Environmental monitoring refers to a host of activities involving the sampling or sensing of diverse properties from an environment in an effort to monitor, study and overall better understand it. While potentially rich and scientifically valuable, these data often create challenging interpretation tasks because of their volume and complexity. This thesis explores the efficiency of Computer Vision-based frameworks towards the processing of large amounts of visual environmental monitoring data. While considering every potential type of visual environmental monitoring measurement is not possible, this thesis elects three data streams as representatives of diverse monitoring layouts: visual out-of-water stream, visual underwater stream and active acoustic underwater stream. Detailed structure, objectives, challenges, solutions and insights from each of them are presented and used to assess the feasibility of Computer Vision within the environmental monitoring context. This thesis starts by providing an in-depth analysis of the definition and goals of environmental monitoring, as well as the Computer Vision systems typically used in conjunction with it. The document continues by studying the visual out-of-water stream via the design of a novel system employing a contrast-guided approach towards the enhancement of low-light underwater images. This enhancement system outperforms multiple state-of-the-art methods, as supported by a group of commonly-employed metrics. A pair of detection frameworks capable of identifying schools of herring, salmon, hake and swarms of krill are also presented in this document. The inputs used in their development, echograms, are visual representations of acoustic backscatter data from echosounder instruments, thus contemplating the active acoustic underwater stream. These detectors use different Deep Learning paradigms to account for the unique challenges presented by each pelagic species. Specifically, the detection of krill and finfish is accomplish with a novel semantic segmentation network (U-MSAA-Net) capable of leveraging local and contextual information from feature maps of multiple scales. In order to explore the out-of-water visual data stream, we examine a large dataset composed by years-worth of images from a coastal region with strong marine vessels traffic, which has been associated with significant anthropogenic footprints upon marine environments. A novel system that involves ``traditional'' Computer Vision and Deep Learning is proposed for the identification of such vessels under diverse visual appearances on this monitoring imagery. Thorough experimentation shows that this system is able to efficiently detect vessels of diverse sizes, shapes, colors and levels of visibility. The results and reflections presented in this thesis reinforce the hypothesis that Computer Vision offers an extremely powerful set of methods for the automatic, accurate, time- and space-efficient interpretation of large amounts of visual environmental monitoring data, as detailed in the remainder of this work. / Graduate