratton, 1990. Evaluation of grey-level co-occurrence matrix method for land-cover classification using SPOT imagery, IEEE Transactions on Geoscience and Remote Sensing, 28(4):513-519.
49. Marceau, D.J., P.J. Howarth, and D.J. Gratton, 1994. Remote sensing and the measurement of geographical entities in a forested environment - 1.:The scale and spatial aggregation problem, Remote Sensing of Environment, 49:93-104.
50. Marceau, D.J., and G.J. Hay, 1999. Remote sensing contributions to the scale issue, Canadian Journal of Remote Sensing, 25(4):357-366.
51. Meetemeyer, V., and E.O. Box, 1987. Scale effects in landscape studies, Landscape Heterogeneity and Disturbance (M. Turner, editor), Springer-Verlag, New York, pp. 15-34.
52. th ISPRS Congress, 12-23 July, Istanbul, Turkey (International Society for Photogrammetry and Remote Sensing), unpaginated CD-ROM.
53. Moody, A., and C.E. Woodcock, 1995. The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing, Landscape Ecology, 10(6):363-379.
54. National Park Service (NPS), 2008. Great Smoky Mountains National Park. URL: http://www.nps.gov/grsm/naturescience/index.htm (last date accessed: 01 December 2009).
55. Parker, A. J., 1982. The topographic relative moisture index: An approach to soil-moisture assessment in mountain terrain, Physical Geography, 3(2):160-168.
56. PCI, 2003. Geomatica OrthoEngine User Guide, PCI Geomatics Enterprises, Inc.
57. Ryherd, S., and C. Woodcock, 1996. Combining spectral and texture data in the segmentation of remotely sensed images, Photogrammetric Engineering & Remote Sensing, 62(2):181-194.
58. Sader, S.A., D. Ahl, and W. Liou, 1995. Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine, Remote Sensing of Environment, 53:133-144.
59. Sayn-Wittgenstein, L., and A.H. Aldred, 1967. Tree volumes from large-scale photos, Photogrammetric Engineering & Remote Sensing, 33(1):69-73.
60. Schiewe, J., L. Tufte, and E. Ehlers, 2001. Potential and problems of multi-scale segmentation methods in remote sensing, GIS-Zeitschrift für Geoinformationssysteme, 6:34-39.
61. Spurr, S.H., and C.T. Brown Jr., 1946. Specifications for aerial photographs used in forest management, Photogrammetric Engineering & Remote Sensing, 12(1):131-141.
62. Tao, C.V., Y. Hu, and W. Jiang, 2004. Photogrammetric exploitation of Ikonos imagery for mapping applications, International Journal of Remote Sensing, 25(14):2833-2853.
63. Teng, W.T., E.R. Loew, D.I. Ross, V.G. Zsilinsky, C.P. Lo, W.R. Philipson, W.D. Philpot, and S.A. Morain, 1997. Fundamentals of photographic interpretation, Manual of Photographic Interpretation (W.R. Philipson, editor), American Society of Photogrammetry and Remote Sensing, pp. 49-110.
64. Tian, J., and D.M. Chen, 2007. Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition, International Journal of Remote Sensing, 28(20):4625-4644.
65. Treitz, P., and P. Howarth, 2000. Integrating spectral, spatial and terrain variables for forest ecosystem classification, Photogrammetric Engineering & Remote Sensing, 66(3):305-317.
66. 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.
67. Welch, R., M. Madden, and T. Jordan, 2002. Photogrammetric and GIS techniques for the development of vegetation databases of mountainous areas: Great Smoky Mountains National Park, ISPRS Journal of Photogrammetry & Remote Sensing, 57(1-2):53-68.
68. Woodcock, C.E., and A.H. Strahler, 1987. The factor of scale in remote sensing, Remote Sensing of Environment, 25:349-379.
69. Yu, Q., P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Shirokauer, 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery, Photogrammetric Engineering & Remote Sensing, 72(7):799-811.
70. Zhang, Y., 1999. Optimization of building detection in satellite images by combining multispectral classification and texture filtering, International Journal of Remote Sensing, 54:50-60.