Surface hydrology of an arctic ecosystem: Multiscale analysis of a flooding and draining experiment using spectral reflectance
Bibliographic record
Abstract
[1] In the Arctic, surface hydrology plays an important role in controlling plant community composition and ecosystem processes such as land‐atmosphere carbon and energy balance. Investigating how climate change in this region will affect surface hydrology and subsequent biotic, atmospheric, and climatic feedbacks could be key to understanding the future state of the Arctic and Earth systems. Improved methods for monitoring surface hydrology at large spatial scales are needed in the Arctic. Near Barrow, Alaska, a large‐scale experiment with flooded, drained, and control treatment areas, each exceeding 9 ha, was initiated during summer 2008 following 3 years of monitoring under nonmanipulative conditions. Throughout the 2008 growing season, hyperspectral reflectance data were collected in the visible to near‐infrared (IR) range using a 300 m long robotic tram system. Water table depth, surface water depth, and percent surface water cover were also measured. A spectral index (Normalized Difference Surface Water Index (NDSWI)) was developed using reflectance in the IR region (R1000 strong absorbance) and blue region (R460 poor absorbance). NDSWI was strongly correlated with both surface water depth and surface water cover, and was used to monitor spatial and temporal patterns of surface hydrology in the experimental treatment. Using 2002 and 2008 Quickbird satellite imagery, the index was also used to examine differences in NDSWI between experimental treatments. Using this approach, we demonstrate that the flooded treatment was significantly different from the other two treatments (drained and control) and that the new index can be used to monitor surface hydrology in arctic wetlands.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".