Differentiating plant functional types using reflectance: which traits make the difference?

Abstract

Abiotic ecosystem properties together with plant species interaction create differences in structural and physiological traits among plant species. Certain plant traits cause a spatial and temporal variation in canopy reflectance that enables the differentiation of plant functional types, using earth observation data. However, it often remains unclear which traits drive the differences in reflectance between plant functional types, since the spectral regions in which electromagnetic radiation is influenced by certain plant traits are often overlapping. The present study aims to assess the relative (statistical) contributions of plant traits to the separability of plant functional groups using their reflectance. We apply the radiative transfer model PROSAIL to simulate optical canopy reflectance of 38 herbaceous plant species based on field-measured traits such as leaf area index, leaf inclination distribution, chlorophyll content, carotenoid content, water and dry matter content. These traits of the selected grassland species were measured in an outdoor plant experiment. The 38 species differed in growth form and strategy types according to Grime 0 s CSR model and hence represented a broad range of plant functioning. We determined the relative (statistical) contribution of each plant trait for separating plant functional groups via reflectance. Therein we used a separation into growth forms, that is graminoids and herbs, and into CSR strategy types. Our results show that the relative contribution of plant traits to differentiate between the examined plant functional types (PFT) groups using canopy reflectance depends on the PFT scheme applied. Plant traits describing the canopy structure were more important in this regard than leaf traits. Accordingly, leaf area index (LAI) and leaf inclination showed consistently high importance for separating the examined PFT groups. This indicates that the role of canopy structure for spectrally differentiating PFT might have been underestimated.

Remotely Sensing Variation in Ecological Strategies and Plant Traits of Willows in Perialpine Floodplains

“…On the contrary, a set of heterogeneous individuals offers a larger spread in variation to be detected and is therefore less affected by measurement noise. In brief, the leaf area, SLA, and LDMC are better retrieved than the pigment information, which is in line with other PLS regression models predicting plant traits (Asner et al, ; Kattenborn et al, ; Serbin et al, ; Singh et al, ). Moreover, the CSR‐score group shows the highest prediction power among all the plant parameters predicted.…”

Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing

“…In particular, at SPRUCE, differences in the relative cover of shrubs versus forbs had a strong effect on spectral reflectance. This is likely due to the differences in stature, leaf structure, and foliar chemistry between high-growing woody shrubs compared with ground-layer herbaceous PFTs such as forbs [36,99]. We also predict that collecting data in the early fall improved our ability to detect variation among treatments because carotenoids and anthocyanins present in leaf tissues around senescence may have increased optical diversity, allowing for greater distinction among PFTs.…”

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

“…Kattenborn et al. 2018b). The fraction of shadows is highly dependent on the sun‐angle during the acquisition of optical remote sensing data.…”

“…Species-specific canopy architecture influences the way species interact with light and hence their reflectance (e.g. Kattenborn et al 2018b). The fraction of shadows is highly dependent on the sun-angle during the acquisition of optical remote sensing data.…”