利用独立分量分析进行火山灰云遥感检测

火山灰云不但引起全球气候和环境系统的重大变化,而且还会威胁航空安全。热红外遥感技术为检测火山灰云提供了新手段,但是遥感数据自身的冗余和波段相关性大大降低了火山灰云的检测精度。独立分量分析(Independent Component Analysis,ICA)能够实现遥感数据的去相关和消除冗余,在火山灰云检测中具有一定的潜力。通过探索火山灰云的物理、化学性质,文中以2010年4月19日冰岛艾雅法拉(Eyjafjallajokull)火山灰云MODIS图像为数据源,在对MODIS数据进行主成分分析处理的基础上,利用ICA进行火山灰云检测。结果表明:ICA能够较好地从MODIS图像中获取火山灰云信息,所得结果与美国地质调查局标准光谱数据库和火山灰云SO2浓度分布具有较好的一致性,取得了较好的检测效果。     

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.     

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.     

Improved prediction and tracking of volcanic ash clouds

During the past 30 years, more than 100 airplanes have inadvertently flown through clouds of volcanic ash from erupting volcanoes. Such encounters have caused millions of dollars in damage to the aircraft and have endangered the lives of tens of thousands of passengers. In a few severe cases, total engine failure resulted when ash was ingested into turbines and coating turbine blades. These incidents have prompted the establishment of cooperative efforts by the International Civil Aviation Organization and the volcanological community to provide rapid notification of eruptive activity, and to monitor and forecast the trajectories of ash clouds so that they can be avoided by air traffic. Ash-cloud properties such as plume height, ash concentration, and three-dimensional ash distribution have been monitored through non-conventional remote sensing techniques that are under active development. Forecasting the trajectories of ash clouds has required the development of volcanic ash transport and dispersion models that can calculate the path of an ash cloud over the scale of a continent or a hemisphere. Volcanological inputs to these models, such as plume height, mass eruption rate, eruption duration, ash distribution with altitude, and grain-size distribution, must be assigned in real time during an event, often with limited observations. Databases and protocols are currently being developed that allow for rapid assignment of such source parameters. In this paper, we summarize how an interdisciplinary working group on eruption source parameters has been instigating research to improve upon the current understanding of volcanic ash cloud characterization and predictions. Improved predictions of ash cloud movement and air fall will aid in making better hazard assessments for aviation and for public health and air quality.     

Impact on volcanic ash detection caused by the loss of the 12.0 μm “Split Window” band on GOES Imagers

Geostationary Operational Environmental Satellite (GOES) Imager and Sounder data were evaluated to determine the potential effects of volcanic ash detection without the use of a 12 μm infrared (IR) band, on GOES-M (12) through Q (a period of at least 10 years). Principal component analysis (PCA) images with and without 12 μm IR data were compared subjectively for six weak to moderate eruptions using pattern recognition techniques, and objectively by determining a false detection rate parameter. GOES Sounder data were also evaluated in a few instances to assess any potential contributions from the new 13.3 μm Imager band.Results indicated that, during periods of daylight, there was little apparent difference in the quality of IR detection without the 12 μm IR, likely due to a maximum in solar reflectance of silicate ash in a shortwave IR (SWIR) band centered near 3.9 μm. At night when SWIR reflectance diminished, the ash detection capability appeared to be significantly worse, evidenced by increased ambiguity between volcanic ash and meteorological clouds or surface features. The possible effects of this degradation on aviation operations are discussed. The new 13.3 μm IR band on GOES has the capability to help distinguish ash from cirrus clouds, but not from low level clouds consisting of water droplets.Multi-spectral data from higher resolution polar orbiting satellites may also be used to supplement analyses from lower resolution GOES for long-lived ash cloud events. The Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments appear to be the best options in accomplishing this, with additional satellite missions becoming available later in the decade. In summary, it will still be possible to observe and track significant volcanic ash clouds in the GOES-M through Q era (2003–2012) without the benefit of 12 μm IR data, but with some degradation that will be most significant at night.     

SO2 emission of the 1974 eruption of Volcán Fuego,Guatemala

Volcán Fuego in the Central American Republic of Guatemala erupted violently in October, 1974. A remote sensing correlation spectrometer. COSPEC IV, which utilizes the characteristic molecular absorption of SO₂ in the ultraviolet was used to monitor the SO₂ content of the volcanic plume. Over a 60-day period measurements were made on 37 days between and following major eruptive phases. SO₂ emission rates corrected for atmospheric scattering of the spectral signal average 423 metric tons/day with a standard deviation of 252 metric tons/days. Late stage peaks in SO₂ emission at Fuego are consistent with the presence of anomalously high contents of soluble materials on the stratigraphically highest ashes from other Central American eruptions. Indications are that the SO₂ concentration within the volcanic plume increased as activity waned. These features imply that remote spectroscopic sensing of SO₂ and perhaps other gases in a volcanic plume may provide a relatively easy and inexpensive means of determining the cessation of violent eruptive activity.     

Electrification of volcanic plumes

Volcanic lightning, perhaps the most spectacular consequence of the electrification of volcanic plumes, has been implicated in the origin of life on Earth, and may also exist in other planetary atmospheres. Recent years have seen volcanic lightning detection used as part of a portfolio of developing techniques to monitor volcanic eruptions. Remote sensing measurement techniques have been used to monitor volcanic lightning, but surface observations of the atmospheric electric Potential Gradient (PG) and the charge carried on volcanic ash also show that many volcanic plumes, whilst not sufficiently electrified to produce lightning, have detectable electrification exceeding that of their surrounding environment. Electrification has only been observed associated with ash-rich explosive plumes, but there is little evidence that the composition of the ash is critical to its occurrence. Different conceptual theories for charge generation and separation in volcanic plumes have been developed to explain the disparate observations obtained, but the ash fragmentation mechanism appears to be a key parameter. It is unclear which mechanisms or combinations of electrification mechanisms dominate in different circumstances. Electrostatic forces play an important role in modulating the dry fall-out of ash from a volcanic plume. Beyond the local electrification of plumes, the higher stratospheric particle concentrations following a large explosive eruption may affect the global atmospheric electrical circuit. It is possible that this might present another, if minor, way by which large volcanic eruptions affect global climate. The direct hazard of volcanic lightning to communities is generally low compared to other aspects of volcanic activity.     

Observations of Mt. Etna volcanic ash plumes in 2006: An integrated approach from ground-based and polar satellite NOAA–AVHRR monitoring system

Mt. Etna, in Sicily (Italy), is one of the world's most frequent emitters of volcanic plumes. During the last ten years, Etna has produced copious tephra emission and fallout that have damaged the inhabited and cultivated areas on its slopes and created serious hazards to air traffic. Recurrent closures of the Catania International airport have often been necessary, causing great losses to the local economy. Recently, frequent episodes of ash emission, lasting from a few hours to days, occurred from July to December 2006, necessitating a look at additional monitoring techniques, such as remote sensing. The combination of a ground monitoring system with polar satellite data represents a novel approach to monitor Etna's eruptive activity, and makes Etna one of the few volcanoes for which this surveillance combination is routinely available.In this work, ash emission information derived from an integrated approach, based on comparing ground and NOAA–AVHRR polar satellite observations, is presented. This approach permits us to define the utility of real time satellite monitoring systems for both sporadic and continuous ash emissions. Using field data (visible observations, collection of tephra samples and accounts by local inhabitants), the duration and intensity of most of the tephra fallout events were evaluated in detail and, in some cases, the order of magnitude of the erupted volume was estimated. The ground data vs. satellite data comparison allowed us to define five different categories of Etna volcanic plumes according to their dimensions and plume height, taking into account wind intensity. Using frequent and good quality satellite data in real time, this classification scheme could prove helpful for investigations into a possible correlation between eruptive intensity and the presence and concentration of ash in the volcanic plume. The development and improvement of this approach may constitute a powerful warning system for Civil Protection, thus preventing unnecessary airport closures.     

Gas and hydrogen isotopic analyses of volcanic eruption clouds in Guatemala sampled by aircraft

Gas samples were collected by aircraft entering volcanic eruption clouds of three Guatemalan volcanoes. Gas chromatographic analyses show higher H₂ and S gas contents in ash eruption clouds and lower H₂ and S gases in vaporous gas plumes. H isotopic data demonstrate lighter isotopic distribution of water vapor in ash eruption clouds than in vaporous gas plumes. Most of the H₂O in the vaporous plumes is probably meteoric. The data are the first direct gas analyses of explosive eruptive clouds, and demonstrate that, in spite of atmospheric admixture, useful compositional information on eruptive gases can be obtained using aircraft.     

Preliminary sensitivity study of eruption source parameters for operational volcanic ash cloud transport and dispersion models — A case study of the August 1992 eruption of the Crater Peak vent, Mount Spurr, Alaska

Ash clouds are one of the major hazards that result from volcanic eruptions. Once an eruption is reported, volcanic ash transport and dispersion (VATD) models are used to forecast the location of the ash cloud. These models require source parameters to describe the ash column for initialization. These parameters include: eruption cloud height and vertical distribution, particle size distribution, and start and end time of the eruption. Further, if downwind concentrations are needed, the eruption mass rate and/or volume of ash need to be known. Upon notification of an eruption, few constraints are typically available on many of these source parameters. Recently, scientists have defined classes of eruption types, each with a set of pre-defined eruption source parameters (ESP). We analyze the August 18, 1992 eruption of the Crater Peak vent at Mount Spurr, Alaska, which is the example case for the Medium Silicic eruption type. We have evaluated the sensitivity of two of the ESP – the grain size distribution (GSD) and the vertical distribution of ash – on the modeled ash cloud. HYSPLIT and Puff VATD models are used to simulate the ash clouds from the different sets of source parameters. We use satellite data, processed through the reverse absorption method, as reference for computing statistics that describe the modeled-to-observed comparison. With the grain size distribution, the three options chosen, (1) an estimated distribution based on past eruption studies, (2) a distribution with finer particles and (3) the National Oceanic and Atmospheric Administration HYSPLIT GSD, have little effect on the modeled ash cloud. For the initial vertical distribution, both linear (uniform concentration throughout the vertical column) and umbrella shapes were chosen. For HYSPLIT, the defined umbrella distribution (no ash below the umbrella), apparently underestimates the lower altitude portions of the ash cloud and as a result has a worse agreement with the satellite detected ash cloud compared to that with the linear vertical distribution for this particular eruption. The Puff model, with a Poisson function to represent the umbrella cloud, gave similar results as for a linear distribution, both having reasonable agreement with the satellite detected cloud. Further sensitivity studies of this eruption, as well as studies using the other source parameters, are needed.     

利用卫星热红外遥感技术监测长白山天池火山活动性

介绍了卫星热红外遥感技术用于火山监测的国内外研究现状,阐述了热红外遥感技术的原理,分析了卫星热红外遥感技术用于火山活动性监测的可行性。以长白山天池火山为例,基于Landsat TM/ETM影像和ASTER影像反演获得了1999—2008年的温度场,并选取了其中的3种地面覆盖类型(森林植被、土壤和植被(矮草)以及裸露岩石),从而去除了地表环境因素的影响;从每种地面覆盖类型中扣除了当日天池气象站的平均气温,去除了气象因素的影响,得到了由火山热活动可能导致的温度热异常。结果显示,从1999—2005年,由火山活动导致的温度热异常伴随着扰动发生了明显的上升,自2005年以后逐渐下降,2006—2008年趋于稳定。这些结果与测震、GPS形变以及He同位素比值变化趋势保持了较好的一致性,表明卫星热红外遥感技术用于火山活动性监测的巨大潜力和优势,可以作为一种常规的监测手段尝试性地纳入日常的火山监测工作中     

卫星热红外遥感在火山活动性监测中的应用

介绍了卫星热红外遥感在国内外火山监测研究中的应用现状,结合热红外遥感在地震中的应用成果,对利用卫星热红外遥感监测火山活动的可行性及方法进行了探讨,提出通过火山区热红外亮温旬变、月变和年变模型扣除地形地貌、岩性、植被等地表环境因素的影响,通过火山区和邻近参照区红外亮温差值运算扣除气象因素影响的火山活动性热红外异常提取方法。并以长白山火山为例,利用1999、2003和2004年的NOAA卫星影像资料,对长白山火山及周围地区的热红外影像特征进行了分析解译,对长白山火山区与外围参照区的红外亮温年变差异进行了统计分析。结果表明:1)长白山火山区的红外亮温分布特征在空间上主要受地形控制,总体表现为以天池为中心,向外围逐渐升高的漏斗状,天池则是低温背景上的明显高温标记。在时间上,长白山火山区红外亮温的演变过程主要受季节变化的影响,具有明显的夏高冬低年变特征。2)相对于1999年,2003和2004年长白山火山区均显示出明显的升温趋势,升温幅度可达2K左右。我们认为这可能是近年来天池火山活动性逐年增强的反映。这也意味着利用卫星热红外遥感监测火山活动性将是行之有效的新途径,也是值得深入研究的课题     

Stratospheric dust loading from early 1981 to September 1985 based on the twilight sounding method and stratospheric dust collections

Stratospheric aerosol loading from early 1981 to late 1985 was investigated by remote optical measurements using the twilight sounding method and by in situ mineral dust collections. Both experiments tracked the decay of aerosol abundances after the El Chichón eruption. A comparison between the remote optical observations and dust samplings suggests that aerosol maxima in 1985 were probably associated with a minor eruption of the Bezymianny volcano. Considering the different dynamical behavior of volcanic ash and condensed sulfuric acid aerosol, we traced the origin of collected dust to a minor eruption of Una Una volcano. This collected dust that could not be detected by remote sensing techniques against the high background level due to condensed aerosol from El Chichón highlights the complimentary nature of stratospheric dust collections and the twilight sounding method.     

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.     

Monitoring Etna volcanic plumes using a scanning LiDAR

In this paper, we use data obtained from LiDAR measurements during an ash emission event on 15 November 2010 at Mt. Etna, in Italy, in order to evaluate the spatial distribution of volcanic ash in the atmosphere. A scanning LiDAR system, located at 7?km distance from the summit craters, was directed toward the volcanic vents and moved in azimuth and elevation to analyse different volcanic plume sections. During the measurements, ash emission from the North East Crater and high degassing from the Bocca Nuova Crater were clearly visible. From our analysis we were able to: (1) evaluate the region affected by the volcanic plume presence; (2) distinguish volcanic plumes containing spherical aerosols from those having non-spherical ones; and (3) estimate the frequency of volcanic ash emissions. Moreover, the spatial distribution of ash mass concentration was evaluated with an uncertainty of about 50?%. We found that, even during ash emission episodes characterised by low intensity like the 15 November 2010 event, the region in proximity of the summit craters should be avoided by air traffic operations, the ash concentration being greater than 4?×?10^?3?g/m³. The use of a scanning permanent LiDAR station may usefully monitor the volcanic activity and help to drastically reduce the risks to aviation operations during the frequent Etna eruptions.     

基于梯度变换的浅水湖泊围网区遥感提取算法

获取并掌握浅水湖泊围网养殖区域的时空分布信息对合理规划围网养殖进而提升湖泊水质具有重要意义.本文以长江下游典型的围网养殖浅水湖泊——阳澄湖作为研究区,利用资源三号(ZY-3)高分遥感影像,针对围网区与非围网区的光谱空间变化特征,采用梯度变换方法,尝试提出一种浅水湖泊围网区的遥感提取算法;并以人工解译结果作为参考,对提取结果进行验证.研究结果发现该算法对浅水湖泊围网养殖区的提取精度为90.66%,可进一步用于开展长时序的浅水湖泊围网区动态变化研究,进而为湖泊环境的政府部门制定湖泊水质提升和围网区合理规划政策提供决策依据.     

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。     

环境一号卫星高光谱遥感数据的内陆水质监测适宜性——以巢湖为例

环境一号星座的A星(HJ-1A)上搭载了超光谱成像仪(HSI),它是具有高光谱分辨率的全新国产遥感数据源,为水质遥感特别是内陆水质遥感提供了新的高光谱数据.但HSI影像的处理方法尚不完善,在广泛应用于水质遥感前对其进行质量研究和评价非常必要.本文针对HSI数据在巢湖水质监测应用方面的适宜性,对信噪比和数据真实性、倾斜条...     

卫星遥感技术在火山监测中的应用

作为一门新兴技术，卫星遥感已被有效地应用于火山活动监测、通过SAR(合成孔径雷达)和InSAR(合成孔径雷达干涉成像)资料可以监测火山地表形变、使用热辐射数据以及将遥感与其它技术相结合能够研究火山区的热活动和火山喷发物。在我国的火山监测中，广泛地应用卫星遥感技术是非常必要的。     

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.