Hyperspectral characterization of vegetation in hemiboreal, boreal and Arctic peatlands using a geographically extensive field dataset

• Published in: Ecological Informatics
• Main Authors: Salko, Sini-Selina, Hovi, Aarne, Burdun, Iuliia, Juola, Jussi, Rautiainen, Miina
• Other Authors: Department of Built Environment, Geoinformatics, Aalto-yliopisto, Aalto University
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2024
• Subjects: Fractional cover; Plant functional type; Reflectance spectra; Remote sensing; Arctic; Climate change
• Online Access: https://aaltodoc.aalto.fi/handle/123456789/130321; https://doi.org/10.1016/j.ecoinf.2024.102772

| openaire: EC/H2020/771049/EU//FREEDLES Northern peatlands store up to 25% of global soil organic carbon and function as important hotspots for biodiversity. However, they are facing degradation from climate change driven by human activities as well as anthropogenic land use changes, up to the point of endangering the ecosystems' functioning and the storage of soil organic carbon entailed within them. The surface vegetation of northern peatlands is an important indicator of the ecosystem's functioning and ecohydrology, highlighting the importance of its large-scale, continuous monitoring. Approaches utilizing hyperspectral data for monitoring vegetation health and species composition can also be applied to peatland vegetation. To support the development of methods for interpreting hyperspectral satellite data from peatlands, we conducted a comprehensive in situ study of hemiboreal, boreal, sub-Arctic and Arctic peatland vegetation. We measured the reflectance spectra (350–2500 nm), soil moisture, and various vegetation-related attributes from a total of 446 vegetation plots in Estonia and Finland, from a 1500 km south-north interval. We then investigated (i) the spectral variation in surface vegetation of hemiboreal, boreal, sub-Arctic and Arctic peatlands and (ii) explored its connection to plant functional types (PFTs) and soil moisture, as well as evaluated the potential of hyperspectral data in estimating PFT cover using simple vegetation indices and partial least square (PLS) regression. Our results indicate that (i) the best spectral regions to retrieve information regarding the PFT vary greatly especially between vascular plants and bryophytes, (ii) the reflectance at an individual wavelength as well as simple vegetational index can, to some extent, predict the PFT, and that (iii) the PLS regression can predict the PFT with good accuracy. Overall, our findings demonstrate the potential of using hyperspectral data in monitoring PFTs in northern peatlands. The spectral library and the ancillary data from ...