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Prediction of grassland biodiversity using measures of spectral variance: a meta-analytical review

Thornley, R. H., Gerard, F. F., White, K. and Verhoef, A. (2023) Prediction of grassland biodiversity using measures of spectral variance: a meta-analytical review. Remote Sensing, 15 (3). 668. ISSN 2072-4292

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To link to this item DOI: 10.3390/rs15030668


Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess test results of the hypothesis across studies using several moderating variables, relating to the botanical and spectral sampling strategies, and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatment due to their dynamic phenology and the taxonomic complexity of their canopies over small scales. Across studies, results suggest an overall positive relationship between spectral variation and species diversity (mean correlation co-efficient = 0.36). However, high levels of both within study and between study heterogeneity was found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf level studies displaying a stronger relationship compared to canopy level studies. We highlight the challenges facing synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multi-temporal assessments to resolve controversy in the field.

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:109888


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