The TERN-ANU Landscape Data Visualiser

There is an abundance of data on Australia’s natural resources and ecosystems. Often, however, the data are hard to find and cannot easily be explored and analysed by a non-expert user. That is why the ANU Centre for Water and Landscape Dynamics (ANU-WALD) and the Terrestrial Ecosystem Research Network (TERN) have teamed up to develop the TERN-ANU Landscape Data Visualiser, a web atlas of spatial data on our landscapes, soils, ecosystems and water resources available from ANU, TERN and other organisations.

The Visualiser allows you to drill into time series for any location, compare to data sets, and download data for further processing.

You can visualise and analyses data from airborne data collection (e.g. LiDAR and hyperspectral imaging), a range of time series from satellite remote sensing and modelling, data from field surveys (e.g. the TERN Ecosystem Surveillance Plots) and time series from station measurements such as the OzFlux energy, water and carbon flux measurement network.

You can compare time series for any two locations or variables, and easily download the any of the data shown.

 

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Terms of Use

By using the TERN-ANU Landscape Data Visualiser you are agreeing to be bound by these Terms of Use. These terms may change from time to time.

Copyright: Copyright or other rights in material included within the TERN-ANU Landscape Data Visualiser may belong to Terrestrial Ecosystem Research Network (TERN) at University of Queensland, the Australian National University (ANU) or third parties. Logos are used with consent. Content contained within this portal is accessible under licenses specified by data providers. Please refer to specific licences associated with specific content for relevant terms of use. Requests for further authorisation should be directed via our about page.

Disclaimer: The TERN-ANU Landscape Data Visualiser and its content is provided on an “as is” and “as available” basis. You understand and agree that you use the TERN-ANU Landscape Data Visualiser at your own discretion and risk and that you will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through the TERN-ANU Landscape Data Visualiser. Web links to this site from external, third party websites should not be constructed as implying any relationships with and/or endorsement of the external site or its content by TERN or the ANU. If you have any concerns about the veracity of the data or website content, please inform us via our about page.

 

Data Documentation

The Landscape Data Visualiser contains a broad array of data from various sources. Links to further documentation are provided below:

Biomass and Carbon

Water

Soils and Landscape

Biodiversity and Habitat

 

 

Questions and Feedback

Any trouble using the tools on the Landscape Data Visualiser, difficulty finding the right data, or any other questions?

Please send us an email or contact us in another way

We will be delighted to help you and your feedback can help us improve the usefulness of the TERN-ANU Landscape Data Visualiser.

 

       

Satellite-based river gauging

On this web page you can find background information and data for satellite gauging reaches (SGRs) as published in Van Dijk et al. (2016). You are welcome to use these data in any way you see fit. To cite them do not refer to these web site but to the original paper, which includes this URL:

Van Dijk, A. I. J. M., G. R. Brakenridge, A. J. Kettner, H. E. Beck, T. De Groeve, and J. Schellekens (2016), River gauging at global scale using optical and passive microwave remote sensing, Water Resour. Res., 52, doi:10.1002/2015WR018545. LINK TO ARTICLE  

The easiest way to explore the available SGRs is by downloading the KML-formatted files and exploring them in Google Earth, which also gives you direct access to estimated hydrographs, performance metrics and the location of SGR cells. The KML files are organised and named by upstream catchment area, viz. large (1000-10,000 km2) and largest (>10,000 km2) and by performance, viz. best (Rmax>0.9), good (0-8-0.9), moderate (0.6-0.8) and poor (<0.6) (see paper for further details on the calculation of Rmax). DATA LINK

Alternatively, if you wish to replicate our experiment or undertake your own research using the same data we used, you can download all constructed SGRs including estimated the inundation signal from MODIS or GFDS, as well as the discharge observations used to construct and evaluate the SGRs. The data are formatted in 3 separate, self-described NetCDF files. DATA LINK

Fig7 Examples of good, bad and ugly satellite-gauging reaches (see paper for detailed explanation)



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.



Australia’s Environment: routine, comprehensive national reporting

National-scale, comprehensive information on the condition, change and trajectory of our environment is essential for successful environmental management. At national scale, the State of Environment report is produced once every five years, with measurements that are often already some years old. There is an urgent need for a continuous and up-to-date environmental monitoring system that can provide the basis for regular state-of-environment and environmental accounts.

Since 2015, we have been developing a data processing system that integrates and summarises spatial data to produce an annual report. The system provides continuity and regularity in environmental condition data. A backbone to the system is our OzWALD technology, a model-data fusion system that integrates satellite remote sensing into spatial computer models to estimate important components of the water and carbon cycles.

In collaboration with the Terrestrial Ecosystem Research Network, Integrated Marine Observing System, Geoscience Australia, CSIRO and the Australian Bureau of Statistics, we have developed ‘’Australia’s Environment”, an annual briefing on the state of our environment. We provide the information at different formats and levels of detail to make them as relevant, accessible and easily interpreted as possible.

The information can be accessed in digest through the annual Fact Sheet, Briefing Material, and summary article.

For those wishing to use the data in accounting or reporting, we provide Australia’s Environment Explorer, a web atlas that allows you to visualise and investigate environmental changes by region, location or land cover type.

 


        



Water license compliance monitoring from space

In New South Wales, monitoring whether water licence holders use river and groundwater in accordance with their licence is a major challenge. Distances are vast, and extraction is often directly from river floodplains during floods, which makes it very hard to keep an eye on compliance.
Together with New South Wales Government, we are developing a satellite-based system to monitor irrigation water use and water extraction. The proposed system will provide guidance to assist compliance officers in targeting properties for inspection, by determining the likelihood that the amount of irrigation on individual properties estimated from satellite observations exceeds extraction limits.

 

 

       

Improving spatial water information through data assimilation

Up-to-date spatial water information and forecasts are tremendously valuable for farmers, land and water managers, emergency services, and many other users. For example, to assess crop and pasture growing conditions, soil mechanical properties and fire risk.
Computer models, satellite remote sensing and station measurements all provide valuable information on the water cycle. The best possible up-to-date information and forecasts are derived by combining these data sources through data assimilation: a set of data integration tools that is already used in weather forecasting.
We are working closely with the Bureau of Meteorology to improve the operational water information produced by their Australian Water Resources Assessment (AWRA) system. Together, we are developing practical data assimilation techniques to blend satellite observations, for example relating to soil moisture, total water storage and evapotranspiration, as well as station observations of river flows.



Environmental model software: W3 and OzWALD

The World-Wide Water model (W3) and its Australian version, OzWALD, are two near-identical models for grid-based estimation of daily water balance dynamics and water-related vegetation dynamics. Both are evolutions of the original AWRA-L model. The example Matlab implementation of the models is freely available for download. You can find the code, relevant literature, and some course material via this link or the download button below.
(Note that this is not the AWRA Community Modelling System (AWRA CMS), which also an evolution of the original AWRA-L model but is maintained by the Bureau of Meteorology and can be found here: https://github.com/awracms/awra_cms)

 

       

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