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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Development of WTI and turbidity estimation model using SMA - application to Kushiro Mire, eastern Hokkaido, Japan
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Development of WTI and turbidity estimation model using SMA - application to Kushiro Mire, eastern Hokkaido, Japan

机译:使用SMA的WTI和浊度估算模型的开发-在日本北海道东部Ku路市泥潭中的应用

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A new water-turbidity index (WTI) based on multispectral images was developed and tested at Kushiro Mire, eastern Hokkaido, Japan. An algorithm for turbidity estimation was developed and applied to Landsat TM images to monitor the turbid water on the mire surface during the snow-melting season. We used spectral mixture analysis (SMA) to produce a turbidity estimation model. The SMA "unmixes" a mixed pixel determining the fractions due to each spectral end member. In this study, we used four end members (1, alder; 3, reed; 3, high-concentration turbid water (485 ppm); 4, low-concentration turbid water(10 ppm) measured in the test site. The WTI was determined by the following equation: WTI = a(max)/(a(max) + a(min)), where a(max) is abundance of high-concentration turbid water and a(min) is abundance of low concentration turbid water. The end-member spectra of alder and reed were measured in the laboratory using specimens collected at the test site. The spectrum of turbid water was measured at the test sites. The relative abundance of each end member was estimated based on this spectral information using SMA. The same formula was applied to Landsat TM images. Then we applied the WTI equation to the end-member images to obtain a WTI map. In the mire wetland region, turbid water spreads under alder trees and reed grasses. Tb verify our turbidity estimation method based on WTI under these conditions, we constructed a small experimental wetland consisting of mixed stands of alder acid reed. WTI was calculated from the mixed spectrum of this "artificial wetland" and the regression curve for the relation between WTI and the actual turbidity was determined (R-2=.91). Finally, this regression equation was used to derive a turbidity map from the WTI image. (C) 2001 Elsevier Science Inc. All rights reserved. [References: 30]
机译:在日本北海道东部的Ku路市,开发了一种基于多光谱图像的新浊度指数(WTI)并进行了测试。开发了一种浊度估算算法,并将其应用于Landsat TM图像,以监测融雪季节泥潭表面的浑浊水。我们使用光谱混合分析(SMA)来生成浊度估算模型。 SMA“解混”混合像素,确定归因于每个光谱末端成员的分数。在这项研究中,我们使用了四个末端成员(1 、,木,3,芦苇; 3,高浓度混浊水(485 ppm); 4,低浓度混浊水(10 ppm)。由以下公式确定:WTI = a(max)/(a(max)+ a(min)),其中a(max)是高浓度混浊水的丰度,而a(min)是低浓度混浊水的丰度在实验室中使用测试点收集的标本测量al木和芦苇的末端成员光谱,在测试地点测量浑浊水的光谱,并根据该光谱信息使用以下光谱信息估算每个末端成员的相对丰度SMA,将相同的公式应用于Landsat TM图像,然后将WTI方程应用于最终成员图像以获得WTI图,在泥泞湿地区域,浑浊的水在s木和芦苇草下扩散,Tb验证了我们的浊度在这些条件下基于WTI的估算方法,我们构建了一个小型实验湿地consi al木酸芦苇混合林的刺痛。从该“人工湿地”的混合光谱中计算出WTI,并确定了WTI与实际浊度之间关系的回归曲线(R-2 = .91)。最后,该回归方程用于从WTI图像中得出浊度图。 (C)2001 Elsevier Science Inc.保留所有权利。 [参考:30]

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