Tropical rain forests are important carbon stores and harbours of biodiversity but are being cleared at an unprecedented rate. There is an estimated 2 billion hectares of degraded forest globally, which retains a large proportion of its biodiversity. Restoration of these lands is needed to meet global commitments to combat the interlinked climate and biodiversity crises, and effective, scalable and affordable monitoring of the restoration process is essential. High resolution remote sensing technologies offer the best hope for monitoring at scale. In particular, unoccupied aerial vehicles (UAVs) offer a viable option for high spatial and temporal resolution remote sensing, though methods to guide forest restoration with these are still in their infancy. This thesis introduces approaches for the use of remote sensing data to guide tropical forest management, with particular focus on the use of UAV data in the context of restoration, looking at canopy structure, composition and dynamics.

First, I introduce the context of tropical forest restoration, discussing the contribution of remote sensing to monitoring and understanding projects, with a focus on the recent developments around the use of UAVs. I also introduce the main study site of this thesis --- an ecosystem restoration concession of nearly 100 km^2 in Sumatra, Indonesia, known as Hutan Harapan. Next, I introduce a method for delineating individual tree crowns in three dimensions from remote sensing data in the form of point clouds, as created by light detection and ranging (LiDAR) and UAV structure from motion (SfM) approaches. This method, MCGC, makes use of graph cut concepts from mathematics combined with understanding of tree crown geometry and allometric scaling to automatically map tree crowns. I validate this approach using data collected in Borneo, comparing forests with three distinctive structures, showing the power of this approach to both map trees and estimate aboveground biomass. In Chapter 3, I develop a pipeline for automatic mapping of key tree species prevalence at Hutan Harapan from photographs taken from a UAV. I show it is possible to break up imagery over management units into superpixels, and through a combination of spectral and textural patterns in the imagery, train an automatic classifier to detect the species of interest from UAV imagery. I then show the power of this approach to map prevalence of key tree species indicative of the successional stage of forest recovery and demonstrate the utility of this approach for guiding management. I find that using an extra camera to take photographs with additional wavebands only slightly improved mapping accuracy. Finally, I use a combination of a LiDAR survey in 2014 and UAV surveys in 2017 and 2018 to track the effects of the strong El Niño event of 2015-16 on the canopy at Hutan Harapan, looking at 3 sites of varying recovery status spanning 100 ha of forest. I find that early-successional forest was less resistant to the drought than taller secondary forest – with canopy height loss and high mortality. However, in the subsequent high-rainfall period, I observe that early-successional forests recovered strongly. Together, the analyses demonstrate that early-successional stages lost and then regained canopy height to a greater extent that taller forest, highlighting the power of repeat surveys using LiDAR and UAVs to track canopy dynamics. Finally, I critically evaluate the methods developed, highlighting how the insights they provide can be useful for restoration practitioners, underlining the key role that remote sensing, especially with a UAV, can play whilst also needing further development.