Landsat assessment of variable spectral recovery linked to post-fire forest structure in dry sub-boreal forests
Bibliographic record
Abstract
Forest disturbances such as wildfires can dramatically alter forest structure and composition, increasing the likelihood of ecosystem changes. Up-to-date and accurate measures of post-disturbance forest recovery in managed forests are critical, particularly for silvicultural planning. Measuring the live and dead vegetation post-fire is challenging because areas impacted by wildfire may be remote, difficult to access, and/or dangerous to survey. The difficulties of post-fire monitoring are compounded by the global increase in the frequency and severity of disturbances, as expansion of disturbed areas also increases the number and size of areas requiring post-disturbance monitoring. Methods that safely, efficiently, and extensively differentiate silviculturally beneficial coniferous growth from barren ground or deciduous shrubs are necessary to inform post-fire forest management. Satellite imagery can detect burn patterns, but monitoring changes in forest structure post fire is challenging due to complex vegetation responses. To overcome this challenge, this study combines post-disturbance spectral trajectory measures from a time series of historical Landsat imagery with field and remotely piloted aircraft (RPA) lidar (light detection and ranging) data to examine vegetation recovery of lodgepole pine (Pinus contorta) dominated sub-boreal forests after high-severity fires in 2006 in central British Columbia, Canada. Distinct spectral recovery trajectories were identified using data-clustering from a combination of seven Landsat spectral indices, with trajectories varying by recovery magnitude and rate. The forest structure associated with each distinct trajectory of spectral recovery was analyzed using 430 ha of spatially explicit forest structure measures (e.g., basal area, stem counts) and composition (e.g., percent coniferous) derived from 26 coincident field plots and high density RPA lidar (>200 points/m2) data. By comparing spectral trajectories to forest structure measures, we found the most spatially abundant cluster of spectral recovery coincided with a basal area of 0.62 m2/ha, high stem densities (>5000 stems/ha) and a high abundance of coniferous trees (>95 % coniferous). Around 10 % of the landscape was associated with relatively high abundance of deciduous vegetation (>20 %) in addition to very high conifer stem densities (>8000 stems/ha). By identifying the structural characteristics associated with unique Landsat spectral trajectories, we highlight the combined value of RPA lidar data and satellite image time series in providing a detailed and spatially explicit characterization of post-fire recovery relevant to managed forests.
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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 | 0.001 |
| 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".