Reef Cover, a coral reef classification for global habitat mapping from remote sensing.

Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale reef mapping to date are typically poorly defined, not shared or region-specific, limiting end-users' ability to interpret outputs. Here we present Reef Cover, a coral reef geomorphic zone classification, developed to support both producers and end-users of global-scale coral reef habitat maps, in a transparent and version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge of reef form and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. Two case studies demonstrate application of the Reef Cover classification scheme and its scientific and conservation benefits: 1) detailed mapping of the Cairns Management Region of the Great Barrier Reef to support management and 2) mapping of the Caroline and Mariana Island chains in the Pacific for conservation purposes.

Benthic and coral reef community field data for Heron Reef, Southern Great Barrier Reef, Australia, 2002-2018.

This paper describes benthic coral reef community composition point-based field data sets derived from georeferenced photoquadrats using machine learning. Annually over a 17 year period (2002-2018), data were collected using downward-looking photoquadrats that capture an approximately 1 m2 footprint along 100 m-1500 m transect surveys distributed along the reef slope and across the reef flat of Heron Reef (28 km2), Southern Great Barrier Reef, Australia. Benthic community composition for the photoquadrats was automatically interpreted through deep learning, following initial manual calibration of the algorithm. The resulting data sets support understanding of coral reef biology, ecology, mapping and dynamics. Similar methods to derive the benthic data have been published for seagrass habitats, however here we have adapted the methods for application to coral reef habitats, with the integration of automatic photoquadrat analysis. The approach presented is globally applicable for various submerged and benthic community ecological applications, and provides the basis for further studies at this site, regional to global comparative studies, and for the design of similar monitoring programs elsewhere.

Global protected-area coverage and human pressure on tidal flats.

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 (HMc ) (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 (HMc > 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 (HMc > 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.

Cobertura Mundial de Áreas Protegidas y la Presión Humana sobre las Planicies Mareales Resumen Las planicies mareales son un ecosistema costero con distribución global e importancia para el mantenimiento de la biodiversidad y los servicios ambientales. Existen estudios, desde locales hasta continentales, que han documentado la pérdida acelerada del hábitat mareal causado por el impacto humano, aunque las evaluaciones sobre el progreso en su conservación son muy pocas. Ahora que está disponible una estimación internamente coherente de la distribución y el cambio, basado en las imágenes satelitales de Landsat, de las planicies mareales del mundo, examinamos la representación de estas planicies dentro de las áreas protegidas (APs) y la presión humana sobre las mismas. Determinamos la representación de las planicies mareales y su cambio neto dentro de las APs mediante la superposición espacial de los mapas de las planicies mareales y la Base de Datos Mundial de Áreas Protegidas. De manera similar, superpusimos el mapa más reciente de la distribución de las planicies mareales (2014-2016) en el mapa de modificaciones humanas (MH) (abarca desde 0, ninguna presión humana, hasta 1, presión humana muy alta) para estimar la presión humana ejercida sobre este ecosistema. El 68% de la extensión actual de las planicies mareales está sujeta a una presión humana desde moderada hasta muy alta (MH > 0.1), aunque el 31% de la extensión de las planicies mareales se encuentra dentro de las APs, lo que excede por mucho el porcentaje de protección de los dominios marino (6%) y terrestre (13%). El cambio neto de la extensión de las planicies mareales dentro de las APs fue similar al cambio neto de las planicies fuera de las APs entre 1999 y 2016. La insuficiencia sustancial de la protección de las planicies mareales ocurrió en Asia, en donde grandes extensiones intermareales coincidieron con una presión humana alta y muy alta (MH > 0.4-1.0) y la pérdida neta de planicies mareales de hasta 86.4 km² (95% IC 83.9-89.0) ocurrió dentro de una sola AP durante el periodo de estudio. Si se consideran en conjunto, nuestros resultados muestran un progreso importante en la designación de AP para las planicies mareales a nivel mundial, aunque el solo estado de AP no previene la pérdida de hábitat. Salvaguardar las planicies mareales del planeta por lo tanto requerirá de un entendimiento más profundo de los factores que rigen sobre sus dinámicas y de políticas efectivas que promuevan estrategias holísticas de manejo costero y de captación.

Mapping precipitation-corrected NDVI trends across Namibia.

Savannas comprise a major component of the Earth system and contribute ecosystem services and functions essential to human livelihoods. Monitoring spatial and temporal trends in savanna vegetation and understanding change drivers is therefore crucial. Widespread greening has been identified across southern Africa; yet its drivers and manifestations on the ground remain ambiguous. This study removes the effects of precipitation on an NDVI time-series, thereby identifying trends not driven by rainfall. It utilizes the significant correlation between vegetation and precipitation as captured using MODIS and rainfall estimates. A linear regression between variables was used to derive its residual (corrected) time-series, and the rate and spatial extent of trends were evaluated in relation to biomes. A random sample-based qualitative interpretation of high spatial resolution imagery was then used to evaluate the nature of the trend on the ground. 23.25% of the country, including all biomes exhibited positive trends. We propose that greening may be related to a reduction in woody species richness, loss of the large trees and a shift towards drought tolerant shrub species, as has been shown in other sub-Saharan environments. 3.23% of the country exhibited negative trends, which were mostly associated with more humid (forested) regions pointing to deforestation as a cause; these manifested as vegetation clearing, identifiable using high resolution multi-temporal imagery. Greening trends could not be identified using this approach; instead, they point to the occurrence of gradual vegetation change caused by indirect drivers.

The global distribution and trajectory of tidal flats.

Increasing human populations around the global coastline have caused extensive loss, degradation and fragmentation of coastal ecosystems, threatening the delivery of important ecosystem services1. As a result, alarming losses of mangrove, coral reef, seagrass, kelp forest and coastal marsh ecosystems have occurred1-6. However, owing to the difficulty of mapping intertidal areas globally, the distribution and status of tidal flats-one of the most extensive coastal ecosystems-remain unknown7. Here we present an analysis of over 700,000 satellite images that maps the global extent of and change in tidal flats over the course of 33 years (1984-2016). We find that tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation7, occupy at least 127,921 km2 (124,286-131,821 km2, 95% confidence interval). About 70% of the global extent of tidal flats is found in three continents (Asia (44% of total), North America (15.5% of total) and South America (11% of total)), with 49.2% being concentrated in just eight countries (Indonesia, China, Australia, the United States, Canada, India, Brazil and Myanmar). For regions with sufficient data to develop a consistent multi-decadal time series-which included East Asia, the Middle East and North America-we estimate that 16.02% (15.62-16.47%, 95% confidence interval) of tidal flats were lost between 1984 and 2016. Extensive degradation from coastal development1, reduced sediment delivery from major rivers8,9, sinking of riverine deltas8,10, increased coastal erosion and sea-level rise11 signal a continuing negative trajectory for tidal flat ecosystems around the world. Our high-spatial-resolution dataset delivers global maps of tidal flats, which substantially advances our understanding of the distribution, trajectory and status of these poorly known coastal ecosystems.

Seagrass morphometrics at species level in Moreton Bay, Australia from 2012 to 2013.

Seagrass above, below and total biomass, density and leaf area, length and width were quantified at a species level for 122 sites over three sampling periods in Moreton Bay, Australia. Core samples were collected in two regions: (1) a high water quality region with varying species assemblages and canopy complexity (98 sites); and (2) along a turbidity gradient in the bay (24 sites within four locations). Core samples were collected using a 15 cm diameter×20 cm long corer. Seagrass dry biomass per component was quantified per species present in each sample. A total of 220 biomass and density data records are included, 130 from the high water quality region and 90 from the turbidity gradient. These data provide a detailed assessment of biomass, density and leaf metrics per species sampled from Moreton Bay over 2012-2013. In future, these can be used as a baseline to assess seasonal and spatial variation within the bay, within the region and among regions.

Field data sets for seagrass biophysical properties for the Eastern Banks, Moreton Bay, Australia, 2004-2014.

This paper describes seagrass species and percentage cover point-based field data sets derived from georeferenced photo transects. Annually or biannually over a ten year period (2004-2014) data sets were collected using 30-50 transects, 500-800 m in length distributed across a 142 km(2) shallow, clear water seagrass habitat, the Eastern Banks, Moreton Bay, Australia. Each of the eight data sets include seagrass property information derived from approximately 3000 georeferenced, downward looking photographs captured at 2-4 m intervals along the transects. Photographs were manually interpreted to estimate seagrass species composition and percentage cover (Coral Point Count excel; CPCe). Understanding seagrass biology, ecology and dynamics for scientific and management purposes requires point-based data on species composition and cover. This data set, and the methods used to derive it are a globally unique example for seagrass ecological applications. It provides the basis for multiple further studies at this site, regional to global comparative studies, and, for the design of similar monitoring programs elsewhere.

Comparability of slack water and Lagrangian flow respirometry methods for community metabolic measurements.

Coral reef calcification is predicted to decline as a result of ocean acidification and other anthropogenic stressors. The majority of studies predicting declines based on in situ relationships between environmental parameters and net community calcification rate have been location-specific, preventing accurate predictions for coral reefs globally. In this study, net community calcification and production were measured on a coral reef flat at One Tree Island, Great Barrier Reef, using Lagrangian flow respirometry and slack water methods. Net community calcification, daytime net photosynthesis and nighttime respiration were higher under the flow respirometry method, likely due to increased water flow relative to the slack water method. The two methods also varied in the degrees to which they were influenced by potential measurement uncertainties. The difference in the results from these two commonly used methods implies that some of the location-specific differences in coral reef community metabolism may be due to differences in measurement methods.

Incorporating DEM uncertainty in coastal inundation mapping.

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

Disentangling how landscape spatial and temporal heterogeneity affects Savanna birds.

In highly seasonal tropical environments, temporal changes in habitat and resources are a significant determinant of the spatial distribution of species. This study disentangles the effects of spatial and mid to long-term temporal heterogeneity in habitat on the diversity and abundance of savanna birds by testing four competing conceptual models of varying complexity. Focussing on sites in northeast Australia over a 20 year time period, we used ground cover and foliage projected cover surfaces derived from a time series of Landsat Thematic Mapper imagery, rainfall data and site-level vegetation surveys to derive measures of habitat structure at local (1-100 ha) and landscape (100-1000s ha) scales. We used generalised linear models and an information theoretic approach to test the independent effects of spatial and temporal influences on savanna bird diversity and the abundance of eight species with different life-history behaviours. Of four competing models defining influences on assemblages of savanna birds, the most parsimonious included temporal and spatial variability in vegetation cover and site-scale vegetation structure, suggesting savanna bird species respond to spatial and temporal habitat heterogeneity at both the broader landscape scale and at the fine-scale. The relative weight, strength and direction of the explanatory variables changed with each of the eight species, reflecting their different ecology and behavioural traits. This study demonstrates that variations in the spatial pattern of savanna vegetation over periods of 10 to 20 years at the local and landscape scale strongly affect bird diversity and abundance. Thus, it is essential to monitor and manage both spatial and temporal variability in avian habitat to achieve long-term biodiversity outcomes.

Coastal retreat and improved water quality mitigate losses of seagrass from sea level rise.

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.

Remotely sensed data for ecosystem analyses: combining hierarchy theory and scene models.

Remotely sensed data have been used extensively for environmental monitoring and modeling at a number of spatial scales; however, a limited range of satellite imaging systems often constrained the scales of these analyses. A wider variety of data sets is now available, allowing image data to be selected to match the scale of environmental structure(s) or process(es) being examined. A framework is presented for use by environmental scientists and managers, enabling their spatial data collection needs to be linked to a suitable form of remotely sensed data. A six-step approach is used, combining image spatial analysis and scaling tools, within the context of hierarchy theory. The main steps involved are: (1) identification of information requirements for the monitoring or management problem; (2) development of ideal image dimensions (scene model), (3) exploratory analysis of existing remotely sensed data using scaling techniques, (4) selection and evaluation of suitable remotely sensed data based on the scene model, (5) selection of suitable spatial analytic techniques to meet information requirements, and (6) cost-benefit analysis. Results from a case study show that the framework provided an objective mechanism to identify relevant aspects of the monitoring problem and environmental characteristics for selecting remotely sensed data and analysis techniques.