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 (R2 = 0.85), although there was little difference among the methods (R2 = 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 (R2 = 0.78) than the MARS model (R2 = 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.
Cunningham, S., MAC NALLY, R., Read, J., Baker, P., White, M., THOMSON, J., & Griffioen, P. (2009). A Robust Technique for Mapping Vegetation Condition Across a Major River System. Ecosystems, 12, 207-219. https://doi.org/10.1007/s10021-008-9218-0