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Intra-annual taxonomic and phenological drivers of spectral variance in grasslands

Thornley, R., Gerard, F. F., White, K. and Verhoef, A. (2022) Intra-annual taxonomic and phenological drivers of spectral variance in grasslands. Remote Sensing of Environment, 271. 112908. ISSN 0034-4257

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To link to this item DOI: 10.1016/j.rse.2022.112908


According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We tested the seasonal influence of the taxonomic and phenological metrics on spectral variance using linear mixed models. A significant interaction term of percent mature leaves and species diversity was found, with the most parsimonious model explaining 43% of the intra-annual change. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance -species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits.

Item Type:Article
Divisions:Science > School of Archaeology, Geography and Environmental Science > Earth Systems Science
Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
ID Code:102377


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