A multi-resolution forest stand segmentation algorithm integrating Landsat imagery and forest structural, age, and species attributes
Bibliographic record
Abstract
Strategic forest inventories are required to support management, enable monitoring, and inform policy development. These inventories are typically developed using photointerpretation of aerial imagery, followed by manual stand delineation and attribution. The process of delineating and attributing these segments can be costly, time consuming, and subjective. Many spectrally driven segmentation algorithms have been developed, but few have incorporated pixel-based, wall-to-wall, forest attributes such as stand height or species. As such, we propose a two-phase segmentation algorithm to automatically delineate forest stands from remotely sensed Landsat spectral reflectance data and forest attributes, including height and height variation, stem volume, age, and tree species. Initially at a micro-segmentation phase, small groups of pixels from the Landsat imagery are grouped based on spectral similarity. Next, in the segment-development phase, these groups of pixels are further merged into forest segments based on structure, age, and species information. We applied this approach to five study sites totalling 45 Mha selected to represent distinct forest landscapes across Canada. Our results found that grouping micro-segments by stand height variation minimized variance within forest segments in western Canada, while stem volume minimized variance within forest segments in boreal forest sites. Stand height variation also consistently showed the lowest Global Moran’s I (ranging from 0.1 to 0.2), indicating that variance between segmented forest stands was greatest and most heterogeneous from their neighbours. Interestingly, while using species produced relatively homogeneous forest stands, it was less successful in discriminating between stands. The analysis showed that structural attributes, particularly those related to height and stem volume, were most effective in delineating homogeneous forest stand polygons that were distinct from their neighbours. This work expands upon existing spectrally-driven segmentation algorithms to integrate forest structure, age, and species information, mimicking manual forest stand delineation and attribution practices in a transparent and automated manner.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".