The Quantification and Effect of Data Accuracy on Spatial Configuration and Composition Measurements of Landscape Pattern in Ecology
Alex M. Lechner*, Karin J. Reinke, Elizabeth Farmer, Bill T. Langford and Simon D. Jones
RMIT University Melbourne, Australia
Abstract: In natural resource management, ecological models are increasingly used to investigate the relationship between environmental and ecological processes (e.g. quantifying the response of rare and threatened species to habitat fragmentation). This paper examines the potential effect of data accuracy upon the measurement of landscape pattern using spatial configuration and composition measures commonly used in ecological modelling. This was achieved via an accuracy assessment of two standard environmental metrics calculated from commonly used continental and regional vegetation datasets and compared to a higher spatial resolution dataset treated as if it were the truth. Three study areas were selected from within Victoria, Australia, that represented varying degrees of spatial heterogeneity. The two metrics calculated to describe the environment within each plot were vegetation extent and Nearest Neighbour (NN). The first metric, vegetation extent describes spatial composition, whilst the second metric, NN, describes spatial configuration. Assessments were made at the local scale, represented by 80 one hectare circular plots for each study area. The effect of transforming the data was also tested as in many cases the relationship between ecological phenomenon and land cover measurements may not be linear. Results confirm the expectation that the utilisation of lower accuracy spatial data results in the derivation of less accurate environmental metrics. Further findings showed that vegetation extent was less sensitive to map product than nearest neighbour suggesting that certain metrics may be more susceptible to error. Importantly, the magnitude of error was also influenced by the type of mathematical function used to transform the data. The overall magnitude of error as recorded for vegetation extent, for each of the three landscapes, were qualitatively shown to poorly predict the magnitude of error that would occur using the spatially explicit NN environmental metric. Consequently, it is likely that global error statements (e.g. confusion matrices) that are not spatially explicit, are potentially inadequate for describing error in map products that are to be used for modelling spatially explicit phenomenon.
Keywords: ecology; environmental metrics; error propagation; landscape pattern; remote sensing; spatial error