Initialization of an insect infestation spread model using tree structure and spatial characteristics derived from high spatial resolution digital aerial imagery
Bibliographic record
Abstract
High spatial resolution digital aerial imagery has demonstrated the capacity to enable the derivation of a range of parameters describing the structural characteristics of individual trees and spatial attributes of forest stands. As a result, these data have the potential to provide important information to help initialize models of insect infestations, in particular models addressing the spread of mountain pine beetle, Dendroctonus ponderosae (Hopk.), which has reached epidemic levels within western Canada. In support of this study, ground data and images with 10 cm spatial resolution were collected over a study area straddling the borders of British Columbia and Alberta, Canada, which is experiencing infestation by mountain pine beetles. Images were processed using an object-based classification algorithm, which correctly identified between 50% and 100% (mean 80.2%) of the tree crowns detected on the imagery relative to field-measured tree locations. Unidentified tree crowns primarily included trees with small crown and stem diameters which are less susceptible to infestation by mountain pine beetles. Following accurate identification of tree locations, parameters for stem diameter and stocking density were derived from the imagery and compared with measurements derived from ground survey data. Results indicate that two image-derived individual tree parameters were correlated sufficiently with ground measures to act as model inputs, namely stocking density (r2 = 0.91, standard error (se) = 506.65, p < 0.001) and stem diameter (r2 = 0.51, se = 2.63, p < 0.001). With confidence in our capacity to accurately predict these critical parameters for infestation modelling, we then apply a simple, spatially explicit mountain pine beetle infestation model. These models can be used to predict the potential impact on forest stands caused by mountain pine beetle attack and also to inform forest managers of the resources required to provide rapid and persistent mitigation necessary to control infestations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".