The relationship between species and spectral diversity in grassland communities is mediated by their vertical complexity

Abstract

Aims:The link between spectral diversity and in-situ plant biodiversity is one promising approach to using remote sensing for biodiversity assessment. Nevertheless, there is little evidence as to whether this link is maintained at fine scales, as well as to how it is influenced by vegetation's vertical complexity. Here we test, at the community level in grasslands, the link between diversity of the spectral signal (S Div ) and taxonomic diversity (T Div ), and the influence of vertical complexity. Methods:We used 196 1.5 m × 1.5 m experimental communities with different biodiversity levels. To measure vertical complexity, we quantified height diversity (H Div ) of the most abundant species in the community. T Div was calculated using the Shannon index based on species cover. Canopy spectral information was gathered using an unmanned aerial vehicle (UAV) mounted with a multi-spectral sensor providing spectral information via six 10-nm bands covering the visible and near-infrared region at a spatial resolution of 3 cm. We measured S Div in a core area of 1 m ×1 m within the communities as mean Euclidean distance of all pixels in a feature space spanned between the two first components of a PCA calculated for the complete raster stack. We modelled S Div through mixed-effect linear models, using T Div , H Div , and their interaction as fixed-effect predictors.Results: Contrary to our expectations, T Div was negatively linked to S Div . The diversity in plant height was positively related to S Div . More importantly, diversity in plant height and T Div had a significant negative interaction, meaning the more complex the vegetation was in terms of height, the more the S Div -T Div relationship became negative. Conclusions:Our results suggest that in order to exploit the S Div -T Div link for monitoring purposes, it needs to be contextualized. Moreover, the results highlight that communities' functional characteristics (i.e. plant height) mediate such a link, calling for new insights into the relation between S Div and functional diversity.

Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species‐rich grasslands

“…Thus, it is crucial to understand the effect of canopy structure on spectral diversity and its impact on the relationship between spectral diversity and plant species richness. In this light, it is surprising that spectral metrics from UAV data are rarely used (Conti et al., 2021; Villoslada et al., 2020), despite increasing data availability, high spatial resolution and the potential of the onboard sensors to quantify the VIS and NIR part of the spectrum. Despite the relatively small areas covered, UAVs are very beneficial for studying the spectral diversity–biodiversity relationship and assessing the capabilities of other platforms.…”

“…Furthermore, a range of factors may confound the spectral diversity–biodiversity relationship. For instance, the fraction of bare soil (Gholizadeh et al., 2018), dead biomass (Schweiger et al., 2015), the size of plants (Conti et al., 2021), phenology, flowering patterns, short‐term weather conditions and management (Gholizadeh et al., 2020; Rossi et al., 2021), as well as the amount of biomass (Villoslada et al., 2020) and the composition of the plant community (different life forms such as graminoids, forbs and legumes, Wang, Gamon, Schweiger, et al., 2018) affect spectral diversity and, thus, interfere with the estimation of plant species richness. Such confounding factors may in part be mitigated with an extensive use of spectral information.…”

Assessing the Impact of Soil on Species Diversity Estimation Based on UAV Imaging Spectroscopy in a Natural Alpine Steppe

“…Besides the biochemical characteristics, the functional diversity of grassland also includes the structural characteristics [45,79]. The vertical canopy structure even would mediate the link between spectral diversity and species diversity [80]. Although there are still uncertainties for obtaining grassland canopy structures, several structural characteristics of grassland have been estimated by terrestrial laser scanning.…”

Spectral-Based Classification of Plant Species Groups and Functional Plant Parts in Managed Permanent Grassland

“…The large number of recent publications concerning remote sensing in grassland emphasizes the importance of this area. Common applications include modeling of grassland successional stages [14], forage quality parameters [15], biomass [16][17][18][19], legume N-fixation [17], chlorophyll content [20], species richness [21,22], leaf area index [23,24], and (species) classification [25][26][27][28][29][30]. Next to the vast number of applications, there is a large variability of grassland types due to local and regional factors [18,26,27,30].…”

“…Conti et al [21] successfully used a six-channel multispectral camera and a spatial resolution of approximately 3 cm to assess the link between species diversity and spectral characteristics for permanent grassland biodiversity. The work and results of Suzuki et al [29] are promising for spectral-based classification of grassland.…”