Large-scale biodiversity monitoring is urgently needed in the face of global change and ongoing mass extinction of species, but technically challenging due to the large effort needed to measure biodiversity on the ground, sparsity of monitoring sites, and accessibility of remote ecosystems. Recent advances in remote sensing move towards global measurements of plant canopy structure and leaf biochemical and biophysical traits that are used to map functional diversity, an important dimension of biodiversity. GEDI, a new spaceborne lidar instrument that measures Earth surface structure, is currently operating on the International Space Station (ISS) collecting data on global forests between 51.6° north and south to characterize vegetation 3D structure and biomass. At the same time, several spaceborne imaging spectrometers including DESIS, HISUI and PRISMA are now in-orbit and EMIT, ENMAP, SBG and CHIME are planned for this decade. These new instruments will be the first to fulfill the instrument requirements to derive a broad suite of vegetation products including plant foliar traits and diversity. However, it is unknown yet to what degree functional diversity can be mapped, e.g. at what spatial grain and extent, and how large-scale measurements connect to traditional biodiversity estimates and ecosystem functioning.
Results/Conclusions
Therefore, we developed an approach to simulate spaceborne lidar and imaging spectroscopy data over California, a biodiversity hotspot offering a unique range of ecosystems. Data similar to the upcoming Surface Biology and Geology designated observable was simulated using airborne imaging spectroscopy covering over 40% of California, and spaceborne lidar was simulated according to GEDI over an area of over 7,000 km2. We mapped plant foliar traits (nitrogen, LMA, lignin and others) and canopy structural traits (canopy height, layering, density) to derive functional diversity over a broad range of ecosystems and spatial scales. Results are promising especially for structural richness at 1 km scale (r2=0.75). Trait turnover was more difficult to characterize (r2=0.40) and needs further development of algorithms and data fusion approaches to profit from the combined strength of different remote sensing methods. Important functional traits such as leaf nitrogen and LMA can be mapped across a large range of ecosystems and similarly, the mapping of canopy height is robust and broadly applicable. These unique datasets offer the possibility to explore the capabilities of global functional trait and diversity mapping, the linking to biodiversity and ecosystem functioning measurements on the ground (e.g. from eDNA, flux towers) and integration into Earth system models.