Remote sensing of leaf area index : enhanced retrieval from close-range and remotely sensed optical observations
Notice bibliographique
Résumé
A wide range of models used in agriculture, ecology, carbon cycling, climate and other related studies require information on the amount of leaf material present in a given environment to correctly represent radiation, heat, momentum, water, and various gas exchanges with the overlying atmosphere or the underlying soil. Leaf area index (LAI) thus often features as a critical land surface variable in parameterisations of global and regional climate models, e.g., radiation uptake, precipitation interception, energy conversion, gas exchange and momentum, as all areas are substantially determined by the vegetation surface. Optical wavelengths of remote sensing are the common electromagnetic regions used for LAI estimations and generally for vegetation studies. The main purpose of this dissertation was to enhance the determination of LAI using close-range remote sensing (hemispherical photography), airborne remote sensing (high resolution colour and colour infrared imagery), and satellite remote sensing (high resolution SPOT 5 HRG imagery) optical observations. The commonly used light extinction models are applied at all levels of optical observations. For the sake of comparative analysis, LAI was further determined using statistical relationships between spectral vegetation index (SVI) and ground based LAI. The study areas of this dissertation focus on two regions, one located in Taita Hills, South-East Kenya characterised by tropical cloud forest and exotic plantations, and the other in Gatineau Park, Southern Quebec, Canada dominated by temperate hardwood forest. The sampling procedure of sky map of gap fraction and size from hemispherical photographs was proven to be one of the most crucial steps in the accurate determination of LAI. LAI and clumping index estimates were significantly affected by the variation of the size of sky segments for given zenith angle ranges. On sloping ground, gap fraction and size distributions present strong upslope/downslope asymmetry of foliage elements, and thus the correction and the sensitivity analysis for both LAI and clumping index computations were demonstrated. Several SVIs can be used for LAI mapping using empirical regression analysis provided that the sensitivities of SVIs at varying ranges of LAI are large enough. Large scale LAI inversion algorithms were demonstrated and were proven to be a considerably efficient alternative approach for LAI mapping. LAI can be estimated nonparametrically from the information contained solely in the remotely sensed dataset given that the upper-end (saturated SVI) value is accurately determined. However, further study is still required to devise a methodology as well as instrumentation to retrieve on-ground green leaf area index . Subsequently, the large scale LAI inversion algorithms presented in this work can be precisely validated. Finally, based on literature review and this dissertation, potential future research prospects and directions were recommended.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».