Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experiment
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. L...
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000378 |
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| author | Ramesh K. Ningthoujam Keith J. Bloomfield Michael J. Crawley Catalina Estrada I. Colin Prentice |
| author_facet | Ramesh K. Ningthoujam Keith J. Bloomfield Michael J. Crawley Catalina Estrada I. Colin Prentice |
| author_sort | Ramesh K. Ningthoujam |
| collection | DOAJ |
| description | 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 (Rcv2 = 0.36 compared with a Pearson correlation of 0.21). The predictive power for our PLSR model of biomass (Rcv2 = 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. |
| format | Article |
| id | doaj-art-8406c080a41c42dea6140e2c01b4979b |
| institution | DOAJ |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-8406c080a41c42dea6140e2c01b4979b2025-08-20T03:12:51ZengElsevierEcological Informatics1574-95412025-05-018610302810.1016/j.ecoinf.2025.103028Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experimentRamesh K. Ningthoujam0Keith J. Bloomfield1Michael J. Crawley2Catalina Estrada3I. Colin Prentice4Georgina Mace Centre for Living Planet, Life Sciences, Imperial College London, Ascot SL5 7PY, United Kingdom; Leverhulme Centre for Wildfires, Environment and Society, Imperial College London, London SW7 2BW, United KingdomGeorgina Mace Centre for Living Planet, Life Sciences, Imperial College London, Ascot SL5 7PY, United Kingdom; Corresponding author.Georgina Mace Centre for Living Planet, Life Sciences, Imperial College London, Ascot SL5 7PY, United KingdomGeorgina Mace Centre for Living Planet, Life Sciences, Imperial College London, Ascot SL5 7PY, United KingdomGeorgina Mace Centre for Living Planet, Life Sciences, Imperial College London, Ascot SL5 7PY, United Kingdom; Leverhulme Centre for Wildfires, Environment and Society, Imperial College London, London SW7 2BW, United KingdomVegetation 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 (Rcv2 = 0.36 compared with a Pearson correlation of 0.21). The predictive power for our PLSR model of biomass (Rcv2 = 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.http://www.sciencedirect.com/science/article/pii/S1574954125000378Canopy spectral reflectanceProductivityRemote sensingVegetation indexNitrogen fertilisation |
| spellingShingle | Ramesh K. Ningthoujam Keith J. Bloomfield Michael J. Crawley Catalina Estrada I. Colin Prentice Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experiment Ecological Informatics Canopy spectral reflectance Productivity Remote sensing Vegetation index Nitrogen fertilisation |
| title | Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experiment |
| title_full | Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experiment |
| title_fullStr | Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experiment |
| title_full_unstemmed | Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experiment |
| title_short | Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experiment |
| title_sort | hyperspectral sensing of aboveground biomass and species diversity in a long running grassland experiment |
| topic | Canopy spectral reflectance Productivity Remote sensing Vegetation index Nitrogen fertilisation |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000378 |
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