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Record W2026327286 · doi:10.1080/01431160152518642

Application of vertical skyward wide-angle photography and airborne video data for phenological studies of beech forests in the German Alps

2001· article· en· W2026327286 on OpenAlexfundno aff
Petri Pellikka

Bibliographic record

VenueInternational Journal of Remote Sensing · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsnot available
FundersCanadian Forest ServiceJenny ja Antti Wihurin RahastoAcademy of Finland
KeywordsBeechCanopyEnvironmental scienceRemote sensingPhenologyPhotosynthetically active radiationTree canopyGeographyForestryEcology

Abstract

fetched live from OpenAlex

Vertical skyward wide-angle photography, photosynthetically active radiation (PAR) measurements and analysis of leaf parameters were used as ground data for the assessment of phenological change of montane beech forest using airborne video data in the German Alps. The main objectives of the study were: (1) to test the feasibility of wide-angle photographs as ground data for remotely sensed data, (2) to evaluate the feasibility of airborne video data in a change detection study, and (3) to study the phenology of beech forests in different elevation zones to acquire information for regional photosynthesis and evapotranspiration studies. The results showed an increase in canopy closure from April to early June, no changes during the summer months, and a slow decrease in autumn. The results also showed a strong correlation between canopy closure estimation using the photographic method, PAR measurements, and leaf variables. The adjusted r 2 values between photographic canopy closure estimation and PAR ratio ranged from 0.72 to 0.88 and between canopy closure estimation and leaf size from 0.77 to 0.88. The problem of using photographic canopy closure estimation in phenological studies is that canopy closure is difficult to compare with spectral information of remote sensing data. The estimations had high correlation (r 2=0.71) only with the red band of the airborne data, while with green and the near-infrared (NIR) band the correlations were very weak.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.034
GPT teacher head0.322
Teacher spread0.288 · 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 designOther design
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

Citations13
Published2001
Admission routes1
Has abstractyes

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