COMPARISON BETWEEN VEGETATIVE INDEXES OBTAINED BY AERIAL IMAGES WITH UNMANNED AERIAL VEHICLES (UAV) AND SATELLITE - Sampaio, H. S.; Bourscheidt, V.; Sarracini, L. H.; Jorge, L. A. C.; Bettiol, G. M.; Bernardi, A. C. C. (Revista Brasileira de Engenharia de Biossistemas, 2020)
Remote sensing (RS), which is widely employed in different areas, may be used as well for the management of agricultural systems. Among its different tools and applications, the use of vegetation indexes, which attempt to relate the variations of the measured spectral behavior with different biophysical parameters of the plants, may be one of the most important. And the quality of the resulting products will depend intrinsically on the precision of the images being used. On this way, this work aims to compare the vegetation indexes obtained from satellite images and with unmanned aerial vehicle (UAV) in an irrigated pasture area. The images were obtained from the Landsat-8 satellite and with UAV (both conventional RGB and infrared adapted NBG cameras), and the following indices were calculated: NDVI, the classic index for monitoring vegetation; the MPRI, which is related to the NDVI, but uses only the bands of the visible spectrum; NDVI adapted to RGB digital cameras and; ENDVI, an enhancement of the NDVI proposed to optimize the use of RGB cameras. The values of the indices obtained by these images were further correlated. In order to make the comparison with Landsat-8 images more appropriate, the digital values of UAV images were converted to reflectance values. The results of this study indicated an intermediate positive correlation of the NDVI and ENDVI indices obtained from satellite and UAV images, which could be influenced by the scale difference across the images, as well as by the temporal variation in the data acquisition from the different sources. The better calibration of the images taken with UAVs is also important to ensure the conversion to reflectance to be adequate. The studied indexes were sensitive to indicate the variations in the studied area and confirmed that they can be tools of precision agriculture, helping in the planning of pasture management with the application of Precision Agriculture.