Data fusion for high spatial resolution LAI estimation

Leaf Area Index (LAI) is a critical variable for forest management. It is difficult to obtain accurate LAI estimations of high spatial resolution over large areas. Local estimations can be obtained from in situ field measurements. Extrapolation of local measurements is prone to error. Remote sensing LAI estimation products, such as the one provided by MODIS are of very low resolution and subject to criticism in recent validation works. Forest management requires increasingly high resolution estimations of LAI. We propose a data fusion process for high spatial resolution estimation of the LAI over a large area, combining several heterogeneous information sources: field sampled data, elevation data and remote sensing data. The process makes use of spatial interpolation techniques. We follow a hybrid validation approach that combines the conventional prediction error measures with a spatial validation based on image segmentation. We obtain encouraging results of this information fusion process on data from a forest area in the north of Portugal.