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Cyanobacteria Dataset of Random Forest Algorithm for Satellite Monitoring in Taihu Lake (2019)


YANG Zi1,2PAN Xin*3YUAN Jie1,2SONG Hao1,2XU Kun1,2WU YuHang1,2YANG YingBao*3
1 School of Earth Sciences and Engineering,Hohai University,Nanjing 211100,China2 Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing,Hohai University,Nanjing 211100,China3 School of Geography and Remote Sensing,Hohai University,Nanjing 211100,China

DOI:10.3974/geodb.2023.12.01.V1

Published:Dec. 2023

Visitors:2117       Data Files Downloaded:52      
Data Downloaded:30.53 MB      Citations:

Key Words:

GF6 satellite,Taihu Lake,cyanobacteria,random forest,2019

Abstract:

The cyanobacteria are essential and important factors for water resource management in Taihu Lake. The cyanobacteria dataset in Taihu Lake (2019) was developed using the GF-6 satellite images, integrated with the random forest method based on multiple remote sensing factors (Normalized Difference Vegetation Index and Normalized Difference Water Index). The dataset was validated using overall classification accuracy, Kappa coefficient, producer accuracy, user accuracy, misclassification error, and omission error. The validation results showed that the mean of overall classification accuracy and Kappa coefficient for this dataset reached 0.97 and 0.95, respectively. The dataset includes cyanobacteria distribution data from May to December in 2019 for six periods. The spatial resolution of the dataset is 20 meters. The dataset is archived in .tif format, consisting of 6 data files with data size of 0.98 MB (compressed into 1 file with 601 KB). The research paper based on the dataset was published in Journal of Lake Sciences, Vol. 34, No. 6, 2022.Browse

Foundation Item:

National Natural Science Foundation of China (41701487, 42071346, 42371397)

Data Citation:

YANG Zi, PAN Xin*, YUAN Jie, SONG Hao, XU Kun, WU YuHang, YANG YingBao*. Cyanobacteria Dataset of Random Forest Algorithm for Satellite Monitoring in Taihu Lake (2019)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2023. https://doi.org/10.3974/geodb.2023.12.01.V1.

YANG Zi, PAN Xin, YUAN Jie, et al. Dataset of blue algae in Taihu Lake based on random forest algorithm and satellite monitoring (2019) [J]. Journal of Global Change Data & Discovery, 2023, 7(3): 321-326

References:


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     [4] Wang, M., Zheng, W., Liu, C., Application of Himawari-8 data with high-frequency observation for Cyanobacteria bloom dynamically monitoring in Lake Taihu [J]. Journal of Lake Sciences, 2017, 29(5): 1043-1053.
     [5] Shi, H., Li, X. W., Niu, Z. C., et al. Remote sensing information extraction of aquatic vegetation in Lake Taihu based on Random Forest Model [J]. Journal of Lake Sciences, 2016, 28(3): 635-644.
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     [12] Yang, Z., Pan, X., You, C. S., et al. Spatio-temporal variation of fractional vegetation coverage in the Aydingkol Lake Basin [J]. Journal of Applied Remote Sensing, 2022, 16(1): 1-23.
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0Datapaper_Taihu_Cyanobacteria.pdf2379.00kbDownLoad
1 Taihu_Cyanobacteria.rar 601.14KB
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