Object-Oriented Automatic Identification of Forest Gaps Using Digital Orthophoto Maps and LiDAR Data
Bibliographic record
Abstract
Identification of forest gaps is a prerequisite for quantification of their size, shape, and dynamics, and for clarification of both complex structural forest species regeneration and understory species diversity. Although airborne LiDAR and digital orthophoto maps (DOM) have been used separately to identify forest gaps, few studies have considered integration of the two data sources for forest gap segmentation and classification. True color DOM (20 cm) and airborne LiDAR (3.7 points/m2) data were used to study object-oriented gap identification in the typical natural secondary forest of the Maoershan Experimental Forest Farm (China). Three segmentation schemes based on DOM only data, LiDAR data, and integrated DOM & LiDAR were adopted when processing the object-oriented classification. Based on the segmentation results, the support vector machine classifier was used with DOM spectral features, LiDAR height features, and integrated features from both data sources to identify forest gaps. The Modified Euclidean Distance 3 (ED3Modified) index was selected to assess segmentation quality. Comparison of the three segmentation schemes revealed that segmentation based on LiDAR was the best and the classification accuracy using integrated spectral and height features was the highest (OA = 87%, Kappa = 0.81). Those results could provide technical support for the quantitative analysis of forest gap features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".