Water Quality Mapping of Yamuna River Stretch Passing Through Delhi State Using High Resolution Geoeye-2 Imagery

S. Said, A. Hussain, G. Sharma
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引用次数: 2

Abstract

The present article utilizes high resolution Geoeye 2 imagery for mapping and monitoring pollution concentrations of 22 km stretch of river Yamuna passing through Delhi state, by developing regression models between water quality parameters (WQP's) and the corresponding spectral reflectance values. Water samples collected from the sampling locations were analysed for 20 WQP's and grouped into four classes namely; (WQP)organic, (WQP)inorganic, (WQP)anion and (WQP)cation. Several spectral band combinations as well as single bands were probed for performing multiple linear regression (MLR) analysis with the four WQP classes. Results reveal relatively strong positive correlations for band combination viz. [mean RGB × √B/R] with all four WQP classes yielding high R2 value (∼0.85) and RMSE (∼1.03) amongst other selected band combinations. Spatial distribution maps were generated that substantiates to the actual in-situ pollution concentration levels thereby evidences the potential of high resolution Geoeye-2 imagery for monitoring and mapping pollution concentrations in the water bodies.
利用高分辨率Geoeye-2图像对流经德里邦的亚穆纳河进行水质测绘
本文利用高分辨率Geoeye 2图像,通过开发水质参数(WQP)与相应光谱反射率值之间的回归模型,对流经德里邦的亚穆纳河22公里河段的污染浓度进行制图和监测。从采样地点收集的水样被分析为20个WQP,并分为四类,即;(WQP)有机,(WQP)无机,(WQP)阴离子和(WQP)阳离子。对四个WQP类进行多元线性回归(MLR)分析,探索了多个光谱波段组合以及单个波段。结果显示,波段组合(即[平均RGB ×√B/R])具有相对较强的正相关性,所有四个WQP类别在其他选择的波段组合中产生较高的R2值(~ 0.85)和RMSE(~ 1.03)。生成了与实际现场污染浓度水平相吻合的空间分布图,从而证明了Geoeye-2高分辨率图像在监测和绘制水体污染浓度方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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