Use of remote sensing to map occurrence and spread of Phytophthora cinnamomi in Banksia woodlands on the Gnangara Groundwater System, Western Australia
Bibliographic record
Abstract
The soilborne plant pathogen Phytophthora cinnamomi is listed as one of the world’s 100 worst invasive alien species by the International Union for Conservation of Nature (IUCN). The impacts on native flora and fauna habitats have been identified as a key threatening process in Australia. Identifying and mapping diseased vegetation and the rate of spread of the disease is required for management; however, this is often difficult and costly. This study investigated the ability of using a time series of orthophotos (1953–2008) in combination with Landsat satellite imagery, including trend analysis, and GIS to identify the presence of vegetation impacted by P. cinnamomi at four sites in Banksia woodlands in Western Australia. Further, the historical extent and rate of spread of P. cinnamomi was assessed at one site between 1953 and 2008. Our assessment identified that three of the four sites were affected by P. cinnamomi, results that are consistent with on-ground surveys. Investigation of disease progression at one site found a large increase in the area impacted between 1974 and 1988 and the rate of spread was highest between 1953 and 1963 (1.286 m year-1) and lowest between 1997 and 2008 (0.526 m year-1). The techniques presented provide a cost-effective tool to monitor broad-scale vegetation dynamics over time for management of this plant pathogen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".