Integrating multi-source geospatial data to monitor peatland red-listed plant species, greenhouse gas dynamics, and the water table

Peatlands provide vital ecosystem services, yet over half of Finland's peatlands have been drained for forestry, agriculture, and peat extraction, leading to biodiversity loss and increased greenhouse gas emissions. This dissertation explores the use of geospatial environmental and remote sensing data, combined with statistical modelling, as a cost-effective approach to peatland monitoring. The research demonstrates that integrating environmental and remote sensing data can predict the distribution of red-listed peatland plant species, identify potential greenhouse gas sink and source patterns, and model water table dynamics across different spatial scales. These findings support more efficient peatland conservation, restoration planning, and long-term peatland monitoring in Finland.