SPECTRAL FEATURE ANALYSIS FOR QUANTITATIVE ESTIMATION OF CYANOBACTERIA CHLOROPHYLL-A
Notice bibliographique
Résumé
In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. <i>Chlorophyll-a</i> is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of <i>chlorophyll-a</i> by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of <i>chlorophyll-a</i> with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. <br><br> In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding <i>chlorophyll-a</i> concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between <i>chlorophyll-a</i> and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria <i>chlorophyll-a</i>, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative estimation of <i>chlorophyll-a</i>, and more effective than the traditional single band model; the best regression models for SR, NDVI with <i>chlorophyll-a</i> are linear and power, respectively. Under the condition without water disturbance, the single band model works the best. For the SR index, there are two optimal band combinations, which is comprised of infrared (700nm-900nm) and blue-green range (450nm-550nm), infrared and red range (600nm-650nm) respectively, with band width between 45nm to 125nm. For NDVI, the optimal band combination includes the range from 750nm to 900nm and 700nm to 750nm, with band width less than 30nm. For single band model, band center located between 733nm-935nm, and its width mustn’t exceed the interval where band center located in. <br><br> This study proved , as for SR or NDVI, the centers and widths are crucial factors for quantitative estimating <i>chlorophyll-a</i>. As for remote sensor, proper spectrum channel could not only improve the accuracy of recognizing cyanobacteria bloom, but reduce the redundancy of hyperspectral data. Those results will provide better reference for designing the suitable spectrum channel of customized sensors for cyanobacteria bloom monitoring at a low altitude. In other words, this study is also the basic research for developing the real-time remote sensing monitoring system with high time and high spatial resolution.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».