, Volume 12, Issue 2, pp 207–219 | Cite as

A Robust Technique for Mapping Vegetation Condition Across a Major River System

  • S. C. CunninghamEmail author
  • R. Mac Nally
  • J. Read
  • P. J. Baker
  • M. White
  • J. R. Thomson
  • P. Griffioen


Ecologists need to develop tools that allow characterization of vegetation condition over scales that are pertinent to species’ persistence and appropriate for management actions. Our study shows that stand condition can be mapped accurately over the floodplain of a major river system (ca 100,000 ha of forest over 1600 km of river)—the Murray River in southeastern Australia. It demonstrates the value of using quantitative ground surveys in conjunction with remotely sensed data to model vegetation condition over very large spatial domains. A comparison of four modelling methods found that stand condition was best modelled using the multivariate adaptive regression spline (MARS) method (R 2 = 0.85), although there was little difference among the methods (R 2 = 0.77–0.85). However, a subsequent validation survey of condition at new locations showed that use of artificial neural networks had substantially higher predictive power (R 2 = 0.78) than the MARS model (R 2 = 0.28). This discrepancy demonstrates the value of using several modelling approaches to determine relationships among vegetation responses and environmental variables, and stresses the importance of validating ecological models with predictive surveys conducted after model building. The artificial neural network was used to produce a stand condition map for the whole floodplain, which predicted that only 30% of the area containing Eucalyptus camaldulensis stands is currently in good condition. There is a downstream decline in stand condition, which is related to more extreme declines in flooding, due to water harvesting, and drier climate found in the Lower Murray region. Rigorous surveying and modelling approaches, such as those used here, are necessary if vegetation health is to be effectively monitored and managed.


Eucalyptus camaldulensis floodplains neural networks regression splines regression trees remote sensing river regulation validation vegetation condition 



This research was funded by an ARC linkage Grant (LP0560518, which was partially funded by the Victorian Department of Sustainability and Environment (DSE) and four Catchment Management Authorities (Mallee CMA, North Central CMA, Goulburn-Broken CMA, and North East CMA)). We thank Rachael Nolan for assistance with fieldwork. Environmental data layers were supplied by the DSE’s Corporate Geospatial Data Library and the Australian Greenhouse Office (Landsat7 data). This is publication No. 146 from the Australian Centre for Biodiversity: Analysis, Policy and Management.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • S. C. Cunningham
    • 1
    Email author
  • R. Mac Nally
    • 1
  • J. Read
    • 2
  • P. J. Baker
    • 1
  • M. White
    • 3
  • J. R. Thomson
    • 1
  • P. Griffioen
    • 4
  1. 1.Australian Centre for Biodiversity, School of Biological SciencesMonash UniversityMelbourneAustralia
  2. 2.School of Biological SciencesMonash UniversityMelbourneAustralia
  3. 3.Arthur Rylah Institute for Environmental ResearchDepartment of Sustainability and EnvironmentHeidelberg, MelbourneAustralia
  4. 4.acroMapHeidelberg, MelbourneAustralia

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