Remote Sensing of Spatial and Temporal Dynamics of Vegetation

  • Richard J. Hobbs
Part of the Ecological Studies book series (ECOLSTUD, volume 79)


Most of the world’s vegetation is in a state of flux at a variety of spatial and temporal scales. Plant growth and reproductive patterns respond to seasonal fluctuations in climate. Yearly climatic variations are also responsible for differences in species growth and establishment patterns, leading to changes in species composition and distributions. Over long periods of time, directional vegetational changes may occur through succession. Vegetation changes may take place at extremely small scales, for instance, in canopy gaps created by the death of individual trees (Shugart and West, 1981; Runkle, 1985), or over larger scales where vegetation responds to such disturbances as fires or floods. Species distributions may change rapidly in response to episodic events (e.g., Hobbs and Mooney, 1989), or over longer periods in response to climatic shifts (e.g., Davis, 1986; Delcourt and Delcourt, 1987). Evidence of past vegetational changes resulting from changes in climate during glaciation cycles reinforce the view that major vegetational shifts are possible.


Normalize Difference Vegetation Index Remote Sensing Vegetation Change Leaf Water Content Landsat Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adamson, D.A., and Fox, M.D. (1982). Change in Australasian vegetation since European settlement, pp. 109–146. In J.M.B. Smith (ed.), A History of Australasian Vegetation. McGraw-Hill, Sydney, Australia.Google Scholar
  2. Adomeit, E.M., Jupp, D.L.B., Margules, C., and Mayo, K.K. (1981). The separation of traditionally mapped land cover classes by LANDSAT data, pp. 150–165. In A.N. Gillison and D.J. Anderson (eds.), Vegetation Classification in Australia. Australian Nat. Univ. Press, Canberra.Google Scholar
  3. Ayyad, M.A., (1981). Soil-vegetation-atmosphere interactions, pp. 9–31. In D.W. Goodall, R.A. Perry, and K.M.W. Hones (ed.), Arid-land Ecosystems: Structure, Function and Management, Vol. 2. Cambridge Univ. Press, Cambridge, England.Google Scholar
  4. Bell, D.T., and Stephens, L.J. (1984). Seasonality and phenology of kwongan species, pp. 205–226. In J.S. Pate, and J.S. Beard (eds.), Kwongan Plant Life of the Sandplain. Univ. of Western Australia Press, Nedlands.Google Scholar
  5. Bolin, B., Doos, B.R., Jager, J., and Warrik, R.A. (eds.) (1987). The Greenhouse Effect, Climatic Change, and Ecosystems. SCOPE 29. Wiley, NY.Google Scholar
  6. Botkin, D.B., Estes, J.E., MacDonald, R.M., and Wilson, M.V. (1984). Studying the earth’s vegetation from space. BioScience 34:508–514.CrossRefGoogle Scholar
  7. Broeker, W.S. (1987). Unpleasant surprises in the greenhouse? Nature 328:123–126.CrossRefGoogle Scholar
  8. Byrne, G.R., Crapper, P.F., and Mayo, K.K. (1980). Monitoring land cover changes by principal components analysis of multitemporal Landsat data. Remote Sens. Envir. 10:175–184.CrossRefGoogle Scholar
  9. Chaudhury, M.U. (1985). Landsat series: technical properties and application to vegetation studies, pp. 23–29. In Remote Sensing in Vegetation Studies. ESCAP-BIOTROP, Bogor, Indonesia.Google Scholar
  10. Christensen, E.J., Jensen, J.R., Ramsey, E.W., and Mackey, H.E. (1988). Aircraft MSS data registration and vegetation classification for wetland change detection. Int. J. Remote Sens. 9:23–38.CrossRefGoogle Scholar
  11. Clark, C.A., Cate, R.B., Trenchard, M.H., Boatright, J.A., and Bizzell, R.M. (1986). Mapping and classifying large ecological units. BioScience 36:476–478.Google Scholar
  12. Committee on Planetary Biology. (1986). Remote Sensing of the Biosphere. Nat. Academy Press, Washington, DC.Google Scholar
  13. Connell, J.H., and Slatyer, R.O. (1977). Mechanisms of succession in natural communities and their role in community stability and organisation. Amer. Nat. 111:1119–1144.CrossRefGoogle Scholar
  14. Currey, B., Fraser, A.S., and Bardsley, K.L. (1987). How useful is Landsat monitoring? Nature 328:587–589.CrossRefGoogle Scholar
  15. Davis, M.B. (1986). Climatic instability, time lags, and community disequilibrium, pp. 268–284. In J. Diamond and T.J. Case (eds.), Community Ecology. Harper and Row, NY.Google Scholar
  16. Delcourt, P.A., and Delcourt, H.R. (1987). Long-Term Forest Dynamics of the Temperate Zone. A Case Study of Late-Quaternary Forests in Eastern North America. Springer-Verlag, NY.Google Scholar
  17. Dregne, H.E., and Tucker, C.J. (1988). Green biomass and rainfall in semi-arid sub-Saharan Africa. J. Arid Envir. 15:245–252.Google Scholar
  18. Earth System Sciences Committee (1988). Earth System Science, a Closer View. NASA, Washington, DC.Google Scholar
  19. Foran, B.D. (1988). Detection of yearly cover change with Landsat MSS on pastoral landscapes in Central Australia. Remote Sens. Envir. 23:333–350.CrossRefGoogle Scholar
  20. Frank, T.D. (1984). The effect of change in vegetation cover and erosion patterns on albedo and texture of Landsat images in a semi-arid environment. Ann. Assoc. Amer. Geogr. 74:393–407.CrossRefGoogle Scholar
  21. Fung, T., and LeDrew, E. (1987). Application of principal components analysis to change detection. Photogramm. Eng. Remote Sens. 12:1649–1658.Google Scholar
  22. Goetz, A.F.H., Rock, B.N., and Rowan, L.C. (1983). Remote sensing for exploration: an overview. Econ. Geol. 78:573–590.CrossRefGoogle Scholar
  23. Goward, S.N., Tucker, C.J., and Dye, D.G. (1985). North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio 64:3–14.CrossRefGoogle Scholar
  24. Graetz, R.D. (1987). Satellite remote sensing of Australian rangelands. Remote Sens. Environ. 23:313–331.CrossRefGoogle Scholar
  25. Graetz, R.D., Gentle, M.R., Pech, R.P., O’Callaghan, J.R., and Drewien, G. (1983). The application of Landsat image data to rangeland assessment and monitoring: an example from South Australia. Aust. Rangel. J. 5:63–73.CrossRefGoogle Scholar
  26. Graetz, R.D., Walker, B.H., and Walker, P.A. (1988). The consequences of climatic change for seventy percent of Australia, pp. 399–420. In G.I. Pearman (ed.), Greenhouse. Planning for Climatic Change. CSIRO, Melbourne, Australia.Google Scholar
  27. Gray, A.J., Crawley, M.J. and Edwards, P.J. (1986). Colonisation, Succession and Stability. Blackwell, Oxford, England.Google Scholar
  28. Green, G.M. (1986). Use of SIR-A and Landsat MSS data in mapping shrub and intershrub vegetation at Koonamore, South Australia. Photogramm. Eng. Remote Sens. 52:659–670.Google Scholar
  29. Griffin, G.F., and Friedel, M.H. (1985). Discontinuous change in central Australia: some major implications of ecological events for land management. J. Arid Environ. 9:63–82.Google Scholar
  30. Hamburg, S.P., and Cogbill, C.V. (1988). Historical decline of red spruce populations and climatic warming. Nature 331:428–430.CrossRefGoogle Scholar
  31. Herrmann, K., Rock, B.N., Ammer, U., and Paley, H.N. (1988). Preliminary assessment of airborne imaging spectrometer and airborne thematic mapper data acquired for forest decline areas in the Federal Republic of Germany. Remote Sens. Environ. 24:129–149.CrossRefGoogle Scholar
  32. Hobbs, R.J., and Hopkins, A.J.M. (1990). From frontier to fragments: European impact on Australia’s vegetation. Proc. Ecol. Soc. Aust. 16 (in press).Google Scholar
  33. Hobbs, R.J., and Mooney, H.A. (1989). Effects of episodic events on Mediterranean-climate ecosystems. In F. di Castri, C. Floret, S. Rambal, and J. Roy (eds.), Timescales of Water Stress Response of Mediterranean Biota (in press).Google Scholar
  34. Hobbs, R.J., Wallace, J.F., and Campbell, N.A. (1989). Classification of vegetation in the Western Australian wheatbelt using Landsat MSS data. Vegetatio 80:91–105.CrossRefGoogle Scholar
  35. Horler, D.N.H., Barber, J., and Barringer, A.R. (1980). Effects of heavy metals on the absorbance and reflectance spectra of plants. Int. J. Remote Sens. 1:121–136.Google Scholar
  36. Horler, D.N.H., Dockray, M., Barber, J., and Barringer, A.R. (1983). Red edge measurements for remotely sensing plant chlorophyll content. Adv. Space Res. 3:273–277.CrossRefGoogle Scholar
  37. Huston, M., and Smith, T. (1987). Plant succession: life history and competition. Amer. Nat. 130:168–198.CrossRefGoogle Scholar
  38. Jenson, J.R., and Toll, D.L. (1982). Detecting residential land use development at the urban fringe. Photogramm. Eng. Remote Sens. 48:629–643.Google Scholar
  39. Johnson, A.H., and Siccama, T.C. (1984). Decline of red spruce in the northern Appalachians: Assessing the possible role of acid deposition. Tappi J. 67:68–72.Google Scholar
  40. Jupp, D.L.B., Walker, J., and Penridge, L.K. (1986). Interpretation of vegetation structure in Landsat MSS imagery: A case study in disturbed semi-arid eucalypt woodlands. Part 2. Model-based analysis. J. Envir. Manag. 23:35–37.Google Scholar
  41. Malingreau, J.P., Stephens, G., and Fellows, L. (1985). Remote sensing of forest fires: Kalimantan and North Borneo in 1982–83. Ambio 14:314–321.Google Scholar
  42. Malingreau, J.P., and Tucker, C.J. (1988). Large-scale deforestation in the southeastern Amazon Basin of Brazil. Ambio 17:49–55.Google Scholar
  43. Mooney, H.A. (1988). Ecologists and the global change program. Trends Ecol. Evol. 3:4–5.CrossRefGoogle Scholar
  44. Mooney, H.A., Hobbs, R.J., Gorham, J., and Williams, K. (1986). Biomass accumulation and resource utilisation in co-occurring grassland annuals. Oecologia (Berlin) 70:555–558.CrossRefGoogle Scholar
  45. Morton, A.J. (1986). Moorland plant community recognition using Landsat MSS data. Remote Sens. Environ. 20:291–298.CrossRefGoogle Scholar
  46. National Academy Press (1986). Global Change in the Geosphere-Biosphere: Initial Priorities for and IGBP. Nat. Acad. Press, Washington, DC.Google Scholar
  47. Pech, R.P., and Davis, A.W. (1987). Reflectance modeling of semiarid woodlands. Remote Sens. Environ. 23:365–377.CrossRefGoogle Scholar
  48. Pech, R.P., Davis, A.W., and Graetz, R.D. (1986a). Reflectance modeling and the derivation of vegetation indices for an Australian semi-arid shrubland. Int. J. Remote Sens. 7:389–403.CrossRefGoogle Scholar
  49. Pech, R.P., Davis, A.W., Lamcraft, R.R., and Graetz, R.D. (1986b). Calibration of Landsat data for sparsely vegetated arid rangelands. Int. J. Remote Sens. 8:1829–1850.Google Scholar
  50. Perry, C.R., Jr. and Lanternschlager, L.F. (1984). Functional equivalence of spectral vegetation indices. Remote Sens. Envir. 14:169–182.CrossRefGoogle Scholar
  51. Pickett, S.T.A., Collins, S.L., and Armesto, J.J. (1987). Models, mechanisms and pathways of succession. Bot. Rev. 53:335–371.CrossRefGoogle Scholar
  52. Pickup, G., and Chewings, V.H. (1986). Random field modelling of spatial variations in erosion and deposition in flat alluvial landscapes in arid central Australia. Ecol Model. 33:269–296.CrossRefGoogle Scholar
  53. Pickup, G., and Chewings, V.H. (1988). Forecasting patterns of soil erosion in arid lands from Landsat MSS data. Int. J. Remote Sens. 9:69–84.CrossRefGoogle Scholar
  54. Pitt, M.D., and Heady, H.F. (1978). Responses of annual vegetation to temperature and rainfall patterns in northern California. Ecology 59:336–350.CrossRefGoogle Scholar
  55. Postel, S. (1984). Acid pollution, acid rain and the future of forests. Worldwatch Paper 58:1–22.Google Scholar
  56. Richards, J. A. (1984). Thematic mapping from multitemporal image data using the principal components transformation. Remote Sens. Environ. 16:35–46.CrossRefGoogle Scholar
  57. Richards, J.A. (1986). Remote Sensing Digital Image Analysis. Springer-Verlag, Berlin.Google Scholar
  58. Richards, J.A., and Kelly, D.J. (1984). On the concept of spectral class. Int. J. Remote Sens. 5:987–991.CrossRefGoogle Scholar
  59. Rock, B.N., Hohsizaki, T., and Miller, J.R. (1988). Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sens. Environ. 24:109–127.CrossRefGoogle Scholar
  60. Rock, B.N., Vogelmann, J.E., Williams, D.L., Vogelmann, A.F., and Hoshizaki, T. (1986). Remote detection of forest damage. BioScience 36:439–445.CrossRefGoogle Scholar
  61. Rohde, W.G., and Olson, C.E. Jr. (1971). Estimating foliar moisture content from infrared reflectance data. pp. 144–164. In Third Biennial Workshop: Color Aerial Photography in the Plant Sciences and Related Fields. Amer. Soc. Photo-grarrim., Falls Church, VA.Google Scholar
  62. Roller, N.E.G., and Colwell J.E. (1986). Course-resolution satellite data for ecological surveys. BioScience 36:468–475.CrossRefGoogle Scholar
  63. Runkle, J.R. (1985). Disturbance regimes in temperate forests, pp. 17–33. In S.T.A. Pickett and P.S. White (eds), The Ecology of Natural Disturbance and Patch Dynamics. Academic Press, NY.Google Scholar
  64. Saunders, D.A., Arnold, G.W., Burbidge, A.A., and Hopkins, A.J.M. (Eds.) (1987). Nature Conservation: The Role of Remnants of Native Vegetation. Surrey Beatty, Sydney, Australia.Google Scholar
  65. Saxon, B.C., and Dudzinski, M.L. (1984). Biological survey and reserve design by Landsat mapped ecolines—A catastrophe theory approach. Aust. J. Ecol. 9:117–123.CrossRefGoogle Scholar
  66. Schutt, P., and Cowling, E.B. (1985). Waldsterben, a general decline: symptoms, development. Plant. Dis. 69:548–558.Google Scholar
  67. Shugart, H.H., and West, D.C. (1981). Long-term dynamics of forest ecosystems. Amer.Sci. 69:647–652.Google Scholar
  68. Silvertown, J. (1980). The dynamics of a grassland ecosystem: botanical equilibrium in the park grass experiment. J. Appl. Ecol. 17:491–504.CrossRefGoogle Scholar
  69. Sing, A. (1983). Univariate image-differencing for forest change detection with Landsat. pp. 154–160. In Remote Sensing for Rangeland Monitoring and Management. Remote Sensing Society, Reading, England.Google Scholar
  70. Sing, A. (1987). Spectral separability of tropical forest classes. Int. J. Remote Sens. 8:971–979.CrossRefGoogle Scholar
  71. Taylor, W.P. (1934). Significance of extreme or intermittent conditions in distribution of species and management of natural resources, with a restatement of Leibig’s law of minimum. Ecology 15:374–379.CrossRefGoogle Scholar
  72. Tilman, D. (1982). Resource Competition and Community Structure. Princeton Univ. Press, Princeton, NJ.Google Scholar
  73. Tilman, D. (1988). Plant Strategies and the Dynamics and Structure of Plant Communities. Princeton Univ. Press, Princeton, NJ.Google Scholar
  74. Townshend, J.R.G., and Justice, C.O. (1986). Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int. J. Remote Sens. 7:1435–1445.CrossRefGoogle Scholar
  75. Townshend, J.R.G., and Tucker, C.J. (1984). Objective assessment of advanced very high resolution radiometer data for land cover mapping. Int. J. Remote Sens. 5:497–504.CrossRefGoogle Scholar
  76. Tucker, C.J. (1980). Remote sensing of leaf water content in the near infrared. Remote Sens. Environ. 10:23–32.CrossRefGoogle Scholar
  77. Tucker, C.J. (1986). Maximum normalized difference vegetation index images for sub-Saharan Africa for 1983–1985. Int. J. Remote Sens. 7:1383–1384.CrossRefGoogle Scholar
  78. Tucker, C.J., Justice, C.O., and Prince, S.D. (1986). Monitoring the grasslands of the Sahel 1984–1985. Int. J. Remote Sens. 7:1715–1731.CrossRefGoogle Scholar
  79. Tucker, C.J., Townshend, J.R.G., and Goff, T.E. (1985a). African land-cover classification using satellite data. Science 227:369–375.PubMedCrossRefGoogle Scholar
  80. Tucker, C.J., Vanpraet, C.L., Sharman, M.J., and Van Ittersum, G. (1985b) Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984. Remote Sens. Envir. 17:233–249.CrossRefGoogle Scholar
  81. Ustin, S.L., Adams, J.B., Elvidge, C.D., Rejmanek, M., Rock, B.N., Smith, M.O., Thomas, R.W., and Woodward, R.A. (1986). Thematic mapper studies of semiarid shrub communities. BioScience 36:446–456.CrossRefGoogle Scholar
  82. Walker, B.H. (1979). Management principles for semi-arid ecosystems, pp. 379–388. In B.H. Walker (ed.), Management of Semi-arid Ecosystems. Elsevier, Amsterdam, Netherlands.Google Scholar
  83. Walker, J., Jupp, D.L.B., Penridge, L.K., and Tian, G. (1986). Interpretation of vegetation structure in Landsat MSS imagery: A case study in disturbed semi-arid eucalypt woodlands. Part 1. Field data analysis. J. Envir. Manag. 23:19–33.Google Scholar
  84. Walsh, S.J. (1987). Variability of Landsat MSS spectral responses of forests in relation to stand and site characteristics. Int. J. Remote Sens. 8:1289–1299.CrossRefGoogle Scholar
  85. Wardley, N.W., Milton, E.J., and Hill, C.T. (1987). Remote sensing of structurally complex semi-natural vegetation—an example from heathland. Int. J. Remote Sens. 8:31–42.CrossRefGoogle Scholar
  86. Warren, P.L., and Huchinson, C.F. (1984). Indicators of rangeland change and their potential for remote sensing. J. Arid Envir. 7:107–126.Google Scholar
  87. Watt, A.S. (1981). A comparison of grazed and ungrazed grassland in East Anglian Breckland. J. Ecol. 69:509–536.CrossRefGoogle Scholar
  88. Weaver, R.E. (1987). Spectral separation of moorland vegetation in airborne Thematic Mapper data. Int. J. Remote Sens. 8:43–55.CrossRefGoogle Scholar
  89. Weismiller, R.A., Kristof, S.J., Scholtz, D.K., Anuta, P.E., and Momin, S.A. (1977). Change detection in coastal zone environments. Photogramm. Eng. Remote Sens. 43:1533–1539.Google Scholar
  90. West, D.C., Shugart, H.H., and Botkin, D.B. (eds.) (1981). Forest Succession Concepts and Application. Springer-Verlag, NY.Google Scholar
  91. Wickware, G.M., and Howarth, P.J. (1981). Change detection in the Peace-Athabasca Delta using digital Landsat data. Remote Sens. Envir. 11:9–25.CrossRefGoogle Scholar
  92. Williams, K., Hobbs, R.J., and Hamburg, S.P. (1987). Invasion of annual grassland in northern California by Baccharis pilularis ssp. consanguinea. Oecologia (Berlin) 72:461–465.CrossRefGoogle Scholar
  93. Yates, H., Strong, A., McGinnis, D., Jr., and Tarpley, D. (1986). Terrestrial observations from NOAA operational satellites. Science 231:463–470.PubMedCrossRefGoogle Scholar
  94. Yool, S.R., Star, J.L., Estes, J.E., Botkin, D.B., Eckhardt, D.W. and Davis, F.W. (1986). Performance analysis of image processing algorithms for classification of natural vegetation in the mountains of southern California. Int. J. Remote Sens. 7:683–702.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York Inc. 1990

Authors and Affiliations

  • Richard J. Hobbs

There are no affiliations available

Personalised recommendations