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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., NERC RSPB

The determination of key phenological growth stages of banana plantations, such as flower emergence and plant establishment, is difficult due to the asynchronous growth habit of banana plants. Identifying phenological events assists growers in determining plant maturity, and harvest timing and guides the application of time-specific crop inputs. Currently, phenological monitoring requires repeated manual observations of individual plants’ growth stages, which is highly laborious, time-inefficient, and requires the handling and integration of large field-based data sets. The ability of growers to accurately forecast yield is also compounded by the asynchronous growth of banana plants. Satellite remote sensing has proved effective in monitoring spatial and temporal crop phenology in many broadacre crops. However, for banana crops, very high spatial and temporal resolution imagery is required to enable individual plant level monitoring. Unoccupied aerial vehicle (UAV)-based sensing technologies provide a cost-effective solution, with the potential to derive information on health, yield, and growth in a timely, consistent, and quantifiable manner. Our research explores the ability of UAV-derived data to track temporal phenological changes of individual banana plants from follower establishment to harvest. Individual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters (R2 of 0.77 and 0.69 respectively). A temporal profile of canopy reflectance and plant morphology for 15 selected banana plants were derived from UAV-captured multispectral data over 21 UAV campaigns. The temporal profile was validated against ground-based determinations of key phenological growth stages. Derived measures of minimum plant height provided the strongest correlations to plant establishment and harvest, whilst interpolated maxima of normalised difference vegetation index (NDVI) best indicated flower emergence. For pre-harvest yield forecasting, the Enhanced Vegetation Index 2 provided the strongest relationship (R2 = 0.77) from imagery captured near flower emergence. These findings demonstrate that UAV-based multitemporal crop monitoring of individual banana plants can be used to determine key growing stages of banana plants and offer pre-harvest yield forecasts.

Recent developments in technology and data processing for Unoccupied Aerial Vehicles (UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to fill the critical gap between limited-scope field surveys and limited-customization satellite and piloted aerial platforms. These advances are especially ground-breaking for supporting management, restoration, and conservation of landscapes with limited field access and vulnerable ecological systems, particularly wetlands. This study presents a scoping review of the current status and emerging opportunities in wetland UAV applications, with particular emphasis on ecosystem management goals and remaining research, technology, and data needs to even better support these goals in the future. Using 122 case studies from 29 countries, we discuss which wetland monitoring and management objectives are most served by this rapidly developing technology, and what workflows were employed to analyze these data. This review showcases many ways in which UAVs may help reduce or replace logistically demanding field surveys and can help improve the efficiency of UAV-based workflows to support longer-term monitoring in the face of wetland environmental challenges and management constraints. We also highlight several emerging trends in applications, technology, and data and offer insights into future needs.

Accurately quantifying tree and forest structure is important for monitoring and understanding terrestrial ecosystem functioning in a changing climate. The emergence of laser scanning, such as Terrestrial Laser Scanning (TLS) and Unoccupied Aerial Vehicle Laser Scanning (UAV-LS), has advanced accurate and detailed forest structural measurements. TLS generally provides very accurate measurements on the plot-scale (a few ha), whereas UAV-LS provides comparable measurements on the landscape-scale (>10 ha). Despite the pivotal role dense tropical forests play in our climate, the strengths and limitations of TLS and UAV-LS to accurately measure structural metrics in these forests remain largely unexplored. Here, we propose to combine TLS and UAV-LS data from dense tropical forest plots to analyse how this fusion can further advance 3D structural mapping of structurally complex forests. We compared stand (vertical point distribution profiles) and tree level metrics from TLS, UAV-LS as well as their fused point cloud. The tree level metrics included the diameter at breast height (DBH), tree height (H), crown projection area (CPA), and crown volume (CV). Furthermore, we evaluated the impact of point density and number of returns for UAV-LS data acquisition. DBH measurements from TLS and UAV-LS were compared to census data. The TLS and UAV-LS based H, CPA and CV measurements were compared to those obtained from the fused point cloud. Our results for two tropical rainforest plots in Australia demonstrate that TLS can measure H, CPA and CV with an accuracy (RMSE) of 0.30 m (Haverage =27.32 m), 3.06 m2 (CPAaverage =66.74 m2), and 29.63 m3 (CVaverage =318.81 m3) respectively. UAV-LS measures H, CPA and CV with an accuracy (RMSE) of

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (