Characterizing Boreal Peatland Plant Composition and Species Diversity
Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing Mc. Partland, et al. Remote Sensing doi: 10. 3390/rs 11141685 Background: Peatlands, which account for ~15% of land surface across Arctic and Boreal regions of the globe, are experiencing ecological changes resulting from climate change. Altered hydrology from drought and permafrost thaw, rising temperatures, and elevated levels of carbon dioxide lead to changes in plant community composition, which ultimately affect the productivity, species diversity, and carbon cycling of peatlands. The objective of this research was to assess the ability of remote sensing for characterizing and tracking changes in peatland plant communities in response to global change drivers. Relationship between spectral variation and species diversity indices Analysis: Working within two large peatland manipulation experiments, we leveraged existing field measurements and hand-held spectroscopy to examine climate controls on species composition and diversity. Drone-based hyperspectral images were also used to map plant functional type in an Alaskan peatland. Findings: - We found a strong effect of plant functional type on spectral reflectance. - There was a positive relationship between species diversity and spectral variation. - Hyperspectral imagery was effective for mapping plant functional type in an Alaskan peatland (accuracy ~90%). Significance: Our results demonstrate the utility of hyperspectral remote sensing for charactering the plant diversity and plant function type of peatland systems. Supervised classification of plant functional type in an Alaskan peatland
Notes Citation: Full, formal citation: Mc. Partland, M. Y. ; Falkowski, M. J. ; Reinhardt, J. R. ; Kane, E. S. ; Kolka, R. ; Turetsky, M. R. ; Douglas, T. A. ; Anderson, J. ; Edwards, J. D. ; Palik, B. ; Montgomery, R. A. Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing. Remote Sens. 2019, 11, 1685. https: //doi. org/10. 3390/rs 11141685. Award Information: This research was supported by the NASA Terrestrial Ecology Program (ROSES-11) under NASA Award number NNX 14 AF 96 G (Falkowski, PI)
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