Remote Sensing of Urban Lake Water Quality: A Preliminary Result from Spectral Angle Based Approach
Weiqi Chen
Department of Geography and Anthropology, Louisiana State University, Baton Rouge, USA
Xuelian Meng
Department of Geography and Anthropology, Louisiana State University, Baton Rouge, USA
Shuisen Chen *
Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangdong Key Laboratory of Remote Sensing and GIS Technology Application, Guangzhou Institute of Geography, Guangzhou, China and College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangdong, China
Jia Liu
Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangdong Key Laboratory of Remote Sensing and GIS Technology Application, Guangzhou Institute of Geography, Guangzhou, China
*Author to whom correspondence should be addressed.
Abstract
This study aims to develop a quick method based on spectral angle (SA) to evaluate overall water quality and spatial variation in urban lakes using in-situ water quality parameters of lakes or reservoirs and synchronous SPOT 5 remote sensing imagery, referring to the spectrum of a clear montanic Jiulongtan Reservoir in satellite image. The regression models between SA and water quality parameters were built for analysis, including chlorophyll a (Chl-a), chemical oxygen demand (COD), total phosphorous (TP), total nitrogen (TN) and integrated trophic state index (TSI). The results show that the grades of lake water quality in Guangzhou could be ordered by SA values from most desirable to least desirable as Jiulongtan Reservoir, Luhu Lake, Liwan Lake, Liuhua Lake, and Dongshan Lake. Further, the results also show that the SA of urban lakes correlates potentially with the parameters of water quality (Chl-a, R2 = 0.929569, p < 0.01; COD, R2 = 0.9767916, p < 0.01; TN, R2 = 0.58767495, p < 0.05; TP, R2 = 0.8705, p < 0.05) or TSI (R2 = 0.9066, p < 0.001) in spite of limited data samples collected in the study. The SA classification results by SPOT 5 multi-spectral images roughly reflect the grade difference of water quality as a whole and their spatial variations, i.e. consistent with concurrent result of lake water sampling analysis. The validation shows this approach can be helpfully used to quickly monitor the water quality status of lakes or reservoirs for broad region, to effectively identify the sampling locations for water sample taking and water quality analysis, and provide information for the management of urban lakes (by SPOT 5 or higher resolution satellite image) or large-middle sized reservoir (by Landsat ETM+).
Keywords: Remote sensing, spectral angle, urban lake, reservoir, water quality, trophic state index (TSI)