Percentage Cover of Assessment Units:

Data collection and analysis (4.3) from the 28 drone plots included over 5% of an adjacent assessment unit, which was not the intended target vegetation. However, this only represents a small part of the overall sample site. While we acknowledge this sample error, we confirm that it does not affect the overall Econd® score.

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 assessment could separate them if more detailed benchmarks were made available.

 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 Bioconditon benchmark for native tree size structure is provided as the number of large trees (separated into eucalypt and non-eucalypt) per unit area. A method to calculate native tree size structure (D1 in the explainer tab of the associated Excel workbook) site_reference_observation_ICS was derived which involved several steps to make the published benchmarks comparable with the drone observations of tree canopy size. In essence, this involved the conversion of DBH based benchmarks to tree crown characteristic benchmarks that could be assessed with the drone-based data. However, significant uncertainty stemming from compounding errors in individual tree segmentation, allometry relating benchmark DBH to crown characteristics, and uncertainty / unknown errors in the eucalypt / non-eucalypt large tree benchmarks, remained in the results. Ultimately this level of uncertainty was unsatisfactory, and this indicator was excluded from the account. As more detailed benchmarks and individual tree segmentation / classification approaches are made available, this indicator can be revisited for follow-up accounts.

 Classification accuracy and alternative classification approach

There were sites where low accuracy was reported, which reduced the overall accuracy of the model. This is due to a combination of noise in high resolution data, the blurring of indicators around the edge of objects, occasional inaccuracy in the derived canopy height model, and model inaccuracies.

In sites where less than half of the validation points were accurate, a manual annotation approach was taken to calculate the proportion of cover of the relevant classes. 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. More detail on the calculation of each indicator is provided in the association excel workbook. The sites that were manually digitised were excluded from the validation process, which ensured that only sites reporting relatively high accuracy contributed to the account calculation.

All original data has been preserved to allow for re-processing upon the development and implementation of more advanced classification pipelines.

 Coarse woody debris

The computer vision model applied to classify coarse woody debris was not sufficiently accurate to calculate this indicator. 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. This indicator was directly comparable with the QLD Biocondition benchmarks.