Linking Spaceborne and Ground Observations of Autumn Foliage Senescence in Southern Québec, Canada
Bibliographic record
Abstract
Autumn senescence progresses over several weeks during which leaves change their colors. The onset of leaf coloring and its progression have environmental and economic consequences, however, very few efforts have been devoted to monitoring regional foliage color change in autumn using remote sensing imagery. This study aimed to monitor the progression of autumn phenology using satellite remote sensing across a region in Southern Québec, Canada, where phenological observations are frequently performed in autumn across a large number of sites, and to evaluate the satellite retrievals against these in-situ observations. We used a temporally-normalized time-series of Normalized Difference Vegetation Index (NDVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to monitor the different phases of autumn foliage during 2011–2015, and compared the results with ground observations from 38 locations. Since the NDVI time-series is separately normalized per pixel, the outcome is a time-series of foliage coloration status that is independent of the land cover. The results show a significant correlation between the timing of peak autumn coloration to elevation and latitude, but not to longitude, and suggest that temperature is likely a main driver of variation in autumn foliage progression. The interannual coloration phase differences for MODIS retrievals are larger than for ground observations, but most ground site observations correlate significantly with MODIS retrievals. The mean absolute error for the timing of all foliage phases is smaller than the frequency of both ground observation reports and the frequency of the MODIS NDVI time-series, and therefore considered acceptable. Despite this, the observations at four of the ground sites did not correspond well with the MODIS retrievals, and therefore we conclude that further methodological refinements to improve the quality of the time series are required for MODIS spatial monitoring of autumn phenology over Québec to be operationally employed in a reliable manner.
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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".