Stephanie Duce

Ecosystems and the services they provide are essential for life but continue to undergo degradation worldwide. Satellite remote sensing has been essential for environmental mapping for decades, but can suffer poor accuracy when applied to mapping terrestrial ecosystems. Geospatial Foundation Models (GeoFMs) integrate diverse spatial data, including image data, spatial context, and temporal dynamics, and output readily available covariates called embeddings. GeoFM embeddings likely possess an improved ability to detect ecosystems over traditional approaches. In this study, we investigate whether the use of embeddings from Google’s AlphaEarth Foundations model yields improvements to ecosystem maps developed in the Republic of Maldives compared to single-date Sentinel-2 satellite imagery. We compare (1) per-class accuracies, (2) the effect of decreasing numbers of map classes on overall accuracy, and (3) the relationships between confidence, accuracy, and the number of training samples for each class. AlphaEarth outperforms Sentinel-2 (1) for individual ecosystems, (2) with increasing numbers of classes, and (3) with fewer training data samples while also being more confident in its classifications. We expect that these advantages will promote rapid uptake and expansion in the use of GeoFMs to address challenging spatial analyses, such as mapping global ecosystems and landscapes with limited available data.

Accurate digital elevation models (DEMs) are essential for hydrological modelling and floodplain analysis, particularly in low-relief floodplains where small elevation errors can significantly affect flow routing and inundation extent. This study evaluated the vertical accuracy of six freely available global DEMs across the Flinders River catchment, North Queensland, Australia, using 30,916,100 quality-filtered ICESat-2 ATL06 elevation points for regression-based bias correction and airborne LiDAR datasets from five benchmark regions for independent validation. The evaluated DEMs included TANDEM-X, Copernicus DEM, ALOS AW3D30, SRTM, ASTER GDEM, and the Hydrological DEM. Vertical accuracy was assessed using mean error (ME), root mean square error (RMSE), and residual dispersion before and after calibration. Results showed substantial pre-calibration bias in the Hydrological DEM (ME = −2.93 m) and SRTM (ME = −2.66 m), whereas Copernicus DEM showed minimal initial bias (ME = −0.01 m). Regression-based correction reduced mean errors to within ±0.13 m across all DEMs. SRTM showed the largest improvement, with RMSE decreasing from 3.20 m to 0.55 m, while TANDEM-X achieved the highest post-calibration accuracy (RMSE = 0.14 m). Independent LiDAR validation confirmed improved vertical accuracy while preserving terrain morphology and river gradients.

English Language Marine turtle regional management units (RMUs) are region‐wide population boundaries delineating marine turtle subpopulations. Analyses within RMUs can provide insights into their knowledge gaps and conservation status for those regions. However, their large spatial extent limits their effectiveness for localized conservation planning. This study introduces Marine Turtle Management Areas (MATMAs), a smaller, spatially defined conservation unit aimed at refining RMUs to improve conservation management in Southeast Asia into manageable units to make local and regional management more effective. To define meaningful MATMAs, we collated published and unpublished datasets on marine turtle movements, including satellite tracking, photo identification records and flipper tag recoveries. Nesting groups were also categorized and analyzed using a minimum convex polygon bootstrapping method to create MATMAs. Six green turtle MATMAs were identified for the Andaman Sea, Java Sea, Raja Ampat, South China Sea, northern Borneo/Kalimantan and the Sulu and Sulawesi Sea, while two hawksbill MATMAs were identified in the Melaka Straits and the Sulu Sea. We acknowledge that data availability influenced MATMA delineation, leading to potential gaps in the coverage for both turtle species in Southeast Asia. However, as a proof of concept, this study demonstrates that MATMAs offer a higher‐resolution approach to marine turtle conservation, contextualizing jurisdictional boundaries and enhancing localized to seascape conservation strategies. MATMAs provide a valuable framework for conservation managers, informing targeted decision‐making for immediate action. Indonesian Language Regional Management Units (RMUs) penyu laut merupakan batas populasi yang mendelineasi subpopulasi penyu laut dalam skala regional. Analisis‐analisis dalam RMU dapat memberikan gambaran tentang kesenjangan pengetahuan dan status konservasinya di region tersebut. Meskipun demikian, besarnya cakupan spasial RMU membatasi efektivitas RMU dalam perencanaan pengelolaan konservasi pada skala lokal. Studi ini memperkenalkan konsep Marine Turtle Management Areas (MATMAs) , yaitu unit konservasi dengan cakupan spasial tertentu. Tujuannya adalah untuk meningkatkan pengelolaan konservasi di Asia Tenggara dengan cara membagi RMU menjadi unit—unit yang lebih kecil untuk meningkatkan efektivitas pengelolaan di tingkat lokal maupun regional. Dalam rangka mendefinisikan MATMA , kami mengumpulkan data tentang pergerakan penyu laut, baik yang sudah maupun belum dipublikasikan, termasuk pergerakan melalui pelacakan satelit, catatan‐catatan identifikasi foto, dan penemuan‐penemuan tag yang dipasang pada sirip penyu. Kami juga mengkategorikan dan menganalisis kelompok‐kelompok peneluran dengan menggunakan metode minimum convex polygon bootstrapping untuk membuat MATMA . Kami berhasil mengidentifikasi enam MATMA untuk penyu hijau, yaitu di Laut Andaman, Laut Jawa, Raja Ampat, Laut Cina Selatan, sisi utara Kalimantan, serta Laut Sulu—Sulawesi. Selain itu, dua MATMA teridentifikasi untuk penyu sisik, yaitu di Selat Malaka dan Laut Sulu. Kami menyadari bahwa delineasi MATMA dalam studi ini dibatasi oleh ketersediaan data, sehingga ada kemungkinan bahwa MATMA yang kami hasilkan tidak mencakup semua lokasi penyu hijau dan penyu sisik di Asia Tenggara. Namun, sebagai konsep awal, studi ini menunjukkan bahwa MATMA menawarkan pendekatan resolusi yang lebih tinggi untuk konservasi penyu laut dengan mempertimbangkan batas‐batas jurisdiksi serta memperkuat strategi konservasi dari skala lokal hingga bentang laut ( seascape ). MATMA menawarkan sebuah kerangka kerja yang penting bagi para pengelola konservasi melalui pengambilan keputusan yang lebih terarah dan tepat‐sasaran secara cepat.

Lithological mapping is essential for geological studies, mineral exploration, and environmental assessment. Satellite remote sensing combined with machine learning provides a scalable, cost-effective approach for regional lithological discrimination. This study evaluates hyperspectral and multispectral satellite imagery for lithological mapping in a geologically complex region of northwestern Queensland, Australia. The study area, within the Mount Isa Inlier, comprises diverse sedimentary, volcanic, intrusive, and metamorphic lithologies. PRISMA hyperspectral and ASTER multispectral imagery were analyzed using supervised classification algorithms, including Support Vector Machine (SVM), Mahalanobis Distance (MaDC), Minimum Distance (MDC), and Maximum Likelihood (MLC). Image-derived endmembers from representative lithologies were used as training data. Classification accuracy was assessed using confusion matrices, Overall Accuracy (OA), and the Kappa coefficient. PRISMA imagery outperformed ASTER data. SVM achieved the highest performance for PRISMA (OA = 82.03%, Kappa = 0.81), whereas MLC achieved the highest performance for ASTER (OA = 33.29%, Kappa = 0.30). Classification accuracy was evaluated using an independent set of validation ROIs that were spatially separated from the training samples, providing a more reliable estimate of model performance. These results highlight the benefits of hyperspectral remote sensing with machine learning for lithological discrimination in complex terrain and emphasise the importance of spatially independent validation. The approach demonstrates strong potential for regional-scale applications and may support more efficient mineral exploration and geological mapping workflows.

A detailed understanding of how protected species use their habitats can guide management interventions in areas of high human use. For marine turtles, different food availability and physical habitat characteristics can underpin turtle presence at anthropogenically modified compared to unmodified sites. We develop telemetry-based habitat models with boosted regression trees to identify the environmental characteristics underpinning foraging habitat suitability for green turtles in the Great Barrier Reef region. We fit models to green turtle Fastloc GPS tracks from both modified and unmodified inshore foraging sites and using pseudo-absences (simulated correlated random walks). We assess model performance by the ability to predict known foraging areas, true skill statistic, explanatory power (percent deviance explained) and pedictive skill (AUC) of the models. We then predict potentially suitable foraging areas for green turtles in the Great Barrier Reef region using the model for unmodified habitats. Our model highlights shallow nearshore environments and midshelf reefs as important foraging areas for green turtles. These areas are likely affected by dynamic floods, development and turbidity. In 2022, 46.6% of predicted suitable habitat fell within habitat protection zones, and 16.5% in Marine National Park Zones of the Great Barrier Reef Marine Park. A detailed foraging distribution of the species has not previously been compiled at this regional scale. Identifying biophysical drivers of habitat suitability can inform identification of possible foraging habitat in less data rich regions in Australia and overseas. Evaluating changes over time in habitat distribution provides insights into the degree to which broad-scale environmental changes and anthropogenic activities influence the condition and function of habitats, even within protected area boundaries.

There is an urgent need for improved monitoring approaches to rapidly and accurately assess sea cucumber populations at ecologically relevant scales. Timely surveys are critical for informing effective fisheries management and decision-making. Traditional surveys, undertaken via snorkelling, manta tows, or SCUBA, are limited to shallow and accessible areas; however, sea cucumbers inhabit a broad range of depths, including areas beyond safe diving limits and exposed shallow waters inaccessible by boat. To overcome these limitations and increase the rapidity of field collection, we propose the use of remote sensing technologies to survey sea cucumber populations across a range of depths. Here, we evaluated the effectiveness of aerial drones and in-water remote operated vehicles (ROVs) for assessing sea cucumber species and abundances across various depth ranges (< 50 m) on the Great Barrier Reef, Australia. Aerial drone orthomosaics and ROV video footage were compared to more traditional snorkel and SCUBA-based assessments conducted at similar depths. The vast majority of pairwise comparisons between in-water ROV video counts and snorkel or SCUBA assessments found no significant differences in sea cucumber assemblages. Counts from aerial drone-derived orthomosaics, however, were significantly lower, counting approximately half as many sea cucumbers as snorkel counts. This was largely attributed to poor weather during the drone surveys. Remote methods were significantly faster in the field for surveying a given area than traditional methods. Given that towed ROVs can efficiently cover a broader depth range and aerial drones are effective for survey shallow areas under suitable weather conditions, we recommend using a combination of aerial drones and towed ROVs to survey sea cucumbers, with tool selection guided by prevailing weather conditions. This approach offers the advantages of collecting multiple types of data from a single data source, vastly increasing survey efficiency, and providing a historical record for future assessments. The methods have the potential to be used to survey other benthic–associated species.

Coral reefs protect coastlines from inundation and flooding and serve over 200 million people globally. Wave transformation has previously been studied on coral reef flats with limited focus on forereef zones where wave transformation is greatest during high‐energy conditions. This study investigates the role of forereef spur and groove (SaG) morphology in wave energy dissipation and transmission at the reef crest. Using XBeach on LiDAR‐derived bathymetry from One Tree Island in the southern Great Barrier Reef, we reproduced dissipation rates comparable to SaG field studies. We examined how wave energy dissipation differs between realistic bathymetry and those with SaG features removed, demonstrating an up to 40% decrease in dissipation when SaG features are absent. We then investigated changes to wave energy dissipation and wave transmission at the reef crest based on IPCC AR5 emission scenarios (RCP2.6 and RCP8.5) and a total disaster scenario (TD) for the year 2100. For RCP2.6, an increase in wave heights of 0.8 m and an increase in water level of 0.3 m resulted in a two‐fold increase in dissipation rates. For RCP8.5 and TD, with no increase in incident wave height, dissipation rates were 29% and 395% lower than RCP2.6. This resulted in increased wave transmission at the reef crest by 1.8 and 2.7 m for the RCP8.5‐ and TD based models, respectively, when compared to the RCP2.6‐based model. The results from our novel modeling approach of using long‐shore varying accurate bathymetry on forereefs show increased wave energy dissipation rates with implications for reducing coastal flooding and island inundation on reef‐lined coasts.

Coral reefs protect coastlines from inundation and flooding, servicing over 200 million people globally. Wave transformation has previously been studied on coral reef flats with limited focus on forereef zones where wave transformation is greatest during high-energy conditions. This study investigates the role of forereef spur and groove (SaG) morphology on wave energy dissipation and overtopping on coral reefs. Using XBeach on LiDAR-derived bathymetry, we reproduced dissipation rates comparable to SaG field studies. Our results emphasize accurate bathymetries’ role in forereef wave energy dissipation models by including morphological features (e.g., groove sinuosity, irregular forereef slopes) that control the mode of wave energy dissipation (frictional and breaking). We then investigated changes to wave energy dissipation and wave overtopping based on IPCC AR5 low and high emission scenarios (RCP2.6 and RCP8.5) and a total disaster scenario (TD) for the year 2100 considering changes to SaG morphology, wave power and relative sea-level rise. For RCP2.6, an increase in wave heights of 0.8 m and an increase in water level of 0.3 m resulted in a two-fold increase in dissipation rates. For RCP8.5 and TD, with no increase in incident wave height, dissipation rates were 29% and 395% lower than RCP2.6. This resulted in increased overtopping at the reef crest by 1.8 m and 2.7 m for RCP8.5 and TD scenarios, respectively, when compared to RCP2.6. Decreased dissipation rates and increased wave overtopping in forecasted climate conditions suggest the need for strategies to promote coral growth to facilitate high dissipation rates in the future.

Tidal wetlands are expected to respond dynamically to global environmental change, but the extent to which wetland losses have been offset by gains remains poorly understood. We developed a global analysis of satellite data to simultaneously monitor change in three highly interconnected intertidal ecosystem types—tidal flats, tidal marshes, and mangroves—from 1999 to 2019. Globally, 13,700 square kilometers of tidal wetlands have been lost, but these have been substantially offset by gains of 9700 km ² , leading to a net change of −4000 km ² over two decades. We found that 27% of these losses and gains were associated with direct human activities such as conversion to agriculture and restoration of lost wetlands. All other changes were attributed to indirect drivers, including the effects of coastal processes and climate change.