Data and information services

Data and information services

Our scientific research has been guided by influencing forest policy

The Primary Forests & Climate Program has pioneered improved identification and mapping of primary forests globally. It has developed an improved method that is able to identify remnant primary forest, in addition to much larger intact forest landscapes. These smaller remnant areas still provide significant ecosystem integrity and the resulting benefits. However, existing mapping often failed to identify these areas, risking further loss and degradation.

This improved mapping will support better forest landscape management by ensuring primary forest areas are accurately and fully mapped.

Example outputs are below.

USA forest maturity

Map showing forest maturity and its importance to water quality of forests on continental USA.

Forest stability maps for Siberia and the Amazon, related to our case study areas, are being finalised.

Project members working data and information services

Dr Brendan Mackey

Brendan Mackey

Project Director and Director of the Griffith Climate Action Beacon at Griffith University, contributing to community planning and engagement in forest projects.
Brendan Rogers

Brendan Rogers

Dr. Rogers investigates how boreal forests are responding to climate change and land use, how this feeds back to climate change, and how management and policy can be used for mitigation and adaptation.
Tatiana Shestakova

Tatiana Shestakova

Tatiana is a post-doctoral researcher at Woodwell Climate Research Center research. Her interests span the fields of terrestrial ecology, stable isotope biogeochemistry, ecosystem modelling and climate change impacts on natural ecosystems.
Pat Norman

Patrick Norman

Pat is a spatial research scientist focusing on the environment, forests and protected areas.

Data and information services publications

Mapping forest stability within major biomes using MODIS time series

Forest stability is a key component of ecosystem integrity and primary forests. Current remote sensing products largely focus on deforestation rather than forest degradation, and depend on machine learning calibrated with extensive field measurements. To address this, we used MODIS time series to develop a novel approach for mapping forest stability across forest biomes.