참고문헌 (18)

1. Liu Searching the strategy of responding to the extreme weather events from the archive of Formosat-2 remote sensing imagery Geology 2009 28 50 

2. Liu Processing of Formosat-2 daily revisit imagery for site surveillance IEEE Trans. Geosci. Remote Sens. 2006 10.1109/TGRS.2006.880625 44 3206 

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3. 10.1117/2.1201106.003758 Liu, C.-C., and Chen, N.-Y. (2011). Responding to natural disasters with satellite imagery. SPIE Newsroom. 

   

4. Chung Rapid response to a typhoon-induced flood with an sar-derived map of inundated areas: Case study and validation Remote Sens. 2015 10.3390/rs70911954 7 11954 

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5. Liu Rapidly responding to landslides and debris flow events using a low-cost unmanned aerial vehicle J. Appl. Remote Sens. 2015 10.1117/1.JRS.9.096016 9 096016 

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6. Lee The reconstruction of the processes of catastrophic disasters caused by the 2009 Typhoon Morakot J. Chin. Soil W. Conserv. 2011 42 313 

7. Liu Monitoring and assessment of barrier lakes formed after the Wenchuan earthquake based on multitemporal remote sensing data J. Appl. Remote Sens. 2009 10.1117/1.3153915 3 031665 

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8. 10.1007/978-3-642-32238-9 Ugai, K., Yagi, H., and Wakai, A. (2013). Mechanism of landslide-debris flow-barrier lake disaster chain after the Wenchuan earthquake. Earthquake-Induced Landslides: Proceedings of the International Symposium on Earthquake-Induced Landslides, Kiryu, Japan, 2012, Springer. 

   

9. Liu Image processing of Formosat-2 data for monitoring south asia tsunami Int. J. Remote Sens. 2007 10.1080/01431160601094518 28 3093 

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10. Liu Automatic extraction of ground control regions and orthorectification of Formosat-2 imagery Opt. Express 2009 10.1364/OE.17.007970 17 7970 

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11. Liu Change detection of gravel mining on riverbeds from the multi-temporal and high-spatial-resolution Formosat-2 imagery River Res. Appl. 2009 10.1002/rra.1210 25 1136 

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12. Chang Monitoring reservoir water quality with Formosat-2 high spatiotemporal imagery J. Environ. Monit. 2009 10.1039/b912897b 11 1982 

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13. Liu Vicarious calibration of the Formosat-2 remote sensing instrument IEEE Trans. Geosci. Remote Sens. 2010 10.1109/TGRS.2009.2033179 48 2162 

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14. Chung Spatiotemporal variation of gaoping river plume observed by Formosat-2 high resolution imagery J. Mar. Syst. 2014 10.1016/j.jmarsys.2013.12.011 132 28 

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15. Liu, C.-C. (2018, January 1-2). Towards an automatic change detection system for land use and land cover. Proceedings of the 2nd Sirindhorn Conference on Geoinformatics 2018, Chaeng Watthana, Bangkok, Thailand. 

16. 10.3390/rs9121336 Goldblatt, R., Rivera Ballesteros, A., and Burney, J. (2017). High spatial resolution visual band imagery outperforms medium resolution spectral imagery for ecosystem assessment in the semi-arid brazilian sertão. Remote Sens., 9. 

    

17. 10.3390/rs10060909 Markert, K., Schmidt, C., Griffin, R., Flores, A., Poortinga, A., Saah, D., Muench, R., Clinton, N., Chishtie, F., and Kityuttachai, K. (2018). Historical and operational monitoring of surface sediments in the lower mekong basin using landsat and google earth engine cloud computing. Remote Sens., 10. 

    

18. 10.3390/rs9101065 Xiong, J., Thenkabail, P., Tilton, J., Gumma, M., Teluguntla, P., Oliphant, A., Congalton, R., Yadav, K., and Gorelick, N. (2017). Nominal 30-m cropland extent map of continental africa by integrating pixel-based and object-based algorithms using sentinel-2 and landsat-8 data on google earth engine. Remote Sens., 9.