Insufficient area of target vegetation class covered.

Of the 34 drone plots analysed, 6 had a total target vegetation class of below the required 5-hectares (Table 10). This was the result of erroneous flight planning, where the Incorrect vegetation was measured. Nevertheless, the assessment of these slightly smaller areas is expected to produce reasonable results, and results from these sites were not removed from the final analysis.

Sites not analysed in the remote sensing workflow

Two sites, GLEN002 and WYRA004, were not analysed as part of the remote sensing analysis. As a result, a manual annotation approach was taken to calculate the proportion of cover of the relevant classes for these sites. For this, a 0.1-hectare sub-plot was generated at the centre of the orthomosaic in the cloud-based DroneDeploy platform. Then, the cover of trees, shrubs, bare ground and ground cover was digitised. The area of these classes was calculated and used to derive indicator values.

The K and L indicators were merged to match the approach taken by BioCondition

The BioCondition benchmarks did not separate between indicator K (Native species count for herbaceous species – forbs), and indicator L (Native species count for herbaceous species – other species). In order to make the observations of these indicators comparable with the BioCondition benchmarks, the forbs and other species were merged for the calculation of indicator condition scores. The original information of these two indicators has been preserved, so future assessments could separate them if more detailed benchmarks were made available.

Indicator J to match the approach taken by BioCondition vs expert elicitation reference

According to the BioCondition reference data, only grasses should be included under Indicator J, while graminoids are accounted for under Indicators K and L.

Calculation of native tree species recruitment

While the Method recommends the use of a formula comparing the benchmark for recruitment to the observation, this approach was not deemed appropriate – as the translation of drone derived data did not accurately relate to the field derived benchmarks. As a result, recruitment was calculated as follows: when canopy trees were present, the presence of recruits within field data earned a score of 100, and the absence of recruits led to a score of zero. As more relevant benchmarks are made available, the incorporation of more sensitive recruitment measures will be considered.

Accuracy assessment sampling intensity

Within the Central Queensland Asset Account, the total number of validation points for the remote sensing accuracy assessment was 306, lower than the 683 suggested by the Method. However, this lower number is the result of the lower number of individual sites, not a reduction in sampling intensity on a per site basis – and as such, is not expected to have a material impact on the reliability of the accuracy assessment. Additionally, alternative approaches to the accuracy assessment have been suggested by remote sensing scientists from Astron (now Ecocene) - who managed the geobia classification (see Supplementary Document 1). These suggestions will be considered for future iterations of the Method.

Cryptogam was excluded from the account

Because cryptogam was not available as a benchmark for the chosen reference condition approach (published QLD BioCondition benchmarks), this indicator was excluded from this account. As with the merging of indicators K and L, the observations of cryptogam have been preserved and could be calculated later if benchmarks for this indicator were made available.

Native tree size structure was excluded from the account

The BioCondition benchmark for native tree size structure is provided as the number of large trees (separated into eucalypt and non-eucalypt) per unit area. The accurate measurement of tree size relative to these criteria was not feasible. As more detailed benchmarks and individual tree segmentation / classification approaches are made available, this indicator can be revisited for follow-up accounts.

Coarse woody debris calculation

An automatic approach to calculating the length of coarse woody debris was not feasible. As an alternative, this indicator was calculated by digitising (using a line draw tool in Drone-Deploy) all of the visible coarse woody debris within 0.1-hectare sub-plots for each of the sites. The length of each of the lines was then summed, and multiplied by 10, to provide an estimate of the length of coarse woody debris per hectare.

Insufficient Sampling Intensity

The three Assessment Units in the Central Queensland Environmental Account that did not meet the minimum sampling requirements.

Expectation of Brigalow Presence

At the outset of planning, we anticipated a higher presence of Brigalow ecosystems than was ultimately observed in the field. This influenced the initial allocation of plots and contributed to the shortfall in sampling for certain units.

Differences Between ‘Of Concern’ and ‘No Concern’ Areas

The ‘No Concern’ areas within these Assessment Units are small compared to the ‘Of Concern’ areas. As a result, greater emphasis was placed on sampling Threatened Ecological Communities (TECs) and ‘Of Concern’ areas to ensure adequate representation of higher‑value ecosystems.

Coverage of Sub‑assets vs. Assessment Units

While the sub‑assets themselves were sampled, some individual Assessment Units within those sub‑assets did not reach the minimum number of plots. We plan to address this gap in the next survey round to ensure full compliance with the Method.