Mapping Fractional Vegetation Coverage across Wetland Classes of Sub-Arctic Peatlands Using Combined Partial Least Squares Regression and Multiple Endmember Spectral Unmixing

Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Heidi Cunnick, Joan M. Ramage, Dawn Magness, Stephen C. Peters
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2023
Subjects:
sub-arctic peatland-wetlands
hyperspectral
PLSR
G-LiHT
plant functional type
multiple endmember spectral unmixing
Science
Q
Arctic
Alaska
Online Access:https://doi.org/10.3390/rs15051440
https://doaj.org/article/e22850517b5e4e47a80af255310b521d
Description
Summary:Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember reference library to unmix hyperspectral imagery and map vegetation coverage at the level of plant functional type (PFT), across three wetland sites in sub-arctic Alaska. This study explores the optimization and parametrization of multiple endmember spectral mixture analysis (MESMA) models to estimate coverage of PFTs across wetland classes. We use partial least squares regression (PLSR) to identify a parsimonious set of critical bands for unmixing and compare the reference and modeled coverage. Unmixing, using a full set of 110-bands and a smaller set of 4-bands, results in maps that effectively discriminate between PFTs, indicating a small investment in fieldwork results in maps mirroring the true ground cover. Both sets of spectral bands differentiate between PFTs, but the 4-band unmixing library results in more accurate predictive mapping with lower computational cost. Reducing the unmixing reference dataset by constraining the PFT endmembers to those identified in the field-site produces only a small advantage for mapping, suggesting extensive fieldwork may not be necessary for MESMA to have a high explanatory value in these remote environments.

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