Lacunarity analysis to determine optimum extents for sample‐based spatial information extraction from high‐resolution forest imagery
Bibliographic record
Abstract
Abstract Lacunarity analysis was evaluated as a means to determine multiple pattern scales that are inherent in high‐resolution imagery of forests and to specify an optimal spatial extent for spatial image information extraction. A series of 0.5 m pixel images of temperate hardwood and mixed boreal forests were analysed using lacunarity distributions calculated for spatial extents ranging from 7 m to 40 m. The optimal extent was taken as that which displayed the greatest number of distinct pattern scales. For the temperate hardwood forest dataset, 12–14 m extents were found to be optimal, detecting three pattern scales. For the boreal forest dataset, optimal extents were 14–18 m for five of six plots, detecting two or three pattern scales in each plot. The detected pattern scales ranged from 8 m to 14 m and showed some correspondence to tree crown size, but also responded to clusters of understorey and overstorey trees or to partially exposed tree crowns. The method can aid in determination of the sample extent that best captures the pattern scales present in the imagery. More generally, it can be useful in exploratory analysis of any spatial data for which the fundamental patterns are not known. Acknowledgments This research was funded by grants to D. King from the US National Geographic Society and the Natural Sciences and Engineering Research Council of Canada (NSERC). The Quebec Ministry of Natural Resources kindly provided field data for Dataset 1. The Ontario Ministry of Natural Resources acquired the airborne imagery for Dataset 1. The National Capital Commission of Canada provided field access and logistic support.
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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 | 0.000 | 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 | 0.001 |
| 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".