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Record W1592469096 · doi:10.3390/rs70709410

Potential of C and X Band SAR for Shrub Growth Monitoring in Sub-Arctic Environments

2015· article· en· W1592469096 on OpenAlexafffundabout
Yannick Duguay, Monique Bernier, Esther Lévesque, Benoît Tremblay

Bibliographic record

VenueRemote Sensing · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMinistère des Ressources naturelles et des ForêtsUniversité LavalUniversité du Québec à Trois-RivièresInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyCentre National de la Recherche ScientifiqueArcticNet
KeywordsShrubTundraArcticVegetation (pathology)Arctic vegetationRemote sensingThe arcticEnvironmental sciencePhysical geographyGeologyGeographyEcologyOceanographyBiology

Abstract

fetched live from OpenAlex

The Arctic and sub-Arctic environments have seen a rapid growth of shrub vegetation at the expense of the Arctic tundra in recent decades. In order to develop better tools to assess and understand this phenomenon, the sensitivity of multi-polarized SAR backscattering at C and X band to shrub density and height is studied under various conditions. RADARSAT-2 and TerraSAR-X images were acquired from November 2011 to March 2012 over the Umiujaq community in northern Quebec (56.55°N, 76.55°W) and compared to in situ measurements of shrub vegetation density and height collected during the summer of 2009. The results show that σ0 is sensitive to changes in shrub coverage up to 20% and is sensitive to changes in shrub height up to around 1 m. The cross-polarized backscattering (σ0 HV ) displays the best sensitivity to both shrub height and density, and RADARSAT-2 is more sensitive to shrub height, as TerraSAR-X tends to saturate more rapidly with increasing volume scattering from the shrub branches. These results demonstrate that SAR data could provide essential information, not only on the spatial expansion of shrub vegetation, but also on its vertical growth, especially at early stages of colonization.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.377

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.243
Teacher spread0.198 · 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.

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

Citations51
Published2015
Admission routes3
Has abstractyes

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