Prediction of forest stem volume using kriging adapted to detected edges
Bibliographic record
Abstract
The modern techniques of the global positioning system and geographic information system enable many new approaches to forestry planning problems. Using these it is possible to efficiently geoposition, store, and analyze each field measurement in a spatial context. This work is directed towards the application of a dynamic forestry planning system based on a forest map with very high spatial resolution created from geopositioned field plot data, instead of the traditional forest stand map. The new dynamic system is dependent on accurate methods to create a high-resolution map from a set of field measurements. This problem may be solved using the kriging spatial prediction (interpolation) method. The aim of this paper is to present and empirically evaluate a new kriging method side-by-side with global and stratified kriging. The new method uses the output from an edge-detection algorithm, here applied to Landsat TM image data, to increase the prediction accuracy. Prediction evaluation was made in terms of mean forest stem volume per hectare measured on circular field plots of 10 m radius. The new method showed a prediction root mean square error of 41% of the mean volume, compared with corresponding results of global, 58%, and stratified kriging, 45%.
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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) | 0.000 | 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".