Structure parameters for characterization of vegetation canopies are often estimated from remote optical measurements. Existing methods include those based on measurements of gap fraction, spectral vegetation indices, or the inversion of spectral canopy reflectance models. This paper proposes a novel method based on inversion of multiangular measurements of the abundances of light scattering components, which may be estimated using spectral unmixing. An algorithm is described for predicting the abundances of various scattering components using Monte Carlo simulation with a Poisson canopy model and an ellipsoidal leaf angle distribution. The method was tested using simulated data from ray-traced images in a ground-based measurement scenario. Model fit surfaces were calculated for 20 different values of leaf area index (LAI) and mean leaf angle (MLA). The experiments generally showed good correspondances between observations and predictions, except for high values of LAI and low values of MLA. Future work should include experiments on real data and robust unmixing of scattering components.
This paper shows the potential of using robotics for data acquisition within full-scale field trials. Robotics ensured simultaneously measurements from several sensors from GPS targeted sampling points. This was demonstrated by supporting a project developing methods to measure gap fraction and canopy structure in cereals. The project required measurements from ordinary barley canopy areas using a high- dynamic-range RGB camera, and a multi-spectral Cropscan radiometer. Further, the RGB camera required images from 12 different angles relative to the rows.
In fulfilling these demands an existing robotic platform at Research Center Bygholm, Denmark, was enhanced. A payload software system was developed ensuring a simple and efficient interface between the robotic platform and the multiple sensor systems. The software of the Cropscan Multispectral Radiometer System was also altered to support
remote control by the payload software. The robotic system collected data from a full scale field at two occasions.