Quantification and reduction of bidirectional effects in aerial cir imagery of deciduous forest using two reference land surface types
Notice bibliographique
Résumé
Abstract Bidirectional effects in airborne remote sensing data are governed by land surface reflectance characteristics, atmospheric scattering and the sun‐object‐sensor angular relationship. These factors introduce brightness variations in the data that render quantitative analysis more difficult. On the other hand, bidirectional reflectance distribution functions (BRDF) of land surfaces provide information about the physical characteristics of the surface. This study has two objectives. The first is to reduce brightness variations in aerial false colour imagery using empirical methods. The second is to test if bidirectional effects must be modelled using sample data only for the land surface type under analysis or can another surrogate land cover type, which may be easier to sample in the imagery, be used. The method is based on multiple view angle imaging of two reference land surfaces using highly overlapping stereo frame format imagery. Reduction of BRDF effects includes derivation of a quantification factor used in a formula based on the Rayleigh scattering function. The performance of the method is determined by evaluating the residual variations in mean digital number (DN) of several test plots located in two overlapping images. The study area is a temperate deciduous forest damaged to varying degrees by a severe ice storm in 1998. For sixteen test plots throughout the forest, it was found that the mean DN values of the plots in the two images became more similar when the correction model was derived from samples of the same deciduous forests. However, a BRDF model derived from samples of vegetated fields nearby reduced the variations between images for some of the sixteen forest plots more than the forest BRDF model. These plots were less damaged, had high canopy cover, and were located on steep slopes oriented towards the sun. They had scattering characteristics more similar to the fields than to the deciduous forest as a whole. Two principal conclusions were derived. First, it is essential that the model used to derive the BRDF correction be from the same land cover as the land cover type under study. Second, the suitability of the quantification factors for the BRDF correction provided additional information about the forest structure and damage level. Key words: Remote sensingBRDFAerial photographyCalibrationForest damageForest structure Notes Corresponding author. Tel.: +358–2‐333–5638, Fax: +358–2‐333–5896, e‐mail: petri. pellikka@utu.fi
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| 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) | 0,000 | 0,000 |
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 tête enseignante, 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 ».