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Record W4282926118 · doi:10.1016/j.rse.2022.113121

Dynamic surface water maps of Canada from 1984 to 2019 Landsat satellite imagery

2022· article· en· W4282926118 on OpenAlexaffabout
Ian Olthof, Thomas Rainville

Bibliographic record

VenueRemote Sensing of Environment · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSurface waterRemote sensingHydrographySatellite imagerySatelliteThematic mapEnvironmental scienceWater resourcesScale (ratio)GeographyCartographyHydrology (agriculture)Geology

Abstract

fetched live from OpenAlex

Knowledge of the location of surface water in time and space is critical to inform policy on the environment, wildlife, and human welfare. Dynamic surface water maps have been created at continental to global scales from medium and coarse resolution satellite image archives, most notably by Pekel et al. (2016), who mapped global water dynamics from 1984 to 2015 Landsat data. Their occurrence layer, depicting the percentage of time that water was observed in each 30 m resolution cell, has been relied upon heavily by Natural Resources Canada's (NRCan) Emergency Geomatics Service (EGS) to map surface water during flood events. To generate dynamic surface water maps that are optimized for Canada's unique geography, the fully automated EGS surface water mapping methodology was applied in a cloud environment to the 1984–2019 Landsat archive over Canada. National-scale surface water maps and derived inundation frequency akin to Pekel's occurrence, as well as inter-annual wetting and drying trends calculated using per-pixel logistic regression, were produced to form the complete dataset. Separate comparisons of our frequency layer with Pekel's occurrence layer and Canada's water base layer from the National Hydrographic Network (NHN) were conducted. The comparison with Pekel's occurrence showed that our product contains a large number of unique water objects, the majority of which are correct when assessed against Google Earth (GE) imagery. Comparison with the NHN indicated that the NHN contained a large number of permanent water objects that were mapped as ephemeral water objects in our product, with the majority of these being verified in GE as true ephemeral water objects such as floodplains and wetlands. Wetting trends were found to be more than five times greater than drying trends across Canada, with notable wetting in the Prairie Pothole region and Low Arctic verified with examples of statistically significant wetting and drying features. The dataset will enhance EGS flood and river ice mapping operations, provide information on floodplain location and extent, and give insight into the effects of climate change on surface water availability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.177
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations57
Published2022
Admission routes2
Has abstractyes

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