城市河网尺度的水体光谱指数适宜性分析研究

doi:10.3964/j.issn.1000-0593(2019)11-3482-05

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Abstract Urban surface water is an important part of urban ecological environment. Hyperspectral remote sensing of surface water environment is an important application direction of hyperspectral remote sensing. Water extraction is the first step of hyperspectral remote sensing of surface water environment. Its main task is to obtain the contour of the surface water body from hyperspectral remote sensing data. The water body spectral index makes full use of the spectral information, and the calculation is simple, the implementation is easy, and the extraction effect is excellent. Spectral indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), hyperspectral difference water index (HDWI) and index of water index (IWI) have been widely used in the extraction of open water bodies such as lakes and large rivers. In recent years, with the development of imaging spectroscopy technology, the acquisition capability of hyperspectral remote sensing data has also advanced rapidly, and spatial resolution and spectral resolution have been continuously improved. The rivers and lakes are basically distributed along the topography in the basin while the urban surface water is generally small, criss-crossed, forming a river network. When hyperspectral remote sensing data are used for urban surface water extraction, the spatial resolution of the image, the type of features and the complexity of the ground objects are very different from those of rivers and lakes. Therefore, the applicability of these commonly used spectral indices in urban surface water extraction needs to be evaluated. This article is based on this starting point and goal, taking the Jiaxing City, Zhejiang Province in China, which is in Jiangnan Water Town and has a dense river network as the research object, and using the high spatial resolution airborne hyperspectral remote sensing data acquired by airborne imaging spectrometer for applications (AISA) as data source. The optimal threshold is determined by Youden index. The overall accuracy, commission error, omission error and Kappa coefficient are used as the accuracy evaluation indicators. The suitability of NDVI, NDWI, HDWI and IWI in urban river network extraction was analyzed and evaluated. The results show that the trend of the shadow spectrum is similar to the water spectrum, and is the main factor causing high commission errors in the water body extraction. All four indices accurately suppress the shadows that fall in the vegetation, but do not effectively suppress the shadows that fall in the buildings. Although HDWI can suppress shadows cast in buildings to a certain extent, it cannot effectively suppress the bright buildings. Through further analysis of the spectrum of different types of water bodies and (the ground objects under) shadows, the water and shadow spectral curves are similar, and there are peaks around 560～600 nm, but the heights of water and shadow peaks are different. The water wave peaks are larger while the peak value of the shadow wave is lower. Therefore, by fully excavating the spectrum reflectance information at 560～600 nm in water bodies and shadows, it is expected to further suppress building shadows and improve the accuracy of water extraction in urban river networks.

Key words：Urban river network; Hyperspectral water body extraction; Spectral index; Suitability analysis

Received: 2018-09-27 Accepted: 2019-01-22

ZTFLH:

TP753

Corresponding Authors: XIE Feng E-mail: xf@mail.sitp.ac.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)11-3482-05 OR https://www.gpxygpfx.com/EN/Y2019/V39/I11/3482

[1] HU Wei-guo, MENG Ling-kui, ZHANG Dong-ying, et al(胡卫国, 孟令奎, 张东映, 等). Remote Sensing for Land & Resources(国土资源遥感), 2014, 26(2): 43.
[2] Feyisa G L, Meilby H, Fensholt R, et al. Remote Sensing of Environment, 2014, 140: 23.
[3] Rokni K, Ahmad A, Selamat A, et al. Remote Sensing, 2014, 6(5): 4173.
[4] Jiang Hao, Feng Min, Zhu Yunqiang, et al. Remote Sensing, 2014, 6(6): 5067.
[5] WANG-Lin, ZHAO Dong-zhi, YANG Jian-hong(王林, 赵冬至, 杨建洪). China Environmental Science(中国环境科学), 2012, 32(1): 136.
[6] GUO Yu-long, LI Yun-mei, WANG Qiao, et al(郭宇龙, 李云梅, 王桥, 等). Acta Optica Sinica(光学学报), 2015, 35(11): 1101003.
[7] Mishra S, Mishra D R. Remote Sensing of Environment, 2012, 117: 394.
[8] McFeeters S K. International Journal of Remote Sensing, 1996, 17(7): 1425.
[9] Xie C, Huang X, Zeng W, et al. International Journal of Digital Earth, 2016, 1: 1.
[10] Xie Huan, Luo Xin, Xu Xiong, et al. Journal of Applied Remote Sensing, 2014, 8(1): 085098.
[11] JIA De-wei, ZHONG Shi-quan, LI Xue, et al(贾德伟，钟仕全，李雪，等). Science of Surveying and Mapping(测绘科学), 2011, 36(4): 128.
[12] YANG Wen-liang, YANG Min-hua, QI Hong-xia(杨文亮，杨敏华，祁洪霞). Science of Surveying and Mapping(测绘科学), 2012, 37(1): 148.
[13] YIN Ya-qiu, LI Jia-guo, YU-Tao, et al(殷亚秋，李家国，余涛，等). Bulletin of Surveying and Mapping(测绘通报), 2015，(1): 81.
[14] CHENG Tao, LIU Ruo-mei, ZHOU Xu(程滔, 刘若梅, 周旭). Bulletin of Surveying and Mapping(测绘通报), 2014，(4): 86.
[15] Waske B, van der Linden S, Benediktsson J A, et al. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(7): 2880.
[16] GUO Fei(郭飞). Modern Electronics Technique(现代电子技术), 2015, 38(10): 23.
[17] DU Ya-li, LI Dao-fan, LI Jing(杜雅丽, 李道帆, 李静). Guangdong Medical Journal(广东医学), 2003, 24(9): 993.