Camera traps are a widely used instrument in animal ecology due to their low cost, low power requirements, and low maintenance needs. Many camera trap projects capture time-lapse imagery in addition to motion-triggered imagery to verify camera functionality. These time-lapse images are recorded at frequent, regular intervals over long time periods; however, they are currently underutilised in ecological studies because they typically lack wildlife presence. Projects that incorporate time-lapse capture into their routine offer a valuable secondary data source of environmental information that can be used for further ecological analyses. This study demonstrates multiple ecological applications of semantic segmentation applied to camera trap imagery. These applications include monitoring canopy and understory phenology, particularly seasonal vegetation green-up. They further enable the assessment of habitat heterogeneity through vegetation classification and quantification, as well as the estimation of habitat complexity by mapping the spatial structure of vegetation classes within images. In addition, the model can function as a preprocessing filter for subsequent instance-segmentation analyses, facilitating the identification and counting of forage resources such as fruit. These novel applications can provide insight into habitat quality and ecosystem dynamics that shape wildlife behaviour. A variety of remote sensing techniques are used to monitor vegetation phenology. Phenocams, static cameras positioned above the canopy, take repeated RGB images that allow researchers to track subtle changes in vegetation over time. These systems offer finer-scale monitoring than satellite imagery but can be limited by high upfront costs, significant power requirements, and the practical challenges of deploying them in remote or inaccessible locations. A substantial body of research exists on the use of digital repeat imagery for phenology monitoring, along with established frameworks for processing and interpreting such data. In recent years, several studies have explored the use of camera traps for monitoring vegetation phenology parameters such as green-up and leaf area. Camera traps are cheap, rugged sensors that have the advantage of existing widespread use across the world for wildlife ecology purposes. Although these studies have demonstrated potential for monitoring vegetation, most rely on manual image classification, which restricts their scalability. In addition, while camera traps are widely deployed for wildlife studies, their positioning is often optimised for animal detection rather than vegetation monitoring, which can limit their effectiveness for phenological analysis. Advances in computer vision, particularly deep learning (DL), offer promising opportunities to address these challenges. DL techniques excel in image classification, segmentation, object detection, and depth estimation. By applying these methods to camera trap imagery, it is possible to identify regions of interest and segment vegetation in ways that improve the accuracy and detail of phenological measurements. Beyond traditional time-series approaches, DL could enable a more comprehensive understanding of vegetation dynamics, including fine-scale spatial patterns and structural changes over time. This research aims to investigate the application of camera traps and deep learning methods to monitor vegetation phenology parameters across Europe, assessing their potential for scalable, low-cost, and high-resolution ecological monitoring.