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Record W2154326988 · doi:10.1093/treephys/20.11.755

Use of middle infrared radiation to estimate the leaf area index of a boreal forest

2000· article· en· W2154326988 on OpenAlexfundno aff
Doreen S. Boyd, T. E. Wicks, Paul J. Curran

Bibliographic record

VenueTree Physiology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsnot available
FundersCanadian Forest ServiceNatural Environment Research Council
KeywordsLeaf area indexNormalized Difference Vegetation IndexTaigaEnvironmental scienceBorealVegetation (pathology)CanopyRemote sensingBoreal ecosystemEnhanced vegetation indexAtmospheric sciencesInfraredVegetation IndexGeographyForestryEcologyPhysicsBiology

Abstract

fetched live from OpenAlex

The leaf area index (LAI) of boreal forest can be estimated using reflected radiation recorded by satellite sensors. Measurements of visible and near infrared radiation are commonly used in the normalized difference vegetation index (NDVI) to estimate LAI. However, research, mainly in tropical forest, has demonstrated that LAI is related more closely to radiation of middle infrared wavelengths than of visible wavelengths. This paper derives a vegetation index, VI3, based on radiation from vegetation recorded at near and middle infrared wavelengths. For a boreal forest canopy, the relationship between VI3 and LAI was observed to be much stronger than that between NDVI and LAI. In addition, the LAI estimated using VI3 accounted for about 76% of the variation in field estimates of LAI, compared with about 46% when using the NDVI. We conclude that information provided by middle infrared radiation should be considered when estimating the leaf area index of boreal forest.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.225

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 head0.019
GPT teacher head0.240
Teacher spread0.220 · 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

Citations30
Published2000
Admission routes1
Has abstractyes

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