Examining change and long-term trends in the marine environment using satellite-based time series
Bibliographic record
Abstract
The oceans and coastal areas are dynamic environments in which variability occurs at a wide range of temporal scales, from seconds to years to decades and longer. Some very good time series now exist at specific locations, that permit characterization of this variability as well as of longer-term trends, but for much of the world ocean the in situ data is sparse and such characterization is not possible, or possible only by extrapolation. For these less well-studied areas, satellite imagery and gridded products created from a combination of satellite and in situ data are the only available sources of continuous historical information. Satellite imagery provides regular, spatially synoptic, global information, at spatial resolutions on the order of 1 km and temporal resolutions typically measured in days. Some sensors offer higher spatial resolutions but at lower temporal resolution. With accumulated time series of up to 30 years or longer for some satellites and other gridded datasets, we can now begin to use this technology to identify and track long-term change, as well as to characterize shorter-term variability. In this paper we will discuss the analysis of long-term change and trends in marine and coastal environments using examples from recent projects. We will illustrate vegetation changes in and around the Anderson River delta on the Beaufort Sea coast of the Arctic Ocean using 30 m spatial resolution Landsat imagery acquired over a 31-year period from 1972 to 2003. Using a temporal classification approach, we map both interannual variability and long-term losses on the mudflats of the delta itself, and gains on the nearby tundra. In a second example, we illustrate how chlorophyll and sea surface temperature (SST) vary locally within the northeast Pacific, and how temporal patterns and trends also vary regionally, using 24 years of weekly 4 km resolution sea surface temperature from the weather satellites, and 11 years of weekly 9 km chlorophyll composite data products from ocean colour satellites. Finally, we show examples of trends and anomalies calculated from 41 years of sea ice distribution, concentrations and ice type in the Beaufort Sea, using ice chart maps produced by the Canadian Ice Service from a combination of satellite, airborne and in situ observations. The weekly ice charts provide coverage of the entire western hemisphere of the Arctic Ocean over the continental shelf, slope and rise areas at a spatial resolution of 4 km for a total of 26 distinct ice parameters spanning the years 1968 to the present. Using this rich data source, the analysis of long-term trends in relation to seasonal and interannual variability can be used to estimate potential changes due to climate change for local and regional areas, which are shown to vary considerably from one region to another. The availability of data from all of the sources discussed over extended time periods of 30 years or longer provides a means of assessing the potential degree of climate change that has been occurring over the present and earlier decades. This information can also be useful in calibrating and validating models that can be applied to anticipate and quantify the effects of climate change which may occur in future decades.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".