Pollen analysis of sediment profile WO1 from Wollingster See

A palynological study of a 15 m sediment core from the centre of Lake Wollingst (water depth 14,5 m) is presented. The pollen record shows 3 lateglacial thermomers, called Meiendorf, Bölling, Alleröd and the early holocene Friesland-Thermomer. The succession of forest vegetation taking place on the lake surroundings during the Holocene was typical for older moraine soils which are poor in nutrients: forest vegetation started with birch and pine, followed by hazel, oak and elm in the Boreal and by alder, lime and ash-tree in the Atlantic. Beech and hornbeam reached the area during Subboreal. However, due to the poor soils they spread out only after the Iron Age. With the deforestation during the medieval time the lake lost its character of a primeval forest lake. Lake Wollingst was oligotrophic since its origin at the end of the Pleniglacial. After medieval forest-clearing the lake has changed its quality of water particularly in connection with hemp- and flax-rotting. The modem sediments in this profile are completely disturbed. They contain reworked material, a lot of blue-green algae and remains of Bosmina longirostris indicating eutrophic conditions.

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy.