Biodiversity monitoring is constrained by cost- and labour-intensive field sampling methods. Increasing evidence suggests that remotely sensed spectral diversity (SD) is linked to plant diversity, holding promise for monitoring applications. However, studies testing such a relationship reported conflicting findings, especially in challenging ecosystems such as grasslands, due to their variety and high temporal dynamism. It follows that a thorough investigation of the key factors influencing these relationships, such as the metrics applied (i.e., continuous, categorical) and phenology (e.g., flowering), is necessary. The present study aims to assess the effect of flowering on the applicability of six different SD metrics for plant diversity monitoring at the local scale and to investigate how spatial resolution affects the results. Taxonomic diversity was calculated based on data collected in 159 plots of 1.5 m × 1.5 m with experimental mesic grassland communities. Spectral information was collected using a UAV-borne sensor measuring reflectance across six bands in the visible and near-infrared range at ∼2 cm spatial resolution. Our results showed that, in the presence of flowering, the relationship between SD and plant diversity is significant and positive only when SD is calculated using categorical metrics. Despite the observed significance, the variance explained by the models was very low, with no evident differences when resampling spectral data to coarser pixel sizes. Such findings suggest that new insights into the possible confounding effects on the SD-plant diversity relationship in grassland communities are needed to use SD for monitoring purposes.

“Flower power”: How flowering affects spectral diversity metrics and their relationship with plant diversity / Perrone, M.; Conti, L.; Galland, T.; Komarek, J.; Lagner, O.; Torresani, M.; Rossi, C.; Carmona, C. P.; de Bello, F.; Rocchini, D.; Moudry, V.; Simova, P.; Bagella, S.; Malavasi, M.. - In: ECOLOGICAL INFORMATICS. - ISSN 1574-9541. - 81:(2024). [10.1016/j.ecoinf.2024.102589]

“Flower power”: How flowering affects spectral diversity metrics and their relationship with plant diversity

Bagella S.;Malavasi M.
2024-01-01

Abstract

Biodiversity monitoring is constrained by cost- and labour-intensive field sampling methods. Increasing evidence suggests that remotely sensed spectral diversity (SD) is linked to plant diversity, holding promise for monitoring applications. However, studies testing such a relationship reported conflicting findings, especially in challenging ecosystems such as grasslands, due to their variety and high temporal dynamism. It follows that a thorough investigation of the key factors influencing these relationships, such as the metrics applied (i.e., continuous, categorical) and phenology (e.g., flowering), is necessary. The present study aims to assess the effect of flowering on the applicability of six different SD metrics for plant diversity monitoring at the local scale and to investigate how spatial resolution affects the results. Taxonomic diversity was calculated based on data collected in 159 plots of 1.5 m × 1.5 m with experimental mesic grassland communities. Spectral information was collected using a UAV-borne sensor measuring reflectance across six bands in the visible and near-infrared range at ∼2 cm spatial resolution. Our results showed that, in the presence of flowering, the relationship between SD and plant diversity is significant and positive only when SD is calculated using categorical metrics. Despite the observed significance, the variance explained by the models was very low, with no evident differences when resampling spectral data to coarser pixel sizes. Such findings suggest that new insights into the possible confounding effects on the SD-plant diversity relationship in grassland communities are needed to use SD for monitoring purposes.
2024
“Flower power”: How flowering affects spectral diversity metrics and their relationship with plant diversity / Perrone, M.; Conti, L.; Galland, T.; Komarek, J.; Lagner, O.; Torresani, M.; Rossi, C.; Carmona, C. P.; de Bello, F.; Rocchini, D.; Moudry, V.; Simova, P.; Bagella, S.; Malavasi, M.. - In: ECOLOGICAL INFORMATICS. - ISSN 1574-9541. - 81:(2024). [10.1016/j.ecoinf.2024.102589]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/330429
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