Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework

Abstract

Abstract:We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was "species-specific", in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species' differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species' locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions.

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

“…This is similar to other studies which shows a negative relationship between LDMC and LAI [76]. It was also observed that the moisture content of leaf litter in the forest understory could be estimated by LAI obtained using remotely sensed data due to buffering effect of LAI on the understory microclimate [77].…”

The Feasibility of Leaf Reflectance-Based Taxonomic Inventories and Diversity Assessments of Species-Rich Grasslands: A Cross-Seasonal Evaluation Using Waveband Selection

“…To characterise the site, we used the Ecological Flora of the British Isles database [35], which contains the ecological traits of species, to acquire Ellenberg's indicator values. These values can be interpreted as follows: species light demand from low to high (1-9); moisture demand from low to high (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12); soil pH from very acid to very alkaline (1-9); and nitrogen demand from the least to excessive (1)(2)(3)(4)(5)(6)(7)(8)(9). These values provide evidence of the environmental niche within which these species are typically found.…”

“…It is now possible to create very high-resolution hyperspectral maps of grasslands due to advances in airborne remote sensing, with pixel sizes that are comparable to leaf sizes. Analyses of species-specific leaf and canopy spectra in herbaceous habitats have demonstrated that there is the potential for mapping taxonomic units [4][5][6], phylogenetic groups [7] and plant functional types [8,9].…”

Assessing Suitability of Sentinel-2 Bands for Monitoring of Nutrient Concentration of Pastures with a Range of Species Compositions

“…Such integration of prior information has been found to be useful for retrieval of vegetation parameters such as leaf chlorophyll [49,50]. Further insight into this complex issue of dominance of certain parameters in the overall reflectance can potentially be achieved using physically-based radiative transfer models [51]. However, it will require development of conversion equations to relate the pasture quality parameters with those required for the radiative transfer model simulations.…”