Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R² = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R² = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km² of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m² resolution to underpin evidence-based management and conservation of seagrass.

Accurate knowledge of the spatial extent of seagrass habitats is essential for monitoring and management purposes given their ecological and economic significance. Extent data are typically presented in binary (presence/absence) or arbitrary, semi-quantitative density bands derived from low-resolution satellite imagery, which cannot resolve fine-scale features and intra-habitat variability. Recent advances in consumer-grade unoccupied aerial vehicles (UAVs) have advanced our ability to survey large areas at higher resolution and at lower cost. This has improved the accessibility of mapping technologies to developing coastal nations, where a large proportion of the world’s seagrass habitats are found. Here, we present the application of UAV-gathered imagery to determine seagrass habitat extent and percent of canopy cover. Four contrasting sites were surveyed in the Turneffe Atoll Marine Reserve, Belize, and seagrass canopy cover was ground truthed from in situ quadrats. Orthomosaic images were created for each site from the UAV-gathered imagery. Three modelling techniques were tested to extrapolate the findings from quadrats to spatial information, producing binary (random forest) and canopy cover (random forest regression and beta regression) habitat maps. The most robust model (random forest regression) had an average absolute error of 6.8–11.9% (SE of 8.2–14), building upon previous attempts at mapping seagrass density from satellite imagery, which achieved errors between 15–20% approximately. The resulting maps exhibited great intra-habitat heterogeneity and different levels of patchiness, which were attributed to site energetics and, possibly, species composition. The extra information in the canopy cover maps provides greater detail and information for key management decisions and provides the basis for future spatial studies and monitoring programmes.

Land‐ocean dissolved organic matter (DOM) transport is a significant and changing term in global biogeochemical cycles which is increasing as a result of human perturbation, including land‐use change. Knowledge of the behavior and fate of transported DOM is lacking, particularly in the tropics and subtropics where land‐use change is occurring rapidly. We used Parallel Factor (PARAFAC) Analysis to investigate how land‐use influenced the composition of the DOM pool along a subtropical land‐use gradient (from near‐pristine broadleaf forest to agri‐urban settings) in Belize, Central America. Three humic‐like and two protein‐like components were identified, each of which was present across land uses and environments. Land‐use mapping identified a strong (R² = 0.81) negative correlation between broadleaf forest and agri‐urban land. All PARAFAC components were positively associated with agri‐urban land‐use classes (cropland, grassland, and/or urban land), indicating that land‐use change from forested to agri‐urban exerts influence on the composition of the DOM pool. Humic‐like DOM exhibited linear accumulation with distance downstream and behaved conservatively in the coastal zone whilst protein‐like DOM exhibited nonlinear accumulation within the main river and nonconservative mixing in coastal waters, indicative of differences in reactivity. We used a hydrodynamic model to explore the potential of conservative humics to reach the region's environmentally and economically valuable coral reefs. We find that offshore corals experience short exposures (10 ± 11 days yr⁻¹) to large (∼120%) terrigenous DOM increases, whilst nearshore corals experience prolonged exposure (113 ± 24 days yr⁻¹) to relatively small (∼30%) terrigenous DOM increases.

Reservoirs collectively contribute 1–2% of global anthropogenic greenhouse gas emissions, although individual emissions can vary widely. While emission models have considerably advanced our understanding of the lifetime carbon impacts of reservoirs globally and offer means to inform judicious planning, their widespread adoption is hindered by high manual processing requirements, uncertainties, and linkages to geospatial drivers that can be obscure for planners. Meanwhile, simpler Tier 1 methods fail to capture variability across individual reservoirs and can overestimate national emissions by 50% compared to model-based estimates. Here we introduce an automated and transparent framework for large scale reservoir emission assessments and planning with spatially-explicit emission models to address key limitations in current approaches. By applying our framework to strategic hydropower expansion in Myanmar, we show how emission models can support low-carbon reservoir development at large scales. Our results show that the proposed methodology can yield a hydropower strategy for Myanmar that eliminates 0.94 MtCO _2e in emissions (1% of national total), conserves 239 km ² of forest and arable land, and reduces the number of barriers in lower river reaches from 28 to 7.

Riparian tree canopies are key components of river systems, and influence the provision of many essential ecosystem services. Their management provides the potential for substantial control of the downstream persistence of pollutants. The recent advent of new advances in mass spectrometry to detect a large suite of emerging contaminants, high-frequency observations of water quality and gas exchange (e.g., aquatic eddy covariance), and improved spatial resolution in remote sensing (e.g., hyperspectral measurements and high-resolution imagery), presents new opportunities to understand and more comprehensively quantify the role of riparian canopies as Nature-based Solutions. The paper outlines how we may now couple these advances in observational technologies with developments in water quality modelling to integrate simulation of eutrophication impacts with organic matter dynamics and fate of synthetic toxic compounds. In particular regarding solar radiation drivers, this enables us to scale-up new knowledge of canopy-mediated photodegradation processes at a basin level, and integrate it with ongoing improvements in understanding of thermal control, eutrophication, and ecosystem metabolism.

Reservoirs collectively contribute 1-2% of global anthropogenic greenhouse gas emissions, although individual emissions can vary widely. While emission models have considerably advanced our understanding of the lifetime carbon impacts of reservoirs globally and offer means to inform judicious planning, their widespread adoption is hindered by high manual processing requirements, uncertainties, and linkages to geospatial drivers that can be obscure for planners. Meanwhile, simpler Tier 1 methods fail to capture variability across individual reservoirs and can overestimate national emissions by 50% compared to model-based estimates. In this study, an automated framework for reservoir emission assessment and planning is used to address some of these limitations. By applying a spatially explicit emission model enhanced with human-focused explanations to strategic hydropower expansion planning in Myanmar, we demonstrate how emission models can be used to inform low carbon reservoir planning at a national scale.