Bioindicators of Forest Sustainability: Using Remote Sensing to Monitor Forest Condition
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Bibliographic record
Abstract
The Bioindicators of Forest Sustainability Project has applied a physiological, remote sensing approach to develop practical and objective measures of forest condition,. This project was designed to address ecosystem condition and productivity (C&I, Criteria Number 2), by producing an indicator for disturbance and stress. While stress indicators like chlorophyll fluorescence and pigment content exist at the leaf level, developing reliable indicators at the canopy level is a challenge. In this study, an inverse modelling approach has demonstrated the capability of hyperspectral sensor (compact airborne spectrographic imager (CASI)) reflectance images to map chlorophyll content in 12 stands of a tolerant hardwood sugar maple (Acer saccharum) forest in the Algoma region of Ontario, Canada that vary in condition from healthy to chronically stressed. The practical significance of developing spectral features related to chlorophyll concentration is in identifying whether forests are healthy or stressed. Temporal variations in chlorophyll concentrations could provide an objective, early-warning indicator of stress. The Bioindicators Project has expanded to include the boreal forest species jack pine (Pinus banksiana), black spruce (Picea mariana), trembling aspen (Populus tremuloides), and white birch (Betula papyrifera). Preliminary results suggest that chlorophyll concentrations of jack pine, aspen, and white birch canopies can be estimated from CASI images. Current efforts are focussing on scaling this technique up from the high spatial resolution of CASI images to lower spatial resolution, but larger image size, of satellite images of the hyperion sensor of NASA’s Earth Observing-1 satellite. Once developed, this technique could be used as an efficient means to operationally assess both acute and chronic forest physiological stress and classify forest condition based on chlorophyll content. Forest condition maps developed using this technology could be used for state of the resource reporting, assessing the effects of silvicultural operations, and as indicators of incipient insect and disease outbreaks.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it