QUANTIFICATION OF VEGETATION STRESS BASED ON HYPERSPECTRAL IMAGE PROCESSING

Nowadays, hyperspectral remote sensing is increasingly widely used, which makes possible several precision applications. In particular, actual status as well as change in status of objects on ground surface can be evaluated, providing useful information for e. g. agriculture, and environmental management, and thematic information can also be obtained, such as landuse pattern for a given area. In our study, effect of adverse soil processes on plants at an arable land was investigated by processing airborne remotely sensed data. Hyperspectral images were taken by using an AISA Dual hyperspectral sensor detecting between 400 and 1.000 nm in parallel with field measurement of certain biomass parameters, including plant height, cover, leaf area index, etc. and sampling at points determined by GPS, for laboratory analysis of chlorophyll a content. After radiometric and geometric corrections, channel selecting methods were applied to improve the information content. In addition to the traditionally used vegetation indices, channels potentially useful to estimate biomass and chlorophyll were selected. After evaluating point data, spatial distribution of chlorophyll a content was also examined, and as a result, a vegetation stress map was generated. In addition, based on calculations from field measurement data for coverage, and selection and processing of certain spectra, a coverage map was also generated for the studied area.