耦合小波变换和偏最小二乘的悬浮物浓度和浊度高光谱建模方法

针对高光谱曲线中可能存在噪声以及传统半经验方法不能有效利用全部光谱信息的问题,提出了耦合Haar小波变换和偏最小二乘的水质遥感高光谱建模方法(Haar WT-PLS)。利用该方法,对在南四湖获取的实测高光谱数据经分解尺度为3的Haar小波变换后,将原始光谱数据压缩到47个特征变量;随后利用小波变换重构的光谱数据建立了悬浮物浓度和浊度的Haar WT-PLS反演模型,并进行了验证。结果表明:Haar WT-PLS反演悬浮物浓度和浊度精度较高,验证样本的均方根误差分别为25.05 mg/L和20.10NTU,平均相对误差分别为20.36%和13.88%。通过和单波段模型、一阶微分模型和波段比值模型进行精度对比分析,本文建立的Haar WT-PLS模型反演悬浮物浓度和浊度具有较高的精度和更好的稳定性。     

基于PLSR的珠江口城市河流水质高光谱反演

为研究珠江口城市河流水体高光谱特征与城市河流水质指数(CWQI)的关系,对中山市典型河流开展了高光谱监测和同步水质分析,基于偏最小二乘回归(PLSR)建立了高光谱数据与CWQI的反演模型,并研究了反演模型的最佳光谱分辨率和最优主成分数.结果 表明:基于化学需氧量、总磷、氨氮和溶解氧4项水质指标质量浓度值计算得到的CWQ...     

基于国产卫星多光谱影像的河流水体浊度遥感联合反演研究

基于集合建模思想,在水体光谱特征分析与敏感波段选择基础上,集成多种遥感反演模型优势,构建了河流水体浊度多光谱遥感联合反演模型(Combined Model-BP,CM-BP)。选择汉江中下游典型河段为研究区,利用2012—2013年原位观测数据,以具有较高时间分辨率和空间分辨率的国产卫星数据作为多光谱遥感数据源,测试评估了CM-BP浊度遥感反演模型适用性,并与传统波段组合模型进行精度比较,基于反演结果分析了研究区浊度时空分布特征。结果表明:基于集合建模思想构建的CM-BP模型的反演精度、适应性及稳定性均高于波段组合模型;从光谱分辨率、时空分辨率等角度考虑,环境与灾害监测预报小卫星星座系统、高分一号卫星、资源三号等国产卫星多光谱遥感数据是河流水体水质反演优选数据源;国产卫星遥感数据可以满足河流水体水质高精度、实时性与大尺度等遥感反演需要,为河流水环境监测研究提供数据基础。     

基于高光谱数据的汉江中下游典型河段水体悬浮物遥感反演

选择汉江中下游典型河段作为研究区域,利用2012—2013年实测水质数据及高光谱数据,基于有效信息变量筛选和神经网络算法构建研究区水体悬浮物浓度高光谱反演模型,分析评价了模型性能与估测效果,讨论了研究区水体悬浮物浓度分布特征。研究结果表明:基于变量投影重要性指数和神经网络优势构建的高光谱反演模型在反演精度、稳定性和适应性方面表现出优异的性能;而对于基于简单相关性分析构建的单波段模型和比值模型而言,建模样本选择对模型精度有较大影响,导致模型反演精度、稳定性和适用性较差;汉江中下游典型河段水体总悬浮物浓度整体上在18.8~187.0 mg/L之间变化,季节性差异明显,即春、夏两季悬浮物浓度低于秋季。     

南四湖水体叶绿素a浓度实用化高光谱反演模型

根据2014年7月21-23日在南四湖实测的高光谱数据和同步水质采样分析数据分别构建了反演南四湖水体叶绿素a浓度的单波段模型、一阶微分模型、波段比值模型、三波段模型以及四波段模型,并进行验证,同时尝试用连续统去除和反连续统去除法构建叶绿素a浓度反演模型。结果表明:R*728模型、R'705模型、R*728/R*617波段比值模型、R734/R463波段比值模型、[R-1rs(671)-R-1rs(730)]·Rrs(763)三波段模型以及[R-1rs(682)-R-1rs(720)]·[R-1rs(761)-R-1rs(733)]-1四波段模型均可以较好的估测南四湖水体叶绿素a浓度,均方根误差RMSE小于7μg/L,ARE在15%以内。其中各模型中线性模型估测精度最高,更适合南四湖水体叶绿素a浓度的高光谱反演;连续统去除和反连续统去除法无法建模。     

基于PCA和BP的水体Chl-a含量高光谱反演方法

利用主成分分析法对高光谱数据进行降维,将主成分得分作为输入,将水体20个采样点的叶绿素a(Chl-a)含量实测数据作为输出,对BP神经网络进行训练学习,实现压缩光谱数据与Chl-a含量的自适应非线性映射,并利用另外10个采样点数据对网络进行验证,结果表明预测值与实测值差距较小.     

基于小波变换的高光谱遥感影像光谱匹配技术研究

以中国的高光谱影像OMIS为例,研究了基于小波变换的高光谱遥感影像光谱匹配算法,然后将该方法用于遥感影像智能化解译。实验结果表明,该算法先对光谱曲线进行小波去噪,再对不同频率信号进行小波多分辨率分析,能够在匹配过程中拉大异类间的特征值距离,提高分类精度。     

基于新偏最小二乘回归法的系列水文资料分析

在实际问题中,经常会碰到海量数据或者样本点较少,自变量较多的数据。对此可以利用递阶偏最小二乘回归来建立线性模型。但是一个直接的问题是如何对自变量进行分组。由此提出了基于聚类分析的递阶偏最小二乘回归方法,在对解释变量分组时引入聚类分析。通过对长江宜昌段水沙观测数据作实证分析后发现,基于聚类分析的递阶偏最小二乘回归方法是有效可行的,而且用该方法建立的回归模型比一般的偏最小二乘回归模型拟合能力更强。     

基于无人机高光谱成像技术的河湖水质参数反演

以云南星云湖和深圳茅洲河为例,利用无人机高光谱成像仪获取研究区高光谱影像的同时对地面水质参数进行采样,构建水质参数的最佳双波段监测模型。结果表明：叶绿素a的模型精度最高（R2=0.99,RPD=7.55）,其次是悬浮物（R2=0.95,RPD=4.86）,总氮（R2=0.88,RPD=2.04）和总磷（R2=0.86,RPD=1.93）,最低是浊度（R2=0.60,RPD=1.65）。无人机高光谱成像技术为城市河流、湖泊的水质监测提供了全新的数据来源和技术手段,同时也为湖泊、河流的水环境保护及治理提供了依据。     

黄河高浊度水混凝沉淀试验的研究

介绍了高浊度水混凝沉淀时HPAM(水解聚丙烯酰胺 )一次投加、HPAM分步投加、PAC HPAM联合投加的试验。结果表明 :PAC HPAM联合投加与HPAM一次投加相比 ,处理后水的浊度可降低 1 15～ 9 2 7倍 ,有利于去除水中的有机物。并且在沉淀后浊度大致相等时 ,HPAM用量可减少 4 0 %～ 6 0 % ,有利于降低饮用水中单体丙烯酰胺含量和其它有机物 ,保证饮用水水质     

Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and aerial photography

The potential for mapping in-channel morphology within shallow gravel-bed rivers using airborne multispectral imagery and aerial photography is illustrated using a case study from the River Tummel, Scotland. The technique described relies on a good correlation between observed light reflectance levels from a water body and water depth. Measured water depths are regressed against reflectance levels derived from airborne multispectral imagery and black-and-white aerial photographs, to obtain equations that can be used for mapping channel bathymetry. The technique has a great deal of potential for wide-ranging applications, including detailed morphological surveys, assessing in-channel changes and mapping riverine habitats.© 1997 John Wiley & Sons, Ltd.     

基于 Landsat-TM 卫星影像的中亚水体动态变化研究

当今世界卫星遥感和图像处理技术迅速崛起并飞速发展,利用卫星遥感数据对水资源进行分析和监测已成为必然趋势。中亚五国地处欧亚大陆核心腹地,是丝绸之路经济带的陆上要塞,地区内常年干旱少雨,国家间水体面积差异显著。利用Landsat-TM/ETM+卫星遥感影像数据,基于决策树分类方法,进行提取中亚水体信息,选取中亚主要湖泊、河渠作为典型研究区,研究2000年、2010年和2015年的水体动态变化。研究结果表明,中亚水体总面积为123 984.80km2,其中湖泊面积为111 856.63km2,河渠面积为6 323.59km2,自然坑塘面积为5 804.58km2,湖泊、河渠和自然坑塘面积分别占中亚水体总面积的90.21%、5.10%和4.68%;中亚地区除乌兹别克斯坦国水资源充足外,其余国家水资源相对匮乏;典型研究区中咸海、额尔齐斯河和乌拉尔河变差系数相对较大,分别为41.18%、24.82%和14.68%;总体来看,中亚水体面积正逐年减少,研究中亚水体分布及动态变化有助于推进"一带一路"战略。     

Application of airborne multispectral digital imagery to quantify riverine habitats at different base flows

We employed an integrated system of airborne remote sensing and ground surveys for regional mapping of instream habitats under variable flows over a 70 km section of the Lower Yakima River in southern Washington, USA. Airborne multispectral digital imagery was obtained in conjunction with field survey measurements and used to quantify the spatial extent, condition and temporal changes of selected river habitat characteristics under two different flows (14 and 28 m³ s^?1). Under each flow, geomorphic measures were quantified (e.g. channel complexity, number and size of habitats). Water depth and velocity were also classified for instream habitats, and temperature and turbidity were recorded. Remote sensing classification accuracies for islands, exposed rocks and water surfaces were greater than 99%, while more detailed depth/flow classifications were less accurate (68% and 72%, for the high and low flows, respectively). While high turbidity (>4 Nephelometric Turbidity Units (NTU)), shadows and bidirectional reflectance factor reduced classification accuracies, the overall effect of these factors was minimal. Under the low‐flow situation, off‐channel habitats were less abundant, more isolated and had shallower depths and warmer temperatures. Our analysis suggests that airborne multispectral imagery, coupled with appropriate ground truth data, can be a viable method for regional mapping of diverse riverine habitats under variable flows. We concluded from this analysis that the higher flow situation provided substantially better habitat than currently exists in the Lower Yakima River. Copyright © 2002 John Wiley & Sons, Ltd.     

基于BP神经网络的温榆河水质参数反演模型研究

为进一步提高内陆水体水质参数遥感反演的准确性,北京市温榆河被选为研究对象,研究选取ETM＋数据和准同步实测水质指标（浊度、BOD;）数据,建立了多个隐含层数目为1的BP神经网络模型,并选出分别针对浊度和BOD5的最佳神经网络模型,利用ETM＋影像的波段组合值反演了浊度和BOD,浓度值。最后将其反演结果与常规多元线性回归模型的反演结果进行精度比较。结果表明：温榆河的水质参数遥感反演为非线性问题,使用BP神经网络方法进行浊度与BOD,两种水质参数反演的结果优于线性回归方法的反演结果。     

Spectrally based bathymetric mapping of a dynamic,sand‐bedded channel: Niobrara River,Nebraska, USA

Methods for spectrally based mapping of river bathymetry have been developed and tested in clear‐flowing, gravel‐bed channels, with limited application to turbid, sand‐bed rivers. This study used hyperspectral images and field surveys from the dynamic, sandy Niobrara River to evaluate three depth retrieval methods. The first regression‐based approach, optimal band ratio analysis (OBRA), paired in situ depth measurements with image pixel values to estimate depth. The second approach used ground‐based field spectra to calibrate an OBRA relationship. The third technique, image‐to‐depth quantile transformation (IDQT), estimated depth by linking the cumulative distribution function (CDF) of depth to the CDF of an image‐derived variable. OBRA yielded the lowest depth retrieval mean error (0.005 m) and highest observed versus predicted R² (0.817). Although misalignment between field and image data did not compromise the performance of OBRA in this study, poor georeferencing could limit regression‐based approaches such as OBRA in dynamic, sand‐bedded rivers. Field spectroscopy‐based depth maps exhibited a mean error with a slight shallow bias (0.068 m) but provided reliable estimates for most of the study reach. IDQT had a strong deep bias but provided informative relative depth maps. Overprediction of depth by IDQT highlights the need for an unbiased sampling strategy to define the depth CDF. Although each of the techniques we tested demonstrated potential to provide accurate depth estimates in sand‐bed rivers, each method also was subject to certain constraints and limitations.     

Spatial and temporal scales of variability of cyanobacteria harmful algal blooms from NOAA GLERL airborne hyperspectral imagery

NOAA GLERL has routinely flown a hyperspectral imager to detect cyanobacteria harmful algal blooms (cyanoHABs) over the Great Lakes since 2015. Three consecutive years of hyperspectral imagery over the Great Lakes warn drinking water intake managers of the presence of cyanoHABs. Western basin imagery of Lake Erie contributes to a weekly report to the Ohio Environmental Protection Agency using the cyanobacteria index (CI) as an indicator of the presence of cyanoHABs. The CI is also used for the weekly NOAA NCCOS cyanoHAB Lake Erie bulletin applied to satellite data. To date, there has not been a sensor comparison to look at the variability between the satellite and hyperspectral imagery on a pixel-by-pixel basis, as well as a time scale comparison between measurements from buoys and shipboard surveys. The spatial scale is a measure of size of a cyanobacteria bloom on a scale of meters to kilometers. The change in the spatial scale or spatial variability has been quantified from satellite and airborne imagery using a decorrelation scale analysis to find the point at which the values are not changing or are not correlated with each other. The decorrelation scales were also applied to the buoy and shipboard survey data to look at temporal scales or changes in time on hourly to daytime scales for blue-green algae, chlorophyll and temperature. These scales are valuable for ecosystem modelers and for those initiating sampling efforts to optimize sampling plans and to infer a potential mechanism in an observational study from a synoptic viewpoint.     

紊流浑水异重流交混区的阻力系数的研究

为进一步研究紊流浑水异重流交混区阻力规律,并简化该阻力计算方法,通过对紊流浑水异重流交混区上下两部分流速分布的研究,提出了最大流速点以上流速分布修正公式及适合高含沙水流的最大流速点以下流速分布公式。在此基础上对紊流浑水异重流交混区阻力系数进行了探讨,推导出了水库紊流异重流交界面阻力系数公式,并对其进行了讨论,论证了公式的合理性。并利用天然实测资料和实验室异重流试验数据对公式进行了验证,结果表明:推导出的异重流交界面阻力系数计算方法可以较好地模拟异重流交界面阻力变化规律。     

被动微波遥感土壤水分反演研究进展

准确获取区域土壤水分时空分布和变化对于水文过程模拟、洪旱灾害监测等方面具有重要意义.近几十年,快速发展的遥感技术为大区域地表土壤水分连续观测提供了机遇.尤其,被动微波遥感具有诸多优势,被认为是进行大区域表层土壤水分连续监测的最有潜力的手段.国内外学者对被动微波遥感土壤水分反演开展了大量的研究.为了更好了解国内外研究前沿...