Monitoring the spread of invasive species is crucial for nature conservation; however regularity can only be assured if cost-effectiveness can be achieved. We aimed at testing low-cost remote sensing sources and simple methodology for recognising the invasive species Robinia pseudacacia and thus founding a monitoring scheme. A study area with mixed wooded stands containing R. pseudacacia has been selected for this purpose in NE Slovenia. Four different sources (Landsat ETM and airborne orthophotos from summer and spring) were tested together with a filtering for forested areas. Filtering was based either on Landsat information or on a forest polygon layer as alternatives. Generalised linear models were constructed in a training window within the study area to establish a statistical rule of recognition for the species based on spectral information. Models were tested both within and outside the training window for accuracy. As means of accuracy assessment both the well-established AUC and the specially adapted Jaccard index have been applied.
The best and most reliable recognition was achieved by using the spring orthophoto, in which the species was captured in flower, combined with a GIS filtering by a forest vector layer. The superiority of this combination was especially striking when tested over the full study area. The Jaccard index appeared to be more sensitive in discrimination between models. Thus we conclude that even spectrally less detailed data sources may provide a basis for successful monitoring if the phenology of the target species is also considered.