Natural landscape image classification is a difficult problem in computer vision. Many classes that can be found in such images are often ambiguous and can easily be confused with each other (e.g. smoke and fog), and not just by a computer algorithm, but by a human as well. Since natural landscape video surveillance became relatively pervasive in recent years, in this paper we focus on the classification of natural landscape images taken mostly from forest fire monitoring towers. Since these images usually suffer from the lack of the usual low and middle level features (e.g. sharp edges and corners), and since their quality is degraded by atmospheric conditions, this makes the already difficult problem of natural landscape classification even more challenging. In this paper we tackle the problem of automatic natural landscape classiffication by proposing and evaluating a classifier based on a pretrained deep convolutional neural network and transfer learning.