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Advances in Civil Engineering
Volume 2012 (2012), Article ID 945613, 14 pages
http://dx.doi.org/10.1155/2012/945613
Research Article

Characterization of Forested Landscapes from Remotely Sensed Data Using Fractals and Spatial Autocorrelation

1Universities Space Research Association at NASA Marshall Space Flight Center, National Space Science and Technology Center, NASA Global Hydrology and Climate Center, Huntsville, AL 35805, USA
2Civil and Environmental Engineering Department, University of Alabama in Huntsville, Huntsville, AL 35899, USA
3Earth Science Office at NASA Marshall Space Flight Center, National Space Science and Technology Center, NASA Global Hydrology and Climate Center, Huntsville, AL 35805, USA

Received 5 August 2011; Accepted 27 December 2011

Academic Editor: Kirk Hatfield

Copyright © 2012 Mohammad Z. Al-Hamdan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. A. Musleh and J. F. Cruise, “Functional relationships of resistance in wide flood plains with rigid unsubmerged vegetation,” Journal of Hydraulic Engineering, vol. 132, no. 2, pp. 163–171, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. D. S. Rhee, H. Woo, B. A. Kwon, and H. K. Ahn, “Hydraulic resistance of some selected vegetation in open channel flows,” River Research and Applications, vol. 24, no. 5, pp. 673–687, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Poggi, C. Krug, and G. G. Katul, “Hydraulic resistance of submerged rigid vegetation derived from first-order closure models,” Water Resources Research, vol. 45, no. 10, Article ID W10442, 14 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. E. Q. Hui, X. E. Hu, C. B. Jiang, F. K. Ma, and Z. D. Zhu, “A study of drag coefficient related with vegetation based on the flume experiment,” Journal of Hydrodynamics, vol. 22, no. 3, pp. 329–337, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. M. L. R. Sarker and J. E. Nichol, “Biomass estimation using texture measurements of dual polarization SAR data,” in Proceedings of the Remote Sensing and Photogrammetry Society Conference, May, 2010.
  6. R. M. Lucas, A. A. Held, S. R. Phinn, and S. Saatchi, “Tropical forests,” in Remote Sensing for Natural Resource Management and Environmental Monitoring, S. L. Ustin, Ed., pp. 239–315, John Wiley & Sons, Hoboken, NJ, USA, 2004.
  7. J. R. Santos, M. S. Pardi Lacruz, L. S. Araujo, and M. Keil, “Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data,” International Journal of Remote Sensing, vol. 23, no. 7, pp. 1217–1229, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. J. R. Santos, C. C. Freitas, L. S. Araujo et al., “Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest,” Remote Sensing of Environment, vol. 87, no. 4, pp. 482–493, 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. J. E. S. Fransson and H. Israelsson, “Estimation of stem volume in boreal forests using ERS-1 C- and JERS-1 L-band SAR data,” International Journal of Remote Sensing, vol. 20, no. 1, pp. 123–137, 1999. View at Scopus
  10. K. J. Ranson and G. Sun, “An evaluation of AIRSAR and SIR-C/X-SAR images for mapping northern forest attributes in Maine, USA,” Remote Sensing of Environment, vol. 59, no. 2, pp. 203–222, 1997. View at Publisher · View at Google Scholar · View at Scopus
  11. P. A. Harrell, L. L. Bourgeau-Chavez, E. S. Kasischke, N. H. F. French, and N. L. Christensen, “Sensitivity of ERS-1 and JERS-1 radar data to biomass and stand structure in Alaskan boreal forest,” Remote Sensing of Environment, vol. 54, no. 3, pp. 247–260, 1995. View at Publisher · View at Google Scholar · View at Scopus
  12. J. E. Nichol and M. L. R. Sarker, “Efficiency of texture measurement from two optical sensors from improved biomass estimation,” in Proceedings of the 7th ISPRS TC Symposium—100 Years ISPRS, W. Wagner and B. Székely, Eds., vol. 38 of IAPRS, Vienna, Austria, July, 2010.
  13. T. Kajisa, T. Murakami, N. Mizoue, N. Top, and S. Yoshida, “Object-based forest biomass estimation using Landsat ETM+ in Kampong Thom Province, Cambodia,” Journal of Forest Research, vol. 14, no. 4, pp. 203–211, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Lu, “Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon,” International Journal of Remote Sensing, vol. 26, no. 12, pp. 2509–2525, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Baccini, M. A. Friedl, C. E. Woodcock, and R. Warbington, “Forest biomass estimation over regional scales using multisource data,” Geophysical Research Letters, vol. 31, no. 10, Article ID L10501, 4 pages, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. O. N. Krankina, M. E. Harmon, W. B. Cohen, D. R. Oetter, Z. Olga, and M. V. Duane, “Carbon stores, sinks, and sources in forests of northwestern Russia: can we reconcile forest inventories with remote sensing results?” Climatic Change, vol. 67, no. 2-3, pp. 257–272, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. D. P. Turner, M. Guzy, M. A. Lefsky, W. D. Ritts, S. van Tuyl, and B. E. Law, “Monitoring forest carbon sequestration with remote sensing and carbon cycle modeling,” Environmental Management, vol. 33, no. 4, pp. 457–466, 2004. View at Scopus
  18. G. M. Foody, D. S. Boyd, and M. E. J. Cutler, “Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions,” Remote Sensing of Environment, vol. 85, no. 4, pp. 463–474, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. Å Rosenqvist, A. Milne, R. Lucas, M. Imhoff, and C. Dobson, “A review of remote sensing technology in support of the Kyoto Protocol,” Environmental Science and Policy, vol. 6, no. 5, pp. 441–455, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Dong, R. K. Kaufmann, R. B. Myneni et al., “Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks,” Remote Sensing of Environment, vol. 84, no. 3, pp. 393–410, 2003. View at Publisher · View at Google Scholar · View at Scopus
  21. R. F. Nelson, D. S. Kimes, W. A. Salas, and M. Routhier, “Secondary forest age and tropical forest biomass estimation using thematic mapper imagery,” BioScience, vol. 50, no. 5, pp. 419–431, 2000. View at Scopus
  22. T. Häme, A. Salli, K. Andersson, and A. Lohi, “A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data,” International Journal of Remote Sensing, vol. 18, no. 15, pp. 3211–3243, 1997. View at Scopus
  23. G. L. Anderson, J. D. Hanson, and R. J. Haas, “Evaluating landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands,” Remote Sensing of Environment, vol. 45, no. 2, pp. 165–175, 1993. View at Publisher · View at Google Scholar · View at Scopus
  24. P. J. Curran, J. L. Dungan, and H. L. Gholz, “Seasonal LAI in slash pine estimated with landsat TM,” Remote Sensing of Environment, vol. 39, no. 1, pp. 3–13, 1992. View at Scopus
  25. M. Al-Hamdan, J. Cruise, D. Rickman, and D. Quattrochi, “Effects of spatial and spectral resolutions on fractal dimensions in forested landscapes,” Remote Sensing, vol. 2, no. 3, pp. 611–640, 2010. View at Publisher · View at Google Scholar
  26. D. A. Quattrochi, C. W. Emerson, N. S.-N. Lam, and H. L. Qiu, “Fractal characterization of multitemporal remote sensing data,” in Modeling Scale in Geographic Information Science, N. J. Tate and P. M. Atkinson, Eds., pp. 13–33, John Wiley & Sons, Hoboken, NJ, USA, 2001.
  27. M. Goodchild, R. Haining, and S. Wise, “Integrating GIS and spatial data analysis: problems and possibilities,” International Journal of Geographical Information Systems, vol. 6, no. 5, pp. 407–423, 1992. View at Scopus
  28. W. Zhao, Multiscale analysis for characterization of remotely sensed images, Ph.D. dissertation, Louisiana State University, Baton Rouge, La, USA, 2001.
  29. S. Lovejoy and D. Schertzer, “Generalized scale invariance in the atmosphere and fractal models of rain,” Water Resources Research, vol. 21, no. 8, pp. 1233–1250, 1985. View at Publisher · View at Google Scholar · View at Scopus
  30. J. R. Krummel, R. H. Gardner, G. Sugihara, R. V. O'Neill, and P. R. Coleman, “Landscape patterns in a disturbed environment,” Oikos, vol. 48, no. 3, pp. 321–324, 1987. View at Scopus
  31. M. J. MacLennan and P. J. Howarth, “The use of fractal geometry to identify ranges of scale variance in digital remotely sensed data,” in Proceedings of the 21st International Symposium on Remote Sensing Environment, Ann Arbor, Mich, USA, October 1987.
  32. M. W. Palmer, “Fractal geometry: a tool for describing spatial patterns of plant communities,” Vegetatio, vol. 75, no. 1-2, pp. 91–102, 1988. View at Publisher · View at Google Scholar · View at Scopus
  33. W. B. Cohen, T. A. Spies, and G. A. Bradshaw, “Semivariograms of digital imagery for analysis of conifer canopy structure,” Remote Sensing of Environment, vol. 34, no. 3, pp. 167–178, 1990. View at Scopus
  34. B. Zeide, “Fractal geometry in forestry applications,” Forest Ecology and Management, vol. 46, no. 3-4, pp. 179–188, 1991. View at Scopus
  35. N. D. Lorimer, R. G. Haight, and R. A. Leary, “The fractal forest: fractal geometry and applications in forest science,” General Technical Report NC-170, US Department of Agriculture, Forest Service, North Central Forest Experiment Station, St. Paul, Minn, USA, 1994.
  36. B. A. St-Onge and F. Cavayas, “Automated forest structure mapping from high resolution imagery based on directional semivariogram estimates,” Remote Sensing of Environment, vol. 61, no. 1, pp. 82–95, 1997. View at Publisher · View at Google Scholar · View at Scopus
  37. B. Barbanis, H. Varvoglis, and C. L. Vozikis, “Imperfect fractal repellers and irregular families of periodic orbits in a 3-D model potential,” Astronomy and Astrophysics, vol. 344, no. 3, pp. 879–890, 1999. View at Scopus
  38. N. Coops and D. Culvenor, “Utilizing local variance of simulated high spatial resolution imagery to predict spatial pattern of forest stands,” Remote Sensing of Environment, vol. 71, no. 3, pp. 248–260, 2000. View at Publisher · View at Google Scholar · View at Scopus
  39. J. F. Weishampel, D. L. Urban, and H. H. Shugart, “Semivariograms from a forest transect gap model compared with remotely sensed data,” Journal of Vegetation Science, vol. 3, no. 4, pp. 521–526, 1992.
  40. J. F. Weishampel, J. H. Sloan, J. C. Boutet, and J. R. Godin, “Mesoscale changes in textural pattern of “intact” Peruvian rainforests (1970s–1980s),” International Journal of Remote Sensing, vol. 19, no. 5, pp. 1007–1014, 1998. View at Scopus
  41. J. F. Weishampel, J. R. Godin, and G. M. Henebry, “Pantropical dynamics of “intact” forest canopy texture,” Global Ecology and Biogeography, vol. 10, no. 4, pp. 389–397, 2001. View at Publisher · View at Google Scholar · View at Scopus
  42. J. B. Drake and J. F. Weishampel, “Simulating vertical and horizontal multifractal patterns of a longleaf pine savanna,” Ecological Modelling, vol. 145, no. 2-3, pp. 129–142, 2001. View at Publisher · View at Google Scholar · View at Scopus
  43. C. W. Emerson, N. S.-N. Lam, and D. A. Quattrochi, “A comparison of local variance, fractal dimension, and Moran's I as aids to multispectral image classification,” International Journal of Remote Sensing, vol. 26, no. 8, pp. 1575–1588, 2005. View at Publisher · View at Google Scholar · View at Scopus
  44. N. S.-N. Lam, H. L. Qiu, D. A. Quattrochi, and C. W. Emerson, “An evaluation of fractal methods for characterizing image complexity,” Cartography and Geographic Information Science, vol. 29, no. 1, pp. 25–35, 2002. View at Scopus
  45. B. B. Mandelbort, The Fractal Geometry of Nature, W. H. Freeman, New York, NY, USA, 1983.
  46. M. F. Goodchild, “Fractals and the accuracy of geographical measures,” Mathematical Geology, vol. 12, no. 2, pp. 85–98, 1980. View at Publisher · View at Google Scholar · View at Scopus
  47. C. W. Emerson, N. S.-N. Lam, and D. A. Quattrochi, “Multi-scale fractal analysis of image texture and pattern,” Photogrammetric Engineering and Remote Sensing, vol. 65, no. 1, pp. 51–61, 1999. View at Scopus
  48. M. C. Shelberg, N. S.-N. Lam, and H. Moellering, “Measuring the fractal dimensions of surfaces,” in Proceedings of the 6th International Symposium on Automated Cartography, pp. 319–328, Ottawa, Canada, October 1983.
  49. N. S.-N. Lam and L. De Cola, Eds., Fractals in Geography, Prentice Hall, Englewood Cliffs, NJ, USA, 1993.
  50. S. Jaggi, D. A. Quattrochi, and N. S.-N. Lam, “Implementation and operation of three fractal measurement algorithms for analysis of remote-sensing data,” Computers & Geosciences, vol. 19, no. 6, pp. 745–767, 1993. View at Scopus
  51. D. C. Bragg, “A local basal area adjustment for crown width prediction,” Northern Journal of Applied Forestry, vol. 18, no. 1, pp. 22–28, 2001. View at Scopus
  52. D. Oladi, Developing a framework and methodology for plantation assessment using remotely sensed data, Ph.D. thesis, University of New Brunswick, New Brunswick, Canada, 1996.
  53. J. Guavin, T. Hunt, and R. Tardif, “Edmondstone Applied Forestry Technology Group,” Documentation on Growth Phase Model, 1993.
  54. W. A. Farr, D. J. Demars, and J. E. Dealy, “Height and crown width related to diameter for open-grown western hemlock and Sitka spruce,” Canadian Journal of Forest Research, vol. 19, no. 9, pp. 1203–1207, 1989. View at Scopus
  55. P. T. Sprinz and H. E. Burkhart, “Relationships between tree crown, stem, and stand characteristics in unthinned loblolly pine plantation,” Canadian Journal of Forest Research, vol. 17, pp. 534–538, 1987.
  56. E. G. Roberts and R. D. Ross, “Crown area of free-growing loblolly pine and its apparent independence of age and site,” Journal of Forestry, vol. 63, no. 6, pp. 462–463, 1965.
  57. B. C. Wile, “Crown size and stem diameter in red spruce and balsam fir,” Forestry Pub. 1056, Canada Department of Forestry. Forestry Research Branch, Ontario, Canada, 1964.
  58. J. H. Smith and G. R. Baily, “Influence of stocking and stand density on crown widths of douglas fir and lodgepole pine,” Commonwealth Forestry Review, vol. 43, no. 3, pp. 243–246, 1964.
  59. P. E. Vezina, “More about the crown competition factor,” Department of Forestry. Forest Research Branch, Contribution No. 505, 1963.
  60. C. O. Minor, “Stem-crown diameter relations in southern pine,” Journal of forestry, vol. 49, no. 7, pp. 490–493, 1951.
  61. A. D. Cliff and J. K. Ord, Spatial Autocorrelation, Pion Limited, London, UK, 1973.
  62. N. S.-N. Lam, D. A. Quattarochi, H.-L. Qiu, and W. Zhao, “Environmental assessment and monitoring with image characterization and modeling system using multi-scale remote sensing data,” Applied Geography Studies, vol. 2, no. 2, pp. 77–93, 1998.
  63. D. A. Quattrochi, N. S.-N. Lam, H. L. Qiu, and W. Zhao, “Image characterization and modeling system (ICAMS): a geographic information system for the characterization and modeling of multiscale remote sensing data,” in Scale in Remote Sensing and GIS, D. A. Quattroch and M. F. Goodchild, Eds., pp. 295–307, CRC/Lewis Publishers, Boca Raton, Fla, USA, 1997.
  64. P. A. Burrough, “Fractals and geostatistical methods in landscape studies,” in Fractals in Geography, N. S.-N. Lam, L. De Cola, et al., Eds., pp. 87–121, Prentice Hall, Englewood Cliffs, NJ, USA, 1993.
  65. D. M. Mark and P. B. Aronson, “Scale-dependent fractal dimensions of topographic surfaces: an empirical investigation, with applications in geomorphology and computer mapping,” Mathematical Geology, vol. 16, no. 7, pp. 671–683, 1984. View at Publisher · View at Google Scholar · View at Scopus
  66. M. F. Goodchild, Spatial Autocorrelation, Concepts and Techniques in Modern Geography, 47, Geo Books, Norwich, UK, 1986.
  67. N. S.-N. Lam, M. Fan, and K. B. Liu, “Spatial-temporal spread of the AIDS epidemic, 1982–1990: a correlogram analysis of four regions of the United States,” Geographical Analysis, vol. 28, no. 2, pp. 93–107, 1996. View at Publisher · View at Google Scholar · View at Scopus
  68. C. E. Woodcock and A. H. Strahler, “The factor of scale in remote sensing,” Remote Sensing of Environment, vol. 21, no. 3, pp. 311–332, 1987. View at Scopus
  69. K. C. Clarke, “Computation of the fractal dimension of topographic surfaces using the triangular prism surface area method,” Computers & Geosciences, vol. 12, no. 5, pp. 713–722, 1986. View at Publisher · View at Google Scholar · View at Scopus
  70. N. S.-N. Lam, H.-L. Qiu, and D. Quattrochi, “An evaluation of fractal surface measurement methods using ICAMS (Image Characterization and Modeling System),” in Proceedings of the ACSM/ASPRS Annual Convention, Seattle, Wash, USA, April 1997.
  71. S. W. Myint, Wavelet analysis and classification of urban environment using high-resolution multispectral image data, Ph.D. dissertation, Louisiana State University, Baton Rouge, La, USA, 2001.
  72. R. E. Walpole, R. H. Myers, and S. L. Myers, Probability and Statistics for Engineers and Scientists, Macmillan Publishing, New York, NY, USA, 1998.
  73. R. E. Walpole, Introduction to Statistics, Macmillan Publishing, New York, NY, USA, 1982.
  74. M. A. Lefsky, Lidar remote sensing of canopy height profiles: application to spatial and temporal trends in canopy structure, Ph.D. thesis, The University of Virginia, Charlottesville, Va, USA, 1997.
  75. S. E. Watts, Determining forest productivity and carbon dyanamics in Southeastern Ohio from remotely-sensed data, Ph.D. dissertation, The Ohio State University, Columbus, Ohio, USA, 2001.