Study on the applicability of FAI linear fitting model in the extraction of cyanobacterial blooms.
Cyanobacterial blooms
FAI-L
Lake
NDVI
Remote sensing
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
09 Sep 2024
09 Sep 2024
Historique:
received:
14
03
2024
accepted:
31
08
2024
medline:
9
9
2024
pubmed:
9
9
2024
entrez:
9
9
2024
Statut:
epublish
Résumé
Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.
Identifiants
pubmed: 39249606
doi: 10.1007/s10661-024-13082-3
pii: 10.1007/s10661-024-13082-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
909Subventions
Organisme : Anhui University of Science and Technology Master's and Doctor's Fund Projects
ID : ZY030
Organisme : the National Key Research and Development Project of China
ID : 2018YFC0407703-1
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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