Abstract
Remote sensing technology has gradually become the mainstream means of dynamic monitoring of the large-scale surface environment by virtue of its large-scale, low-cost, and high-efficiency advantages. Based on the Google Earth Engine (GEE), using Landsat and Sentinel satellite images from 1990 to 2018 as the data source, selecting the Jiulong River Basin as the research area, calling CART (Classification And Regression Tree), Random Forest, Naive Bayes, Minimum Distance, Support Vector Machine and carry out accuracy evaluation and result analysis of the classification results. The results show that the land cover classification can be completed quickly based on the GEE platform, and each classification method can achieve better results; the support vector machine algorithm extraction results are the best, the overall accuracy is above 78%, and the Kappa is above 0.75; vegetation and field land is the main land type of Jiulong River Basin.
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References
Xu, D., et al.: J. Clean. Prod. 321, 128948 (2020)
Rawat, J.S., Biswas, V., Kumar, M.: Egypt. J. Remote Sens. Space Sci. 16(1), 111–117 (2013)
Xie, Z., Tang, L., Huang, Y., Huang, J.: J. Subtrop. Resour. Environ. 16(02), 1–9 (2016)
Gorelick, N., Hancher, M., Dixon, M.: Remote Sens. Environ. 202, 18–27 (2017)
Liu, Y.Y., Tian, T., Zeng, P.: Chin. J. Appl. Ecol. 31(9), 3163–3172 (2020)
Zou, D., Li, X., Kang, R., Luo, J.: Geomat. Spat. Inf. Technol. 44(S1), 100–102 (2021)
Su, Z.: Land Dev. Eng. Res. 5(01), 1–5 (2020)
Liang, M.: Beijing Surv. Mapp. 32(12), 1512–1516 (2018)
Rawat, J.S., Kumar, M.: Egypt. J. Remote Sens. Space Sci. 18(1), 77–84 (2015)
Huang, H., Lin, C., Yu, R., Yan, Y., Hu, G., Li, H.: RSC Adv. 9(26), 14736–14744 (2019)
Acknowledgements
This work was funded by National natural science foundation of China (No. 51809222) and the Natural Science Foundation of Fujian Province, China (No. 2020J01261) and the “Scientific Research Climbing Plan” Project from Xiamen University of Technology (No. XPDKT19010).
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Liao, Y., Liu, G., Luan, H., Zheng, M., Deng, G. (2022). Comparative Study on Remote Sensing Image Classifier of Jiulong River Basin. In: Bourennane, S., Kubicek, P. (eds) Geoinformatics and Data Analysis. ICGDA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-031-08017-3_8
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DOI: https://doi.org/10.1007/978-3-031-08017-3_8
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