DG23 Posters

Ash, A., (1)

1 – Cranfield University and UK Centre for Ecology and Hydrology

Artificial flowers have been widely used in pollination behavioural experiments and have come a long way in their development, using robotics and 3D printing. In comparison, the pollinator monitoring community still use traditional attraction methods of pan traps and coloured paper which have been proven to not accurately attract all insect pollinator species. With the rise in automated, remote monitoring and pollinator identification, there is a need for new and improved attractants that can accurately and reliably attract different pollinators. In order to do this, I have been conducting a comprehensive study of the floral attraction cues for different pollinators. The next step is, by going through all the papers utilising artificial flowers in behavioural research, determining the best methods for including multiple attraction cues in one artificial flower. Before designing a complex flower, I need to perform a simple experiment to determine if a flower-naïve pollinator in the lab has an innate preference to an artificial flower over a pan trap or coloured paper. The data so far, using bumblebee colonies, points to a potential preference for the artificial flowers, but so far not enough data has been collected to make definitive conclusions.

Future experiments include repeating the choice experiment in the field with a wider variety of artificial flowers and different coloured pan traps and paper to see which attractant receives the most visitors. Eventually, using the information gathered on artificial flower design, I will design a complex artificial flower with multimodal attraction cues specified to attract a desired pollinator group. Once I have designed these artificial flower systems, I will create a database of artificial flower designs that others can use for pollinator monitoring.

This poster will include an overview of my current stage of PhD research along with prototype designs of future artificial flower systems.

Shuangyu Wei (1), Paige Tien (1), John Calautit (1), Yupeng Wu (1), Hua Zhong (2)

1 – University of Nottingham, UK
2 – Nottingham Trent University, UK

Occupants’ behaviour and the use of electrical equipment can significantly impact the building energy demand. Accurate occupancy and equipment usage information are key to improving the performance of demand-driven control, which can automatically adjust the heating, cooling and ventilation system operation. Employing static schedules is commonly used for the operation of heating, ventilation and air-conditioning systems, while it cannot satisfy the actual requirements due to the dynamic variations within the conditioned spaces. This study introduces a coupled real-time occupancy and equipment usage detection and recognition approach using deep learning and computer vision techniques for efficient building energy controls. The experimental results presented an overall equipment detection and occupancy activity detection accuracy of 78.39% and 93.60%. To investigate the influence of the implementation of the approach on building energy demand, a case study office building was selected to conduct experimental tests and modelled using a building energy simulation tool. Four scenarios with different occupancy and equipment profiles were defined and evaluated. The simulation results showed that heat gains, when employing static profiles were larger than the heat gains predicted when using the deep learning-influenced profiles. Up to 53.95% lower heat gains were estimated when using both occupancy and equipment detection approaches than static schedules solely. The results highlighted the importance of monitoring real-time occupancy and electrical equipment usage and the advantages of using deep learning detection techniques to provide data for demand-driven controls, optimising building energy efficiency while maintaining a comfortable indoor environment.

Kilcoyne, A. (1), Clement, M. (1), Moore, C. (1), Woodget, A. (1), Fancourt, F. (1), Stefaniak, A. (1), Trippier, B. (1), Potter, S. (1)

1 – Natural England

The Living England project is a multi-year programme, led by Natural England, aiming to deliver a satellite-derived national habitat probability map every two years. The maps describe the extent and distribution of England’s diverse broad habitats and is funded by Environmental Land Management (ELM) Schemes and the Natural Capital and Ecosystem Assessment (NCEA) programme. The project combines expertise in ecology, Earth observation and data science to derive national-scale predictions. This evidence is supporting indicator metrics for the Government’s 25-Year Environment Plan, along with contributing to key policy areas such as Spatial Prioritisation under the new agri-environment schemes, Natural Capital assessments and Local Nature Recovery Strategies. The project uses machine learning to classify homogenous objects, derived from Sentinel-2 imagery, into broad habitat classes. The models utilise open-source datasets, including satellite imagery and indices (Sentinel-1, Sentinel-2), terrain, climatic and geological information to derive predictions informed by bespoke field data collection using ESRI Field Maps and other habitat surveys. Multiple reproducible analytical pipelines have been developed, including a Google Earth Engine cloud-masking framework and coherence-based bare ground mapping. The national habitat probability map is published under an Open Government Licence and is widely available for use by environmental practitioners in supporting land management decisions. The poster will provide an overview of the methodologies used and some of the challenges modelling habitats on a national scale. The project aims to deliver a new up-to-date map every two years using the latest field data collected by Natural England to inform land management decisions.

Benjamin Blundell (1), Doug Gillespie (1), Gordon Hastie (1), Jessica Montabaranom (1), Emma Longden (1), Carol Sparling (1)

1 – University of St Andrews

Increasing numbers of structures associated with marine renewable energy are being installed in our oceans. It is important to understand how animals interact with these new environments. Unlike wind turbines, tidal energy generators have large moving parts under water, which could injure or kill a marine mammal if the collide with them. We have recently completed a 12-month data collection period using two multi-beam sonars at an operational tidal turbine in Scotland. The amount of data is considerable – over 6TB per month. Spotting informative events is a tricky and time consuming activity, currently performed semi-manually. Currently, all data processing takes place offline with the aim of summarising animal interactions with the turbine. However, we are undertaking research into automated processes including the use of Artificial Intelligence, as well as a number of Computer Vision techniques.

Chevuturi, A. (1), Brown, M.J.(1), Fry, M. (1)

1 – UK Centre for Ecology and Hydrology

In this study we examine object storage, a cutting-edge cloud-native technology specifically designed for efficiently managing large datasets. While object storage offers significant cost-effectiveness compared to disk storage, it requires data to be appropriately adapted to fully realise its benefits. Data retrieval from object storage is over HTTP in complete “objects,” which are either entire files or file chunks. As this is relatively new technology, there is a clear lack of established tools and best-practice for converting various file types for optimal use with object storage, particularly for large gridded and N-dimensional datasets used in environmental and climate science. The performance and speed of object storage are contingent upon the data’s structure, chunking, and the specific analysis requirements of the user. Consequently, a better understanding of these interactions is essential before widespread adoption. To address this need, our study conducted a series of experiments using gridded data with different chunking strategies, aiming to identify the most efficient approach for utilizing and accessing data stored in an object store. Our findings highlight the need for comprehensive understanding of object storage before its widespread adoption, and serve as a valuable resource for guiding future users in utilizing object storage effectively.

Usman Nazir (1), Murtaza Taj (1), Momin Uppal (1), Sara Khalid (2)

1 – Lahore University of Management Sciences
2 – University of Oxford

Industrial air pollution has a direct health impact and is a major contributor to climate change. Small scale industries particularly bull-trench brick kilns are one of the major causes of air pollution in South Asia often creating hazardous levels of smog that is injurious to human health. To mitigate the climate and health impact of the kiln industry, fine-grained kiln localization at different geographic locations is needed. Kiln localization using multi-spectral remote sensing data such as vegetation index results in a noisy estimates whereas use of high-resolution imagery is infeasible due to cost and compute complexities. This paper proposes a fusion of spatio-temporal multi-spectral data with high-resolution imagery for detection of brick kilns within the “Brick-Kiln-Belt” of South Asia. We first perform classification using low-resolution spatio-temporal multi-spectral data from Sentinel-2 imagery by combining vegetation, burn, build up and moisture indices. Then orientation aware object detector: YOLOv3 (with value) is implemented for removal of false detections and fine-grained localization. Our proposed technique, when compared with other benchmarks, results in a 21 improvement in speed with comparable or higher accuracy when tested over multiple countries.

Vahid Seydi (1), Julian Moore (2), Magnus Harrold (3), David Mills (1), Rachel Gavey (2), Noel Bristow (1)

1 – Centre for Applied Marine Sciences, school of ocean sciences, Bangor University, UK
2 – Applied Petroleum Technology UK 
3 – ORE Catapult’s Marine Energy Engineering Centre of Excellence, UK

The selection process for wind turbine installation locations is influenced by a multitude of factors that collectively contribute to comprehensive site risk assessment. In the case study conducted in the Celtic Sea, these factors encompass bathymetry, visibility, wind speed, protected areas, seabed substrate, shipping route, oil and gas infrastructures, and military danger and exercise areas. Each factor contributes to the overall risk level based on their respective values. However, the challenge lies in effectively integrating these risks to make informed decisions.

Traditional methods for addressing this challenge often rely on weighted linear regression. Analytical hierarchical process (AHP) is commonly used to determine these weights through pairwise comparisons. However, AHP’s static pairwise comparison approach may overlook the dynamic nature of the factors and fail to capture complex relationships. In this research, we propose a novel approach utilizing fuzzy logic and Bivariate Membership Functions (BMFs) to tackle this dynamic nature of risk assessment. By employing fuzzy inference system and BMFs, we can effectively handle linguistic uncertainty in pairwise comparisons. The dynamic fuzzy pairwise comparison approach facilitates a more accurate and interpretable assessment of the relative importance of risk factors across the domain of criteria, capturing the nuanced relationships between them. We anticipate that our research will contribute to improved accuracy in risk assessment and decision-making for wind turbine installation location selection and by extension to a range of potential applications that involve the integration of spatially varying factors. By leveraging fuzzy logic and dynamic pairwise comparison, we can better capture the complexities and dynamics of the risk factors, leading to more informed and robust site risk assessments with reduced uncertainty. Overall, our work aims to advance the understanding and application of fuzzy logic and dynamic pairwise comparison within AHP in the context of wind turbine installation location selection.

Fremand, A. (1)

1 – UK Polar Data Centre – British Antarctic Survey

The use of Uncrewed Aerial Vehicles (UAV) for scientific purposes has significantly increased in the last 10 years and opens the doors to new scientific discoveries. The development of miniaturised sensors, the availability of performant autopilot hardware and software have helped the development of their usage. It is now a cost-effective way to collect data that are used in a large range of environmental research studies. However, the lack of UAV data management best practices, and the lack of open hardware and software tools, limits the full potential reuse of these data.

The NERC Environmental Data Service, a trusted UK facility providing data stewardship services ensuring environmental data of long-term value are findable, accessible, interoperable and reusable (FAIR), is developing recommendations on how to best collect and manage UAV data to reach their full reuse potential. In this poster, we showcase the challenges and opportunities that exist with regards to the stewardship aspects of UAV data and invite users to take part in a survey to better understand their usage and requirements for managing UAV data.

Ankita Bhattacharya (1), Dr.Andrew Barkwith (1), Dr.Peter Robins (2)

1 – British Geological Survey
2 – Bangor University

Compound flooding in estuaries occurs when two or more drivers of flooding, such as rising sea levels, storm surges or river flows, occur simultaneously or in close succession. Multiple driving forces can amplify each other and lead to greater impacts than when they occur in isolation. A better understanding of the interdependence between flood drivers will facilitate a more accurate assessment of compound flood risk in coastal regions. The Conwy estuary, North Wales, is one of the flashiest catchments in Britain, holding several records for past flood events and making headline news at least once per season. To reduce flood-risk now and in the future, it is crucial that we understand the driving interactions of floods accurately and produce appropriate data and assessment methods. This project aims to find out how groundwater influences compound flooding in UK estuaries and it requires development of a coupled catchment and groundwater model for Conwy estuary. Model simulations will be calibrated against past river and tide gauges and UKCP18 sea level data, to show how the river, groundwater and soil moisture drivers are likely to influence the magnitude, behaviour and timings of compound flooding in the future.

Trippier, B. (1), Mein, R. (1), Wade, R. (1), Walker, K. (1)

1 – Natural England

Natural England have been working in partnership with the Botanical Society of Britain and Ireland (BSBI) to develop new mapping products to help inform on land management decisions on a national scale. Poorly targeted tree and woodland establishment can damage wildlife and carbon-rich habitats. With the UK Government’s ambition to increase tree and woodland cover in England from 14 to 17%, this represents a major shift in land use policy, and has a pivotal role in supporting nature recovery and biodiversity.

Under the Natural Capital and Ecosystem Assessment (NCEA) programme, we developed a series of ‘Botanical Heatmaps’ with BSBI, to mobilise their vascular plant datasets collected by their network of expert volunteers, to highlight sites where nationally Rare, Scarce and Threatened species are known to occur and where high quality semi-natural habitat is likely to be present. These help ensure such areas are appropriately considered when making land management decisions. These are available through an ArcGIS Online spatial mapping application, accompanied with technical user guides. An easily interpretable summarised botanical value map has also been published under OGL, which assigns ‘High’, ‘Moderate’ and ‘Low’ value scores for each 1km grid square showing their value for vascular plant species.

Hao Sun, (1), Murtaza Mohammadi, (1), Shuangyu Wei, (1), John Kaiser Calautit, (1), Hua Zhong, (2), and Carlos Jimenez-Bescos, (3)

1 – University of Nottingham, UK
2 – Nottingham Trent University, UK.
3 – University of Derby, UK

Digital technologies and skillsets are vital components in addressing global environmental challenges. This study delves into an innovative application of computational fluid dynamics (CFD) to enhance natural ventilation in buildings with courtyards, mainly focusing on the impact of vegetation. Past research has underexplored indoor spaces surrounding the courtyard and the role of vegetation as a microclimate regulator. Hence, this study introduces the crucial role of digital tools and skills in assessing energy consumption, pollution dispersion, and thermal comfort influenced by the presence and configuration of vegetation in courtyards.

The digital technique was validated using wind tunnel experiments, displaying high accuracy. This application of digital technology can potentially transform the way architectural designs are approached and evaluated. Furthermore, the investigation of pollutant dispersion in courtyard buildings can potentially enhance our understanding of indoor air quality, contributing to building confidence in the usage of these structures.

The impact of vegetation on the aero-thermal comfort conditions was evaluated through a series of scenarios involving different configurations. This study also investigated the vegetation cover, height, and planting area. The results offer significant insights and tools for maximizing the beneficial effects of vegetation, hence building trust in such eco-friendly approaches.

The study underscores the necessity of familiarizing architects and stakeholders with the digital skills required for these advanced techniques. With a focus on thermal comfort, energy consumption, and pollution dispersion, the application of computational fluid dynamics and wind tunnel experiments are identified as key skills in the evolving architectural practice. Future works will expand this methodology to explore the influence of various strategies on different designs and layouts of courtyards, thus illustrating the potential of digital technologies and advanced analytical skills in enhancing environmental sustainability in architectural design.

Keywords: Computational Fluid Dynamics (CFD); Courtyard Microclimate; Digital Technologies in Architecture; Vegetation Impact; Pollution Dispersion

Nichols, M., (1) Stuart, R., (1), Mobbs, D., (1) Leaver, D., (1) Dean, H., (1) Ferguson, S., (1) Zwagerman, T., (1) Lobo-Guerrero Villegas, J.P., (1)

1 – UK Centre for Ecology and Hydrology

NERC-funded environmental science is tackling larger and ever more complex questions. With this greater ambition comes the combined challenges of data deluge, integrative and cost-effective research, as well as meeting expectation and requirements in the age of open science. The internationally recognised FAIR Data Principles can help us manage and increase the reusability and value of our science data holdings. This is beneficial for integrative science, enhances the impact of our research and delivers important aspects of the open science agenda. The Data Stewardship Wizard (DSW, presented here) is a new tool with a fundamentally different approach to data management, designed to help deliver FAIR data in practice. The tool guides researchers through a series of relevant, multi-choice questions in a smart questionnaire, offering links to helpful resources and showing metrics indicating progress towards FAIR data based on the answers. It also provides a bespoke data management plan document based on the questionnaire answers, with no further effort from the researcher. The DSW tool will help to foster a FAIR data culture and uptake of good practice in making UKCEH data findable, accessible, interoperable and re-usable. The DSW has recently rolled out at UKCEH and is being pilot tested across the NERC data centres.

Heysham, Nourhan (1), Kingston, Richard (1), Topping, Dave (1)

1 – University of Manchester

Evidence based policy making has been central to planning policy over the past decades (Rae and Wong, 2021) but we know that access to data and tools to support such approaches is problematic (Kingston and Vlastaras, 2020). With increasing amounts of environmental data playing a central role in decision-making, it is imperative to design tools and technologies that provide inclusive access to various users to support their policy and decision making.

As part of the Natural Environment Research Council’s (NERC) strategy, the Digital Solutions Programme (DSP) is developing a hub for delivering user-centric tools to a wide range of users to support decision making. Unlike methodologies starting with prejudiced software designs, for DSP, designing tools based on what users need came at the heart of our methodological process.

This poster presents findings from over 18 months of research where we adopted a multi-phased user research process, starting with scoping and context exploration, funnelling down in detail, with a target of eliciting user requirements to build tools that users need and would use. We explore the adopted user-centric approach for DSH, which follows literature and practice-based knowledge on user-centric design and requirements engineering. The methods used include user workshops, interviews, network mapping analysis and requirements elicitation techniques. The outcome is cascading user needs analysis and requirements modelling, leading to design scenarios for tools utilising environmental data, based on user needs. This is part of a larger iterative design process, with further user testing and engagement throughout the upcoming stages and years on the programme.