Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images
Bibliographic record
Abstract
The invasive emerald ash borer (EAB, Agrilus planipennis Fairmaire) infects and eventually kills endemic ash trees and is currently spreading across the Great Lakes region of North America. The need for early detection of EAB infestation is critical to managing the spread of this pest. Using WorldView-2 (WV2) imagery, the goal of this study was to establish a remote sensing-based method for mapping ash trees undergoing various infestation stages. Based on field data collected in Southeastern Ontario, Canada, an ash health score with an interval scale ranging from 0 to 10 was established and further related to multiple spectral indices. The WV2 image was segmented using multi-band watershed and multiresolution algorithms to identify individual tree crowns, with watershed achieving higher segmentation accuracy. Ash trees were classified using the random forest classifier, resulting in a user’s accuracy of 67.6% and a producer’s accuracy of 71.4% when watershed segmentation was utilized. The best ash health score-spectral index model was then applied to the ash tree crowns to map the ash health for the entire area. The ash health prediction map, with an overall accuracy of 70%, suggests that remote sensing has potential to provide a semi-automated and large-scale monitoring of EAB infestation.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".