{"schedule": {"version": "0.8", "base_url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/schedule/", "conference": {"acronym": "open-earth-monitor-global-workshop-2024", "title": "Open Earth Monitor \u2014 Global Workshop 2024", "start": "2024-09-30", "end": "2024-10-04", "daysCount": 5, "timeslot_duration": "00:05", "rooms": [{"name": "Theatre Hall (Conference Center Laxenburg)", "guid": null, "description": "Main room", "capacity": 350}, {"name": "Maria Theresia Seminar room (Conference Center Laxenburg)", "guid": null, "description": "Oral talks", "capacity": 46}, {"name": "Wodak Room (IIASA)", "guid": null, "description": "Workshop room 1 - IIASA", "capacity": 40}, {"name": "Raiffa Room (IIASA)", "guid": null, "description": "Workshop room 2 - IIASA", "capacity": 28}, {"name": "Foyer", "guid": null, "description": "Poster sessions", "capacity": null}], "days": [{"index": 1, "date": "2024-09-30", "day_start": "2024-09-30T04:00:00+01:00", "day_end": "2024-10-01T03:59:00+01:00", "rooms": {}}, {"index": 2, "date": "2024-10-01", "day_start": "2024-10-01T04:00:00+01:00", "day_end": "2024-10-02T03:59:00+01:00", "rooms": {}}, {"index": 3, "date": "2024-10-02", "day_start": "2024-10-02T04:00:00+01:00", "day_end": "2024-10-03T03:59:00+01:00", "rooms": {"Theatre Hall (Conference Center Laxenburg)": [{"id": 193, "guid": "b9b403a1-3ffb-5b25-b715-2018d1ad0411", "logo": "", "date": "2024-10-02T09:45:00+01:00", "start": "09:45", "duration": "00:15", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-193-welcome-plenary", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/UVBQKM/", "title": "Welcome plenary", "subtitle": "", "track": null, "type": "Poster presentation", "language": "en", "abstract": "Welcome plenary by Steffen Fritz - International Institute for Applied Systems Analysis (IIASA)", "description": "", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}, {"id": 186, "guid": "fa5ac59f-299c-56c2-81d1-4a5846ce8006", "logo": "", "date": "2024-10-02T10:00:00+01:00", "start": "10:00", "duration": "00:30", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-186-the-role-of-earth-observation-for-the-european-bauhaus-initiative-", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/KD9R8T/", "title": "The Role of Earth Observation for the European Bauhaus Initiative\"", "subtitle": "", "track": null, "type": "Keynote lecture", "language": "en", "abstract": "Please add an abstract as soon as possible", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 237, "code": "H8UKSB", "public_name": "John Schellnhuber", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 194, "guid": "5d181053-f17a-591e-8654-8fdc59425608", "logo": "", "date": "2024-10-02T10:30:00+01:00", "start": "10:30", "duration": "00:15", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-194-the-open-earth-monitor-project", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/SUN8YE/", "title": "The Open-Earth-Monitor Project", "subtitle": "", "track": null, "type": "Poster presentation", "language": "en", "abstract": "The Open-Earth-Monitor Project - Coordinator of the Open-Earth-Monitor project and director of the Opengeohub Foundation", "description": "", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}, {"id": 189, "guid": "008440e4-d7d6-5576-a27e-edf03614193c", "logo": "", "date": "2024-10-02T11:30:00+01:00", "start": "11:30", "duration": "00:30", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-189-10-years-of-global-forest-watch-from-data-to-impact", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/ZTMYJL/", "title": "10 years of Global Forest Watch \u2013 from data to impact", "subtitle": "", "track": null, "type": "Keynote lecture", "language": "en", "abstract": "Please provide an abstract as soon as possible", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 239, "code": "G7NC38", "public_name": "Elizabeth Goldman", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 161, "guid": "574c75ca-ccb7-5902-8edf-ea5dff294a8c", "logo": "", "date": "2024-10-02T12:00:00+01:00", "start": "12:00", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-161-monitoring-livestock-and-agricultural-systems-an-ensemble-approach-based-on-data-harmonization", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/XRPRHR/", "title": "Monitoring livestock and agricultural systems: An ensemble approach based on data harmonization", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Approximate five billion hectares (38%) of global land area is used for agricultural system, contributing significantly to the loss of biodiversity and having a substantial impact on water resources and greenhouse gas emissions of the World. Aiming to support multi-scale environmental policies and decision making process, several land monitoring systems / products were launched in the last years, including WorldCereal, GLaNCE, Dynamic World, UMD GLAD GLCLUC, GLC_FCS30D and Global Pasture Watch. Even though all these systems / products have different advantages, limitation, constraints and resolutions (thematic, spatial and temporal), in general they have a high potential to be combined to support different land cover and land use applications at global, national and local scale. Here we present a framework able integrate global monitoring systems / products in an automated, flexible and reproducible way, taking advantages of new technologies as cloud-optimized formats and cloud services. We demonstrated it integrating different crop and pastures classes in seamless monitoring system for the tropics, allowing the users to define their own area of interest, harmonization rules and overlap criteria. The implementation is publicly available in scikit-map (https://github.com/openlandmap/scikit-map) and all input layers publicly accessible through SpatioTemporal Asset Catalog (STAC).", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 94, "code": "DLL88P", "public_name": "Leandro Leal Parente", "biography": "Computer scientist with a PhD in Environmental Science working with remote sensing, data science, machine learning, high-performance computing and WebGIS applications.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 130, "guid": "7bc54c96-f191-5f66-b52e-7852377b8028", "logo": "", "date": "2024-10-02T12:40:00+01:00", "start": "12:40", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-130-assessing-forest-disturbance-dynamics-and-drivers-using-radar-satellite-data", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/9VNA9W/", "title": "Assessing forest disturbance dynamics and drivers using radar satellite data", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Satellite radar remote sensing utilizes long-wavelength energy that can penetrate clouds and is sensitive to changes in the physical structure of vegetation. These characteristics, in combination with the high spatial and temporal detail of new and near-future radar satellites, provide major opportunities for monitoring forest disturbances and regrowth dynamics.\r\n\r\nWe provide an overview of recent research activities on the use of radar remote sensing to monitor forest dynamics and present key results achieved in the Open-Earth-Monitor project. These include forest disturbance monitoring, monitoring of forest loss drivers and carbon, and assessments of selective logging intensity. We will highlight how the near-future availability of freely available multi-frequency radar data from Sentinel-1 (C-band), NISAR (L-band), and BIOMASS (P-band) will improve our ability to assess forest dynamics. We will also discuss our open-source initiatives aimed at facilitating the adoption of radar data and change detection approaches by both the scientific community and country stakeholders.", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"id": 28, "code": "HBZHEB", "public_name": "Johannes Reiche", "biography": "Dr Johannes Reiche is assistant professor at the Laboratory of Geo-information Science and Remote Sensing, Wageningen University where he leads the radar remote sensing group and the RADD forest disturbance alert development. He obtained his PhD (2015) from Wageningen University and his MSc (2011) from the University of Jena. His research interest is on utilizing radar remote sensing to unravel human activities in and dynamics of forest ecosystems, with a strong focus on multi-sensor methods, near real-time change monitoring and characterisation of drivers, follow-up land use and commodities.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 181, "guid": "66d627a4-0606-5b26-8773-4665c8dd28ce", "logo": "", "date": "2024-10-02T14:30:00+01:00", "start": "14:30", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-181-mapping-land-use-management-in-europe-using-remote-sensing-in-situ-data-and-statistical-information", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/SYHL9S/", "title": "Mapping land use management in Europe using remote sensing, in situ data and statistical information", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "There is currently a lack of high-resolution pan-European information on land use management, especially in terms of how forests, cropland and grassland are intensively and extensively managed. This is partly due to the lack of ground-based information, which is needed to downscale these types of management practices (some of which are captured in different types of agricultural censuses and surveys and National Forest Inventories) as well as the inability of remote sensing to capture different kinds of land use. This type of information is needed for economic land use modelling and for assessing policy impacts, such as the latest reforms from the Common Agricultural Policy (CAP) and other European Union (EU) Green Deal targets. These types of analyses are undertaken using economic land use models such as GLOBIOM and CAPRI, which is one of the main aims of the Horizon Europe funded LAMASUS project (https://www.lamasus.eu/).  \r\n \r\nOne of the main inputs to the development of a land use management map is Corine land cover, which is a remotely sensed product developed by the Copernicus Land Monitoring Service every six years. First, we produced an annual time series of Corine from 2000 to 2018 by using the high-resolution land cover times series produced by OpenGeoHub and the BFAST algorithm applied to MODIS data to determine the year of change between the six-year production cycle of CORINE. Any remaining changes that were unaccounted for had the year of change selected randomly. Transition rules were also applied to ensure that the land cover/land use transitions were reasonable. \r\n\r\nLand use management classes for forest, cropland, grassland and urban areas were then devised in collaboration with the modelers in the LAMASUS project as well as around 30 stakeholders who participated in the first LAMASUS stakeholder workshop. Using different input data sets from remote sensing, in-situ data (from LUCAS), modelled data from CAPRI, and statistical information from agricultural censuses, surveys and other sources, rules were developed to allocate the Corine land cover classes to more detailed land use management classes. Here we will present the results of this mapping along with a method for how the map has been fit to official area statistics so that this information can be used by the economic land use models in LAMASUS.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 229, "code": "CSQZHQ", "public_name": "Linda See", "biography": "Linda See is a Principal Research Scholar in the Novel Data Ecosystems for Sustainability (NODES) research group at the International Institute for Applied Systems Analysis (IIASA). Her main research interests include artificial intelligence-based methods, geographic information systems (GIS), land cover, crowdsourcing, and citizen science. She is currently an editor of Environment and Planning B.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 180, "guid": "979a3808-a886-5602-88ec-cb8bd554502a", "logo": "", "date": "2024-10-02T15:30:00+01:00", "start": "15:30", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-180-high-resolution-spatial-information-on-livestock-density-and-grassland-management-in-europe", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/GW8KJK/", "title": "High-resolution spatial information on livestock density and grassland management in Europe", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Improving the sustainability of the European livestock sector requires high resolution spatial data. Otherwise potential negative impacts of livestock related to local ecosystem degradation, as well as positive ones such as preserving cultural landscapes through grazing cannot be analysed. Data on livestock numbers usually used in scientific analyses are collected and provided by the European statistical office, but are provided on a rather coarse spatial resolution of statistical regions. In addition, data on the actual use of grasslands, whether grazed, mown and the intensity of their use is not collected systematically or not at all. We provide an approach for mapping grazing livestock (cattle, small ruminants) density and the use of grassland for Europe. We first collected livestock numbers on a local level for all EU countries, which we harmonized, and supplemented it with statistics on actual outdoor grazing of animals. We then mapped areas that are grazed by combining EU-wide in-situ data on grazing with a set of socio-economic, terrain, soil and climate characteristics using machine learning. We then allocated grazing livestock on two different earth observation derived land use and land cover products: corine land cover and the high resolution grassland layer. Our approach enables identifying areas that are grazed, and combined with livestock statistics, also how intensively these areas are used either for grazing or mowing. Such information can support tracking the state of european grassland ecosystems, landscape conservation, as well as other environmental dimensions related to the livestock sector, such as nitrogen deposition, with a high spatial detail. Finally, by using regularly updated systematically collected data, we can update the data in the future.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 228, "code": "S7PLGK", "public_name": "Ziga Malek", "biography": "Ziga Malek is a landscape scientist interested in combining statistical and in-situ data on land use with earth observation products to map the way we use our terrestrial surface beyond what we can observe with satellites. This way, he has mapped the spatial distribution of organic farmers worldwide, land-use decision making and grazing in seminatural areas in Europe. In addition, he has developed land use models - many of which used data derived from earth observation - all across the globe and on different scales. Ziga has obtained his engineers degree at the University of Ljubljana, Slovenia, and his PhD at the University of Vienna, Austria.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 172, "guid": "4db516e2-f349-5b3f-be8d-340c17c3b43b", "logo": "", "date": "2024-10-02T16:30:00+01:00", "start": "16:30", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-172-actual-and-potential-habitat-and-vegetation-type-mapping-to-support-conservation-science", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/QBNFXV/", "title": "Actual and potential habitat and vegetation type mapping to support conservation science", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Earth observation data provides an invaluable resource to assess the state and condition of the environment. However, many domain\u2013specific applications, such as the mapping of species-specific habitats and vegetation for conservation, often require specific spatial and thematic resolutions, rather than off\u2013the\u2013shelf products. And although remotely sensed data is critical to assess actual coverage, particularly for the assessment of restoration opportunities, knowledge on the potential distribution of habitats and vegetation is usually required. Here I will provide an overview of ongoing efforts to estimate current and potential vegetation types across global, European and local extents. I will focus both on approaches to integrate existing data sets for global and European habitat estimates, but also demonstrate the potential of earth observation data and deep learning to identify vegetation types at high resolution. Finally, I will highlight opportunities to bring the Earth observation and ecology community closer together particularly in the light of data gaps, harmonization and standards.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 214, "code": "LGJCLH", "public_name": "Martin Jung", "biography": "Martin Jung is a Senior Research Scholar with the Biodiversity, Ecology, and Conservation (BEC) Research Group. His current research focusses broadly on nexus topics of land-use, socio-economic management practices and biodiversity indicators as well as investigating cost-effective solutions for robust monitoring and planning of conservation interventions for species and ecosystems.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 148, "guid": "2641366d-d2f8-5fce-8dc9-3d0d2250ba4f", "logo": "", "date": "2024-10-02T16:50:00+01:00", "start": "16:50", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-148-land-cover-change-and-biodiversity-pressures-a-global-analysis-leveraging-eo-data", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/Q9UQSZ/", "title": "Land cover change and biodiversity pressures: A global analysis leveraging EO data", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Biodiversity loss is a critical environmental concern, with habitat destruction and degradation identified as key drivers. Recent advancements in computational methods and the ever-growing availability of Earth Observation (EO) data enable detailed analyses of land cover changes at unprecedented spatial and temporal scales. This paper develops a set of indicators of land cover and land cover conversions to assess potential pressures on terrestrial biodiversity and ecosystems. Key land cover conversions include deforestation/reforestation, cropland expansion/contraction, and urban/infrastructure development. We leverage two high-resolution datasets (i.e. the Copernicus Climate Change Initiative Land Cover [CCI-LC] and the Global Human Settlement Layer [GHSL] built-up area) to develop national and subnational indicators for all countries globally, spanning 2000-2020 for CCI-LC and 1975-2030 for GHSL. The analysis reveals a continued decline in natural and semi-natural vegetation cover in many OECD countries and partner countries since the 2000 baseline. For example, Brazil experienced a substantial loss of tree cover (200,000 km\u00b2) between 2000 and 2020, equivalent to an area exceeding Switzerland's landmass by a factor of six. Meanwhile, most OECD countries exhibited a net gain in tree cover during the same period. Urban development is another key reason for the observed decline in natural and semi-natural vegetated land where countries such as China and India displayed a significantly higher increase in artificial surfaces compared to OECD countries over the past two decades. Results currently only account for the ecosystem extent and do not account for the ecosystem condition. For instance, some grassland land cover may have been significantly modified by long-term grazing and is in fact intensively managed grassland (wild prairies vs grassland pastures). Therefore, these results should be considered alongside complementary data sources to provide a more comprehensive picture of biodiversity pressures and highlight that current global land monitoring EO products do not adequately meet the needs of policy analysts who require data at the interface of land cover and land use.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 197, "code": "U83UEX", "public_name": "Mika\u00ebl Maes", "biography": "Mika\u00ebl Maes is a climate and environmental data scientist with over 9 years of professional experience in international organizations, academia, and the private sector. He currently works at the Organisation for Economic Co-Operation and Development (OECD) in Paris (France), working with OECD countries and analyzing large geospatial data sources to support them with environmental and climate change challenges. Previously, Mika\u00ebl obtained a doctorate degree in Environmental Science at University College London, where he was part of a large epidemiological study researching the associations between nature exposure and children's mental health and cognitive development. Mika\u00ebl also holds an MSc degree in Environmental Science from King's College London, as well as MSc and BSc degrees in Biology from Ghent University.", "answers": []}, {"id": 234, "code": "GXD3S8", "public_name": "Ivan Ha\u0161\u010di\u010d", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 144, "guid": "7212a7ed-3e5d-5607-9a0c-4e1817c0fa7c", "logo": "", "date": "2024-10-02T17:10:00+01:00", "start": "17:10", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-144-dynamic-flood-susceptibility-assessment-harnessing-high-resolution-data-for-effective-risk-reduction", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/LYJ8FV/", "title": "Dynamic Flood Susceptibility Assessment: Harnessing High-Resolution Data for Effective Risk Reduction", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Accurate and timely flood risk assessment is paramount for effective disaster mitigation and preparedness. Traditional flood susceptibility maps (FSMs) often fall short by providing static representations, failing to capture the dynamic nature of flood risk in a changing climate. This study presents a novel dynamic FSM framework that integrates high-resolution climate data and temporal analysis to address these limitations. Developed within the context of the Open-Earth-Monitor Cyberinfrastructure (OEMC) project, our methodology offers a significant advancement in flood risk modeling.\r\nTo generate dynamic, high-resolution (1 km) FSMs for the Mediterranean region, we utilized the Random Forest algorithm. These maps uniquely adapt to varying seasonal conditions, precipitation intensities, and post-drought scenarios. Our model's adaptability stems from its training on a comprehensive dataset that combines flood and non-flood locations from the Copernicus Emergency Management Service (EMS) and the Global Flood Database v1. Additionally, we incorporated crucial factors influencing flood events, including elevation, slope, land cover, drainage density, soil moisture, and precipitation. Model evaluation employed cross-validation techniques utilizing both training and testing datasets. This comprehensive assessment confirmed the superior performance of the Random Forest model, solidifying its effectiveness as a robust tool for flood susceptibility mapping.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 196, "code": "UMMSWN", "public_name": "Hamidreza Mosaffa", "biography": "I'm a Postdoctoral Researcher at the National Research Council of Italy (IRPI-CNR), working with Prof. Luca Brocca. I'm interested in developing machine learning tools to enhance hydrological monitoring and modeling. This work aims to improve our ability to mitigate natural hazards. My current research is focused on high-resolution satellite products with deep learning algorithm and create hybrid physics-aware models.", "answers": []}], "links": [], "attachments": [], "answers": []}], "Maria Theresia Seminar room (Conference Center Laxenburg)": [{"id": 167, "guid": "321cec30-ba1f-553c-b299-def5ae23fb62", "logo": "", "date": "2024-10-02T12:00:00+01:00", "start": "12:00", "duration": "00:20", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-167-quantifying-the-mis-match-between-in-situ-and-satellite-time-series-the-case-of-eddy-covariance-flux-observations", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/XQPMSH/", "title": "Quantifying the (mis)match between in-situ and satellite time series: the case of eddy covariance flux observations", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Eddy covariance (EC) systems are commonly used to measure the net exchanges of energy, water carbon dioxide (CO2) and other trace gasses between the ecosystems and the atmosphere. Such measuring systems have been established in different ecosystems and climate regimes across the globe, thereby providing invaluable ground information to understand ecosystem dynamics at global scale. Although the number of EC stations installed worldwide (e.g. FLUXNET sites) are constantly growing with time, their spatial distribution is limited in comparison to the vast complexity of land ecosystems. Furthermore, EC towers track the exchange of energy and matter from an area (often referred to as a footprint) that spans some few hundred meters around and upstream of the measurement site (the so-called fetch), and which can vary according to meteorological conditions. Remote sensing (RS) and in-situ flux datasets are commonly combined to upscale the exchanges of carbon and energy at a global scale (e.g. the FLUXCOM project), as well as for calibration and validation activities. The challenge to do this correctly lies in trying to link the footprints of the EC measurements to those of the satellite measurements, a task that is often disregarded or oversimplified. In this study we designed a methodological approach within the Open-Earth-Monitor (OEMC) project to estimate dynamically the match (or mismatch) between some likely proxies of EC footprints (approximated as circles with radius from 50 to 200 meters) and the footprints of (coarse) spatial resolution RS time series. To quantify the degree of mismatch we collect Sentinel-2 images at 10 meters resolution for several EC sites over Europe. Then, we compute the kNDVI vegetation index for all the sites masking clouds and cloud shadows. We also define proxies for different pixel sizes of satellite data ranging from 500 meters to 1500 meters radius around the tower. To compare the EC footprints with the Satellite pixel resolution we compute the Jensen-Shannon index that quantifies the amount of information (in terms of kNDVI) shared between both scales at every available time step. As a result, we provide initial recommendations of when in the year the sites are more suitable to be matched with satellite data according to the surrounding phenology. We expect these will open the possibility to correct biases in future upscaling fluxes exercises and remote sensing products calibration.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 48, "code": "KMEEGP", "public_name": "Daniel E. Pabon-Moreno", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 157, "guid": "f19ce471-5210-5c99-bf8a-3514fc27d93e", "logo": "", "date": "2024-10-02T12:40:00+01:00", "start": "12:40", "duration": "00:20", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-157-worldcereal-a-dynamic-open-source-system-for-global-scale-seasonal-and-reproducible-crop-mapping", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/WMWG8N/", "title": "WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop mapping", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "The WorldCereal project, funded by the European Space Agency (ESA), aims to provide a comprehensive understanding of global cropped areas, irrigation practices, and the distribution of major commodity crops. WorldCereal has developed a dynamic open-source system that generates a range of products, including temporary crop extent, seasonal maize and cereal maps, seasonal irrigation maps, seasonal active cropland maps, and confidence layers. These products are based on the analysis of Sentinel-1 and Sentinel-2 imagery at 10 m spatial resolution, complemented by Landsat 8 imagery and AgERA5 meteorological information, and are updated at seasonal intervals for each agricultural system. WorldCereal has demonstrated the feasibility of global crop mapping by producing the first global, seasonally updated crop and irrigation maps for the year 2021. WorldCereal has also released a fully open, harmonized database of in-situ reference data related to land cover, crop type, and irrigation, enabling a broad community to access and contribute to this growing resource. WorldCereal is now entering a new phase, in which the system is being implemented as a cloud-based processing service in the new Copernicus Data Space Ecosystem. The system will offer more flexibility and customization options to users, allowing them to generate tailored crop type products for their regions of interest. Moreover, the WorldCereal product suite will be extended with eight new crops, and the in-situ reference database will be updated and expanded. WorldCereal will also conduct a series of regional use cases and capacity building activities to demonstrate the system\u2019s capabilities and to boost user uptake by the broad agricultural monitoring community. WorldCereal provides a vital tool for policymakers, international organizations, and researchers to better understand local to global cropping patterns and to inform decision-making related to food security and sustainable agriculture.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 200, "code": "UMJGLK", "public_name": "Kristof Van Tricht", "biography": "Remote sensing researcher active in the agricultural domain, from crop classification to crop monitoring, in support of global food security.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 152, "guid": "00983b5a-b9ca-504b-8037-294b5fd71a85", "logo": "", "date": "2024-10-02T14:30:00+01:00", "start": "14:30", "duration": "00:20", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-152-assessing-population-exposure-to-air-pollution-a-multi-pollutant-indicator-framework-for-oecd-countries-and-partners", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/WHDWVK/", "title": "Assessing population exposure to air pollution: A multi-pollutant indicator framework for OECD countries and partners", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Air pollution, particularly fine particulate matter (PM2.5), ground-level ozone (O3), and nitrogen dioxide (NO2), poses a significant global health risk, contributing to early mortality. Measuring population exposure is crucial for understanding and mitigating these health impacts. This paper leverages recent advancements in air pollution data to review various global air pollution datasets based on a criteria set. The framework facilitates comparisons between various hybrid datasets (combining ground-based and satellite measurements) and offers a methodology for constructing air pollution exposure indicators for PM2.5, O3, and NO2. It uses the Global Burden of Disease data to update the indicator set on the national and subnational levels across the 1990-2020 period. Results reveal that most OECD countries fall short of the World Health Organization's (WHO) 2021 air quality guidelines for PM2.5, O3, and NO2. Countries such as Chile, Korea, Poland, and T\u00fcrkiye exhibit PM2.5 concentrations (population weighted) exceeding safe levels by a factor of four. Similarly, several OECD countries such as Korea, Italy, and Slovenia experienced severe O3 exposure in 2020, while non-OECD countries such as India displayed even higher population weighted O3 concentrations, exceeding safe levels by more than double. A sensitivity analysis further indicates that despite similar trends observed across different air pollution datasets, considerable differences are found between global datasets and national statistics. This highlights the need to further examine the accuracy of the various data sources and help guide policy analysis at the national and subnational levels. Given the widespread failure to meet safe air quality standards, our findings emphasize the urgent need for global policy actions to reduce population exposure to air pollution and safeguard public health.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 197, "code": "U83UEX", "public_name": "Mika\u00ebl Maes", "biography": "Mika\u00ebl Maes is a climate and environmental data scientist with over 9 years of professional experience in international organizations, academia, and the private sector. He currently works at the Organisation for Economic Co-Operation and Development (OECD) in Paris (France), working with OECD countries and analyzing large geospatial data sources to support them with environmental and climate change challenges. Previously, Mika\u00ebl obtained a doctorate degree in Environmental Science at University College London, where he was part of a large epidemiological study researching the associations between nature exposure and children's mental health and cognitive development. Mika\u00ebl also holds an MSc degree in Environmental Science from King's College London, as well as MSc and BSc degrees in Biology from Ghent University.", "answers": []}, {"id": 234, "code": "GXD3S8", "public_name": "Ivan Ha\u0161\u010di\u010d", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 134, "guid": "81a4c4b2-6306-5fcb-bd6b-66e277e77cfd", "logo": "", "date": "2024-10-02T15:30:00+01:00", "start": "15:30", "duration": "00:20", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-134-user-and-producer-perspectives-for-fair-environmental-data", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/VHWEHP/", "title": "User and producer perspectives for FAIR environmental data", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Findable, Accessible, Interoperable, and Reusable (FAIR) data principles are composed of a set of guidelines focused on efficient discovery and data utilization, which are crucial for sharing scientific data effectively. Hence, adapting to the FAIR principles benefits diverse environmental applications and supports a diversity of policies. This study presents the findings of an extended user survey conducted within the Open Earth Monitor Cyberinfrastructure (OEMC) project, exploring user perspectives on FAIR environmental data. For this purpose, an existing survey targeted at both users and producers of geospatial data was extended to enhance the representability and have the widest feedback for understanding users' and producers' needs, expectations, experiences, and understanding of FAIR principles. \r\nThe survey included three blocks. The first block addressed the background and general information of the survey respondents. The second block inquired about the characteristics of the geospatial data that has been primarily used or produced. The third block investigated how user and producer group participants are familiar with the FAIR principles and which of those seemed most relevant to them. In addition, we fostered a target-specific participant selection strategy to cover the main institutions and relevant user groups. \r\nThe survey revealed a discrepancy in the preferred observational scales between data producers and users. While producers primarily focus on generating data at global scales, users frequently require data at local and regional levels. This finding underscores the need for improved communication and collaboration between data providers and users to ensure data production aligns with user needs. Furthermore, the survey identified findability and openness as the top priorities for FAIR environmental data, alongside clear licensing, comprehensive metadata availability, and detailed documentation. \r\nThese findings emphasize the crucial role of robust data management practices and user-centric approaches in promoting the effective utilization of environmental data. \r\nFurther key findings from user responses will be presented, highlighting user perceptions of FAIRness in environmental data, current gaps in FAIR implementation, and identified challenges. Based on these insights, we will discuss the implications of the survey results and propose recommendations for advancing the FAIRness of environmental data in the future. \r\nThis research contributes to ongoing efforts within the OEMC project and beyond, informing strategies for improving the discoverability, usability, and overall value of environmental data for various stakeholders.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 184, "code": "QZLE7H", "public_name": "Katja Berger", "biography": "Dr Katja Berger received the PhD in Agriculture and Remote Sensing at the University of Applied Life Sciences in Vienna, Austria, in remote sensing, agriculture and forestry. She worked as senior scientist at the Department of Geography (Physical Geography and Remote Sensing), at the Ludwigs-Maximilians Universit\u00e4t (LMU) M\u00fcnchen in the framework of the preparations of the German hyperspectral EnMAP mission. In this context she focused on efficient retrieval algorithms for multiple crop traits. In 2022 she joined the Image Processing Laboratory (IPL) at the University of Valencia, Spain, supporting research activities in the context of ESA CHIME projects. Since February 2024 she is employed as Post Doc at GFZ Potsdam. The work focus is on engaging with stakeholders and building networks to promote the uptake of EO data and products for forest and land monitoring applications, from national to global levels. She participated actively in COST actions, such as the \u201cPan-European Network of Green Deal Agriculture and Forestry Earth Observation Science\u201d (PANGEOS) leading science communications with a focus on stakeholder engagement. She is organizing as main chair the EARSeL Imaging Spectroscopy workshop in April 2024 in Valencia and is an active member of the ESA\u2019s Sentinel-2 next generation Mission Advisory Group (MAG).", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 131, "guid": "5d7ddfb6-8643-51a3-9989-208c54146891", "logo": "", "date": "2024-10-02T16:30:00+01:00", "start": "16:30", "duration": "00:45", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-131-workshop-data-spaces-the-ec-solution-for-environmental-biodiversity-and-climate-challenges-different-approaches-on-multisource-data-semantics-fairness-and-sovereignty", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/FJCFNF/", "title": "Workshop: Data Spaces: the EC solution for environmental, biodiversity and climate challenges. Different approaches on multisource data, semantics, FAIRness and sovereignty", "subtitle": "", "track": "Pre-conference training sessions", "type": "Workshop proposal", "language": "en", "abstract": "Integrity of natural ecosystems is one of the main concerns of current European and Global Green Policies, e.g., the European Green Deal. Public administration managers need reliable and long-term information for a better monitoring of the ecosystems and climate evolution and inform decision makers. Data Spaces are intended to become the EC comprehensive solution to integrate data from different sources with the aim to generate and provide a more ready to use knowledge on climate change, circular economy, pollution, biodiversity, and deforestation. This workshop aims to discuss pros and cons of some technological solutions in terms of Data Spaces, OGC standards, semantic descriptions, datacubes, FAIR principles and sovereignty of data. It also intends to share lessons learned from main EC projects dealing with the topic: AD4GD, GREAT, B-cubed, Fairicube, etc.\r\n\r\nRecording of the session: \r\nhttps://youtu.be/JH14NmIazpc?si=I8jQUkY5uWhLfzE8", "description": "Solutions for multisource geospatial integration will be shared between:\r\n- AD4GD (Joan Mas\u00f3, Ivette Serral)\r\n- Fairicube (Kathi Schleidt)\r\n- USAGE (Giacomo Martirano), and \r\n- OEMC (Milutin Milenkovic)\r\n\r\nOpen discussion topics will focus on:\r\n- Semantic interoperability\r\n- Metadata\r\n- Multisource Data Integration\r\n- Connectors\r\n- Authentication & Privacy\r\n- Geospatial User Feedback (GUF)", "recording_license": "", "do_not_record": false, "persons": [{"id": 32, "code": "3TB8US", "public_name": "Joan Maso", "biography": "Dr. Joan Mas\u00f3 (m) is a Principal investigator of CREAF leading specialized group on geospatial interoperability, GIS, remote sensing . (PhD in Geography, MSc in Physics, and a MSc in Electronic Engineering all in the UAB). Since 1995 he is a researcher at CREAF and GIS developer. Co-creator of the MiraMon compressed map  in 1997 that has evolved into a distribution and preservation format. Teacher in a RS and GIS master in the UAB. Creator of Remote Sensing imagery visualization and download software for web data portals (the MiraMon Map Browser). Expert in JPEG2000 format. He is an active member of the TC of the Open Geospatial Consortium (OGC) since 2003 (editor OGC 07- 057r7 WMTS standard and the new OGC 20-057 OGC API Tiles among others and chair of the Iberian and Latin American Forum; ILAF). Spanish representative in the TC 211 and editor of the ISO 19165 Preservation of geospatial data and metadata. OGC Gardels gold medal in 2018. Coordinator of GeoViQua FP7 project (research project about visualization of quality information in GEOSS), H2020 ConnectinGeo and currently HE AD4GD. Participant in several European projects related with biodiversity, citizen science, remote sensing and research infrastructures such as H2020 ECOPOTENTIAL, H2020 GroundTruth 2.0, H2020 WeObserve H2020 ERA-PLANET, H2020 E-Shape, H2020 BestMap, H2020 COS4Cloud (INTRAEOSC project), H2020 Framework-biodiversity, H2020 WQeMS, HE OEMC, HE CitiObs, HE More4Nature, - ESA-IHE Phenotandem, EEA InCASE, as well as some other national and local projects related both with remote sensing and geospatial standards and applications. Earth and Space Science Informatics (ESSI) former division president in the European Geosciences Union (EGU). Chair of the OGC API Common, OGC API Tiles and OGC API Maps working groups and member of the OGC Architecture Board. Co-chair of the Citizens Science GEOSS working group. Senior member of IEEE and member of the International Society of Digital Earth (ISDE) council. Chair of the Community of Practice in Interoperability for Citizen Science.", "answers": []}, {"id": 183, "code": "AHF7PW", "public_name": "Ivette Serral", "biography": "Ivette Serral. BSc in Environmental Sciences and MSc in Remote Sensing and GIS for the Universitat Aut\u00f2noma de Barcelona, Spain. With more than ten years of experience in environmental geospatial research related to European and national projects. At CREAF she is focused on geospatial data management, from standardization to analysis and visualization following FAIR principles, She also contributes to the MiraMon GIS software development and applications. She co-coordinated the GeoViQua FP7 project (2007-2013) and ConnectinGEO H2020 (2015-2017). She is participating in AD4GD HE (2022-2024) on the development of the Green Deal Data Space and in coordinating the tasks in Pilot 2 on biodiversity and terrestrial habitat connectivity.\r\nShe is leading the GEO Community Activity on Essential Variables,\r\nShe is member of the EuroGEO Action Group on Data Spaces.", "answers": []}], "links": [], "attachments": [], "answers": []}], "Raiffa Room (IIASA)": [{"id": 139, "guid": "17c9b740-8e7d-5fa8-837b-0c176ca51bc5", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/ARXWB7/Task_5.8_LbFa5iF.jpg", "date": "2024-10-02T12:00:00+01:00", "start": "12:00", "duration": "00:45", "room": "Raiffa Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-139-workshop-meteoeurope1km-a-high-resolution-daily-gridded-meteorological-dataset-for-europe-for-the-1961-2020-period", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/ARXWB7/", "title": "Workshop: MeteoEurope1km: a high-resolution daily gridded meteorological dataset for Europe for the 1961\u20132020 period", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "Daily gridded meteorological datasets are an important source of information for analysis of historical weather and many other research areas since they have no gaps in the spatio-temporal domain they cover. Most of the daily gridded meteorological datasets represent reanalysis or estimations from different remote sensing sensors or are generated by downscaling procedures. A daily gridded meteorological dataset for Europe at 1 km spatial resolution, named MeteoEurope1km, is created, covering the 1961\u20132020 period and consists of five variables: maximum (TMAX), minimum (TMIN), and mean (TMEAN) temperature, sea-level pressure (SLP), and total precipitation (PRCP). Spatio-temporal regression kriging, an interpolation method that combines multiple linear regression for trend modeling and space-time kriging for the estimation of the residuals, is used for interpolation of daily temperature variables. Ordinary kriging is used for SLP and PRCP, except that for PRCP an additional step to predict PRCP occurrence is applied using Indicator kriging. Combination of GHCN-daily, ECA&D, and SYNOP observations from OGIMET service is used as an observational dataset, with previous removal of duplicated stations and outliers. Geometric temperature trend, digital elevation model and topographic wetness index are used as auxiliary variables for temperature datasets.  Accuracy assessment (leave-one-station-out cross-validation) shows high accuracy of the fitted models. Coefficient of determination for all temperature parameters and SLP is greater than 96%, while for PRCP is greater than 76 %. Root mean square error is 1.3\u00b0C, 1.6\u00b0C, 1.8\u00b0C, 1.5 mbar, and 2.5 mm for TMEAN, TMAX, TMIN, SLP, and PRCP, respectively. MeteoEurope1km is available as cloud optimized GeoTIFFs, and are accessible through dailymeteo.com portal, ZENODO, and R meteo package. Future work will be oriented towards increasing the spatial extent to other continents besides Europe, interpolation of other daily meteorological variables, and improving models performances by applying spatial machine learning methods, such as Random Forest Spatial Interpolation.", "description": "The workshop will be structured in two parts. The first part will be focused on the methodology applied, accuracy assessment, drawbacks and future plans. The second part of the workshop will focus on MeteoEurope1km data access and usage (COGs, ZENODO, QGIS, R meteo, Python, REST API, portal).", "recording_license": "", "do_not_record": false, "persons": [{"id": 188, "code": "BDCAAG", "public_name": "Aleksandar Sekuli\u0107", "biography": "Aleksandar Sekuli\u0107 is a technical manager of GILAB DOO BEOGRAD and an assistant professor at the Department of Geodesy and Geoinformatics, Faculty of Civil Engineering, University of Belgrade. His main areas of expertise are: GIS, Geostatistics, Machine Learning, and modelling of spatial and spatio-temporal phenomena, with an application in spatio-temporal interpolation of meteorological phenomena.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 128, "guid": "8601cb9f-78d2-5c90-a4d7-ec2bb68ee761", "logo": "", "date": "2024-10-02T14:50:00+01:00", "start": "14:50", "duration": "00:45", "room": "Raiffa Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-128-workshop-streamlining-earth-observation-data-sharing-with-the-zen-python-library", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/DNNSQM/", "title": "Workshop: Streamlining Earth Observation Data Sharing with the zen Python Library", "subtitle": "", "track": "Pre-conference training sessions", "type": "Workshop proposal", "language": "en", "abstract": "This workshop equips participants with hands-on experience managing their EO data on Zenodo using the zen Python library. Participants will learn to customize metadata and automate data management workflows using zen scripts.", "description": "This workshop introduces the zen library developed as part of the OEMC project. zen can simplify the upload and management of Earth Observation (EO) datasets on the Zenodo platform, making it easier for researchers and developers to adhere to Open Science principles and share their data openly.\r\n\r\nObjectives:\r\n- Familiarize participants with the Zenodo platform and its role in Open Science.\r\n- Introduce the zen library and its functionalities for managing EO datasets.\r\n- Guide participants through hands-on exercises on uploading EO datasets; customizing metadata; and automating data management workflows.", "recording_license": "", "do_not_record": true, "persons": [{"id": 182, "code": "XYH7SW", "public_name": "Deleted User", "biography": "Rolf obtained his doctoral degree in Environmental Sciences in 2021 and has a background in geoinformatics, complex systems modeling, and business administration. At OpenGeoHub, he is a postdoctoral researcher.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 174, "guid": "1fe86d1d-bdff-5335-9cd6-f4f8c82a42b0", "logo": "", "date": "2024-10-02T16:30:00+01:00", "start": "16:30", "duration": "00:45", "room": "Raiffa Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-174-workshop-the-nrt-ecosystem-a-unified-approach-to-forest-disturbance-monitoring", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/XRCSRU/", "title": "Workshop: The nrt Ecosystem: A Unified Approach to Forest Disturbance Monitoring", "subtitle": "", "track": null, "type": "Workshop proposal", "language": "en", "abstract": "*nrt* is a Python package designed to streamline environmental monitoring efforts by offering a unified Application Programming Interface (API) for a diverse array of forest disturbance monitoring algorithms. This unified API simplifies the process for users, enabling easy comparison and integration of different algorithms that are optimized for rapid computation and scalable deployment.\r\n\r\nBeyond its core functionality, the *nrt* ecosystem encompasses additional tools that enhance its utility and versatility. These include **diagnostics**, **time-series simulation**, **generation of reference data**, and **computation of accuracy metrics**. Collectively, these features make *nrt* a valuable resource for environmental monitoring and analysis, catering to a wide range of research and operational needs.\r\n\r\nDuring the workshop, participants will engage in hands-on demonstrations covering the various aspects of the *nrt* ecosystem. This practical experience aims to equip attendees with the knowledge and skills necessary to effectively utilize this tool in their projects, enhancing their capability to leverage any of its components for their projects.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 243, "code": "EGMNDU", "public_name": "Kenji Ose", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}], "Foyer": [{"id": 135, "guid": "25b840dc-fe02-5e80-9954-79f1608812aa", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/PTP77L/Fig.1_3JFhwuM.jpg", "date": "2024-10-02T18:00:00+01:00", "start": "18:00", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-135-landscape-scale-and-spatially-explicit-representation-of-tropical-vegetation-dynamics-and-ecosystem-carbon-stocks-laser-", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/PTP77L/", "title": "Landscape-scale And Spatially Explicit Representation of tropical vegetation dynamics and ecosystem carbon stocks (LASER)", "subtitle": "", "track": "OEMC project workshop", "type": "Poster presentation", "language": "en", "abstract": "Tropical vegetation dynamics and ecosystem carbon (C) stocks typically vary with local topography and forest disturbance history. Yet, neither remote sensing nor vegetation modeling captures the underlying mechanistic processes determining ecosystem functioning and therefore the resulting estimates often do not match field observations of vegetation C stocks, especially so in hyperdiverse tropical forest ecosystems. This mismatch is further aggravated by the fact that multiple interacting factors, such as climatic drivers (i.e., temperature, precipitation, climate seasonality), edaphic factors (i.e., soil fertility,\r\ntopographic diversity) and diversity-related parameters (i.e., species composition and associated plant functional traits) in concert determine ecosystem functioning and therefore affect tropical forest C sink-strength. Here, we propose a novel framework designed for integrating in-situ observations of local plant species diversity with remotely sensed estimates of plant functional traits, with the goal to deduce parameters for a recently developed trait- and size-structured demographic vegetation model. Plant-FATE (Plant Functional Acclimation and Trait Evolution) captures the acclimation of plastic traits within individual plants in response to the local environment and simulates shifts in species composition through demographic changes between coexisting species, in association with differences in their life-history strategies. Our framework allows to project the functional response of tropical forest ecosystems under present and future climate change scenarios and thus should have crucial implications for assisted restoration and management of tropical plant species threatened by extinction.", "description": "Figure legend: [A] Geographic location of the study area and LiDAR transects flown across environmental gradients of the Osa peninsula; [B] Vegetation structure and plant functional diversity observed across permanent monitoring plots; [C] Example for airborne laser scanning (ALS, upper panel) and terrestrial laser scanning (TLS, lower panel) imagery obtained for each transect; [D] Parameters for model simulation of vegetation structure and plant functional diversity.\r\n\r\nFurther information about this research project can be found here: https://iiasa.ac.at/blog/feb-2024/rainforest-gets-digital-twin", "recording_license": "", "do_not_record": false, "persons": [{"id": 185, "code": "KJW3CW", "public_name": "Florian Hofhansl", "biography": "I am a research scholar in the Biodiversity, Ecology, and Conservation (BEC) Research Group of the IIASA Biodiversity and Natural Resources (BNR) Program exploring the relationship between biodiversity and ecosystem functioning with a focus on plant functional traits, life-history theory, and spatial ecology.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 164, "guid": "776ac42e-d95e-59fd-a053-1a17549203af", "logo": "", "date": "2024-10-02T18:05:00+01:00", "start": "18:05", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-164-exploring-additional-in-situ-measurements-for-the-integration-of-eddy-covariance-system-observations-with-remote-sensing-time-series", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/ADD7VA/", "title": "Exploring additional in-situ measurements for the integration of eddy covariance system observations with remote sensing time series", "subtitle": "", "track": null, "type": "Poster presentation", "language": "en", "abstract": "Among the many services in-situ datasets can provide to society, one of the more pressing interests currently active in the Earth Observation (EO) sector is the integration of in-situ and satellite datasets. The remote sensing community is actively using ICOS (Integrated Carbon Observation System) outputs for calibration and validation activities of satellite products. However, there are additional measurements currently excluded from the ICOS portfolio that could be beneficial for calibration and validation opportunities: for example, fraction of absorbed photosynthetic active radiation (fAPAR) and land surface temperature (LST) from thermal cameras. \r\nAn experimental setup was implemented on a subset of ICOS stations for estimating leaf area index (LAI), strictly related to fAPAR, from above- and below-canopy measurements of photosynthetic active radiation (PAR). The first longer-than-1-year datasets being available, we present some relevant preliminary results and the future direction of this activity. \r\nNASA recently published some best practices on LST measurements for validation of satellite products. At this scope, a single thermal camera of high accuracy is deployed on a network of measuring stations. We intend to check how this setup relates to different configurations, such as different camera models, or the deployment of 3-4 lower-standard sensors looking at different angles, thus increasing the spatial resolution. \r\nAdditional points under scrutiny are: what is the heterogeneity of these variables in the eddy covariance footprint, and how can these measurements add value to the net ecosystem exchange (NEE) and its derived products? And how can the integration between satellite imagery and ground observations benefit from them?", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 93, "code": "PV3JKJ", "public_name": "Simone Sabbatini", "biography": "Simone Sabbatini has a PhD in Forest Ecology, obtained in 2014 at the DIBAF department of the University of Tuscia, Viterbo, Italy. His background consists in a BSC in Forestry and Environmental Science, and a MSC in Management of Forestry Systems, both held at the University of Florence, Italy. Currently he is a Junior Researcher at the Euri-Mediterranean Center on Climate Change (CMCC), where he is involved in the activities of the Ecosystem Thematic Center (ETC), a facility of the Integrated Carbon Observatory System Research Infrastructure (ICOS-RI). At the ETC he deals with giving support to the ICOS stations concerning eddy covariance (EC), air meteorological measurements, and file submission. He is also in charge of running quality routines on EC data during the labelling time, and of caring the correct metadata ingestion of sensors by the processing routines.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 155, "guid": "e4f64bfc-6dc2-5380-a1f2-b46ded9b9203", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/3MQPHQ/Screenshot_from_2024-03-15_10_NbOkm1Q.png", "date": "2024-10-02T18:10:00+01:00", "start": "18:10", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-155-a-european-air-quality-monitor", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/3MQPHQ/", "title": "A European Air Quality Monitor", "subtitle": "", "track": null, "type": "Poster presentation", "language": "en", "abstract": "Air pollution is a health risk to millions of citizens in Europe. Critical concentrations of nitrogen-dioxide (NO2), ozone (O3), and particulate matter (PM10 and PM2.5) occur predominantly in densely populated areas affected by high volumes of traffic or industry. Although several thousand air quality stations scattered over Europe record hourly measurements, the EEA publishes continuous maps on an annual basis with considerable time lag. However, there is a public benefit in accessing such maps more timely.\r\nWith the OEMC Air Quality Monitor we design tools which streamline the mapping workflow building on top of the EEA methodology. The process includes gathering and pre-processing data (both measurement and covariates) and making spatial predictions for the four mentioned air pollutants. We leverage public station measurements, gridded climate and atmospheric transport model outputs, and land cover and traffic information as well as open source software. This combination facilitates a transparent way to map air quality in Europe at one kilometer spatial resolution for daily, monthly, and annual intervals.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 25, "code": "G7HNTQ", "public_name": "Johannes Heisig", "biography": "- Research Associate (Open Earth Monitor Cyberinfrastructure, ongoing)\r\n- PhD Geoinformatics (University of M\u00fcnster, ongoing)\r\n- M.Sc. Environmental Geography (University of Bayreuth, 2020)\r\n- B.Sc. Geography (Ludwig-Maximilians-University Munich, 2017)", "answers": []}, {"id": 233, "code": "3Z3CGT", "public_name": "Brian Pondi", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 149, "guid": "dbc8153c-c034-57dc-a392-a38d7f665351", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/KDHV8A/session_image_oikon_z5e8SLq.png", "date": "2024-10-02T18:15:00+01:00", "start": "18:15", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-149-satellite-based-maximum-entropy-modelling-for-identifying-potential-soil-microplastics-hotspots", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/KDHV8A/", "title": "Satellite-based maximum entropy modelling for identifying potential soil microplastics hotspots", "subtitle": "", "track": "OEMC project workshop", "type": "Poster presentation", "language": "en", "abstract": "The pervasive presence of microplastics in terrestrial ecosystems has emerged as a pressing environmental concern. Recent studies have identified soil as a major sink for microplastics contamination, potentially surpassing oceanic levels by factors ranging from 4 to 23-fold. The small size of microplastics and the complexity of soil matrices as sink substrates pose challenges for quantifying soil pollution. As a result, current analytical methods are limited in efficiency, making large-scale environmental assessments unfeasible. The vertical incorporation of microplastics into soil, along with the challenges of recognizing microscopic objects in satellite images, restricts the practicality of using remote sensing for direct large-scale environmental assessments. Hence, a more comprehensive approach is necessary to tackle these challenges. One potential solution involves utilizing satellite imagery combined with a maximum entropy model. By integrating locations  where microplastic presence has been confirmed and extracted from soil samples, the maximum entropy model can establish a connection between satellite-derived environmental predictors and the presence of microplastics in soil. The aim of this research was to assess the practicality and viability of employing this approach in a real-world setting.\r\n\r\nTo test our approach, we designed a case study covering wider administrative area of the City of Osijek, Croatia. For training data, we utilized 31 sampled locations where soil microplastics have been confirmed through previous research, along with environmental variables primarily derived through signal enhancement of Sentinel-based imagery. After literature review, a preliminary list of 31 environmental predictor variables was generated, covering various facets of microplastics input to the soil and their dispersion in the environment. These were tested for variance inflation factor (VIF) and spatial autocorrelation to identify statistically significant variables for model calibration. To relate environmental variables to microplastics presence, we leveraged maximum entropy model. The best-performing model underwent additional testing using various permutation tests to evaluate its robustness. We identified 4491 different sets of three environmental variables eligible for further examination. We employed each combination to train maximum entropy models using 5-fold cross-validation to identify the most robust model. Additional testing included jackknife cross-validation to identify and remove outlier samples.\r\n\r\nThe best performing model, with an AUC under the ROC of 0.863, was the one trained using combination of environmental predictors including land cover (CLC+ Backbone raster product), soil moisture derived from Sentinel-1 imagery, and catchment areas determined through hydrological analysis of the digital elevation model. The output prediction map clearly delineates areas that highly likely represent pollution hotspots. This research demonstrates the feasibility of utilizing satellite imagery, in conjunction with topological analysis and maximum entropy models, to conduct large-scale environmental assessment and accurately pinpoint hotspots of soil microplastics contamination.  This approach could significantly aid future stakeholders since the EU has taken proactive steps as of 2018 to tackle soil microplastics pollution, by implementing regulations, action plans, and initiatives to prevent plastic pellet loss. Furthermore, the European Commission has incorporated impact assessments into its decision-making process regarding microplastics. Advanced environmental monitoring techniques offer potential in tracking progress and quantifying effectiveness of forthcoming measures.", "description": "Based in Croatia, Oikon Ltd. \u2013 Institute of Applied Ecology is recognized as a leading consulting company and research institute in the field of applied ecology across the region. Our expertise spans a wide array of environmental services, including nature conservation, industrial ecology, renewable energy, and more. With our interdisciplinary approach to ecology, we wanted to propose a practical solution for large-scale microplastics pollution monitoring, leveraging remote sensing and modelling techniques. Microplastics have been quietly infiltrating our soil, posing a significant threat to terrestrial ecosystems. Recent research suggests that soil might even outpace oceans as the primary sink for these pollutants. By integrating sample data and environmental predictors derived from satellite imagery with ecological modelling, we were able to confidently pinpoint hotspots of microplastics pollution in our case study area.", "recording_license": "", "do_not_record": false, "persons": [{"id": 194, "code": "WAYAZ7", "public_name": "Bruno \u0106aleta", "biography": "Master of Nature and Environmental Protection working in the realm of Remote Sensing. Fascinated by the intersection of technology and nature, with a focus on machine learning, ecological modeling, and biodiversity monitoring. \ud83c\udf0d", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 163, "guid": "5498a241-752a-5a3c-b0a5-9b0160df2cfa", "logo": "", "date": "2024-10-02T18:20:00+01:00", "start": "18:20", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-163-ground-measurements-and-in-situ-observations-from-the-oemc-project-for-the-support-of-environmental-policies-and-the-benefit-of-society", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/TTZ89A/", "title": "Ground measurements and in-situ observations from the OEMC project for the support of environmental policies and the benefit of society", "subtitle": "", "track": null, "type": "Poster presentation", "language": "en", "abstract": "The collection of representative observational datasets in environmental sciences is crucial for advancing the understanding of the phenomena under consideration. The integration between in-situ datasets with remote sensing and machine learning techniques makes possible reliable predictions and analyses with enhanced precision and resolution. The OEMC project aims at supporting informed decision making on environmental policies for the benefit of the whole society, by combining in-situ measurements and remote sensing datasets. Here we investigate the impacts of some of the  OEMC in-situ datasets on society and policymakers: how are the in-situ datasets supporting the use-cases of the project? What is their combined potential in terms of technological advancement and knowledge boost? The following categories of OEMC in-situ data, their benefits, and relation to sustainable development goals (SDGs) are scrutinised.\r\nGHG fluxes: GHG fluxes ground observations, combined with satellite data, can be proficiently used for calibration and validation of models, with benefits in terms of better predictions, development of early warning systems, better understanding of climate change impacts, ecosystem services, etc. Current and potential stakeholders are the Intergovernmental Panel on Climate Change (IPCC) and international projects such as the Global Carbon Project (GCP) and FluxCom initiative. UNFCCC is also using GHG flux data. Related SDGs include 11, 12, 13 and 15.\r\nForest biomass: in-situ observations of forest biomass are fundamental in refining the assessment of global forest carbon stocks and their change under natural and anthropogenic drivers. These data serve the needs of a wide range of stakeholders, from both the scientific and the policy making sectors, interested in quantifying the actual carbon sequestration capacity of forests and refining estimates of forest inventories. Policies such as the European Forest strategy and monitoring of SDG 15 will benefit from such datasets.\r\nMarine and terrestrial biodiversity: these datasets support projects and activities of biodiversity conservation, a fundamental branch of Earth science and a crucial aspect for the survival of humanity. Potential stakeholders include the European Environmental Agency (EEA) and the Joint Research Centre of the European Commission (JRC), and policies such as the European Biodiversity strategy and SDGs 14 and 15.\r\nOcean and coastal datasets: the importance of ocean and coastal organisms for the balance of the biosphere becomes more and more evident, but scientific knowledge is still limited in comparison with the terrestrial counterpart. Increasing the monitoring of these ecosystems is crucial, in particular for human communities living in coastal areas. EEA and JRC are included in the stakeholders interested. Related SDG: 14\r\nLCLU: in-situ land use and land cover information derived from processing land surveys data and satellite imagery support land degradation alert systems and EO mapping. Potentially supported SDGs are 11, 12, 13, 14 and 15.\r\nAutomated ground observations: automated measurements of biological processes support the validation of EO products and provide input for ecological modelling. Data consistency is enhanced by the availability of a continuous dataflow from field sites where sampling is logistically or financially constrained. Possible applications include early warning systems in agricultural, forestry, and urban greening sectors, improved agronomic and silvicultural practices, monitoring ecosystems productivity and biodiversity levels. Potential stakeholders are the EEA, the JRC, entities involved in mandatory and voluntary carbon markets (UNFCCC, UNDP, private companies), national governments and local administrations. Related SDGs are 11, 12, 13 and 15.\r\nCitizen science: citizen science in-situ data for training and validation of EO mapping models can play a fundamental part in supporting environmental policies, covering a wide range of topics, from deforestation to aboveground biomass assessment, from crop type to land use and land cover distributions. The European Green Deal is expected to greatly benefit from this type of in-situ datasets, and SDGs 13, 14 and 15 will potentially be supported.\r\nIn-situ and gridded integration: although the combination of in-situ and gridded datasets is common, their spatial resolution often differs. A case study focusing on eddy covariance data tries to shed light on the overlapping degree of ground and satellite footprints, with benefits for society in terms of technological advancements and a deeper understanding of how ecosystems react to climate change, with potential benefits for SDGs 13 and 15.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 93, "code": "PV3JKJ", "public_name": "Simone Sabbatini", "biography": "Simone Sabbatini has a PhD in Forest Ecology, obtained in 2014 at the DIBAF department of the University of Tuscia, Viterbo, Italy. His background consists in a BSC in Forestry and Environmental Science, and a MSC in Management of Forestry Systems, both held at the University of Florence, Italy. Currently he is a Junior Researcher at the Euri-Mediterranean Center on Climate Change (CMCC), where he is involved in the activities of the Ecosystem Thematic Center (ETC), a facility of the Integrated Carbon Observatory System Research Infrastructure (ICOS-RI). At the ETC he deals with giving support to the ICOS stations concerning eddy covariance (EC), air meteorological measurements, and file submission. He is also in charge of running quality routines on EC data during the labelling time, and of caring the correct metadata ingestion of sensors by the processing routines.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 143, "guid": "b5f6c497-eefb-56c6-8776-acad9ab67add", "logo": "", "date": "2024-10-02T18:25:00+01:00", "start": "18:25", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-143-assimilating-leaf-area-index-and-soil-moisture-from-optical-and-sar-data-into-the-wofost-model-to-improve-maize-zea-mays-l-yield-estimation", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/UTXEKX/", "title": "Assimilating Leaf Area Index and Soil Moisture from Optical and SAR Data into the WOFOST Model to Improve Maize (Zea mays L.) Yield Estimation", "subtitle": "", "track": "OEMC project workshop", "type": "Poster presentation", "language": "en", "abstract": "Assimilating Leaf Area Index and Soil Moisture from Optical and SAR Data into the WOFOST Model to Improve Maize (Zea mays L.) Yield Estimation\r\n\r\nGebeyehu Abebe 1,2, Odunayo David Adeniyi1,3, Amazirh Abdelhakim1,4, Zoltan Szantoi1\r\n1European Space Agency (ESA)/ESRIN, Frascati RM 00044, Italy\r\n2Department of Natural Resources Management, Debre Berhan University, Debre Berhan, Ethiopia. \r\n3Department of Earth and Environmental sciences, University of Pavia, Italy, Via Ferrata 1, Pavia, 27100, Italy.\r\n4Centre for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Hay My Rachid, Ben Guerir 43150, Morocco\r\nAbstract \r\nCrop Simulation Models (CSM) are commonly used to estimate crop yield at a local scale. Meanwhile, Remote Sensing (RS) data provides valuable information on crop parameters like soil moisture and leaf area index (LAI) across different spatial scales. Data Assimilation (DA) is a powerful technique that combines CSM and RS data from satellite imagery to enhance simulated crop state variables and model outputs, such as total biomass and yield. In this study, we aimed to implement a joint assimilation strategy for LAI and soil moisture data in the WOFOST model. The goal was to simulate rainfed grain maize yield at the field scale and evaluate its performance at both the field and administrative zone levels. The Ensemble Kalman Filter (EnKF) algorithm was applied to achieve this integration. The LAI and soil moisture data were sourced from Sentinel 3 and Soil Moisture Active Passive (SMAP) L3 Radiometer Global Daily 9 km Soil Moisture, respectively. The study tested various assimilation scenarios, including deterministic modeling, independent assimilation of LAI from Sentinel 3, independent assimilation of soil moisture from SMAP, and joint assimilation of both LAI and soil moisture data. Ongoing validation involves comparing the simulated grain maize yield with field observations and independent grain maize statistics data in the major maize-growing administrative zones of western and southwestern Ethiopia. The expected outcomes include improved accuracy in grain maize yield predictions at the field scale and enhanced crop monitoring and forecasting at local and national levels.\r\nKeywords: Data assimilation; EnKF; LAI; soil moisture; WOFOST; grain maize yield", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 195, "code": "EKXGU9", "public_name": "Gebeyehu A. Zeleke", "biography": "Gebeyehu Abebe (Ph.D.), an assistant professor at Debre Berhan University in Ethiopia, specializes in remote sensing. His research interests span crop growth monitoring, SAR image processing, data fusion, machine learning algorithms, and crop modeling. Currently, he\u2019s conducting research as part of the 2023 African Research Fellow program at ESA/ESRIN in Frascati, Italy.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 176, "guid": "f7cf9538-e108-5c39-be97-840b708b4fa2", "logo": "", "date": "2024-10-02T18:30:00+01:00", "start": "18:30", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-176-eo-exploitation-platform-common-architecture", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/BSGLJR/", "title": "EO Exploitation Platform Common Architecture", "subtitle": "", "track": null, "type": "Poster presentation", "language": "en", "abstract": "The \u2018Exploitation Platform\u2019 concept derives from the need to access and process an ever-growing volume of data. Many web-based platforms have emerged - offering access to a wealth of satellite Earth Observation (EO) data. Increasingly, these are collocated with cloud computing resources and applications for exploiting the data. Rather than downloading the data, the exploitation platform offers a cloud environment with access to EO data and associated compute and tools that facilitate the analysis and processing of large data volumes. The Exploitation Platform benefits users, data providers and infrastructure providers. Users benefit from the scalability & performance of the cloud infrastructure, the added-value services offered by the platform \u2013 and avoid the need to maintain their own hardware. Data hosted in the cloud infrastructure reaches a wider audience and Infrastructure Providers gain an increased cloud user base.\r\n\r\nUsers are beginning to appreciate the advantages of exploitation platforms. However, the market now offers a plethora of platforms with various added value services and data access capabilities. This ever-increasing offer is rather intimidating and confusing for most users. In order to fully exploit the potential of these complementary platform resources we anticipate the need to encourage interoperation amongst the platforms, such that users of one platform may consume the services of another directly platform-to-platform.\r\n\r\nEOEPCA (EO Exploitation Platform Common Architecture) is a European Space Agency (ESA) funded project with the goal to define and agree a re-usable exploitation platform architecture using standard interfaces to encourage interoperation and federation between operational exploitation platforms - facilitating easier access and more efficient exploitation of the rapidly growing body of EO and other data. Interoperability through open standards is a key guiding force for the Common Architecture: platform developers are more likely to invest their efforts in standard implementations that have wide usage; off-the-shelf clients and software are more likely to be found for standards-based solutions.\r\n\r\nThe EOEPCA system architecture is designed to meet a set of defined use cases for various levels of user, from expert application developers to consumers. The architecture is defined as a set of Building Blocks (BBs), exposing well-defined open-standard interfaces. These include Identity and Access Management, Resource Discovery, Data Access, Processing Workflows, Data Cube Access, Machine Learning Operations, and more. Each of these BBs are containerized for Kubernetes deployment, which provides an infrastructure-agnostic deployment target.\r\n\r\nThe exploitation platform is conceived as a \u2018virtual work environment\u2019 where users can access data, develop algorithms, conduct analysis and share their value-adding outcomes. The EOEPCA architecture facilitates this through a Workspace BB that provides a user-centric platform experience in which the standard discovery, visualisation and access interfaces are re-used for user-owned resources maintained within the platform - including data, applications, added-value products (from processing), etc. This is supported by an Application Hub building-block that provides interactive web-tooling for analysis, algorithm development, data exploitation and provides a web dashboard capability through which added-value outcomes can be showcased.\r\n\r\nOur presentation will highlight the generalised architecture, standards, best practice and open source software components available.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 222, "code": "X7SKE3", "public_name": "Chandra Taposeea-Fisher", "biography": "I have been working in the Earth Observation in some capacity since 2009, where I had an internship at ESA in The Netherlands. Since then, I have been a Young Graduate Trainee at ESA in Rome, completed a PhD in Numerical and Marine Geophysics from Imperial College London, and have been working full time in the Earth Observation sector since 2017. Now a Senior Project Manager at Telespazio UK, I have worked on primarily ESA projects, including a Digital Twin Earth for Food Systems precursor, EO4SD Lab, CCI Sea State, several Thematic Exploitation Platforms, the TRUTHS Satellite. I am project manager for both the EOEPCA+ and EarthCODE ESA projects, both having FAIR Open Science principles at their forefront.", "answers": []}, {"id": 244, "code": "3FTVAB", "public_name": "Garin Smith", "biography": "Ground Segment Architect specialising in Reproducible Science, AI (Artificial Intelligence), ARD (Analysis Ready Data) and Quality initiatives in the space industry. Heavily involved in the architecture of Open Source solutions, frameworks and platforms. \r\n\r\nLeading a number of initiatives that exploit the capability of the ESA EOEPCA+ platform including ESA EarthCODE, ESA Open Science Catalogue and other Data Quality initiatives.\r\n\r\nTechnical lead (Prime) for ESA EarthCODE and ESA AI4DTE Artificial Intelligence initiative. \r\n\r\nTechnical lead and project manager for the ESA AIOPEN Artificial Intelligence initiative Telespazio component.\r\n\r\nTechnical lead (Prime) for UK Space Agency UK EO Data Architecture Report.\r\n\r\nTechnical lead (Prime) and project manager on an ESA project to deliver Analysis Ready Data on the ASAR CARD4L NRB Product Development Project.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 138, "guid": "9617fced-2b22-57c0-9047-99d2329afc2b", "logo": "", "date": "2024-10-02T18:35:00+01:00", "start": "18:35", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-138-satellite-based-methane-discovering-and-monitoring-revolutionizing-air-pollution-control", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/MSTF9Y/", "title": "Satellite-based methane discovering and monitoring:  Revolutionizing air pollution control", "subtitle": "", "track": "OEMC project workshop", "type": "Poster presentation", "language": "en", "abstract": "Air pollution has emerged as a critical global concern, exerting adverse impacts on natural ecosystems and exacerbating the pace of climate change. Despite the existence of mitigation strategies, the accurate quantification of methane emissions remains a formidable challenge, impeding progress towards meeting emission reduction targets set for 2030. This study is dedicated to addressing the urgent global issue of air pollution, with a particular focus on methane emissions, known for their significant contribution to climate change and associated environmental and health hazards. Conventional monitoring techniques have proven inadequate, leaving millions of abandoned oil wells unchecked in their methane emissions, thus demanding a comprehensive solution.  In response, we present a novel technological advancement based on satellite data, to facilitate the precise measurement, detection, and ongoing monitoring of methane leaks. By harnessing breakthroughs in deep tech disciplines such as Earth observation integrated with machine learning, astrophysical methodologies, theoretical chemistry, and computational fluid dynamics, this technology enables the identification of methane leaks across diverse geographical locations worldwide.\r\nFurthermore, this study underscores the critical importance of fostering collaboration and information exchange among stakeholders to optimize the effectiveness of emission reduction endeavors. Through its innovative approach and interdisciplinary collaboration, this work aspires to deliver a significant contribution towards mitigating climate change impacts and safeguarding natural resources for the benefit of future generations.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 186, "code": "LK37K7", "public_name": "Santiago Vargas", "biography": "Dr. Santiago Vargas Dom\u00ednguez earned his bachelor's degree in Physics and went on to receive both an M.Sc and Ph.D. in Astrophysics. With over 20 years of research experience acquired at institutions across America and Europe, Dr. Vargas specializes in image processing and algorithm application ranging from astrophysics to Earth observation. His expertise extends to utilizing artificial intelligence for data extraction, enabling the development of strategic solutions across various domains.", "answers": []}, {"id": 189, "code": "GAMZUB", "public_name": "Maria Fernanda Gonz\u00e1lez", "biography": "Mar\u00eda Fernanda is a serial entrepreneur and innovator. She has been creating technological Startups, and business solutions since 2010. She has a Ph.D in Quantum Physics (University of Barcelona, Spain), a Master in Numerical Methods (Polytechnic University of Catalu\u00f1a, Spain), and MBA from IESE Business School. She has undergraduate studies in Theoretical Physics, Civil Engineering and Physical Oceanography in Colombia. Mar\u00eda Fernanda is a specialist in creating business solutions based on Big Data and Machine Learning for large corporations, improving their annual turnover between 5 and 7 percent. In 2013 she was invited by the United States and nominated by Spain to represent the country in its prestigious IVLP (International Visitor Leadership Program). During her professional career she has received the support of several governments through different grants such as the Catalan Government, the Spanish Government, the American Government, the South Korean Government and the Dutch Government. \r\n\r\nHer latest venture, of which she is the CEO, is called Planetai Space, which provides services to quantify the Natural Capital of our planet and the negative effects of air pollution on the environment.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 162, "guid": "01a52c1b-9588-58b7-b8d0-7fb1cf2bc467", "logo": "", "date": "2024-10-02T18:40:00+01:00", "start": "18:40", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-162-the-interoperable-alternative-map-browser-for-the-datasets-produced-in-oemc", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/TWTXRD/", "title": "The interoperable alternative map browser for the datasets produced in OEMC", "subtitle": "", "track": null, "type": "Poster presentation", "language": "en", "abstract": "<p align=\"justify\">One of the major challenges in data management is (and in the project OEMC) is demonstrating the correct implementation of the <a href=\"https://www.openaire.eu/how-to-make-your-data-fair\">FAIR</a> (Findable, Accessible, Interoperable and Reproducible) principles. To make data accessible, it is required that \u201cdata is retrievable by their identifier using a standardised communications protocol that should be open, free, and universally implementable\u201d.<br>\r\nOEMC has produced a list of datasets that are exposed to the public with and elegant <a href=\"https://app.earthmonitor.org/\">Open-Earth-Monitor App</a>. Our talk will focus on demonstrating the interoperability of the taken approach, showing an alternative web map browser that gives access to the same OEMC datasets. This web map browser was deployed using the original <a href=\"https://github.com/grumets/MiraMonMapBrowser\">MiraMon Map Browser</a> technology without any customization and using only Open Geospatial Consortium (OGC) standards web services calls, demonstrating the technical interoperability of the OEMC services. The presented  <a href=\"https://maps.oemc.grumets.cat\">visualization portal</a> goes beyond a simple visualization by combining the OGC WMS standard with modern web browser capabilities. During the talk, we will demonstrate how to access OEMC datasets through MiraMon browser functionalities, such as query by location, multiple projections support, reading storymaps, and data multidimensional support among others. An important feature of the visualization portal is that it allows the final users to provide common feedback about the data (such as star rating and comments) that are shared with other users as well as to produce and share their own storymaps and this way share the knowledge gained by analysing the data.</p>", "description": "This talk targets anyone interested in enhanced interoperability and application of OGC standards.", "recording_license": "", "do_not_record": false, "persons": [{"id": 32, "code": "3TB8US", "public_name": "Joan Maso", "biography": "Dr. Joan Mas\u00f3 (m) is a Principal investigator of CREAF leading specialized group on geospatial interoperability, GIS, remote sensing . (PhD in Geography, MSc in Physics, and a MSc in Electronic Engineering all in the UAB). Since 1995 he is a researcher at CREAF and GIS developer. Co-creator of the MiraMon compressed map  in 1997 that has evolved into a distribution and preservation format. Teacher in a RS and GIS master in the UAB. Creator of Remote Sensing imagery visualization and download software for web data portals (the MiraMon Map Browser). Expert in JPEG2000 format. He is an active member of the TC of the Open Geospatial Consortium (OGC) since 2003 (editor OGC 07- 057r7 WMTS standard and the new OGC 20-057 OGC API Tiles among others and chair of the Iberian and Latin American Forum; ILAF). Spanish representative in the TC 211 and editor of the ISO 19165 Preservation of geospatial data and metadata. OGC Gardels gold medal in 2018. Coordinator of GeoViQua FP7 project (research project about visualization of quality information in GEOSS), H2020 ConnectinGeo and currently HE AD4GD. Participant in several European projects related with biodiversity, citizen science, remote sensing and research infrastructures such as H2020 ECOPOTENTIAL, H2020 GroundTruth 2.0, H2020 WeObserve H2020 ERA-PLANET, H2020 E-Shape, H2020 BestMap, H2020 COS4Cloud (INTRAEOSC project), H2020 Framework-biodiversity, H2020 WQeMS, HE OEMC, HE CitiObs, HE More4Nature, - ESA-IHE Phenotandem, EEA InCASE, as well as some other national and local projects related both with remote sensing and geospatial standards and applications. Earth and Space Science Informatics (ESSI) former division president in the European Geosciences Union (EGU). Chair of the OGC API Common, OGC API Tiles and OGC API Maps working groups and member of the OGC Architecture Board. Co-chair of the Citizens Science GEOSS working group. Senior member of IEEE and member of the International Society of Digital Earth (ISDE) council. Chair of the Community of Practice in Interoperability for Citizen Science.", "answers": []}, {"id": 205, "code": "3QHGZS", "public_name": "Imma Serra", "biography": "Research technician at CREAF.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 146, "guid": "d1cb408c-c964-5add-8b80-eec212137a43", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/GBXGKF/image_sae3WJd.png", "date": "2024-10-02T18:45:00+01:00", "start": "18:45", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-146-integrating-different-remote-sensing-products-to-produce-high-spatial-and-temporal-snow-estimates-in-the-cloud", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/GBXGKF/", "title": "Integrating different remote sensing products to produce high spatial and temporal snow estimates in the cloud", "subtitle": "", "track": "OEMC project workshop", "type": "Poster presentation", "language": "en", "abstract": "Hydrological planners need accurate and up-to-date information on snow dynamics. The OEMC project aims to improve the measurement of the Snow Water Equivalent as an estimation of the available water stored in snow covered areas in the Alps to support planning activities such as hydropower, agriculture and drinking water. To reach this objective high spatial and temporal information is required. Several remote sensing sensors exist with different spatial and temporal resolutions and hence different potentiality. To produce optimal results, an integration of different data sources is necessary. This requires large computational resources as well as huge data amounts. In this context, standardized cloud processing APIs such as OpenEO serve as powerful processing tools that can promote openness and reproducibility. In this talk we will present how we exploited cloud native EO to improve the development of snow products, such as snow cover fraction and snow water equivalent maps.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 202, "code": "H8YUKK", "public_name": "Valentina Premier", "biography": "Valentina Premier received her Ph.D. degree in information engineering and computer science at the University of Trento, Trento, Italy within the Remote Sensing Laboratory and with Eurac Research, Bolzano, Italy, within the Institute for Earth Observation. She is working on snow water equivalent retrieval by using remote sensing data.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 151, "guid": "011940a3-2ff5-5670-a779-9d24ef3faff3", "logo": "", "date": "2024-10-02T18:50:00+01:00", "start": "18:50", "duration": "00:05", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-151-the-evolution-of-the-oss4geo-a-foss4g-resources-platform-initiative", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/PYZRQT/", "title": "The evolution of the OSS4gEO, a FOSS4G resources platform initiative", "subtitle": "", "track": "OEMC project workshop", "type": "Poster presentation", "language": "en", "abstract": "At the OEMC Global Workshop in 2023, we presented a community led initiative part of the wider Open Innovation framework at European Space Agency that worked to implement an open, interactive, user intuitive platform for a constantly updated, comprehensive and detailed overview of the dynamic environment of the open source digital infrastructure for geospatial data storage, processing and visualisation systems.  Today, we have over 450 documented geospatial FOSS projects, interconnected into the FOSS4G ecosystem. \r\nAt the OEMC Global Workshop of 2024, the team presents the work done within the next steps, identifying quality metrics for open source software and assess the connection with the health of the associated project and thus paving the way to understand the benefits as well as the pitfalls of certification in geospatial open source software. \r\nThe work is supported by ESA, under the Permanently Open Call for Proposals for Future EO-1: EO Science for Society.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 65, "code": "NL8NGQ", "public_name": "Codrina Maria Ilie", "biography": "Codrina Ilie is a technical geographer, an open source GIS/RS power user, actively working in improving open data services development at Terrasigna. In her 12 years of activity, Codrina has essentially focused on using open source GIS and RS solutions for data management, processing and visualization. As an advocate for foss4g, since 2010 she has been a volunteer trainer in the Romanian geospatial community, geo-spatial.org. Since 2013, Codrina has been a Charter Member and today serves the geospatial community as an OSGeo Board of Directors member, within her second term.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 195, "guid": "f9cceda0-de06-5a5f-844b-050436642ec4", "logo": "", "date": "2024-10-02T18:55:00+01:00", "start": "18:55", "duration": "00:03", "room": "Foyer", "slug": "open-earth-monitor-global-workshop-2024-195-optimizing-uav-data-processing-for-pattern-classification-with-cnn-on-low-to-moderate-quality-imagery", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/TJJTXG/", "title": "OPTIMIZING UAV DATA PROCESSING FOR PATTERN CLASSIFICATION WITH CNN ON LOW TO  MODERATE-QUALITY IMAGERY", "subtitle": "", "track": null, "type": "Poster presentation", "language": "en", "abstract": "Over the last decade, UAV systems have enabled high-resolution data collection for various applications at relatively low\r\n\r\ncosts and with great flexibility in acquisition time and parameters [5]. This data can serve as a valuable reference for large-\r\nscale space-borne applications. However, the flexibility in image acquisition presents challenges related to varying data types\r\n\r\nand quality, which are affected by environmental conditions, sensor specifications, and radiometric calibration. Capturing\r\ncomparable reflectance values with UAV systems is particularly challenging, and many early studies relied on minimal\r\npreprocessing or raw digital number (DN) values [4]. Given that some datasets' spectral information (reflectance/DN) may\r\nnot be directly comparable, a classifier that emphasizes generalized texture information is needed rather than relying solely\r\non spectral data. Among common machine learning (ML) techniques, the convolutional neural network (CNN) of deep\r\nlearning (DL) has proven to be a successful tool in classifying images of land use from remote sensing data [1, 2]. CNN\r\nallows high-order representation based on generalized texture information already used in crop classification [7, 3, 6].\r\nOur research explores the potential of using low-to-moderate-quality UAV data for agricultural pattern classification,\r\nfocusing on how color-balancing techniques can enhance data consistency when images are captured under variable lighting\r\n\r\nconditions. We evaluated the performance of CNNs in classifying agricultural patterns using moderate- to low-quality, high-\r\nresolution (0.07-meter) optical multispectral data collected from three agricultural test sites in Germany between 2019 and\r\n\r\n2021. We used models trained exclusively on samples converted to reflectance values and applied them to images impacted\r\nby different sunlight conditions, including digital number (DN) and reflectance data. The models were trained to classify\r\nsmall-scale agricultural patterns, such as damaged and undamaged canopy, weed-infested and bare soil areas, across four\r\ncrop types: winter wheat, rapeseed, corn, and spring barley.\r\nThis study, funded by the German Federal Ministry for Economic Affairs and Energy (FKZ: 50EE1901), is carried out in\r\ncollaboration with CLAAS E-Systems GmbH to develop an application for crop monitoring based on Sentinel-1 data.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 245, "code": "NUJFKL", "public_name": "Linara Arslanova", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}]}}, {"index": 4, "date": "2024-10-03", "day_start": "2024-10-03T04:00:00+01:00", "day_end": "2024-10-04T03:59:00+01:00", "rooms": {"Theatre Hall (Conference Center Laxenburg)": [{"id": 185, "guid": "a647e002-e0fc-52e1-9e20-f0170d0f5c48", "logo": "", "date": "2024-10-03T09:00:00+01:00", "start": "09:00", "duration": "00:30", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-185-open-data-open-science-and-open-platforms-way-forward-with-earth-observation-in-the-actual-climate-crisis", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/MKMTKA/", "title": "Open data, open science and open platforms: way forward with Earth Observation in the actual climate crisis", "subtitle": "", "track": null, "type": "Keynote lecture", "language": "en", "abstract": "In the face of the escalating global climate crisis we are facing, the integration of open data, open science, and open platforms has emerged as a transformative approach in Earth Observation (EO) and its applications. This abstract explores the pivotal role of these interconnected principles in addressing these climate challenges in the European Space Agency (ESA).\r\nOpen data initiatives have democratized access to valuable EO datasets, fostering collaboration and innovation across a wide range of stakeholders from policy makers, policy owners to scientists, and end users as farmers. By facilitating transparency and accessibility, these initiatives enable a deeper understanding of Earth's systems, crucial for informed decision-making amidst climate uncertainty.\r\nCoupled with open data, open science practices advocate for transparency, reproducibility, and the sharing of methodologies, results, and findings. This collaborative view not only accelerates scientific discovery but also cultivates a culture of accountability essential in confronting the multifaceted complexities of climate change and the climate finance behind it.\r\nFurthermore, the integration of open platforms also in ESA provides a dynamic infrastructure for EO research and application development. These platforms not only streamline data management and analysis but also empower communities to co-create solutions tailored to their unique challenges, fostering resilience in the face of environmental threats, which ESA is supporting through many of its projects and programmes.\r\nAs the climate crisis intensifies, the synergy between open data, open science, and open platforms offers a promising pathway forward in EO endeavours. By fostering inclusivity, innovation, and collective action, this integrated approach holds the potential to catalyse transformative change, safeguarding our planet for future generations making good use of all ESA\u2019s EO missions and options.", "description": "Overview of ESA's view on Open data, open science and open platforms", "recording_license": "", "do_not_record": false, "persons": [{"id": 236, "code": "3ABDGC", "public_name": "Inge Jonckheere", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 184, "guid": "4cf4714a-a8e7-5f4d-be16-dead6da08a50", "logo": "", "date": "2024-10-03T09:30:00+01:00", "start": "09:30", "duration": "00:30", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-184-towards-a-multi-frequency-sar-datacube-for-global-monitoring-of-dynamic-land-surface-processes", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/8WPVGA/", "title": "Towards a multi-frequency SAR datacube for global monitoring of dynamic land surface processes", "subtitle": "", "track": null, "type": "Keynote lecture", "language": "en", "abstract": "Due to their ability to observe the land surface irrespective of weather and lightning conditions, radar satellite constellations are indispensable for monitoring of highly dynamic land surface processes. While in the past only scatterometer missions allowed consistent monitoring at global scale, albeit at very coarse spatial scales, this has changed fundamentally with the Copernicus Sentinel-1 mission that stands out as one of the most successful Synthetic Aperture Radar (SAR) missions. With its novel combination of high spatial and temporal resolution, long-term mission planning, and open data policy it has served as a role model for the conceptualization of future radar missions. With the upcoming launches of the Japanese Advanced Land Observing Satellite-4 (ALOS-4) satellite, the NASA-ISRO SAR Mission (NISAR), ESA\u2019s Biomass mission, and the Copernicus Radar Observing System for Europe in L-band (ROSE-L) satellites, there is now the opportunity to monitor dynamic processes at high spatial resolution (10-20m) with short revisit times (1-3 days) at multiple frequencies (C-, L-, and P-band). In this presentation I will discuss a collaborative effort of the Vienna University of Technology (TU Wien) and the EODC Earth Observation Data Centre to build a global multi-frequency SAR datacube suited for applying hybrid algorithms combining physical models and machine learning. Furthermore, I will show examples of how we use this tailored datacube for the monitoring of soil moisture, floods, vegetation, and soil structural characteristics.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 235, "code": "TLJYCC", "public_name": "Wolfgang Wagner", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 190, "guid": "d425cc28-0eed-5c25-869b-2d8fdda9567a", "logo": "", "date": "2024-10-03T10:00:00+01:00", "start": "10:00", "duration": "00:30", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-190-brazilian-use-case-of-economic-land-use-modelling-to-impact-policy", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/QV3EBZ/", "title": "Brazilian use case of economic land-use modelling to impact policy", "subtitle": "", "track": null, "type": "Keynote lecture", "language": "en", "abstract": "Please provide an abstract and exact title as soon as possible", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 11, "code": "WRMKR3", "public_name": "Gilberto Camara", "biography": "Gilberto C\u00e2mara is a Brazilian researcher in Geoinformatics, GIScience, Spatial Analysis, and Land Use Modelling from INPE (Brazil's National Institute for Space Research). He was INPE\u2019s director general (2005-2012), visiting professor at the Institute for Geoinformatics at the University of M\u00fcnster (2013-2015), and Director of the Secretariat of GEO (Group on Earth Observations) from 2018-2021.  He published 180 papers and cited more than 17,500 times with an H-index of 55 (Google Scholar, February 2024).", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 169, "guid": "0a77fdaa-1425-5ef2-96ea-75ae376dad51", "logo": "", "date": "2024-10-03T11:30:00+01:00", "start": "11:30", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-169-a-multi-source-remote-sensing-approach-for-large-scale-mapping-of-other-wooded-lands", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/DSGFLG/", "title": "A Multi-Source Remote Sensing Approach for Large-Scale Mapping of Other Wooded Lands", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "The primary objective of this study was to develop and evaluate different remote sensing techniques for mapping Other Wooded Lands (OWL), while also assessing the accuracy and uncertainties associated with classifying OWL class compared to forest and grasslands. Additionally, we aimed to design a scalable process for large-scale OWL mapping. As defined by the Food and Agriculture Organization (FAO), OWLs are areas with 5-10% tree canopy cover for trees reaching a height of 5 meters at maturity, or with a combined cover of shrubs, bushes, and trees above 10 percent. Also, OWLs must span a minimum land area of 0.5 hectares and exclude predominantly agricultural or urban land uses. Three diverse landscapes were chosen based on expert input, encompassing natural regions globally and representing the three main land cover classes of interest: forest, OWL, and grassland. The selected areas were (1) Cheringoma, Sofala, Mozambique; (2) Cerrado biome, Goi\u00e1s, Brazil; and (3) Albacete and Ja\u00e9n, Spain. For each Area of Interest (AOI), we selected a Sentinel-2 MGRS tile that entirely covered the area. A stratified random sampling approach ensured robust sample collection across all land cover classes within each scene, resulting in over 1.7 million samples per scene. High-resolution imagery from Google Earth/Bing was utilized for visual interpretation. The mapping utilized data from 2022, encompassing a six-month window before and after the year of interest (totaling two years). A total of 174 metrics were calculated on data from various sources to characterize land cover for OWL modeling. Data processing was conducted using Google Earth Engine (GEE), and a Random Forest algorithm was employed for OWL land cover modeling. The resulting maps exhibited a global accuracy of 74.5% (Mozambique) and 76.5% (Brazil), Spain is currently under analysis. In Mozambique, the producer accuracy for OWL was 42.4%, with omissions associated with grasslands and forests at 34.5% and 21.5%, respectively. For the Cerrado region, both user and producer accuracies were notably higher, at 71.6% and 74.7% respectively. Mapping results were combined with ICESat-2 satellite lidar, where available, to investigate the vegetation height and structure of land cover classes. Top of canopy heights, median heights, and percent forest cover decreased between forest, OWL, and grassland classes. This methodology offers a scalable approach for mapping OWLs, contributing to improved deforestation monitoring and environmental protection efforts.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 211, "code": "SZH3XZ", "public_name": "Nath\u00e1lia Teles", "biography": "I'm a veterinarian with a background in animal science and currently a PhD student in Environmental Sciences. I work as a researcher and field lead at the Image Processing and GIS laboratory at the Federal University of Goi\u00e1s. I'm also involved in the Global Pasture Watch Initiative as a member of Lapig's team.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 158, "guid": "86674e5c-6425-52be-a8a8-1b5a5bbf10b1", "logo": "", "date": "2024-10-03T11:50:00+01:00", "start": "11:50", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-158-multi-decadal-trend-analysis-and-forest-disturbance-assessment-of-european-tree-species-concerning-signs-of-a-subtle-shift", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/FN3KHE/", "title": "Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Climate change poses a significant threat to the distribution and composition of forest tree species worldwide. European forest tree species\u2019 range is expected to shift to cope with the increasing frequency and intensity of extreme weather events, pests and diseases caused by climate change. Despite numerous regional studies, a continental scale assessment of current changes in species distributions in Europe is missing due to the difficult task of modeling a species realized distribution and to quantify the influence of forest disturbances on each species. In this study we conducted a trend analysis on the realized distribution of 6 main European forest tree species (<i>Abies alba</i> Mill., <i>Fagus sylvatica</i> L., <i>Picea abies</i> L. H. Karst., <i>Pinus nigra</i> J. F. Arnold, <i>Pinus sylvestris</i> L. and <i>Quercus robur</i> L.) to capture and map the prevalent trends in probability of occurrence for the period 2000\u20132020. We also analyzed the impact of forest disturbances on each species\u2019 range and identified the dominant disturbance drivers. Our results revealed an overall trend of stability in species\u2019 distributions (85% of the pixels are considered stable by 2020 for all species) but we also identified some hot spots characterized by negative trends in probability of occurrence, mostly at the edges of each species\u2019 latitudinal range. Additionally, we identified a steady increase in disturbance events in each species\u2019 range by disturbance (affected range doubled by 2020, from 3.5% to 7% on average) and highlighted species-specific responses to forest disturbance drivers such as wind and fire. Overall, our study provides insights into distribution trends and disturbance patterns for the main European forest tree species. The identification of range shifts and the intensifying impacts of disturbances call for proactive conservation efforts and long-term planning to ensure the resilience and sustainability of European forests.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 34, "code": "FU3F7S", "public_name": "Carmelo Bonannella", "biography": "Carmelo has a PhD in GIS Science and Remote Sensing from Wageningen University and Research (WUR) with a specialization in forest resources monitoring and management through geospatial data science applications and time series analysis.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 156, "guid": "40f4c36c-8469-55e6-8642-b58ec146f9c2", "logo": "", "date": "2024-10-03T12:10:00+01:00", "start": "12:10", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-156-seea-carbon-accounting-using-earth-observation-datasets-and-its-comparison-with-carbon-accounts-following-the-unfccc-framework", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/LGDAKZ/", "title": "SEEA carbon accounting using Earth Observation datasets and its comparison with carbon accounts following the UNFCCC  framework", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Earth Observation (EO) biomass and carbon datasets are increasing and their potential as inputs to the environmental-economic accounting framework based on SEEA was assessed in this study toward accounting for all carbon pools: above-ground, below-ground, deadwood, litter and soil carbon. This demonstration allowed the compilation of carbon accounts in four accounting periods 2010-2017, 2017-2018, 2018-2019 and 2019-2020 for six case countries namely Brazil, Mozambique, the Netherlands, the Philippines, Sweden and USA, and later on compared with the accounts from a counterpart carbon accounting framework based on UNFCCC. The compiled carbon accounts revealed the above-ground component being the dominant carbon pool in Brazil and the Philippines, while soil organic carbon outweighs other carbon pools in the Netherlands, Sweden and surprisingly Mozambique. We found decreasing carbon stocks especially for Brazil even in shorter accounting periods i.e., 2018-2019 captured by the EO dataset. This is in contrast to what has been reported by countries to UNFCCC mostly reporting stability in the carbon flows over the years. Part of the discrepancy is the country definitions of managed forests which can be inconsistent with forest management datasets from EO (this study). Another reason is the dependency of countries on national forest inventories which are rarely updated on an annual basis. Moreover, our compiled accounts showed minimal carbon emissions from forest degradation mainly driven by the choice of ecosystem extent input, and lower soil carbon emissions than UNFCCC reports, potentially underestimating peatland emissions. The findings and outputs from this demonstration echo the potential of EO datasets for carbon accounting especially with the advent of time series biomass data, higher spatial resolution of ecosystem extent maps 5-10 m and online ecosystem accounting tools for efficient use cases.", "description": "Environmental-Economic Accounting, Carbon Accounting, SEEA, UNFCCC, Earth Observation, CCI Biomass", "recording_license": "", "do_not_record": true, "persons": [{"id": 100, "code": "HD3CLP", "public_name": "Arnan Araza", "biography": "Postdoctoral researcher focusing on the applications of Earth Observation into environmental-economic accounting", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 150, "guid": "5ec6d825-349f-54a5-84ed-2b8470bc7b68", "logo": "", "date": "2024-10-03T13:30:00+01:00", "start": "13:30", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-150-large-scale-eo-processing-with-xcube-on-cdse", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/BMUDD8/", "title": "Large-scale EO processing with xcube on CDSE", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "xcube is a mature and capable Python software package and framework for EO data ingestion, processing, analysis, visualization, and dissemination. In the scope of the Open Earth Monitor project, xcube is being updated and expanded to support new data sources, improve on-demand cluster processing capabilities, and run seamlessly on the new Copernicus Data Space Ecosystem. Recent work also focuses on providing a maximally preconfigured turnkey distribution of xcube, increasing its suitability as a drop-in compute engine for cloud infrastructures such as CDSE. xcube\u2019s features are complemented by the new zappend tool, which provides robust creation and updating of large, slice-structured Zarr datasets.\r\n\r\nThis talk will describe and demonstrate a typical large-scale processing workflow using the xcube framework in the CDSE ecosystem \u2013 running the gamut through data ingestion from multiple sources through the xcube data store subsystem, data cube construction and normalization, data synthesis and processing to export, dissemination, and seamless visualization via the server and viewer components. Scalability and big data capability is accounted for throughout through approaches such as object storage, parallelization, on-demand cluster processing, dataset pyramidization, and lazy computation. The newly implemented components and improved integration make xcube an ideal tool for the realization of typical Open Earth Monitor workflows.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 198, "code": "DMPAEC", "public_name": "Pontus Lurcock", "biography": "Pontus Lurcock is a software engineer at Brockmann Consult GmbH, with a strong focus on geodatacubes and analysis-ready earth observation data. He has extensive experience of working at the interface between informatics and geosciences, and holds an MSc in Computer Science and a PhD in Geology.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 159, "guid": "551a6816-34eb-53af-9bff-08e03d196e1d", "logo": "", "date": "2024-10-03T14:30:00+01:00", "start": "14:30", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-159-time-series-reconstruction-of-global-scale-historical-earth-observation-data-by-seasonally-weighted-average", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/LT8X3B/", "title": "Time-series reconstruction of global scale historical Earth observation data by seasonally weighted average", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "While various imputation methods are available to reconstruct gappy time series of images, most of them are inadequate for large datasets like the full Landsat archive.\r\nTo address this need, this work proposes a new methodology called seasonally weighted average generalization (SWAG). SWAG works solely on the time dimension, reconstructing images by employing a weighted average of available samples in the original time series. It prioritizes images collected at integer multiples of a year to enforce annual seasonality and gives higher weights to more recent images to avoid propagating land cover changes. The method is implemented as part of the open source Python package scikit-map and optimized for computational efficiency.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 181, "code": "XXHGN8", "public_name": "Davide Consoli", "biography": "Davide Consoli received the Ph.D. degree from Politecnico di Torino, Turin, Italy, in 2023, thesis on fast methods for computational electromagnetics with biomedical applications. \r\n\r\nHe is currently working as a Post-Doctoral Researcher at the OpenGeoHub Foundation, The Netherlands. At OpenGeoHub, Davide supports the foundation\u2019s work at international projects developing solutions for high performance computing and modeling on large scale spatiotemporal earth-observation data.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 165, "guid": "b3756206-aee9-5219-8f0e-a3d9e355c611", "logo": "", "date": "2024-10-03T15:30:00+01:00", "start": "15:30", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-165-exploring-the-biophysical-impacts-of-potential-changes-in-tree-cover-in-africa", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/CCLAG9/", "title": "Exploring the biophysical impacts of potential changes in tree cover in Africa", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "The UN has declared this to be the Decade of Ecosystem Restoration, which should foster the development of restoration projects in many parts of the world suffering from land degradation. In parallel, there is growing demand for deforestation-free and sustainably produced products, as reflected partly by the establishment of the new EU Regulation on Deforestation-free products. The combination of these trends will likely lead to local land use changes resulting in increases in landscape heterogeneity. Here we place an interest in the effects that such changes have on biophysical variables that directly impact the Earth system and the local climate, such as short-wave radiation, land surface temperature and evapotranspiration, as estimated diurnally from geostationary satellite observations. In this study, we explore how the tree density and tree spatial arrangement in different ecosystems of the African continent have an impact on the energetic budget at local and regional scales. We perform a space for time analysis where local changes on vegetation are used to disentangle the effect of land cover transitions on biophysical variables. We expect the results of the study to provide insights into where increasing landscape complexity may provide additional benefits in terms of ecosystem services and thereby contribute towards guidelines in sustainable land planning.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 8, "code": "VLMNWP", "public_name": "Gregory Duveiller", "biography": "Gregory Duveiller holds a PhD in agronomical science and biological engineering from the Universit\u00e9 catholique of Louvain (UCLouvain), Belgium. After his PhD, he spent 10 years working at the European Commission Joint Research Centre (JRC), in Ispra, Italy. He has specialized in developing methods to combine different satellite remote sensing data streams to better monitor and understand land processes, including crop yield monitoring, land cover change and land-atmosphere interactions. Since 2021 he is a project group leader at the Max Planck Institute for Biogeochemistry in Jena, Germany. His main research aims at improving our understanding of the role of terrestrial ecosystems in the Earth System by using data-driven yet process-based thinking applied to satellite Earth Observation data. A key focus is on exploring the complexity and diversity of terrestrial ecosystems, and how their specific functional properties affect land-atmosphere interactions.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 182, "guid": "336ff2e8-30bc-50d1-b654-1cdd37e4398d", "logo": "", "date": "2024-10-03T16:30:00+01:00", "start": "16:30", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-182-forestnavigator-combining-forest-monitoring-and-modelling-for-assessing-policy-pathways-towards-eu-climate-neutrality", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/MWTFSU/", "title": "ForestNavigator: combining forest monitoring and modelling for assessing policy pathways towards EU climate neutrality", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "The achievement of ambitious LULUCF mitigation targets for 2030 and the EU 2050 climate\r\nneutrality goals strongly rely on forests. Currently, there is a large discrepancy in data for monitoring\r\nthe status of EU forests with large differences across sources of information. In particular, remote\r\nsensing data and national statistics are not sufficiently detailed and consistently integrated to allow\r\nfor comprehensive monitoring of forest status and consistently modelling biomass and carbon over\r\ntime, by showing a latency in capturing changes in forest cover and forest biomass.\r\nForestNavigator aims at modelling a series of forest sector policy pathways aligned to EU climate\r\nneutrality goals. These pathways rely on integrating various data sources, including high resolution\r\nremote sensing derived datasets (forest area, disturbances), ground data sources (NFI structural\r\ndata) and national statistics (forest harvest and products). In ForestNavigator, we consistently\r\ncombine these sources allowing for for a consistent representation of forests and forest sector\r\nstatus featured in forest biophysical and socioeconomic models. Additionally, ForestNavigator\r\ndevelops workflows that enable to timely update mitigation pathways according to near-real time\r\ndetection of changes in forests and in the forest bioeconomy. This near-real time update of policy\r\npathways, according to the continuously changing conditions, enables to timely correct efforts for\r\nachieving policy mitigation targets. We present recent developments ongoing in ForestNavigator\r\nproject for a model-data fusion towards the assessment of EU consistent forest policy pathways.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 230, "code": "EJZD9Y", "public_name": "Fulvio Di Fulvio", "biography": null, "answers": []}, {"id": 231, "code": "S88UNA", "public_name": "Andrey Lessa Derci Augustynczik", "biography": null, "answers": []}, {"id": 232, "code": "SNVMFT", "public_name": "Petr Havlik", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}], "Maria Theresia Seminar room (Conference Center Laxenburg)": [{"id": 129, "guid": "0ffcc777-b419-52d8-a8e0-a223a9e46875", "logo": "", "date": "2024-10-03T11:30:00+01:00", "start": "11:30", "duration": "00:45", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-129-workshop-big-data-analytics-in-open-geo-hub-cloud-using-sits", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/W3JBXW/", "title": "Workshop: Big Data Analytics in Open Geo Hub Cloud using SITS", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "This hands-on workshop will present the use of big data analytics to work with data available at the Open GEO Hub cloud service", "description": "The workshop will focus on the analysis of image time series extracted from big Earth observation data cubes available at the Open GEO Hub (OGH). The demonstration will present case studies of land use and land cover classification, including: (a) accessing and visualising data using OGH STAC; (b) building data cubes; (c) generating composite indexes; (d) combining multi-source data; (e) controlling the quality of training samples; (f) creating models using machine learning and deep learning algorithms; (g) tuning deep learning models; (h) parallel image classification using GPUs and CPUs; (i) removing outliers by post-processing; (j) using best practices for accuracy assessment. The workshop will use the open source R package SITS, which is one of the technologies whose development is being supported by the OEMC project.", "recording_license": "", "do_not_record": true, "persons": [{"id": 11, "code": "WRMKR3", "public_name": "Gilberto Camara", "biography": "Gilberto C\u00e2mara is a Brazilian researcher in Geoinformatics, GIScience, Spatial Analysis, and Land Use Modelling from INPE (Brazil's National Institute for Space Research). He was INPE\u2019s director general (2005-2012), visiting professor at the Institute for Geoinformatics at the University of M\u00fcnster (2013-2015), and Director of the Secretariat of GEO (Group on Earth Observations) from 2018-2021.  He published 180 papers and cited more than 17,500 times with an H-index of 55 (Google Scholar, February 2024).", "answers": []}, {"id": 182, "code": "XYH7SW", "public_name": "Deleted User", "biography": "Rolf obtained his doctoral degree in Environmental Sciences in 2021 and has a background in geoinformatics, complex systems modeling, and business administration. At OpenGeoHub, he is a postdoctoral researcher.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 166, "guid": "5145acec-235a-597c-a39e-20b60bbf2bbf", "logo": "", "date": "2024-10-03T13:30:00+01:00", "start": "13:30", "duration": "00:20", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-166-systemic-human-biosphere-atmosphere-monitoring-and-diagnostics", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/CW3LLQ/", "title": "Systemic human-biosphere-atmosphere monitoring and diagnostics", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Here we propose a planetary health diagnostic framework, which aims to track, understand, and characterize the Earth system during the onset and progression of both chronic change (such as climate change) and abrupt disruptions (stemming from climate extremes and socio-economic shocks). However, monitoring a single component of the Earth system to guide policy, but ignoring other essential components, could lead to misleading diagnostics and maladaptation of global sustainability. To gain insights into the integration of climate, biosphere, and society, we apply an interactive dimensionality reduction to the annual variability of multi-stream global data from 2003-2022, including data representing the biosphere and climate combined with national socio-economic indicators.\r\n \r\nWe find that the interactions between biosphere, atmosphere and socio-economy can be captured by three principal axes, which cumulatively explain 17.3%, 22.8% and 24.5% of the variability condensed by non-interactive dimensionality reduction in each individual domain, respectively. The 1st and 2rd pairs of Biosphere-atmosphere-socioeconomic interactive axes describe terrestrial vegetation and land surface water syndromes. The first axes positively correlate to terrestrial vegetation productivity, air temperature, and technology and public health. The second axes negatively correlate to soil moisture, potential evaporation, and reflect several combined socioeconomic aspects such as land use and inequality. We find distinct trajectories across countries with high-income countries more resistant COVID-19-induced economic shock. High and low income groups show contrasting trajectories that are related to poverty reduction and methane emission in the low-income country group. This study advocates for a data-driven paradigm to jointly monitor the recent trajectories of the biosphere, atmosphere, and society that could provide a better understanding and early warning of the state of the Earth system for human well-being.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 8, "code": "VLMNWP", "public_name": "Gregory Duveiller", "biography": "Gregory Duveiller holds a PhD in agronomical science and biological engineering from the Universit\u00e9 catholique of Louvain (UCLouvain), Belgium. After his PhD, he spent 10 years working at the European Commission Joint Research Centre (JRC), in Ispra, Italy. He has specialized in developing methods to combine different satellite remote sensing data streams to better monitor and understand land processes, including crop yield monitoring, land cover change and land-atmosphere interactions. Since 2021 he is a project group leader at the Max Planck Institute for Biogeochemistry in Jena, Germany. His main research aims at improving our understanding of the role of terrestrial ecosystems in the Earth System by using data-driven yet process-based thinking applied to satellite Earth Observation data. A key focus is on exploring the complexity and diversity of terrestrial ecosystems, and how their specific functional properties affect land-atmosphere interactions.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 197, "guid": "b82172e5-7321-5a19-80e3-282ab0973abc", "logo": "", "date": "2024-10-03T14:30:00+01:00", "start": "14:30", "duration": "00:20", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-197-spatiotemporal-prediction-of-socd-for-europe-2000-2022-in-3d-t", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/LAQNPD/", "title": "Spatiotemporal prediction of SOCD for Europe (2000\u20132022) in 3D+T", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Spatiotemporal prediction of SOCD for Europe (2000\u20132022) in 3D+T", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 248, "code": "PSRWAB", "public_name": "Xuemeng Tian", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 191, "guid": "aaeaa146-4926-5d88-b249-4305b316aa7b", "logo": "", "date": "2024-10-03T15:30:00+01:00", "start": "15:30", "duration": "00:20", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-191-the-geo-trees-project", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/PLVP8N/", "title": "The GEO-trees project", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "Please provide an abstract of your talk as soon as possible", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 241, "code": "3NJR8F", "public_name": "Dmitry Shchepashchenko", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 142, "guid": "062838a9-c09d-5e0f-bdee-83e893151922", "logo": "", "date": "2024-10-03T16:30:00+01:00", "start": "16:30", "duration": "00:20", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-142-drought-monitoring-across-scales-with-open-soil-moisture-remote-sensing-data", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/S8FTSE/", "title": "Drought monitoring across scales with open soil moisture remote sensing data", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Drought monitoring across scales is increasingly feasible with the use of open data. Multiple missions dedicated to monitor specific variables as indicators of the status of the earth system contribute to the growing availability of earth observation datasets. Soil moisture is one of these key indicators to monitor the status of drought. \r\n\r\nHowever, drought, as a process dependent on multiple conditions from the atmospheric scale to the local land surface scale, expresses itself as a pattern of patterns. This nested nature consisting of vast anomalies conditioned in fragments, frequently complicates the characterization of drought from only one type of observations (e.g. ground data or only certain scale of remote sensing observations). Therefore, soil moisture data at multiple spatial scales are needed. \r\n\r\nCurrently, soil moisture datasets cover a reasonably wide range of scales to enable the monitoring of drought from continental to local scale. Multiple products exist to cover the monitoring of soil moisture anomalies with resolutions in the order of tens of kilometres either from active and passive radiometric technologies like ASCAT (Advanced SCATterometer) and the European Space Agency - Climate Change Initiative (ESA-CCI) products. Similarly, the pursuit of high-resolution observations is already evidencing the advantage of high-resolution data such as that of Sentinel-1 mission for dealing with the small-scale heterogeneity. Evaluation of these two scales of available data over Europe and Italy serve as examples of their suitability for multiple drought applications, also in an operational context \r\n\r\nFor this study we benefit from the Open Earth Monitoring Cyberinfrastructure project aiming to democratize the use of earth observations open the path to generalize the integration of open datasets across scales.  Overall, our goal is to support this initiative and improve the comparison and combination of open data sources. This is crucial for addressing the multi-scale challenges of earth system sciences.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 192, "code": "SFDNQZ", "public_name": "Jaime Gaona", "biography": "Jaime Gaona was born in Burgos, Spain in 1986. Jaime has a background specialized in hydrology during his Civil Engineering studies from the University of Burgos (2013) and his M.Sc. in Hydraulics and Environment from the Polytechnic University of Valencia (2015).\r\n\r\nJaime holds a PhD supported by an Erasmus Mundus Joint Doctorate scholarship in river Sciences (2019) from Freie Universit\u00e4t Berlin and Universit\u00e1 Degli Studi di Trento, associated with the Leibniz Institute of Freshwater Ecology (Berlin IGB), focused on characterizing and modeling the groundwater-surface water interactions (hyporheic exchange) using innovative measurement techniques such as FO-DTS and hydrogeophysics directed by J\u00f6rg Lewandowski and Alberto Bellin.\r\n\r\nHe started as postdoc in 2019 to study soil moisture and evaporation in the Spanish National Science Project HUMID devoted to the analysis of Iberian drought based on remote sensing and land surface modelling at Ebro Observatory with Pere Quintana-Segu\u00ed, while helping to lecture hydraulics and irrigation systems at the Polytechnic University of Barcelona (2020). \r\n\r\nJaime was from 2021 JCYL-supported researcher at the University of Salamanca, Spain, group of Water resources led by Jos\u00e9 Mart\u00ednez Fern\u00e1ndez at the Research Institute of Agrobiotechnology (CIALE), working on the analysis of soil moisture relevance to vegetation responses.\r\n\r\nJaime is currently research fellow working in soil moisture analysis at the Hydrology group led by Luca Brocca of the Research Institute for Geo-Hydrological Protection IRPI of the Italian National Research Council in Perugia, Italia.", "answers": []}], "links": [], "attachments": [], "answers": []}], "Wodak Room (IIASA)": [{"id": 173, "guid": "1fb39d0a-f3a8-5bce-b961-84d9049be7cc", "logo": "", "date": "2024-10-03T11:30:00+01:00", "start": "11:30", "duration": "00:45", "room": "Wodak Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-173-workshop-global-forest-watch-the-latest-data-and-tools-to-better-protect-forests", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/8NF7GF/", "title": "Workshop: Global Forest Watch: the latest data and tools to better protect forests", "subtitle": "", "track": null, "type": "Workshop proposal", "language": "en", "abstract": "The workshop aims to present new data sets related to tree management available on the GFW website and tools for collecting feedback on these data sets and tools to collect training and validation data.\r\n The list of new data sets includes the new version of Spatial Database of Planted Trees, the\r\nNatural Lands Map, and a new version of the Forest management layer for the year 2020. Discussion will focus on current challenges data producers face such as dataset definitions, data gaps, and quality assurance of the presented datasets.", "description": "Initial Agenda: \r\n\u00b7  Spatial Database of Planted Trees (SDPT) by WRI (Elise Mazur)- 10 minutes\r\n\u00b7  Natural Lands Map by WRI (Liz Goldman) \u2013 10 minutes \r\n\u00b7  Forest management layer 2020 by IIASA (Myroslava Lesiv) \u2013 10 minutes \r\n\u00b7  Discussion \u2013 15 minutes", "recording_license": "", "do_not_record": false, "persons": [{"id": 213, "code": "JF3SQQ", "public_name": "Myroslava Lesiv", "biography": null, "answers": []}, {"id": 218, "code": "TY3DZ7", "public_name": "Elise Mazur", "biography": null, "answers": []}, {"id": 219, "code": "Y3WKBW", "public_name": "Liz Goldman", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 120, "guid": "d6ae9a3d-9ab2-588e-b6f5-4100e159f4fd", "logo": "", "date": "2024-10-03T13:30:00+01:00", "start": "13:30", "duration": "00:45", "room": "Wodak Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-120-workshop-monitoring-deforestation-related-land-use-change-and-carbon-emissions-for-eudr-and-climate-policies", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/7H8WET/", "title": "Workshop: Monitoring Deforestation-related land use change and Carbon Emissions for EUDR and climate policies", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "The workshop aims to exchange on recent policy requirements, progress in providing EO-based data and products and equip participants with better knowledge and skills to analyze the drivers of deforestation and associated carbon emissions using remote sensing and Machine learning. The workshop aligns with recent European Union(EU) regulations to curb the EU market\u2019s impact on global deforestation and provides valuable information for monitoring land use following deforestation, crucial for environmental initiatives and carbon neutrality goals.", "description": "Part 1: Guest Speaker \r\nIntroduction to Deforestation and EUDR requirements (JRC - F. Achard team)\r\n\u25cf\tDefinition and significance of deforestation\r\n\u25cf\tRelevant policy initiatives; in particular EUDR and implications for monitoring\r\n\u25cf\tRequirements \r\n\r\nPart 2: Mapping Drivers of Deforestation (R. Masolele)\r\nSection 1: Mapping Land Use and Change Detection\r\n\u25cf\tChange detection methods using remote sensing data\r\n\u25cf\tMonitoring deforestation over time\r\n\u25cf\tTechniques for land use classification\r\n\r\nSection 2: Identifying Deforestation Drivers\r\n\u25cf\tUnderstanding direct drivers of deforestation\r\n\u25cf\tApplying machine learning techniques to identify deforestation drivers\r\n\u25cf\tCase studies on driver identification\r\n\r\nPart 3: Estimating and Mapping Carbon Emissions Associated with Deforestation (Camilo Zamora, Arnan Araza)\r\nSession 1: Carbon Emissions Estimation\r\n\u25cf\tIntroduction to carbon emissions modeling\r\n\u25cf\tUsing remote sensing for above-ground biomass estimation\r\n\u25cf\tQuantifying carbon emissions from deforestation drivers\r\n\r\nAdditional Components:\r\nOpen Discussion and Q&A:\r\nDedicated time for participants to discuss challenges, share experiences, and seek guidance on specific issues.\r\n\r\nWorkshop Outcome:\r\nParticipants will leave the workshop with practical knowledge in mapping deforestation drivers and estimating associated carbon emissions using remote sensing and machine learning. They will gain insights into the interdisciplinary nature of deforestation analysis and the application of machine learning for informed decision-making.", "recording_license": "", "do_not_record": true, "persons": [{"id": 82, "code": "UBMC3H", "public_name": "Robert Masolele", "biography": "Dr. Robert Masolele is a post-doctoral researcher renowned for his work at the intersection of artificial intelligence and remote sensing, particularly in the field of classifying land use changes with a specific emphasis on commodity crops. His innovative research has contributed significantly to our understanding of how agricultural expansion, particularly in the cultivation of commodity crops, impacts global landscapes.\r\n\r\nEducation and Early Career:\r\nDr. Masolele earned his Ph.D. in Remote sensing and Machine learning from Wageningen University, where his passion for harnessing cutting-edge technologies to address pressing environmental challenges first took root. His early career saw him working on various projects related to satellite imagery analysis and machine learning applications, laying the foundation for his expertise in the intricate field of land use classification.\r\n\r\nExpertise in AI and Satellite Imagery:\r\nSpecializing in the fusion of artificial intelligence and high-resolution satellite imagery, Dr. Masolele has developed advanced models and algorithms that excel in classifying land use changes with high accuracy. His research focuses on monitoring the expansion of commodity crops such as cacao, oil palm, rubber, coffee, avocado, pasture, and soy, providing valuable insights into the environmental repercussions of large-scale agricultural practices.\r\n\r\nNotable Contributions:\r\nOne of Dr. Masolele's most notable contributions includes the development of a novel convolutional neural network (CNN) architecture tailored to handle the complexities of commodity crop identification. His work has led to breakthroughs in mapping the spatial distribution of land use following deforestation, understanding the dynamics of deforestation, and quantifying the ecological impact of crop expansion.\r\n\r\nCollaborations and Impact:\r\nDr. Masolele is a sought-after collaborator in interdisciplinary research endeavors, fostering partnerships between environmental scientists, ecologists, and computer scientists. His work has had a tangible impact on sustainable land management practices and has been influential in shaping conservation policies in regions vulnerable to agricultural encroachment.\r\n\r\nPublications:\r\nIn recognition of his outstanding contributions, Dr. Robert Masolele's work has been published in leading academic journals, media outlets, and he frequently presents his findings at international conferences.\r\n\r\nAs Dr. Masolele continues to push the boundaries of knowledge in his field, his dedication to leveraging artificial intelligence and satellite imagery for the betterment of global ecosystems remains a beacon of inspiration for the scientific community.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 133, "guid": "8e8868b0-f6de-543c-9b4f-d731d94b4f17", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/LGF33B/Screenshot_from_2024-03-05_11_TbRUTkZ.png", "date": "2024-10-03T15:30:00+01:00", "start": "15:30", "duration": "00:45", "room": "Wodak Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-133-workshop-spatiotemporal-machine-learning-fitting-models-and-generating-predictions-using-time-series-data", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/LGF33B/", "title": "Workshop: Spatiotemporal Machine Learning: fitting models and generating predictions using time-series data", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "Machine Learning is commonly used to map environmental variables in 2D, but what about generating predictions of dynamic variables such as above ground biomass, forest species, soil carbon and similar? The difference between spatiotemporal vs purely 2D / 3D mapping is in the three main aspects: (1) points and covariate layers are matched in spacetime (usually month-year period or at least year), (2) covariate layers are based on time-series data and include also accumulative indices (e.g. cumulative rainfall, cumulative snow cover, cumulative cropping fraction and similar) and derivatives, (3) during model training and validation, points are subset in both spacetime to avoid overfitting and bias in predictions. The rationale for using spatiotemporal machine learning is fitness of data for reliable time-series analysis: the predictions for anywhere in the spacetime cube need to be unbiased, with objectively quantified prediction errors (uncertainty), so that hence changes can be derived without a risk for serious over-/under-estimation. We have tested this framework on local and regional data sets (e.g. LUCAS soil samples covering 2009, 2012, 2015, 2018 for Europe) and can be now potentially applied using global compilations of soil points (https://opengeohub.github.io/SoilSamples/). Spatiotemporal machine learning could also potentially be used for predicting future states of soil, e.g. by extrapolating models to future climate scenarios and future land use systems (Bonannella et al., 2023).", "description": "The workshop will provide tutorials in R (https://opengeohub.github.io/spatial-prediction-eml/) and Python (https://github.com/openlandmap/scikit-map) and will focus primarily in the mlr3 and scikit-learn frameworks for ML and ensemble methods based on model stacking (3-4 base learners and 1 meta-learner).\r\n\r\nCited references:\r\n- Bonannella, C., et al. (2023). Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ, 11, e15593. https://doi.org/10.7717/peerj.15593 \r\n- Hackl\u00e4nder, J., et al. (2023). Land potential assessment and trend-analysis using 2000\u20132021 FAPAR monthly time-series at 250 m spatial resolution. PeerJ, in review. https://doi.org/10.21203/rs.3.rs-3415685/v1\r\n- Witjes, M., et al. (2023). Ecodatacube. eu: Analysis-ready open environmental data cube for Europe. PeerJ, 11, e15478. https://doi.org/10.7717/peerj.15478", "recording_license": "", "do_not_record": true, "persons": [{"id": 1, "code": "8QMFTU", "public_name": "Tom Hengl (OpenGeoHub)", "biography": "Tom is the Director at the OpenGeoHub foundation. He has more than 20 years of experience as an environmental modeler, data scientist and spatial analyst with background in soil mapping and geo-information science. He continuously runs hands-on-R training courses to promote use of Open Source software for spatial analysis / spatial modeling purposes. He is currently the project leader of the OpenLandMap \u2014 a system for automated global soil and vegetation mapping at fine spatial resolutions (100 m, 250 m to 1 km) and which aspires to be recognized as an \u201cOpenStreetMap-type\u201d system for environmental data. Tom\u2019s core core philosophy is outlined in this document (see also our Medium article on OpenLandMap.org). Tom is recipient of the Clarivate Highly Cited Researchers for 2021/2022/2023.", "answers": []}], "links": [], "attachments": [], "answers": []}], "Raiffa Room (IIASA)": [{"id": 127, "guid": "8e50916a-8b92-554e-b15d-c200a91990d4", "logo": "", "date": "2024-10-03T11:30:00+01:00", "start": "11:30", "duration": "00:45", "room": "Raiffa Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-127-workshop-geo-automl-with-scikit-map", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/BXHX3T/", "title": "Workshop: Geo-AutoML with Scikit-map", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "In this workshop, the participants will apply an automated machine learning framework suitable for EO data.", "description": "In this workshop, the participants will apply an automated machine learning framework suitable for EO data able to:\r\n- Define an optimum number of models to produce per-pixel values and prediction interval considering the mapping requirements and computational resources available\r\n- Evaluate the computation efficiency and model performance for several ML models considering specific input EO data and target variable\r\n- Establish the model hyper-parameters able to reduce the computation time without significantly reducing the model performance - Establish a plan for distributing the processing in a multi-node high performance computing (HPC) environment.", "recording_license": "", "do_not_record": true, "persons": [{"id": 94, "code": "DLL88P", "public_name": "Leandro Leal Parente", "biography": "Computer scientist with a PhD in Environmental Science working with remote sensing, data science, machine learning, high-performance computing and WebGIS applications.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 121, "guid": "318894ab-c61b-5a0b-aaa9-1b1081134c06", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/AMCPTF/whatif_flood_video__iEZON9a.png", "date": "2024-10-03T13:30:00+01:00", "start": "13:30", "duration": "00:45", "room": "Raiffa Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-121-workshop-playing-the-water-cycle-game-data-from-space-for-flood-risk-mitigation-and-better-managing-water-resources", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/AMCPTF/", "title": "Workshop: Playing the water cycle game: data from space for flood risk mitigation and better managing water resources", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "Climate change is profoundly affecting the global water cycle, increasing the likelihood and severity of extreme water-related events. Droughts are becoming more frequent and intense. Extreme precipitation events are more localised and of unprecedented magnitude, causing widespread flooding and severe impacts on our lives and assets.\r\nAccurately predicting and monitoring water-related environmental disasters, as well as optimal water resource management, require better decision support systems. These systems should integrate remote sensing, in-situ and citizen observations with high-resolution Earth system modelling, artificial intelligence, information and communication technologies, and high-performance computing.\r\nWithin the Digital Twin Earth for Hydrology and the Open Earth Monitor Cyberinfrastructure projects, we have developed advanced interactive tools for building what-if scenarios for flood risk assessment, drought monitoring and water resources management. The workshop will describe the developed tools (current version here: https://explorer.dte-hydro.adamplatform.eu/) and the recent advances developed within the Open Earth Monitor Cyberinfrastructure and related projects. An interactive session will be held to demonstrate the potential and limitations of the developed what-if scenarios.", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"id": 20, "code": "8RHQVS", "public_name": "Luca Brocca", "biography": "Luca Brocca received the Master's degree and the PhD in Civil Engineering in 2003 and 2008 respectively. Since (2009) 2019 he is (Researcher) Director of Research at the National Research Council, Research Institute for Geo-Hydrological Protection of (CNR-IRPI) in Perugia. He is author and co-author of 190+ journal refereed papers (17000+ citations), 80+ papers in peer-reviewed conference proceedings/book chapters, and 10+ regional and global datasets.\r\nLuca Brocca is involved as PI (and co-PI) in several projects funded by the European Commission and Space Agencies (ESA, EUMETSAT). \r\nAmong others, in 2012, he received the \u201cEarly Career Research Excellence\u201d award by iEMSs society, in 2018 he has been the winner of the Copernicus Masters competition \u201cBayWa Smart Farming Challenge\u201d, in 2019 and 2020 he has been nominated \u201cHighly Cited Researchers\u201d by Web of Science Group \u2013 Clarivate, and in 2021 he won the \u201cESA\u2013EGU Earth Observation Excellence Award\u201d.\r\nThe main research interest of Luca Brocca lies in the development of innovative methods for exploiting satellite observations (soil moisture, rainfall, river discharge) for hydrological applications including floods, landslides, rainfall, droughts, irrigation, water resources management (e.g., SM2RAIN, irrigation from space).", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 125, "guid": "4c4b6acc-e9b8-5080-af5d-e07028c30d88", "logo": "", "date": "2024-10-03T15:30:00+01:00", "start": "15:30", "duration": "00:45", "room": "Raiffa Room (IIASA)", "slug": "open-earth-monitor-global-workshop-2024-125-workshop-accessing-global-scale-historical-and-complete-landsat-data", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/YMSRCM/", "title": "Workshop: Accessing global scale, historical and complete Landsat data", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "Access methods and processing pipeline of Landsat bi-monthly, complete and cloud optimized collection", "description": "The first part of the workshop will describe the processing pipeline applied to produce the Landsat bi-monthly, complete (gaps free) and cloud optimized collection derived from the GLAD Landsat ARD-2 collection, from 1997 to 2022. The second part of the workshop will be focused on how to access the collection and its derived biophysical parameters, such as fraction of absorbed photosynthetically active radiation (FAPAR), normalized difference water index (NDWI) and bare soil fraction (BSF). All the data and the code presented will open source and freely available.", "recording_license": "", "do_not_record": true, "persons": [{"id": 181, "code": "XXHGN8", "public_name": "Davide Consoli", "biography": "Davide Consoli received the Ph.D. degree from Politecnico di Torino, Turin, Italy, in 2023, thesis on fast methods for computational electromagnetics with biomedical applications. \r\n\r\nHe is currently working as a Post-Doctoral Researcher at the OpenGeoHub Foundation, The Netherlands. At OpenGeoHub, Davide supports the foundation\u2019s work at international projects developing solutions for high performance computing and modeling on large scale spatiotemporal earth-observation data.", "answers": []}], "links": [], "attachments": [], "answers": []}]}}, {"index": 5, "date": "2024-10-04", "day_start": "2024-10-04T04:00:00+01:00", "day_end": "2024-10-05T03:59:00+01:00", "rooms": {"Theatre Hall (Conference Center Laxenburg)": [{"id": 187, "guid": "18f5f646-2921-5ecf-82d5-875502720e1d", "logo": "", "date": "2024-10-04T09:00:00+01:00", "start": "09:00", "duration": "00:30", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-187-implementing-open-eo-knowledge-and-the-journey-towards-users-engagement", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/QNCDG9/", "title": "Implementing Open EO Knowledge and the journey towards users engagement", "subtitle": "", "track": null, "type": "Keynote lecture", "language": "en", "abstract": "GEO now since 2 decades  has been working, to advocate Earth Observations Open data and Open knowledge, it is urgent to make sure that users are able to discover, access and re-use the available open applications, enhance knowledge sharing and solve most urgent countries socio environmental issues. The GEO Knowledge Hub is a promising tool to enhance knowledge sharing among the scientific community and accelerate the impact that EO Data and EO Knowledge can have.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 23, "code": "9AH9BY", "public_name": "Paola De Salvo", "biography": "Paola De Salvo, Italian National, after receiving her Master\u2019s degree in Environmental Biology cum laude, from the University of Roma Tre, Rome Italy, she started her Geospatial career within the International Institute of Aerospace  Survey and Earth Science (ITC) in the Netherlands.   \r\nShe  later brought her GIS and Remote Sensing competencies to the United Nations Specialized Agencies of Food and Agriculture Organization (FAO) and World Food Programme (WFP) to ensure Earth Observations are used for decision making in developing countries. \r\nShe has been instrumental in establishing and training local staff in Web-based Spatial Data Infrastructures in order to increase timely data discoverability and accessibility . \r\nAfter 12 years of applying her skills within the UN System, she transitioned to the private sector where she worked for Esri Inc, as a solution engineer in support of  United Nations and NGO GIS / Remote Sensing related projects.  \r\nBelieving in the power of Open  Earth Observations Data  and Knowledge she joined the Group on Earth Observations (GEO) Secretariat as an \u200bInformation Technology Officer coordinating GEOSS Platform and GEO Knowledge Hub development, implementation and users uptake.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 183, "guid": "748946d4-1f53-54b0-aa4c-3519bd8b4a87", "logo": "", "date": "2024-10-04T09:30:00+01:00", "start": "09:30", "duration": "00:30", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-183-rethinking-the-grid-towards-less-distorted-imagery-and-ai", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/HJNYMX/", "title": "Rethinking the grid: Towards less distorted imagery and AI", "subtitle": "", "track": null, "type": "Keynote lecture", "language": "en", "abstract": "Satellite imagery is traditionally stored and processed on rectangular grids. However, the widespread usage of such grids has normalized their inherent distortions, particularly near the poles. Previous attempts to address this issue, such as employing multiple local projections like the UTM-based Sentinel 2 L1C grid, have led to inefficiencies, including a significant increase in data volume (~30%) due to overlaps that need to be stored, downloaded, and processed. Additionally, there is a lack of a unified global indexing system and the choice of pixel cell shape, which further complicate the analysis.\r\n\r\nIn this keynote talk, we advocate for a paradigm shift towards Discrete Global Grid Systems (DGGS) to mitigate these challenges. DGGS tessellate the Earth's surface with hierarchical cells of equal area, minimizing distortion and reducing loading time of large geospatial datasets. This approach would greatly improve spatial statistics and convolutional Machine Learning models, where accurate representation of global phenomena is paramount at a global scale.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 91, "code": "REJEED", "public_name": "Daniel Loos", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 188, "guid": "88b6d262-039a-59bc-ac81-7ceeb2942455", "logo": "", "date": "2024-10-04T10:00:00+01:00", "start": "10:00", "duration": "00:30", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-188-eo-for-policy-making-in-the-eu", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/BMCQN9/", "title": "EO for Policy making in the EU", "subtitle": "", "track": null, "type": "Keynote lecture", "language": "en", "abstract": "An abstract needs to be submitted as soon as possible", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 238, "code": "CK7E9Q", "public_name": "Mark Dowell", "biography": "Please complete this field", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 136, "guid": "554b9afd-d842-5257-8f24-509b38a3fcc6", "logo": "", "date": "2024-10-04T11:00:00+01:00", "start": "11:00", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-136-deriving-policy-relevant-geodata-from-satellite-images-lessons-learned-in-the-geo-informed-project", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/9ELMGE/", "title": "Deriving policy-relevant geodata from satellite images: lessons learned in the GEO.INFORMED project", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Evidence-based policy is gaining importance, also in the environmental policy domain in Flanders, Belgium. However, the most prevalent source of policy-relevant information still remains ground sampling, with limited spatial and temporal detail and coverage. The ease of access to freely available (Sentinel) satellite imagery from the Copernicus program through the new OpenEO API provides a golden opportunity for filling this information gap. During the GEO.INFORMED project, remote sensing and deep learning researchers engaged in a co-creation trajectory with regional environmental policy makers to develop machine learning workflows for transforming Copernicus satellite data into policy-relevant geodata. The main challenges encountered in the project where associated with ensuring mutual understanding between scientists and policy-makers; and with the technical implications of non-standard model inputs and limited reference data availability. Within the project, a range of strategies for overcoming these challenges were tested, and the lessons learned will be the main focus of this talk.", "description": "In this talk Dr. Stien Heremans (KU Leuven) will share some of the lessons learned in the GEO.INFORMED project (2020-2024). The main aim of this project was to derive policy-relevant geodata from satellite images. During this talk, she will give an overview of the specific policy-relevant geodata developed within the project. She will also dig deeper into the challenges associated with (a) the selection of policy-relevant and technically feasible 'use cases'; (b) the co-creation trajectory where scientists and policy makers cooperated to conceptualize, develop and fine-tune the geodata (workflows) for these use cases; and (c) the technicalities associated with implementing machine (and deep) learning workflows in these (often) reference data-scarce environments. And of course she will also share some of the solutions.", "recording_license": "", "do_not_record": false, "persons": [{"id": 187, "code": "VVLSAZ", "public_name": "Stien Heremans", "biography": "Stien Heremans obtained her master in Earth Observation from Leuven University in 2009. She then proceeded with a PhD about the use of machine learning methods for sub-pixel crop classification, which she successfully defended in 2015. Since then, she has been working as a remote sensing expert in the Research Institute for Nature and Forest (INBO) of the Flemish government and as a post-doctoral researcher at Leuven University. Her research focuses on the integration of remote sensing data into policy-relevant environmental monitoring and modeling.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 141, "guid": "82817b63-61d8-5a31-9eed-a79ecc40856e", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/CNSFUF/Screenshot_2024-03-14_at_14.1_Fdhtnbs.png", "date": "2024-10-04T11:40:00+01:00", "start": "11:40", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-141-global-trait-based-vegetation-monitoring", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/CNSFUF/", "title": "Global Trait-Based Vegetation Monitoring", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Global Trait-Based Vegetation Monitoring: Leveraging Multispectral Imagery for Restoration Project Assessment\r\n\r\nRestoration projects are crucial for ecosystem recovery and biodiversity conservation, but their large-scale monitoring poses significant challenges. Conventional approaches often rely on intensive manual work, incur high costs and need help with standardisation, making monitoring on a global scale impossible. Public satellite missions such as Sentinel-2 have great potential to transform ecosystem monitoring due to their high spatial and temporal resolution when linked directly to ecosystem characteristics. Here, we present several global, high-resolution (20m) maps of vegetation traits derived from Sentinel-2 multispectral imagery, reflecting the mean trait value during the vegetation period at annual intervals from 2019 onwards. Using a hybrid inversion approach of the physically-based radiative transfer model PROSAIL, we estimate leaf functional traits (e.g. chlorophyll content, equivalent water thickness, or leaf mass per area) and canopy structural traits (e.g. leaf area index). Validation using in-situ data suggests that the trait maps can effectively track local temporal changes. Further, we show how the generated trait maps can map functional trait diversity at a coarser resolution. Altogether, these products provide deeper insights into ecosystem health, biodiversity status and restoration efforts.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 169, "code": "S8BPN9", "public_name": "Felix Specker", "biography": "Geospatial Data Scientist at the [Crowther Lab](https://crowtherlab.com/meet-the-lab/), ETH Zurich.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 154, "guid": "994a17ef-3299-5e8b-8777-bc2d8a82f8d7", "logo": "", "date": "2024-10-04T14:00:00+01:00", "start": "14:00", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-154-mapping-cocoa-farms-across-pantropical-regions-using-high-resolution-satellite-imagery-and-deep-learning", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/3BJ8YK/", "title": "Mapping Cocoa Farms Across Pantropical Regions Using High-Resolution Satellite Imagery and Deep Learning", "subtitle": "", "track": "OEMC project workshop", "type": "Oral talk", "language": "en", "abstract": "Cocoa cultivation serves as a crucial source of income for countless farmers across pantropical regions. However, this agricultural practice often leads to deforestation in tropical forests. While previous studies have highlighted the expansion of cocoa farms, particularly in select African countries, there remains a significant gap in comprehensive data regarding the location of cocoa farms on a pantropical scale. To address this challenge, our study employs deep learning models trained on Sentinel-1 and Sentinel-2 satellite imagery, coupled with annotated reference datasets, to map cocoa farms across pantropical regions.\r\nOur findings provide valuable insights for governments, cocoa companies, consumers, NGOs, and international organizations striving to mitigate the challenges associated with escalating deforestation linked to cocoa production. Of particular significance is the utility of this dataset in addressing the recent European Union Regulation mandating companies to refrain from importing commodity crops associated with deforestation. By providing a comprehensive understanding of cocoa farm distribution across pantropical regions, our research contributes to informed decision-making and sustainable practices in cocoa production and trade.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 82, "code": "UBMC3H", "public_name": "Robert Masolele", "biography": "Dr. Robert Masolele is a post-doctoral researcher renowned for his work at the intersection of artificial intelligence and remote sensing, particularly in the field of classifying land use changes with a specific emphasis on commodity crops. His innovative research has contributed significantly to our understanding of how agricultural expansion, particularly in the cultivation of commodity crops, impacts global landscapes.\r\n\r\nEducation and Early Career:\r\nDr. Masolele earned his Ph.D. in Remote sensing and Machine learning from Wageningen University, where his passion for harnessing cutting-edge technologies to address pressing environmental challenges first took root. His early career saw him working on various projects related to satellite imagery analysis and machine learning applications, laying the foundation for his expertise in the intricate field of land use classification.\r\n\r\nExpertise in AI and Satellite Imagery:\r\nSpecializing in the fusion of artificial intelligence and high-resolution satellite imagery, Dr. Masolele has developed advanced models and algorithms that excel in classifying land use changes with high accuracy. His research focuses on monitoring the expansion of commodity crops such as cacao, oil palm, rubber, coffee, avocado, pasture, and soy, providing valuable insights into the environmental repercussions of large-scale agricultural practices.\r\n\r\nNotable Contributions:\r\nOne of Dr. Masolele's most notable contributions includes the development of a novel convolutional neural network (CNN) architecture tailored to handle the complexities of commodity crop identification. His work has led to breakthroughs in mapping the spatial distribution of land use following deforestation, understanding the dynamics of deforestation, and quantifying the ecological impact of crop expansion.\r\n\r\nCollaborations and Impact:\r\nDr. Masolele is a sought-after collaborator in interdisciplinary research endeavors, fostering partnerships between environmental scientists, ecologists, and computer scientists. His work has had a tangible impact on sustainable land management practices and has been influential in shaping conservation policies in regions vulnerable to agricultural encroachment.\r\n\r\nPublications:\r\nIn recognition of his outstanding contributions, Dr. Robert Masolele's work has been published in leading academic journals, media outlets, and he frequently presents his findings at international conferences.\r\n\r\nAs Dr. Masolele continues to push the boundaries of knowledge in his field, his dedication to leveraging artificial intelligence and satellite imagery for the betterment of global ecosystems remains a beacon of inspiration for the scientific community.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 175, "guid": "60a3d3ac-0cff-5c6b-be10-f9df42371b35", "logo": "", "date": "2024-10-04T14:20:00+01:00", "start": "14:20", "duration": "00:20", "room": "Theatre Hall (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-175-earthcode-a-fair-open-science-environment-for-the-earth-sciences", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/LEYNUZ/", "title": "EarthCODE \u2013 A FAIR Open Science environment for the Earth sciences", "subtitle": "", "track": null, "type": "Oral talk", "language": "en", "abstract": "The EarthCODE (Earth Science Collaborative Open Development Environment) vision provides an integrated, cloud-based, user-centric development environment which can be used to support the European Space Agency\u2019s (ESA) science activities and projects. Building on activities that developed the European EO open-source ecosystem and the Open Earth System Science community (e.g. EOEPCA - Exploitation Platform Common Architecture, DeepESDL - Deep Earth System Data Lab, openEO Platform, ESA Euro Data Cube, etc.), ESA is implementing EarthCODE as a collaborative platform for conducting Earth System Science sustainably and adhering to FAIR and Open Science Principles. EarthCODE will enable the long-term persistence of research outputs from science activities. \r\n\r\nEarthCODE looks to maximise reproducibility, reuse, and consumption of research outputs by the wider community, promoting a flexible and scalable architecture developed with interoperable open-source blocks, with a long-term vision evolving by incrementally integrating industrially provided services from a portfolio of the Network of Resources. EarthCODE platform collaborators will participate in creating integrated architecture, with interoperable solutions and federated capabilities.\r\n \r\nEarthCODE will use EOEPCA Open Standards to help support Open Science, and help drive these standards. Open science principles are increasingly being embraced in the field of Earth Sciences, promoting transparency, collaboration, and accessibility of research. This is being done by promoting open access publications, preprints and open review processes, sharing data/methodologies for verification, reproducibility and reuse. In software development, these principles allow inspection, modification, and code contribution, encouraging collaboration among researchers through various platforms (i.e. GitHub, GitLab, etc.). Sharing of educational resources openly allow for global audience, and involvement of the public through citizen science for scientific research.\r\n \r\nEarthCODE will provide an Integrated Development Platform, giving developers the tools needed to develop high quality workflows that allow experiments to be executed in the cloud and the reproduced by other scientists, following Open Science principles. Our solution is built around existing open-source solutions and building blocks, primarily the Open Science Catalogue, EOxHub and EOEPCA. With it\u2019s adopted federated approach, EarthCODE will have the capability to facilitate processing on other platforms, i.e. DeepESDL, ESA EURO Data Cube, Open EO Cloud/Open EO Platform and AIOPEN/AI4DTE.  \r\n \r\nCollaboration and Federation are at the heart of EarthCODE. As EarthCODE evolves we expect providing solutions allowing allow federation of data and processing. EarthCODE has ambition to deliver a model for a Collaborative Open Development Environment for Earth system science, where researchers can leverage the power of the wide range of EO platform services available to conduct their science, while also making use of FAIR Open Science tools to manage data, code and documentation, create end-to-end reproducible workflows on platforms, and have the opportunity to discover, use, reuse, modify and build upon the research of others in a fair and safe way. EarthCODE thus aims to make possible the eight enabling elements of the EO Open Science and Innovation vision: open data, open-source code, linked data & code, open access documentation, end-to-end workflows reproducible on platforms, open science resources, open science tools, and a healthy community applying all the elements in their practice.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 244, "code": "3FTVAB", "public_name": "Garin Smith", "biography": "Ground Segment Architect specialising in Reproducible Science, AI (Artificial Intelligence), ARD (Analysis Ready Data) and Quality initiatives in the space industry. Heavily involved in the architecture of Open Source solutions, frameworks and platforms. \r\n\r\nLeading a number of initiatives that exploit the capability of the ESA EOEPCA+ platform including ESA EarthCODE, ESA Open Science Catalogue and other Data Quality initiatives.\r\n\r\nTechnical lead (Prime) for ESA EarthCODE and ESA AI4DTE Artificial Intelligence initiative. \r\n\r\nTechnical lead and project manager for the ESA AIOPEN Artificial Intelligence initiative Telespazio component.\r\n\r\nTechnical lead (Prime) for UK Space Agency UK EO Data Architecture Report.\r\n\r\nTechnical lead (Prime) and project manager on an ESA project to deliver Analysis Ready Data on the ASAR CARD4L NRB Product Development Project.", "answers": []}], "links": [], "attachments": [], "answers": []}], "Maria Theresia Seminar room (Conference Center Laxenburg)": [{"id": 137, "guid": "920f989f-6562-5da5-85bc-72c20d1aa8a8", "logo": "", "date": "2024-10-04T11:00:00+01:00", "start": "11:00", "duration": "00:45", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-137-workshop-citizen-science-mobile-app-and-data-in-support-of-forest-mapping-laxenburg-park-campaign", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/DMBRC7/", "title": "Workshop: Citizen Science Mobile App and Data in Support of Forest Mapping: Laxenburg Park Campaign", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "Policies on opening satellite image archives have shifted earth observation to the big data era. However, due to the associated data-hungry analytics, such as deep learning, satellite observations have to be combined (trained and then validated) with a large amount of in-situ data to get meaningful results. Yet, the collection of in-situ data is often laborious, and the resulting observations are rarely open for others to use. To bridge this in-situ data gap, this workshop will analyze the suitability of a citizen science mobile app for measuring biomass and tree species of individual trees and forest plots, i.e., the TreeQuest and ForestQuest modules, respectively. The app has been developed by the International Institute for Applied Systems Analysis (IIASA) and will be freely available for Android and iOS phones by the workshop. We will first present the app and then initiate a citizen science campaign motivating the conference participants to take part by testing the app and surveying selected trees around the conference center. Members from TU Wien will measure and model selected trees using a terrestrial laser scanner. The resulting 3D point cloud will allow the extraction of detailed information on vegetation structure, which will be used for comparison with the mobile app and forest inventory measurements acquired with traditional forest measurement tools (e.g. caliper, vertex). Finally, we will present the results and discuss the performance and potential further development of the app with workshop participants. \r\n\r\nThe workshop will also discuss the relevance of collected data and the approach for the two ongoing initiatives such as (a) the Citizens for Copernicus project that is funded by the Austrian Research Promotion Agency, application No. 47907528, and (b) the Open Earth Monitor Cyberinfrastructure project funded from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101059548.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 27, "code": "SMRNWK", "public_name": "Milutin Milenkovi\u0107", "biography": "Milutin Milenkovi\u0107 is a research scholar in the Novel Data Ecosystems for Sustainability Research Group of the IIASA Advancing Systems Analysis Program. His current research combines Earth observation and citizen science to monitor environmental change, particularly tropical forest growth and resilience. He is also a guest researcher in the Geo-information Science and Remote Sensing lab at Wageningen University, The Netherlands, and a guest lecturer at Vienna University of Technology, Austria.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 124, "guid": "49fc467c-b51b-5416-b855-8efc17618e89", "logo": "", "date": "2024-10-04T11:45:00+01:00", "start": "11:45", "duration": "00:45", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-124-workshop-inferring-spatiotemporal-dynamics-of-mosquitoes-in-italy-using-machine-learning", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/SMBD8J/", "title": "Workshop: Inferring spatiotemporal dynamics of mosquitoes in Italy using machine learning", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The choice of the modelling approach, i.e., the algorithm, is a crucial factor in addressing the multiple sources of variability that can lead to disparate outcomes when different models are applied to the same dataset. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or models as inputs for a meta-learner. The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. Our study utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. This dataset spans various locations in southern Europe, covering four countries -Albania, France, Italy, and Switzerland- and encompasses multiple seasons from 2010 to 2022. Utilising these ovitrap observations and a set of environmental predictors, we employed a stacked machine learning model to forecast the weekly average number of mosquito eggs. This approach enabled us to i) unearth the seasonal dynamics of Ae. albopictus for 12 years; ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. Beyond its immediate application for public health management, our work presents a versatile modelling framework adaptable to infer the spatio-temporal abundance of various species, extending its relevance beyond the specific case of Ae. albopictus.", "description": "The workshop will be split into two parts: the first part will delve into the methods used to preprocess and harmonize the ovitraps dataset, the selection and preparation of the predictor variables, and the modelling framework and rationale used to model the species' spatiotemporal dynamics (i.e., mosquito egg abundance). The second part will instead demonstrate the integration of this data into a web application, designed to visually inform both public health institutions and private citizens, thereby facilitating tailored intervention strategies and boosting public awareness of mosquito-borne disease risks.", "recording_license": "", "do_not_record": true, "persons": [{"id": 34, "code": "FU3F7S", "public_name": "Carmelo Bonannella", "biography": "Carmelo has a PhD in GIS Science and Remote Sensing from Wageningen University and Research (WUR) with a specialization in forest resources monitoring and management through geospatial data science applications and time series analysis.", "answers": []}, {"id": 250, "code": "K8JFMX", "public_name": "Daniele Da Re", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 196, "guid": "50c3f683-00f2-5af2-b800-3e59653c61ac", "logo": "", "date": "2024-10-04T14:00:00+01:00", "start": "14:00", "duration": "00:30", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-196-workshop-high-resolution-gross-primary-productivity-modeling-and-mapping-dynamics", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/WGKG8X/", "title": "Workshop: High-Resolution Gross Primary Productivity: Modeling and Mapping Dynamics", "subtitle": "", "track": null, "type": "Workshop proposal", "language": "en", "abstract": "This workshop will explore the high-resolution mapping of gross primary productivity (GPP) using light-use efficiency models. During the workshop, we will cover how to access the bi-monthly GPP maps and assess the accuracy of the maps via eddy covariance flux measurements. Participants will gain insight on how to exploit high-resolution GPP maps across diverse ecosystems.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"id": 247, "code": "FWUSQK", "public_name": "Mustafa Serkan Isik", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 126, "guid": "59252341-861f-5351-b384-4f285c4a2b30", "logo": "/media/open-earth-monitor-global-workshop-2024/submissions/PHH7DJ/oemc_WyS4mUb.png", "date": "2024-10-04T14:30:00+01:00", "start": "14:30", "duration": "00:30", "room": "Maria Theresia Seminar room (Conference Center Laxenburg)", "slug": "open-earth-monitor-global-workshop-2024-126-workshop-using-grass-saga-and-whiteboxtool-to-map-global-high-resolution-land-relief-parameterization-adopting-equi7-projection-system", "url": "https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-2024/talk/PHH7DJ/", "title": "Workshop: Using GRASS, SAGA and Whiteboxtool to map global high-resolution land relief parameterization adopting Equi7 projection system", "subtitle": "", "track": "OEMC project workshop", "type": "Workshop proposal", "language": "en", "abstract": "The workshop starts with accessing a global ensemble digital terrain model, cropping to tile and reprojecting to Equi7 projection system. Secondly, the attendants will set up a docker containing GRASS, SAGA and Whtieboxtool with R/Python that enables to script land relief parameterization process. Lastly, attendant will follow a workflow that produces different land relief parameters by tiles and mosaics with consideration of boundary effect, in order to achieve high-resolution global scale terrain parameter mapping.", "description": "", "recording_license": "", "do_not_record": true, "persons": [{"id": 176, "code": "9S9G3L", "public_name": "Yu-Feng Ho", "biography": "Yu-Feng works in OpenGeoHub, specializing in geocomputing with geo spatial data, optimize and automate modeling frameworks, and parallelization, and back-end development.", "answers": []}], "links": [], "attachments": [], "answers": []}]}}]}}}