[论文]于之锋等人.Comparative Study on Recognition Models of Black-Odorous Water in Hangzhou Based on GF-2 Satellite Data
Comparative Study on Recognition Models of Black-Odorous Water in Hangzhou Based on GF-2 Satellite Data
Yu, ZF (Yu, Zhifeng) ; Huang, QY (Huang, Qiyu) ; Peng, XX (Peng, Xiaoxue) ; Liu, HJ (Liu, Haijian) ; Ai, Q (Ai, Qin) ; Zhou, B (Zhou, Bin) ; Yuan, XH (Yuan, Xiaohong) ; Fang, MH (Fang, Meihong) ; Wang, B (Wang, Ben)
卷 22 期 12 文献号 4593
出版时间 JUN 2022
To improve the ability of remote sensing technology in recognizing black-odorous water bodies in Hangzhou, this study analyzed the typical spectral characteristics of black-odorous water in Hangzhou based on measured spectral data and water quality parameters, including the transparency, dissolved oxygen, oxidation reduction potential, and ammonia nitrogen. The single-band threshold method, the normalized difference black-odorous water index (NDBWI) model, the black-odorous water index (BOI) model, and the color purity on a Commission Internationale de L'Eclairage (CIE) model were compared to analyze the spatial and temporal distribution characteristics of the black-odorous water in Hangzhou. The results showed that: (1) The remote sensing reflectance of black-odorous water was lower than that of ordinary water, the spectral curve was gentle, and the wave peak shifted toward the near-infrared direction in the wavelength range of 650-850 nm; (2) Among the aforementioned models, the normalized and improved normalized black-odorous water index methods had a higher accuracy, reaching 87.5%, and the threshold values for black-odorous water identification were 0.14 and 0.1, respectively; (3) From 2015 to 2018, the quantity of black-odorous water in the main urban area of Hangzhou showed a decreasing trend, and black-odorous water was mainly distributed in the Gongshu District and tended to appear in narrow rivers, densely populated areas, and factory construction sites. This study is expected to be of great practical value for the rapid tracking and monitoring of urban black-odorous water by using remote sensing technology for future work.