Quantifying Submerged Fluvial Topography Using Hyperspatial Resolution UAS Imagery and Structure From Motion Photogrammetry.

Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process-oriented investigations of flow hydraulics, sediment dynamics and in-stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through-water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. Whilst the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM-photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary-winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02 m) for two different river systems over channel lengths of 50-100 m. Errors in submerged areas range from 0.016 m to 0.089 m, which can be reduced to between 0.008 m and 0.053 m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM-photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10 m to a few hundred metres). This article is protected by copyright. All rights reserved.

Ragweed Pollen Source Inventory for France - the Second Largest Centre of Ambrosia in Europe

France, in particular the Rhône-Alpes region, is one of the three main centres of ragweed (Ambrosia) in Europe. The aim of this study is to develop a gridded ragweed pollen source inventory for all of France that can be used in assessments, eradication plans and by atmospheric models for describing concentrations of airborne ragweed pollen. The inventory combines information about spatial variations in annual Ambrosia pollen counts, knowledge of ragweed ecology, detailed land cover information and a Digital Elevation Model. The ragweed inventory consists of a local infection level on a scale of 0–100% (where 100% is the highest plant abundance per area in the studied region) and a European infection level between 0% and 100% (where 100% relates to the highest identified plant abundance in Europe using the same methodology) that has been distributed onto the EMEP grid with 5 km × 5 km resolution. The results of this analysis showed that some of the highest mean annual ragweed pollen concentrations were recorded at Roussillon in the Rhône-Valley. This is reflected by the inventory, where the European infection level has been estimated to reach 67.70% of the most infected areas in Europe i.e. Kecskemét in central Hungary. The inventory shows that the Rhône Valley is the most heavily infected part of France. Central France is also infected, but northern and western parts of France are much less infected. The inventory can be entered into atmospheric transport models, in combination with other components such as a phenological model and a model for daily pollen release, in order to simulate the dispersion of ragweed pollen within France as well as potential long-distance transport from France to other European countries.

Ragweed (Ambrosia) Pollen Source Inventory for Austria

This study improves the spatial coverage of top-down Ambrosia pollen source inventories for Europe by expanding the methodology to Austria, a country that is challenging in terms of topography and the distribution of ragweed plants. The inventory combines annual ragweed pollen counts from 19 pollen-monitoring stations in Austria (2004–2013), 657 geographical observations of Ambrosia plants, a Digital Elevation Model (DEM), local knowledge of ragweed ecology and CORINE land cover information from the source area. The highest mean annual ragweed pollen concentrations were generally recorded in the East of Austria where the highest densities of possible growth habitats for Ambrosia were situated. Approximately 99% of all observations of Ambrosia populations were below 745 m. The European infection level varies from 0.1% at Freistadt in Northern Austria to 12.8% at Rosalia in Eastern Austria. More top-down Ambrosia pollen source inventories are required for other parts of Europe.

Italian Ragweed Pollen Inventory

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.

Designing and Developing Digital and Blended Learning Solutions

The continuous advancement in computing, together with the decline in its cost, has resulted in technology becoming ubiquitous (Arbaugh, 2008, Gros, 2007). Technology is growing and is part of our lives in almost every respect, including the way we learn. Technology helps to collapse time and space in learning. For example, technology allows learners to engage with their instructors synchronously, in real time and also asynchronously, by enabling sessions to be recorded. Space and distance is no longer an issue provided there is adequate bandwidth, which determines the most appropriate format such text, audio or video. Technology has revolutionised the way learners learn; courses are designed; and ‘lessons’ are delivered, and continues to do so. The learning process can be made vastly more efficient as learners have knowledge at their fingertips, and unfamiliar concepts can be easily searched and an explanation found in seconds. Technology has also enabled learning to be more flexible, as learners can learn anywhere; at any time; and using different formats, e.g. text or audio. From the perspective of the instructors and L&D providers, technology offers these same advantages, plus easy scalability. Administratively, preparatory work can be undertaken more quickly even whilst student numbers grow. Learners from far and new locations can be easily accommodated. In addition, many technologies can be easily scaled to accommodate new functionality and/ or other new technologies. ‘Designing and Developing Digital and Blended Learning Solutions’ (5DBS), has been developed to recognise the growing importance of technology in L&D. This unit contains four learning outcomes and two assessment criteria, which is the same for all other units, besides Learning Outcome 3 which has three assessment criteria. The four learning outcomes in this unit are: • Learning Outcome 1: Understand current digital technologies and their contribution to learning and development solutions; • Learning Outcome 2: Be able to design blended learning solutions that make appropriate use of new technologies alongside more traditional approaches; • Learning Outcome 3: Know about the processes involved in designing and developing digital learning content efficiently and what makes for engaging and effective digital learning content; • Learning Outcome 4: Understand the issues involved in the successful implementation of digital and blended learning solutions. Each learning outcome is an individual chapter and each assessment unit is allocated its own sections within the respective chapters. This first chapter addresses the first learning outcome, which has two assessment criteria: summarise the range of currently available learning technologies; critically assess a learning requirement to determine the contribution that could be made through the use of learning technologies. The introduction to chapter one is in Section 1.0. Chapter 2 discusses the design of blended learning solutions in consideration of how digital learning technologies may support face-to-face and online delivery. Three learning theory sets: behaviourism; cognitivism; constructivism, are introduced, and the implication of each set of theory on instructional design for blended learning discussed. Chapter 3 centres on how relevant digital learning content may be created. This chapter includes a review of the key roles, tools and processes that are involved in developing digital learning content. Finally, Chapter 4 concerns delivery and implementation of digital and blended learning solutions. This chapter surveys the key formats and models used to inform the configuration of virtual learning environment software platforms. In addition, various software technologies which may be important in creating a VLE ecosystem that helps to enhance the learning experience, are outlined. We introduce the notion of personal learning environment (PLE), which has emerged from the democratisation of learning. We also review the roles, tools, standards and processes that L&D practitioners need to consider within a delivery and implementation of digital and blended learning solution.