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。     

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.     

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.     

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.     

MODIS亮温增温异常点比值的时序分析方法及其在台湾一些5级以上地震前的异常变化

首次提出了MODIS卫星遥感数据用于地震预测研究的MODIS亮温增温异常点比值定量化方法以及尽量减少云层干扰的初步方案。结合近期台湾地区一系列中强地震的震例研究,归纳了分析区域内中强地震前的MODIS亮温增温异常的量化指标。研究表明,大部分研究震例在地震发生前1~20天都出现了突跳性质的增温异常比值(活跃度)增强的情况,而正常情况下增温异常比值都在零值附近波动。利用在一定程度上排除了云层影响的MODIS卫星遥感热红外信息得到的亮温增温异常点比值的变化,在一些多震地区有针对性地系统开展研究,有望找到识别地震短临前兆的一种新方法     

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.     

Time Series Analysis on the Ratio for Pixels with Abnormal Brightness Temperature Increase and Its Variation Before Some Earthquakes with Ms ≥ 5.0 in the Taiwan Area

In the study of the application of MODIS satellite remote sensing data to earthquake prediction, the paper puts forward for the first time a quantitative method to estimate the ratio for the pixels with abnormal brightness temperature (BT) increase and a preliminary scheme for cloud removal. The principle is that, firstly, the cloudless data observed by the same satellite at the same period of time but in different days (usually 1 day to 3 days) are mosaiced to get high ratio of clear sky, and then the BT variation curve and mean square difference (MSD) of each pixel are calculated with the data from the covered area to determine daily whether the BT data of the day is normal or not at a certain pixel by using double the MSD as the criterion. The ratio for the pixels with abnormal BT increase can be calculated by dividing the total number of abnormal pixels with the total pixels of the whole area. Analysis on a series of recent earthquakes in the Taiwan area shows that the ratio for pixels with abnormal BT increase, which normally undulates around zero, has a sudden enhancement 1 day to 20 days before medium-strong earthquakes. It is expected that a new method for identifying earthquake auspice could be found through special studies in regions with frequent seismic activity by analyzing the change of the ratio for the pixels with abnormal BT increase from MODIS satellite remote sensing infrared (IR) information from which the effect of clouds has been removed to a certain extent.     

Time Series Analysis on the Ratio for Pixels with Abnormal Brightness Temperature Increase and Its Variation Before Some Earthquakes with Ms ≥ 5.0 in the Taiwan Area

In the study of the application of MODIS satellite remote sensing data to earthquake prediction, the paper puts forward for the first time a quantitative method to estimate the ratio for the pixels with abnormal brightness temperature (BT) increase and a preliminary scheme for cloud removal. The principle is that, firstly, the cloudless data observed by the same satellite at the same period of time but in different days (usually 1 day to 3 days) are mosaiced to get high ratio of clear sky, and then the BT variation curve and mean square difference (MSD) of each pixel are calculated with the data from the covered area to determine daily whether the BT data of the day is normal or not at a certain pixel by using double the MSD as the criterion. The ratio for the pixels with abnormal BT increase can be calculated by dividing the total number of abnormal pixels with the total pixels of the whole area. Analysis on a series of recent earthquakes in the Taiwan area shows that the ratio for pixels with abnormal BT increase, which normally undulates around zero, has a sudden enhancement 1 day to 20 days before medium-strong earthquakes. It is expected that a new method for identifying earthquake auspice could be found through special studies in regions with frequent seismic activity by analyzing the change of the ratio for the pixels with abnormal BT increase from MODIS satellite remote sensing infrared (IR) information from which the effect of clouds has been removed to a certain extent.     

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.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

Evaluation of the Physical and Chemical Properties of Eyjafjallajökull Volcanic Plume Using a Cloud-Resolving Model

The Eyjafjallajökull volcanic eruption, which occurred on April 14, 2010, caused many environmental, air traffic and health problems. An attempt has been made to demonstrate for the first time that certain improvements could be made in the quantitative prediction of the volcanic ash parameters, and in the accounting of the processes in the immediate vicinity of the volcano, using a cloud-resolving model. This type of explicit modeling by treatment of volcanic ash and sulfate chemistry parameterization, with input of a number parameters describing the volcanic source, is the way forward for understanding the complex processes in plumes and in the future plume dispersion modeling. Results imply that the most significant microphysical processes are those related to accretion of cloud water, cloud ice and rainwater by snow, and accretion of rain and snow by hail. The dominant chemical conversion rates that give a great contribution to the sulfate budget are nucleation and dynamic scavenging and oxidation processes. A three-dimensional numerical experiment has shown a very realistic simulation of volcanic ash and other chemical compounds evolution, with a sloping structure strongly influenced by the meteorological conditions. In-cloud oxidation by H₂O₂ is the dominant pathway for SO₂ oxidation and allows sulfate to be produced within the SO₂ source region. The averaged cloud water pH of about 5.8 and rainwater pH of 4.5 over simulation time show quantitatively how the oxidation may strongly influence the sulfate budget and acidity of volcanic cloud. Compared to observations, model results are close in many aspects. Information on the near field volcanic plume behavior is essential for early preparedness and evacuation. This approach demonstrates a potential improvement in quantitative predictions regarding the volcanic plume distribution at different altitudes. It could be a useful tool for modeling volcanic plumes for better emergency measures planning.     

Patterns in open vent, strombolian behavior at Fuego volcano, Guatemala, 2005–2007

Fuego volcano, Guatemala is a high (3,800 m) composite volcano that erupts gas-rich, high-Al basalt, often explosively. It spends many years in an essentially open vent condition, but this activity has not been extensively observed or recorded until now. The volcano towers above a region with several tens of thousands of people, so that patterns in its activity might have hazard mitigation applications. We conducted 2 years of continuous observations at Fuego (2005–2007) during which time the activity consisted of minor explosions, persistent degassing, paroxysmal eruptions, and lava flows. Radiant heat output from MODIS correlates well with observed changes in eruptive behavior, particularly during abrupt changes from passive lava effusion to paroxysmal eruptions. A short-period seismometer and two low-frequency microphones installed during the final 6 months of the study period recorded persistent volcanic tremor (1–3 Hz) and a variety of explosive eruptions. The remarkable correlation between seismic tremor, thermal output, and daily observational data defines a pattern of repeating eruptive behavior: 1) passive lava effusion and subordinate strombolian explosions, followed by 2) paroxysmal eruptions that produced sustained eruptive columns, long, rapidly emplaced lava flows, and block and ash flows, and finally 3) periods of discrete degassing explosions with no lava effusion. This study demonstrates the utility of low-cost observations and ground-based and satellite-based remote sensing for identifying changes in volcanic activity in remote regions of underdeveloped countries.     

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.     

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.     

基于SVM测井数据的火山岩岩性识别——以辽河盆地东部坳陷为例

辽河盆地东部坳陷储集层由火山多期喷发形成,岩相岩性复杂,岩性以中、基性火山岩为主.本文将火山岩的岩心及岩矿鉴定资料与测井数据进行整合,应用测井数据建立支持向量机(SVM)两分类和多分类岩性识别模式.首先,深入研究支持向量机二分类及"一对一"、"一对多"和有向无环图三种经典多分类算法的基本原理及结构;然后,总结研究区域火山岩岩石特征,分析测井数据的测井响应组合特征,选择40口井中岩心分析和薄片鉴定资料完整、常规五种测井曲线(RLLD,CNL,DEN,AC,GR)齐全的1200个测井数据作为训练样本,构造三种支持向量机岩性识别模式;最后,对4测试井中800个测井数据进行岩性识别,识别结果与取心段岩心描述和岩心/岩屑薄片鉴定资料对比,实验结果表明有向无环图更适合辽河盆地火山岩的识别,识别正确率达到82.3%.     

地震灾后区域无人机GPS遥感定位技术研究

传统基于云计算的地理位置定位方法进行目标定位时需要分析大量的地理数据信息,造成目标定位过程复杂且容易出现定位差错。提出新的地震灾后区域无人机GPS遥感定位方法,其功能包括遥感传感子系统、遥感空中控制子系统、无人机平台、无人机地面控制子系统、三维模型影像重建以及目标定位分析。重建地震灾区的三维影像模型时,先提取地震灾区的DEM数据,将无人机GPS遥感测量得到的影像纹理映射到DEM上,构建地震灾区的三维地物模型;目标定位分析采用测距目标定位法,利用无人机在不同位置对目标进行定位分析,实现对地震灾后区域的定位分析,得到目标的坐标结果。实验结果说明,所提方法能提高目标定位的精度,缩短目标定位用时。     

基于稀疏成分分析的遥感影像分类

遥感影像分类一直是遥感研究的重点、难点和热点之一.针对经典的主成分分析法在不同地物的光谱存在重叠相关时,分类效果欠佳的缺陷,提出一种基于稀疏成分分析的遥感影像分类法.该方法利用稀疏性提取源信号,不要求源成分之间互不相关.实验结果表明,与主成分分析方法相比,基于稀疏成分分析的分类结果更可靠、更准确.     

利用 SQL Server 实现 MODIS 数据存储与读取

应用 SQL Server 服务器数据库,存储 MODIS 卫星遥感热红外影像数据,并探索较为合理的节省服务器磁盘空间的数据存储方法。此项工作对应用于地震观测预报领域的其他遥感数据的存储、读取等基础工作具有参考性。