TAILIEUCHUNG - Mapping land cover using multi-temporal sentinel-1A data: A case study in Hanoi
In this article, the authors present a new approach to the classification of land cover by using multi-temporal Sentinel-1A data. The experience data are single-pole (VV) in Interferometric Wide Swath mode (IW) collected from December 2014 to October 2015 along descending orbit over Hanoi, Vietnam. Decision tree method is applied base on analyzing threshold of standard deviation, mean backscatter value of land cover patterns, and combining double-crop rice classification image. | Vietnam Journal of Earth Sciences, 39(4), 345-359, DOI: Vietnam Academy of Science and Technology (VAST) Vietnam Journal of Earth Sciences Mapping land cover using multi-temporal sentinel-1A data: A case study in Hanoi Le Minh Hang*1, Vu Van Truong1, Nguyen Dinh Duong2, Tran Anh Tuan3 1 Military Technical Academy, 236 Hoang Quoc Viet street, Cau Giay, Hanoi, Vietnam Institute of Geography (VAST), Hanoi, Vietnam 3 Institute of Ecology and Biological Resources (VAST), Hanoi, Vietnam 2 Received 22 November 2016. Accepted 01 September 2017 ABSTRACT Land cover mapping is one of the most important applications of both optical and microwave remote sensing. The optical remote sensing recognizes land cover objects using spectral reflectance of the material constituting the land cover. The microwave remote sensing recognizes ground objects using backscatter, of which the intensity depends on the roughness of the ground’s surface. Therefore, the multi temporal SAR images owning a lot of phenology information of land cover are the potential ideal data source for land cover mapping, in particular in the urban area. In this article, the authors present a new approach to the classification of land cover by using multi-temporal Sentinel-1A data. The experience data are single-pole (VV) in Interferometric Wide Swath mode (IW) collected from December 2014 to October 2015 along descending orbit over Hanoi, Vietnam. Decision tree method is applied base on analyzing threshold of standard deviation, mean backscatter value of land cover patterns, and combining double-crop rice classification image. The double-crop rice image is classified by rice phenology using multi-temporal Sentinel-1A images. The threshold in decision tree method is analyzed by field surveying data. The resulting classified image has been assessed using the test points in high-resolution images of Google Earth and field data. The accuracy of .
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