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1.
通过对实测光谱数据和水深数据的分析研究发现,泥沙浓度在水深遥感中具有重要意义。泥沙含量决定水体光谱反射率与 水深的相关性方向: 泥沙含量较少的清水区,光谱反射率与水深呈负线性相关; 而在泥沙含量较高的浊水区,光谱反射率与水深 数据呈正线性相关。以此为依据,对水体进行清/浊水体的光谱分别处理,可提高水深的光谱反演精度。同时,结合实测水体光谱 进行不同光谱分辨率的模拟分析,发现高光谱分辨率遥感将有助于提高水深反演精度。  相似文献   

2.
传统测量水中悬浮物质如泥沙、叶绿素等的方法是通过大面积、大范围的逐点采样,存在着速度慢、周期长、成本较高等不足,不利于进行区域的科学研究。而利用水体光谱分析能够克服以上诸多缺点。本文分析了含沙水体、含叶绿素水体及污染水体等水体的光谱特征,阐述了利用水体光谱的分析来测定水中泥沙、叶绿素含量的方法和原理,从而说明水体光谱分...  相似文献   

3.
利用细分光谱仪数据分析水体泥土含量的方法研究   总被引:8,自引:1,他引:8  
分析了水中泥土含量对水体光谱特征的影响。对利用水体的光谱反射率计算水体中泥土含量的方法进行了分析,找出了用于泥土含量计算的最佳波段。该方法对今后的利用高光谱遥感图像计算河流和海水中的泥沙含量及估算水土流失具有一定的参考意义。  相似文献   

4.
中国近岸浑浊水体大气修正的迭代与优化算法   总被引:5,自引:0,他引:5  
中国近岸水体泥沙含量较高,且变化梯度大;泥沙浓度可从几千mg/l变化到1mg/l以下。针对上述浑浊二类水体的大气修正一直是水色遥感应用的难点之一。利用水体近红外固有光学特性的Arnone光谱迭代算法在泥沙含量较低时适用,其近红外光谱迭代关系与根据2003年春季黄东海水色试验数据得出的现场光谱关系大致相当。当泥沙含量达到某一程度时,该算法失效,导致蓝光等较短波段的离水辐亮度为负。根据现场数据分析结果,这一分界点大概在泥沙浓度10—20mg/l。因此,本文首次提出将中国近岸浑浊水体进一步区分为中低和中高浑浊水体,并给出初步的划分标准,采用光谱优化方法对中高浑浊水体进行水色大气修正。优化误差函数的选取以现场试验获取的可见光波段光谱关系式为基础。结果表明,优化算法在近岸高浑浊水体可给出满足光谱分布规律的反演结果。与其他大气校正方法一样,优化方法也需要进一步的微调。将Gordon标准算法、Arnone光谱迭代和优化方法结合,对SeaWiFS图像进行处理,分别得出归一化离水辐亮度和总悬浮物(TSM)浓度分布图像,结果令人满意。  相似文献   

5.
肖青  闻建光  柳钦火  周艺 《遥感学报》2006,10(4):559-567
遥感监测水体叶绿素含量是水质遥感研究的难点之一。本文通过研究不同叶绿素含量水体反射率的光谱特征,确定了提取叶绿素a含量的最佳特征波段,建立了太湖水体叶绿素a的混合光谱模型。研究发现,混合光谱模型提取的水体叶绿素a百分比浓度与同步采样分析的叶绿素a浓度之间有较好的线性相关性。并基于TM和HEPERION图像利用此模型生成了叶绿索a浓度分布图。研究结果表明,混合光谱分解模型可以作为遥感监测水体叶绿素a含量的定量模型。  相似文献   

6.
福建省海岸带泥沙分布的气象卫星遥感监测   总被引:3,自引:0,他引:3  
张春桂 《国土资源遥感》1999,10(2):25-28,42
根据水体对太阳辐射光谱的反射率变化特性,利用NOAA极轨气象卫星遥感资料,动态监测福建省海岸带悬浮泥沙的分布,并从十几幅质量较好的不同时相的泥沙分布图中得出初步的监测结果。  相似文献   

7.
近海Ⅱ类海水反射率与表面悬浮泥沙相关性的研究   总被引:5,自引:0,他引:5  
阐述了1998年5月2日至27日“东海重点试验区水下光谱观测”试验期间,利用上海技术物理研究所研制的PIS-B型光谱辐射计测得的现场光谱数据和用常规吊桶表面水质取样,经实验室泥沙样和光谱数据处理后,用最小二乘法建立光谱反射率与表层悬浮泥沙浓度之间的关系。结果表明,水体表面的光谱反射率与表层悬浮泥沙浓度之间呈指数关系。实践证明,遥测东海海区表面悬浮泥沙浓度的最佳波带为555nm和670nm。  相似文献   

8.
黄河口水体光谱特性及悬沙浓度遥感估测   总被引:10,自引:1,他引:10  
通过黄河口含沙水体野外遥感光谱反射率的观测实验,探讨了黄河口水体表观光谱特性,分析了悬浮体中有机颗粒含量和悬沙粒度对光谱特性的影响。针对Landsat TM/ETM^+影像波段特性,对黄河口含沙水体在其可见光至近红外4个波段的光谱特性进行了模拟分析,并结合表观光谱观测数据建立了经验回归函数,以估测不同时相黄河口水体表层悬沙的浓度。  相似文献   

9.
高光谱遥感技术在水环境监测中的应用研究   总被引:26,自引:4,他引:26  
以黄土高原几类常见土壤为试验品,定量研究了不同泥土含量与水体光谱反射率的关系,探索了利用反射率反演水体深度的方法。文中还介绍了利用高光谱遥感技术分析水污染类型和强度的方法。最后以靖边县城的芦河为例,分析了高光谱遥感探测水污染程度的可行性。  相似文献   

10.
在实验室单一成分叶绿素水体与巢湖复杂水体的条件下,比较了基于反射率光谱与基于偏振度光谱所建模型精度上的区别。结果显示,对于复杂水质条件水体,在叶绿素蓝绿光特征波段处,偏振度光谱比反射率光谱具有更好地克服其他光学活性物质影响和保留叶绿素浓度信息的能力;而在红光特征波段处,两种光谱反演效果的差异性则较小。  相似文献   

11.
Chlorophyll-a (Chl-a) and Suspended Solid Concentration (SSC) shows the productivity of water and their surrounding environment. These parameters can be effectively estimated through several remote sensing techniques. From the recent reports on the Gulf of Thailand, it is found that Chl-a and SSC are increasing in coastal areas due to changing environment caused by variations in the global carbon cycle, climate change and water pollution linking to anthropogenic conditions such as high population density and rapid urbanization in neighbouring coastal areas deteriorating the coastal and marine environment. Various models are evaluated in this study for estimation of marine Chl-a and SSC by employing Ocean Colour Monitor-2 sensor of Oceansat-2 satellite for Northern Gulf of Thailand. The retrieval of Chl-a and SSC by the atmospheric correction of visible bands from 400 to 700 nm to attain normalized water-leaving radiances and then a suitable algorithm is applied. The In-situ reflectance values of sea waters are measured using the ASD spectroradiometer. The reflectance values of the spectroradiometer are correlated for the same day atmospherically corrected satellite reflectance and the analysis offers high correlation R2 0.73. Satellite derived, Chl-a and SSC are correlated with observed in situ Chl-a and SSC. This analysis offered better correlation of R2 0.86 and 0.85 respectively with the algorithms of Chl-a and SSC.  相似文献   

12.
三峡工程蓄水以来,清水下泄,坝下游河段发生了长时间、长距离的沿程冲刷,河流悬浮泥沙浓度发生改变,给沿岸生态系统带来了不利影响。随机森林算法灵活、稳健,已被广泛应用于各类生态环境变量的回归预测分析,但其在水体悬浮泥沙浓度估算方面的能力尚未得到充分认识。基于泥沙站点监测数据和MODIS卫星遥感反射率数据,通过构建随机森林非参数回归预测模型,对三峡工程坝下游宜昌至城陵矶河段在建坝前后14年间(2002年—2015年)各月的悬浮泥沙浓度进行遥感估算。研究表明:(1)基于随机森林的悬浮泥沙浓度估算模型表现较好,模型预测值与实测值间相关性好、预测精度高,优于其他模型(线性回归、支持向量机、人工神经网络模型)。(2)在参与模型构建的MODIS波段变量中,红波段被认为是最重要的预测变量,但不能单独使用它进行预测,悬浮泥沙遥感预测需要多变量共同参与。(3)将悬浮泥沙数据按季节分类所构建的随机森林模型,其平均误差为0.46 mg/L,平均相对均方根误差为12.33%,估算效果最优,能够满足较高精度下悬浮泥沙浓度估算的需求。综上,可以考虑以季节为划分依据,用随机森林回归模型估算悬浮泥沙浓度,并用于后期坝下游河道悬浮泥沙浓度时空反演。  相似文献   

13.
Sampling for suspended sediment concentrations (SSC) in inland waters is traditionally based on collecting samples at sparse locations and in limited intervals. A number of investigators explored the utility of earth-observing satellites and air-borne sensors for monitoring of SSC over vast areas. Two approaches are commonly deployed: (1) empirical relationships between a chosen remotely sensed quantity and the actual in-situ SSC; and (2) bio-optical models founded on radiative transfer modeling. Unfortunately, in-situ measurements are often unavailable for direct image calibration, and inherent optical properties of optically active constituents (specific scattering and absorption coefficients) are usually unknown. This paper examines the possibility to retrieve SSC from multispectral satellite imagery without any in-situ data, i.e. using only image-derived information. The fundamental principle of image selfcalibration relies on the fact that in the visual domain of wavelengths (∼400–700 nm) the at-sensor reflectance becomes “saturated“ at high SSC, whereas the near-infrared domain (∼700–900 nm) remains almost perfectly linearly related to sediment concentrations. The core idea of the self-calibrating procedure is rather simple and is based on fitting an exponential function between reflectance and SSC, with SSC replaced by a linear relationship between SSC and reflectance in the near-infrared domain. As a first approximation of the non-linearity between reflectance and SSC levels in the 400–700 nm range, we used the equation proposed by Schiebe et al. (1992), although other equations, especially those arising from optical theory could be used as well. The technique is illustrated on a moderately sediment-laden reservoir and two scenes acquired from Landsat ETM+. The standard error of the estimated SSC was below 15 mg/L (i.e. ∼25 % relative error for the observed range of SSC). Although the proposed algorithm does not yield better results than other models mentioned in the literature, the primary advantage of the outlined methodology is that no in-situ measurements (water sampling nor spectral profiling) are needed — i.e. only image-derived information is used.  相似文献   

14.
杭州湾HJ CCD影像悬浮泥沙遥感定量反演   总被引:6,自引:0,他引:6  
利用环境小卫星CCD(HJ CCD)影像对杭州湾悬浮泥沙浓度(SSC)进行了反演研究。通过对杭州湾水体遥感反射率(Rrs)与SSC进行相关性分析发现,在690nm和830nm左右出现显著的反射峰,分别位于HJ CCD影像的第3和第4波段范围内;大于700nm波长处的Rrs与SSC相关性较好。基于实测Rrs和SSC之间的相关关系,利用第4和第3波段比值作为遥感因子建立SSC反演模型,模型决定系数达到0.90。借鉴近红外-短波红外(NIR-SWIR)结合的大气校正方法反演出的准同步MODIS气溶胶数据,实现了HJ CCD影像的大气校正,第3、第4波段的大气校正结果相对误差分别为5.54%和6.97%。结果显示,HJ CCD影像反演的SSC相对误差为7.12%;杭州湾悬浮泥沙浓度要显著高于长江口,且内部差异明显。研究表明,通过适当的大气校正方法和反演算法,HJ CCD影像可用于杭州湾悬浮泥沙浓度的估计。  相似文献   

15.
根据建立的垂直于大洋中脊的二维热对流有限元数值模型,采用常粘性以及与温度相关的粘性两种粘性结构对小尺度地幔对流对海底地形的影响进行了重新研究。该模型的主要特点是采用开放边界条件,从而可以避免原有封闭模型因回流问题而产生的复杂效应。数据结果显示,在常粘性模型中,小尺度地幔对流可造成海底地形抬升;但对与温度相关的粘性模型,小尺度对流对地形几乎不产生影响。  相似文献   

16.
杭州湾最大浑浊带(turbidity maximum zone,TMZ)受自然和人类活动的双重影响,年际变化显著。为探究杭州湾水域TMZ和表层悬浮泥沙浓度的年际变化特征,优选1984-2015年间30幅Landsat卫星影像,建立杭州湾水域表层悬沙浓度反演模型,模型经实测数据验证,平均相对误差为23.3%。对每张卫星影像进行悬沙浓度反演,进而提取TMZ面积数据。结果表明,杭州湾悬沙浓度面积分布类型均为正偏分布,且偏态系数由0.63增长至2.03,高悬沙浓度区域占比不断缩小。杭州湾各区域悬沙浓度均呈下降趋势,北岸芦潮港站下降趋势最为显著,减幅达73%。杭州湾TMZ面积年化下降率为4.57%,大于长江和钱塘江年输沙量的年化下降率3.74%。河流来沙减少和潮滩围垦导致的当地泥沙来源减少及水流携沙能力降低是影响TMZ面积降低的重要因素。  相似文献   

17.
Suspended sediment yield is a very important environmental indicator within Amazonian fluvial systems, especially for rivers dominated by inorganic particles, referred to as white water rivers. For vast portions of Amazonian rivers, suspended sediment concentration (SSC) is measured infrequently or not at all. However, remote sensing techniques have been used to estimate water quality parameters worldwide, from which data for suspended matter is the most successfully retrieved. This paper presents empirical models for SSC retrieval in Amazonian white water rivers using reflectance data derived from Landsat 5/TM. The models use multiple regression for both the entire dataset (global model, N = 504) and for five segmented datasets (regional models) defined by general geological features of drainage basins. The models use VNIR bands, band ratios, and the SWIR band 5 as input. For the global model, the adjusted R2 is 0.76, while the adjusted R2 values for regional models vary from 0.77 to 0.89, all significant (p-value < 0.0001). The regional models are subject to the leave-one-out cross validation technique, which presents robust results. The findings show that both the average error of estimation and the standard deviation increase as the SSC range increases. Regional models were more accurate when compared with the global model, suggesting changes in optical proprieties of water sampled at different sampling stations. Results confirm the potential for the estimation of SSC from Landsat/TM historical series data for the 1980s and 1990s, for which the in situ database is scarce. Such estimates supplement the SSC temporal series, providing a more comprehensive SSC temporal series which may show environmental dynamics yet unknown.  相似文献   

18.
目前世界上最大的工程超导超级对撞机(SSC)正在建造之中,作者参与这项大型精密工程测量的方案设计和部分咨询工作。根据工程的特点,设计测量投影方式是其中一项工作。本文讨论所设计的双重正形投影(stereographic double conformal projection)以及各种观测量投影归化中的问题。  相似文献   

19.
基于BP神经网络模型的太湖悬浮物浓度遥感定量提取研究   总被引:6,自引:1,他引:6  
构建了含有一个隐含层的两层BP神经网络反演模型,以TM数据的前4个波段的反射率作为输入,以悬浮物浓度值作为输出,成功反演了太湖水体的悬浮物浓度。  相似文献   

20.
Indian Remote Sensing Satellite (IRS) — P4 Ocean Colour Monitor (OCM) data were used to estimate Suspended Sediment Concentration (SSC) in the coastal waters off Chennai and to study the distribution along the coast. Surface water samples were collected during May and October 2000 synchronized with satellite overpass, and quantitative estimates of Suspended Sediment Concentration (SSC) were done. OCM — Data Analysis System (DAS) software developed by the Space Applications Centre, Ahmedabad was used for OCM data processing and analysis. The field data and OCM derived SSC showed a correlation of r = 0.85 and r = 0.95 for the months of May and October respectively.  相似文献   

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