Detecting Spatial Patterns of Peatland Greenhouse Gas Sinks and Sources with Geospatial Environmental and Remote Sensing Data.

Peatlands play a key role in the circulation of the main greenhouse gases (GHG) - methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). Therefore, detecting the spatial pattern of GHG sinks and sources in peatlands is pivotal for guiding effective climate change mitigation in the land use sector. While geospatial environmental data, which provide detailed spatial information on ecosystems and land use, offer valuable insights into GHG sinks and sources, the potential of directly using remote sensing data from satellites remains largely unexplored. We predicted the spatial distribution of three major GHGs (CH4, CO2, and N2O) sinks and sources across Finland. Utilizing 143 field measurements, we compared the predictive capacity of three different data sets with MaxEnt machine-learning modeling: (1) geospatial environmental data including climate, topography and habitat variables, (2) remote sensing data (Sentinel-1 and Sentinel-2), and (3) a combination of both. The combined dataset yielded the highest accuracy with an average test area under the receiver operating characteristic curve (AUC) of 0.845 and AUC stability of 0.928. A slightly lower accuracy was achieved using only geospatial environmental data (test AUC 0.810, stability AUC 0.924). In contrast, using only remote sensing data resulted in reduced predictive accuracy (test AUC 0.763, stability AUC 0.927). Our results suggest that (1) reliable estimates of GHG sinks and sources cannot be produced with remote sensing data only and (2) integrating multiple data sources is recommended to achieve accurate and realistic predictions of GHG spatial patterns.

Cost-efficient management of peatland to enhance biodiversity in Finland.

Peatlands provide a variety of ecosystem services besides being important ecosystems for biodiversity. Sustainable peatland management requires that its impacts are identified, and all management is allocated in a cost-efficient manner. In this study, we assessed how peatland management influences the habitat suitability of red-listed plant species and the financial performance of management measured as net present value (NPV). The study was done in three landscapes in Finland. We considered four peatland management scenarios i.e., no management activity (NOMANAGE), hydrological restoration (REST), wood harvesting for bioenergy (BIOENERGY), and timber production (TIMBER). The NPVs of different management scenarios were compared to the habitat suitability of red-listed peatland plant species. A cost-impact analysis was used, with TIMBER as a baseline scenario, to find out which alternative scenario would be the most cost-efficient in contributing to habitat suitability. Our study shows that potential habitat areas were significantly different between the scenarios. REST provided the largest potential habitat areas, followed by BIOENERGY, NOMANAGE, and TIMBER. TIMBER provided the best financial performance when low interest rates were used. REST and BIOENERGY were more cost-efficient in enhancing potential habitat areas than NOMANAGE. REST would improve suitable habitats and provide financial benefits when a higher interest rate was used. In conclusion, even a win-win condition could be achieved in some cases (33%), in which higher NPV was achieved simultaneously with improved potential habitat areas. The study provides information for alleviating the economic barriers of restoration and targeting land use and management options cost-efficiently.