Javier X Leon

Interactions between sea-level rise and wave exposure on reef island dynamics in the Solomon Islands

Low-lying reef islands in the Solomon Islands provide a valuable window into the future impacts of global sea-level rise. Sea-level rise has been predicted to cause widespread erosion and inundation of low-lying atolls in the central Pacific. However, the limited research on reef islands in the western Pacific indicates the majority of shoreline changes and inundation to date result from extreme events, seawalls and inappropriate development rather than sea-level rise alone. Here, we present the first analysis of coastal dynamics from a sea-level rise hotspot in the Solomon Islands. Using time series aerial and satellite imagery from 1947 to 2014 of 33 islands, along with historical insight from local knowledge, we have identified five vegetated reef islands that have vanished over this time period and a further six islands experiencing severe shoreline recession. Shoreline recession at two sites has destroyed villages that have existed since at least 1935, leading to community relocations. Rates of shoreline recession are substantially higher in areas exposed to high wave energy, indicating a synergistic interaction between sea-level rise and waves. Understanding these local factors that increase the susceptibility of islands to coastal erosion is critical to guide adaptation responses for these remote Pacific communities.

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.

Filling the ‘white ribbon’ – a multisource seamless digital elevation model for Lizard Island, northern Great Barrier Reef

Hydrographers have traditionally referred to the nearshore area as the ‘white ribbon’ area due to the challenges associated with the collection of elevation data (elevation hereafter refers to both topography and bathymetry) in this highly dynamic transitional zone between terrestrial and marine environments. Accordingly, available information in this zone is typically characterized by a range of data sets from disparate sources. In this article, we propose a framework to fill the white ribbon area of a coral reef system by integrating multiple elevation data sets acquired by a suite of remote-sensing technologies into a seamless digital elevation model (DEM). A range of data sets are integrated, including field-collected global positioning system (GPS) elevation points, topographic and bathymetric light detecting and ranging (lidar), single and multibeam echosoundings, nautical charts, and bathymetry derived from optical remote-sensing imagery. The proposed framework ranks data reliability internally, thereby avoiding the requirements to quantify absolute error and results in a high-resolution, seamless product. Nested within this approach is an effective spatially explicit technique for improving the accuracy of bathymetry estimates derived empirically from optical satellite imagery through modelling the spatial structure of residuals. The approach was applied to data collected on and around Lizard Island in northern Australia. Collectively, the framework holds promise for filling the white ribbon zone in coastal areas characterized by similar data availability scenarios.

Improving the synoptic mapping of coral reef geomorphology using object-based image analysis

Monitoring coral reefs is of great importance for environmental management of these ecosystems. The use of remote sensing and geographical information systems enables rapid and effective mapping of the geomorphology of reefs that can be used as a basis for biodiversity and habitat assessments. However, pixel-based approaches have not been appropriate for detailed mapping of such complex systems. An object-based image analysis (OBIA) approach was used in this study to map intra-reef geomorphology of coral reefs across the Torres Strait region using Landsat ETM+ imagery. By combining image analysis techniques and a non-parametric neural network classifier and incorporating additional spatial information such as context, shape and texture, the accuracy of the segmentation and classification was improved considerably. A large-scale synoptic map of 10 geomorphological classes was produced for Torres Strait with an overall accuracy of 75%. The OBIA approach employed in this research has enabled the geomorphology of reef platforms to be mapped for the first time at such accuracy and descriptive resolution.

Supporting local and traditional knowledge with science for adaptation to climate change: lessons learned from participatory three-dimensional modeling in BoeBoe, Solomon Islands

Coastal communities in the Coral Triangle are increasingly threatened by climate change. Sea-level rise (SLR) will result in biophysical and socioeconomic impacts that could increase the loss of livelihoods, cultural heritage and infrastructure. Effective adaptation requires a holistic approach that incorporates scientific knowledge together with local and traditional knowledge. Community-based adaptation built on local knowledge is of great value for environmental management, particularly when scientific data are lacking. This article reports a case study that integrated traditional and scientific knowledge using participatory three-dimensional modeling (P3DM) in BoeBoe village, Solomon Islands. P3DM is a process by which members of the local community build a physical terrain model and overlay it with the location of important resources such as protected areas or harvesting sites. Additionally, SLR inundation scenarios based on surveyed elevations were incorporated into a geographic information system (GIS), allowing for a real-time integration of science with local knowledge. Despite discrepancies in scales and accuracy, information from both the P3DM and GIS were complementary. The process, itself, provided a forum for discussion between many members of the village who would normally not be involved and highlighted the importance of community engagement when building capacity for adaptation to climate change.

Spatial and temporal variability of seagrass at Lizard Island, Great Barrier Reef

Abstract Increasing threats to natural ecosystems from local and global stressors are reinforcing the need for baseline data on the distribution and abundance of organisms. We quantified spatial and/or temporal patterns of seagrass distribution, shoot density, leaf area index, biomass, productivity, and sediment carbon content in shallow water (0–5 m) at Lizard Island, Great Barrier Reef, Australia, in field surveys conducted in December 2011 and October 2012. Seagrass meadows were mapped using satellite imagery and field validation. A total of 18.3 ha of seagrass, composed primarily of Thalassia hemprichii and Halodule uninervis, was mapped in shallow water. This was 46% less than the area of seagrass in the same region reported in 1995, although variations in mapping methods may have influenced the magnitude of change detected. There was inter-annual variability in shoot density and length, with values for both higher in 2011 than in 2012. Seagrass properties and sediment carbon content were representative of shallow-water seagrass meadows on a mid-shelf Great Barrier Reef island. The data can be used to evaluate change, to parameterize models of the impact of anthropogenic or environmental variability on seagrass distribution and abundance, and to assess the success of management actions.

Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment

Structure from Motion with Multi-View Stereo photogrammetry (SfM-MVS) is increasingly used in geoscience investigations, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via Unmanned Aerial Vehicle, ‘UAV’) and ground-based (via handheld digital camera, ‘ground’) SfM-MVS in modelling hillslope gully systems in a dry-tropical savanna, and to assess the strengths and limitations of the approach at a hillslope scale and an individual gully scale. UAV surveys covered three separate hillslope gully systems (with areas of 0.412–0.715 km2), while ground surveys assessed individual gullies within the broader systems (with areas of 350–750 m2). SfM-MVS topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate that UAV SfM-MVS can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., approximately 0.1 m resolution with 0.4–1.2 m elevation error), while ground-based SfM-MVS is more capable of quantifying gully morphology (e.g., approximately 0.01 m resolution with 0.04–0.1 m elevation error). Despite difficulties in reconstructing vegetated surfaces, uncertainty as to optimal survey and processing designs, and high computational demands, this study has demonstrated great potential for SfM-MVS to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in savanna landscapes and elsewhere.

Marine turtles are not fussy nesters: a novel test of small-scale nest site selection using structure from motion beach terrain information

Background Nest selection is widely regarded as a key process determining the fitness of individuals and viability of animal populations. For marine turtles that nest on beaches, this is particularly pivotal as the nesting environment can significantly control reproductive success.The aim of this study was to identify the environmental attributes of beaches (i.e., morphology, vegetation, urbanisation) that may be associated with successful oviposition in green and loggerhead turtle nests. Methods We quantified the proximity of turtle nests (and surrounding beach locations) to urban areas, measured their exposure to artificial light, and used ultra-high resolution (cm-scale) digital surface models derived from Structure-from-Motion (SfM) algorithms, to characterise geomorphic and vegetation features of beaches on the Sunshine Coast, eastern Australia. Results At small spatial scales (i.e., <100 m), we found no evidence that turtles selected nest sites based on a particular suite of environmental attributes (i.e., the attributes of nest sites were not consistently different from those of surrounding beach locations). Nest sites were, however, typically characterised by occurring close to vegetation, on parts of the shore where the beach- and dune-face was concave and not highly rugged, and in areas with moderate exposure to artificial light. Conclusion This study used a novel empirical approach to identify the attributes of turtle nest sites from a broader ‘envelope’ of environmental nest traits, and is the first step towards optimizing conservation actions to mitigate, at the local scale, present and emerging human impacts on turtle nesting beaches.

Benthic and substrate cover data derived from photo-transect surveys in Lizard Island, Great Barrier Reef conducted on October 3-7, 2012

Underwater georeferenced photo-transect surveys were conducted on October 3-7, 2012 at various sections of the reef and lagoon at Lizard Island, Great Barrier Reef. For this survey a snorkeler swam while taking photos of the benthos at a set distance from the benthos using a standard digital camera and towing a GPS in a surface float which logged the track every five seconds. A Canon G12 digital camera was placed in a Canon underwater housing and photos were taken at 1 m height above the benthos. Horizontal distance between photos was estimated by three fin kicks of the survey snorkeler, which corresponded to a surface distance of approximately 2.0 - 4.0 m. The GPS was placed in a dry bag and logged the position at the surface while being towed by the photographer (Roelfsema, 2009). A total of 1,265 benthic photos were taken. Approximation of coordinates of each benthic photo was conducted based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the GPS coordinates that were logged at a set time before and after the photo was captured. Benthic or substrate cover data was derived from each photo by randomly placing 24 points over each image using the Coral Point Count for Microsoft Excel program (Kohler and Gill, 2006). Each point was then assigned to 1 of 79 cover types, which represented the benthic feature beneath it. Benthic cover composition summary of each photo scores was generated automatically using CPCE program. The resulting benthic cover data of each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.

Benthic and substrate cover data derived from photo-transect surveys in Lizard Island Reef conducted on December 10-15, 2011

Underwater georeferenced photo-transect surveys were conducted on December 10-15, 2011 at various sections of the reef at Lizard Island, Great Barrier Reef. For this survey a snorkeler or diver swam over the bottom while taking photos of the benthos at a set height using a standard digital camera and towing a GPS in a surface float which logged the track every five seconds. A standard digital compact camera was placed in an underwater housing and fitted with a 16 mm lens which provided a 1.0 m x 1.0 m footprint, at 0.5 m height above the benthos. Horizontal distance between photos was estimated by three fin kicks of the survey diver/snorkeler, which corresponded to a surface distance of approximately 2.0 - 4.0 m. The GPS was placed in a dry-bag and logged the position as it floated at the surface while being towed by the photographer. A total of 5,735 benthic photos were taken. A floating GPS setup connected to the swimmer/diver by a line enabled recording of coordinates of each benthic photo (Roelfsema 2009). Approximation of coordinates of each benthic photo was conducted based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the GPS coordinates that were logged at a set time before and after the photo was captured. Benthic or substrate cover data was derived from each photo by randomly placing 24 points over each image using the Coral Point Count for Microsoft Excel program (Kohler and Gill, 2006). Each point was then assigned to 1 of 78 cover types, which represented the benthic feature beneath it. Benthic cover composition summary of each photo scores was generated automatically using CPCE program. The resulting benthic cover data of each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.