基于Landsat TM/ETM+数据提取巢湖悬浮泥沙相对浓度的信息与空间分布变化

悬浮泥沙是重要的水质参数之一.应用遥感技术监测悬浮泥沙,学者们提出了众多的悬浮泥沙遥感的经验模型和推导模型.但在缺乏大气参数或没有足够实测数据的情况下,这些模式的精度和准确性得不到保证.针对这种情况,以巢湖为实验区,对三景的巢湖卫星遥感数据进行了如下的数据处理:(1)利用内部平均相对反射率法进行图像的大气校正,得到的相对反射率与真实反射率具有相似的波谱特征;(2)对图像进行了水体提取、二值化、掩膜处理,并通过湖泊泥沙指数SI=(TM2 TM3)/(TM2/TM3)提取了TM数据下的泥沙信息,得到水体含沙量图;(3)按照本文提出的基于遥感图像的不同浓度等级泥沙的划分依据,在泥沙指数图上进行密度分割处理,得到了巢湖泥沙相对浓度分布图.在上述的处理基础上,利用谱间关系法对巢湖水体进行准确提取;结果表明,与实测资料对比,巢湖泥沙相对浓度分布与验证数据一致,实测数据和SI值相关系数为0.89(置信度水平在0.001),表明泥沙指数方法可以直观和定量地反映悬浮泥沙相对浓度的分布与变化;研究结果显示,1987-2000年间,巢湖高浓度悬浮泥沙范围增大了约1.5倍.通过影像差值图清楚地识别出变化区域,主要位于西湖的中心、河口入湖区和东湖的南岸,这种变化的最主要原因是由于各入湖河流携带的大量悬浮泥沙进入水体,其次是岸坡崩塌物形成的.     

基于泥沙因子的水深遥感反演模型

针对悬浮泥沙影响水体遥感测深精度的问题,选择长江口南港至南槽为研究区,通过对遥感测深方法研究,结合悬浮泥沙光谱特性分析,把"泥沙因子"引入到水体遥感测深反演模型中,研究表明:1)单因子非线性模型中,指数模型对0-2m的水深反演效果较好,对数模型对2-7m的水深反演较好,二次回归模型对7-14m的水深反演效果较好:2)建立的BP人工神经网络水深反演模型综合了多个波段具有的水深信息,模型的反演效果好于单因子非线性模型;3)实验构建的泥沙遥感参数综合了不同波段具有的悬沙信息,削弱了叶绿素和外界环境条件对泥沙信息的干扰,可较好地反映悬沙浓度变化特征;4)建立的BP人工神经网络泥沙因子水深反演模型削弱了悬浮泥沙对遥感测深的影响,模型实际反演能力明显优于单因子非线性模型和多因子BP人工神经网络水深反演模型.     

基于浮油膜双光束干涉模型的油膜厚度遥感反演理论

海面浮油膜是海洋石油污染监测与海洋油气资源勘探的共同关注对象,其厚度的遥感定量反演是研究难点之一.随着油膜厚度的变化,入射光在浮油膜中的辐射传输有所不同,是海面浮油膜厚度光学遥感反演的理论依据.本研究将均匀分布的浮油膜视为一个平行平板,分析其可见光范围内的辐射传输过程,探讨油膜反射光的双光束干涉,构建以油膜厚度及其光谱反射率为变量的油膜厚度遥感定量反演模型,并结合菲涅耳公式进行模型参数的物理意义分析,揭示油膜厚度遥感定量反演的关键在于油膜的消光特性.利用油膜光谱响应实验数据,开展理论模型的分析与验证,结果表明：油膜厚度遥感定量反演的理论模型不仅具有明确的光学物理意义,同时也具有较好的模拟精度;浮油膜光谱反射率随厚度变化响应的主要因素是油膜对入射光的分子散射作用,该散射作用与入射光波长具有显著的关系,因此油膜光学遥感散射特性研究将有助于油膜种类识别与厚度的遥感定量反演;模型参数的理论意义与数据分析表明,建立典型油种油膜的模型参数查找表,将有效提高海面浮油膜厚度遥感定量反演的效率.     

基于环境一号卫星高光谱遥感数据的巢湖水体叶绿素a浓度反演

三波段模型是基于生物光学模型构建的叶绿素a浓度反演半分析模型,是目前反演内陆富营养化浑浊水体叶绿素a浓度效果较好的方法.本文通过星地同步实验,分析巢湖水体各组分光谱特征,分别基于地面实测数据与环境一号卫星高光谱遥感数据建立三波段模型反演巢湖水体叶绿素a浓度.结果表明,由于特征波段在不同数据源的位置不同,导致了两个模型波段选择及反演精度的差异.因此,只有在充分考虑遥感数据的光谱特征的条件下,分析遥感信息理论和实际图幅影像有效结合在一起的地物信息,才能进一步优化三波段模型的波段选择,实现遥感数据定量反演水体叶绿素a浓度的目标.     

考虑采砂影响的鄱阳湖丰水期悬浮泥沙浓度模拟

针对受采砂活动影响显著的鄱阳湖高浑浊水体,结合数值模拟和遥感技术,利用已有的鄱阳湖采砂区遥感监测结果,在构建的鄱阳湖水动力-悬浮泥沙输移模型中添加泥沙点源,对2011年7月1-31日采砂影响下的鄱阳湖丰水期悬浮泥沙浓度进行数值模拟.利用悬浮泥沙浓度实测数据和MODIS影像反演结果对模拟结果的有效验证表明,考虑采砂影响后,悬浮泥沙浓度模拟值与实测值具有强相关关系,确定性系数为0.831,均方根误差为15.5 mg/L,悬浮泥沙浓度空间分布趋势与遥感反演结果基本一致.模拟结果显示,采砂活动对鄱阳湖南部主湖区、河流入湖口影响较小,其主要影响由南向北,经棠荫以西和松门山岛以北航道、入江水道延伸到湖口区域,是鄱阳湖北湖区高浑浊水体形成的重要原因.     

MODIS陆地气溶胶遥感反演--利用TERRA和AQUA双星MODIS数据协同反演算法

利用卫星数据遥感陆地气溶胶一直是国际上研究的难点与热点.利用新一代传感器MODIS(中分辨率成像光谱仪)数据,DDV(Dark Dense Vegetation)算法反演陆地气溶胶的分布以及性质已经取得了较好的效果.然而,该算法只适用于诸如水体、浓密植被等较低地表反射率区域,大大限制了该算法的实际应用范围,尤其是无法应用于城市等亮地表区域气溶胶的遥感反演.文中提出了基于利用TERRA和AQUA双星MODIS数据的协同反演模型算法(SYNTAM-Synergy of Terra and Aqua MODIS),用以反演陆地气溶胶的光学厚度等信息.该算法实现了地表反射率与气溶胶光学厚度的同时反演,可应用于各种地表反射率类型,包括城市等亮地表区域.通过与国际AERONET的地面观测数据对比做初步的反演验证,结果表明,该算法具有较高的精度,进一步的验证工作还在继续.     

MODIS陆地气溶胶遥感反演

利用卫星数据遥感陆地气溶胶一直是国际上研究的难点与热点. 利用新一代传感器MODIS(中分辨率成像光谱仪)数据, DDV(Dark Dense Vegetation)算法反演陆地气溶胶的分布以及性质已经取得了较好的效果. 然而, 该算法只适用于诸如水体、浓密植被等较低地表反射率区域, 大大限制了该算法的实际应用范围, 尤其是无法应用于城市等亮地表区域气溶胶的遥感反演. 文中提出了基于利用TERRA和AQUA双星MODIS数据的协同反演模型算法(SYNTAM-Synergy of Terra and Aqua MODIS), 用以反演陆地气溶胶的光学厚度等信息. 该算法实现了地表反射率与气溶胶光学厚度的同时反演, 可应用于各种地表反射率类型, 包括城市等亮地表区域. 通过与国际AERONET的地面观测数据对比做初步的反演验证, 结果表明, 该算法具有较高的精度, 进一步的验证工作还在继续.     

湖泊水质遥感的几个关键问题

我国目前约有面积大于1km2的湖泊有3000个,绝大部分属高叶绿素和高悬浮物浓度水体,属于典型的Ⅱ类水体,物质组成多样,水体的光学辐射传输复杂,且有大范围的光学浅水.我国的湖泊水质/水色遥感虽然取得了一定进展,但借鉴海洋水色遥感的相关理论和经验,还需要解决以下四个关键问题:1)兼顾海洋沿海水质遥感,发展专用的静止卫星湖泊水质遥感器;2)在当前多光谱遥感资料基础上研发高光谱湖泊水质因子提取的遥感定量化模型,提高反演精度;3)深化湖底底质对湖泊水质/水质遥感影响研究,发展湖底水质遥感反射率精确计算模型;4)发展适用于湖泊水体区域性Ⅱ类水体大气校正方法,并集成反演、遥感产品制作、分发等技术,构建湖泊水体水质/水色业务化运行体系.     

乌梁素海沉水植物群落光谱特征及冠层水深影响分析

沉水植物对于改善富营养化水体和重建水生生态系统起着至关重要的作用.应用遥感技术可以实时、大面积监测沉水植物的分布和生长情况,而冠层水深直接影响沉水植物在湖泊、河流中的准确遥感解译.本研究基于实测光谱数据,分析了乌梁素海沉水植物光谱特征,并研究了冠层水深对乌梁素海沉水植物反射光谱的影响,建立了乌梁素海沉水植物冠层水深反演模型.结果表明:1)挺水植物在短波红外1662 nm和2223 nm附近分别有一个反射峰,这是挺水植物区别于沉水植物和漂浮藻类的重要波段; 0深度沉水植物(WDC=0)与漂浮藻类的光谱反射率非常接近,但是在绿波段(550～690 nm)有明显差异,因此,可以利用绿波段和短波红外波段的光谱特征来区分挺水植物、沉水植物和漂浮藻类.2)沉水植物群落的光谱反射率随冠层水深的增加而降低,在700～900 nm波段范围内变化最为明显,且在700～735 nm波段附近,沉水植物群落光谱反射率与冠层水深呈显著负相关.3)在建立的单波段/波段比沉水植物冠层水深反演模型中,波段比反演模型要优于单波段反演模型,波段比反演模型的决定系数R2 0.70,均方根误差13.70 cm,平均相对误差28%,反演精度较好,适用于10～60 cm沉水植物冠层水深的反演.4)利用波段响应函数,将实测光谱反射率积分到Landsat-8 OLI波段上,建立OLI了冠层水深反演模型,其中,波段比幂函数模型反演效果最好,R2为0.49,均方根误差为18.17 cm,平均相对误差40.05%.可用于精确大气校正后乌梁素海沉水植物冠层水深的反演.     

吉林查干湖水体叶绿素a含量高光谱模型研究

叶绿素a含量能够在一定程度上反映水质状况,高光谱遥感可有效反演叶绿素a含量.该研究通过分析水体叶绿素a浓度与其高光谱反射特征之间的相关关系,采用单波段相关分析、波段比值、微分光谱和神经网络模型等多种算法建立了叶绿素a高光谱定量模型.结果表明:叶绿素a与单波段光谱在蓝、绿波段相关系数较低,而在红光与近红外波段有明显提高,微分光谱也表现出同样的趋势;反射率比值算法模拟效果好于线性回归法;神经元网络模型可以大大提高实测光谱数据的反演能力,确定性系数高达0.95.这为今后利用星载高光谱传感器在查干湖进行叶绿素a浓度大面积遥感反演提供了研究基础.     

基于反射光谱和模拟MERIS数据的太湖悬浮物遥感定量模型

利用地物光谱仪研究了太湖水体的反射光谱特征,通过对比分析,发现580nm反射率值和810nm的反射峰高是太湖悬浮物的敏感波段,并通过光谱微分的方法,发现840nm附近的一阶微分与悬浮物浓度相关性最好,基于上述结论,分别建立了太湖悬浮物的反射光谱和一阶微分遥感定量模型,并利用反射光谱数据,模拟MERIS数据的波段设置,结果表明MERIS第5、12、13波段可以很好的估测太湖的悬浮物浓度.     

Remote sensing application system for water environments developed for Environment Satellite 1

Remote sensing data collected by the Environment Satellite I are characterized by high temporal resolution, high spectral resolution and mid-high spatial resolution. We designed the Remote Sensing Application System for Water Environments (RSASWE) to create an integrated platform for remote sensing data processing, parameter information extraction and thematic mapping using both remote sensing and GIS technologies. This system provides support for regional water environmental monitoring, and prediction and warning of water pollution. Developed to process and apply data collected by Environment Satellite I, this system has automated procedures including clipping, observation geometry computation, radiometric calibration, 6S atmospheric correction and water quality parameter inversion. RSASWE consists of six subsystems: remote sensing image processing, basic parameter inversion, water environment remote sensing thematic outputs, application outputs, automated water environment outputs and a non-point source pollution monitoring subsystem. At present RSASWE plays an important role in operations at the Satellite Environment Center.     

Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

The spatial distribution of sub-pixel components has an impact on retrieval accuracy, and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index (LAI). To investigate this effect, we constructed three realistic scenarios with the same LAI values and other properties, except that the simulated plants had different distributions. We implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor (BRF) datasets based upon these simulated scenes. The inversion was conducted using these data, which showed that spatial distribution affects retrieval accuracy. The inversion was also conducted for LAI based on charge-coupled device (CCD) data from the Environment and Disaster Monitor Satellite (HJ-1), which depicted both forest and drought-resistant crop land cover. This showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI inversion. The spatial distribution of global fractal dimension index, which can be used to describe the area of sub-pixel components and their spatial distribution modes, shows good consistency with the coarse resolution LAI inversion error.     

Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

The spatial distribution of sub-pixel components has an impact on retrieval accuracy, and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index (LAI). To investigate this effect, we constructed three realistic scenarios with the same LAI values and other properties, except that the simulated plants had different distributions. We implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor (BRF) datasets based upon these simulated scenes. The inversion was conducted using these data, which showed that spatial distribution affects retrieval accuracy. The inversion was also conducted for LAI based on charge-coupled device (CCD) data from the Environment and Disaster Monitor Satellite (HJ-1), which depicted both forest and drought-resistant crop land cover. This showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI inversion. The spatial distribution of global fractal dimension index, which can be used to describe the area of sub-pixel components and their spatial distribution modes, shows good consistency with the coarse resolution LAI inversion error.

     

查干湖冰封期光谱特征及影响因素

湖冰光谱特征是湖冰遥感反演的物理基础,是研究湖冰光学特性和空间分布的理论依据。本文以查干湖为例,使用ASD Field Spec 4便携式地物光谱仪采集冰封期不同类型湖冰、积雪和水体光谱,利用Savitzky-Golay滤波法和包络线去除法分析白冰、灰冰、黑冰、雪冰、积雪和水体的反射光谱特征,探索气泡对湖冰反射光谱特征的影响。积雪和雪冰、白冰和灰冰、黑冰和水体的反射特征随着波长的变化特征基本一致,冰的反射率介于积雪和水体之间,其中白冰的反射率高于灰冰和黑冰,在包络线去除结果中,黑冰和水体在440 nm吸收谷处的吸收面积为5.184和10.878、吸收深度为0.052和0.106,雪、雪冰、白冰、灰冰在800和1030 nm吸收谷处的吸收面积和吸收深度的变化表现为雪<雪冰<灰冰<白冰。气泡是影响湖冰光谱特征的重要因素,气泡使白冰反射率减小和黑冰反射率增大,并且气泡使得白冰在800/1030nm和黑冰在440 nm处的吸收面积和吸收深度减小,其中气泡大小和疏密程度的不同会导致湖冰反射率的影响程度存在差异。同时,本文选取时间同步的Landsat 8 OLI遥感影像,在完成辐...     

Linear spectral unmixing algorithm for modelling suspended sediment concentration of flash floods,upper Tekeze River,Ethiopia

Flash floods are the highest sediment transporting agent,but are inaccessible for in-situ sampling and have rarely been analyzed by remote sensing technology.Laboratory and field experiments were done to develop linear spectral unmixing(LSU) remote sensing model and evaluate its performance in simulating the suspended sediment concentration(SSC) in flash floods.The models were developed from continuous monitoring in the laboratory and the onsite spectral signature of river bed sediment deposits and flash floods in the Tekeze River and in its tributary,the Tsirare River.The Pearson correlation coefficient was used to determine the variability of correlations between reflectance and SSCs.The coefficient of determination(R2) and root mean square of error(RMSE) were used to evaluate the performance of the generated models.The results found that the Pearson correlation coefficient between SSCs and reflectance varied based on the level of the SSCs,geological colors,and grain sizes.The performance of the LSU model and empirical remote sensing approaches were computed to be R2=0.92,and RMSE=±0.76 g/1 in the Tsirare River and R2=0.91,and RMSE=±0.73 g/1 in the Tekeze River and R2=0.81,RMSE=±2.65 g/l in the Tsirare river and R2=0.76,RMSE=±10.87 g/l in the Tekeze River,respectively.Hence,the LSU approach of remote sensing was found to be relatively accurate in monitoring and modeling the variability of SSCs that could be applied to the upper Tekeze River basin.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

多分类器组合方法的遥感技术研究

将光谱聚类方法应用于高光谱遥感数据处理,对低反射率地物信息的提取取得较好效果;同时采用决策树的多分类器组合方法提取高光谱遥感影像信息,经对比研究发现其效果明显优于单个分类器。     

基于无人机和卫星遥感影像的升金湖草滩植被地上生物量反演

湿地植被地上生物量是衡量湿地生态系统健康状况的重要指标,对于珍稀水禽越冬繁殖、全球碳循环、生态净化具有重要意义,是生态学与遥感解译的研究热点之一.针对于地上生物量的测算,卫星遥感数据覆盖范围广但其空间分辨率较低,无人机遥感数据空间分辨率高但采集范围小,同时受湿地面积、观测系统及外界环境等条件的影响,使得遥感影像地上生物量反演更加复杂和困难.本研究基于无人机和高分一号数据对升金湖草滩植被地上生物量反演进行研究,结合升金湖保护区4个样区无人机可见光影像与相应样区实测样本数据,建立地上生物量与可见光波段、多种可见光植被指数的线性、幂函数、多项式、对数回归模型,并通过可决系数（R²）、平均绝对误差（MAE）和均方根误差（RMSE）对模型进行精度评价,选择最优模型对无人机影像进行地上生物量反演;通过可见光波段反演得到的生物量,与高分一号WFV归一化差分植被指数（Normalized Difference Vegetation Index,NDVI）影像相结合进行回归建模,获取整个升金湖草滩植被地上生物量分布.结果表明,利用无人机红光波段建立的多项式方程对地上生物量反演有着最高模拟精度,R²=0.86、预测精度MAE=111.33 g/m²、RMSE=145.42 g/m²,且红光波段生物量反演方法得到的结果与实际生物量分布一致性较高,高分一号WFV NDVI与无人机反演生物量构建的多项式模型为最优模型,R²为0.91.本研究利用无人机和高分一号数据进行生物量反演研究,整合多源遥感数据优点,以获取更加丰富和准确的信息,进而提高地上生物量反演精度,为湿地监测和湿地恢复管理提供数据和技术支撑,具有重要研究意义和应用价值.     

Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams

Remote sensing has rarely been used as a tool to map and monitor submerged aquatic vegetation (SAV) in rivers, due to a combination of insufficient spatial resolution of available image data and strong attenuation of light in water through absorption and scattering. The latter process reduces the possibility to use spectral reflectance information to accurately classify submerged species. However, increasing availability of very high resolution (VHR) image data may enable the use of shape and texture features to help discriminate between species by taking an object based image analysis (OBIA) approach, and overcome some of the present limitations.This study aimed to investigate the possibility of using optical remote sensing for the detection and mapping of SAV. It firstly looked at the possibilities to discriminate submerged macrophyte species based on spectral information only. Reflectance spectra of three macrophyte species were measured in situ across a range of submergence depths. The results showed that water depth will be a limiting factor for the classification of species from remote sensing images. Only Spiked Water Milfoil (Myriophyllum spicatum) was indicated as spectrally distinct through ANOVA analysis, but subsequent Jeffries–Matusita distance analysis did not confirm this. In particular Water Crowfoot (Ranunculus fluitans) and Pondweed (Potamogeton pectinatus) could not be discriminated at 95% significance level. Spectral separability of these two species was also not possible without the effect of an overlying water column.Secondly, the possibility to improve species discrimination, using spatial and textural information was investigated for the same SAV species. VHR image data was acquired with a Near Infrared (NIR) sensitive DSLR camera from four different heights including a telescopic pole and a Helikite UAS. The results show that shape and texture information can improve the detection of the spectrally similar Pondweed and Water Crowfoot from VHR image data. The best performing feature ‘length/width ratio of sub-objects’ was obtained through expert knowledge. All of the shape and texture based features performed better at species differentiation than the spectrally based features.In conclusion this study has shown that there is considerable potential for the combination of VHR data and OBIA to map SAV in shallow stream environments, which can benefit species monitoring and management.