Estimation of vegetation fraction using RGB and multispectral images from UAV

The vegetation fraction (VF) monitoring in a specific area is a very important parameter for precision agriculture. Until a few years ago, high-cost flights on aeroplanes and satellite imagery were the only option to acquire data to estimate VF remotely. Recently, Unmanned Aerial Vehicles (UAVs) have emerged as a novel and economic tool to supply high-resolution images useful to estimate VF. VF is usually estimated by spectral indices using red-green-blue (RGB) and near-infrared (NIR) bands data. For this study, a UAV equipped with both kinds of sensors (RGB and NIR) was used to obtain high-resolution imagery over a maize field in progressive dates along the mid-season and the senescence development stages. The early-season stage was also monitored using only RGB spectral indices. Flights were performed at 52 m over the terrain, obtaining RGB images of 1.25 cm pixel(-1) and multispectral images of 2.10 cm pixel(-1). Three spectral indices in the visible region, Excess Green (ExG), Colour Index of Vegetation (CIVE), and Vegetation Index Green (VIg), and three NIR-based vegetation indices, Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Normalized Green (NG), were evaluated for VF estimation. Otsu's method was applied to automatically determine the threshold value to classify the vegetation coverage. Results show that ExG presents the higher mean accuracy (85.66%) among all the visible indices, with values ranging from 72.54% to 99.53%, having its best performance in the earlier development stage. Nevertheless, GNDVI mean accuracy (97.09%) overcomes all the indices (visible and multispectral), ranging in value from 92.71% to 99.36%. This allowed comparing the accuracy difference gained by using a NIR sensor, with a higher economic cost than required using a simple RGB sensor. The results suggest that ExG can be a very suitable option to monitor VF in the early-season growth stage of the crop, while later stages could require NIR-based indices. Thus, the selection of the index will depend on the objectives of the study and the equipment capacity.