We face a series of unprecedented global challenges, especially climate change, that threaten to undermine our socio-economic well-being as well as our biodiverse ecosystems. Achieving sustainable development is the challenge of our age, as encapsulated in the Paris agreement in 2015. Global leaders agreed on 17 sustainable development goals and 169 associated targets to be met by 2030. To monitor these environmental targets effectively on a global scale requires major advances in satellite earth observation systems and associated data analytics.

This interdisciplinary meeting will highlight recently operational and forthcoming satellite systems providing new sensors supporting sustainability. Advances in the processing of big satellite data will be presented alongside novel analytics focused on delivering actionable sustainability intelligence. The meeting will also highlight advances in approaches to issues of sustainability in the building, launching and operation of satellite themselves.

We encourage contributions from researchers working in all aspects of satellite systems emphasizing sustainability. The themes and topics for this conference address the need for new performant sustainability systems, the critical elements of their performance and the technology for their implementation.

Global Sustainability: Challenges and Opportunities for Space and Satellites
  • satellite data-informed climate change detection and attribution
  • sustainability data solutions in areas such as agriculture, disease, humanitarian aid, re/afforestation
  • industry perspectives: implementation of analyses to support commercial demand for sustainability impact, and market opportunities
  • identification of global hotspots for environmental stress/fragility and response prioritisation
  • satellite data-informed disaster preparation and response.


  • Satellite Missions for Sustainability: New Assets and Capability
  • new and forthcoming missions with sustainability impact
  • novel sensors used in quantifying sustainability action impact
  • crop and forestry damage, stress and disease measurement
  • monitoring of population and movement
  • inspection of physical assets at risk from environmental change.


  • Satellite Data for Sustainability: Solutions at Scale
  • combining satellite data with airborne/terrestrial data, including IOT
  • visualization of large and complex multi-level data and analytical products (including VR / AR / MXR)
  • artificial intelligence and machine learning supported applications
  • sustainability data platforms-characteristics and applications
  • open data structures for sustainability
  • capacity building for innovative global applications.


  • Sustainability Issues for Satellites: Build, Launch and Orbit
  • minimizing environmental impact from launch pad build
  • green fuels and propulsion
  • satellite capture, refueling and de-orbiting
  • rocket stage capture
  • launch and orbit regulations and their impact.
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    Conference 11888

    Space, Satellites and Sustainability II

    29 September 2021 | Alsh 1
    View Session ∨
    • 1: Scotland Leading in Sustainability through Space
    • 2: Global Sustainability: Challenges and Opportunities for Space and Satellites
    • 3: Satellite Data for Sustainability: Solutions at Scale
    • Poster Session
    • 4: Satellite Missions for Sustainability: New Assets and Capability
    • Closing Remarks
    Session 1: Scotland Leading in Sustainability through Space
    29 September 2021 • 9:30 AM - 10:00 AM BST | Alsh 1
    Session Chair: Kristina Tamane, The Univ. of Edinburgh (United Kingdom)
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    Author(s): Sarah Middlemiss, Ecometrica (United Kingdom)
    On demand | Presented live 29 September 2021
    Session 2: Global Sustainability: Challenges and Opportunities for Space and Satellites
    29 September 2021 • 10:00 AM - 12:30 PM BST | Alsh 1
    Session Chair: Mathew Williams, The Univ. of Edinburgh (United Kingdom)
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    Author(s): Susanne Mecklenburg, European Space Research and Technology Ctr. (Netherlands)
    On demand | Presented live 29 September 2021
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    Climate change represents the biggest global threat of the 21st century. This has been widely recognized and is currently responded to by major international initiatives, summarizing the most pressing, globally relevant requirements in addressing the effects of a changing climate, such as the United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement, the UN’s “2030 Agenda for Sustainable Development” and the Sendai Framework for Disaster Risk Reduction 2015–2030. The European Space Agency (ESA) is already addressing a large number of the requirements that respond to the above main drivers for climate action through being a main developer of European Earth Observation (EO) capabilities to deliver climate science and services. ESA’s satellites provide the global view, enabling the science community to detect signs of change, identify significant trends and constrain the models to predict the future. Through its role as a major provider of systematic and global climate observations ESA interacts with a number of international organisations, stakeholders and users, within the climate landscape that are working toward strengthening the scientific understanding and projection of climate and addressing the consequences of future change. One of the keystones of ESA’s climate activities is the Climate Change Initiative (CCI), which has been running for more than 10 years and is led by the ESA Climate Office. This unique scientific effort involves ca. 450 world-leading experts across ESA Member States to generate global multi-mission and multi-decadal datasets satisfying the requirements for 22 Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS), on behalf of UNFCCC. These datasets have fully characterised uncertainties and are validated using independent, traceable, in-situ measurements. They provide an impartial yardstick to understand climate processes and to improve and validate climate models, thereby enhancing the quality, credibility and exploitation of model predictions. In association with Earth System Models (ESMs), CCI data also provide the observational record to study drivers, interactions and feedbacks due to climate change, as well as reservoirs, teleconnections, tipping points, global energy, water and carbon budgets and other Earth-system cycles, etc. The scientific results of the CCI programme, published in more than 900 papers to date, are a major contribution to the physical science base of IPCC Assessment Reports. The keynote will provide an assessment on the challenges and opportunities that space-borne data provide in our quest to tackle climate change, and will focus on the current and future satellite missions developed by ESA, underlying data storage and processing systems and how users will be able to integrate such data into potential scientific and commercial applications.
    Coffee Break 10:30 AM - 10:50 AM
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    Author(s): Jenny Kingston, Leonard Felicetti, Stephen Hobbs, Cranfield Univ. (United Kingdom)
    On demand | Presented live 29 September 2021
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    Sustainability in space is often discussed, but as a community we are only gradually learning what it actually means. To inform this understanding, a set of three parallel projects ran at Cranfield University (Oct 2020 to Mar 2021) to develop a scenario of sustainable use of the geostationary orbit region. The three projects were to develop mission designs for (a) a Scavenger spacecraft equipped with tools, actuators and sensors to perform rendezvous with selected satellites at their end of life, to harvest selected parts and components (i.e. solar panels, radiators, antenna reflectors), store and deliver them to the Recycler for refurbishment or recycling, (b) a Recycler space station located in GEO, capable of receiving parts and materials obtained by the Scavenger spacecraft and performing a range of inspection, recycling and repurposing operations on them, and (c) a candidate customer mission: a huge communications satellite based on the Airbus VASANT (VASt ANTenna) concept, with two antenna arrays, each 35 m square, sized to be able to communicate directly from GEO to mobile phone users at Earth’s surface. Some of the features highlighted by these studies are (a) the technical challenges of re-using parts from old satellites: modularity and design-for-reuse seem to be key enablers, (b) the advanced robotics and autonomy implied by the on-orbit operations, (c) the challenge of long-term orbit control without excessive propellant consumption, and (d) although the technology is challenging, there are major non-technical challenges for the business case and for aspects such as the legal use of debris, liability for accidents, and compliance with any regulations. Sustainability is challenging, but nature leaves us no alternative.
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    Author(s): Steven Hancock, The Univ. of Edinburgh (United Kingdom); Ludwig Prade, Gerald Bonner, Fraunhofer Ctr. for Applied Photonics (United Kingdom); Stephen P. Todd, UK Astronomy Technology Ctr. (United Kingdom); Christopher Lowe, Ciara N. McGrath, Univ. of Strathclyde (United Kingdom); Johannes N. Hansen, Ian J. Davenport, Iain H. Woodhouse, The Univ. of Edinburgh (United Kingdom); Brynmor E. Jones, Haochang Chen, Fraunhofer Ctr. for Applied Photonics (United Kingdom)
    On demand | Presented live 29 September 2021
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    Satellites have become essential tools for providing information for weather forecasts, monitoring agriculture and studying climate. In recent years satellite lidar (laser ranging) has come of age, with three missions launched in 2018. These spaceborne lidars are collecting ground-breaking data. They are the only current in-orbit technology capable of directly measuring bare Earth topography under vegetation and of making non-saturating measurements of forest biomass, height and cover. However, the energy requirements of a lidar lead to very sparse coverage. NASA's GEDI mission, the densest sampling lidar yet, is expected to directly sample between 2-4% of the Earth's surface, leading to sampling errors and preventing its use in applications that require continuous coverage without interpolation. The coverage of a lidar satellite is controlled by how much laser power they can emit, collect and make a usable measurement from. This is controlled by the payload power, telescope collecting area, energy per shot needed for an accurate measurement, laser efficiency and detector efficiency. Initial calculations using currently in-orbit technology suggest that global, continuous coverage could be achieved within 5 years at 30 m resolution using ten ICESat-2 class lidar satellites. This would be prohibitively expensive. The Global Altimeter MISSion: GLAMIS project aimed to see whether recent developments in photonics, deployable optics and small-satellites could make continuous coverage lidar more cost-effective. All current lidar satellites use a solid-state laser. These produce short, powerful bursts of light but are only ~5% efficient. Tapered laser diodes are 25% efficient, but cannot emit the same amount of energy in as short a burst as solid state lasers can. Diode laser would need to spread the energy over a longer time, using pulse compressed lidar (PCL) to allow measurements, which would lead to very different noise behaviour to sold state lasers, possibly preventing their use in spaceborne lidar. A satellite lidar simulator was used to determine that PCL is suitable for satellite lidar over a range of biomes. The mirror size and cost for fixed and deployable telescopes were estimated for three different satellite size classes; a 12U satellite, a 150 kg satellite and a 500 kg satellite. The electric power and cost of each of these satellites was also estimated and used, along with the laser efficiencies, to calculate the lidar coverage each could achieve. Orbital constellation simulations were used to determine how many satellites of each configuration would be needed to achieve global coverage within a given timeframe, accounting for loss due to clouds. It was found that deployable optics would allow a more cost effective coverage than fixed optics. It was also found that the larger satellites would be a more cost-effective solution than smaller satellites. In conclusion, using tapered laser diodes in pulse compressed mode and deployable optics are promising technologies to increase the coverage of satellite lidar. For a future constellation of lidar satellites capable of covering the whole Earth, the optimum configuration was found to be a constellation of eleven 150 kg (roughly microwave sized) satellites fitted with foldable mirrors.
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    29 September 2021 • 11:30 AM - 11:50 AM BST | Alsh 1
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    Two major topics of discussion in space sector in recent years have been the recognition of space debris as a critical threat to future and on-going missions, and the growth in the number of small satellite (10-500 kg) missions, and satellites in general. The proliferation of small satellites have invited commercialisation and subsequently, advancements in satellite technologies have helped us improve life on Earth, but the growing number of satellites are adding to the already high population of objects in low-Earth orbit. Now is the time to act and ensure future satellites aren’t destined to become space debris. In response to the growing number of small satellites unable to de-orbit from low-Earth orbit within 25 years, Cranfield University has developed a family of drag augmentation systems. These are lightweight, cost-effective drag sails deployed at end of mission, increasing the drag area of a spacecraft, minimising the de-orbit period and thus reducing the probability of significant collisions. The LEOniDAS team from Cranfield University are taking part in this year’s edition of ESA’s Fly Your Thesis! programme and aims to aid in the further development of the drag sail family, including a more scalable and adaptable hybrid design. This paper will focus on the challenges associated with developing this de-orbit technology; testing the devices for deployment in microgravity, de-risking the devices, improving customer confidence and understanding the deployment dynamics post sail deployment. The experiment aims to lend credibility to the drag sail concept, further accelerating the maturation and commercialisation of the devices. The paper will conclude in an overview of space debris mitigation and show where drag sails are applicable in the overall sustainable space ecosystem. The technology has a strong enabling potential for future space activities, allowing satellites to operate responsibly and sustainably, and ensuring we’re preserving space for the future.
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    • Developments in commercial Space projects frequently draw on lessons learnt from the maritime and seafaring worlds, in particular in the fields of indemnity, security and salvaging. But in discussions on the greening of seaports and the greening of spaceports, there is a notable difference. • Large seaports and the global shipping industry contribute significantly to Green Gas house emissions and, hence, are under media scrutiny and at the forefront of political discussions on how to achieve Netzero commercial operations by 2050 • In response, many freight-centric seaports increasingly embrace green management plans, that go far beyond current legislation in an attempt to create economic and environmental competitive advantage, while also (in theory) fulfilling environmental and social responsibilities. • In contrast, for small spaceports, discussions on greening commercial operations are negligible - beyond an environmental impact assessment during the planning stage. Instead the main focus, and funding, is on economic competition and technological innovation. • Notably, green/bio rocket fuel and sourcing renewable energies for local operations are in discussion. But these innovations, while important, are unlikely to lead to an organisation being labelled ‘NetZero’ by 2050, as defined by the International Greenhouse Gas protocol, that underpins the Environmental Green Deal. This discussion paper outlines three different green scenarios for small commercial vertical launch spaceports (microlaunchers), delivering low-weight payloads into polar orbits. I focus on: ==> Lesson learnt from large maritime seaports and freight networks, in particular how to identify, measure and address the challenging Scope 3 emissions (indirect emissions outside an organisation’s financial control, both upstream and downstream in their supply chain). ==> The Scope 3 ethical conundrum : how responsible should/could spaceports be for the lifecycle of the small cubsats/payloads being launched into Low Earth Orbit? (a question framed by different regulatory, ethical and trade contexts in Europe than in the USA).
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    Author(s): Diletta C. Invernizzi, Jacobs (United Kingdom); Harriet Brettle, Astroscale Ltd. (United Kingdom); Doug Kerr, Jacobs (United Kingdom); Phillip D. Anz-Meador, Jacobs Technology, Inc. (United States); Sion Edwards, Andrew Horner, Jayne Dale, Dominic Coy, Jacobs (United Kingdom); Mark Chang, PA Consulting Group (United Kingdom)
    On demand | Presented live 29 September 2021
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    The danger posed by human-created, orbital debris has been well documented and there is a broad consensus that, if unchecked, such debris poses a significant threat to the future of space activity. Besides, there has been much discussion on the difficulties inherent in trying to remove large, non-functioning satellites from Earth orbit. However, while the physical aspects of the space domain make this a unique technical challenge and never before has society had to face the problem of decommissioning redundant infrastructures on this scale, the issues faced by the space community in tackling the economic and legal difficulties of this environmental threat are not without terrestrial parallels. The nuclear industry is also well versed in dealing with the decommissioning of potentially harmful assets that have reached the end of their operational lifespan, and has developed considerably in the last decades across disciplines and approaches. Nevertheless, only very recently there have been some attempts to investigate decommissioning and develop lessons learned across sectors. In this paper, we address this topic, and we highlight the similarities that characterise these sectors, also presenting the magnitude of the nuclear and space decommissioning challenges. Moreover, we investigate to what extent the nuclear and outer space decommissioning industries can learn from each other. Results include considerations for the future such as: • the importance of promoting decommissioning as an exciting and socio-environmentally responsible industry for new generations; • the need for “design for decommissioning” to be implemented since the early stage(s) of a project; • policies specific to the end-of-life of assets and infrastructure that focus on financial obligations of the entities responsible for decommissioning.
    Break
    Lunch/Exhibition Break 12:30 PM - 1:30 PM
    Session 3: Satellite Data for Sustainability: Solutions at Scale
    29 September 2021 • 1:30 PM - 3:20 PM BST | Alsh 1
    Session Chair: Murray Collins, Space Intelligence (United Kingdom)
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    Author(s): Richard Tipper, Ecometrica (United Kingdom)
    On demand | Presented live 29 September 2021
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    Author(s): Vasilis Myrgiotis, Mathew Williams, The Univ. of Edinburgh (United Kingdom)
    On demand | Presented live 29 September 2021
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    Grasslands are the most widespread terrestrial ecosystem in the United Kingdom (UK). They represent a critical carbon (C) reservoir and provide forage and fodder to millions of livestock. Quantifying how management and climate affect grassland C dynamics is key to achieving climate-resilient farming, to shaping and monitoring data-informed policies, and to transitioning to a zero-C agri-food sector. To this end, remote sensing systems provide information on grassland vegetation in near-real time, across large domains and at high resolution. Biogeochemical models use our continuously-developing knowledge on ecosystem functioning to describe ecosystem C dynamics and the effects of weather and human management on them. Field measurements of C pools and fluxes provide direct ground observations of grassland C losses and gains. The combination of earth and ground observations with modelling represents a robust way for quantifying, monitoring and verifying grassland ecosystem C stocks. This presentation demonstrates our current capabilities in regards to this. We have developed and tested a model-data fusion (MDF) framework that uses earth observation data (Proba-V and Sentinel-2) on vegetation canopy (leaf area index) to infer vegetation management (grazing, cutting) and inform a validated process-based model of field-scale C dynamics. The framework was applied for 2017-2018 at 1855 grassland fields that were sampled from across the UK. The MDF-predicted livestock density per area and the corresponding agricultural census-based data had a correlation coefficient of 0.68. The MDF-predicted annual yield (harvested and cut biomass) was within the range of relevant measured data and reflected the variation of grassland management intensity across the UK. On average, the simulated grasslands were C sinks in 2017 and 2018 but the 2018 European summer heatwave resulted in a 9-fold increase in the number of simulated fields that were C sources in 2018 compared to 2017. We argue that earth observation data can be used in a MDF framework to monitor grassland vegetation management and to simulate its impacts on the C balance of any UK grassland field as well as to attribute changes in annual C balance to human activities and weather anomalies.
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    Author(s): David T. Milodowski, The Univ. of Edinburgh (United Kingdom), National Ctr. for Earth Observation (United Kingdom); Alexander T. Merrington, Nicholas P. Ross, Dave McKay, The Univ. of Edinburgh (United Kingdom); Paula McGregor, Ecometrica (United Kingdom); Mathew Williams, The Univ. of Edinburgh (United Kingdom), National Ctr. for Earth Observation (United Kingdom)
    On demand | Presented live 29 September 2021
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    Expansion of forests and woodlands are key elements of global strategies to capture carbon from the atmosphere and therefore mitigate climate change. Fundamental to the successful planning and management of woodland conservation and restoration is the ability to map accurately the spatial extent and character of woodland ecosystems. Satellite remote sensing increasingly provides a powerful tool to facilitate these monitoring efforts at scale. However, woodland cover in Scotland is highly fragmented, with marked differences in structure between the remnant native woodlands and more common commercial plantation systems. In addition, the landscape is topographically complex and frequently shrouded in cloud. These factors pose challenges for satellite remote sensing. Therefore, the capacity to characterise fragmented woodland cover accurately in such landscapes remains uncertain. In this contribution, we assess the extent to which trees can be mapped at 10m spatial resolution using a combination of openly available Sentinel 1 C-band radar backscatter data and Sentinel 2 multi-spectral imagery in fragmented woodland landscapes in NW Scotland. We assess the predictive accuracy of the resultant classifiers using a spatially rigorous site-level cross-validation across six sites of varied woodland cover, and explore the role of canopy characteristics and topography in modulating the accuracy with which trees are detected. We find that the accuracy with which we can detect trees is strongly dependent on the stand structure. Trees are mapped more accurately in dense woodland (≥50% tree cover) than more open woodlands (≥20% tree cover), and especially compared to isolated trees. Accuracy also varied with topography, with highest accuracies in flat terrain and reduced accuracies on steeper slopes. These results demonstrate clear potential for integrating Sentinel satellite monitoring systems within woodland management frameworks, while highlighting the importance of reporting context-dependent accuracy statistics with remotely sensed maps of tree or forest cover.
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    29 September 2021 • 2:40 PM - 3:00 PM BST | Alsh 1
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    The Paris Agreement to mitigate climate change and the associated UN initiative on Reducing greenhouse gas Emissions from Deforestation and forest Degradation (REDD+) necessitate Earth observation solutions for frequent monitoring of compliance to pledges for reducing deforestation rates. Near-real-time forest cover change monitoring at relevant spatial scales (30-50 m resolution) also improves efficiencies in forest management practices, supports local communities in managing natural and anthropogenic disturbances, helps to update forest inventories more frequently and enables a rapid response to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation data from multi-spectral optical/near-infrared wavelengths every 5 days at global scale. Here, we present a Sentinel-2 based near-real-time forest cover change monitoring system and an example of its application to areas of interest in Mexico and Kenya. The forest cover loss detections are carried out with a trained random forest machine learning model that is then applied automatically to the latest image acquisition with low enough cloud cover to provide a clear view of some of the area of interest. The results are transmitted to the user organisations via email alerts providing details on the number of forest loss detections, areas of each disturbed forest polygon, latitude and longitude and other metadata. The forest monitoring system is implemented in Python as an open-source library called pyeo, available on Github. It provides functionality for training the random forest model, searching for and downloading Sentinel-2, Landsat and Planet images and applying the random forest model to the new image data. An independent validation with very high-resolution Planet (~3m) and RapidEye (~5m) imagery is carried out to assess the accuracy of the forest cover loss data from Sentinel-2. The results indicate higher accuracies and faster detection using the monitoring system presented in this paper than with other monitoring systems. The proposed automated forest monitoring system can accurately detect forest cover loss with 92.5% accuracy and forest gain with 70.8% accuracy. It is scalable to larger regions, countries and continents.
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    Author(s): Andrew Revill, Vasilis Myrgiotis, Mathew Williams, The Univ. of Edinburgh (United Kingdom)
    On demand | Presented live 29 September 2021
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    Farmers are under increasing pressure to manage agricultural resources in a more sustainable and efficient manner. Information on crop nitrogen (N) status can be used to support variable rate fertiliser applications. Furthermore, yield forecasts can aid the logistical planning of harvest operations and ameliorate any negative economic impacts on food supply chains. Complex crop models simulating crop N dynamics and yields often require extensive model inputs that are seldom available. By combining observations of leaf area index (LAI) with a process-based crop model, this research presents a novel and scalable analytical solution for generating robust daily estimate of wheat N and yield, which can be applied at the sub-field scale. The crop model, DALEC-Crop, is a carbon cycle model that simulates the key processes involved in crop growth and development in response to daily meteorological observations. The model was first calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha-1 for two consecutive growing seasons. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 11%). Using these developments at the plot scale, the model yield estimates had a high agreement with observations (mean R^2 = 0.7 and NRMSE = 7%) when applied at the sub-field scale across field sites under the constraints of Sentinel-2 data. Although additional field sites and seasons are required for further testing, the modelling approach could be feasibly applied to estimate yields across large areas with only minimal inputs.
    Break
    Coffee Break/Poster Session 3:20 PM - 4:00 PM
    Poster Session
    29 September 2021 • 3:20 PM - 4:00 PM BST | Exhibition Hall 4
    Poster authors will be available during the Wednesday afternoon coffee break to further engage with their research in front of their posters.
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    Author(s): Viktor A. Shishko, V.E. Zuev Institute of Atmospheric Optics (Russian Federation); Alexander V. Konoshonkin, V.E. Zuev Institute of Atmospheric Optics (Russian Federation), National Research Tomsk State Univ. (Russian Federation); Natalia V. Kustova, Dmitriy N. Timofeev, Nadezhda Kan, Ilya V. Tkachev, Vasiliy Slesarev, Alexey Kozodoev, Anatoli G. Borovoi, V.E. Zuev Institute of Atmospheric Optics (Russian Federation)
    On demand | Presented live 29 September 2021
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    The purpose of this work is to solve an important issue: the light scattering problem for ice crystals of cirrus clouds less than 10 μm and matching the obtained solution with the existing solution obtained within the physical optics approximation. The article presents a solution to the problem of light scattering by hexagonal ice particles of cirrus clouds with sizes from 0.05 to 5.17 μm for a wavelength 0.532 μm, obtained within the discrete dipole approximation. It is found that the obtained solution is in good agreement with the physical optics approximation in the vicinity of scattering angles of 0–10º (the vicinity of forward direction scattering). However, to solve the problem of light scattering in the vicinity of the backward scattering direction, which is important for the interpretation of lidar data, it is necessary to continue the calculations to sizes of the order of 20 μm. The results obtained are necessary for constructing algorithms for the interpretation of lidar data obtained by sounding cirrus clouds.
    Session 4: Satellite Missions for Sustainability: New Assets and Capability
    29 September 2021 • 4:00 PM - 5:50 PM BST | Alsh 1
    Session Chairs: Richard Tipper, Ecometrica (United Kingdom), Callum J. Norrie, The Univ. of Edinburgh (United Kingdom)
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    Author(s): Justyna Kosianka, Michael A. Allen, Nicholas Rodgers, Ursa Space Systems Inc. (United States)
    On demand | Presented live 29 September 2021
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    Synthetic aperture radar (SAR) offers persistent, all-weather, day or night remote sensing capabilities for a dynamic characterization of the Earth. Ursa Space Systems’ unique and growing virtual constellation of over 20 SAR satellites provides complex data that is processed into imagery, enabling advanced analytics to detect and track change, including mapping rapidly-evolving spatio-temporal change events such as natural disasters such as hurricanes. Our collection of SAR-based change analytics provides up-to-date information via rapid mobilization of the analytic, from the event alert to satellite image tasking, data ingest, processing, and analytic generation delivered to the user for decision making. As satellites provide continuous monitoring of the earth, we derive climate change related insights before, during, and after disaster events. We start to understand a vulnerable region by examining a time series low-resolution SAR data and generating historical change maps which we then analyze to detect trends in the detection maps and infer significant change events. Based on these preliminary results, we task high-resolution SAR data for further investigation. This can specifically alert us to phenomena such as sea level rise. Ahead of the hurricane, we strategically task imagery using expected storm tracks from Spire and other weather-forecasting data providers to perform flood mapping during the storm. After the storm passes, we continue to collect data to monitor the region, generating fused data layers describing storm damage and standing water with additional land-use land-cover (LULC) context. With specially tasked imagery, we also examine soil moisture and other detailed ground disturbance through interferometric SAR (InSAR). The above processes will be demonstrated in a series of use cases including examples following Hurricanes Irma in 2017 and Dorian in 2019.
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    Author(s): Alexander T. Merrington, David T. Milodowski, Mathew Williams, The Univ. of Edinburgh (United Kingdom)
    On demand | Presented live 29 September 2021
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    Habitat mapping is key to meeting land management and conservation objectives, from supporting estimation of natural capital in a landscape to monitoring habitat change over time. Current land management challenges, including conserving rare species and habitats, illustrate a growing need for spatially extensive and rapidly updatable biodiversity information; a need that can best be met through remotely sensed imagery combined with the remarkable data processing potential of machine learning. We assess the potential for optical satellite data to classify complex upland habitat types in the Scottish Highlands, following two UK national habitat classification frameworks. We explore how the differences in spatial and spectral resolution of satellite sensors affects the accuracy of derived habitat maps. Specifically, we contrast the performance of open-source Sentinel-2 data (20 m spatial resolution) against higher spatial-resolution data from the commercial WorldView-2 satellite (0.5 m resolution). We then compare the results produced with these satellite datasets against equivalent results obtained with high-resolution (25 cm) colour airborne photographs, to better inform users on the utility of available optical data before subsequent analysis, e.g. natural capital assessments, in comparable settings. We demonstrate that high-fidelity habitat maps (93% overall accuracy) can be produced using high resolution optical satellite data (WorldView-2). This level of accuracy exceeded that of maps derived from airborne surveys (~75%) and is deemed sufficient to be useful to ecologists in-situ. In contrast, the capacity of Sentinel 2 data was considerably more limited (~50% overall accuracy). This highlights the importance of spatial resolution for characterising habitat mosaics at scale, especially in settings such as upland Scotland where shifts in habitat and species composition of importance to land managers may occur at relatively fine length scales (<10m). Provided high spatial resolution optical data is available, the framework developed is scalable to a national scale, therefore, facilitating effective land management strategies.
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    Author(s): Blanca Arellano, Josep Roca Cladera, Dolors Martinez, Carina Serra, Javier Lana, Rolando Biere, Univ. Politècnica de Catalunya (Spain)
    On demand | Presented live 29 September 2021
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    The estimation of the Earth's surface temperature (LST) from the infrared thermal radiation (TIR) emitted by the Earth detected by means of remote sensing has allowed a giant leap in climate analysis. The specialized literature has highlighted the singular importance of the LST in the generation of the Urban Heat Island (UHI), especially at night. It is during the night that the effects of UHI become more apparent, due to the low cooling capacity of urban construction materials and is during nighttime that temperatures can cause higher health risks, leading to the aggravation of negative impacts on people’s health and comfort in extreme events such as heat waves becoming more and more frequent and lasting longer. However, the study of nocturnal UHIs is still poorly developed, due to the structural problems. On the one hand, the scarcity of meteorological stations that allow obtaining the air temperature (Ta) with an adequate degree of spatial resolution. And, on the other, due to the limited temporal availability of medium / high resolution nocturnal satellite images that make it possible to know the LST at night. Traditional methods for obtaining nocturnal UHI have been directed either to extrapolation of data from weather stations, or obtaining Ta through urban transects. In the first case, the lack of weather stations in urban landscapes makes it extremely difficult to obtain data to extrapolate and propose models at a detailed resolution scale. In the second case, there is a manifest difficulty in obtaining data simultaneously and significantly representative of urban and rural zones. Remote sensing images are another methodology used to measure nighttime UHI, but the greatest limitation of this method is the scarcity of high-resolution images that allow rigorous nighttime LST to be obtained. Only MODIS, or Sentinel 3, offer free mid-resolution nighttime thermal imaging for LST and UHI analysis. Furthermore, the integration of LST (obtained from remote sensing imagery) with Ta (obtained from weather stations) continues to be a pending challenge. The right estimation of the temperature of the air at ≈ 2-m height above ground (Ta) from LST is possible but complex. The vertical lapse rate to be applied is function of the surface energy balance, which varies in function of the nature of the surface and of the instant of the day. The objective of this paper is to integrate the information derived from the thermal band of satellite images (LST) with the "in situ" measurements of the Ta obtained at meteorological stations. The methodology used consists, first, of developing a model by means of multi-regression analysis of the night air temperatures, using as explanatory variables, in addition to the physical characteristics of the territory (longitude, latitude, altitude, distance to the sea, slope, orientation, NDVI, albedo), the characteristics derived from urbanization (NDVI, NDBI, Sky View Factor, building index, land use, ...) as well as the LST obtained by means of MODIS and Sentinel 3. And, secondly, downscaling the previous model, by means of the derived information of Landsat 8 and Sentinel 2. In this way, a set of models is obtained at different resolutions that allow estimating the nighttime temperature at a detailed level (a grid of 100 x 100 meters). The case study is the Metropolitan Area of Barcelona (636 km2, 3.3 million inhabitants).
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    Author(s): Tomasz S. Tkaczyk, Rice Univ. (United States)
    On demand | Presented live 29 September 2021
    Closing Remarks
    29 September 2021 • 5:50 PM - 5:55 PM BST | Alsh 1
    Mathew Williams, The Univ. of Edinburgh (United Kingdom)
    Conference Chair
    Mathew Williams
    The Univ. of Edinburgh (United Kingdom)
    Conference Chair
    The Univ. of Edinburgh (United Kingdom)
    Conference Chair
    The Univ. of Edinburgh (United Kingdom)
    Program Committee
    Space Intelligence (United Kingdom)
    Program Committee
    Mathias Disney
    Univ. College London (United Kingdom)
    Program Committee
    Jenni Doonan
    The Univ. of Edinburgh (United Kingdom)
    Program Committee
    The Univ. of Edinburgh (United Kingdom)
    Program Committee
    Gay Jane P. Perez
    Univ. of the Philippines Diliman (Philippines)
    Program Committee
    Tristan Quaife
    The Univ. of Reading (United Kingdom)
    Program Committee
    Science and Technology Facilities Council (United Kingdom)