  Plant phenology studies rely on long-term monitoring of life cycles of
plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface
technologies have been used for plant monitoring, demanding the creation of
methods capable of locating and identifying plant species through time and
space. However, this is a challenging task given the high volume of data, the
constant data missing from temporal dataset, the heterogeneity of temporal
profiles, the variety of plant visual patterns, and the unclear definition of
individuals' boundaries in plant communities. In this letter, we propose a
novel method, suitable for phenological monitoring, based on Convolutional
Networks (ConvNets) to perform spatio-temporal vegetation pixel-classification
on high resolution images. We conducted a systematic evaluation using
high-resolution vegetation image datasets associated with the Brazilian Cerrado
biome. Experimental results show that the proposed approach is effective,
overcoming other spatio-temporal pixel-classification strategies.
