Grants and residencies

Research

Sphagnum Mosses as Ecosystem Engineers in transitional Mires: A Drone-based Analysis

Application summary

The state of peatlands, or mires, concerns us all, as they cover nearly 30 percent of Finland’s land area and provide several ecosystem services to humankind. The role and usage of peatlands are highly debated topics in today’s society. Although peatlands appear to be in a state of equilibrium, most of them have been exposed to severe disturbances by either land use or climate change, with natural fluctuations occurring as well. The resulting ecosystem shifts or transitions among peatland types can lead to significant carbon dioxide release and a decline of biodiversity. Therefore, it is crucial to recognize transitions and changes in order to adapt land use management and restoration measures. My PhD project is highly interdisciplinary, involving a broad team and bridging the fields of geography, ecology and remote sensing. It seeks to detect transitions of peatland ecosystems with help of archived aerial images and a variety of modern, high-resolution drone imagery complimented by field data. My hypothesis is that transitions can be detected on drone images by identifying vegetation patterns and hydrological features. The key element in my research are Sphagnum mosses, which are ecosystem engineers distributed along the hummock-hollow gradient. Ergo, Sphagnum mosses can be used as an indicator for change detection. My PhD project further aims to develop reliable methods for detailed peatland vegetation classification and mapping on species level, and lastly to model on a multidimensional level vegetation distribution and hydrological flow networks. Detecting transitions can show dynamic processes within mires and point out important carbon sinks, thus increasing our understanding of mires and the ecological interactions therein. The developed methods will be of high importance in land use planning and management, as well as for peatland restoration and evaluating ecosystem services. The research outputs will be broadly transferable - nationally and globally.

The PhD project “Sphagnum mosses as ecosystem engineers: A drone-based analysis” dealt with the state and dynamics of peatlands. It aimed at detecting vegetation and hydrological patterns in transitional mires, that is, peatlands that undergo natural and climate-induced changes. By this, it addressed vegetation succession with focus on Sphagnum mosses as ecosystem engineers and drones equipped with different sensors as remote sensing tools for peatland monitoring. The choice of sensor and level of detail shown in the drone images must be chosen according to the monitoring goal and known before data acquisition. It is necessary to adapt common field methods (i.e., vegetation surveys and sampling) to provide appropriate reference data for geospatial modelling and image analysis. These might deviate significantly from those that address purely ecological questions followed by statistical analysis (without any geospatial aspect). This project’s results suggest the following guidelines for further studies involving remote sensing-based assessments of peatlands ecosystems: 1.) Low costs RGB sensors mounted on drones combined with simple algorithms may provide simplified vegetation distribution maps along a wetness gradient, 2.) common multispectral sensors that provide more spectral information can accurately map microhabitats, as well as plant communities, and 3.) state-of-the-art sensors, such as hyperspectral sensors, may allow for species-level detection to model Sphagnum moss distribution but require careful selection of field data and additional information of hydrological site-conditions. However, this data type has the advantage to examine the relationship between vegetation and hydrological conditions. In conclusion, drone-based assessments profit from a detailed monitoring goal, while data collected as addition might lack proper quality or reference data. Different vegetation level can be mapped accurately, and even low-cost options allow for good results.

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