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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake
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Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake

机译:机载高光谱数据,用于评估浅浊湖中的悬浮颗粒物和水生植物

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This paper presents an application of a physic-based method that relies on spectral inversion procedures to simultaneously estimate concentrations of water constituents, water column heights (cH) and benthic substrate types in Lake Trasimeno (Italy) from airborne imaging spectrometry. Complex waters of this lake are challenging due to the coexistence of optically-deep turbid waters and of optically-shallow waters, mostly characterised by dense submerged aquatic vegetation (SAV) beds. Airborne data acquired on 12 May 2009 by Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) were converted into remote sensing reflectance R-rs(lambda) with the atmospheric correction code ATCOR. A spectral inversion procedure implementing a bio-optical model (namely BOMBER), parameterised with in-situ-data; was-firstly run-to-retrieve concentrations of suspended particulate matter (SPM), chlorophyll-a (chl-a) and coloured dissolved organic matter (i.e. a(CDOM)(440)) in the optically-deep waters. The areas where the retrieved optimisation error was higher than 10% were instead assumed as optically-shallow. In these areas two maps depicting the linear unmixing of three substrate types (i.e., siltyclay, Chara ssp. and other hydrophyte) and the water column heights were produced. The MIVIS-derived products were validated with field data providing a reliable estimation of SPM, chl-a, a(CDOM)(440) and cH (determination coefficients always R-2 > 0.7). SPM concentrations were also similar to a 5.4-km long transect of flow-through turbidity data, and the SAV map was comparable to in situ observations. Generally, the colonisation patterns of SAV were reflecting the spatial distribution of SPM concentrations. In particular, the positive role of Chara on keeping SPM concentrations low was observed. Future research should extend this application to remote sensing data acquired in other seasons to trace the dynamics of SAV and its effect on spatial water clarity. (C) 2014 Elsevier Inc. All rights reserved.
机译:本文介绍了一种基于物理的方法的应用,该方法依赖于光谱反演程序,可以通过航空成像光谱同时估算特拉西梅诺湖(意大利)中水成分的浓度,水柱高度(cH)和底栖底质类型。该湖的复杂水域具有挑战性,这是因为浅色浑浊的水域和浅色浅色的水域并存,主要特征是密集的水下水生植物(SAV)床。 2009年5月12日通过多光谱红外和可见光成像光谱仪(MIVIS)获得的机载数据被转换为具有大气校正代码ATCOR的遥感反射率R-rs(λ)。一种实现生物光学模型(即BOMBER)的光谱反演程序,该模型使用原位数据进行参数化;首先是在光学较深的水中检索悬浮颗粒物(SPM),叶绿素a(chl-a)和有色溶解有机物(即a(CDOM)(440))的浓度。取回的优化误差高于10%的区域被假定为光学浅层。在这些区域中,绘制了两个图,描述了三种底物类型(即粉质粘土,Chara ssp。和其他水生植物)的线性解混和水柱高度。 MIVIS衍生产品已通过现场数据验证,可提供SPM,chl-a,a(CDOM)(440)和cH的可靠估计(测定系数始终为R-2> 0.7)。 SPM浓度也类似于5.4公里长的流通浊度样带,并且SAV图可与原位观测结果相媲美。通常,SAV的定殖模式反映了SPM浓度的空间分布。特别是,观察到Chara在保持SPM浓度较低方面的积极作用。未来的研究应将此应用程序扩展到其他季节获得的遥感数据,以追踪SAV的动态及其对空间水净度的影响。 (C)2014 Elsevier Inc.保留所有权利。

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