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Data Details

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：4616       Data Files Downloaded：59      
Data Downloaded：34.64 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：


     [1] Qin, B. Q., Gao, G., Zhu, G. W., et al. Lake eutrophication and its ecosystem response [J]. Chinese Science Bulletin, 2013, 58(10): 855-864.
     [2] Zhu, L. Y. Remote Sensing Monitoring and Assessment of Water Quality for Lakes [D]. Beijing: Institute of Remote Sensing Applications, Chinese Academy of Sciences. 2006
     [3] Yang, Y., Hang, W. L., Xie, H. B., et al. A study on water information extraction method of cyanobacteria lake based on Landsat8 [J]．Remote Sensing for Land and Resources, 2020, 32(4): 130-136.
     [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.
     [6] Xia, X. R., Wei, Y. C., Xu, N., et al. Decision tree model of extracting blue-green algal blooms information based on Landsat TM/ETM+ imagery in Lake Taihu [J]. Journal of Lake Sciences, 2014, 26(6): 907-915.
     [7] Huang, J. Z., Zhao, R., Satellite remote sensing monitoring of cyanobacteria outbreak in Taihu Lake [J]. Remote Sensing Information, 1999(4): 43-43.
     [8] Li, X. W., Shi, H., Zhang, Y., et al. Cyanobacteria blooms monitoring in Taihu Lake based on the Sentinel-2A satellite of European Space Agency [J]. Environmental Monitoring in China, 2018, 34(4): 169-176.
     [9] Miao, S., Wang, R., Li, J. C., et al. Retrieval algorithm of phycocyanin concentration in inland lakes from Sentinel 3A-OLCI images [J]. Journal of Infrared and Millimeter Waves, 2018, 37(5): 621-630.
     [10] Li, X. Z., Lv, H., Li, Y. M., et al. Spatial scale difference analysis of cyanobacteria bloom extraction based on MODIS and GOCI data [C]. Jiangsu Society of Oceanology and Limnology, 2013.
     [11] Li, Y. C., Sun, J. L., Xie, Z. Q., et al. Extraction methods of cyanobacteria bloom in Lake Tai based on MODIS vegetation index. Journal of the Meteorological Sciences, 2011, 31(6): 737-741.
     [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.
     [13] Breiman, L. Random Forests [J]. Machine Learning, 2001, 45(1): 5-32. DOI: https://doi.org/10.1023/A:1010933404324.
     [14] Yin, J., Zhu, Y. F. Comparative study of water extraction methods in different regions of OLI images [J]. Jiang Xi Science, 2020, 38(5): 743-747.
     

Data Product:

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0Datapaper_Taihu_Cyanobacteria.pdf2379.00kbDownLoad
1 Taihu_Cyanobacteria.rar 601.14KB
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