Mitchell Lyons

Bringing an ecological view of change to Landsat-based remote sensing

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, longterm trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007

Shallow coastal ecosystems are the interface between the terrestrial and marine environment. The physical and biological composition and distribution of benthic habitats within these ecosystems determines their contribution to ecosystem services and biodiversity as well as their connections to neighbouring terrestrial and marine ecosystem processes. The capacity to accurately and consistently map and monitor these benthic habitats is critical to developing and implementing management applications. This paper presents a method for integrating field survey data and high spatial resolution, multi-spectral satellite image data to map bathymetry and seagrass in shallow coastal waters. Using Quickbird 2 satellite images from 2004 and 2007, acoustic field survey data were used to map bathymetry using a linear and ratio algorithm method; benthic survey field data were used to calibrate and validate classifications of seagrass percentage cover and seagrass species composition; and a change detection analysis of seagrass cover was performed. The bathymetry mapping showed that only the linear algorithm could effectively and accurately predict water depth; overall benthic map accuracies ranged from 57–95%; and the change detection produced a reliable change map and showed a net decrease in seagrass cover levels, but the majority of the study area showed no change in seagrass cover level. This study demonstrates that multiple spatial products (bathymetry, seagrass and change maps) can be produced from single satellite images and a concurrent field survey dataset. Moreover, the products were produced at higher spatial resolution and accuracy levels than previous studies in Moreton Bay. The methods are developed from previous work in the study area and are continuing to be implemented, as well as being developed to be repeatable in similar shallow coastal water environments.

Unravelling complexity in seagrass systems for management: Australia as a microcosm

Environmental decision-making applies transdisciplinary knowledge to deliver optimal outcomes. Here we synthesise various aspects of seagrass ecology to aid environmental decision-making, management and policy. Managers often mediate conflicting values and opinions held by different stakeholders. Critical to this role is understanding the drivers for change, effects of management actions and societal benefits. We use the diversity of seagrass habitats in Australia to demonstrate that knowledge from numerous fields is required to understand seagrass condition and resilience. Managers are often time poor and need access to synthesised assessments, commonly referred to as narratives. However, there is no single narrative for management of seagrass habitats in Australia, due to the diversity of seagrass meadows and dominant pressures. To assist the manager, we developed a classification structure based on attributes of seagrass life history, habitat and meadow form. Seagrass communities are formed from species whose life history strategies can be described as colonising, opportunistic or persistent. They occupy habitats defined by the range and variability of their abiotic environment. This results in seagrass meadows that are either transitory or enduring. Transitory meadows may come and go and able to re-establish from complete loss through sexual reproduction. Enduring meadows may fluctuate in biomass but maintain a presence by resisting pressures across multiple scales. This contrast reflects the interaction between the spatial and temporal aspects of species life history and habitat variability. Most management and monitoring strategies in place today favour enduring seagrasses. We adopt a functional classification of seagrass habitats based on modes of resilience to inform management for all seagrass communities. These concepts have world-wide relevance as the Australian case-studies have many analogues throughout the world. Additionally, the approach used to classify primary scientific knowledge into synthesised categories to aid management has value for many other disciplines interfacing with environmental decision-making.

Mapping fish community variables by Integrating field and satellite data, object-based image analysis and modeling in a traditional Fijian fisheries management area

Abstract: The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km 2 ) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.

Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data

In the face of increasing climate-related impacts on coral reefs, the integration of ecosystem resilience into marine conservation planning has become a priority. One strategy, including resilient areas in marine protected area (MPA) networks, relies on information on the spatial distribution of resilience. We assess the ability to model and map six indicators of coral reef resilience—stress-tolerant coral taxa, coral generic diversity, fish herbivore biomass, fish herbivore functional group richness, density of juvenile corals and the cover of live coral and crustose coralline algae. We use high spatial resolution satellite data to derive environmental predictors and use these in random forest models, with field observations, to predict resilience indicator values at unsampled locations. Predictions are compared with those obtained from universal kriging and from a baseline model. Prediction errors are estimated using cross-validation, and the ability to map each resilience indicator is quantified as the percentage reduction in prediction error compared to the baseline model. Results are most promising (percentage reduction = 18.3%) for mapping the cover of live coral and crustose coralline algae and least promising (percentage reduction = 0%) for coral diversity. Our study has demonstrated one approach to map indicators of coral reef resilience. In the context of MPA network planning, the potential to consider reef resilience in addition to habitat and feature representation in decision-support software now exists, allowing planners to integrate aspects of reef resilience in MPA network development.

The role of satellite remote sensing in structured ecosystem risk assessments

The current set of global conservation targets requires methods for monitoring the changing status of ecosystems. Protocols for ecosystem risk assessment are uniquely suited to this task, providing objective syntheses of a wide range of data to estimate the likelihood of ecosystem collapse. Satellite remote sensing can deliver ecologically relevant, long-term datasets suitable for analysing changes in ecosystem area, structure and function at temporal and spatial scales relevant to risk assessment protocols. However, there is considerable uncertainty about how to select and effectively utilise remotely sensed variables for risk assessment. Here, we review the use of satellite remote sensing for assessing spatial and functional changes of ecosystems, with the aim of providing guidance on the use of these data in ecosystem risk assessment. We suggest that decisions on the use of satellite remote sensing should be made a priori and deductively with the assistance of conceptual ecosystem models that identify the primary indicators representing the dynamics of a focal ecosystem.

Integrating field survey data with satellite image data to improve shallow water seagrass maps: the role of AUV and snorkeller surveys?

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow (<5 m) seagrass habitats, these maps can be created by integrating high spatial resolution imagery with field survey data. Field survey data for seagrass are often collected via snorkelling or diving. However, these methods are limited by environmental and safety considerations. Autonomous underwater vehicles (AUVs) are used increasingly to collect field data for habitat mapping, albeit mostly in deeper waters (>20 m). Here, we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (<5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photographs of the seagrass were collected along transects via snorkelling or an AUV. Photographs from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object-based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller-collected field data-sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison with snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5% of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programmes.

Seagrass ecosystem trajectory depends on the relative timescales of resistance, recovery and disturbance

Seagrass ecosystems are inherently dynamic, responding to environmental change across a range of scales. Habitat requirements of seagrass are well defined, but less is known about their ability to resist disturbance. Specific means of recovery after loss are particularly difficult to quantify. Here we assess the resistance and recovery capacity of 12 seagrass genera. We document four classic trajectories of degradation and recovery for seagrass ecosystems, illustrated with examples from around the world. Recovery can be rapid once conditions improve, but seagrass absence at landscape scales may persist for many decades, perpetuated by feedbacks and/or lack of seed or plant propagules to initiate recovery. It can be difficult to distinguish between slow recovery, recalcitrant degradation, and the need for a window of opportunity to trigger recovery. We propose a framework synthesizing how the spatial and temporal scales of both disturbance and seagrass response affect ecosystem trajectory and hence resilience.

Long term monitoring of seagrass distribution in Moreton Bay, Australia, from 1972–2010 using Landsat MSS, TM, ETM+

Seagrass ecosystems are well studied and seagrass is recognised as a vital contributor to overall ecosystem health and productivity. However, a significant gap in knowledge exists in terms of the large scale temporal and spatial dynamics of cover level and distribution of seagrass communities. Remotely sensed satellite imagery offers a means to map seagrass cover and distribution over large temporal and spatial scales. At present, no operational methods have been produced to map seagrass on large spatio-temporal scales (> 100km2). This study presents a combined per-pixel/object-based method to rapidly map seagrass cover and distribution from a full Landsat archive, from 1972–2010 (MSS, TM and ETM+), with no in-situ data and at accuracies as good or better than existing mapping methods. The products provide management agencies with a baseline assessment as well as the capacity to continue to map seagrass distribution and predict changes in the future.

Assessing the reliability of avian biodiversity measures of urban greenspaces using eBird citizen science data

Assessing the reliability of avian biodiversity measures of urban greenspaces using eBird citizen science data Corey T. Callaghan , Mitchell B. Lyons , John M. Martin , Richard E. Major 3,4 and Richard T. Kingsford 1 Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, UNSW, Sydney, Australia, Royal Botanic Gardens and Domain Trust, Sydney, Australia, Australian Museum Research Institute, Australian Museum, Sydney, Australia, Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, Sydney, Australia