Creating a digital treeless peatland map using satellite image interpretation

Abstract

In the satellite image-based estimation and classification of forest variables in Finland peatlands are usually processed separately from mineral soil forests, to improve the accuracy of the results. The division into peatlands and mineral soil forests is based on a mask provided by the National Land Survey. It would be advantageous, however, to update the mask with the satellite imagery used for estimating forest variables. The aim here was to compare methods for treeless peatland detection on a Landsat ETM+ satellite image. The area concerned was located within the southern aapa mire zone in Finland. The classification methods tested included sequential maximum a posteriori (SMAP), supervised maximum likelihood (ML) and unsupervised ML with Iso Cluster-based signatures. The unsupervised Iso Cluster ML method performed poorly, while the overall accuracies of SMAP and supervised ML were better and quite similar (88–94% and 89–90% on forestry land, respectively). SMAP produced more usable maps, by forming compact and unspeckled treeless peatland regions. The existing peatland mask was slightly more accurate than SMAP and ML, although it performed less well in the treeless peatland class. The updating of the existing mask by combining it with the best classification result did not succeed. The main conclusion is that a peatland mask can be based on Landsat TM classification, but in areas where a good topographic mask exists the latter is more useful, and cannot easily be updated with help of satellite image data.