作者:Yao, FF (Yao, Fangfang)[ 1,2 ] ; Wang, C (Wang, Chao)[ 3 ] ; Dong, D (Dong, Di)[ 1,2 ] ; Luo, JC (Luo, Jiancheng)[ 1 ] ; Shen, ZF (Shen, Zhanfeng)[ 4 ] ; Yang, KH (Yang, Kehan)[ 1,2 ]

REMOTE SENSING

卷: 7  期: 9  页: 12336-12355

DOI: 10.3390/rs70912336

出版年: SEP 2015

摘要

Accurate information of urban surface water is important for assessing the role it plays in urban ecosystem services under the content of urbanization and climate change. However, high-resolution monitoring of urban water bodies using remote sensing remains a challenge because of the limitation of previous water indices and the dark building shadow effect. To address this problem, we proposed an automated urban water extraction method (UWEM) which combines a new water index, together with a building shadow detection method. Firstly, we trained the parameters of UWEM using ZY-3 imagery of Qingdao, China. Then we verified the algorithm using five other sub-scenes (Aksu, Fuzhou, Hanyang, Huangpo and Huainan) ZY-3 imagery. The performance was compared with that of the Normalized Difference Water Index (NDWI). Results indicated that UWEM performed significantly better at the sub-scenes with kappa coefficients improved by 7.87%, 32.35%, 12.64%, 29.72%, 14.29%, respectively, and total omission and commission error reduced by 61.53%, 65.74%, 83.51%, 82.44%, and 74.40%, respectively. Furthermore, UWEM has more stable performances than NDWI's in a range of thresholds near zero. It reduces the over- and under-estimation issues which often accompany previous water indices when mapping urban surface water under complex environmental conditions.

通讯作者地址: Luo, JC (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[ 3 ] Univ Puerto Rico, Dept Environm Sci, San Juan, PR 00931 USA

[ 4 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Natl Engn Res Ctr Geoinformat, Beijing 100101, Peoples R China