Abstract
As a result of extensive farmland clearing over the last hundred years or so, dry-land salinity is a major problem in Western Australia. In fact, in some parts of the state, over 20 percent of Agricultural land is no longer productive. Prior to the work to be described in this chapter, no reliable large scale estimates of the extent or progression of salinity were available. This chapter describes a methodology for monitoring the historical extent of salinity, using a time series of satellite imagery, landform information derived from digital elevation models and ground truth data collected by experts with local knowledge. This work has served to highlight the salinity problem to decision makers in government and to provide input into the process of developing and applying remedial measures to arrest the spread of salinity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Besag, J. E. Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society B, 36, 1974, pp. 192–326.
Besag, J. E. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society B 48, 1986, pp. 259–302.
Caccetta, P., Campbell, N., West, G., Kiiveri, H., and Gahegan, M. Aspects of reasoning with uncertainty in an agricultural GIS environment. The New Review of Applied Expert Systems 1, 1995, pp. 161–177.
Caccetta, P. Remote Sensing, GIS and Bayesian Knowledge-based Methods for Monitoring Land Condition. PhD thesis, Department of Computer Science, Curtin University of Technology, Western Australia, 1997.
Caccetta, P. C., Campbell, N. A. C., Evans, F., Furby, S. L., Kiiveri, H. T., and Wallace, J. F. (2000). Mapping and monitoring land use and condition change in the south west of Western Australia using remote sensing and other data. In Proceedings of the Europa 2000 Conference, Barcelona.
Campbell, N. A. and Atchley, W. R. (1981), ‘The geometry of canonical variate analysis’, Syst. Zoology, Vol. 30, No. 3, pp. 268–280.
Subpixel matching using cross correlation and second derivatives. Submitted to ISPRS Journal of Photogrammetry and Remote Sensing.
Darroch, J. N., Lauritzen, S. L. and Speed, T. P. Log-linear models for contingency tables and Markov fields over graphs. Annals of Statistics 8, 1980, pp. 522–539.
Dempster, A. P., Laird, N. M., and Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39, 1977, pp. 1–21.
Furby, S. L., (1994) Discriminating between pasture and barley grass and saltbush using multi-temporal imagery. CMIS technical report.
Furby, S. L. and Campbell (2001), ‘Calibrating images from different dates to like value digital counts’, Remote Sensing of the Environment, 77, 186–196.
Jensen, F. V. An Introduction to Bayesian Networks. Springer Verlag, New York, 1996.
Kiiveri, H. T. and Caccetta, P. Data fusion, uncertainty and causal probabilistic networks for monitoring the salinisation of farmland. Digital signal processing, 8, 225-230.
Kiiveri, H. T. Some statistical models for remotely sensed data. In SISC96 Imaging Interface Workshop Proceedings, 1996.
Lauritzen, S. L., and Spiegelhalter, D. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B 50, 1988, pp. 157–224.0
Lauritzen, S. L. Propagation of probabilities, means, and variances in mixed graphical association models. Journal of the American Statistical Association 87, 1992, pp. 1098–1108.
Lauritzen, S. L. (1995). ‘The EM algorithm for graphical association models with missing data’, Computational Statistics and Data Analysis, 19, pp. 191-201.
NASA, (2001). Landsat 7 Science data users handbook. Available on line at http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbooks_toc.html.
Rao, C. R. (1966), Linear statistical inference and its applications. Second Edition, Wiley, New York.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media New York
About this chapter
Cite this chapter
Kiiveri, H., Caccetta, P., Campbell, N., Evans, F., Furby, S., Wallace, J. (2003). Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Sets. In: Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B., Yu, B. (eds) Nonlinear Estimation and Classification. Lecture Notes in Statistics, vol 171. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21579-2_4
Download citation
DOI: https://doi.org/10.1007/978-0-387-21579-2_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95471-4
Online ISBN: 978-0-387-21579-2
eBook Packages: Springer Book Archive