How to measure habitat conditions
Conserving Australia's wildlife and natural heritage requires reliable data on habitat condition and how it is changing over time.
Obtaining this data is challenging due to the vast size of Australia with its incredible array of unique habitats and species located in some of the world's most remote environments.
Australia needs a consistent way to accurately measure the condition of its habitats at regional, state, and national scales that does not always necessitate local, on-site assessments.
A national habitat condition monitoring program
Since 2015, CSIRO and the Australian Government have worked together to create the Habitat Condition Assessment System (HCAS), a consistent and robust national habitat condition monitoring program.
The HCAS combines biological and physical ecosystem data, and information about the location of remaining intact ecosystems, to estimate habitat condition across Australia's diverse landscape using satellite remote sensing.
The HCAS is based on the premise that habitat occurring at places with similar environmental characteristics (e.g. same average temperature and rainfall) should look similar when viewed from space (e.g. similar canopy cover). Where two such places have markedly different remotely sensed signals, this may indicate a difference in their condition.
By comparing locations in Australia to similar areas that are in excellent condition, we should be able to estimate and compare the condition of any habitat on the continent.
New and improved flexible platform
The HCAS is a flexible platform that can continuously evolve as site data, satellite imagery, and statistical modelling methods advance . As a result, the HCAS has been incrementally improved in several progressive versions.
The HCAS v3.0 is currently in development and has an anticipated launch date of mid-2024. It will include extensive updates such as increasing the output resolution and improving condition estimate accuracy in riparian zones and wetland habitats.
This new version will not only include substantial improvements, but also a clearer evaluation of its limitations and areas of uncertainty. Further enhancements will be identified to take advantage of new developments in site data, remote sensing and machine learning toward more 'real-time' mapping.