Remote sensing-based characterisation of functional diversity for polar ecosystems (RS of functional diversity)

This project focuses on the patterns in different scales of functional diversity in polar ecosystems. We will develop a method to map ? and ?functional diversity remotely trough UAVs using hyperspectral imaging and light detection and ranging. We will empirically connect data acquired from the air to trait information collected in the field.

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  • field work
  • remote sensing


  • terrestrial biology

Project Keywords

  • biosphere / vegetation / plant characteristics

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To protect (sub-)arctic ecosystems, we need a better understanding of spatial biodiversity patterns and quantitative methodologies to assess biodiversity of this fragile environment. This project aims to provide relevant data on how to best protect the biodiversity of these ecosystems. Biodiversity is often used to explain ecosystem functions and services ,and it has become clear that traits-based functional biodiversity has a closer link to these than species-based diversity (Fukami et al. 2005; Hillebrand & Matthiessen 2009). Functional diversity can be examined on multiple scales, within and between communities. To fully understand vegetation diversity patterns, large areas have to be sampled in detail. With in-situ sampling, research is often limited to either small scale or limited detail. Remote sensing can be used to map biodiversity on a large scale, identifying hotspots and helping to understand patterns. So far, multiple attempts to map biodiversity and traits using remote sensing have been made, with different extents of success. There are correlations between spectral indices, like NDVI, and species diversity, but they come with some drawbacks (Nagendra 2001; Kerr & Ostrovsky 2003). Likewise, heterogeneity in the spectral signal can reflect heterogeneity in species composition (spectral variance theory, (Palmer et al. 2002; Schmidtlein & Sassin 2004; Rocchini et al. 2004) but homogeneity in the spectral signal does not necessarily reflect homogeneity in species composition. Using remote sensing to assess functional diversity is promising, as relating spectral data to canopy leaf traits (Roelofsen et al. 2013; Homolová et al. 2013; Elmendorf et al. 2012; Asner et al. 2015), or individual leaf traits (Roelofsen et al. 2014), is possible. However, remote sensing techniques have never before been used to assess the entire complexity of the vegetation trait diversity phenomenon. Studies on leaf traits have had differing outcomes, and often the data is poorly transferable to other regions (Roelofsen et al. 2013), a problem which we hope to assess by including a wide range of polar ecosystems in the initial modelling phase. Trait means for each species are often used when calculating functional diversity (Albert 2015; Flynn et al. 2009; Fukami et al. 2005). Individuals within species, however, vary in their trait values due to environmental constraints (Albert 2015; Cianciaruso et al. 2009; Fajardo & Siefert 2016). This is essential for calculating functional diversity, for species-environment relationships, and also for remote sensing of traits. By measuring the trait values in situ for each plot, we will be able to better relate trait data to spectral variance. A major challenge of remote sensing vegetation diversity is the scattering caused by the canopy structure (Knyazikhin et al. 2013). Scattering determines the spectral signal for a large part, complicating the interpretation of measurements. To understand this scattering, and incorporate canopy structure in diversity measurements, we will be using Light Detection and Ranging (LiDAR). LiDAR can scan the canopy and produce a detailed image of its height. It can improve the accuracy and give extra information on traits that are left unmonitored with hyperspectral data (Asner et al. 2015). This research focuses on the patterns in different scales of functional diversity in (sub-) arctic (polar) ecosystems. These systems are under threat, the arctic climate is changing faster than in temperate and tropic (Screen et al. 2012; Alexeev et al. 2005), and locations are often remote and not readily accessible. The extent of intraspecific variation will be explored, as well as the traits for which it varies most. The main goal of this research is to develop a method for characterizing alpha and beta functional diversity in polar ecosystems through remote sensing. This method can be used in further research on biodiversity in these areas, to improve monitoring intensity.

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