Citizen science data validates aerial imagery to track the ‘rise and fall’ of woody vegetation through extremes of climate
Bibliographic record
Abstract
Context Ground truthing remotely sensed imagery for detecting changes in wetland vegetation can be time-consuming and costly for monitoring. Harnessing the resources of citizen scientists (CS) using mobile devices has been under utilised in Australia. Aims The project aimed to test the feasibility and practicality of using CS to collect data using mobile devices to ground truth remotely sensed imagery. Methods Using high-resolution aerial imagery, we detected the establishment of woody vegetation over a 20-year dry phase from 2000 to 2020 in Thirlmere Lakes National Park, NSW, Australia. To ground truth these woody species, we engaged with a local community group using a customised, freely available mobile device application. Key results During the dry event of 2020, CS documented well-established woody species, such as Melaleuca linariifolia (flax-leaved paperbark), amongst the Lepironia articulata grey rush. With the La Niña wet events in early 2020–22 and subsequent higher water levels, the CS documented the survival of M. linariifolia but the dieback of eucalypts, and other woody species. Conclusions Observations at higher temporal frequencies by CS using mobile devices, augmented with researchers’ observations, proved to be a valuable, quality-controlled method to ground truth high-resolution aerial imagery. Implications This case study showed that monitoring the phenology of vegetation in a peat wetland can be supplemented by the inclusion of a CS programme. This under-utilised resource can increase coverage and frequency of data observations, lower costs as well as create community awareness, capability and engagement in scientific research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".