3 resultados para inventory management method
em Worcester Research and Publications - Worcester Research and Publications - UK
Resumo:
Surface flow types (SFT) are advocated as ecologically relevant hydraulic units, often mapped visually from the bankside to characterise rapidly the physical habitat of rivers. SFT mapping is simple, non-invasive and cost-efficient. However, it is also qualitative, subjective and plagued by difficulties in recording accurately the spatial extent of SFT units. Quantitative validation of the underlying physical habitat parameters is often lacking, and does not consistently differentiate between SFTs. Here, we investigate explicitly the accuracy, reliability and statistical separability of traditionally mapped SFTs as indicators of physical habitat, using independent, hydraulic and topographic data collected during three surveys of a c. 50m reach of the River Arrow, Warwickshire, England. We also explore the potential of a novel remote sensing approach, comprising a small unmanned aerial system (sUAS) and Structure-from-Motion photogrammetry (SfM), as an alternative method of physical habitat characterisation. Our key findings indicate that SFT mapping accuracy is highly variable, with overall mapping accuracy not exceeding 74%. Results from analysis of similarity (ANOSIM) tests found that strong differences did not exist between all SFT pairs. This leads us to question the suitability of SFTs for characterising physical habitat for river science and management applications. In contrast, the sUAS-SfM approach provided high resolution, spatially continuous, spatially explicit, quantitative measurements of water depth and point cloud roughness at the microscale (spatial scales ≤1m). Such data are acquired rapidly, inexpensively, and provide new opportunities for examining the heterogeneity of physical habitat over a range of spatial and temporal scales. Whilst continued refinement of the sUAS-SfM approach is required, we propose that this method offers an opportunity to move away from broad, mesoscale classifications of physical habitat (spatial scales 10-100m), and towards continuous, quantitative measurements of the continuum of hydraulic and geomorphic conditions which actually exists at the microscale.
Resumo:
The use of simulation games as a pedagogic method is well established though its effective use is context-driven. This study adds to the increasing growing body of empirical evidence of the effectiveness of simulation games but more importantly emphasises why by explaining the instructional design implemented reflecting best practices. This multimethod study finds evidence that student learning was enhanced through the use of simulation games, reflected in the two key themes; simulation games as a catalyst for learning and simulation games as a vehicle for learning. In so doing the research provides one of the few empirically based studies that support simulation games in enhancing learning and, more importantly, contextualizes the enhancement in terms of the instructional design of the curriculum. This research should prove valuable for those with an academic interest in the use of simulation games and management educators who use, or are considering its use. Further, the findings contribute to the academic debate concerning the effective implementation of simulation game-based training in business and management education.
Resumo:
This study provides the first spatially detailed and complete inventory of Ambrosia pollen sources in Italy – the third largest centre of ragweed in Europe. The inventory relies on a well tested top-down approach that combines local knowledge, detailed land cover, pollen observations and a digital elevation model that assumes permanent ragweed populations mainly grow below 745m. The pollen data were obtained from 92 volumetric pollen traps located throughout Italy during 2004-2013. Land cover is derived from Corine Land cover information with 100m resolution. The digital elevation model is based on the NASA shuttle radar mission with 90m resolution. The inventory is produced using a combination of ArcGIS and Python for automation and validated using cross-correlation and has a final resolution of 5km x 5km. The method includes a harmonization of the inventory with other European inventories for the Pannonian Plain, France and Austria in order to provide a coherent picture of all major ragweed sources. The results show that the mean annual pollen index varies from 0 in South Italy to 6779 in the Po Valley. The results also show that very large pollen indexes are observed in the Milan region, but this region has smaller amounts of ragweed habitats compared to other parts of the Po Valley and known ragweed areas in France and the Pannonian Plain. A significant decrease in Ambrosia pollen concentrations was recorded in 2013 by pollen monitoring stations located in the Po Valley, particularly in the Northwest of Milan. This was the same year as the appearance of the Ophraella communa leaf beetle in Northern Italy. These results suggest that ragweed habitats near to the Milan region have very high densities of Ambrosia plants compared to other known ragweed habitats in Europe. The Milan region therefore appears to contain habitats with the largest ragweed infestation in Europe, but a smaller amount of habitats is a likely cause the pollen index to be lower compared to central parts of the Pannonian Plain. A low number of densely packed habitats may have increased the impact of the Ophraella beetle and might account for the documented decrease in airborne Ambrosia pollen levels, an event that cannot be explained by meteorology alone. Further investigations that model atmospheric pollen before and after the appearance of the beetle in this part of Northern Italy are needed to assess the influence of the beetle on airborne Ambrosia pollen concentrations. Future work will focus on short distance transport episodes for stations located in the Po Valley, and long distance transport events for stations in Central Italy that exhibit peaks in daily airborne Ambrosia pollen levels.