Hyperspectral remote sensing for monitoring coral reef and seagrass Ecosystems: capabilities, limitations and future directions - Joanna N. Smart; Dylan Cowley; David E. Carrasco Rivera; Muhammed Hafizt; Mitchell Lyons; Stuart Phinn; Chris Roelfsema (Marine Environmental Research, 2026)

RayExtract: A fast, scalable method for tree volume reconstruction from terrestrial laser scanning - Timothy Devereux; Thomas Lowe; Joshua Rivory; Rafael Bohn Reckziegel; Kim Calders; Raja Ram Aryal; Glen Eaton; Zane Cooper; Shaun Levick; Stuart Phinn; William Woodgate (Remote Sensing of Environment, 2026)

The impact of neotectonics on the geomorphology of the northern Great Barrier Reef - Atefeh Sansoleimani; Gregory E. Webb; Daniel L. Harris; Stuart R. Phinn; Chris M. Roelfsema (Coral Reefs, 2025)
The Great Barrier Reef (GBR) is assumed to be a tectonically stable area, but recent studies show that neotectonic activity could have affected the geomorphology and evolution of the coral reefs. We used bathymetry, free-air gravity and 2D seismic data to test the hypothesis that neotectonic (i.e. geologically recent or currently active) faults have affected the morphology and major aspects of reefs’ growth in the northernmost zone of the GBR, 13.5°S–10.5° S. Bathymetry data show that elongated reef platforms, oriented WNW-ESE, are systematically deflected at their ends or, in some cases, offset through their bodies, along interpreted structural lineaments. Submarine stream channels, running mostly in WNW-ESE orientations, have deepened their courses within the areas occupied by elongated platforms. The deflection or offset of the coral reefs is interpreted to result from movement on neotectonic faults based on the following lines of evidence: (1) fault lineaments based on reef platform morphology are almost parallel to those interpreted from free-air gravity data, consistent with deep-seated faults; (2) offsets along the deflected zones of reef platforms are oriented sub-parallel to interpreted fault lineaments, consistent with those faults having deformed reef morphology since reef initiation; (3) some inferred fault positions correspond to faults interpreted from limited 2D seismic reflection data; (4) fluvial incision of the continental shelf between elongated reefs is consistent with neotectonic fault activity; and (5) M > 4 earthquakes have been recorded adjacent to some interpreted lineaments. We propose that the GBR has been affected by localised active tectonism in different ways. Small, localised faults active in the late Pleistocene displaced and deflected coral reefs laterally in the northernmost zone of the GBR. In the central zone of the GBR larger (NNE-SSW trending) bounding faults controlled regional subsidence, thus affecting accommodation and broader reef growth patterns. Our results suggest that recently active faults in the GBR have had, and may continue to have, important ramifications for the developmental history of the reef province. These findings are significant given that the assumption of tectonic stability underpins many interpretations of the potential effects of changing climate and sea level on reef growth.

Bushfire recovery at a long-term tall eucalypt flux site through the lens of a satellite: Combining multi-scale data for structural-functional insight - William Woodgate; Stuart Phinn; Timothy Devereux; Raja Ram Aryal (Remote Sensing of Environment, 2025)

Fire resilience analysis: Using high temporal and spatial satellite imagery for rehabilitated landscapes - Phillip B. McKenna; Stuart Phinn; Peter D. Erskine (Ecological Engineering, 2025)

Vegetation recovery of rehabilitated pasture at three coal mine sites following fire disturbance - Phillip B. McKenna; David Doley; Natasha Ufer; Vanessa Glenn; Stuart Phinn; Peter D. Erskine (Ecological Engineering, 2024)

Remote sensing of artisanal and small-scale mining: A review of scalable mapping approaches - Ilyas Nursamsi; Stuart R. Phinn; Noam Levin; Matthew Scott Luskin; Laura Jane Sonter (Science of The Total Environment, 2024)

Mapping functional diversity of canopy physiological traits using UAS imaging spectroscopy - Emiliano Cimoli; Arko Lucieer; Zbyněk Malenovský; William Woodgate; Růžena Janoutová; Darren Turner; Ryan S. Haynes; Stuart Phinn (Remote Sensing of Environment, 2024)

Characterizing Watersheds to Support Land-use Planning in Indonesia: A Case Study of Brantas Tropical Watershed - Bagus Setiabudi Wiwoho; Stuart Phinn; Neil McIntyre (Ecohydrology & Hydrobiology, 2023)

Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography - Andrew Clark; Stuart Phinn; Peter Scarth (PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2023)
Convolutional Neural Networks (CNN) consist of various hyper-parameters which need to be specified or can be altered when defining a deep learning architecture. There are numerous studies which have tested different types of networks (e.g. U-Net, DeepLabv3+) or created new architectures, benchmarked against well-known test datasets. However, there is a lack of real-world mapping applications demonstrating the effects of changing network hyper-parameters on model performance for land use and land cover (LULC) semantic segmentation. In this paper, we analysed the effects on training time and classification accuracy by altering parameters such as the number of initial convolutional filters, kernel size, network depth, kernel initialiser and activation functions, loss and loss optimiser functions, and learning rate. We achieved this using a well-known top performing architecture, the U-Net, in conjunction with LULC training data and two multispectral aerial images from North Queensland, Australia. A 2018 image was used to train and test CNN models with different parameters and a 2015 image was used for assessing the optimised parameters. We found more complex models with a larger number of filters and larger kernel size produce classifications of higher accuracy but take longer to train. Using an accuracy-time ranking formula, we found using 56 initial filters with kernel size of 5 × 5 provide the best compromise between training time and accuracy. When fully training a model using these parameters and testing on the 2015 image, we achieved a kappa score of 0.84. This compares to the original U-Net parameters which achieved a kappa score of 0.73.

Decision-making with ecological process for coastal and marine planning: current literature and future directions - Debbie A. Chamberlain; Hugh P. Possingham; Stuart R. Phinn (Aquatic Ecology, 2022)

Fine-scale time series surveys reveal new insights into spatio-temporal trends in coral cover (2002–2018), of a coral reef on the Southern Great Barrier Reef - Chris Roelfsema; Eva M. Kovacs; Julie Vercelloni; Kathryn Markey; Alberto Rodriguez-Ramirez; Sebastian Lopez-Marcano; Manuel Gonzalez-Rivero; Ove Hoegh-Guldberg; Stuart R. Phinn (Coral Reefs, 2021)

Global protected‐area coverage and human pressure on tidal flats - Narelle K. Hill; Bradley K. Woodworth; Stuart R. Phinn; Nicholas J. Murray; Richard A. Fuller (Conservation Biology, 2021)
Tidal flats are a globally distributed coastal ecosystem important for supporting biodiversity and ecosystem services. Local to continental‐scale studies have documented rapid loss of tidal habitat driven by human impacts, but assessments of progress in their conservation are lacking. With an internally consistent estimate of distribution and change, based on Landsat satellite imagery, now available for the world's tidal flats, we examined tidal flat representation in protected areas (PAs) and human pressure on tidal flats. We determined tidal flat representation and its net change in PAs by spatially overlaying tidal flat maps with the World Database of Protected Areas. Similarly, we overlaid the most recent distribution map of tidal flats (2014–2016) with the human modification map (HM c ) (range from 0, no human pressure, to 1, very high human pressure) to estimate the human pressure exerted on this ecosystem. Sixty‐eight percent of the current extent of tidal flats is subject to moderate to very high human pressure (HM c > 0.1), but 31% of tidal flat extent occurred in PAs, far exceeding PA coverage of the marine (6%) and terrestrial (13%) realms. Net change of tidal flat extent inside PAs was similar to tidal flat net change outside PAs from 1999 to 2016. Substantial shortfalls in protection of tidal flats occurred across Asia, where large intertidal extents coincided with high to very high human pressure (HM c > 0.4–1.0) and net tidal flat losses up to 86.4 km² (95% CI 83.9–89.0) occurred inside individual PAs in the study period. Taken together, our results show substantial progress in PA designation for tidal flats globally, but that PA status alone does not prevent all habitat loss. Safeguarding the world's tidal flats will thus require deeper understanding of the factors that govern their dynamics and effective policy that promotes holistic coastal and catchment management strategies.

Workflow for the Generation of Expert-Derived Training and Validation Data: A View to Global Scale Habitat Mapping - Chris M. Roelfsema; Mitchell Lyons; Nicholas Murray; Eva M. Kovacs; Emma Kennedy; Kathryn Markey; Rodney Borrego-Acevedo; Alexandra Ordoñez Alvarez; Chantel Say; Paul Tudman; Meredith Roe; Jeremy Wolff; et al. (Frontiers in Marine Science, 2021)
Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.

A global coral reef probability map generated using convolutional neural networks - Jiwei Li; David E. Knapp; Nicholas S. Fabina; Emma V. Kennedy; Kirk Larsen; Mitchell B. Lyons; Nicholas J. Murray; Stuart R. Phinn; Chris M. Roelfsema; Gregory P. Asner (Coral Reefs, 2020)

Habitat maps to enhance monitoring and management of the Great Barrier Reef - Chris M. Roelfsema; Eva M. Kovacs; Juan Carlos Ortiz; David P. Callaghan; Karlo Hock; Mathieu Mongin; Kasper Johansen; Peter J. Mumby; Magnus Wettle; Mike Ronan; Petra Lundgren; Emma V. Kennedy; et al. (Coral Reefs, 2020)

Cloud and cloud shadow masking for Sentinel-2 using multitemporal images in global area - Danang Surya Candra; Stuart Phinn; Peter Scarth (International Journal of Remote Sensing, 2020)

Observing Geomorphological Change on an Evolving Coastal Sand Dune Using SLAM-Based UAV LiDAR - Jeremy Sofonia; Stuart Phinn; Chris Roelfsema; Farid Kendoul (Remote Sensing in Earth Systems Sciences, 2019)

Adaptive management and planning for the conservation of four threatened large Asian mammals in a changing climate - Jiban Chandra Deb; Stuart Phinn; Nathalie Butt; Clive A. McAlpine (Mitigation and Adaptation Strategies for Global Change, 2019)

Unmanned Aerial Systems (UAS) for environmental applications special issue preface - Anita Simic Milas; Joaquim João Sousa; Timothy A. Warner; Ana Cláudia Teodoro; Emanuel Peres; José Alberto Gonçalves; Jorge Delgado Garcia; Ricardo Bento; Stuart Phinn; Amy Woodget (International Journal of Remote Sensing, 2018)

Estimating aboveground woody biomass change in Kalahari woodland: combining field, radar, and optical data sets - Vladimir R. Wingate; Stuart R Phinn; Nikolaus Kuhn; Peter Scarth (International Journal of Remote Sensing, 2018)

Is Climate Change Shifting the Poleward Limit of Mangroves? - Sharyn M. Hickey; Stuart R. Phinn; Nik J. Callow; Kimberly P. Van Niel; Jeff E. Hansen; Carlos M. Duarte (Estuaries and Coasts, 2017)

Climatic-Induced Shifts in the Distribution of Teak (Tectona grandis) in Tropical Asia: Implications for Forest Management and Planning - Jiban Chandra Deb; Stuart Phinn; Nathalie Butt; Clive A. McAlpine (Environmental Management, 2017)

Measuring fire severity using UAV imagery in semi-arid central Queensland, Australia - Phill McKenna; Peter D. Erskine; Alex M. Lechner; Stuart Phinn (International Journal of Remote Sensing, 2017)

A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series - Tony Gill; Kasper Johansen; Stuart Phinn; Rebecca Trevithick; Peter Scarth; John Armston (International Journal of Remote Sensing, 2017)

Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: a case study in Bangladesh - Muhammad Al-Amin Hoque; Stuart Phinn; Chris Roelfsema; Iraphne Childs (International Journal of Remote Sensing, 2016)

Incorporating benthic community changes into hydrochemical-based projections of coral reef calcium carbonate production under ocean acidification - Emily C. Shaw; Sarah M. Hamylton; Stuart R. Phinn (Coral Reefs, 2016)

Tectonic subsidence provides insight into possible coral reef futures under rapid sea-level rise - Megan I. Saunders; Simon Albert; Chris M. Roelfsema; Javier X. Leon; Colin D. Woodroffe; Stuart R. Phinn; Peter J. Mumby (Coral Reefs, 2016)

Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia - Ammar Abdul Aziz; Stuart Phinn; Paul Dargusch; Hamdan Omar; Sanjiwana Arjasakusuma (Wetlands Ecology and Management, 2015)

Natural in situ relationships suggest coral reef calcium carbonate production will decline with ocean acidification - Emily C. Shaw; Stuart R. Phinn; Bronte Tilbrook; Andy Steven (Limnology and Oceanography, 2015)
There are few in situ studies showing how net community calcification ( G net ) of coral reefs is related to carbonate chemistry, and the studies to date have demonstrated different predicted rates of change. In this study, we measured net community production ( P net ), G net , and carbonate chemistry of a reef flat at One Tree Island, Great Barrier Reef. Diurnal p CO 2 variability of 289–724 μatm was driven primarily by photosynthesis and respiration. The reef flat was found to be net autotrophic, with daily production of ∼ 35 mmol C m −2 d −1 and net calcification of ∼ 33 mmol C m −2 d −1 . G net was strongly related to P net , which drove a hysteresis pattern in the relationship between G net and aragonite saturation state (Ω ar ). Although P net was the main driver of G net , Ω ar was still an important factor, where 95% of the variance in G net could be described by P net and Ω ar . Based on the observed in situ relationship, G net would be expected to reach zero when Ω ar is ∼ 2.5. It is unknown what proportion of a decline in G net would be through reduced calcification and what would occur through increased dissolution, but the results here support predictions that overall calcium carbonate production will decline in coral reefs as a result of ocean acidification.

Filling the ‘white ribbon’ – a multisource seamless digital elevation model for Lizard Island, northern Great Barrier Reef - Javier X. Leon; Stuart R. Phinn; Sarah Hamylton; Megan I. Saunders (International Journal of Remote Sensing, 2013)

Mapping coral reefs at reef to reef-system scales, 10s–1000s km 2 , using object-based image analysis - Chris Roelfsema; Stuart Phinn; Stacy Jupiter; James Comley; Simon Albert (International Journal of Remote Sensing, 2013)

Coastal retreat and improved water quality mitigate losses of seagrass from sea level rise - Megan I. Saunders; Javier Leon; Stuart R. Phinn; David P. Callaghan; Katherine R. O'Brien; Chris M. Roelfsema; Catherine E. Lovelock; Mitchell B. Lyons; Peter J. Mumby (Global Change Biology, 2013)
The distribution and abundance of seagrass ecosystems could change significantly over the coming century due to sea level rise ( SLR ). Coastal managers require mechanistic understanding of the processes affecting seagrass response to SLR to maximize their conservation and associated provision of ecosystem services. In Moreton Bay, Queensland, Australia, vast seagrass meadows supporting populations of sea turtles and dugongs are juxtaposed with the multiple stressors associated with a large and rapidly expanding human population. Here, the interactive effects of predicted SLR , changes in water clarity, and land use on future distributions of seagrass in Moreton Bay were quantified. A habitat distribution model of present day seagrass in relation to benthic irradiance and wave height was developed which correctly classified habitats in 83% of cases. Spatial predictions of seagrass and presence derived from the model and bathymetric data were used to initiate a SLR inundation model. Bathymetry was iteratively modified based on SLR and sedimentary accretion in seagrass to simulate potential seagrass habitat at 10 year time steps until 2100. The area of seagrass habitat was predicted to decline by 17% by 2100 under a scenario of SLR of 1.1 m. A scenario including the removal of impervious surfaces, such as roads and houses, from newly inundated regions, demonstrated that managed retreat of the shoreline could potentially reduce the overall decline in seagrass habitat to just 5%. The predicted reduction in area of seagrass habitat could be offset by an improvement in water clarity of 30%. Greater improvements in water clarity would be necessary for larger magnitudes of SLR . Management to improve water quality will provide present and future benefits to seagrasses under climate change and should be a priority for managers seeking to compensate for the effects of global change on these valuable habitats.

Observations of Surface Energy Fluxes and Boundary-Layer Structure Over Heron Reef, Great Barrier Reef, Australia - Mellissa C. MacKellar; Hamish A. McGowan; Stuart R. Phinn; Joshua S. Soderholm (Boundary-Layer Meteorology, 2013)

Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef, Australia - Ian Leiper; Stuart Phinn; Arnold G. Dekker (International Journal of Remote Sensing, 2012)

Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs - Stuart R. Phinn; Chris M. Roelfsema; Peter J. Mumby (International Journal of Remote Sensing, 2012)

Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland, Australia - Santosh Bhandari; Stuart Phinn; Tony Gill (International Journal of Remote Sensing, 2011)

The specific inherent optical properties of three sub-tropical and tropical water reservoirs in Queensland, Australia - Glenn Campbell; Stuart R. Phinn; Paul Daniel (Hydrobiologia, 2011)

Natural and anthropogenic changes to mangrove distributions in the Pioneer River Estuary (QLD, Australia) - Stacy D. Jupiter; Donald C. Potts; Stuart R. Phinn; Norman C. Duke (Wetlands Ecology and Management, 2007)

Characterizing a tropical deforestation wave: a dynamic spatial analysis of a deforestation hotspot in the Colombian Amazon - ANDRES ETTER; CLIVE MCALPINE; STUART PHINN; DAVID PULLAR; HUGH POSSINGHAM (Global Change Biology, 2006)
Tropical deforestation is the major contemporary threat to global biodiversity, because a diminishing extent of tropical forests supports the majority of the Earth's biodiversity. Forest clearing is often spatially concentrated in regions where human land use pressures, either planned or unplanned, increase the likelihood of deforestation. However, it is not a random process, but often moves in waves originating from settled areas. We investigate the spatial dynamics of land cover change in a tropical deforestation hotspot in the Colombian Amazon. We apply a forest cover zoning approach which permitted: calculation of colonization speed; comparative spatial analysis of patterns of deforestation and regeneration; analysis of spatial patterns of mature and recently regenerated forests; and the identification of local‐level hotspots experiencing the fastest deforestation or regeneration. The colonization frontline moved at an average of 0.84 km yr −1 from 1989 to 2002, resulting in the clearing of 3400 ha yr −1 of forests beyond the 90% forest cover line. The dynamics of forest clearing varied across the colonization front according to the amount of forest in the landscape, but was spatially concentrated in well‐defined ‘local hotspots’ of deforestation and forest regeneration. Behind the deforestation front, the transformed landscape mosaic is composed of cropping and grazing lands interspersed with mature forest fragments and patches of recently regenerated forests. We discuss the implications of the patterns of forest loss and fragmentation for biodiversity conservation within a framework of dynamic conservation planning.

Remotely Sensed Data for Ecosystem Analyses: Combining Hierarchy Theory and Scene Models - Stuart R. PHINN; DOUGLAS A. STOW; JANET FRANKLIN; LEAL A. K. MERTES; JOEL MICHAELSEN (Environmental Management, 2003)

Hyperspectral analysis of chlorophyll content and photosynthetic capacity of coral reef substrates - Karen E. Joyce; Stuart R. Phinn (Limnology and Oceanography, 2003)
Few studies have assessed the biophysical properties controlling reflection and absorption of light in coral reef environments and their relationships with quantitative measures of reef health and productivity. The present article examines the relationship between spectral reflectance, photosynthetic capacity, and chlorophyll a from common coral reef substrates. Reflectance readings of several targets (massive corals Montipora sp., n = 49, and Porites sp., n = 80; macroalgae Chlorodesmis sp., n = 24; and sediment interspersed with benthic microalgae, n = 35) were obtained in situ on Heron Reef, southern Great Barrier Reef (23°27'S, 151°55'E). Measurements of photosynthetic capacity and chlorophyll content were acquired simultaneously. Linear correlations were examined between spectral reflectance at all wavelengths and both photosynthetic capacity and pigment content (Chl a ). Reflectance plots for all targets exhibited an absorption feature centered at 675 nm, and spectral reflectance at this wavelength decreased with increasing Chl a levels. The strength of this correlation varied between features, being highest for Porites sp. and lowest for sediment, highlighting the complexities of coral reef environments and the difficulties associated with relating spectral reflectance to biophysical properties. Photosynthetic capacity did not exhibit statistically significant correlations to spectral reflectance or absorption at any wavelength. Our results demonstrate the capabilities and difficulties associated with field scale hyperspectral data for measuring select biophysical properties of coral reefs and the need for assessment of the capabilities of airborne and satellite imaging sensors for similar purposes.