Detecting changes in understorey and canopy vegetation cycles in West Central Alberta using a fusion of Landsat and MODIS
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Aims To model regional vegetation cycles through data fusion methods for creating a 30‐m daily vegetation product from 2000 to 2018 and to analyze annual vegetation trends over this time period. Location The Yellowhead Bear Management Area, a 31,180‐km 2 area in west central Alberta, Canada. Methods In this paper, we use Dynamic Time Warping (DTW) as a data fusion technique to combine Landsat 5, 7 and 8 satellite data and Moderate Resolution Image Spectroradiometer (MODIS) Aqua and Terra imagery, to quantify daily vegetation using Enhanced Vegetation Index at a 30‐m resolution, for the years 2000–2018. We validated this approach, entitled DRIVE (Daily Remote Inference of VEgetation), using imagery acquired from a network of ground cameras. Results When DRIVE was compared to start and end of season dates (SOS and EOS respectively) derived from ground cameras, correlations were r = 0.73 at SOS and r = 0.85 at EOS with a mean absolute error of 7.17 days at SOS and 10.76 days at EOS. Results showed that DRIVE accurately increased spatial and temporal resolution of remote‐sensing data. We demonstrated that SOS is advancing at a maximum rate of 0.78 days per year temporally over the 18‐year time period for varying elevation gradients and land cover classes over the region. Conclusions With DRIVE, we demonstrate the utility of DTW in quantifying vegetation cycles over a large heterogeneous region and determining how changing climate is affecting regional vegetation. DRIVE may prove to be an important method to determine how carbon sequestration is varying within fine‐scale individual plant communities in response to changing climate and likely will be beneficial to wildlife movement and habitat selection studies examining the varying response of wildlife species to changing vegetation cycles under shifting climatic conditions.
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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 | 0.000 | 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 | 0.000 |
| 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 it