The Open-Earth-Monitor (OEMC) design workshop, which took place at the project kick-off meeting in Wageningen in 2022, put use cases at the center of the OEMC project. Since then, 30+ OEMC use cases were established, each with at least one stakeholder directly influencing the design and results (tools and data) developed by the use case.
The UNCCD currently measures land degradation neutrality at 300 m spatial resolution, while the modern open EO data is available publicly at finer resolutions even up to 10 m resolution. To make LDN data more usable and matching the field conditions, OEMC project aims at developing opensource tools for measuring land potential and productivity at higher resolution (up to 30 m) and providing analysis ready data in a distributed system with Cloud-Optimized GeoTIFFs.
IDH and Mirova both need high spatial resolution tools to track the impacts of their projects and investments on biodiversity. OEMC project plants to create an open-data monitor that can provide a global overview of biodiversity trends at any site for the last 25+ years, primarily based on EO images (FPAR monthly, fire disturbance monthly, canopy height, vegetation diversity, bare earth and surface water percentage), which could be used in combination with local data to refine and improve estimates.
The Nature Conservancy and Wetlands International track global mangrove dynamics and share data via the global mangrove watch platform. OEMC project will help TNC and Wetlands Int to produce harmonized GIS data layers, including soil carbon estimates for world mangrove forests, aiming at the spatial resolution of 30 m and annual updates. These layers can then be used to do carbon accounting and estimate losses and gains in the years to come.
WRI Global Forest Watch and Land & Carbon Lab are developing forest carbon emissions estimation tools that can be potentially used in all climate zones. OEMC project will combine forests and land change information with earth observation and in-situ biomass data to improve frequency and quality of developed tools. We will work on cases in Peru, Guyana and Mozambique and we will follow the SEEA and the IPCC reporting frameworks.
There is a high interest today in combining global environmental Earth System Science variables and socio-economic variables e.g. from statistics offices to try to understand how does the ecological health of the planet relates to "socio-economic health". OEMC project plans to build a tool to analyse the impact of various economic activities on environmental degradation, such as climate change, biodiversity loss, and ecosystem services; as well as the opposite: to measure the impact of ecosystem degradation on financial systems. This "Planet Health Index" tool will use the UN System of Environmental Economic Accounting as a basis for the analysis.
Within the Global Carbon Project (GCP), RECCAP2 aims to establish the greenhouse gas budgets of large regions at continental scale, as well as produce new synthesis of global land, ocean and coastal GHG fluxes. The OEMC project proposes to improve the coarse spatial resolution of satellite observation of sun-induced chlorophyll fluorescence (SIF) signal to enable a better monitor of carbon uptake by terrestrial ecosystems. We will try a novel downscaling approach and combine different sources of remote sensing with AI modeling.
The European Drought Observatory (EDO) and the Global Drought Observatory (GDO) uses mainly modeled data for monitoring drought at the European and Global scale. The OEMC project will build 1km drought maps on a seasonal scale based on soil moisture, vegetation, and precipitation data obtained from the integration of satellite, modeled, and ground-based observations. Integrated satellite data will allow us to include the human component in drought assessment with improved reliability in areas impacted by human activity (ie extensively irrigated areas).
Monitoring soil carbon / increase soil carbon sequestration is of exteme importance for climate change mitigation. The OEMC project will produce 1km daily climate element (max., min., mean temperature, sea level pressure, total precipitation) maps to support the creation and monitoring of carbon credit projects under VERRA methodology for the estimation of baseline scenario carbon emissions. This will help to fill a need in financial analysis and design of Regenerative Agriculture projects, specifically in the estimation of future soil carbon sequestration.
Digital Terrain Model i.e. map of the land surface elevation and morphology is one of the key inputs to ecological modeling / vegetation and soil mapping. Our results for Continental Europe show that machine learning can be used to remove canopy and buildings, although some post-processing is still needed to create hydrologically correct DTMs. The OEMC project aims at building such a system for the whole world and then also derive some key terrain variables that can be used as input layers for further research and monitoring.
Reforestation (i.e. re-greening of the planet) is one of the key climate change mitigation strategies. The OEMC project will be modeling and monitoring forest change (gain and loss) at 1 km scale, ideally near-real time with at least stable annual products available openly.
The European Commission's JRC is developing EU Soil Observatory (EUSO), a system of dashboards to warn organizations about potential negative trends. The OEMC aims to support this work by building a web-service with dynamic soil data that is based on using streams of EO data e.g. Sentinel and/or Landsat. OEMC will use the existing platform EcoDataCube.eu to visualize and serve pan-European predictions that can then be further used for processing flows and decisions at the level of EUSO.
IITA, together with a number of partners, is building a monitoring system able to predict yield for specific crop types to support tillage and conservation agriculture practices (mixed farming systems MFS initiatives). OEMC project can speed up the predictive mapping of crop fields developed by IITA and help upscale the process to cover the entire African continent. OEMC can also help train producers and users of agronomy data on how to update the predictions and how to communicate associated uncertainty and connected risks.
In this use-case, the OEMC project will expand the RADD alerts for European forest monitoring and disturbance detection and mapping by creating a 10-m forest disturbance alert and characterization tool for Europe using Sentinel-1/2 data that can differenciate human and natural forest disturbances. This will directly support the EU forest management efforts and climate laws such as the European Green Deal and the EU Forest Strategy for 2030.
OEMC aims to produce a dataset that provides an estimation of local surface temperature changes in relation to forest cover variations. It will serve to inform the JRC Forest Observatory and advise on thermal consequences of land-based mitigation strategies such as tree planting. It will be based on SEVIRI data and previous work.
The European Flood Alert System (EFAS) monitors flood risk across Europe. The OEMC project will develop 1 km resolution flood risk maps, updated yearly, based on the two main drivers of extreme flood risk: soil moisture and precipitation.
Success of wildfire risk mapping is largely a function of quality meteorological data / forecasting. The OEMC project aims to produce 1km resolution daily climate element (max., min., mean temperature, sea level pressure, total precipitation) maps from 1990 to present. This data set will be valuable for wildfire risk identification modeling.
Crop yield predictions need accurate up-to-date meteorological data. The OEMC project will produce 1km daily climate element (max., min., mean temperature, sea level pressure, total precipitation) maps. This data set will provide Agriculture Insurance actors a concrete base for more accurate underwriting and premium calculation, such as calculation of weather risk probability and potential crop risks related with the prevailing and weather conditions.
Currently CAMS provides air quality forecasts on the European and global scale, while in-situ observations are collected and redistributed by the EEA. The OEMC project aims to provide a simple means for exploring the relationship between CAMS forecasts and in situ sensor data.
Currently CAMS provides air quality forecasts on the European and global scale, while in-situ observations are collected and redistributed by the EEA. Some local authorities and research institutes try to downscale CAMS forecasts using local data, such as emissions, traffic, and in situ observations. The OEMC project aims to provide a simple way for downscaling CAMS forecasts to regional and local settings, potentially also using local monitoring network data and local traffic information.
Under the European Green Deal, the EU biodiversity strategy for 2030 commits to planting at least 3 billion additional trees in the EU by 2030. The OEMC project will create a tool to help identify what kind of tree species can be planted in specific areas of interest based on a list of suitable species per location with at a spatial resolution of 30 m. Each pixel will show the number of suitable species and the users can then decide which ones to plant according to specific needs (increasing biodiversity, production, protection etc). The tool will be limited to areas currently covered by forests, croplands (to incentivize the creation of agroforestry systems), grasslands and transitional (shrubs, bushes) areas.
Biodiversity is a complex property of landscape not trivial to map, especially not at high spatial resolution. The OEMC project aims to create a spatial product that estimates biodiversity by using spatial characteristics as proxy.
Emilia-Romagna region has been collecting daily info on mosquito occurrence since 2017 using point data to generate a warning system. OEMC aims to provide at least two complementary open-source model applications whose outputs will support and integrate Emilia-Romagna efforts to manage and control Ae. albopictus populations, including an automated framework for such data to demonstrate the accuracy of distribution maps. Automation of spatiotemporal predictions could help assess health risks and generate data in close to real-time.
Papuk National Park and similar national and nature park agencies in Croatia currently have limited access to forest inventory data and operates using offline layers. OEMC project will build high spatial resolution forest management and tracking tools which will be seamlessly available to all agencies as open data. The agencies can then collect local on-ground data and use them to locally train/refine models to compare protected and managed forest over the last 25+ years and potentially derive ecosystem services of forests using FAIR procedures / EO-based data.
LAPIG is involved in two mapping initiatives (MapBiomas and Global Pasture Watch) using Landsat data, producing 30-m annual maps, which could be downscaled using Sentinel-2 data to 10-m. OEMC project aims to produce recurrent 30-m mapping products for grasslands / pasture areas and livestock from 2000 onward to support the Land & Carbon Lab initiative, seeking to contribute for a better understanding of land use conversion, food production, climate change and land productivity at global scale. All produced EO data and training samples will be open data allowing further refinements for the mapping products at national and local scales by user communities.
INPE (Instituto Nacional de Pesquisas Espaciais) is National Institute for Space Research. One of its main mission is to monitor forests in Brazil. The OEMC project will create a 10-m radar-based disturbance tool to monitor and characterize deforestation in tropical forests to be tested in Brazil. We will mainly use Sentinel-1 data and future SAR missions associated with real-time drivers mapped by optical Sentinel-2 and Planet data to enable separation of natural and human disturbances.
Hydrological Office of the Province of South Tyrol needs 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 now covered areas in the Alps to support the planning activies of the Hydrological Office of the Province of South Tyrol.
The DPC currently uses coarse resolution maps to assess seasonal conditions with a moderated reliability of human activities impact on measured areas. The OEMC project will produce 1km drought maps on a monthly basis exploiting soil moisture, vegetation and precipitation data obtained from the integration of satellite, modeled, and ground-based observations. Changing to high resolution is expected to improve the system and to open new opportunities that will allow to include the human component in the drought assessment, with a significantly better reliability.
Various Sinai land re-greening restoration projects require most detailed and most up-to-date environmental data to feed various scenario testing / landscape design project. The OEMC project will build a data cube for Sinai which will include vegetation, carbon, and water cycle changes.
Ethiopian ministries and universities require easy access to EO and environmental data to help land restoration projects. During the OEMC project, online maps and tools to monitor vegetation and land productivity will be produced in order to support Ethiopia's strategic investment framework and natural resource management policy development.
The OEMC project will analyze whether and how satellite LiDAR data and data on forest management contribute to the biomass model. In addition to this, we will analyze the potential of mobile biomass apps to provide open forest field measurements and support the validation of EO-based biomass.