Spatial Analysis of High Resolution Aerial Photographs to Analyze the Spread of Mountain Pine Beetle Infestations
Bibliographic record
Abstract
Mountain pine beetle (Dendroctonus Ponderosae) infestations in Western Canada have reached alarming proportions. The spread of attacks has significantly impacted pine tree stocks, the forest ecosystem in general and the overall socio-economic condition of residents in communities that depend on the forest industry. The ability to track these attacks and anticipate their trajectories will allow forest managers to target intervention measures and improve mitigation efforts. Spatial analysis methods are well suited to characterize the dynamics of the mountain pine beetle attacks. This study implemented a multi-scale spatiotemporal analysis to describe the patterns of three consecutive attack years (2000-2002) for a 19 km2 study site in British Columbia, Canada. In the first stage, baseline complete spatial randomness (CSR) was examined using average nearest neighbour analysis and quadrat counts on infestation data extracted from 19 cm resolution black and white aerial photographs. In the second stage, global and local Moran’s I spatial autocorrelation statistics, Getis-Ord General G global high/low clustering and Getis-Ord G_i^* test statistic techniques were used to evaluate the spread patterns over time. The results show a significant clustering of attack sites in the first two years, with considerable changes in the attack spread in the third year. These changes were independent of the two levels of spatial resolution used. Knowledge about infestation patterns can be used to anticipate needed social and environmental assistance for affected communities. Further, the information can aid overall sustainable forestry management.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".