Essential Biodiversity Variables (EBV) and Plant Functional Traits (PFT) from Hyperspectral Remote Sensing

Through the development of variables (EBVs), policy and scientific bodies such as IPBES and GEOSS seek consensus around which essential biodiversity variables could form the basis of a global monitoring program for biodiversity. It is argued that essential climate variables (ECVs) can be calculated directly or indirectly from remotely sensed data. However a number of the proposed essential biodiversity variables essential biodiversity variables are challenging to derive from remote sensing. In this presentation, the derivation of plant functional traits (PFTs) using hyperspectral remote sensing is explored. The plant functional traits are then examined as a proxy for a number of the proposed essential biodiversity variables. For example, suitable plant functional traits that may be used as proxies for essential biodiversity variables include ecosystem extent, species occurrence, cover (biomass, LAI, plant height) and leaf nitrogen content. The accurate derivation of plant functional traits from hyperspectral remote sensing using empirical as well as radiatve transfer models is described at a local scale. Radiative transfer models explain the transfer and interaction of radiation inside vegetation canopies based on physical laws, offering an explicit connection between biophysical and biochemical variables and canopy reflectance. However, specificity to local conditions limits the applicability of physical and empirical models to other regions - in other words the generalization of physical models to larger extents require information to constrain the parameter range. The generalization of physical models is a problem particularly where plant species heterogeneity limits accuracy. An emerging approach to generate essential biodiversity variables at a global level is to upscale empirical models. A possible solution to the problem of transferability and upscaling of both empirical and physical model approaches for essential biodiversity variables is to add data driven models to radiative transfer models, using a data assimilation technique.