共查询到18条相似文献，搜索用时 140 毫秒
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鄱阳湖叶绿素a浓度遥感定量模型研究 总被引：1，自引：0，他引：1
叶绿素a浓度是反映湖泊水体营养状况的重要指标,本研究通过分析水体叶绿素a浓度与高光谱反射特征的相互关系,采用一阶微分值和峰值比值法分别建立了叶绿素a的高光谱定量反演模型,在此基础上与同步MODIS数据敏感波段建立卫星定量反演模型。结果表明:叶绿素a荧光峰出现在波段690nm-700nm,波段696nm一阶微分值相关系数最大;波段700nm与波段680nm的比值与其对数相关性较好,MODIS数据波段2和波段1比值的指数模型为最佳的回归模型。 相似文献
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基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型 总被引：19，自引：1，他引：19
旨在寻找叶绿素a的高光谱遥感敏感波段并建立其定量估算模型。通过对太湖水体的连续监测，获得了从2004年6月到8月3个月的太湖水体高光谱数据和水质化学分析数据。利用实测的高光谱数据分析计算太湖水体的离水辐亮度和遥感反射比；然后，通过相关分析寻找反演叶绿素a浓度的高光谱敏感波段，进而建立反演太湖水体叶绿素a浓度的高光谱遥感定量估算模型，并用相关数据对模型进行精度分析。研究发现，水体的遥感反射比光谱在719nm和725nm存在两个峰，其中719nm处的峰更明显且稳定。通过模型的对比分析，发现用这两个峰值处的遥感反射比参与建模可以提高叶绿素a的估算精度；并且认为由反射比比值变量R719／R670所建立的线性模型对叶绿素a浓度的估算精度最理想。 相似文献
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基于季节分异的太湖叶绿素浓度反演模型研究 总被引：4，自引：0，他引：4
叶绿素浓度反演算法主要有经验方法、半经验/分析方法和分析方法,其中半经验/分析方法应用最为广泛。但是反演的模型以及模型中的参数和反演精度都随着水体中叶绿素浓度的变化而改变。不同的季节水体中叶绿素浓度不同,水体反射光谱曲线特征和与叶绿素浓度相关性较高的敏感波段也不一致,使得各季节选用的反演模型和模型中的参数也存在一定的差异。本文在对2005年1—10月份叶绿素a（Chla）浓度季节差异进行分析的基础上,对4—10月份同步测量的水体光谱数据分春、夏、秋三个季节进行分析,分季节建立叶绿素浓度反演模型,并对它们进行比较,旨在为各季节选择最佳的反演模型。研究结果表明：春季和秋季选用波段比值算法反演精度较高,其中对数模型,线性模型和一元二次模型都有较高的相关性;夏季选用微分算法较好,该算法所建立的三种模型均具有较高的相关性。 相似文献
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昆承湖水质状况遥感监测与空间特征分析 总被引：2，自引：0，他引：2
针对镇域水资源常规的监测方法不能满足对水质适时、大尺度的监测评价要求等问题,该文以江苏常熟辛庄镇昆承湖为例,分析叶绿素a(Chl-a)和悬浮物(SS)浓度值与水体归一化反射光谱、一阶微分反射光谱特征的关系,运用PEARSON法分析两者之间的相关性,确定Chl-a和SS的敏感波段,利用Chl-a和SS敏感波段归一化光谱反射值和实测浓度数据建立了水质参数反演模型。采用同时相ETM遥感数据对水质参数(叶绿素a和悬浮物)浓度进行遥感定量反演,并根据反演结果分析镇域水体污染空间分布特征。结果表明,对水质参数几个最大正(负)相关的光谱值进行波段组合处理可以提高反演精度,并且模型反演值和实测浓度值之间误差较低。通过对叶绿素、悬浮物等水质参数反演实现了对昆承湖水体污染状况的快速、准确和动态的信息获取和评价,有效地实时监测镇域水体在空间和时间上的变化状况和特性。 相似文献
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应用实测光谱估测太湖梅梁湾附近水体叶绿素浓度 总被引：40，自引：1，他引：40
遥感方法测定水体中叶绿素含量的核心问题是建立遥感数据和叶绿素含量的定量关系;利用太湖梅梁湾附近水体的实测光谱和水质采样实验室分析,从数量上揭示了位于682nm附近和706nm附近对叶绿素含量估测最重要的两个光谱特征,分别通过比值(R706/R682、R706/R572)、微分、面积、峰高、峰谷距离以及反射峰位置等建立与叶绿素浓度的线性或非线性相关回归模型,通过R2、平均误差以及RMS误差等的分析对比,认为比值和反射峰位置对叶绿素浓度有很好的指示作用,是估测太湖梅梁湾附近水体叶绿素浓度的最好方法,其中与反射峰位置的指数关系R2达0.9199,与R706/R682的直接线性关系R2达0.9038。相对于706nm附近的峰谷距离、峰高以及反射峰面积而言,反射峰的位置是指示叶绿素浓度最敏感的变量;572nm附近、682nm附近以及706nm附近等三个波段对探测叶绿素a具有重要作用。 相似文献
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叶绿素a荧光遥感研究进展 总被引：11，自引：0，他引：11
继叶绿素a反演的“蓝绿比值法”后,叶绿素a荧光遥感成为海水叶绿素a浓度反演的重要方法,对提高二类水体和赤潮水体的叶绿素a浓度的反演精度效果明显。本文回顾了人们对水体叶绿素a荧光的认识、测量和研究的历史过程,介绍了荧光产生的生物学机理以及它随叶绿素a浓度的正相关和“红移现象”等主要光谱特征。本文还总结了荧光量子产量、不同藻种生理状态、水体其他物质及大气的吸收等多种因素对叶绿素a荧光遥感的影响。基于对叶绿素a荧光光谱特征和影响因素的认识,人们相继建立了两种荧光遥感方法———基线荧光高度法和归一化荧光高度法。对于前景广阔的叶绿素荧光遥感领域,人们正进行着更深入的研究与探索,积累更多的现场数据和卫星同步数据,逐步完善和改进反演模型。 相似文献
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利用MODIS影像数据,对水华现象出现的重要物质——巢湖水体叶绿素a信息进行反演。在对数据进行预处理后,采集影像相应位置的像元灰度值,与现实中在对应位置观测的湖水叶绿素a浓度进行相关性拟合分析,得到叶绿素a浓度与像元灰度值的相关性方程与相关系数。在建立相关性模型时采用了单波段和波段比值法,得到的最高相关系数为R2=0.881 8,表明利用这种方法反演巢湖水体叶绿素a浓度空间分布具有一定的可靠性。选择相关系数性较高的2个拟合方程反向演算MODIS影像,最后成功得到巢湖湖面当日叶绿素a浓度分布状况图,为巢湖防治水华发生提供了比较准确可靠的信息。 相似文献
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鄱阳湖富营养化高光谱遥感监测模型初探 总被引：4，自引：1，他引：3
基于对光谱反射率与水质参数的相关分析,分别选取特征波长建立水质参数高光谱估测模型。结合修正营养状态指数,对湖泊的富营养化程度进行了监测和评价。结果表明:①总氮、总磷含量和透明度值的高光谱估测模型效果较理想;②单项指数评价水体富营养化水平其结果存在较大差异,综合考虑多个指标,计算营养指数的平均值,可以对富营养化程度进行正确的评价;③由于悬浮物浓度变化较大,掩盖了水体的叶绿素a信息,以致叶绿素a估测模型不具有通用性,为了完善叶绿素a浓度估测模型需要获得大范围、多季节的光谱数据,以便建立更有代表性和通用性的模型;④评价结果显示,鄱阳湖呈现轻度到中度富营养化状态,需要采取有力的保护措施防止进一步恶化。 相似文献
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S. Kasi Viswanath N. K. Tripathi K. R. Salin 《Journal of the Indian Society of Remote Sensing》2018,46(4):675-685
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
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黄浦江水质指标与反射光谱特征的关系分析 总被引：16，自引：0，他引：16
利用地物光谱测量技术及同步配套的常规水质采样分析实验，对上海市黄浦江全河段水体进行调查研究。共选取31个典型站位进行了光谱测量和同步水质取样，每个测点分析了9个水质指标，包括TP，TN，CODMn，CODCr，NH3-N，BOD5，DO，悬浮物浓度和浊度。各水质参数之间存在较大的相关性，以总氮（TN）和悬浮物浓度两个水质指标为例，分析了这两个水质指标与单波段归一化反射率、反射率的一阶微分、不同波段之间反射率的比值以及反射率取对数等之间的相关关系，给出了以上两个水质指标的单波段归一化反射率、一阶微分反射率识别的特征波段，以及两个波段比值的最佳波段组合。建立了常规水质参数与水体光谱反射率之间的关系模型，为利用遥感技术监测水环境提供了基础。 相似文献
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T. Preethi Latha P. V. Nagamani K. H. Rao S. K. Dash S. B. Choudhury Aziz Rehman V. V. S. S. Sarma M. Naveen Babu P. Amarendra B. Srinivasa Rao T. D. V. Prasad 《Journal of the Indian Society of Remote Sensing》2014,42(1):129-138
In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R2 value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R2 value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations. 相似文献
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Kumar Arun Prasad 《国际地球制图》2016,31(8):891-912
This study aims at discriminating eight mangrove species of Rhizophoraceae family of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra. 相似文献
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N. K. Patel R. K. Saxena Ajay Shiwalkar 《Journal of the Indian Society of Remote Sensing》2007,35(1):73-79
A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover. 相似文献