Cutting of Phragmites australis as a lake restoration technique: Productivity calculation and nutrient removal in Wuliangsuhai Lake, northern China

Reed is one of the most frequent and dominant species in wetlands all over the world, with common reed (Phragmites australis (Cav.) Trin. ex Steud.) as the most widely distributed species. In many wetlands, P. australis plays a highly ambivalent role. On the one hand, in many wetlands it purifies wastewater, provides habitat for numerous species, and is a potentially valuable raw material, while on the other hand it is an invasive species which expands aggressively, prevents fishing, blocks ditches and waterways, and builds monospecies stands. This paper uses the eutrophic reed-swamp of Wuliangsuhai Lake in Inner Mongolia, northern China, as a case to present the multiple benefits of regular reed cutting. The reed area and aboveground biomass production are calculated based on field data. Combined with data about water and reed nutrient content, the impact of reed cutting on the lake nutrient budget (N and P) is investigated. Currently, at this lake around 100,000 tons of reed are harvested in winter annually, removing 16% and 8% of the total nitrogen and phosphorus influx, respectively. Harvesting all available winter reed could increase the nutrient removal rates to 48% and 24%, respectively. We also consider the effects of summer harvesting, in which reed biomass removal could overcompensate for the nutrient influx but could potentially reduce reed regrowth.     

Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping

Sampling efforts are constrained by limited availability of resources. Therefore, methods to reduce the number of samples, while still achieving reasonable accuracy are needed. Land-surface segmentation (LSS) has proven a powerful technique to partition digital elevation models (DEMs) and their derivatives into relatively homogeneous areas, which can be further employed as support in soil sampling. Though topography is one of the main soil forming factors, a robust assessment of the potential of this technique to digital soil mapping (DSM) is still missing. In this study, we aimed at evaluating the potential of LSS in stratifying a landscape into relatively homogeneous areas, which can be used as strata for guiding the selection of sampling points in DSM. The experiments were carried out in two study areas where soil samples were available. Land-surface derivatives were derived from DEMs and segmented with a tool based on the multiresolution segmentation algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment from the existing sample data, based on which predictions of soil classes/sub-orders and properties, i.e. soil texture and A-horizon thickness, were made. Results were compared with predictions based on simple random sampling (SRS) and conditioned Latin hypercube (cLHS). The segmentation-based sampling (SBS) scheme performed better than SRS and cLHS schemes in predicting the A-horizon thickness, soil texture fractions and soil classes, showing a high potential of LSS in stratifying a landscape for the purposes of DSM. The novelty of this study is in the way strata are constructed, rather than in the sampling design itself. Further research is needed to demonstrate the value of a SBS design for practical use. The analyses presented here further highlight the importance of considering locally adaptive techniques in optimization of sampling schemes and predictions of soil properties.     

Why GPS makes distances bigger than they are

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.     

Geochronology,stratigraphy and geochemistry of Cambro-Ordovician,Silurian and Devonian volcanic rocks of the Saxothuringian Zone in NE Bavaria (Germany)—new constraints for Gondwana break up and ocean–island magmatism

International Journal of Earth Sciences - Stratigraphically well-defined volcanic rocks in Palaeozoic volcano-sedimentary units of the Frankenwald area (Saxothuringian Zone, Variscan Orogen) were...