Johannes Heisig
Johannes is a research associate at ifgi, University of Münster, and leads the air quality monitor within OEMC. His interests include spatial data science and EO for forest applications. In his PhD he studies wildfire hazard in Germany.
Sessions
Accurate global forest aboveground biomass (AGB) mapping is important for reducing the uncertainties in terrestrial carbon sink assessments. Current global approaches for AGB mapping, such as ESA CCI Biomass maps, rely on methods that are globally optimized; thus, they may result in lower performance when evaluated in specific regions. In this workshop, we aim to present the cloud-optimized and quality-filtered GEDI point observations and analyze how they can contribute to improved global biomass mapping. The outcomes of the workshop will help identify potential improvements to the datasets and define future experimentation to assess their contribution to AGB mapping.
The workshop will start with a presentation of the OEMC high-quality GEDI point dataset. Then, the OEMC Stakeholder, Gamma Remote Sensing, will present their global and regional AGB activities and requirements for the satellite LiDAR data. The workshop will conclude with a discussion about the presented OEMC GEDI dataset and define future directions of the experimentation.
Air pollution is a health risk to millions of people in Europe. Heavier pollution occurs in densely populated or industrial areas where we can observe, e.g., more combustion of fossil fuels. Air quality maps are commonly based on point measurements. At European scale, hourly data from official stations collected by the EEA member states represents the gold standard. At a local level, where a higher station density is required, authorities need to establish their own networks. Here, Citizen Science or Civic Tech initiatives collecting Low-Cost-Sensor (LCS) data from private individuals could offer an alternative to expensive station networks. Despite providing large data quantity, the LCSs lack accuracy and thereby data quality.
This OEMC use case explores the potential of LCS data for mapping the concentration of particles with sizes of ~2.5 µm (PM2.5). It tries to answer the question whether a large volume of LCS measurements and advanced modeling techniques can account for the lower data quality.
We predict hourly PM2.5 at 100 meter spatial resolution for a study area encasing both urban (Stuttgart, Germany) and rural areas. Predictors include static (land cover, terrain, population, traffic network) and dynamic variables (wind, humidity, temperature). In an attempt to leverage temporal air quality dynamics we explore time series forecasting methods such as exponential smoothing. Official EEA station measurements act as a reliable validation data source.