Temporally Consistent Forest Stand Segmentation Using Landsat Imagery
Bibliographic record
Abstract
Object-based image segmentation techniques are widely utilised in environmental disciplines to partition remotely sensed imagery into objects representing distinct conditions such as vegetation structure or landform. However, most approaches are applied to a single temporal snapshot, limiting their ability to update polygons over time. To address this, we proposed a temporally consistent segmentation algorithm based on a two-phase region growing approach designed to be applied to time series of annual Landsat surface reflectance composites. We developed and demonstrated this new approach over six fire-disturbed forested study areas in British Columbia, Canada, to dynamically delineate polygons over time as they underwent land cover change. Our approach maintained existing boundaries for forest polygons with no land cover change while updating those subject to change as forest regenerated and followed successional processes. Rapidly recovering areas such as Cariboo and Fraser-Fort George showed increases in mean segment area from 12 to 21 ha and 14 to 25 ha, respectively, approaching or exceeding pre-disturbance values. Additionally, segment shape complexity increased over time, reflecting the structural development of recovering stands. This work demonstrated the potential of utilizing Landsat surface reflectance data to update forest polygons over time with reference to forest development and increasing maturity.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".