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Title: Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing

Abstract

Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO2. Hyperspectral remote sensing techniques offer the ability to complete landscape-scale analyses of ecological responses to climate disturbance when paired with plot-level measurements that link ecosystem biophysical properties with spectral reflectance signatures. Working within two large ecosystem manipulation experiments, we examined climate controls on composition and diversity in two types of common boreal peatlands: a nutrient rich fen located at the Alaska Peatland Experiment (APEX) in central Alaska, and an ombrotrophic bog located in northern Minnesota at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found a strong effect of plant functional cover on spectral reflectance characteristics. We also foundmore » a positive relationship between species diversity and spectral variation at the APEX field site, which is consistent with other recently published findings. Based on the results of our field study, we performed a supervised land cover classification analysis on an aerial hyperspectral dataset to map peatland plant functional types (PFTs) across an area encompassing a range of different plant communities. Our results underscore recent advances in the application of remote sensing measurements to ecological research, particularly in far northern ecosystems.« less

Authors:
ORCiD logo; ; ORCiD logo; ORCiD logo; ; ; ORCiD logo; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1543014
Grant/Contract Number:  
Cooperative agreement with USDA Forest Service Northern Research Station
Resource Type:
Published Article
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Name: Remote Sensing Journal Volume: 11 Journal Issue: 14; Journal ID: ISSN 2072-4292
Publisher:
MDPI AG
Country of Publication:
Switzerland
Language:
English

Citation Formats

McPartland, Mara, Falkowski, Michael, Reinhardt, Jason, Kane, Evan, Kolka, Randy, Turetsky, Merritt, Douglas, Thomas, Anderson, John, Edwards, Jarrod, Palik, Brian, and Montgomery, Rebecca. Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing. Switzerland: N. p., 2019. Web. doi:10.3390/rs11141685.
McPartland, Mara, Falkowski, Michael, Reinhardt, Jason, Kane, Evan, Kolka, Randy, Turetsky, Merritt, Douglas, Thomas, Anderson, John, Edwards, Jarrod, Palik, Brian, & Montgomery, Rebecca. Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing. Switzerland. doi:10.3390/rs11141685.
McPartland, Mara, Falkowski, Michael, Reinhardt, Jason, Kane, Evan, Kolka, Randy, Turetsky, Merritt, Douglas, Thomas, Anderson, John, Edwards, Jarrod, Palik, Brian, and Montgomery, Rebecca. Tue . "Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing". Switzerland. doi:10.3390/rs11141685.
@article{osti_1543014,
title = {Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing},
author = {McPartland, Mara and Falkowski, Michael and Reinhardt, Jason and Kane, Evan and Kolka, Randy and Turetsky, Merritt and Douglas, Thomas and Anderson, John and Edwards, Jarrod and Palik, Brian and Montgomery, Rebecca},
abstractNote = {Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO2. Hyperspectral remote sensing techniques offer the ability to complete landscape-scale analyses of ecological responses to climate disturbance when paired with plot-level measurements that link ecosystem biophysical properties with spectral reflectance signatures. Working within two large ecosystem manipulation experiments, we examined climate controls on composition and diversity in two types of common boreal peatlands: a nutrient rich fen located at the Alaska Peatland Experiment (APEX) in central Alaska, and an ombrotrophic bog located in northern Minnesota at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found a strong effect of plant functional cover on spectral reflectance characteristics. We also found a positive relationship between species diversity and spectral variation at the APEX field site, which is consistent with other recently published findings. Based on the results of our field study, we performed a supervised land cover classification analysis on an aerial hyperspectral dataset to map peatland plant functional types (PFTs) across an area encompassing a range of different plant communities. Our results underscore recent advances in the application of remote sensing measurements to ecological research, particularly in far northern ecosystems.},
doi = {10.3390/rs11141685},
journal = {Remote Sensing},
number = 14,
volume = 11,
place = {Switzerland},
year = {2019},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.3390/rs11141685

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