REFERENCES

Daubenmire, R., 1968. Plant Communites: A Textbook of Plant Synecology. Harper and Row, New York.

Kittredge, J., 1948. Forest Influences: the Effects of Woody Vegetation on Climate, Water and Soil. Dover Publications, New York.

Klijn, F., 1994. Spatially nested ecosystems: guidelines for classification from a hierarchical perspective. In Ecosystem Classification for Environmental Management, Kluwer Academic, Dordrecht, The Netherlands.

Loveland, T.R., J. W. Merchant, D. Ohlen, and J.F. Brown. 1991. Development of a land-cover characteristics data base for the conterminous U.S. Photogrammetric Engineering and Remote Sensing. 57(11):1453-1463. See also http://3dcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

Oneill, R.V., D.L. DeAngelis, J.B. Wade, and T.F.H. Allen, 1986. A Hierarchical Concept of Ecosystems. Princeton University Press, Princeton, New Jersey. pp. 59-71.

Shimwell, D.W., 1971. The Description and Classification of Vegetation. University of Washington Press, Seattle.

Walter, H. 1984. Vegetation of the Earth, and Ecological Systems of the Geo-biosphere. Springer-Verlag, Berlin.

Source Material for Figures and Tables

Figure 6.01: Global potential vegetation type map.

Created by Thomas C. Hart from the BIOME2 model of latitude, soil, and mean monthly climate data. Prentice, I.C., W. Cramer, S.P. Harrison, R. Leemans, R.A. Monserud, and A.M. Solomon. 1992. A global biome model based on the plant physiology and dominance, soil properties, and climate. J. Biogeogr. 19:117-134.

http://www.epri.com/MECCA/Ecology.html

Figure 6.02: Spectral reflectance changes with viewing angle.

Goetz, S.J. Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. 1997. International Journal of Remote Sensing 18(1):71-94.

Figure 6.03: Effect of multitemporal data on classification accuracy.

After Moik, J.G. Digital Processing of Remotely Sensed Images. Scientific and Technical Information Branch, Goddard Space Flight Center. p. 286, Figure 8-17. 1980. NASA SP-431, U.S. National Aeronautic and Space Administration.

Figure 6.04: Correlation of sparse vegetation with low VI and high surface temperatures.

Goetz, S.J. Multisensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. 1997. International Journal of Remote Sensing 18(1):71-94.

Figure 6.05: Maximum likelihood and parallelepiped decision boundaries.

After Moik, J.G. Digital Processing of Remotely Sensed Images. Scientific and Technical Information Branch, Goddard Space Flight Center. p. 272, Figure 8-13. 1980. NASA SP-431, U.S. National Aeronautic and Space Administration.

Figure 6.06: An index of surface wetness as determined by the slope of NDVI against surface temperature.

Czajkowski, K. P., S. N. Goward, T. Mulhern, S. J. Goetz, S. D. Prince, R.O. Dubayah, A. Walz, D. Shirey and M. Thawley. Recovery of environmental variables from thermal remote sensing. In Thermal Remote Sensing in Land Surface Processes (J. Luvall and D. Quattrochi, Ed.) forthcoming. Ann Arbor Press.

Table 1: Anderson land use and land cover classification.

Adapted from Anderson, J.R., Hardy, E.E., Roach, J.T., Richard, E.W. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper 964. 1976. United States Government Printing Office.

Table 2: International Geosphere-Biosphere Programme Classification.

Adapted from Belward, A.S., ed. The IGBP-DIS global 1 km land cover data set (DISCover) proposal and implementation plans. 1996. IGBP-DIS Working Paper No. 13, Toulouse, France.

Table 3: A simple error matrix of four land cover classes.

Provided by Ned Horning.

Suggested Reading

Anderson, J.R., Hardy, E.E., Roach, J.T., Richard, E.W., 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper 964. United States Government Printing Office (see URL below).

Dale, V. H. (1997), The relationship between land-use change and climate change, Ecological Applications 7(3):753.

Defries, R. S. and J. R. G. Townshend (1994), NDVI-derived land cover classifications at a global scale, International Journal of Remote Sensing 15(17):3567-3586.

Defries, R., M. Hansen and J. Townshend (1995), Global discrimination of land cover types from metrics derived from the AVHRR Pathfinder data, Remote Sensing of Environment 54:209-222.

Defries, R., C. B. Field, I. Fung, C. O. Justice, S. Los, P. A. Matson, E. A. Mooney, C. S. Potter, K. A. Prentice, P. J. Sellers, J. R. G. Townshend, C. J. Tucker, S. Ustin and P. M. Vitousek (1995), Mapping the land surface for global atmosphere-biosphere models: towards continuous distributions of vegetation's functional properties, Journal of Geophysical Research 100 (D10):20867-20882.

Kuchler, A.W. (1983), World map of natural vegetation, Goode's World Atlas, 16th Edition, Rand McNally, pp. 16–17.

Lambin, E. F. and D. Ehrlich (1996), The surface temperature -- vegetation index space for land cover and land-cover change analysis, International Journal of Remote Sensing 17(3):463-487.

Laporte, N., C. Justice and J. Kendall (1995), Mapping the dense humid forest of central Africa using AVHRR satellite data, International Journal of Remote Sensing 16(6):1127-1145.

Lloyd, D. (1990), A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery, International Journal of Remote Sensing 11(12):2269-2279.

Moik, J. G. (1980), Digital processing of remotely sensed images, Washington DC, NASA Scientific and Technical Information Services. 330 Pages.

Nemani, R. and S. W. Running (1996), Implementation of a hierarchical global vegetation classification in ecosystem function models, Journal of Vegetation Science 7:337-347.

Saatchi, S. S., J. V. Soares and D. S. Alves (1997), Mapping Deforestation and Land Use in Amazon Rainforest by Using SIR-C Imagery, Remote Sensing of Environment 59(2):191-202.

Townshend, J. R. G., C. O. Justice and V. Kalb (1987), Characterisation and classification of South American land cover types using satellite data, International Journal of Remote Sensing 8:1189-1207.

Townshend, J., C. O. Justice and W. Li (1992), Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sensing of Environment 35(2):243-253.

Tucker, C. J., J. R. G. Townshend and T. E. Goff (1985), African land cover characterization using satellite data, Science 227:369-375.

Woodcock, C. E., J. B. Collins and R. Warbington (1994), Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model, Remote Sensing of Environment 50(3):240.

URLs

Global land cover products:

http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html

 

Space Imaging /EOSAT satellite data:

http://www.spaceimaging.com/

 

SPOT satellite homepage:

http://www.spot.com

 

Landsat homepage:

http://geo.arc.nasa.gov/sge/landsat/landsat.html

 

The Anderson classification system:

http://globe.fsl.noaa.gov/edu/exp/lc/lc9.html

 

Kuchler Map Archive:

http://www2.lib.muohio.edu/libinfo/depts/brill/Kuchler.html

 

NASA Land Cover / Land Use Change (LCLUC) Program:

http://lcluc.gecp.virginia.edu/

 

International Geosphere - Biosphere Program (IGBP) Global Analysis, Interpretation and Modelling (GAIM) homepage:

http://gaim.unh.edu/

 

Monitoring Tropical Vegetation Project (European Program):

http://wwwmtv.jrc.it/home.html

 

University of Maryland LGRSS (Laboratory for Global Remote Sensing Studies) Global Land Cover Project:

http://www.inform.umd.edu/GEOG/landcover/global-cover.html