Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland.
Konza Prairie
airborne spectroscopy
foliar traits
functional traits
hyperspectral remote sensing
tallgrass prairie
Journal
Ecology
ISSN: 1939-9170
Titre abrégé: Ecology
Pays: United States
ID NLM: 0043541
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
revised:
20
07
2021
received:
17
03
2021
accepted:
03
09
2021
pubmed:
18
11
2021
medline:
1
4
2022
entrez:
17
11
2021
Statut:
ppublish
Résumé
Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network's (NEON's) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of readily available data products of NEON's AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total Biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380-2,500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation
Substances chimiques
Neon
4VB4Y46AHD
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e03590Informations de copyright
© 2021 The Ecological Society of America.
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