Individual Tree Crown (ITC) Delineation on Ikonos and QuickBird Imagery: the Cockburn Island Study. *
Bibliographic record
Abstract
For forestry applications with high spatial resolution (< 1 m/pixel) imagery, an Individual Tree Crown (ITC) approach is generally preferable to pixel-based analyses. This paper presents preliminary results of ITC delineations using a valley following approach over Cockburn Island (Lake Huron, Ontario), first with an Ikonos image (100 cm/pixel), then with a QuickBird image (70 cm/pixel), to examine some of the benefits of the added spatial resolution. Six test areas, typically containing only one situation (i.e., big or small trees, deciduous or coniferous), were analysed on both images. For reference, local maxima (or Tree Top) analyses were also performed and are shown for both media. As expected, the results favour QuickBird with gains in areas of trees with narrow crown (less omission errors), better separation of tree clusters and finer delineation of crown boundaries. In general, the QuickBird image analysis produced 50% more tree crowns and is in closer agreement with its Tree Top counts. From visual inspection, it is obvious that a significant number of tree clusters remain. This work is part of a larger “proof of concept” project, done in collaboration with forestry and forest inventory companies, that intends to check if an ITC approach can lead to better forest management maps. The project will evaluate the automatic creation of forest stand polygons, their species composition (up to 10 species) and parameters such as stem density and crown closure, as well as, the synergistic effects of using a large scale photography (LSP) sampling strategy for volume estimation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".