Estimation of leaf area index using ground-based remote sensed NDVI measurements: validation and comparison with two indirect techniques
Bibliographic record
Abstract
AbstractThis study took place in an evergreen scrub oak ecosystem in Florida. Vegetation reflectance was measured in situ with a laboratory-made sensor in the red (640‐9665 nm) and near-infrared (750‐9950 nm) bands to calculate the normalized difference vegetation index (NDVI) and derive the leaf area index (LAI). LAI estimates from this technique were compared with two other nondestructive techniques, intercepted photosynthetically active radiation (PAR) and hemispherical photographs, in four contrasting 4 m2 plots in February 2000 and two 4 m2 plots in June 2000. We used Beer's law to derive LAI from PAR interception and gap fraction distribution to derive LAI from photographs. The plots were harvested manually after the measurements to determine a "true" LAI value and to calculate a light extinction coefficient (k). The technique based on Beer's law was affected by a large variation of the extinction coefficient, owing to the larger impact of branches in winter when LAI was low. Hemispherical photographs provided satisfactory estimates, slightly overestimated in winter because of the impact of branches or underestimated in summer because of foliage clumping. NDVI provided the best fit, showing only saturation in the densest plot (LAI = 3.5). We conclude that in situ measurement of NDVI is an accurate and simple technique to nondestructively assess LAI in experimental plots or in crops if saturation remains acceptable.A l'aide d'un capteur réalisé au laboratoire, nous avons mesuré in situ la réflectance d'un peuplement bas de chênes à 655 et 825 nm pour calculer la NDVI et estimer le LAI. Ces estimations de LAI ont été comparées à celles obtenues par deux autres techniques non destructrices dans six placettes de 4 m2, en février et juin 2000 : la mesure du PAR intercepté (rayonnement photosynthétiquement actif) et la technique des photographies hémisphériques. Pour calculer le LAI, nous avons utilisé la loi de Beer à partir du PAR intercepté et la fraction de trouées pour exploiter les photographies. Les feuilles de chaque placette ont été récoltées à la main après les mesures afin d'obtenir un LAI de référence et de calculer un coefficient d'extinction (k). La technique du PAR intercepté a été affectée par une importante variation du coefficient d'extinction, due à l'interception plus forte des branches en hiver lorsque le LAI était peu élevé. Les photographies hémisphériques ont fourni des valeurs de LAI satisfaisantes, légèrement surestimées en hiver à cause des branches et sous-estimées en été à cause de l'agrégation du feuillage. Le NDVI a fourni les meilleurs résultats, montrant toutefois une tendance à la saturation dans la placette la plus dense.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".