Spatial Enhancement of MODIS-based Images of Leaf Area Index: Application to the Boreal Forest Region of Northern Alberta, Canada
Bibliographic record
Abstract
Leaf area index (LAI) is one of the most commonly used ecological variables in describing forests. Since 2000, 1-km resolution Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composites of LAI have been operationally available from the National Aeronautics and Space Administration (NASA), USA, at no cost to the user. In this paper, we present a simple protocol to enhance the spatial resolution of NASA-produced LAI composites to 250-m resolution. This is done by fusing MODIS-based estimates of enhanced vegetation index (EVI), consisting of 16-day 250-m resolution composites (also from NASA), with estimates of LAI. We apply the protocol to derive 250-m resolution maps of LAI for the boreal forest region of northern Alberta, Canada. Data fusion was possible in this study because of the inherent linear correlation that exists between EVI and LAI for the April to October growing period of 2005–2008, producing r2-values of 0.85–0.95 and p-values < 0.0001. Comparison of MODIS-based LAI with field-based measurements using the Tracing Radiation and Architecture of Canopies (TRAC) sensor and LAI-2000 Plant Canopy Analyzer showed reasonable agreement across values; statistical comparison of LAI data points produced an r2-value of 0.71 and a p-value < 0.0001. Seventy one percent of MODIS-based LAI were within ±20% of field estimates.
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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 | 0.000 | 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".