FOREST SPECIES CLASSIFICATION BASED ON STATISTICAL POINT PATTERN ANALYSIS USING AIRBORNE LIDAR DATA
Bibliographic record
Abstract
Abstract. The paper investigated the effectiveness of point pattern methods in the application of forest species classification using airborne LiDAR data. The forest stands and individual trees in our study area were classified as either shade tolerant or intolerant species. The purpose of adopting the point pattern methods is to develop new features to effectively characterize the pattern of internal foliage distribution of forest stands or individual trees. Three methods including Quadrat Count, Ripley's K-function, and Delaunay Triangulation were applied, and six feature groups were derived for a stand or tree sample. Feature selection was performed based on the derived features in order to find the best ones for the following classification procedure, which was implemented by two supervised and two unsupervised methods. These newly derived features were proved effective for the classification. The highest classification accuracy 97% was achieved at stand level and 90% at individual tree level. The sensitivity of classification accuracy to the number of features used was also investigated in this paper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".