On Friday 25th of August 2017, Kaja Kandare, defended her thesis on “Fusion of airborne laser scanning and hyperspectral data for predicting forest characteristics at different spatial scales”.

The major objective of Kaja’s thesis was to evaluate the potential of fusing ALS and hyperspectral data for the prediction of forest characteristics and to evaluate the benefits of different spatial details in the predictions.

In her thesis, Kaja has focused in particular on two remotely sensed data sources that right now seem most promising for forest sensing: airborne laser scanning (ALS), and airborne hyperspectral data. Whereas ALS technology, light detection and ranging (LiDAR) consists of a sender of a laser pulse and a receiver, hyperspectral sensors are passive systems that collect and record through a detector the electromagnetic energy that is reflected or emitted by the surface of the objects.

Congratulations and good luck for her future research!