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Remote Sensing of Terrestrial Ecosystem Structure: An Ecologist’s Pragmatic View

  • R. Dean Graetz
Chapter
Part of the Ecological Studies book series (ECOLSTUD, volume 79)

Abstract

This chapter reviews the scientific concepts involved in the application of remote sensing technology to current and future problems in terrestrial ecology. The approach is pragmatic, being decisively user oriented, and is based on the proposition that currently available technology far exceeds the scientific capability of interpreting and applying it. For most terrestrial ecological problems of current and future concern, data types and volumes are not immediately limiting. Rather it is the understanding of the ecological significance of what has already been acquired that fetters the wider, more constructive use of remote sensing technology.

Keywords

Normalize Difference Vegetation Index Remote Sensing Vegetation Index Leaf Area Index Vegetation Structure 
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.

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References

  1. Aase, J.K., Frank, A.B., and Lorenz, RJ. (1987). Radiometric reflectance measurements of northern great plains rangeland and crested wheatgress pastures. J. Range Manag. 40:299–302.CrossRefGoogle Scholar
  2. Aase, J.K., Millard, J.P., and Brown, B.S. (1986). Spectral radiance estimates of leaf area and leaf phytomass of small grains and native vegetation. IEEE Trans. Geosci. Remote Sens. GE-24:685–692.CrossRefGoogle Scholar
  3. Asrar, G., Kanemasu, E.T., Miller, G.P., and Weiser, R.L. (1986). Light interception and leaf area estimates from measurements of grass canopy reflectance. IEEE Tram. Geosci. Remote Sens. GE-24:76–82.CrossRefGoogle Scholar
  4. Badhwar, G.D., MacDonald, R.B., Hall, F.G., and Carnes, J.G. (1986a). Spectral characterization of biophysical characteristics in a boreal forest: relationship between thematic mapper band reflectance and leaf area index for aspen. IEEE Trans. Geosci. Remote Sens. GE-24:322–326.CrossRefGoogle Scholar
  5. Badhwar, G.D., MacDonald, R.B., and Mehta, N. (1986b). Satellite-derived leaf-area-index and vegetation maps as inputs to global carbon cycle models—a hierarchical approach. Int. J. Remote Sens. 7:265–281.CrossRefGoogle Scholar
  6. Bartlett, D.S., Hardisky, M.A., Johnson, R.W., Gross, M.F., Klemas, V., and Hartman, J.M. (1988). Continental scale variability in vegetation reflectance and its relationship to canopy morphology. Int. J. Remote Sens. 9:1223–1241.CrossRefGoogle Scholar
  7. Becker, F., and Choudhury, B.J. (1988). Relative sensitivity of normalised difference index (NDVI) for vegetation and desertifiation monitoring. Remote Sens. Envir. 24:297–311.CrossRefGoogle Scholar
  8. Clevers, J.G.P.W. (1988). The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sens. Envir. 25:53–69.CrossRefGoogle Scholar
  9. Curran, P.J., and Wardley, N.W. (1988). Radiometric leaf area index. Int. J. Remote Sens. 9:259–274CrossRefGoogle Scholar
  10. Curran, P.J., and Williamson, H.D. (1985). The accuracy of ground data used in remote-sensing investigations. Int. J. Remote Sens. 6:1637–1651.CrossRefGoogle Scholar
  11. Curran, P.J., and Williamson, H.D. (1986). Sample size for ground and remotely sensed data. Remote Sens. Envir. 20:31–41.CrossRefGoogle Scholar
  12. Curran, P.J., and Williamson, H.D. (1987a). Airborne MSS data to estimate GLAI. Int. J. Remote Sens. 8:57–74.CrossRefGoogle Scholar
  13. Curran, P.J., and Williamson, H.D. (1987b). GLAI estimation using measurements of red, near-infrared, and middle infrared radiance. Photogram. Eng. Remote Sens. 53:181–186.Google Scholar
  14. Curran, P.J., and Williamson, H.D. (1987c). Estimating green leaf area index of grassland with airborne multispectal scanner data. Oikos 49:141–148.CrossRefGoogle Scholar
  15. Delcourt, H.R., Delcourt, P.A., and Webb, T. (1983). Dynamic plant ecology: the spectrum of vegetation change in space and time. Quat. Sci. Rev. 1:153–175.CrossRefGoogle Scholar
  16. FAO. (1973). FAO Manual of Forest Inventory, with special reference to mixed tropical forests. UN, FAO, Rome, Italy.Google Scholar
  17. Foran, B.D. (1987). Detection of yearly cover change with Landsat MSS on pastoral landscapes in central Australia. Remote Sens. Envir. 23:333–350.CrossRefGoogle Scholar
  18. Foran, B.D., and Pickup, G. (1984). Relationship of aircraft radiometric measurements to bare ground on semi-desert landscapes in central Australia. Austral. Rangelands J. 6:59–68.CrossRefGoogle Scholar
  19. Franklin, J., Logan T.L., Woodcock, C.E., and Strahler, A.H. (1986). Coniferous forest classification and inventory using Landsat and digital terrain data. IEEE Trans. Geosci. Remote Sens. GE-24:139–149.CrossRefGoogle Scholar
  20. Franklin, J., and Strahler, A.H. (1988). Invertible canopy reflectance modelling of vegetation structure in semiarid woodland. IEEE Trans. Geosci. Remote Sens. GE-26:809–825.CrossRefGoogle Scholar
  21. Gallo, K.P., and Eidenshink, J.C. (1988). Differences in visible and near-IR responses, and derived vegetation indices, for the NOAA-9 and NOAA-10 AVHRRs: A case study. Photogram. Eng. Remote Sens. 54:485–490.Google Scholar
  22. Gillison, A.N., Walker, J. (1981). Woodlands, pp. 177–197. In R.H. Groves (ed.), Australian Vegetation. Cambridge University Press, Cambridge, England.Google Scholar
  23. Goward, S.N., Dye, D., Kerber, A., and Kalb, V. (1987). Comparison of North and South American biomes from AVHRR observations. Geocarto Int. 1:27–39.CrossRefGoogle Scholar
  24. Goward, S.N., Tucker, C.J., and Dye, D.G. (1985). North American vegetation patterns observed with Nimbus-7 Advanced Very High Resolution Radiometer. Vegetatio 64:3–14.CrossRefGoogle Scholar
  25. Graetz, R.D., and Pech, R.P. (1988). The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data. Int. J. Remote Sens. 9:1201–1222.CrossRefGoogle Scholar
  26. Graetz, R.D., Pech, R.P., Gentle, M.R., and O’Callaghan, J.F. (1986). The application of Landsat image data to rangeland assessment and monitoring: the development and demonstration of a land image-based resource information system (LIBRIS). J. Arid Envir. 10:53–80.Google Scholar
  27. Hall, F.G., and Badhwar, G.D. (1987). Signature-extendible technology: global space-based crop recognition. IEEE Trans. Geosci. Remote Sens. GE-25:93–103.CrossRefGoogle Scholar
  28. Holben, B.N., and Fraser, R.S. (1984). Red and near-infrared response to off-nadir viewing. Int. J. Remote Sens. 5:145–160.CrossRefGoogle Scholar
  29. Holben, B.N., Tucker, C.J., and Fan, C.J. (1980). Assessing soybean leaf area and leaf biomass with spectral data. Photogram. Eng. Remote Sens. 26:651–656.Google Scholar
  30. Honey, F.R., and Tapley, I.J. (1981). A vegetation response model applied to range inventory using Landsat MSS data. Proc. 15 th Int. Symp. on Remote Sensing of the Environment, Ann Arbor, MI.Google Scholar
  31. Jupp, D.L.B., Strahler, A.H., and Woodcock, C.E. (1988). Autocorrelation and regularization in digital images I: Basic theory. IEEE Tran. Geosci. Remote Sens. GE-26,463–473.CrossRefGoogle Scholar
  32. Justice, C.O., and Hiernaux, P.H.Y. (1986). Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983. Int. J. Remote Sens. 7:1475–1497.CrossRefGoogle Scholar
  33. Justice, C.O., Townshend, J.R.G., Holben, B.N., and Tucker, C.J. (1985). Analysis of the phenology of global vegetation using meteorological satellite data. Int. J. Remote Sens. 6:1271–1318.CrossRefGoogle Scholar
  34. Karaska, M.A., Walsh, S.J., and Butler, D.R. (1986). Impact of environmental variables on spectral signatures acquired by the Landsat thematic mapper. Int. J. Remote Sens. 7:1653–1667.CrossRefGoogle Scholar
  35. Li, X., and Strahler, A.H. (1985). Geometrical-optical modelling of a conifer forest canopy. IEEE Trans. Geosci. Remote Sens. GE-23:705–721.CrossRefGoogle Scholar
  36. Malingreau, J.P. (1986). Global vegetation dynamics: satellite observations over Asia. Int. J. Remote Sens. 7:1121–1146.CrossRefGoogle Scholar
  37. Mathews, E. (1983). Global vegetation and land use: new high-resolution data bases for climate studies. J. Clim. App. Meteorol. 22:474–487.CrossRefGoogle Scholar
  38. Matson, M., and Holben, B. (1987). Satellite detection of tropical burning in Brazil. Int. J. Remote Sens. 8:509–516.CrossRefGoogle Scholar
  39. Matson, M., Stephens, G., and Roinson, J. (1987). Fire detection using data from the NOAA-N satellites. Int. J. Remote Sens. 8:961–970.CrossRefGoogle Scholar
  40. McLachlan, G.J., and Basford, K.E. (1988). Mixture Models: Inference and Applications to Clustering. Marcel Dekker, NY.Google Scholar
  41. Mueller-Dombois, D. (1984). Classification and mapping of plant communities: a review with emphasis on tropical vegetation, pp. 21–88. In G.M. Woodwell (ed.), The Role of Terrestrial Vegetation in the Global Carbon Cycle: Measurement by Remote Sensing, SCOPE 23. Wiley, NY.Google Scholar
  42. Musick, H.B. (1986). Temporal change of Landsat MSS albedo estimates in arid rangeland. Remote Sens. Envir. 20:107–120.CrossRefGoogle Scholar
  43. Nelson, R., Horning, N., and Stone, T.A. (1987). Determining the rate of forest conversion I. Mato Grosso, Brazil, using Landsat MS and AVHRR data. Int. J. Remote Sens. 8:1767–1784.CrossRefGoogle Scholar
  44. Nelson, R., Krabill, W., and Tonelli, J. (1988). Estimating forest biomass and volume using airborne laser data. Remote Sens. Envir. 24:247–267.CrossRefGoogle Scholar
  45. Olson, J.S., Watts, J.A., and Allison, L.J. (1983). Carbon in Live Vegetation of Major World Ecosystems. ORNL-5862, Pub. No. 1997, Envir. Sci. Div., Oak Ridge Nat. Lab., Oak Ridge, TN.Google Scholar
  46. Owe, M.F., Chang, A., and Golus, R.E. (1988). Estimating soil moisture from satellite microwave measurements and a satellite derived vegetation index. Remote Sens. Envir. 24:331–345.CrossRefGoogle Scholar
  47. Paris, J.F., and Kwong, H.H. (1988). Characterization of vegetation with combined thematic mapper (TM) and shuttle imaging radar (SIR-B) image data. Photogram. Eng. Remote Sens. 54:1187–1193.Google Scholar
  48. Pech, R.P., and Davis, W.A. (1987). Reflectance modelling of semi arid woodlands. Remote Sens. Envir. 23:365–377.CrossRefGoogle Scholar
  49. Pech, R.P., Davis, A.W., Lamacraft, R.P., and Graetz R.D. (1986a). Calibration of Landsat data for sparsely vegetated semi-arid rangelands. Int. J. Remote Sens. 7:1729–1750.CrossRefGoogle Scholar
  50. Pech, R.P., Graetz, R.D., and Davis, A.W. (1986b). Reflectance modelling and the derivation of vegetation indices for an Australian semi-arid shrubland. Int. J. Remote Sens. 7:389–403.CrossRefGoogle Scholar
  51. Peterson, D.L., Spanner, M.A., Running, S.W., and Teuber, K.B. (1987). Relationship of thematic mapper simulator data to leaf area index of temperate coniferous forests. Remote Sens. Envir. 2:323–341.CrossRefGoogle Scholar
  52. Peterson, D.L., Westman, W.E., Stephenson, N.J., Ambrosia, V.G., Brass, J.A., and Spanner, M.A. (1986). Analysis of forest structure using thematic mapper simulator data. IEEE Tran. Geosci. Remote Sens. GE-24:113–121.CrossRefGoogle Scholar
  53. Pickup, G., and Foran, B.D. (1987). The use of spectral and spatial variability to monitor cover change on inert landscapes. Remote Sens. Envir. 23:351–363.Google Scholar
  54. Ranson, K.J., and Doughtry, C.S.T. (1987). Scene shadow effects on multispectral response. IEEE Trans. Geosci. Remote Sens. GE-25:502–509.CrossRefGoogle Scholar
  55. Running, S.W. (1986). Global primary production from terrestrial vegetation: estimates integrating satellite remote sensing and computer simulation technology. Sci. Total Envir. 56:233–242.CrossRefGoogle Scholar
  56. Sellers, P.J. (1985). Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens. 6:1335–1372.CrossRefGoogle Scholar
  57. Sellers, P.J. (1987). Canopy reflectance, photosynthesis, and transpiration. II. The role of biophysics in the linearity of their interdependence. Remote Sens. Envir. 11:171–190.Google Scholar
  58. Sellers, P.J., and Dorman, J.L. (1987). Testing the simple biosphere model (SiB) using point micrometeorological and biophysical data. J. Clim. Appl. Meteorol. 26:622–651.CrossRefGoogle Scholar
  59. Specht, R.L. (1981). Foliage projective cover and standing biomass. pp. 10–21. In A.N. Gillison, and D.J. Anderson (eds.), Vegetation Classification in Australia. Australian Nat. Univ. Press, Canberra, Australia.Google Scholar
  60. Strahler, A.H. (1981). Stratification of natural vegetation for forest and rangeland inventory using Landsat digital imagery and collateral data. Int. J. Remote Sens. 2:15–41.CrossRefGoogle Scholar
  61. Strahler, A.H., Woodcock, C.E., and Smith J. A. (1986). On the nature of models in remote sensing. Remote Sens. Envir. 20:121–139.CrossRefGoogle Scholar
  62. Suits, G., Malila, W., and Weller, T. (1988a). The prospects for detecting spectral shifts due to satellite sensor aging. Remote Sens. Envir. 26:17–29.CrossRefGoogle Scholar
  63. Suits, G., Malila, W., and Weller, T. (1988b). Procedures for using signals from one sensor as substitutes for signals of another. Remote Sens. Envir. 25:395–408.CrossRefGoogle Scholar
  64. Taconet, O., Carlson, T., Bernard, R., and Vidal-Madjar, D. (1986). Evaluation of a surface/vegetation parameterization using satellite measurements of surface temperature. J. Clim. Appl. Meteorol. 25:1752–1767.CrossRefGoogle Scholar
  65. Thomas, R.W., and Ustin, S.L. (1987). Discriminating semiarid vegetation using airborne imaging spectrometer data: a preliminary assessment. Remote Sens. Envir. 23:273–290.CrossRefGoogle Scholar
  66. 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. 7:1435–1446.CrossRefGoogle Scholar
  67. Townshend, J.R.G., and Justice, C.O. (1988). Selecting the spatial resolution of satellite sensors required for global monitoring of land transforms. Int. J. Remote Sens. 9:187–236.CrossRefGoogle Scholar
  68. Tucker, C.J., Fung, I.Y., Kealing, D.C., and Gammon, R.H. (1986a). Relationship between atmospheric CO2 variations and a satellite derived vegetation index. Nature 319:195–199.CrossRefGoogle Scholar
  69. Tucker, C.J., Justice, C.O., and Prince, S.D. (1986b). Monitoring the grasslands of the Sahel 1984–1985. Int. J. Remote Sens. 7:1571–1582.CrossRefGoogle Scholar
  70. Tucker, C.J., Townshend, J.R.G., and Goff, T.E. (1985). African land-cover classification using satellite data. Science 227:369–375.PubMedCrossRefGoogle Scholar
  71. Van Gils, H.A.M.J., and Van Wijngaarden, W. (1984). Vegetation structure in reconnaissance and semi-detailed vegetation surveys. ITC J. 13:213–218.Google Scholar
  72. Vujakovic, P. (1987). Monitoring extensive “buffer zones” in Africa: an application for satellite imagery. Biol. Conserv. 39:195–208.CrossRefGoogle Scholar
  73. Walker, J., and Hopkins, M.S. (1984). Vegetation, pp. 44–67. In R.C McDonald, R.F. Isbel, J.G. Speight, J. Walker, M.S. Hopkins (eds.), Australian Soil and Land Survey: Field Handbook. Inkata Press. Melbourne, Australia.Google Scholar
  74. 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 semiarid Eucalypt woodland. Part 1. Field data analysis. J. Envir. Manag. 23:19–33.Google Scholar
  75. Wanjura, D.F., and Hatfield, J.L. (1988). Vegetative and optical characteristics of four row crop canopies. Int. J. Remote Sens. 9:249–258.CrossRefGoogle Scholar
  76. Weigand, C.L., Richardson, A.J. (1987). Spectral components analysis: rationale, and results for three crops. Int. J. Remote Sens. 8:1011–1032.CrossRefGoogle Scholar
  77. Weigand, C.L., Richardson, A.J., Jackson, R.D., Pinter, P.J., Aase, J.K., Smika, D.E., Lautenschlager, L.F., and McMurtrey, J.E. (1986). Development of agrometeorological crop model inputs from remotely sensed information. IEEE Trans. Geosci. Remote Sens. GE-24:90–98.CrossRefGoogle Scholar
  78. Williamson, H.D. (1988). Evaluation of middle and thermal infrared radiance in indices used to estimate GLAI. Int. J. Remote Sens. 9:275–283.CrossRefGoogle Scholar
  79. Wilson, M.F., Henderson-Sellers, A., Dickinson, R.E., and Kennedy, P.J. (1987). Sensitivity of the biosphere-atmosphere transfer scheme (BATS) to the inclusion of variable soil characteristics. J. Clim. Appl. Meteorol. 26:341–362.CrossRefGoogle Scholar
  80. Woodcock, C.E., and Strahler, A.H.(1987). The factor of scale in remote sensing. Remote Sens. Envir. 21:311–332.CrossRefGoogle Scholar
  81. Woodcock, C.E., Strahler, A.H., and Jupp, D.L.B. (1988a). The use of variograms in remote sensing I: Scene models and simulated images. Remote Sens. Envir. 25:323–348.CrossRefGoogle Scholar
  82. Woodcock, C.E., Strahler, A.H., and Jupp, D.L.B. (1988b). The use of variograms in remote sensing II: Real digital images. Remote Sens. Envir. 25:349–379.CrossRefGoogle Scholar

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© Springer-Verlag New York Inc. 1990

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  • R. Dean Graetz

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