Predictive mapping of floristic site conditions across mire habitats: Evaluating data requirements

Ecker, K. and Küchler, M. and Feldmeyer-Christe, E. and Graf, U. and Waser, L. T. (2008) Predictive mapping of floristic site conditions across mire habitats: Evaluating data requirements. Community Ecology, 9 (2). pp. 133-146. ISSN 1585-8553

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Abstract

As conservation of sensitive habitats is a high priority issue in European environmental policy, there is considerable interest in mapping and monitoring specific habitats of high conservation value. In this study, we discuss the potential of the Swiss mire monitoring program to monitor small area habitats in sufficient detail. The monitoring scheme combines nationwide probability sampling and predictive habitat mapping based on a field data sample. Thus, it is designed to identify spatiotemporal changes at the stand level and to derive hard statistics for the sub-national level. For feasibility reasons, the thematic focus is on semi-quantitative mean indicator values derived from vegetation records. These measures provide robust estimates of essential floristic site conditions. Regression models based on CIR aerial photographs are applied to continuously map respective measures across the sample mires. The present study explores the required investment of data for model-based mapping. Exemplary mapping results are presented and validated within a reference mire. Repeated tests show that about one hundred field records are needed to guarantee optimal prediction accuracy and reliable error estimates for all target variables. The corresponding 95% error quantiles in a test data set are below 0.7. To evaluate the benefit of high resolution orthophotos (30 cm resolution), the model prediction is compared with results obtained from coarsened images. Although the original CIR images produce the best model performance, the models based on resolutions comparable to modern satellite images still show considerable potential to assess larger areas where the use of digital aerial photographs is limited. The resulting spatially-explicit in-depth information can resolve the common thematic limitations of stand-alone remote sensing applications in conservation monitoring. As the method is applicable consistently across a range of habitat types, we argue that it has the potential to become a standard method for operational monitoring of priority habitats in European nature conservation.

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