The conservation of tropical forests is a social and ecological relevant subject because of its important role in the global ecosystem. Forest monitoring is mostly done by extraction and analysis of remote sensing imagery (RSI) information. In the literature many works have been successful in remote sensing image classification through the use of machine learning techniques. Generally, traditional learning algorithms demand a representative and huge training set which can be an expensive procedure, especially in RSI, where the imagery spectrum varies along seasons and forest coverage. A semi-supervised learning paradigm known as active learning (AL) is proposed to solve this problem, as it builds efficient training sets through iterative improvement of the model performance. In the construction process of training sets, unlabeled samples are evaluated by a user-defined heuristic, ranked and then the most relevant samples are labeled by an expert user. In this work two different AL approaches (Confidence Heuristics and Committee) are presented to classify remote sensing imagery. In the experiments, our AL approaches achieve excellent effectiveness results compared with well-known approaches existing in the literature for two different datasets.