Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska
Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quant...
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Online Access: | http://www.osti.gov/servlets/purl/1958558 https://www.osti.gov/biblio/1958558 https://doi.org/10.1016/j.rse.2022.113430 |
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ftosti:oai:osti.gov:1958558 2024-01-28T10:02:58+01:00 Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska Yang, Dedi Morrison, Bailey D. Hanston, Wouter McMahon, Andrew Baskaran, Latha Hayes, Daniel J. Miller, Charles E. Serbin, Shawn P. 2023-12-28 application/pdf http://www.osti.gov/servlets/purl/1958558 https://www.osti.gov/biblio/1958558 https://doi.org/10.1016/j.rse.2022.113430 unknown http://www.osti.gov/servlets/purl/1958558 https://www.osti.gov/biblio/1958558 https://doi.org/10.1016/j.rse.2022.113430 doi:10.1016/j.rse.2022.113430 54 ENVIRONMENTAL SCIENCES 59 BASIC BIOLOGICAL SCIENCES 2023 ftosti https://doi.org/10.1016/j.rse.2022.113430 2023-12-30T23:50:57Z Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG ... Other/Unknown Material Arctic Climate change Seward Peninsula Tundra Alaska SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Remote Sensing of Environment 286 113430 |
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Open Polar |
collection |
SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
op_collection_id |
ftosti |
language |
unknown |
topic |
54 ENVIRONMENTAL SCIENCES 59 BASIC BIOLOGICAL SCIENCES |
spellingShingle |
54 ENVIRONMENTAL SCIENCES 59 BASIC BIOLOGICAL SCIENCES Yang, Dedi Morrison, Bailey D. Hanston, Wouter McMahon, Andrew Baskaran, Latha Hayes, Daniel J. Miller, Charles E. Serbin, Shawn P. Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska |
topic_facet |
54 ENVIRONMENTAL SCIENCES 59 BASIC BIOLOGICAL SCIENCES |
description |
Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG ... |
author |
Yang, Dedi Morrison, Bailey D. Hanston, Wouter McMahon, Andrew Baskaran, Latha Hayes, Daniel J. Miller, Charles E. Serbin, Shawn P. |
author_facet |
Yang, Dedi Morrison, Bailey D. Hanston, Wouter McMahon, Andrew Baskaran, Latha Hayes, Daniel J. Miller, Charles E. Serbin, Shawn P. |
author_sort |
Yang, Dedi |
title |
Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska |
title_short |
Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska |
title_full |
Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska |
title_fullStr |
Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska |
title_full_unstemmed |
Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska |
title_sort |
integrating very-high-resolution uas data and airborne imaging spectroscopy to map the fractional composition of arctic plant functional types in western alaska |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1958558 https://www.osti.gov/biblio/1958558 https://doi.org/10.1016/j.rse.2022.113430 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Seward Peninsula Tundra Alaska |
genre_facet |
Arctic Climate change Seward Peninsula Tundra Alaska |
op_relation |
http://www.osti.gov/servlets/purl/1958558 https://www.osti.gov/biblio/1958558 https://doi.org/10.1016/j.rse.2022.113430 doi:10.1016/j.rse.2022.113430 |
op_doi |
https://doi.org/10.1016/j.rse.2022.113430 |
container_title |
Remote Sensing of Environment |
container_volume |
286 |
container_start_page |
113430 |
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1789328280738856960 |