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Record W4308580604 · doi:10.3390/w14223593

Monitoring Shoreline and Land Use/Land Cover Changes in Sandbanks Provincial Park Using Remote Sensing and Climate Data

2022· article· en· W4308580604 on OpenAlexaffabout
Esmaeil Kouhgardi, Mohammadali Hemati, Elaheh Shakerdargah, Hodjat Shiri, Masoud Mahdianpari

Bibliographic record

VenueWater · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsCentre For Cold Ocean Resources EngineeringMemorial University of Newfoundland
Fundersnot available
KeywordsShoreClimate changeAccretion (finance)Change detectionLand coverPopulationPhysical geographyCoastal erosionEnvironmental scienceChange analysisLand useGeographyNational parkHydrology (agriculture)Remote sensingGeologyOceanographyEcology

Abstract

fetched live from OpenAlex

Climate change-driven forces and anthropogenic interventions have led to considerable changes in coastal zones and shoreline positions, resulting in coastal erosion or sedimentation. Shoreline change detection through cost-effective methods and easy-access data plays a key role in coastal management, where other effective parameters such as land-use/land-cover (LULC) change should be considered. This paper presents a remotely sensed shoreline monitoring in Sandbanks Provincial Park, Ontario, Canada, from 1984 to 2021. The CoastSat toolkit for Python and a multilayer perceptron (MLP) neural network classifier were used for shoreline detection, and an unsupervised change detection framework followed by a postclassification change detection method was implemented for LULC classification and change detection. The study assessed the recent coastal erosion and accretion trends in the region in association with spatiotemporal changes in the total area of the West and East Lakes, the transition between LULC classes, extreme climate events, population growth, and future climate projection scenarios. The results of the study illustrate that the accretion trend apparently can be seen in most parts of the study area since 1984 and is affected by several factors, including lake water-level changes, total annual precipitations, sand movements, and other hydrologic/climatic parameters. Furthermore, the observed LULC changes could be in line with climate change-driven forces and population growth to accelerate the detected accretion trend in the East and West Lakes. In total, the synergistic interaction of the investigated parameters would result in a greater accretion trend along with a lower groundwater table amid even a low carbon scenario. The discussed findings could be beneficial to regional/provincial authorities, policymakers, and environmental advocates for the sustainable development of coastal communities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.035
GPT teacher head0.231
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2022
Admission routes2
Has abstractyes

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