Estimation of the essential climate variables (ECVs), such as photosynthetically active radiation (FAPAR) and the leaf
area index (LAI), is largely based on satellite-based remote sensing and the subsequent inversion of radiative transfer
(RT) models. In order to build models that accurately describe the radiative transfer within and below the canopy,
detailed 3D structural (geometrical) and spectral (radiometrical) information of the canopy is needed. Close-range
remote sensing, such as terrestrial remote sensing and UAV-based 3D spectral measurements, offers significant
opportunity to improve the RT modelling and ECV estimation of forests.
Finnish Geospatial Research Institute (FGI) has been developing active and passive high resolution 3D hyperspectral
measurement technologies that provide reflectance, anisotropy and 3D structure information of forests (i.e. hyperspectral
point clouds). Technologies include hyperspectral imaging from unmanned airborne vehicle (UAV), terrestrial
hyperspectral lidar (HSL) and terrestrial hyperspectral stereoscopic imaging. A measurement campaign to demonstrate
these technologies in ECV estimation with uncertainty propagation was carried out in the Wytham Woods, Oxford, UK,
in June 2015.
Our objective is to develop traceable processing procedures for generating hyperspectral point clouds with geometric and
radiometric uncertainty propagation using hyperspectral aerial and terrestrial imaging and hyperspectral terrestrial laser
scanning. The article and presentation will present the methodology, instrumentation and first results of our study.