Contribution of topographic features and categorization uncertainty for a tree species classification in the boreal biome of Northern Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Variations within local topography can effectively impact the location of tree species within naturally forested areas. Furthermore, the uncertainty of prediction for classification can vastly differ amongst topography and the overlying tree species groupings. This study investigated the supplementation of a suite of topographic features corresponding to morphometry and hydrological considerations, in addition to multispectral imagery and other LiDAR-derived features, at fine (2 m) spatial resolution for a pixel-based tree species classification of a forested region of the boreal biome in northern Ontario, Canada. The study area conforms to the Abitibi River Forest (ARF) and consists of the tree species of black spruce (Picea mariana), balsam fir (Abies balsamea), trembling aspen (Populus tremuloides), balsam poplar (Populus balsamifera), tamarack (Larix laricina), white spruce (Picea glauca), and eastern white cedar (Thuja occidentalis). Random forest (RF) and support vector machines (SVMs) were implemented for the classification. Topographic features, specifically those conforming to channel base level, valley depth, and multi-resolution valley bottom flatness (MRVBF), were among the most important features for species predictors. The RF and SVM methods were trained on pixels of pure stands (composed of 70%+ of same tree species) for the tree species groupings, which were split by site level. Modelling accuracies for both the pixel and site level were reported, with the best model attaining an overall site level accuracy and corresponding Cohen’s kappa score of 0.79 and 0.69 for classification, respectively. Entropy maps were generated to characterize the uncertainty of prediction, and substantiate that the regions of lowest uncertainty correspond to wetlands, which are dominated by black spruce (Picea mariana). A modified entropy map was calculated from the normalized top two probabilities of tree species groupings predicted per pixel, so as to better highlight regions of prediction uncertainty. A prediction map for the second most-likely tree species groupings was also computed, which supports the presence of balsam fir (Abies balsamea) as a secondary tree species throughout the ARF region.
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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 it