THE DETECTION OF DISTURBANCE EFFECTS OF NORWAY SPRUCE (PICEA ABIES (L.) KARST.) IN UAV MULTISPECTRAL IMAGERY

Abstract. This study analyses spectral separability of Norway spruce (Picea abies (L.) Karst.) trees several months after induction of mechanical damage (a simulated wind damage), and one year after the damage – at the beginning of vegetation season and then following a period of drought. Experiment includes sample group of trees (Bent trees) over three plots that underwent static pulling test, therefore simulating survival after storm event. They were compared to a group of trees of the same dimensions that did not undergo static pulling test (Control trees). Spectral reflectance data was collected using unmanned aerial vehicle (UAV) and multispectral sensor. Reflectance of four bands were extracted, converted into indices, and analysed with statistical tests. There were differences in plot response to experiment, as one of the plots failed to show significant difference between groups. Overall, multiple indices proved great results for spectral separation between Control and Bent trees. MACI (Modified Anthocyanin Content Index) was the most consistent in differentiating between groups. Other indices that represent chlorophyll content and photosynthetic activity were also relatively sensitive for stress detection. However, on the contrary to what was expected, period of drought did not seem to affect spectral reflectance of canopies. There was a distinct difference of chlorophyll and anthocyanin content, and photosynthetic activity in sample trees, but these deviations did not manifest in larger scale post drought. Moreover, MACI and GNDVI (Green Normalized Difference Vegetation Index) even reduced mean value gap after the period of low precipitation.