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Project - Remote sensing based characterisation of functional diversity for polar ecosystems

Summary

To protect Arctic ecosystems, we need a better understanding of spatial biodiversity patterns and quantitative methodologies to assess biodiversity of this fragile environment. Here, we present a new methodology to directly derive traits-based functional diversity estimates from hyperspectral remote sensing techniques (instead of indirectly assessing biodiversity through mapping environmental conditions). Our conceptual and methodological advances will be applied to and validated in two highly different arctic (Svalbard) and subarctic (Northern Sweden) regions with a large range of representative ecosystem types. Based on comprehensive traits measurements from 56 subarctic permanent plots, we will evaluate α- and β-functional diversity patterns in polar ecosystems and their relative importance, hitherto unknown. Subsequently, we will link α-functional diversity to the mean and variance in spectral reflectance as measured with a 2D-hyperspectral frame to evaluate the feasibility of determining functional diversity and the need for including spectral variance information therein. The quality of simpler indices will be evaluated and the impacts of canopy structure, measured with LiDAR on functional diversity estimates will be determined in different polar ecosystems. Subsequently, the relationships will be validated in Svalbard. Once validated and evaluated for its robustness, we will map α-functional diversity (to identify hotspots of biodiversity) and derive patterns in β -functional diversity by fingerprinting hyperspectral reflectance with an unmanned octocopter at high spatial resolution in both regions. Altogether, we will provide a generic, validated, cost-effective methodological framework to quantify and map functional diversity that can be applied to any polar region, despite the limited accessibility.

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UUID: bd2e3494-acee-5057-ab87-b1d492fb76ce | Version:22 (current)1 (archived) | Added on: 4 June 2018 10:48