Advertisement

Broad-Scale Land Condition Monitoring using Landsat TM and DEM-Derived Data

  • Fiona Evans
  • Adrian Allen
  • Peter Caccetta
  • Suzanne Furby
  • Jeremy Wallace
Part of the IFIP — The International Federation for Information Processing book series (IFIPAICT, volume 39)

Abstract

We describe a project that is monitoring land condition in the south west of Western Australia using Landsat Thematric Mapper satellite images and terrain data derived from digital elevation models (DEMs). Land Monitor is a multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust. The focus of the project is on broad-scale monitoring of land condition for environmental mangement at both catchment and policy levels. Land Monitor will provide land managers and administrators with baseline salinity and vegetation data for monitoring changes over time, and accurate land height data. Land Monitor will cover the 18 million hectares of agricultural land of south west Western Australia. Sequences of calibrated Landsat Thematic Mapper satellite images integrated with landform information derived from height data, ground truthing and other existing mapped data sets are used as the basis for monitoring changes in salinity and woody vegetation. Heights are derived on a 10m grid from stereo aerial photography flown at 1:40,000 scale, using soft-copy automatic terrain extraction techniques. Land Monitor products will include salinity maps, enhanced imagery, vegetation status maps and spectral/temporal statistics. These products will be available in a range of formats and scales, from paddock, farm to catchment and regional scales.

Key words

Remote Sensing DEM Salinity Monitoring 

References

  1. Caccetta, P. C. (1997), Remote Sensing, GIS and Bayesian Knowledge-based Methods for Monitoring Land Condition, PhD Thesis Computer Science Curtin University, Australia. Campbell, N. A. and Atchley, W. R. (1981), `The geometry of canonical variate analysis’, Syst. Zoology, Vol. 30, No. 3, pp. 268–280.Google Scholar
  2. Dempster, A. P., Laird, N. and Rubin, D. B. (1977), `Maximum likelihood from incomplete data via the EM algorithm’, Journal of the Royal Statistical Society, Series B, Vol, 39, pp. 1–38.Google Scholar
  3. Evans, F. H. (1998), An investigation into the use of maximum likelihood classifiers, decision trees, neural networks and conditional probabilistic networks for mapping and predicting salinity, MSc thesis Computer Science, Curtin University, Australia.Google Scholar
  4. Ferdowsian, R., George, R., Lewis, R., McFarlane, D. and Speed, R. (1996), `The extent of dryland salinity in Western Australia’, Proceedings of the 4th National Workshop on the Productive Use and Rehabilitation of Saline Lands, pp. 89–98.Google Scholar
  5. Furby, S. L., Campbell, N. A. and Palmer, M. J. (1997), `Calibrating images from different dates to like value digital counts’, submitted to Remote Sensing of the Environment. Furby, S. L. and Wallace, J. F. (1998), `Land condition monitoring in the Fitzgerald Biosphere region’, Proceedings of the 9th Australasian Remote Sensing Conference, available on CDROM.Google Scholar
  6. Furby, S. L., Evans, F. H., Wallace, J. F. Ferdowsian, R. and Simons, J. (1998), Collecting ground truth data for salinity mapping and monitoring, Land Monitor task report.Google Scholar
  7. Jensen, S. K. and Domingue, J. O. (1988), `Extracting topographic structure from digital elevation data for geographic information system analysis’, Photogrammetric Engineering and Remote Sensing, Vol. 54, No. 11, pp. 1593–1600.Google Scholar
  8. Lauritzen S. L. and Spiegelhalter D. J. (1988), `Local computations with probabilities on graphical structures and their application to expert systems’, Journal of the Royal Statistical Society, Vol. 50, No. 2, pp. 157–224.Google Scholar
  9. Moore, I. D., Grayson, R. B. and Ladson, A. R. (1991), `Digital terrain modelling: a review of hydrological, geomorphological and biological applications’, Hydrological Processes, Vol. 5, No. 1, pp. 3–30.CrossRefGoogle Scholar
  10. Neapolitan, R. E. (1990), Probabilistic reasoning in expert systems, John Wiley and Sons, USA.Google Scholar
  11. O’Callaghan, J. F. and Mark, D. M. (1984), `The extraction of drainage networks from digital elevation data’, Computer Vision, Graphics and Image Processing, Vol. 28, pp. 323–344.CrossRefGoogle Scholar
  12. Quinn, P., Beven, K., Chevallier, P. and Planchon, O. (1991), `The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models’, Hydrological Processes, V5, # 1, pp. 59–79.CrossRefGoogle Scholar
  13. Richards, J. A. (1986), Remote sensing digital image analysis: an introduction, Springer-Verlag, New York.CrossRefGoogle Scholar
  14. Rousseeuw, P. J. and Leroy, A. M. (1984), `Robust regression by means of S-estimators’, in Robust and Nonlinear Time Series Analysis, ed. Franke, J., Hardie, W. and Martin, R. D., Lecture Notes in Statistics, Springer-Verlag, pp. 256–272.CrossRefGoogle Scholar
  15. Schultz, G. A. (1994), `Meso-scale modelling of runoff and water balances using remote sensing and other GIS data’, Hydrological Sciences–Journal des Science Hydrologiques, Vol. 39, No. 2, pp. 121–142.CrossRefGoogle Scholar
  16. Wheaton, G., Wallace, J. F., McFarlane, D. and Campbell, N. A. (1992), `Mapping salt-affected land in Western Australia’, Proceedings of the 6th Australasian Remote Sensing Conference, Vol. 2, pp. 369–377.Google Scholar
  17. Wheaton, G., Wallace, J. F., McFarlane, D., Campbell, N. A. and Caccetta, P. C. (1994), `Mapping and monitoring salt-affected land in Western Australia’, Proc. Resource Technology ‘84 Conference, pp. 531–543.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2000

Authors and Affiliations

  • Fiona Evans
    • 1
  • Adrian Allen
    • 2
  • Peter Caccetta
    • 1
  • Suzanne Furby
    • 1
  • Jeremy Wallace
    • 1
  1. 1.CSIRO Mathematical and Information SciencesAustralia
  2. 2.WA Department of Land AdministrationAustralia

Personalised recommendations