Abstract
Currently, images from unmanned aerial vehicles (UAVs) are being used due to their high spatial and temporal resolution. Studies comparing different mobile data acquisition platforms, such as satellites, are important due to the limited spatial and temporal resolution of some satellites as well of the presence of clouds in such images. The objective of this study was to compare the vegetation indices (VIs) generated from images obtained by orbital (satellite) and sub-orbital (unmanned aerial vehicles - UAV) platforms. The experiment was conducted in a maize-growing area in Paraná, Brazil. Landsat 8 and UAV images of the study area were collected. Four VIs were applied: NDVI, VIgreen, ExG and VEG. The NDVI was selected as the control and compared with the other VIs. There was a good correlation (0.79) between the NDVI and the VEG for the UAV images. For the Landsat images, the highest correlation found was between the NDVI and the VIgreen derived from UAV images, which was 0.89. It is concluded that the images obtained by UAVs generated better indices, mainly in the dry season.
Highlights
Applications of products obtained by remote sensing increasingly include monitoring agriculture and its processes
The objective of this study was to analyse data provided by unmanned aerial vehicles (UAVs) and from Landsat satellites sensors based on the normalized difference vegetation index (NDVI), the green vegetation index (VIgreen), the excess green (ExG) and the vegetative index (VEG)
The image processing and the subsequent calculation of the Pearson R correlation coefficients resulted in the values presented in Table 2, which shows the correlations between the NDVI and the VIgreen, the ExG and the VEG obtained by from the Landsat 8 satellite images
Summary
Applications of products obtained by remote sensing increasingly include monitoring agriculture and its processes. Remote sensing tools provide time-series data and orbital images with high temporal and spectral resolution. The identification of vegetation levels by means of reflectance in data from the Landsat satellite series can be exploited in several agricultural applications (GRAESSER & RAMANKUTTY, 2017). With a spatial resolution of 30 m, Landsat satellites are able to identify cyclic vegetation phenomena with a vast image collection that extends through 40 years (HE et al, 2015). One of the difficulties of satellite remote sensing is the revisit time, which on average is 16 days. This makes agricultural applications, those related to water, nutrient and short-cycle crop management, difficult (XUE & SU, 2017). Passive sensors cannot penetrate clouds; no data are collected on overcast days, making it difficult to collect data during the rainy season
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