Evaluation of applicability of airborne AISA DUAL hyperspectral imaging system to map environmental conditions in orchards

J. Tamas, C. Lenárt, P. Burai

Abstract


Airborne remote sensing data are increasingly used in precision agriculture. Applicability of RS data is mostly depending on the spatial and spectral resolution. Nowadays, a new generation of airborne hyperspectral imaging systems is available and applicable to map the environment. Since effects caused by soil or climatic stress are well-detectable in the plants, vegetation is a promising indicator of water and nutrient supply, as well as degradation processes occurred in soils. Biophysical properties of the leaf show photosynthetic activity, mutations, stress, and the state of plant nutrient content, which has particularly high significance in precision agriculture. In our study, hyperspectral data were collected by an AISA DUAL hyperspectral imaging system, in the wavelength range of 398-973 nm, in 63 channels, with 0.5m ground resolution. The radiometric and geometric calibrations were processed by Caligeo and ITT ENVI software. Band selection method was developed to reduce the noise, which allowed to collect the most reliable bands for plant properties, while, hyperspectral indices were calculated to evaluate the narrow waveband properties of hyperspectral reflectance spectra. Indices designed to detect different physical and chemical properties related to nutrient and water contents were in focus (NDVI, SIPI, PRI, etc.). Dry, senescent or damaged plants not using nitrogen and light efficiently, indicate agricultural stress, whereas a crop showing healthy, productive vegetation indicates (VI) low stress. Indices from canopy-scale hyperspectral reflectance data taken under field conditions were used to derive the spatial patterns of stressed damage of peach varieties in orchards. More than 150 VIs have been published in literature, but only a small subset has substantial biophysical basis or have been systematically tested under field conditions. In addition, based on the evaluated indices, it is possible to map the properties at individual tree's level, which can provide important information for precision agriculture.

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