Hyperspectral sensing of aboveground
biomass and species diversity in a longrunning
grassland experiment
biomass and species diversity in a longrunning
grassland experiment
Author(s)
Ningthoujam, Ramesh
Bloomfield, Keith J
Crawley, Michael J
Estrada, Catalina
Prentice, I Colin
Type
Journal Article
Abstract
Vegetation properties can be assessed through analysis of canopy reflectance spectra. Early techniques relied on
simple two-band vegetation indices (VIs) that exploit leaf reflectance properties at key wavelengths. As the
technology matures it is now possible to gather and test hyperspectral data. Little evidence exists on how
different management regimes, such as nutrient addition, might affect hyperspectral reflectance and thus influence derived estimates of plant diversity and productivity. At a grassland experiment in southern England, we used a portable spectroradiometer to sample 96 plots exposed to multifactorial treatments combining herbivory, plant competition, soil pH and fertility. Our objective was to compare the predictive performance of popular two-band VIs with a multivariate partial least square regression (PLSR) model that uses all available wavelengths. We found that the PLSR models showed higher predictive power than the best performing VIs – that was especially true for our measure of species diversity (R2cv = 0.36 compared with a Pearson correlation of 0.21). The predictive power for our PLSR model of biomass (R2cv = 0.54) compares favourably with values reported in earlier grassland studies. These results confirm that hyperspectral measurement combined with multivariate regression techniques is a promising approach for monitoring grassland properties. There is evidence of particular benefit in capturing narrow bands associated with the red edge region of the spectrum (700–750 nm). Remotely sensed hyperspectral images at a fine spatial scale offer the prospect for matching with sampling units as small as the 2 × 2 m nutrient subplots measured here.
simple two-band vegetation indices (VIs) that exploit leaf reflectance properties at key wavelengths. As the
technology matures it is now possible to gather and test hyperspectral data. Little evidence exists on how
different management regimes, such as nutrient addition, might affect hyperspectral reflectance and thus influence derived estimates of plant diversity and productivity. At a grassland experiment in southern England, we used a portable spectroradiometer to sample 96 plots exposed to multifactorial treatments combining herbivory, plant competition, soil pH and fertility. Our objective was to compare the predictive performance of popular two-band VIs with a multivariate partial least square regression (PLSR) model that uses all available wavelengths. We found that the PLSR models showed higher predictive power than the best performing VIs – that was especially true for our measure of species diversity (R2cv = 0.36 compared with a Pearson correlation of 0.21). The predictive power for our PLSR model of biomass (R2cv = 0.54) compares favourably with values reported in earlier grassland studies. These results confirm that hyperspectral measurement combined with multivariate regression techniques is a promising approach for monitoring grassland properties. There is evidence of particular benefit in capturing narrow bands associated with the red edge region of the spectrum (700–750 nm). Remotely sensed hyperspectral images at a fine spatial scale offer the prospect for matching with sampling units as small as the 2 × 2 m nutrient subplots measured here.
Date Issued
2025-05-01
Date Acceptance
2025-01-16
Citation
Ecological Informatics, 2025, 86
ISSN
1574-9541
Publisher
Elsevier
Journal / Book Title
Ecological Informatics
Volume
86
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Sponsor
The Eric & Wendy Schmidt Fund for Strategic Innova
Identifier
10.1016/j.ecoinf.2025.103028
Grant Number
PO:35013333(1005109-LEMONTREE)
Subjects
06 Biological Sciences
08 Information and Computing Sciences
Ecology
Publication Status
Published
Article Number
ARTN 103028
Date Publish Online
2025-01-18