Fixed cameras installed at the Tumbarumba Wet Eucalypt SuperSite provide a time series of fine scale data as a long-term record of vegetation structure and condition. This dense time series of phenocam images provides data for analysis of ecological responses to climate variability, and when consolidated across the entire terrestrial ecosystem research network, supports calibration and validation of satellite-derived remote sensing data, ensuring delivery of higher quality results for broader scale environmental monitoring products.
Images are captured regularly during daylight hours. Images and data products for a region-of-interest (ROI) that delineates an area of specific vegetation type, are made available on a six monthly basis.
The Tumbarumba Flux site was established in 2000 by CSIRO and started measurements in 2001. The 1 hectare (ha) SuperSite plot was established in 2015 in a collaboration with TERN. The overstorey is dominated by Eucalyptus delegatensis (alpine ash) and Eucalyptus dalrympleana (mountain gum). For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/tumbarumba-wet-eucalypt-supersite/ .
Other images collected at the site include photopoints, digital cover photography (DCP), and ancillary images of fauna and flora.
Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
The Tumbarumba Wet Eucalypt SuperSite was established in 2015 and is managed by CSIRO Land and Water and is supported by TERN.
Purpose
Time series of vegetation phenological observations are collected to understand ecosystems annual cycles. Phenological timeseries can be used for ground-truthing remote sensing data products, for studies of climate change impacts on terrestrial ecosystems, and as a standard for earth system models.
Lineage
For generating ROI chromatic indices the python library vegindex (0.7.2) in python is used. For calculating hazeness values the R hazer (1.1.1.) and jpeg (0.1) libraries are used.
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