Global functional diversity from space: progress and challenges

Advances in remote sensing now address some of the dimensions of the diversity of life on earth,  Imagining specrtrometers can quantify plant functional diversity, detect features diagnostic of taxonomic identity and assess landscape patters with far more ecological resolution resolution than legacy sensors.  Several high fedlity instruments are now on orbit and more are under development, meaning these data are potentially available anywhere and soon will be available everywhere as time series.  Space-based spectroscopy compliments and are synergistic with in-situ data, while requiring in-situ data for calibration and validation of ecosystem-sensitive algorithms.  These new space based data contribute to change detection, habitat analysis, ecosystem function and patterns of functional diversity.  Imaging spectroscopy from space samples canopies, while in-situ date sample species and this disconnect is not only a methological challlenge, it shapes science and applications for which the image data are useful. Current global data sample many of the world's ecosystems and calibration data are available spanning the climatic and much of the phylogenetic variation for overstory vegetation.  We plot extant field studies linked to remote sensing in climate and taxonomic space to show coverage and gaps in information for synergistic and calibration activity. Field data now available, and reasonably consistent span eco-climatic space, using the Olsen mapping approach, and cover most of the branches on the tree of life for vegetation in high, mid-and low-latitude ecosystems. A recent project in Panama adds coverage of tropical forests and a large number of families not previously sampled in conjunction with spectral data from air and space.  We will discuss scaling challenges from the cm scale to the tens of meters sampled by space-borne imagers and the important role of aircraft or done intermediates. We will present results from a global survey of tropical ecosystems to demonstrate the potential of these new data through variation in trait space, scaling patterns and environmental variation and contrast these with upscaled in -situ approaches.