复杂气象条件下火山灰云遥感监测新方法

火山灰云是严重影响航空飞行安全的因素之一.从长期来看,大规模的火山灰云还对全球气候和环境产生深远影响.在国际上普遍应用＂分裂窗亮温差算法＂监测火山灰云的基础上,针对＂分裂窗亮温差算法＂受气象云、冰雪等复杂气象条件影响而容易产生误差的问题,构建了基于我国自主新一代极轨气象卫星FY-3A/MERSI和VIRR数据融合技术的短波红外-热红外火山灰云算法（SWIR-TIR Volcanic Ash method,STVA）.比较了STVA算法、＂分裂窗亮温差算法＂在冰岛艾雅法拉冰河火山爆发中的应用效果.结果表明：与分裂窗亮温差算法相比,STVA算法能够较好地将火山灰云和其他地物区分出来,且不受气象云和海洋的影响.利用欧洲Metop-A卫星资料计算的火山灰云遥感监测产品以及美国Terra/MODIS短波红外和热红外光谱数据对STVA的应用能力进行了对比验证和误差分析,证明了基于FY-3A卫星数据的STVA算法具有良好的稳定性和准确性,表明该方法在全球火山灰云监测中具有应用潜力.     

卫星遥感技术在火山灰云监测中的应用

在航空运输业蓬勃发展的背景下,火山喷发形成的火山灰云严重威胁着航空安全.遥感技术能够快速、准确获取地表和大气的变化信息,在火山活动监测中发挥着重要作用,逐渐成为火山灰云监测的重要手段.文中阐述了火山灰云对自然环境和航空安全的危害;以火山灰云遥感监测平台为例,系统地介绍了火山灰云遥感监测的遥感传感器类型和监测方法的发展概况;对中国火山灰云遥感监测的研究基础进行了评述;最后,对火山灰云遥感监测的发展进行了展望和思考.     

基于Contourlet变换和ICA的多时相遥感图像变化检测

为了提高多时相遥感图像变化检测的精确度和运算效率,本文提出了一种基于Contourlet变换和独立分量分析(ICA-Independent component analysis)的变化检测算法.利用Contourlet变换多尺度、多方向性和各向异性等性质,对图像数据进行多尺度分解,再对分解后的数据进行独立分量分析,利用改进的基于牛顿迭代的固定点ICA算法分离出互相独立的数据分量,然后将分离后的数据分量转变成图像分量,最终对变化图像分量经阈值分割实现变化检测.实验结果表明,与现有的基于PCA、基于ICA、基于小波变换与ICA三种变化检测算法相比,本文算法能有效地分离出变化信息,减少了计算的复杂性,得到的变化图像具有更高的精确度,且对背景有较强的稳健性.     

多核处理器环境下遥感数据处理的并行与串行算法的性能比较

卫星遥感能够提供(准)实时地面信息,在地震研究中的应用越来越广泛.MODIS是搭载在Terra和Aqua两颗卫星上的重要的“图谱合一”光学遥感观测仪器.由于MODIS数据具有多频段、高分辨率、高时效性、应用广泛等特点,使得MODIS数据对地球科学的综合研究和对陆表、生物圈、固态地球、大气和海洋的长期观测有着重要意义,在自然灾害的监测和分析研究领域也有着广泛的应用.     

MODIS数据云检测算法在西南地区的改进

根据云在MODIS数据几个可见光和红外波段的特性,改进了现今国内外应用较广泛的多光谱综合云检测算法,进行中国大陆西南地区的云检测应用试验。通过对该区不同时期的云检测应用试验,调整该方法的各光谱阈值。试验结果表明,该多光谱综合云检测法效果理想,对可见光波段难以识别的薄卷云也有很好的效果,为MODIS数据进一步在中国大陆西南地区的应用打下了良好的基础。     

建筑物震害多源遥感特征与机理分析

随着遥感信息源的不断增加,多种遥感数据被用于详细判读建筑物的震害情况.为准确判读震害等级与建立震害自动识别模式,本文收集整理了汶川地震震区的震害遥感图像,通过目视判读、图像处理、统计分析,重点分析了各类震害建筑物在光学影像中的特征表现、在合成孔径雷达图像中的成像机理特征以及在激光雷达图像中的三维特征.在此基础上构建了建筑物简化模型,并联合光学影像和雷达图像对震害建筑物的影像特征剖面予以分析.结果显示:光学遥感图像色彩信息符合人眼色觉原理,具有较好的直观判读效果;合成孔径雷达图像能够记录地物侧面、表面的粗糙程度和角反射特点,信息量丰富但不直观;激光雷达图像能获取建筑物的三维信息,因此震害评估工作中需有效地综合利用多源遥感数据,才能实现最佳的判识效果.      

南海东北部内孤立波包的地震海洋学和遥感研究

在南海东北部东沙环礁附近,内孤立波被大量地观测报道.在该地区内孤立波的传播和演化过程仍然存在许多待解决的问题.利用改进的地震海洋学处理方法对2009年夏的一段海洋勘探地震测线进行了重新处理,获得了50 m水深之下的水层反射图像,发现了包含8个内孤立波的下沉型内孤立波包.遥感仪器中分辨率成像光谱仪(MODIS)图像在该段地震测量的3 h内,捕捉到同一个内孤立波包,经处理分析,获得前5个内孤立波的清晰图像.本文采用了两种方法计算内孤立波相速度,方法一是利用不同的叠前共偏移距道集剖面估算内孤立波的视相速度,并根据MODIS图像上内孤立波的传播方向对其进行校正;方法二是利用地震与MODIS图像联测直接获得传播相速度.将这两种方法得到的相速度分别进行对比,发现它们在数值大体一致.地震海洋学剖面可直接获得内孤立波包中8个内孤立波的特征参数,包括振幅、视半高宽和视波间距.该内孤立波包的最大振幅为117 m,最大视半高宽为1020 m,最大视波间距为4100 m.由于地震采集船和内孤立波之间存在类多普勒效应,且二者前进方向存在夹角,所以利用地震与MODIS图像联测得到的传播相速度,结合MODIS图像所推断的内孤立波传播方向对视半高宽和视波间距进行校正.首波的半高宽与遥感估算的特征半波宽的比值是1.75,接近理论比值的1.763;校正后的真波间距和遥感量测波间距数值基本一致.最后,结合地震观测前后的遥感图像和潮流数据推断这一内孤立波包为Type-b型.结果表明,将地震海洋学和遥感方法结合,可以更好地研究内孤立波的特征.     

一种新的卫星热红外遥感信息数据源:EOS/MODIS数据

概略介绍了四川省地震局近年来所开展的卫星遥感热红外辐射信息用于地震监测预报的研究工作及其进展；对目前地震系统卫星遥感热红外辐射信息研究工作所使用的数据源进行了比较；着重介绍了一种新的卫星热红外遥感信息数据源——搭载于美国地球观测系统(EOS)的Terra和Aqua极轨卫星上的中分辨率成像光谱仪(MODIS)这一唯一进行直接广播的对地观测仪器的基本参数及数据特性。对MODIS数据与目前正在使用的AVHRR数据进行对比分析后认为，EOS／MODIS数据的产生，将进一步推动利用卫星热红外辐射资料研究强震前的热异常场的工作，并可望在我国地震监测预报实践中得到应用。     

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.     

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.     

Remote Sensing Monitoring of Volcanic Ash Clouds Based on PCA Method

Volcanic ash clouds threaten the aviation safety and cause global environmental effects. It is possible to effectively monitor the volcanic ash cloud with the aid of thermal infrared remote sensing technology. Principal component analysis (PCA) is able to remove the inter-band correlation and eliminate the data redundancy of remote sensing data. Taking the Eyjafjallajokull volcanic ash clouds formed on 15 and 19 April 2010 as an example, in this paper, the PCA method is used to monitor the volcanic ash cloud based on MODIS bands selection; the USGS standard spectral database and the volcanic absorbing aerosol index (AAI) are applied as contrasts to the monitoring result. The results indicate that: the PCA method is much simpler; its spectral matching rates reach 74.65 and 76.35%, respectively; and the PCA method has higher consistency with volcanic AAI distribution.     

Volcanic ash cloud detection from MODIS image based on CPIWS method

Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.     

Near-real-time volcanic ash cloud detection: Experiences from the Alaska Volcano Observatory

Volcanic eruptions produce ash clouds, which are a major hazard to population centers and the aviation community. Within the North Pacific (NOPAC) region, there have been numerous volcanic ash clouds that have reached aviation routes. Others have closed airports and traveled for thousands of kilometers. Being able to detect these ash clouds and then provide an assessment of their potential movement is essential for hazard assessment and mitigation. Remote sensing satellite data, through the reverse absorption or split window method, is used to detect these volcanic ash clouds, with a negative signal produced from spectrally semi-transparent ash clouds. Single channel satellite is used to detect the early eruption spectrally opaque ash clouds. Volcanic Ash Transport and Dispersion (VATD) models are used to provide a forecast of the ash clouds' future location. The Alaska Volcano Observatory (AVO) remote sensing ash detection system automatically analyzes satellite data of volcanic ash clouds, detecting new ash clouds and also providing alerts, both email and text, to those with AVO. However, there are also non-volcanic related features across the NOPAC region that can produce a negative signal. These can complicate alerts and warning of impending ash clouds. Discussions and examples are shown of these non-volcanic features and some analysis is provided on how these features can be discriminated from volcanic ash clouds. Finally, there is discussion on how information of the ash cloud such as location, particle size and concentrations, could be used as VATD model initialization. These model forecasts could then provide an improved assessment of the clouds' future movement.     

Integrated satellite observations of the 2001 eruption of Mt. Cleveland, Alaska

Satellite data were the primary source of information for the eruption of Mt. Cleveland, Alaska on 19 February, and 11 and 19 March 2001. Multiple data sets were used pre-, syn- and post-eruption to mitigate the hazard and determine an eruption chronology. The 19 February eruption was the largest of the three, resulting in a volcanic cloud that formed an arc over 1000 km long, moved to the NE across Alaska and was tracked using satellite data over more than a 50-h period. The volcanic cloud was “concurrently” detected on the GOES, AVHRR and MODIS data at various times and their respective signals compared. All three sensors detected a cloud that had a very similar shape and position but there were differences in their areal extent and internal structural detail. GOES data showed the largest volcanic cloud in terms of area, probably due to its oblique geometry. MODIS bands 31 and 32, which are comparable to GOES and AVHRR thermal infrared wavelengths, were the least effective single channels at detecting the volcanic cloud of those investigated (MODIS bands 28, 29, 31 and 32). MODIS bands 28 and 29 detected the largest volcanic clouds that could easily be distinguished from weather clouds. Of the split-window data, MODIS bands 29 minus band 32 detected the largest cloud, but the band 31 minus band 32 data showed the volcanic cloud with the most internal structural detail. The Puff tracking model accurately tracked the movement, and predicted the extent and shape of this complex cloud even into areas beyond satellite detection. Numerous thermal anomalies were also observed during the eruption on the twice-daily AVHRR data and the high spatial-resolution Landsat data. The high-resolution Radarsat data showed that the AVHRR thermal anomalies were due to lava and debris flow features and a newly formed fan along the west coast of the island. Field observations and images from a hand-held Forward Looking Infrared Radiometer (FLIR) showed that the flow features were ′a′a lava, debris flows and a warm debris fan along the west coast. Real-time satellite data were the primary tool used to monitor the eruption, track changes and to mitigate hazards. High-resolution data, even though coverage is infrequent, were critical in helping to identify volcanic processes and to compile an eruption chronology.     

活动断裂调查中的高分辨率遥感技术应用方法研究

本文系统分析了高分辨率遥感在活动断裂调查中应用的技术现状、工作流程,梳理了各类遥感数据的要求、适用条件和处理方法,总结了活动断裂的遥感解译方法、解译要素和测量参数,并通过实例解析了一些典型的断错地貌,给出了相应的遥感特征. 基于资源三号卫星的立体像对和影像,判读了大青山活动断裂的几何特征和活动特性. 结果表明: 人工改造较大的地区宜收集早期遥感影像,利用不同波段间地物光谱的差异来增强隐伏活动断裂的信息,使用空间增强方法来识别断层陡坎等线性构造;雷达数据多极化分解是检测隐伏构造信息的有效方法;由宏观信息向局部信息追踪是活动断裂解译的有效途径;将遥感影像与数字高程模型(DEM)联合可进行活动断层参数的高精度测量. 本文结果可为活动断裂大比例尺、定量调查提供参考.      

Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor(PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.     

主成分监督分类及其在水质特征遥感图像识别中的应用

建立了一种水域水质状况图像识别的主成分监督分类方法,首先通过ＴＭ水域图像数据的主成分分析,将原有各波段图谱的显著且独立的信息集中在数目尽可能少的合成图象中,再依据不同类型水体的光谱特征,分析各主成分图像的构成及其环境生态学含义,由此对整个研究区域内存在的不同标志类型及其分布特征有所了解,在此基础上,选定训练样本集,从而人有清楚的环境生态意义的标志类型,应用监督法得到较好的识别分类结果,分析表明,这