Estimating boreal forest ground cover vegetation composition from nadir photographs using deep convolutional neural networks
Bibliographic record
Abstract
Ground cover and surface vegetation information are key inputs to wildfire propagation models and are important indicators of ecosystem health. Often these variables are approximated using visual estimation by trained professionals but the results are prone to bias and error. This study analyzed the viability of using nadir or downward photos from smartphones (iPhone 7) to provide quantitative ground cover and biomass loading estimates. Good correlations were found between field measured values and pixel counts from manually segmented photos delineating a pre-defined set of 10 discrete cover types. Although promising, segmenting photos manually was labor intensive and therefore costly. We explored the viability of using a trained deep convolutional neural network (DCNN) to perform image segmentation automatically. The DCNN was able to segment nadir images with 95% accuracy when compared with manually delineated photos. To validate the flexibility and robustness of the automated image segmentation algorithm, we applied it to an independent dataset of nadir photographs captured at a different study site with similar surface vegetation characteristics to the training site with promising results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".