Environmental-economic accounts with Earth observation data

Environmental-economic accounting continues to receive attention as a robust means of measuring and reporting on our environment and to quantify the societal and economic benefits it generates. However, while the usefulness of environmental-economic accounts (EEA) is widely acknowledged, there remain several institutional and technical challenges to making environmental-economic accounting a reality for Australia.

Important among those challenges is the requirement for spatial data on different aspects of environmental composition and condition (e.g., land cover type, vegetation health) and the natural resources and other ecosystem services it provides (e.g., biomass, soil protection). The scientific literature shows that Earth observation should be able to provide at least some of these data in a cost-efficient manner, but it currently does not.

This Fenner Synthesis workshop brought together experts in (a) the use of environmental-economic accounting data, (b) the framing and production of EEA, and (c) satellite Earth observation of environmental variables. The goal was to identify the main constraints and opportunities to the better use of Earth observation in environmental-economic accounting.

The workshop was organised on 10-11 May 2018 in Canberra by Fenner School of Environment & Society, Australian Bureau of Statistics, Australian Government Department of the Environment and Energy, and Geoscience Australia (GA)

All materials and outputs from the workshop can be found on this page.


Global above-ground biomass carbon (v1.0)

ABC_450_ms Global estimates of annual average above-ground biomass carbon (ABC) for 1993-2012, based on a harmonised time series of Vegetation Optical Depth (VOD) derived from a series of satellite passive microwave instruments. Both VOD and ABC are available. [data description – download data]
Reference: Liu, Y.Y., A.I.J.M. van Dijk, R.A.M. de Jeu, J.G. Canadell, M.F. McCabe, J.P. Evans and G. Wang (2015) Recent reversal in loss of global terrestrial biomass, Nature Climate Change 5, doi: 10.1038/NCLIMATE2581. [read]



Global Water Cycle Reanalysis

 Estimates of monthly average water storage in different components of the water cycle for 2003-2012 at 1⁰. Estimates were derived by assimilating satellite data into a model ensemble. Data are available for sub-surface (soil and groundwater) storage and storage in ice & snow, rivers, lakes, and oceans.

Reference: Van Dijk, A. I. J. M., Renzullo, L. J., Wada, Y., and Tregoning, P. A global water cycle reanalysis (2003–-2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble (2014) Hydrology and Earth System Sciences 18, 2955-2973. [read]



Global Canopy Conductance

Canopy conductance at a global scale based on three vegetation indices (NDVI, EVI and Kc) derived from the MODIS MCD43C4 reflectance product. The data is produced globally at 0.05⁰ and every 8 days. Monthly and annual climatologies are also available.

Reference: Yebra, M., Van Dijk, A., Leuning, R., Huete, A., Guerschman, J.P., 2013. Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance. Remote Sensing of Environment, 129, 250-261 [read]



High-resolution Evapotranspiration for Australia

CMRSETEstimates of actual evapotranspiration across Australia based on MODIS reflectance and short wave infra-red data, and gridded meteorological surfaces.  The data have 8-day and 500 m resolution.

Reference: Guerschman J.P., van Dijk, A.I.J.M., Mattersdorf, G., Beringer, J., Hutley, L.B., Leuning, R., Pipunic, R.C. and Sherman, B.S. (2009), Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia. Journal of Hydrology, 369, 107-119.  [read]



Global 0.05° Gross Primary Production estimates

GPP in GPP.mean.2000-2012 AmazonGlobal estimates of monthly gross primary production for 2000-2012 at 0.05° resolution (~5 km) derived from MODIS remote sensing using a simple and effective method considering radiation and canopy conductance limitations on GPP.

Reference: Yebra, M, Van Dijk, A.I.J.M., Leuning, R., Guerschman, J.P. (2015) Global vegetation gross primary production estimation using satellite-derived light-use efficiency and canopy conductance, Remote Sensing of Environment 163: 206–216 [ read ]



MSWEP : Multi-Source Weighted-Ensem­ble Pre­cip­i­ta­tion


A new global terres­trial pre­cip­i­ta­tion (P) dataset (1979–2015) with a high 3-hourly temporal and 0.25° spa­tial res­o­lu­tion (Beck et al., 2016). The dataset is unique in that it takes advan­tage of a wide range of data sources, includ­ing gauge, satel­lite, and reanaly­sis data, to obtain the best pos­si­ble P esti­mates at global scale. download data [Australia (THREDDS)Europe (FTP)]

Reference: Beck, H.E., A.I.J.M. van Dijk, V. Lev­iz­zani, J. Schellekens, D.G. Miralles, B. Martens, A. de Roo: MSWEP: 3-hourly 0.25° global grid­ded pre­cip­i­ta­tion (1979–2015) by merg­ing gauge, satel­lite, and reanaly­sis data, Hydrol­ogy and Earth Sys­tem Sci­ences Dis­cus­sions, doi:10.5194/hess-2016–236, 2016.[ read ]



The Phenomic and Environmental Sensor Array

Based at our ANU research forest in the National Arboretum Canberra, the Phenomic and Environmental Sensor Array collects and integrates data from micrometeorological towers, a distributed wireless environmental sensor network, and overlapping multi-billion pixel time-lapse cameras that cover the research site at 1 cm resolution. The Array provides extremely detailed information from individual trees to the entire forest, every minute and hour. It captures how weather and climate affect growth as the individual trees mature into a closed forest. It will allow us to predict drought effects on growth and development for different eucalyptus species and genotypes, with implications for regenerating forest ecosystems under climate change, nationally and globally. [view latest measurements and images]


Forecasting drought impacts months ahead using satellite data

Skilful seasonal water and crop forecasts can do much to help cope with drought and water-related crises. Rapid advances in computing and in satellite remote sensing of precipitation, soil moisture, landscape water storage and vegetation biomass have created the opportunity to produce such forecasts over large areas with fine detail.
With support from the Australian Research Council and in collaboration with Princeton University, Monash University and Deltares, we have been developing technologies to measure and forecast river flows, soil moisture, irrigation water use and vegetation condition with local relevance and global coverage.
For example, we have developed methods to assimilate water storage observations from the GRACE satellite mission and soil moisture observations from passive microwave satellite instruments to achieve remarkable improvements in the estimation of soil moisture at different depths. This has allowed us to predict vegetation response to developing droughts several months in advance. In other examples, we have developed a technology to accurately measure irrigation water use at fine scale with global coverage, and we developed methods to use river water extent remote sensing to monitor river flows.