Data analysis in hyperspectral remote sensing: vitality analysis.

The objective is to develop a methodology to improve data inter- pretation of data acquired by hyperspectral sensors more specifi- cly methods for stress- and vitality detection. A first step in this process is a field measurement campaign with a fieldspectro- meter on leaf and canopy level of stresses trees. Based upon those data, algorithms for data extraction and interpretation will be developed. Correlations between leaf biochemical proper- ties and reflectance data will be looked after by means REP- methods, reflectance ratios,... The final step in this process is and airborne campaign with an airborne hyperspectral sensor. Methods developed to evaluate the lower level data will be up scaled and evaluated against the acquired data. This should pro- vide us information on stressed trees and typical response pat- terns of well-defined diseases and infections. This must be con- sidered in a context of precision farming.