Protection of tropical forests is key to achieving global climate and biodiversity conservation goals. They play an essential role in carbon sequestration, water cycling, and nutrient exchanges, thereby regulating atmospheric composition and global climate patterns. However, they are under interlinked threats from deforestation and climate change, exacerbating biodiversity loss and potentially pushing these systems toward ecological tipping points. Computer vision techniques based on deep learning have emerged as potent tools for monitoring, conservation, and prediction efforts within these expansive and intricate ecosystems. This thesis uses these emerging technologies to understand forest dynamics at scales ranging from the phenology of individual tree crowns to large-scale deforestation. Chapter 1 introduces tropical forests, explores their ecological and societal value, and discusses the technological challenges and opportunities of studying them, with a focus on deep learning as applied to remote sensing data. Chapter 2 develops a tool to predict deforestation patterns based on convolutional neural networks (CNNs), working with freely accessible data to successfully forecast spatiotemporal patterns in the Southern Peruvian Amazon. Predicting the location of deforestation is difficult as it results from complex interactions within human-ecological systems but doing so may enable effective, adaptable prevention measures and conservation planning. The models, through their ability to discern deforestation drivers such as new access routes from remote sensing data, highlight the potentially transformational role of deep learning in conservation. In Chapter 3, I develop a new approach named detectree2, building on the Mask R-CNN architecture, which is capable of accurately detecting and delineating individual tree crowns from airborne RGB imagery taken over dense tropical forests. The foundation for any remote-sensing study of individual tree dynamics is accurate tree delineation. Trialled in diverse geographies, including Malaysian Borneo and French Guiana, I show this tool holds promise for large-scale forest studies. The performance of the detection and delineation, especially for tall trees, enables tracking of tree growth and mortality for the study of carbon dynamics from cheap, widely accessible photographic data. Chapter 4 develops a pipeline for identifying and mapping tropical tree species, building on the detectree2 approach. This pipeline combines aerial photographic images taken every three weeks using a UAV with hyperspectral survey. Training and testing on a carefully crafted ground truth dataset, the two-step approach applies detectree2 to multitemporal UAV-RGB data in order to automatically segment trees and then applies Linear Discriminant Analysis (LDA) to hyperspectral data to assign species. This new approach identified over sixty tree species with high confidence, achieving accurate species level mapping over 70 % of the total crown area of the landscape. Key to the improved mapping was the temporal stacking of imagery to delineate tree crowns accurately and a large, rigorously validated dataset of labelled tree crowns to train on. In Chapter 5, I use the data and techniques developed in the previous two Chapters to address ecological questions related to the phenology of tropical forests. Seasonal variation in canopy greenness has been observed from space, but the extent to which all species in diverse forests follow a similar pattern of leaf pigment changes, leaf flushing and loss remains unknown. I begin to address that knowledge gap by tracking phenology through drone-mounted sensors, providing a dataset that tracked individual trees in French Guiana at 3-weekly intervals over 34 months. 3,000 tree crowns were mapped and tracked using UAV LiDAR, revealing significant spatiotemporal variability in Plant Area Density (PAD) and distinct species-specific phenological patterns. By juxtaposing PAD with spectral metrics, I start to decipher variation in leaf amount'' and leaf quality'', offering some insights into how individual tree changes might impact forest productivity. Concluding Chapter 6 discusses ways in which integration of deep learning technologies and remote sensing into ecology research is helping to broaden understanding and conservation capabilities for tropical forests, by providing precise, scalable solutions spanning deforestation prediction, tree level monitoring, species identification, and phenological studies.