Turning environmental monitoring into management


The current paradigm for earth observation systems involves data collection to monitor ecological/ environment systems with data analysis informing decision makers on actions that may deliver certain outcomes. Moving to a management focused approach requires access to a wider range of data with better data governance, coupled with advanced analytics/machine learning techniques for sense-making and greater use of spatial digital twins. The key is to develop phenomena-specific systems purposely designed to respond to (societal, environmental and economic) pressures to produce the highly valuable information products that end users ideally want, rather than just creating more low value data. This will enable the introduction of prognostic/ predictive capabilities supporting better, more timely decision making and intervention. It includes identifying what technology developments and investments is required to enable this evolved approach. Critical priority issues include responding to the effects of global climate change, urbanization, population growth and building a circular economy model (including reducing system wastage).

Fundamental and essential environmental information is required for citizens and communities to make decisions. One example is monitoring beach water quality which is affected by a number of factors including storm water run-off. Monitoring the water quality of vulnerable beaches improves government and community planning and decision-making processes.

To turn environmental monitoring into environmental management and help with the decision- making process, a multidisciplinary data collaboration is needed to collect and integrate diverse types of datasets from many organisations, often where data is acquired and used in silos with respect to data collection, management, analysis and dissemination. The Victorian State of the Environment 2018 Report, which is an environmental report card that measures the health of Victoria’s environment, has identified a similar trend. The Report has recommended that the Victorian government develops their spatial information capability and database to inform decision-making across the environment portfolio. Similarly, in the Australian Space Agency report on the role of space-based earth observations to support planning, response and recovery from bushfires, they have also identified the need for an easy-to-use directory of satellite imagery for use by all stakeholders including emergency management for better use of earth observation data.

Systemic integration of spatial datasets from various jurisdictions and fields will provide insights that can lead to smarter decisions and the construction of more comprehensive strategies. Advanced computing power, cloud computing and big data analytics such as artificial intelligence and machine learning technologies can demonstrate how faster and better, decision making can be done Australian Energy Market Operator CEO, Audrey Zibelman reflected on ‘‘What I have learned in Australia is how important advanced computing and the application of artificial intelligence (AI)and machine learning is to our industry to navigate to greater electrification of the economy and a diverse, decarbonised power system.’’ (AFR 1/10/20)


Identified opportunities relate to technology and while land and land use planning are traditional domains for the spatial industry, in terms of application of spatial to environmental monitoring and management, the opportunity is vast from application to bays, waterways, marine and coastal environments to air quality management and green-house gas reduction by decarbonization of the energy and transport systems for example.

  • Data governance, accountabilities and a systematic approach to data management for spatial data collection, integration, storage and ongoing management across government and portfolio agencies. Currently, there is limited accountability for data management and integration from multi-agencies and the requirement to maintain high-quality datasets that lead to enabling sophisticated analyses. Data governance should be established and clearly articulate roles and responsibilities for relevant agencies.
  • Long-term earth observation information: Various satellites have different temporal scales with varying levels of image resolutions. Integration of data from those satellites to create decades of information for various environmental themes with analytic applications such as machine learning will be highly useful for environmental management and decision-making processes. One example is Landsat Surface Reflectance statistics for land cover mapping developed by DEA. For example Victorian government use these statistics to map the dynamic changes in land cover through time in Victoria (from 1985 to present). They model land cover across Victoria, including native vegetation (herbaceous, woody and wetlands), intensive agriculture and recreation, forestry and the built environment, including urban areas. Time-series of spatial optical data have demonstrated high capacity for characterisation of environmental phenomena, describing trends as well as discrete change events.
  • Higher accuracy positioning systems: SBAS is currently in development in Australia and New Zealand and expected to be operational in 5-10 years. SBAS can improve positioning accuracy from a meter level to a centimeter level. This has very strong implications for enhancing environmental and disaster preparedness and management and protecting life and assets – environmental and physical infrastructure – as well as for industries such as forestry and quarries where accuracy is key to ensure boundary management and species protection during operations to maintain a social license to operate.
  • Measuring three-dimensional structure of Australian forests using satellite: Currently, three- dimensional mapping of forest structure has been performed by LiDAR technology using aircraft. This mapping exercise is important for forest biomass and structure monitoring, leading to enabling a solid estimation of time-series forest carbon storage from the ground. However, this is time-consuming and labor intensive. Satellites harnessed with LiDAR technology will provide a significant impact on responding to the effects of global climate change. In 2018, NASA launched two sensors into space that will play a prominent role in monitoring forest biomass and structure over the next decade: the Global Ecosystem Dynamics Investigation (GEDI) now attached to the International Space Station, and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2).These two satellites, which in combination provide complete coverage of the planet, are equipped with LiDAR sensors that record forest structure in 3D, contributing to an ongoing wave of large-scale forest ecosystem measurements. This technology also has the potential to monitor climate impacted environments such as coastal settlements – to manage coastal inundation and erosion due to sea level rise and storms from climate change.


1. Ongoing- and cross-agency collaboration across industry and governments is key to improving spatial information capability and datasets to inform decision-making across the environment portfolios of government/s. In addition, data governance and clearly defining accountability for data collection, storage, management and integration across agencies could provide a systematic approach to ensure high quality data capture to empower analytic methods such as artificial intelligence and machine learning.

2. It is important that end users of spatial technology are regularly informed of megatrends in spatial technologies so current information and understanding can be applied to their land and environmental monitoring, management and decision-making processes and diminish the barriers to adopting new technologies for sustainable environment management.