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1

Automatic Cloud Detection from Multi-Temporal Satellite Images: Towards the Use of PLÉIADES Time Series  

NASA Astrophysics Data System (ADS)

Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, developed at the French Mapping agency. Our approach is based on the availability of multi-temporal satellite images (i.e. time series that generally contain between 5 and 10 images) and is based on a region-growing procedure. Seeds (corresponding to clouds) are firstly extracted through a pixel-to-pixel comparison between the images contained in time series (the presence of a cloud is here assumed to be related to a high variation of reflectance between two images). Clouds are then delineated finely using a dedicated region-growing algorithm. The method, originally designed for panchromatic SPOT5-HRS images, is tested in this paper using time series with 9 multi-temporal satellite images. Our preliminary experiments show the good performances of our method. In a near future, the method will be applied to Pléiades images, acquired during the in-flight commissioning phase of the satellite (launched at the end of 2011). In that context, this is a particular goal of this paper to show to which extent and in which way our method can be adapted to this kind of imagery.

Champion, N.

2012-08-01

2

Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery  

Microsoft Academic Search

Abstract. The framework,of mining of moving objects from image data sequence is presented. Scenes are first clustered and labeled by using two-stage SOM that is modified to recognize images including similar moving objects as the same cluster, and that well recognizes scenes including prominent,objects. After extraction of images which include prominent objects based on clustering result, the position and the

Rie Honda; Shuai Wang; Tokio Kikuchi; Osamu Konishi

2002-01-01

3

Mapping Impervious Surface Expansion using Medium-resolution Satellite Image Time Series: A Case Study in the Yangtze River Delta, China  

NASA Technical Reports Server (NTRS)

Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China-Brazil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived from this study agree well with an existing urban extent polygon data set that was previously developed independently. The overall mapping accuracy was estimated at about 92.5% with 3% commission error and 12% omission error for the impervious type from all images regardless of image quality and initial spatial resolution.

Gao, Feng; DeColstoun, Eric Brown; Ma, Ronghua; Weng, Qihao; Masek, Jeffrey G.; Chen, Jin; Pan, Yaozhong; Song, Conghe

2012-01-01

4

A radar image time series  

NASA Technical Reports Server (NTRS)

A set of ten side-looking radar images of a mining area in Arizona that were aquired over a period of 14 yr are studied to demonstrate the photogrammetric differential-rectification technique applied to radar images and to examine changes that occurred in the area over time. Five of the images are rectified by using ground control points and a digital height model taken from a map. Residual coordinate errors in ground control are reduced from several hundred meters in all cases to + or - 19 to 70 m. The contents of the radar images are compared with a Landsat image and with aerial photographs. Effects of radar system parameters on radar images are briefly reviewed.

Leberl, F.; Fuchs, H.; Ford, J. P.

1981-01-01

5

Monitoring agricultural crop growth: comparison of high spatial-temporal satellite imagery versus UAV-based imaging spectrometer time series measurements  

NASA Astrophysics Data System (ADS)

In 2012, the Dutch National Satellite Data Portal (NSD) was launched as a preparation to the launch of the European SENTINEL satellites in the framework of the Copernicus Programme. At the same time the Unmanned Aerial Remote Sensing Facility (UARSF: www.wageningenUR.nl/uarsf) has been established as research facility at Wageningen University and Research Centre. The NSD became available for the development of services and advice through an investment from the Dutch government in collaboration with the Netherlands Space Office (NSO) in order to develop new services for precision agriculture. The NSD contains Formosat, Radarsat as well as DMC satellite imagery. The processing of the DMC imagery resulted in the Greenmonitor service (www.groenmonitor.nl). The Greenmonitor is an unique product that covers the Netherlands with a high spatial and temporal resolution. The Greenmonitor is now being exploited for various applications, amongst others crop identification, crop phenology, and identification of management activities. The UARSF of Wageningen UR has three objectives: 1) to develop innovation in the field of remote sensing science using Unmanned Aerial Vehicles (UAV) by providing a platform for dedicated and high-quality experiments; 2) to support high quality UAV services by providing calibration facilities and disseminating processing procedures to the UAV user community; 3) to promote and test the use of UAV in a broad range of application fields such as precision agriculture and habitat monitoring. Through this coincidence of new developments the goal of our study was to compare the information for the measurements of spatial variation in crops and soils as derived from high spatial-temporal satellite imagery from the national data portal compared to the exploitation of UAVs, in our case an Altura octocopter with a hyperspectral camera. As such, the focus is on the applications in precision agriculture. Both primary producers and chain partners and service providers are involved in the consortium. First results show that the Greenmonitor is much more suitable for comparison in growth between fields at regional scale, while UAV based imagery is much more suitable for mapping variation in crop biochemistry (i.e., chlorophyll, nitrogen) within the fields, which requires in the Netherlands a spatial resolution of a few meters. Finally, the spatial and spectral dimension of satellite and UAV derived vegetation indices (i.e., weighted difference vegetation index, chlorophyll red-edge index) to evaluate to which extent UAV based image acquisition could be adopted to complement missing data in satellite time-series.

Mucher, Sander; Roerink, Gerbert; Franke, Jappe; Suomalainen, Juha; Kooistra, Lammert

2014-05-01

6

Drought Monitoring by Time Series Analysis of Satellite Land Surface Temperature  

NASA Astrophysics Data System (ADS)

With the development of remote sensing in the last thirty years massive satellite data have been accumulated by different satellite sensors. These continuous satellite data record the information on changes in land surface conditions. The research on the information retrieving from satellite time series data is of great significance, including applications to climate change research, identification of phenology, hydrological modeling, ecosystem and drought monitoring, etc. In this paper a methodology is presented for drought early warning by analyzing the time series of MODIS Land surface Temperature (LST) product. LST, representing the thermal properties of land surface and in turn canopy water stress conditions, is a vital parameter in the drought monitoring. The continuous increasing of LST relative to the historical average implies that drought might be happening. The proposed methodology is to use the satellite time series data to retrieve the trend in LST changes for drought monitoring. Missing observations always exist in the satellite time series due to cloud cover, which affects the reliability of the information retrieved from the time series. The first problem to solve when using such incomplete time series data is, therefore, to evaluate the quality of the time series and reconstruct a new time series without gaps. We have designed a set of criteria to classify the time series quality by taking into account the percent of the missing observations in the time series, the length of the gap in the series, and the retrieval quality of the parameter. A modified version of HANTS (Harmonic ANalysis of Time Series) is implemented to reconstruct the time series. The modification on HANTS is made to fit the rapidly changing character of LST time series. Then the time series of the LST anomaly relative to the historical average is calculated. Based on the time series of the LST anomaly an index to depict the accumulated temperature anomaly and its changing direction is designed. This index is capable of describing drought evolution and drought severity. To process the massive satellite data in time for quasi-real time monitoring, an algorithm is designed and coded to implement a tool for drought early warning through procedures including dataset management, image mosaic and resampling, image degradation, time series reconstruction, calculation of historical average, calculation of anomaly, and generation of drought severity index, etc. We have chosen China mainland as the study area to implement the developed method for drought monitoring. The MODIS daily (MOD11A1) and 8-day (MOD11A2) LST products are collected to construct the time series of satellite data. Then we applied the above methodology over the whole China in particularly to analyze the severe drought event occurred in Sichuan-Chongqing in 2006.

Li, J.; Jia, L.

2009-04-01

7

Using time series of satellite SAR images to calibrate channel depth and friction parameters in the LISFLOOD-FP hydraulic model  

NASA Astrophysics Data System (ADS)

Synthetic Aperture Radar (SAR) satellites are capable of all-weather day/night observations that allow discrimination between land and smooth open water surfaces over large scales. Because of this there has been much interest in the use of SAR images to estimate flood extent and flood elevation in order to improve understanding of fluvial flood inundation processes. In past studies it has been proven that integrating SAR derived information with hydraulic models can improve simulations of flooding mechanisms. A repository of River Severn at Tewkesbury flood images from the ENVISAT satellite (wide-swath mode) have been processed and catalogued for events between 2005 and 2012. Information such as flood extent is taken from these images for floods occurring in 2007, 2008 and 2011 as the focus of this study. The flood events are simulated within a 2D LISFLOOD-FP Sub-Grid hydraulic model of the River Severn covering an area of 50x70km. The Sub-Grid capabilities of this particular version of LISFLOOD-FP allows any size of river channel below that of the grid resolution to be represented thus allowing improved hydraulic connectivity within the flooded area. The objective of this study is to calibrate the parameters of the LISFLOOD-FP Sub-Grid 2-D model that govern channel depth and channel roughness using SAR derived images of flood extent. Parameters 'r' and 'p' are variables used in the Sub-Grid model to define depth of channel at full bank. The equation estimates bank full depth D being equivalent to rWp¬¬, where W is bank full channel width. By varying the parameters 'r' and 'p' around an initial estimate we can create a number of unique models with ranging channel depth. It follows that models with differing channel depths generate results of differing flood levels and extent. The model calibration is achieved by selecting those models results with simulated flood extent which fit best with the observations of flood extent derived from the SAR images. A similar experiment was carried out to vary Manning's value 'n' (associated with channel roughness) with the channel depth parameter 'p' to determine whether the method would also work with pairings of two unrelated parameters. The study first compared the model simulations with a single SAR derived flood extent, then compared model results against a time series of images acquired over multiple flood events. This was to determine whether it is more useful in this method of calibration to use a time series of SAR derived extents. Validation is accomplished by use of surveyed cross sections of the Severn riverbed and aerial photographs, together with gauged records of discharge and water level located inside the limits of the model.

Wood, Melissa; Neal, Jeff; Hostache, Renaud; Corato, Giovanni; Bates, Paul; Giustarini, Laura; Chini, Marco; Matgen, Patrick

2014-05-01

8

The impact of the 2011 T¯o hoku-oki tsunami in the Sendai area: interpretations of time series satellite images and videos.  

NASA Astrophysics Data System (ADS)

The Tohoku-oki tsunami of March 11th 2011 was the most devastating tsunami to strike Japan in recorded history. Approximately 20,000 people died and 100's of square kms of the coast were inundated. Runup heights (local tsunami height above sea level) were at a maximum of 40 m along the northern Honshu coast. Farther south, on the Sendai coastal plain, tsunami runup heights were lower, with a maximum of 20 m recorded. The tsunami inundated up to 5 km inland across the Sendai Plain, which remained partly flooded for several weeks after the event. For the first time after a major natural disaster, post-event satellite imagery of the affected areas was immediately released. Numerous helicopter videos were also acquired. This contribution presents on pre- and post-tsunami satellite time-series data and video imagery to show the impact of the tsunami along a 15 km stretch of coastline of the Sendai Plain between Yuriagi and Iwanuma. The video and satellite data, in association with other data from tide gauges, ocean buoys, survivor observations and fieldwork, are used to identify the tsunami inundation path, tsunami timing, inundation limits and impact on the coastal areas.

Tappin, D.

2012-04-01

9

Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region.  

PubMed

Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E ~ 1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period. PMID:22972316

Song, Youngkeun; Njoroge, John B; Morimoto, Yukihiro

2013-05-01

10

Automated analysis of protein subcellular location in time series images  

PubMed Central

Motivation: Image analysis, machine learning and statistical modeling have become well established for the automatic recognition and comparison of the subcellular locations of proteins in microscope images. By using a comprehensive set of features describing static images, major subcellular patterns can be distinguished with near perfect accuracy. We now extend this work to time series images, which contain both spatial and temporal information. The goal is to use temporal features to improve recognition of protein patterns that are not fully distinguishable by their static features alone. Results: We have adopted and designed five sets of features for capturing temporal behavior in 2D time series images, based on object tracking, temporal texture, normal flow, Fourier transforms and autoregression. Classification accuracy on an image collection for 12 fluorescently tagged proteins was increased when temporal features were used in addition to static features. Temporal texture, normal flow and Fourier transform features were most effective at increasing classification accuracy. We therefore extended these three feature sets to 3D time series images, but observed no significant improvement over results for 2D images. The methods for 2D and 3D temporal pattern analysis do not require segmentation of images into single cell regions, and are suitable for automated high-throughput microscopy applications. Availability: Images, source code and results will be available upon publication at http://murphylab.web.cmu.edu/software Contact: murphy@cmu.edu

Hu, Yanhua; Osuna-Highley, Elvira; Hua, Juchang; Nowicki, Theodore Scott; Stolz, Robert; McKayle, Camille; Murphy, Robert F.

2010-01-01

11

Time Series of High Resolution Satellite Data as a Means to Produce and Validate Biogeophysical Parameters  

NASA Astrophysics Data System (ADS)

The retrieval of biogeophysical parameters time series from space has up to now concerned mostly low or medium resolution satellite data, the highest resolution being on the order of 250 m with MODIS or MERIS observations. However, the forthcoming of new satellite data at high resolution and high revisit frequency such as the Sentinel-2 satellite from the European Space Agency (ESA) may induce new efforts to process time series of biogeophysical variables at higher spatial resolution. Sentinel-2 will produce data at 10-20 m resolution in the visible and near infrared and data at 60 m resolution in the blue and middle infrared with a wide field of view allowing a repetitiveness of 10 days with 1 satellite and 5 days with two satellites at the Equator. The expected launch dates of the two Sentinel-2 satellites are 2014 and end of 2015. Orthorectified top of atmosphere reflectances will be delivered globally free of charge within 24 hour delay after acquisition by ESA. The Sentinel-2 data will offer great opportunities to generate time series of biogeophysical variables exhaustively over the world with high spatial resolution, in particular Essential Climate Variables over land such as the Leaf Area Index (LAI) or the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Algorithms are currently being developed to transform such data into products ready to be used by the community. To validate the corresponding products it is important to build appropriate satellite data sets of high spatial resolution and high temporal frequencies well before launch along with the associated ground measurements. CNES, CESBIO and INRA have been active these last ten years in building such data sets. Over the last ten years, the Kalideos program of CNES has provided time series of SPOT data over 3 sites in France and 1 site in Romania to the scientific community. CESBIO has gathered over 800 Landsat images and 400 Formosat-2 images (multispectral imagery at 8 m resolution, 1 day repetitiveness) over the South of France for the years 2002, 2003, 2006 to 2011. These data have served as a platform for the test and validation of an atmospheric correction scheme based on both the multispectral and multitemporal dimensions of the signal. Their use for the validation of biophysical variable retrieval has also started. To anticipate the massive coming of these high spatial resolution time series of biophysical products, a validation strategy should be designed and implemented. It should capitalize on the experience gained with the validation of medium spatial resolution products, in order to reach stage 4 of the validation, i.e. "Quantify uncertainties in the product and its associated structure with systematic updates when new product versions are released and as the time-series expand". For this a collection of satellite data and ground data on a variety of sites and ecoclimatic situations should be progressively built with an easy and transparent access for the community. The data sets described above could be part of such a collection.

Leroy, M.; Hagolle, O.; Demarez, V.; Claverie, M.; Baret, F.

2012-12-01

12

Volcanic SO2 flux time series from MSG-SEVIRI satellite measurements.  

NASA Astrophysics Data System (ADS)

Quantitative retrieval maps of SO2 and ash columnar abundances retrieved from thermal infrared (TIR) satellite images of volcanic plumes can be converted into flux time series if the wind field is known. In a recently published work we showed how to reconstruct SO2 and ash fluxes from a single TIR MODIS image instrument aboard TERRA and AQUA polar satellites. The results obtained were then successfully compared with the SO2 flux measured with the FLAME ground-based network of DOAS instruments in a case study of the December, 2006 Mt. Etna (Sicily, Italy) eruption. The key point of this work was that a single multispectral image framing a volcanic cloud can be regarded as the evolution in time of physical and volcanological parameters, and effectively records many hours of volcanic activity. We highlight that the flux reconstruction obtained from satellite data with this technique offers new perspectives that are particularly valuable for the monitoring of remote volcanoes and allows some insights on the volcanic processes driving the eruptions. Here we show how this promising approach can be easily extended to a collection of TIR MSG-SEVIRI images exploiting the high acquisition frequency achieved by an instrument on board on a geostationary platform.

Merucci, L.; Corradini, S.

2012-04-01

13

Vegetation classification in eastern China using time series NDVI images  

NASA Astrophysics Data System (ADS)

The SPOT/VGT NDVI (S10) time series data of eastern China (1998-2005) are smoothed with two methods, the moving average and the Savitzky-Golay filter, after they are downloaded from the official website of VITO. Then the monthly maximal NDVI images (total 93 images) are extracted from 279 NDVI (S10) images and the Principal Component Analysis (PCA) is applied on the 93 images. There are 3 components that each explains more than 1% of the variance, in which the principal components 1, 2 and 3 explain respectively 93.25%, 2.77% and 1.21% of the variance in the original 93 maximum NDVI images. The principal component 1 is interpreted as the "climate" component, and principal components 2 and 3 are interpreted as the "growth season" and "non-growth season" components respectively. Principal components 1, 2 and 3 are composed to a 3-band color image which is classified into 7 classes (including 18 subclasses) by ISODATA. The overall accuracy of classification in five samples is 83.6%, and the kappa index is 0.82. Finally, the unique intra-annual NDVI curve of each vegetation class is displayed.

Han, Guifeng; Xu, Jianhua

2007-11-01

14

Extracting dune mobility time series from sequences of optical satellite imagery  

NASA Astrophysics Data System (ADS)

COSI-Corr (Co-registration of Optically Sensed Images and Correlation) is an exciting new tool for the automatic detection and quantification of Earth surface movement from pairs of satellite imagery. The program was originally developed by geophysicists interested in earthquakes, but has quickly found applications in geomorphology, including the study of glaciers, landslides, and sand dunes both on Earth and, recently, on Mars. Given two optical images of the same dune area taken at different times, COSI-Corr calculates the displacement field that maximizes the correlation between the two exposures. Temporal changes of dune celerity can serve as a sensitive proxy for the windiness of desert areas. It can be shown that any change in shear velocity (u*) causes a three times larger change in dune celerity (v): ? {v}/{v} = 3 ? {u*}/{u_*} We have developed an algorithm to use COSI-Corr to compare a sequence multiple satellite images in order to extract time series of dune celerity and monitor the windiness of remote field locations devoid of weather stations and anemometers. The algorithm involves the following steps: Georeference, orthorectify and resample the images to a common resolution. Measure the displacement field for each time step with COSI-Corr. Destripe the raw correlation results to remove uncorrected attitude effects. `Warp' the destriped displacement fields back to a common reference, e.g. the first image in the sequence. `Clean' the correlation results using a combination of two filters, requiring the displacements to (a) have a high signal-to-noise ratio and (b) move in a consistent direction with time. Connect the `surviving' pixels of the displacement map and track them across the image with time, yielding a map-view of dune migration paths. Project the stepwise displacements of each dune track on the resultant migration direction to obtain the cumulative displacements. Select those pixels with a total displacement near the mode of this distribution, and divide their stepwise displacements by the time elapsed between the satellite exposures. This algorithm was applied to a sequence of seven Landsat, SPOT and ASTER images from the Bodélé Depression in northern Chad spanning the past 26 years, and was extended using declassified Corona imagery from 1965. The resulting time series indicates less than ten percent change in windiness of the area over the past 45 years.

Vermeesch, P.; Leprince, S.

2012-12-01

15

Satellite time series analysis to study the ephemeral nature of archaeological marks  

NASA Astrophysics Data System (ADS)

Archaeological structures buried beneath the ground often leave traces at the surface. These traces can be in the form of differences in soil moisture and composition, or vegetation growth caused for example by increased soil water retention over a buried ditch, or by insufficient soil depth over a buried wall for vegetation to place deep roots. Buried structures also often leave subtle topographic traces at the surface. Analyses is carried out on the ephemeral characteristics of buried archaeological crop and soil marks over a number of sites around the city of Rome using satellite data from both optical and SAR (Synthetic Aperture Radar) sensors, including Kompsat-2, ALOS PRISM and COSMO SkyMed. The sensitivity of topographic satellite data, obtained by optical photogrammetry and interferometric SAR, is also analysed over the same sites, as well as other sites in Egypt. The analysis includes a study of the interferometric coherence of successive pairs of a time series of SAR data over sites containing buried structuresto better understand the nature of the vegetated or bare soil surface. To understand the ephemeral nature of archaeological crop and soil marks, the spectral reflectance characteristics of areas where such marks sometimes appear are extracted from a time series of optical multispectral and panchromatic imagery, and their backscatter characteristics extracted from a time series of SAR backscatter amplitude data. The results of this analysis is then compared with the results of the coherence analysis to see if any link can be established between the appearance of archaeological structures and the nature of ground cover. Results show that archaeological marks in the study areas are more present in SAR backscatter data over vegetated surfaces, rather than bare soil surfaces, but sometimes appear also in bare soil conditions. In the study areas, crop marks appear more distinctly in optical data after long periods without rainfall. The topographic analysis shows that very high resolution Digital Elevation Models (DEMs) and derived hill-shade images extracted from satellite optical stereo and interferometric SAR data are capable of identifying archaeological features buried beneath the ground that leave a topographic signature at the surface.

Stewart, Chris

2014-05-01

16

Mesoscale variability in time series data: Satellite-based estimates for the U.S. JGOFS Bermuda Atlantic Time-Series Study (BATS) site  

Microsoft Academic Search

Objectively analyzed fields of satellite sea surface temperature (SST, advanced very high resolution radiometer (AVHRR) Pathfinder) and sea surface height anomaly (SSHA, combined TOPEX\\/Poseidon-ERS-1\\/2) are used to characterize, statistically, the mesoscale variability about the U.S. Joint Global Ocean Flux Study (JGOFS) Bermuda Atlantic Time-Series Study (BATS) site. These results are applied to the in situ BATS time series data and

David M. Glover; Scott C. Doney; Arthur J. Mariano; Robert H. Evans; Scott J. McCue

2002-01-01

17

Scintillation Time Series Synthesis for Satellite Links with Hidden Markov Model  

Microsoft Academic Search

This paper introduces a method to model the rapid attenuation fluctuations (the scintillation) on satellite links with generating the time series of attenuation levels. The applied model is a hidden Markov model which is parameterized from an appropriate filtered Gaussian white noise signal. For the parameterization of the Markov chain the Baum-Welch expectation-maximization algorithm has been used. The resulting Markov

L. Csurgai-Horvath; J. Bito

2007-01-01

18

Time series analysis of satellite derived surface temperature for Lake Garda  

NASA Astrophysics Data System (ADS)

Remotely sensed satellite imageryis the most suitable tool for researchers around the globe in complementing in-situ observations. Nonetheless, it would be crucial to check for quality, validate and standardize methodologies to estimate the target variables from sensor data. Satellite imagery with thermal infrared bands provides opportunity to remotely measure the temperature in a very high spatio-temporal scale. Monitoring surface temperature of big lakes to understand the thermal fluctuations over time is considered crucial in the current status of global climate change scenario. The main disadvantage of remotely sensed data is the gaps due to presence of clouds and aerosols. In this study we use statistically reconstructed daily land surface temperature products from MODIS (MOD11A1 and MYD11A1) at a better spatial resolution of 250 m. The ability of remotely sensed datasets to capture the thermal variations over time is validated against historical monthly ground observation data collected for Lake Garda. The correlation between time series of satellite data LST (x,y,t) and the field measurements f (x,y,t) are found to be in acceptable range with a correlation coefficient of 0.94. We compared multiple time series analysis methods applied on the temperature maps recorded in the last ten years (2002 - 2012) and monthly field measurements in two sampling points in Lake Garda. The time series methods STL - Seasonal Time series decomposition based on Loess method, DTW - Dynamic Time Waping method, and BFAST - Breaks for Additive Season and Trend, are implemented and compared in their ability to derive changes in trends and seasonalities. These methods are mostly implemented on time series of vegetation indices from satellite data, but seldom used on thermal data because of the temporal incoherence of the data. The preliminary results show that time series methods applied on satellite data are able to reconstruct the seasons on an annual scale while giving us a graphical view of intra-annual variations in the trends with residuals. The time series trend analysis shows similar pattern from both the datasets reinstating the importance of remotely sensed data in climate change related studies.

Pareeth, Sajid; Metz, Markus; Rocchini, Duccio; Salmaso, Nico; Neteler, Markus

2014-05-01

19

Consistent Long-Time Series of GPS Satellite Antenna Phase Center Corrections  

NASA Astrophysics Data System (ADS)

The current IGS processing strategy disregards satellite antenna phase center variations (pcvs) depending on the nadir angle and applies block-specific phase center offsets only. However, the transition from relative to absolute receiver antenna corrections presently under discussion necessitates the consideration of satellite antenna pcvs. Moreover, studies of several groups have shown that the offsets are not homogeneous within a satellite block. Manufacturer specifications seem to confirm this assumption. In order to get best possible antenna corrections, consistent ten-year time series (1994-2004) of satellite-specific pcvs and offsets were generated. This challenging effort became possible as part of the reprocessing of a global GPS network currently performed by the Technical Universities of Munich and Dresden. The data of about 160 stations since the official start of the IGS in 1994 have been reprocessed, as today's GPS time series are mostly inhomogeneous and inconsistent due to continuous improvements in the processing strategies and modeling of global GPS solutions. An analysis of the signals contained in the time series of the phase center offsets demonstrates amplitudes on the decimeter level, at least one order of magnitude worse than the desired accuracy. The periods partly arise from the GPS orbit configuration, as the orientation of the orbit planes with regard to the inertial system repeats after about 350 days due to the rotation of the ascending nodes. In addition, the rms values of the X- and Y-offsets show a high correlation with the angle between the orbit plane and the direction to the sun. The time series of the pcvs mainly point at the correlation with the global terrestrial scale. Solutions with relative and absolute phase center corrections, with block- and satellite-specific satellite antenna corrections demonstrate the effect of this parameter group on other global GPS parameters such as the terrestrial scale, station velocities, the geocenter position or the tropospheric delays. Thus, deeper insight into the so-called `Bermuda triangle' of several highly correlated parameters is given.

Steigenberger, P.; Schmid, R.; Rothacher, M.

2004-12-01

20

Surveillance of Vrancea active seismic region in Romania through time series satellite data  

NASA Astrophysics Data System (ADS)

Cumulative stress energy in active seismic regions caused by tectonic forces creates various earthquake precursors. This energy transformation may result in enhanced transient thermal infrared (TIR) emission, which can be detected through satellites equipped with thermal sensors like MODIS (Terra/Aqua) and AVHRR (NOAA). Satellite time-series data, coupled with ground based observations, where available, can enable scientists to survey pre-earthquake signals in the areas of strong tectonic activity. This paper presents observations made using time series MODIS and NOAA-AVHRR satellite data for derived multi-parameters including land surface temperature (LST), outgoing long-wave radiation (OLR), and mean air temperature (AT) for the moderate, 5.9 magnitude earthquake, which took place on the 27th of October, 2004, inthe seismic region of Vrancea, in Romania. Anomalous thermal infrared signals, reflected by a rise of several degrees celsius (°C) in LSTs, and higher OLR values were seen several days before the earthquake. AT values in the epicentral area also increased almost two days prior to the earthquake and intensified three days after the main shock.Increases in LSTs and OLR disappeared three days after the main shock. The survey and joint analysis of geospatial and in-situ geophysical information on land surface temperatures and outgoing long-wave radiation provides new insights into the field of seismic hazard assessment in Vrancea, a significant area of tectonic activity in Romania and Europe.

Zoran, Maria A.; Savastru, Roxana S.; Savastru, Dan M.

2014-02-01

21

Coastal changes in the Sendai area from the impact of the 2011 T?hoku-oki tsunami: Interpretations of time series satellite images, helicopter-borne video footage and field observations  

NASA Astrophysics Data System (ADS)

A combination of time-series satellite imagery, helicopter-borne video footage and field observation is used to identify the impact of a major tsunami on a low-lying coastal zone located in eastern Japan. A comparison is made between the coast protected by armoured 'engineered' sea walls and the coast without. Changes are mapped from before and after imagery, and sedimentary processes identified from the video footage. The results are validated by field observations. The impact along a 'natural' coast, with minimal defences, is erosion focussed on the back beach. Along coasts with hard engineered protection constructed to defend against erosion, the presence of three to six metre high concrete-faced embankments results in severe erosion on their landward faces. The erosion is due to the tsunami wave accelerating through a hydraulic jump as it passes over the embankment, resulting in the formation of a ditch into which the foundations collapse. Engineered coastal defences are thus found to be small defence against highly energetic tsunami waves that overtop them. There is little erosion (or sedimentation) of the whole beach, and where active, it mainly forms V-shaped channels. These channels are probably initiated during tsunami inflow and then further developed during tsunami backflow. Tsunami backflow on such a low lying area takes place energetically as sheet flow immediately after tsunami flooding has ceased. Subsequently, when the water level landward of the coastal dune ridges falls below their elevation, flow becomes confined to rivers and breaches in the coast formed during tsunami inflow. Enigmatic, short lived, 'strand lines' are attributed to the slow fall of sea level after such a major tsunami. Immediately after the tsunami coastal reconstruction begins, sourced from the sediment recently flushed into the sea by tsunami backflow.

Tappin, David R.; Evans, Hannah M.; Jordan, Colm J.; Richmond, Bruce; Sugawara, Daisuke; Goto, Kazuhisa

2012-12-01

22

Measuring vertical deformation in the Seattle, WA urban corridor with satellite radar interferometry time series analysis  

NASA Astrophysics Data System (ADS)

Satellite radar interferometry (InSAR) time series analysis (e.g., Lundgren et al., 2001) can reveal rich patterns of deformation in both time and space. As the technique is sensitive to mm-scale vertical deformation over large and spatially extensive regions, it provides a useful geodetic tool where satellite coverage and radar phase coherence permit. Here we apply InSAR time series techniques based on the Small BAseline Subset Algorithm (SBAS) (Berardino et al., 2002) using data from three satellites (ERS 1, ERS2, and RADARSAT) to the urban corridor between Tacoma, Seattle and Everett, WA, over the time period 1992 - 2007. The target of our work is to better characterize the nature of active faulting and deep-seated landsliding within the densely populated study area. Additionally, we seek to independently quantify how localized short-wavelength deformation is contaminating data collected from the ~ 12 GPS stations in the eastern Puget Sound region. Comparisons of InSAR time series inversions to data from 4 GPS stations temporally and spatially overlapping the available InSAR observations reveal that surface displacement computed from InSAR matches the GPS deformation within the range of error reported for vertical GPS data (~ 4mm). Contemporaneous surface velocity maps generated via linear regression to two independent time series inversions from overlapping ERS satellite tracks 428 and 156 show striking agreement in the pattern of surface velocity, and effectively resolve rates as low as 1 mm/yr. Based on the results of our velocity mapping, we provide new constraints on surface deformation in the Seattle metro region. First, between 1992 and 2007 we document subsidence (~ 1-3 mm/yr) over much of the region characterized by Holocene infilling of the Puget Sound by lahar and floodplain sedimentation. This deformation is consistent with subsidence due to sediment compaction and de-watering. Second, between 1992 and 2007 we document no slow landslide deformation on any of the numerous mapped slide complexes within Seattle. Regions of known active landsliding, such as along Perkins Lane in Seattle, exhibit radar phase de- correlation. These observations are therefore consistent with relatively infrequent and rapid landslide deformation within Seattle. Finally, we note a NW-SE striking, sharp linear gradient in deformation near Federal Way, WA. As this feature is located just north of the Tacoma Fault Zone, it may mark the location of a previously unmapped fault splay that is serving as a barrier to local groundwater flow.

Finnegan, N. J.; Pritchard, M. E.; Lohman, R.; Lundgren, P. R.

2007-12-01

23

2-D Time Series Model Based Support Vector Machine for Remote Sensing Image Segmentation  

Microsoft Academic Search

A method of modeling an image based on 2-D Time Series that merges with the popular MultiClass Support Vector Machine (SVM) as a generalized linear classifier is proposed. In this paper, we present the classification results on both remote sensing and texture type images. Both Binary SVM Classifier and Multiclass SVM are implemented and discussed. The 2-D Time Series models

Pei-gee Peter Ho; Chi Hau Chen

2007-01-01

24

Comparison analysis in growth process in Asian cities by using tandem time series remote sensing of different satellite  

NASA Astrophysics Data System (ADS)

The growth of major cities in Asia, as a consequence of economic development, is feared to have adverse influences on the natural environment of the surrounding areas. Comparison of land cover changes in major cities from the viewpoints of both spatial and time series is necessary to fully understand the characteristics of urban development in Asia. To accomplish this, multiple satellite remote sensing data were analyzed across a wide range and over a long term in this study. The process of transition of a major Asian city in Tokyo, Osaka, Beijing, Shanghai, and Hong Kong was analyzed from the characteristic changes of the vegetation index value and the land cover over about 40 years, from 1972 to 2010. Image data for LANDSAT/MSS, LAND-SAT/TM, ALOS/AVNIR-2, and ALOS/PRISM were obtained using a tandem time series. The ratio and state of detailed distribution of land cover were clarified by the classification processing. The time series clearly showed different change characteristics for each city and its surrounding natural environment of vegetation and forest etc. as a result of development processes.

Hashiba, Hideki; Nakayama, Yasunori; Sugimura, Toshiro

25

TerraLook: GIS-Ready Time-Series of Satellite Imagery for Monitoring Change  

USGS Publications Warehouse

TerraLook is a joint project of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) with a goal of providing satellite images that anyone can use to see changes in the Earth's surface over time. Each TerraLook product is a user-specified collection of satellite images selected from imagery archived at the USGS Earth Resources Observation and Science (EROS) Center. Images are bundled with standards-compliant metadata, a world file, and an outline of each image's ground footprint, enabling their use in geographic information systems (GIS), image processing software, and Web mapping applications. TerraLook images are available through the USGS Global Visualization Viewer (http://glovis.usgs.gov).

U.S. Geological Survey

2008-01-01

26

Classification of time series of multispectral images with limited training data.  

PubMed

Image classification usually requires the availability of reliable reference data collected for the considered image to train supervised classifiers. Unfortunately when time series of images are considered, this is seldom possible because of the costs associated with reference data collection. In most of the applications it is realistic to have reference data available for one or few images of a time series acquired on the area of interest. In this paper, we present a novel system for automatically classifying image time series that takes advantage of image(s) with an associated reference information (i.e., the source domain) to classify image(s) for which reference information is not available (i.e., the target domain). The proposed system exploits the already available knowledge on the source domain and, when possible, integrates it with a minimum amount of new labeled data for the target domain. In addition, it is able to handle possible significant differences between statistical distributions of the source and target domains. Here, the method is presented in the context of classification of remote sensing image time series, where ground reference data collection is a highly critical and demanding task. Experimental results show the effectiveness of the proposed technique. The method can work on multimodal (e.g., multispectral) images. PMID:23743777

Demir, Begum; Bovolo, Francesca; Bruzzone, Lorenzo

2013-08-01

27

Tools for Generating Useful Time-series Data from PhenoCam Images  

NASA Astrophysics Data System (ADS)

The PhenoCam project (http://phenocam.unh.edu/) is tasked with acquiring, processing, and archiving digital repeat photography to be used for scientific studies of vegetation phenological processes. Over the past 5 years the PhenoCam project has collected over 2 million time series images for a total over 700 GB of image data. Several papers have been published describing derived "vegetation indices" (such as green-chromatic-coordinate or gcc) which can be compared to standard measures such as NDVI or EVI. Imagery from our archive is available for download but converting series of images for a particular camera into useful scientific data, while simple in principle, is complicated by a variety of factors. Cameras are often exposed to harsh weather conditions (high wind, rain, ice, snow pile up), which result in images where the field of view (FOV) is partially obscured or completely blocked for periods of time. The FOV can also change for other reasons (mount failures, tower maintenance, etc.) Some of the relatively inexpensive cameras that are being used can also temporarily lose color balance or exposure controls resulting in loss of imagery. All these factors negatively influence the automated analysis of the image time series making this a non-trivial task. Here we discuss the challenges of processing PhenoCam image time-series for vegetation monitoring and the associated data management tasks. We describe our current processing framework and a simple standardized output format for the resulting time-series data. The time-series data in this format will be generated for specific "regions of interest" (ROI's) for each of the cameras in the PhenoCam network. This standardized output (which will be updated daily) can be considered 'the pulse' of a particular camera and will provide a default phenological dynamic for said camera. The time-series data can also be viewed as a higher level product which can be used to generate "vegetation indices", like gcc, for phenological studies so that the details of processing the image series can be avoided. Our goal is to provide access to both the original time-series images and the derived ROI time-series data. The software tools for our processing chains and a description of their use will be made available to the wider scientific community.

Milliman, T. E.; Friedl, M. A.; Frolking, S.; Hufkens, K.; Klosterman, S.; Richardson, A. D.; Toomey, M. P.

2012-12-01

28

A Time-Series of Surface Oil Distribution Detected by Satellite SAR During the Deepwater Horizon Blowout  

NASA Astrophysics Data System (ADS)

Oil discharged as a result of the Deepwater Horizon disaster was detected on the surface of the Gulf of Mexico by synthetic aperture radar satellites from 25 April 2010 until 4 August 2010. SAR images were not restricted by daylight or cloud-cover. Distribution of this material is a tracer for potential environmental impacts and an indicator of impact mitigation due to response efforts and physical forcing factors. We used a texture classifying neural network algorithm for semi-supervised processing of 176 SAR images from the ENVISAT, RADARSAT I, and COSMO-SKYMED satellites. This yielded an estimate the proportion of oil-covered water within the region sampled by each image with a nominal resolution of 10,000 sq m (100m pixels), which was compiled as a 5-km equal area grid covering the northern Gulf of Mexico. Few images covered the entire impact area, so analysis was required to compile a regular time-series of the oil cover. A Gaussian kernel using a bandwidth of 2 d was used to estimate oil cover percent in each grid at noon and midnight throughout the interval. Variance and confidence intervals were calculated for each grid and for the global 12-h totals. Results animated across the impact region show the spread of oil under the influence of physical factors. Oil cover reached an early peak of 17032.26 sq km (sd 460.077) on 18 May, decreasing to 27% of this total on 4 June, following by sharp increase to an overall maximum of 18424.56 sq km (sd 424.726) on 19 June. There was a significant negative correlation between average wind stress and the total area of oil cover throughout the time-series. Correlation between response efforts including aerial and subsurface application of dispersants and burning of gathered oil was negative, positive, or indeterminate at different time segments during the event. Daily totals for oil-covered surface waters of the Gulf of Mexico during 25 April - 9 August 2010 with upper and lower 0.95 confidence limits on estimate. (No oil visible after 4 August.)

MacDonald, I. R.; Garcia-Pineda, O. G.; Solow, A.; Daneshgar, S.; Beet, A.

2013-12-01

29

Cerebral blood flow imaging using time-series analysis of indocyanine green molecular dynamics in mice  

Microsoft Academic Search

Measurement of cerebral perfusion is important for study of various brain disorders such as stroke, epilepsy, and vascular dementia; however, efficient and convenient methods which can provide quantitative information about cerebral blood flow are not developed. Here we propose an optical imaging method using time-series analysis of dynamics of indocyanine green (ICG) fluorescence to generate cerebral blood flow maps. In

Taeyun Ku; Jungsul Lee; Chulhee Choi

2010-01-01

30

Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada  

Microsoft Academic Search

The boreal forest biome is one of the largest on Earth, covering more than 14% of the total land surface. Fire disturbance plays a dominant role in boreal ecosystems, altering forest succession, biogeochemical cycling, and carbon sequestration. We used two time-series data sets of Advanced Very High Resolution Radiometer (AVHRR) Normalized Differenced Vegetation Index (NDVI) imagery for North America to

Scott J. Goetz; Gregory J. Fiske; Andrew G. Bunn

2006-01-01

31

Time Series Comparisons of Satellite and Rocketsonde Temperatures in 1978-1979.  

National Technical Information Service (NTIS)

The Limb Infrared Monitor of the Stratosphere (LIMS) experiment on Nimbus 7 yielded temperature-versus-pressure (T(p)) profiles for each radiance scan. The present report describes time series comparisons between LIMS and rocketsonde T(p) values at rocket...

E. E. Remsberg P. P. Bhatt F. J. Schmidlin

1994-01-01

32

Consistent Long-Time Series of GPS Satellite Antenna Phase Center Corrections  

Microsoft Academic Search

The current IGS processing strategy disregards satellite antenna phase center variations (pcvs) depending on the nadir angle and applies block-specific phase center offsets only. However, the transition from relative to absolute receiver antenna corrections presently under discussion necessitates the consideration of satellite antenna pcvs. Moreover, studies of several groups have shown that the offsets are not homogeneous within a satellite

P. Steigenberger; R. Schmid; M. Rothacher

2004-01-01

33

Satellite time series and in situ data analysis for assessing land slide susceptibility after forest fire: The case study of Pisticci 2012 fire  

NASA Astrophysics Data System (ADS)

Moderate Resolution Imaging Spectroradiometer (MODIS), ASTER, Landsat TM Satellite time series and in situ data analysis have been conducted to assess land slide susceptibility after the large forest fire which affected the Pisticci Municipality in August 2012. These activities are in the framework of the FIRE_SAT project funded by the Civil Protection of the Basilicata Region in order to set up a low cost methodology for fire danger monitoring and fire effect estimation. This project is mainly based on the use of satellite time series available at low cost or free of charge from the NASA website to set up reliable data processing for the pre-operative monitoring of the Basilicata ecosystems. Novel data processing techniques have been developed by researchers of CNR-IMAA for the operative monitoring of fire. In this paper we only focus on the estimation of fire severity we performed after the 2012 summer using satellite time series and in situ data analysis. In particular, Field measurements and laboratory analysis, made on several sample sites selected in area characterized by different levels of fire severity, well fit together confirming the increase in landslide susceptibility after the fire event.

Cavalcante, Francesco; Belviso, Claudia; Stabile, Laura; Savino, Michele; Vitelli, Francesco; Desantis, Fortunato; Lanorte, Antonio; Lasaponara, Rosa

2013-04-01

34

Geostationary Satellite Observations of Gulf Stream Meanders: Infrared Measurements and Time Series Analysis  

Microsoft Academic Search

The infrared capabilities of the Geostationary Operational Environmental Satellite (Goes) are analyzed to obtain multiyear time histories of Gulf Stream meanders. Radiative transfer calculations using monthly mean profiles of atmospheric temperature and moisture are shown to overestimate cloud-free equivalent soundings by 2-5 K. A simple relation is derived between temperature at the satellite, sea surface temperature, and transmissivity of the

George A. Maul; P. Webb deWitt; Alan Yanaway; Stephen R. Baig

1978-01-01

35

Motion Artifact Reduction in Ultrasound Based Thermal Strain Imaging of Atherosclerotic Plaques Using Time Series Analysis  

PubMed Central

Large lipid pools in vulnerable plaques, in principle, can be detected using US based thermal strain imaging (US-TSI). One practical challenge for in vivo cardiovascular application of US-TSI is that the thermal strain is masked by the mechanical strain caused by cardiac pulsation. ECG gating is a widely adopted method for cardiac motion compensation, but it is often susceptible to electrical and physiological noise. In this paper, we present an alternative time series analysis approach to separate thermal strain from the mechanical strain without using ECG. The performance and feasibility of the time-series analysis technique was tested via numerical simulation as well as in vitro water tank experiments using a vessel mimicking phantom and an excised human atherosclerotic artery where the cardiac pulsation is simulated by a pulsatile pump.

Dutta, Debaditya; Mahmoud, Ahmed M.; Leers, Steven A.; Kim, Kang

2013-01-01

36

Acoustic Imaging Time Series of Plume Behavior at Grotto Vent, Endeavour Observatory, Juan de Fuca Ridge  

NASA Astrophysics Data System (ADS)

A time series (24 hours) of acoustic images record the behavior of the principal buoyant plume (height interval 0-40 m above seafloor) discharging from black smoker chimneys on the north tower of the Grotto Vent sulfide edifice in the Main Endeavour Vent Field. The plume imaging was performed using the Simrad SM2000 sonar system (frequency 200 kHz) mounted on ROV Jason from a fixed position on the seafloor with a nearly horizontal slant range to the vent of about 20 m at a water depth of about 2190 m. The acoustic imaging is based on Rayleigh backscattering from mineral particles suspended in the plume that are small (microns) relative to the wavelength of the acoustic pulse (centimeter) such that intensity of backscatter is proportional to particle load. The acoustic time series data were acquired on 26-27 July 2000 as part of the VIP (Vent Imaging Pacific) 2000 cruise. We applied our computer visualization and quantification methods to reconstruct the plume 3D volume object and to measure dimensions and orientation. Plume expansion with height corresponds to model prediction (diameter 2 to 20 meters). Particle load decreases with height following model predications. The plume centerline constructed by joining the local center of mass of successive horizontal slices with height through the buoyant plume alternately bends between 0 and 30 degrees to the northeast and southwest in a complex cycle. The plume bending appears to correspond to the regional mixed semidiurnal tidal cycle (H. Mofjeld, personal communication), with a component related to a prevailing northeasterly current (R. Thomson, personal communication). The effectiveness of tracking plume behavior for this short time series shows the potential of the acoustic method for long-term monitoring of the activity and interactions of plumes in seafloor hydrothermal fields.

Rona, P. A.; Bemis, K. G.; Jackson, D. R.; Jones, C. D.; Mitsuzawa, K.; Palmer, D. R.; Silver, D.

2001-12-01

37

DORIS satellite antenna maps derived from long-term residuals time series  

NASA Astrophysics Data System (ADS)

Recent studies have shown that phase pattern models for the Jason-1 GPS antenna significantly benefit GPS-based precise orbit determination (POD) for the satellite. We have used a similar technique to derive DORIS receiver antenna maps, using all available DORIS tracking data over long time periods (from 1993.0 to 2004.0). We demonstrate that the derived correction models are satellite specific. For a given satellite, year-to-year estimations show clear systematic patterns. Some of these systematic patterns are attributable to the derivative of the multi-path effects in the direction of the satellite velocity. For early SPOT data, the patterns can be explained by an offset in the TAI time tagging (typically 8 ?s). In a second step, we have applied the SPOT2 antenna correction models in precise orbit determination and in the positioning of ground beacons. Preliminary results on DORIS/SPOT2 show that application of the DORIS antenna maps lead to a slight improvement of the derived POD and geodetic results (typically less than 5%).

Willis, P.; Desai, S. D.; Bertiger, W. I.; Haines, B. J.; Auriol, A.

38

Utilizing satellite snow cover data for climatological analysis: a comparison of passive microwave and optically derived time series, 1978 - 1995  

Microsoft Academic Search

When Special Sensor Microwave\\/Imager (SSM\\/I) and Scanning Multichannel Microwave Radiometer (SMMR) data are combined, the time series of spaceborne passive microwave brightness temperatures extends from 1978 to the present. The Meteorological Service of Canada (MSC) has developed a series of operational snow water equivalent (SWE) retrieval algorithms for western Canada that can be applied to both SMMR and SSM\\/I data.

C. Derksen; A. Walker; E. LeDrew; B. Goodison

2002-01-01

39

Time series comparisons of satellite and rocketsonde temperatures in 1978-1979  

NASA Technical Reports Server (NTRS)

The Limb Infrared Monitor of the Stratosphere (LIMS) experiment on Nimbus 7 yielded temperature-versus-pressure (T(p)) profiles for each radiance scan. The present report describes time series comparisons between LIMS and rocketsonde T(p) values at rocketsonde station locations. Sample size has increased up to 665 by this new approach, leading to better statistics for a T(p) validation. The results indicate no clearly significant bias for LIMS versus Datasonde from 10 kPa at low and mid latitudes. There is a positive LIMS bias of 2 to 3 K in the upper stratosphere at high latitudes for the Northern Hemisphere in both winter and spring. LIMS is progressively colder than Datasonde from 0.4 kPa (about -3 K) to 0.1 kPa (about -9 K) at all latitudes. A similar comparison between LIMS and the more accurate falling sphere measurements reveals an equivalent mid-latitude LIMS bias at 0.4 kPa but a much smaller bias at 0.1 kPa (-4.6 K). Because the biases do not vary noticeably with season, it is concluded that they are not a function of atmospheric state. This result confirms the robustness of the LIMS temperature retrieval technique.

Remsberg, Ellis E.; Bhatt, Praful P.; Schmidlin, Francis J.

1994-01-01

40

Time-series measurements of hydrothermal plume volume flux with imaging sonar  

NASA Astrophysics Data System (ADS)

COVIS (Cabled Observatory Vent Imaging Sonar) is an innovative sonar system designed to quantitatively monitor the outputs of deep-sea hydrothermal vent clusters for both high-temperature focused vents and diffuse flows. In September 2010, COVIS was connected to the NEPTUNE Canada underwater ocean observatory network (http://www.NEPTUNEcanada.ca) at the Grotto vent cluster at the Main Endeavour Field on the Endeavour Segment of the Juan de Fuca Ridge. Since then, COVIS has been monitoring the hydrothermal plumes above Grotto by transmitting high-frequency (400 kHz), pulsed acoustic waves towards the plumes and recording the backscattered signals from each pulse, except for a one-year hiatus due to the power-off of the NEPTUNE Canada network between November 2010 and September 2011. The received backscatter signals are transmitted via the NEPTUNE Canada network to the land-based servers in real time, where a combination of automatic and manual data analyses produces a plume volume-flux and flow-rate time series using both the intensity and Doppler shift of the backscatter signals. The initial 30-day time series (Sep-Oct 2010) was presented in AGU Fall meeting, 2011. Evident short-term temporal variations (< 2 days) have been observed, which indicates significant interaction between the plume and the ambient tidal current oscillations. To further investigate such interaction and capture long-term patterns of the system, we present a 10-month time series (since the resumption of COVIS in September 2011 until present) of the volume flux and flow rate of the plume discharging from the North Tower of Grotto. The new time series, with a 3-hour sampling rate and long duration, can reveal the variations of the plume on a wide range of time scales (< 2 days ~ months). Compared with its predecessor, the new time series provides a better chance to capture the episodic events (e.g. geologically driven), low-frequency periodic (e.g. seasonal) oscillations, and long-term trend in the hydrothermal output during the measurement period. In addition, as an extension to the 2010 results, the backscatter data from the smaller plumes on the southeast part of Grotto are also processed to yield a preliminary time series of plume volume flux and flow rate. This time series is further compared with the temperature and chemistry measurements made by the Benthic And Resistivity Sensor (BARS, principal Investigator M. Lilley, University of Washington) and the Remotely Activated water Sampler (RAS, principal Investigator D. Butterfield, National Oceanic and Atmospheric Administration and University of Washington) at the vent orifices to link the variations observed in the buoyant plume with those at the orifices. This work is supported by NSF award OCE-0825088 to Rutgers.

Xu, G.; Jackson, D. R.; Bemis, K. G.; Rona, P. A.

2012-12-01

41

Analysis of HCl and ClO time series in the upper stratosphere using satellite data sets  

NASA Astrophysics Data System (ADS)

Previous analyses of satellite and ground-based measurements of hydrogen chloride (HCl) and chlorine monoxide (ClO) have suggested that total inorganic chlorine in the upper stratosphere is on the decline. We create HCl and ClO time series using satellite data sets extended to November 2008, so that an update can be made on the long term evolution of these two species. We use the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) data for the HCl analysis, and the Odin Sub-Millimetre Radiometer (SMR) and the Aura Microwave Limb Sounder (Aura-MLS) measurements for the study of ClO. Altitudes between 35 and 45 km and two mid-latitude bands: 30° S-50° S and 30° N-50° N, for HCl, and 20° S-20° N for ClO and HCl are studied. ACE-FTS and HALOE HCl anomaly time series (with QBO and seasonal contributions removed) are combined to produce all instrument average time series, which show HCl to be reducing from peak 1997 values at a linear estimated rate of -5.1 % decade-1 in the Northern Hemisphere and -5.2 % decade-1 in the Southern Hemisphere, while the tropics show a linear trend of -5.8 % per decade (although we do not remove the QBO contribution there due to sparse data). Trend values are significantly different from a zero trend at the 2 sigma level. ClO is decreasing in the tropics by -7.1 % ± 7.8 % decade-1 based on measurements made from December 2001 to November 2008. The statistically significant downward trend found in HCl after 1997 and the apparent downward ClO trend since 2001 (although not statistically significant) confirm how effective the 1987 Montreal protocol objectives and its amendments have been in reducing the total amount of inorganic chlorine.

Jones, A.; Urban, J.; Murtagh, D. P.; Sanchez, C.; Walker, K. A.; Livesey, N. J.; Froidevaux, L.; Santee, M. L.

2011-06-01

42

Nonlinear denoising of functional magnetic resonance imaging time series with wavelets  

NASA Astrophysics Data System (ADS)

In functional magnetic resonance imaging (fMRI) the blood oxygenation level dependent (BOLD) effect is used to identify and delineate neuronal activity. The sensitivity of a fMRI-based detection of neuronal activation, however, strongly depends on the relative levels of signal and noise in the time series data, and a large number of different artifact and noise sources interfere with the weak signal changes of the BOLD response. Thus, noise reduction is important to allow an accurate estimation of single activation-related BOLD signals across brain regions. Techniques employed so far include filtering in the time or frequency domain which, however, does not take into account possible nonlinearities of the BOLD response. We here evaluate a previously proposed method for nonlinear denoising of short and transient signals, which combines the wavelet transform with techniques from nonlinear time series analysis. We adopt the method to the problem at hand and show that successful noise reduction and, more importantly, preservation of the shape of individual BOLD signals can be achieved even in the presence of in-band noise.

Stausberg, Sven; Lehnertz, Klaus

2009-04-01

43

Identification of catchment functional units by time series of thermal remote sensing images  

NASA Astrophysics Data System (ADS)

The objective of this study is to demonstrate an approach, based on thermal infrared (TIR) time series data from remote sensing to identify hydrological response units (HRUs) concerning the land surface temperature (LST) within a given catchment without additional input data. For the meso-scale Attert catchment in midwestern Luxembourg, a time series of TIR images from ASTER is used to retrieve, identify and classify possible process based HRUs with mathematical-statistical pattern analysis techniques. These techniques comprise the use of coefficients of correlation and variation, as well as behavioural measures and principal component analysis. This new method will be compared to standard GIS based spatial information for HRU identification. The results highlight the capability of multi-temporal thermal remote sensing data to provide information that are time consuming to retrieve in sparsely monitored regions. Also, we demonstrate how multi-temporal temperature information is related to land cover or geology. Finally, we outline how this approach might support the planning of sensor networks within a catchment or the conceptualisation of a hydrological model.

Müller, Benjamin; Bernhardt, Matthias; Schulz, Karsten

2014-05-01

44

Time Series of Snow Covered Area for Hydrologic Response Units of the Tom River Basin, Russia, from Satellite Imagery, 1980-1985  

NASA Astrophysics Data System (ADS)

The seasonal snowpack dynamics of the Eurasian mountains and Siberian plains play a critical role in the freshwater fluxes of northern rivers into the Arctic Ocean. This study is part of a larger project whose goal is to analyze the freshwater discharge of the Ob' River (Russia) to the Arctic, using satellite remote sensing and hydrologic modeling on a daily time step. Using AVHRR Polar Pathfinder imagery obtained from the National Snow and Ice Data Center (NSIDC), time series of snow cover (accumulation through depletion) are created for hydrologic response units (HRUs) of the Tom River, a tributary of the Ob'. Sub-watersheds and HRUs of the Tom watershed are delineated using digital elevation and land use/land cover data within a Geographical Information System. All digital data are reprojected to the equal area azimuthal EASE-Grid of the NSIDC imagery. Daily remotely-sensed snow images for 1980 to 1985 are overlaid on the HRUs to give time series of percent snow cover for each HRU. These time series are compared with model-generated HRU snow cover, allowing us to evaluate the performance of the model's snow sub-routines in a spatially distributed manner. This study will contribute to better modeling and understanding of snow accumulation and depletion processes, which serve as important and climate-sensitive controls on the fresh water fluxes of Arctic river basins.

Saini, A.; Brubaker, K. L.; Jasinski, M. F.; Stoll, J.

2003-12-01

45

ANALYSIS OF YEAR 2002 SEASONAL FOREST DYNAMICS USING TIME SERIES IN SITU LAI MEASUREMENTS AND MODIS LAI SATELLITE PRODUCTS  

EPA Science Inventory

Multitemporal satellite images are the standard basis for regional-scale land-cover (LC) change detection. However, embedded in the data are the confounding effects of vegetation dynamics (phenology). As photosynthetic vegetation progresses through its annual cycle, the spectral ...

46

Global-scale analysis of satellite-derived time series of naturally inundated areas as a basis for floodplain modeling  

NASA Astrophysics Data System (ADS)

Floodplains play an important role in the terrestrial water cycle and are very important for biodiversity. Therefore, an improved representation of the dynamics of floodplain water flows and storage in global hydrological and land surface models is required. To support model validation, we combined monthly time series of satellite-derived inundation areas (Papa et al., 2010) with data on irrigated rice areas (Portmann et al., 2010). In this way, we obtained global-scale time series of naturally inundated areas (NIA), with monthly values of inundation extent during 1993-2004 and a spatial resolution of 0.5°. For most grid cells (0.5°×0.5°), the mean annual maximum of NIA agrees well with the static open water extent of the Global Lakes and Wetlands database (GLWD) (Lehner and Döll, 2004), but in 16% of the cells NIA is larger than GLWD. In some regions, like Northwestern Europe, NIA clearly overestimates inundated areas, probably because of confounding very wet soils with inundated areas. In other areas, such as South Asia, it is likely that NIA can help to enhance GLWD. NIA data will be very useful for developing and validating a floodplain modeling algorithm for the global hydrological model WGHM. For example, we found that monthly NIAs correlate with observed river discharges.

Adam, L.; Döll, P.; Prigent, C.; Papa, F.

2010-08-01

47

Time series analysis of satellite multi-sensors imagery to study the recursive abnormal grow of floating macrophyte in the lake victoria (central Africa)  

NASA Astrophysics Data System (ADS)

Remote sensing allows multi-temporal mapping and monitoring of large water bodies. The importance of remote sensing for wetland and inland water inventory and monitoring at all scales was emphasized several times by the Ramsar Convention on Wetlands and from EU projects like SALMON and ROSALMA, e.g. by (Finlayson et al., 1999) and (Lowry and Finlayson, 2004). This paper aims at assessing the capability of time series of satellite imagery to provide information suitable for enhancing the understanding of the temporal cycles shown by the macrophytes growing in order to support the monitor and management of the lake Victoria water resources. The lake Victoria coastal areas are facing a number of challenges related to water resource management which include growing population, water scarcity, climate variability and water resource degradation, invasive species, water pollution. The proliferation of invasive plants and aquatic weeds, is of growing concern. In particular, let us recall some of the problems caused by the aquatic weeds growing: Ø interference with human activities such as fishing, and boating; Ø inhibition or interference with a balanced fish population; Ø fish killing due to removal of too much oxygen from the water; Ø production of quiet water areas that are ideal for mosquito breeding. In this context, an integrated use of medium/high resolution images from sensors like MODIS, ASTER, LANDSAT/TM and whenever available CHRIS offers the possibility of creating a congruent time series allowing the analysis of the floating vegetation dynamic on an extended temporal basis. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution, further its spatial resolution can results not always adequate to map the extension of floating plants. Therefore, the integrated use of sensors with different spatial resolution, were used to map across seasons the evolution of the phenomena. The integrated use of satellite resources allowed the estimate of the temporal variability of physical parameters that were used to i) sample the spatio-temporal distribution of the whole floating vegetation (i.e. native vegetation and weed) and ii) assess the seasonal recurrence of the abnormal weeds grow, as well as, their possible relation with the hydrological regimes of the rivers. The paper describes how the 2000 - 2009 MODIS images time series, were analysed (navigated and processed) to derive i) the map the floating vegetation on the test area and ii) identify the areas more interested by the growing iii) to discriminate, whenever possible, according to the spectral and spatial resolution of the sensor applied (i.e. LANDSAT, ASTER, CHRIS), the different vegetation species in order to discriminate the weeds from the floating vegetation. The spectral identification of the different species was performed by exploiting the results of a field campaign performed in the past along the Kenyan coastal areas devoted to define a data base of spectral signatures of the main species. Spectral information was treated to define indexes and spectral analysis procedure customized to multispectral high resolution satellite data. Moreover, the results of the images time series has been analysed to identify a possible definition of the temporal occurrence of the floating vegetation growing considering both the natural phenomenological cycles and the conditions related to the abnormal growing. These results, whenever related to ancillary hydrological information (e.g. the amount of rain), they have shown that the synergy of MODIS images time series with lower temporal frequency time series imagery is a powerful tool to monitor the lake Victoria ecosystem and to follow the floating vegetation extension and even to foresee the possibility to set up a model for the abnormal vegetation growing.

Fusilli, Lorenzo; Cavalli, Rosa Maria; Laneve, Giovanni; Pignatti, Stefano; Santilli, Giancarlo; Santini, Federico

2010-05-01

48

Imaging transient slip events in southwest Japan using reanalyzed Japanese GEONET GPS time series  

NASA Astrophysics Data System (ADS)

The Japanese continuous GPS network (GEONET) with ~1450 stations provide a unique opportunity to study ongoing subduction zone dynamics, and crustal deformation at various spatiotemporal scales. Recently we completed a reanalysis of GPS position time series for the entire GEONET from 1996 to 2012 using JPL GIPSY/OASIS-II based GPS Network Processor [Owen et al., 2006] and raw data provided by Geospatial Information Authority of Japan (GSI) and Caltech. We use the JPL precise GPS orbits reestimated from the present through 1996 [Desai et al., 2011], troposphere global mapping function, and single receiver phase ambiguity resolution strategy [Bertiger et al., 2010] in the analysis. The resultant GPS time series solution shows improved repeatability and consistency over the ~16 yrs span, in comparison with 1996-2006 GPS position estimates used in our previous analysis [Liu et al., 2010a,b]. We apply a time-series analysis framework to estimate bias, offsets caused by instrument changes, earthquakes and other unknown sources, linear trends, seasonal variations, post-seismic deformation and other transient signals. The principal component analysis method is used to estimate the common mode error across the network [Dong et al. 2006]. We construct an interplate fault geometry from a composite plate boundary model [Wang et al. 2004] and apply a Kalman filter based network inversion method to image the spatiotemporal slip variation of slip transient events on the plate interface. The highly precise GPS time series enables the detectability of much smaller transient signals and start to reveal previously unobserved features of slow slip events. For example, the application to 2009-2011 Bungo Channel slow slip event shows it has a complex slip history with the major event initiating in late 2009 beneath the northeast corner of the region and migrating southwestward and updip. At ~2010.75 there is activation of a smaller slip subevent to the east of main slip region, persisting through the end of modeling period (2011.1786). We found clear temporal correspondence of adjacent low frequency earthquakes (LFEs)/tremor following transient slip variation, similar to what we found for other slip events in the region. This indicates that transient slip acts as a driving force to modulate the occurrence of LFEs/tremor. Despite many similarities between 2009-2011 and 2002-2004 slip events, there is considerable difference between the two in terms of slip details, suggesting these recurrent episodic events are not exactly identical. We find the 2009-2011 Bungo Channel event as well as previous ones (e.g, 2002-2004, 1996-1998) all initiated at the same location on the plate interface, suggesting that they are controlled by inherent frictional properties with secondary factors affecting the evolution details. Integrating plate coupling and SSEs confirmed the spatial pattern seen before in other events that the transient slip zones are located in a region between the locked zones and the epicenters of the LFEs/tremor. Recurrent SSEs release nearly all accumulated elastic strain over depths of ~20-30km where slip occurs and at least part of slip deficit at a shallower depth. We are in the process of imaging other slip transients along this part of subduction margin in a systematic effort to improve estimates of slip deficits and future large earthquake hazards.

Liu, Z.; Moore, A. W.; Owen, S. E.

2012-12-01

49

Generation of synthetic but visually realistic time series of cardiac images combining a biophysical model and clinical images.  

PubMed

We propose a new approach for the generation of synthetic but visually realistic time series of cardiac images based on an electromechanical model of the heart and real clinical 4-D image sequences. This is achieved by combining three steps. The first step is the simulation of a cardiac motion using an electromechanical model of the heart and the segmentation of the end diastolic image of a cardiac sequence. We use biophysical parameters related to the desired condition of the simulated subject. The second step extracts the cardiac motion from the real sequence using nonrigid image registration. Finally, a synthetic time series of cardiac images corresponding to the simulated motion is generated in the third step by combining the motion estimated by image registration and the simulated one. With this approach, image processing algorithms can be evaluated as we know the ground-truth motion underlying the image sequence. Moreover, databases of visually realistic images of controls and patients can be generated for which the underlying cardiac motion and some biophysical parameters are known. Such databases can open new avenues for machine learning approaches. PMID:23014716

Prakosa, Adityo; Sermesant, Maxime; Delingette, Hervé; Marchesseau, Stéphanie; Saloux, Eric; Allain, Pascal; Villain, Nicolas; Ayache, Nicholas

2013-01-01

50

Using Landsat image time series to study a small water body in Northern Spain.  

PubMed

Ramsar Convention and EU Water Framework Directive are two international agreements focused on the conservation and achievement of good ecological and chemical status of wetlands. Wetlands are important ecosystems holding many plant and animal communities. Their environmental status can be characterised by the quality of their water bodies. Water quality can be assessed from biophysical parameters (such as Chlorophyll-a concentration ([Chla]), water surface temperature and transparency) in the deeper or lacustrine zone, or from bioindicators (as submerged aquatic vegetation) in the shallow or palustrine zone. This paper proves the use of Landsat time series to measure the evolution of water quality parameters and the environmental dynamics of a small water body (6.57 ha) in a Ramsar wetland (Arreo Lake in the North of Spain). Our results show that Landsat TM images can be used to describe periodic behaviours such as the water surface temperature or the phenologic state of the submerged vegetation (through normalized difference vegetation index, NDVI) and thus detect anomalous events. We also show how [Chla] and transparency can be measured in the lacustrine zone using Landsat TM images and an algorithm adjusted for mesotrophic Spanish lakes, and the resulting values vary in time in accordance with field measurements (although these were not synchronous with the images). The availability of this algorithm also highlights anomalies in the field data series that are found to be related with the concentration of suspended matter. All this potential of Landsat imagery to monitor small water bodies in wetlands can be used for hindcasting of past evolution of these wetlands (dating back to 1970s) and will be also useful in the future thanks to the Landsat continuity mission and the Operational Land Imager. PMID:24452860

Chao Rodríguez, Y; el Anjoumi, A; Domínguez Gómez, J A; Rodríguez Pérez, D; Rico, E

2014-06-01

51

Circulation in Drake Passage revisited using new current time series and satellite altimetry: 1. The Yaghan Basin  

NASA Astrophysics Data System (ADS)

The complex bathymetry of the Drake Passage and the meridional extent of the Shackleton Fracture Zone, in particular, force the Subantarctic Front (SAF) and the Polar Front (PF) to veer to the north, and the flow of the Antarctic Circumpolar Current concentrates in the Yaghan Basin. We have studied the circulation in the Yaghan Basin, using 3 years of velocity data (January 2006-March 2009) at five mooring sites and 18 years of satellite altimetry data. Mean velocities at our mooring sites show a dominant eastward component which decreases with depth, as expected, with a notable exception in the center of the Yaghan Basin, where mean velocities reveal a dominant westward component increasing with depth. The mooring data suggest the existence of a permanent, strong deep cyclonic circulation over the Yaghan seafloor depression in the northeastern part of the Yaghan Basin. The in situ data provide the first opportunity to compare altimetry-derived velocities with high temporal resolution near-surface current meter velocities in a large eddy kinetic energy environment at high latitudes. Globally, altimetry-derived velocities compare rather well with the in situ velocities at 500 m depth both in strength and direction. Correlations are high between the in situ velocities and the surface velocities derived from satellite altimetric data. Mean sea level estimates lead to reasonable mean surface velocities with, however, a slight underestimation of the mean velocity at the mean location of the SAF on the continental slope and a more important underestimation of the westward current in the center of the Yaghan Basin. A dominant mode of velocity variations (23% of the variance) is observed both in the in situ and satellite data, corresponding to a strong southward meander of the SAF upstream of the mooring line and a northward meander of the PF downstream of the latter. The 18 yearlong altimetry time series shows that the mode is robust and has a strong semiannual component.

Ferrari, Ramiro; Provost, Christine; Renault, Alice; SennéChael, Nathalie; Barré, Nicolas; Park, Young-Hyang; Lee, Jae Hak

2012-12-01

52

Constructing new satellite-only time series of global mean, sea surface temperature data for climate from ATSR data  

NASA Astrophysics Data System (ADS)

The Along Track Scanning Radiometers (ATSRs) have provided a near-continuous record of sea surface temperature (SST) data for climate from the launch of ATSR-1 in 1991 to the loss of the Advanced ATSR (AATSR) in April 2012. The intention was always to provide an SST record, independent of in situ data, to corroborate and improve climate data records in recent times. We show that the ATSR record provides a very suitable data set with which to study the recent climate record, particularly during the ATSR-2 and AATSR periods (1995 to 2012) in three major respects. First, ATSR climate time series achieve anomaly accuracies of better than 0.05 K (and high stability). Second, the overlap between instruments allows for excellent determination and removal of biases; between ATSR-2 and AATSR, these are less than 0.05 K for the highest accuracy SST data. Finally, uncertainties on global monthly mean data are less than 0.02 K and hence comparable to those achieved by in situ analyses such as HadSST3. A particular hallmark of the ATSR instruments was their exceptional design for accuracy incorporating high accuracy radiometric calibration, dual-view of the Earth's surface and the use of three thermal emission channels; additional channels are included for cloud clearing in this context. The use of dual-view and multiple thermnal wavelengths allows a number of combinations for retrievals of SST, the most accurate being the dual-view, three-channel retrieval (D3) at nighttime. This restriction is due to the use of the 3.7 micron channel which is sensitive to solar radiation during the day. Extensive work has resulted in a major advances recently resulting in both an operational V2.0 SST product and a further improved ATSR Re-analysis for Climate (ARC) product, a particular feature of the latter being the development of a depth SST product in addition to the skin SST directly determined from satellite data. We will discuss the characteristics of these data sets in terms of accuracy, stability and inter-instrument calibration. We will further describe the methods we use to construct global monthly time series of SST anomalies from ATSR data, noting particularly the significance of sampling maps in quantifying the "global" coverage. For a climate data record, estimation of uncertainty is as important as the parameter values themselves. We will discuss measurement errors, time sampling errors and effects of error correlation in determining the final uncertainty budgets. For use of the D3 ATSR product as the basis for the climate time series, time sampling is the dominant error particularly due to restrictions on valid data points in cloudier regions such as the Pacific at northern mid-latitudes. It will be shown that the overall error budget for the satellite-only data is highly suitable for an independent climate record. The ATSR studies described in this paper mark the culmination of two decades of effort to deliver high accuracy SST data from satellite sensors, giving high confidence also in our understanding of the current in situ record. We conclude that climate-quality must be achieved by the Sea and Land Surface Radiometer (SLSTR) due to be launched on Sentinel-3 in 2014 and we note the importance of gap-bridging and gap-filling between AATSR and SLSTR.

Veal, Karen; Remedios, John; Ghent, Darren

2013-04-01

53

Satellite Imaging Corporation: IKONOS Satellite Images  

NSDL National Science Digital Library

Satellite Imaging Corporation (SIC) acquires and processes imagery from the IKONOS satellite as well as others and makes the products available through their website. The images in the gallery are arranged in several categories based on what applications the images might be useful for, such as Agriculture, Coastal Management, or Sports and Tourism.

Corporation, Satellite I.

54

IMAGE Satellite Scaling  

NSDL National Science Digital Library

This is an activity about satellite size. Learners will calculate the volume of the IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) satellite, the first satellite mission to image the Earth's magnetosphere. They will then determine the effect of doubling and tripling the satellite dimensions on the satellite's mass and cost. This is the first activity in the Solar Storms and You: Exploring Satellite Design educator guide.

55

Mapping agroecological zones and time lag in vegetation growth by means of fourier analysis of time series of NDVI images  

Microsoft Academic Search

Examples are presented of applications of a Fast Fourier transform algorithm to analyse time series of images of Normalized Difference Vegetation Index (NDVI) values. The results obtained for a case study on Zambia indicated that differences in vegetation development among map units of an existing agroclimatic map were not significant, while reliable differences were observed among the map units obtained

M. Menenti; S. Azzali; W. Verhoef; R. van Swol

1993-01-01

56

IMAGE Satellite Scale Model  

NSDL National Science Digital Library

This is an activity about scale model building. Learners will use mathematics to determine the scale model size, construct a pattern, and build a paper scale model of the IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) satellite, the first satellite mission to image the Earth's magnetosphere. This is the second activity in the Solar Storms and You: Exploring Satellite Design educator guide.

57

Monitoring urban development using satellite time series data and GIS technologies: a case study of Vienna 1986-2011  

NASA Astrophysics Data System (ADS)

The spatial and temporal distribution of urban areas is a fundamental information for a series of applications such as land management, future urban planning, ecology and others. This project deals with a classification approach for the area of Vienna during the time from 1986 to 2011 using Landsat 5 TM datasets. Time series of Landsat 5 TM data were downloaded and pre-processed. To minimize the effect of vegetation phenology and sun illumination geometry, Landsat 5 TM acquisitions were limited to a specific time window (June-July). Due to the high amount of data collected an automated approach was necessary. Conventional supervised classification algorithms based only on spectral features were not successful in differentiating urban areas from bare soils due to similarities in the spectral reflectance. To further distinguish these two land cover types their attributes in texture were used. Urban areas showed a high variance in texture data within a uniform reference unit whereas agricultural or bare soil fields demonstrated a very uniform distribution of texture value. For the textural analysis a new pixel value was assigned dependent on the spectral differences of each pixel concerning its neighbouring pixels. The textural analysis was included as an additional feature within the Landsat 5 TM dataset. Furthermore it was evaluated which combination of bands (spectral and textural) was best to discriminate above mentioned areas. These bands were then used for a supervised classification. The distribution of vegetation land cover was calculated and an accuracy assessment was performed based on an independent data set derived from the visual interpretation of high resolution images and ancillary information. Furthermore, a GIS analysis was carried out to evaluate the expansions (in space and time) of the Vienna urban area for each municipality. Preliminary results are presented and discussed.

Neugebauer, N.; Vuolo, F.

2012-04-01

58

The BEYOND center of excellence for the effective exploitation of satellite time series towards natural disasters monitoring and assessment  

NASA Astrophysics Data System (ADS)

BEYOND project (2013-2016, 2.3Meuro) funded under the FP7-REGPOT scheme is an initiative which aims to build a Centre of Excellence for Earth Observation (EO) based monitoring of natural disasters in south-eastern Europe (http://beyond-eocenter.eu/), established at the National Observatory of Athens (NOA). The project focuses on capacity building on top of the existing infrastructure, aiming at unlocking the institute's potential through the systematic interaction with high-profile partners across Europe, and at consolidating state-of-the-art equipment and technological know-how that will allow sustainable cutting-edge interdisciplinary research to take place with an impact on the regional and European socioeconomic welfare. The vision is to set up innovative integrated observational solutions to allow a multitude of space borne and ground-based monitoring networks to operate in a complementary and cooperative manner, create archives and databases of long series of observations and higher level products, and make these available for exploitation with the involvement of stakeholders. In BEYOND critical infrastructural components are being procured for fostering access, use, retrieval and analysis of long EO data series and products. In this framework NOA has initiated activities for the development, installation and operation of important acquisition facilities and hardware modules, including space based observational infrastructures as the X-/L-band acquisition station for receiving EOS Aqua/Terra, NPP, JPSS, NOAA, Metop, Feng Yun data in real time, the setting up of an ESA's Mirror Site of Sentinel missions to be operable from 2014 onwards, an advanced Raman Lidar portable station, a spectrometer facility, several ground magnetometer stations. All these are expected to work in synergy with the existing capacity resources and observational networks including the MSG/SEVIRI acquisition station, nationwide seismographic, GPS, meteo and atmospheric networks. The analysis of the satellite time series from this diverse EO based monitoring network facilities established at NOA covers a broad spectrum of research activities. Indicatively using Landsat TM/ETM+ imagery we have developed algorithms for the automatic diachronic mapping of burnt areas over Greece since 1984 and we have been using MSG/SEVIRI data to detect forest wildfires in Greece since 2007, analyze their temporal and geographical signatures and store these events for further analysis in relation with auxiliary geo-information layers for risk assessment applications. In the field of geophysics we have been employing sophisticated radar interferometry techniques using SAR sensor diversity with multi-frequency, multi-resolution and multi-temporal datasets (e.g. ERS1/ERS2, ENVISAT, TerraSAR-X, COSMO-SkyMED) to map diachronic surface deformation associated with volcanic activity, tectonic stress accumulation and urban subsidence. In the field of atmospheric research, we have developed a 3-dimentional global climatology of aerosol and cloud distributions using the CALIPSO dataset. The database, called LIVAS, will continue utilizing CALIPSO observations but also datasets from the upcoming ADM-Aeolus and EarthCARE ESA missions in order to provide a unique historical dataset of global aerosol and cloud vertical distributions, as well as respective trends in cloud cover, aerosol/cloud amount and variability of the natural and anthropogenic aerosol component. Additionally, our team is involved in Swarm magnetic field constellation, a new Earth Explorer mission in ESA's Living Planet Programme launched on November 22, 2013, as member of the validation team of the mission. Finally, assessment of heat wave risk and hazards is carried out systematically using MODIS satellite data.

Kontoes, Charalampos; Papoutsis, Ioannis; Amiridis, Vassilis; Balasis, George; Keramitsoglou, Iphigenia; Herekakis, Themistocles; Christia, Eleni

2014-05-01

59

Effective Interpolation of Incomplete Satellite-Derived Leaf-Area Index Time Series for the Continental United States  

NASA Technical Reports Server (NTRS)

Many earth science modeling applications employ continuous input data fields derived from satellite data. Environmental factors, sensor limitations and algorithmic constraints lead to data products of inherently variable quality. This necessitates interpolation of one form or another in order to produce high quality input fields free of missing data. The present research tests several interpolation techniques as applied to satellite-derived leaf area index, an important quantity in many global climate and ecological models. The study evaluates and applies a variety of interpolation techniques for the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf-Area Index Product over the time period 2001-2006 for a region containing the conterminous United States. Results indicate that the accuracy of an individual interpolation technique depends upon the underlying land cover. Spatial interpolation provides better results in forested areas, while temporal interpolation performs more effectively over non-forest cover types. Combination of spatial and temporal approaches offers superior interpolative capabilities to any single method, and in fact, generation of continuous data fields requires a hybrid approach such as this.

Jasinski, Michael F.; Borak, Jordan S.

2008-01-01

60

Vegetation Index and Phenology ESDRs from Multiple Long Term Satellite Data Records: Toward a Sensor Independent Time Series  

Microsoft Academic Search

Changes in vegetation phenology depict an integrated response to change in environmental factors and provide valuable information to global change research. Typically, remote sensing of land surface phenology is based on the analysis of vegetation index time series, because of their simplicity, stability, and intrinsic resistance to noise. Most vegetation index based studies are, however, limited to using one sensor

K. Didan; A. Barreto-Munoz

2009-01-01

61

Measuring Mars sand flux seasonality from a time series of HiRISE images and calibrating the threshold for sand mobility  

NASA Astrophysics Data System (ADS)

In this study, focused on the Nili Patera dune field on Mars, we measured the temporal variation of the migration rate of sand ripples from the correlation of a time-series of HiRISE images using COSI-Corr. The time-series covers approximately 1.5 Mars year which allows us to observe seasonal migration rate variability as well as taking an early glimpse on yearly variation. A Principal Component Analysis (PCA) was applied to the time-series to quantify more robustly the time evolution of the signal and filter out noise, in particular due to unrecorded satellite jitter. Using the first two components, which account for 82% of the variance, the seasonal variation of the ripple migration rate was estimated. We clearly observe continuously active migration throughout the year with a strong seasonal quasi-sinusoidal variation which peaks at perihelion. Coupling between surface winds and sand transport is a fundamental factor governing geological activity and climate on Mars. Saltation of sand is likely crucial for both erosion of the surface and for the emission of finer (dust) particles into the atmosphere. Analysis of the distinctive seasonal variation of sand flux with an atmospheric model is used to infer an effective threshold for sand motion. This is the first direct estimate of the stress threshold at Mars on spatial scales relevant for dynamical atmospheric modeling of sand transport, surface erosion, and dust lifting.

Ayoub, F.; Avouac, J.; Newman, C. E.; Richardson, M. I.; Lucas, A.; Leprince, S.; Bridges, N. T.

2013-12-01

62

Vegetation Index and Phenology ESDRs from Multiple Long Term Satellite Data Records: Toward a Sensor Independent Time Series  

NASA Astrophysics Data System (ADS)

Changes in vegetation phenology depict an integrated response to change in environmental factors and provide valuable information to global change research. Typically, remote sensing of land surface phenology is based on the analysis of vegetation index time series, because of their simplicity, stability, and intrinsic resistance to noise. Most vegetation index based studies are, however, limited to using one sensor owing to the inter-sensor continuity challenges. Phenology is used for a variety of research and application topics that revolve around vegetation dynamics change in response to climate and anthropogenic factors. Consequently, the consistency and length of data records are key requirement. Meaningful change research must then merge data sources that are usually dissimilar because of differences in sensor characteristics and variable processing chains. In this NASA Making Earth System data records for Use in Research Environments project (NASA-MEaSUREs) we’re constructing the first Land surface phenology and vegetation index ESDRs from multiple long term satellite data records. 30+ years of AVHRR, MODIS (and eventually VIIRS) daily surface reflectance measurements will be combined into a seamless record useful for environmental and climate impact related research dealing with trends and changes from local to global scale. We developed a data fusion technique based on the homogeneous phenology cluster. This method assumes that land surface seasonality results from the response to the controls exerted by climate, soil, elevation gradient, aspect, and biological limitations. The concept of phenology cluster is very similar to the biotic life-zones used to classify ecosystems, in that regard, a phenology cluster is also a biotic zone, with similar plant species, at around the same elevation gradient, and governed by similar temperature, precipitation and radiation regimes. Inter-sensor-continuity is then modeled in each homogeneous cluster. We constructed a cluster map using elevation data, mean annual temperature and precipitation, soil maps, and a global land cover map. For each cluster we generated seasonal geometric regression models between AVHRR and MODIS daily surface reflectance. These models enable the conversion between the two records and thus the creation of a sensor independent dataset. Our initial analysis indicates an overall high degree of agreement over the full ~30 year record. However, considerable differences (30+%) existed over the tropics and high latitude regions in the vegetation index signal. A large portion of the divergence resulted from poor data quality (aerosols, clouds, etc…). Using a data reliability index technique developed for this NASA MEASURES’s project (Didan and Barreto, 2009) we eliminated most poor quality data, and after applying the cluster based geometric models the difference was reduced to less than 5%, well within current single sensor margin of error. These early results support our hypothesis that a consistent and reliable sensor independent record is achievable using our cluster based continuity method and our reliability index filtering scheme.

Didan, K.; Barreto-Munoz, A.

2009-12-01

63

ASTER's Satellite Image Gallery  

NSDL National Science Digital Library

This site provides access to satellite images acquired by NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite. The images are sorted into eight categories: Archeology, Cities, Geology, Hydrology, Land Use, Natural Hazards, and Volcanoes. Users can also view the most popular images and the most recent additions to the gallery.

Laboratory, Nasa J.

64

Using image area to control CCD systematic errors in spaceborne photometric and astrometric time-series measurements  

NASA Technical Reports Server (NTRS)

The effect of some systematic errors for high-precision time-series spaceborne photometry and astrometry has been investigated with a CCD as the detector. The 'pixelization' of the images causes systematic error in astrometric measurements. It is shown that this pixelization noise scales as image radius r exp -3/2. Subpixel response gradients, not correctable by the 'flat field', and in conjunction with telescope pointing jitter, introduce further photometric and astrometric errors. Subpixel gradients are modeled using observed properties of real flat fields. These errors can be controlled by having an image span enough pixels. Large images are also favored by CCD dynamic range considerations. However, magnified stellar images can overlap, thus introducing another source of systematic error. An optimum image size is therefore a compromise between these competing factors.

Buffington, Andrew; Booth, Corwin H.; Hudson, Hugh S.

1991-01-01

65

A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska  

USGS Publications Warehouse

The U.S. Geological Survey (USGS) has developed a 16-year time series of vegetation condition information for the conterminous United States and, Alaska using 1 km Advanced Very High Resolution Radiometer (AVHRR) data. The AVHRR data have been processed using consistent methods that account for radiometric variability due to calibration uncertainty, the effects of the atmosphere on surface radiometric measurements obtained from wide field-of-view observations, and the geometric registration accuracy. The conterminous United States and Alaska data sets have an atmospheric correction for water vapor, ozone, and Rayleigh scattering and include a cloud mask derived using the Clouds from AVHRR (CLAVR) algorithm. In comparison with other AVHRR time series data sets, the conterminous United States and Alaska data are processed using similar techniques. The primary difference is that the conterminous United States and Alaska data are at 1 km resolution, while others are at 8 km resolution. The time series consists of weekly and biweekly maximum normalized difference vegetation index (NDVI) composites. ?? 2006 American Society for Photogrammetry and Remote Sensing.

Eldenshink, J.

2006-01-01

66

Real time cardiac image registration during respiration: a time series prediction approach  

Microsoft Academic Search

Cardiac image registration is drawing attention for a range of merits in integrating and enhancing real-time (RT) images using\\u000a a priori and complementary images of the myocardium, which might additionally be captured from other modalities. Myocardial\\u000a stem cell delivery and radio-frequency ablation are some of the cases that could benefit from RT registration of high quality\\u000a images. Unfortunately, most of

Mehdi Esteghamatian; Zohreh Azimifar; Perry Radau; Graham Wright

67

Image-based correlation of Laser Scanning point cloud time series for landslide monitoring  

NASA Astrophysics Data System (ADS)

Very high resolution monitoring of landslide kinematics is an important aspect for a physical understanding of the failure mechanisms and for quantifying the associated hazard. In the last decade, the potential of Terrestrial Laser Scanning (TLS) to monitor slow-moving landslides has been largely demonstrated but accurate processing methods are still needed to extract useful information available in point cloud time series. This work presents an approach to measure the 3D deformation and displacement patterns from repeated TLS surveys. The method is based on the simplification of a 3D matching problem in a 2D matching problem by using a 2D statistical normalized cross-correlation function. The computed displacement amplitudes are compared to displacements (1) calculated with the classical approach of Iterative Closest Point and (2) measured from repeated dGPS observations. The performance of the method is tested on a 3 years dataset acquired at the Super-Sauze landslide (South French Alps). The observed landslide displacements are heterogeneous in time and space. Within the landslide, sub-areas presenting different deformation patterns (extension, compression) are detected by a strain analysis. It is demonstrated that pore water pressure changes within the landslide is the main controlling factor of the kinematics.

Travelletti, Julien; Malet, Jean-Philippe; Delacourt, Christophe

2014-10-01

68

The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco  

Microsoft Academic Search

A time series of eight high?resolution Landsat TM images, ranging over the crop season, has been acquired over an irrigated area in central Morocco. From this time series, a Normalized Difference Vegetation Index (NDVI) profile was generated for each pixel. In order to get significant profiles, the images were radiometrically corrected, first, using invariant objects located on the scene, based

V. Simonneaux; B. Duchemin; D. Helson; S. Er-Raki; A. Olioso; A. G. Chehbouni

2008-01-01

69

A satellite time series of sea surface temperatures in the eastern equatorial Pacific Ocean, 1982-1986  

Microsoft Academic Search

Satellite measurements of sea surface temperature (SST) in the eastern equatorial Pacific have been made since 1982 using the multichannel advanced very high resolution radiometers on NOAA polar orbiting satellites. A 4-year data set has been accumulated at weekly intervals and a spatial resolution of about 50 km on an interactive computer system. The time lapse evaluation of the data

Richard Legeckis

1986-01-01

70

Assessing ecosystem function of a Piñon-Juniper woodland using a time series of high resolution satellite imagery and eddy covariance measurements  

NASA Astrophysics Data System (ADS)

Combining recent advancements in satellite remote sensing with current eddy covariance measurement networks is a powerful way to improve our understanding of ecosystem processes. Remote sensing of semi-arid ecosystems requires temporal coverage sufficient to capture discrete responses in productivity as a result of stochastic patterns of precipitation, and adequate spatial resolution to monitor the patchwork of ecosystem heterogeneity. Eddy-covariance towers continuously measure ecosystem-atmosphere carbon and water exchange. However, even with ancillary data regarding phenologic patterns of the region, tower measurements are unable to inform us about differential response from the collection of plant functional types present within the measured tower footprint. We therefore tested the integration of eddy covariance data with a time series of high resolution (5 meter) RapidEye satellite images collected from late 2009 through mid 2011 over a 49 x 49 km area of piñon-juniper (PJ) woodland south of Mountainair, NM that includes two eddy covariance towers. One tower is in intact PJ woodland and the second tower is in a 200 m x 200 m section of PJ woodland in which all piñon >7 cm dbh (~1600 trees) were girdled to simulate the widespread piñon mortality that occurred throughout the SW in 2002. Due to the high spatial and temporal variability in soil moisture and sparse canopy cover at these sites (maximum LAI is ~ 2.1 and 1.8 in the control and girdled sites, respectively), we used site-specific lab based soil moisture reflectance curves to correct for moisture driven variability in soil reflectance. We used three vegetation indices to compare the phenological patterns of specific plant functional types at both tower sites: the traditional vegetation indices NDVI and MSAVI2, as well as a red-edge (690-730 nm) index NDRE which has demonstrated ability to remotely sense plant stress. We combine these remotely-sensed phenological patterns with the flux tower measurements using a site specific assimilation scheme that integrates temporal variability in reflectance from RapidEye with WorldView-2 derived classification maps to examine how widespread piñon mortality alters ecosystem processes in PJ woodlands. Our results suggest that the growing light gaps within the girdled canopy have facilitated an increase in cover by C4 grasses and forbs. The use of high spatial and temporal resolution satellite imagery in this study allowed the quantification of this progression in response to seasonal precipitation patterns and provides a mechanistic approach with which to explain changes in the eddy covariance data. These structural changes, in part, explain why widespread coniferous mortality in PJ woodlands alters ecosystem function to more closely resemble a juniper savanna.

Krofcheck, D. J.; Eitel, J.; Vierling, L. A.; Schulthess, U.; Litvak, M. E.

2011-12-01

71

Time-Series Imaging of Ocean Waves with an Airborne RGB and NIR Sensor  

Microsoft Academic Search

Measurements from the Airborne Remote Optical Spotlight System (AROSS), an airborne, panchromatic, imaging system, have been used to successfully produce frequency-wavenumber spectra of shoaling ocean waves. The fidelity and quality of the spectra have enabled accurate retrievals of water depths, currents, and surf characteristics and these results have been reported in previous publications. A next-generation system, based on AROSS, has

B. A. Hooper; J. Z. Williams; J. P. Dugan; C. Goldman; M. Yi; D. Campion

2005-01-01

72

LIMBO: A time-series Lucky Imaging survey of variability in Galactic globular clusters  

NASA Astrophysics Data System (ADS)

We present a large observing project monitoring globular clusters (GC) over long time baselines, which will lead to a complete census of variable stars in those clusters down to several magnitudes below the horizontal branch (HB). The use of Lucky Imaging (LI) will allow us to obtain high-precision photometry for even faint objects, and long-term monitoring will also mean that observations are sensitive to detecting other slow transient phenomena, such as gravitational microlensing, the primary aim of this project.

Kains, N.; Bramich, D. M.; Jaimes, R. Figuera; Skottfelt, J.

2014-02-01

73

Dynamic Agricultural Land Unit Profile Database Generation using Landsat Time Series Images  

NASA Astrophysics Data System (ADS)

Agriculture requires continuous supply of inputs to production, while providing final or intermediate outputs or products (food, forage, industrial uses, etc.). Government and other economic agents are interested in the continuity of this process and make decisions based on the available information about current conditions within the agriculture area. From a government point of view, it is important that the input-output chain in agriculture for a given area be enhanced in time, while any possible abrupt disruption be minimized or be constrained within the variation tolerance of the input-output chain. The stability of the exchange of inputs and outputs becomes of even more important in disaster-affected zones, where government programs will look for restoring the area to equal or enhanced social and economical conditions before the occurrence of the disaster. From an economical perspective, potential and existing input providers require up-to-date, precise information of the agriculture area to determine present and future inputs and stock amounts. From another side, agriculture output acquirers might want to apply their own criteria to sort out present and future providers (farmers or irrigators) based on the management done during the irrigation season. In the last 20 years geospatial information has become available for large areas in the globe, providing accurate, unbiased historical records of actual agriculture conditions at individual land units for small and large agricultural areas. This data, adequately processed and stored in any database format, can provide invaluable information for government and economic interests. Despite the availability of the geospatial imagery records, limited or no geospatial-based information about past and current farming conditions at the level of individual land units exists for many agricultural areas in the world. The absence of this information challenges the work of policy makers to evaluate previous or current government efforts for a given occurrence at the land unit level, and affecting the potential economic trade-off level in the area. In this study a framework is proposed to create and continuously update a land unit profile database using historical Landsat satellite imagery records. An experimental test is implemented for the agricultural lands in Central Utah. This location was selected because of their success in increasing the efficiency of water use and control along the entire irrigation system. A set of crop health metrics from the literature (NDVI, LAI, NDWI) is calculated and evaluated to measure crop response to farm management for its evaluation in time. The resulting land unit profile database is then tested to determine land unit profile groups based on land unit management characteristics. Comparison with essential inputs (water availability and climate conditions) and crop type (outputs) on a year basis is provided.

Torres-Rua, A. F.; McKee, M.

2012-12-01

74

High Resolution Time Series of Narrowband Ca IIK Images in the Chromosphere  

NASA Astrophysics Data System (ADS)

We have observed a region of quiet Sun near disk center with the Vacuum Tower Telescope (VTT) of the Kiepenheuer-Institut für Sonnenphysik at the Observatorio del Teide, Tenerife, Spain in April 2005 in several wavelengths. Observations were made at the Ca II K line at 393.3 nm, using a Lyot filter with a bandwidth of 30 ± FWHM, centered at the K_{2v} emission peak; at the H? line at 656.3 nm, using a Lyot filter (25 ± FWHM) centered at line core, and in the G-band (430.5 nm), using an interference filter (1 nm FWHM). We acquired a two-hour long sequence of images at a cadence of ten seconds and a spatial resolution of about 0.3 arcsec. We present our Ca observations of excellent spatial resolution which show morphological structures in internetwork regions similar in form, size and lifetime to those present in recent numerical models of the solar chromosphere.

Wöger, F.; Wedemeyer-Böhm, S.; Schmidt, W.; von der Lühe, O.

2006-12-01

75

Image Classification Using Histograms and Time Series Analysis: A Study of Age-Related Macular Degeneration Screening in Retinal Image Data  

Microsoft Academic Search

\\u000a An approach to image mining is described that combines a histogram based representation with a time series analysis technique.\\u000a More specifically a Dynamic Time Warping (DTW) approach is applied to histogram represented image sets that have been enhanced\\u000a using CLAHE and noise removal. The focus of the work is the screening (classification) of retinal image sets to identify age-related\\u000a macular

Mohd Hanafi Ahmad Hijazi; Frans Coenen; Yalin Zheng

2010-01-01

76

Satellite Based Assessment of the NSRDB Site Irradiances and Time Series from NASA and SUNY/Albany Algorithms  

SciTech Connect

In April, 2007, the National Solar Radiation Database (NSRDB) of the National Renewable Energy Laboratory was updated for the period from 1991 to 2005. NSRDB includes monthly averaged summary statistics from 221 Class I sites spanning the entire time period with least uncertainty. In 2008, the NASA GEWEX Surface Radiation Budget (SRB) project updated its satellite-derived solar surface irradiance to Release 3.0. This dataset spans July 1983 to June 2006 at a 1ox1o resolution. In this paper, we compare the NSRDB data monthly average summary statistics to NASA SRB data that has been validated favorably against the BSRN, SURFRAD, WRDC and GEBA datasets. The SRB-NSRDB comparison reveals reasonably good agreement of the two datasets.

Stackhouse, P. W., Jr.; Zhang, T.; Chandler, W. S.; Whitlock, C. H.; Hoell, J. M.; Westberg, D. J.; Perez, R.; Wilcox, S.

2008-01-01

77

A 20-year long volume transport time series of the Antarctic Circumpolar Current obtained from in situ and satellite observations. Part IÂ : production and validation  

NASA Astrophysics Data System (ADS)

A 20-year long volume transport time series of the Antarctic Circumpolar Current (ACC) across the Drake Passage has been produced combining information from in situ mooring data (3 years, 2006-2009, current meter and ADCP) and satellite altimetry data (20 years, 1992-2012). A new method is designed to account for the dependence of the vertical structure on surface velocity and latitude. This method is based on the elaboration of a look-up table of velocity profiles. Yet unpublished velocity profile time series from Acoustic Doppler Current Profilers are analysed and used to provide accurate vertical structure estimates in the upper 500m. The cross-track mean surface geostrophic velocities are estimated using an error/correction scheme in a sensitivity study to the mean velocities deduced from two recent Mean Dynamic Topographies (MDT) : the CNES-CLS09 MDT and the CNES-CLS13 MDT. The look-up table is carefully checked with independent velocity data, and the robustness of the new method established. The volume transport is 140 +/- 2,2 Sv (standard error of the mean) in the upper 3000m and 141 +/- 2,2 Sv from the bottom to the surface. Both transports show a slightly significant decreasing trend with 95% confidence (between -0,15 and -0,35 Sv per year). The small 1 Sv difference between the 0-3000M and 0-bottom transports results from the deep recirculation cells. Partition between baroclinic/ barotropic transport over 3000m is 112 +/-1,6 / 28 +/-1,6 Sv and from the surface to the bottom 136 +/-1,6 Sv / 5 +/-1,6Sv. The barotropic time series present a decreasing trend (-0,35 Sv over 3000m and -0,6 SV from 0 to the bottom) while the baroclinic time series show an increasing trend (0,1 Sv over 3000 m, 0,3 Sv from 0 to the bottom). The total and baroclinic transport time series exhibit 5-year period variations that are significant at the beginning of the series.

Koenig, Zoé; Provost, Christine; Ferrari, Ramiro; Sennéchael, Nathalie; Rio, Marie-Hélène

2014-05-01

78

Reading Satellite Images  

NSDL National Science Digital Library

This lesson plan is part of the DiscoverySchool.com lesson plan library for grades 6-8. It focuses on satellite images and how they are made by active, passive, and remote-sensing instruments. Students analyze satellite images and answer questions about them. Included are objectives, materials, procedures, discussion questions, evaluation ideas, suggested readings, and vocabulary. There are videos available to order which complement this lesson, an audio-enhanced vocabulary list, and links to teaching tools for making custom quizzes, worksheets, puzzles and lesson plans.

79

Remote Sensing Time Series Product Tool  

NASA Astrophysics Data System (ADS)

The TSPT (Time Series Product Tool) software was custom-designed for NASA to rapidly create and display single-band and band-combination time series, such as NDVI (Normalized Difference Vegetation Index) images, for wide-area crop surveillance and for other time-critical applications. The TSPT, developed in MATLAB®, allows users to create and display various MODIS (Moderate Resolution Imaging Spectroradiometer) or simulated VIIRS (Visible/Infrared Imager Radiometer Suite) products as single images, as time series plots at a selected location, or as temporally processed image videos. Manually creating these types of products is extremely labor intensive; however, the TSPT development tool makes the process simplified and efficient. MODIS is ideal for monitoring large crop areas because of its wide swath (2330 km), its relatively small ground sample distance (250 m), and its high temporal revisit time (twice daily). Furthermore, because MODIS imagery is acquired daily, rapid changes in vegetative health can potentially be detected. The new TSPT technology provides users with the ability to temporally process high-revisit-rate satellite imagery, such as that acquired from MODIS and from its successor, the VIIRS. The TSPT features the important capability of fusing data from both MODIS instruments onboard the Terra and Aqua satellites, which drastically improves cloud statistics. With the TSPT, MODIS metadata is used to find and optionally remove bad and suspect data. Noise removal and temporal processing techniques allow users to create low-noise time series plots and image videos and to select settings and thresholds that tailor particular output products. The TSPT GUI (graphical user interface) provides an interactive environment for crafting "what-if" scenarios by enabling a user to repeat product generation using different settings and thresholds. The TSPT Application Programming Interface provides more fine-tuned control of product generation, allowing experienced programmers to bypass the GUI and to create more user-specific output products, such as comparison time plots or images. This type of time series analysis tool for remotely sensed imagery could be the basis of a large-area vegetation surveillance system. The TSPT has been used to generate NDVI time series over growing seasons in California and Argentina and for hurricane events, such as Hurricane Katrina.

Prados, D.; Ryan, R. E.; Ross, K. W.

2006-12-01

80

Remote Sensing Time Series Product Tool  

NASA Technical Reports Server (NTRS)

The TSPT (Time Series Product Tool) software was custom-designed for NASA to rapidly create and display single-band and band-combination time series, such as NDVI (Normalized Difference Vegetation Index) images, for wide-area crop surveillance and for other time-critical applications. The TSPT, developed in MATLAB, allows users to create and display various MODIS (Moderate Resolution Imaging Spectroradiometer) or simulated VIIRS (Visible/Infrared Imager Radiometer Suite) products as single images, as time series plots at a selected location, or as temporally processed image videos. Manually creating these types of products is extremely labor intensive; however, the TSPT development tool makes the process simplified and efficient. MODIS is ideal for monitoring large crop areas because of its wide swath (2330 km), its relatively small ground sample distance (250 m), and its high temporal revisit time (twice daily). Furthermore, because MODIS imagery is acquired daily, rapid changes in vegetative health can potentially be detected. The new TSPT technology provides users with the ability to temporally process high-revisit-rate satellite imagery, such as that acquired from MODIS and from its successor, the VIIRS. The TSPT features the important capability of fusing data from both MODIS instruments onboard the Terra and Aqua satellites, which drastically improves cloud statistics. With the TSPT, MODIS metadata is used to find and optionally remove bad and suspect data. Noise removal and temporal processing techniques allow users to create low-noise time series plots and image videos and to select settings and thresholds that tailor particular output products. The TSPT GUI (graphical user interface) provides an interactive environment for crafting what-if scenarios by enabling a user to repeat product generation using different settings and thresholds. The TSPT Application Programming Interface provides more fine-tuned control of product generation, allowing experienced programmers to bypass the GUI and to create more user-specific output products, such as comparison time plots or images. This type of time series analysis tool for remotely sensed imagery could be the basis of a large-area vegetation surveillance system. The TSPT has been used to generate NDVI time series over growing seasons in California and Argentina and for hurricane events, such as Hurricane Katrina.

Predos, Don; Ryan, Robert E.; Ross, Kenton W.

2006-01-01

81

Trend analysis of precipitation time series in Greece and their relationship with circulation using surface and satellite data: 1955-2001  

NASA Astrophysics Data System (ADS)

In this study, the trends of annual and seasonal precipitation time series were examined on the basis of measurements of 22 surface stations in Greece for the period 1955-2001, and satellite data during the period 1980-2001. For this purpose, two statistical tests based on the least square method and one based on the Mann-Kendall test, which is also capable of detecting the starting year of possible climatic discontinuities or changes, are applied. Greece, in general, presents a clear significant downward trend in annual precipitation for the period 1955-2001, which is determined by the respective decreasing trend in winter precipitation. Both winter and annual series exhibit a downward trend with a starting year being 1984. Satellite-derived precipitation time series could be an alternative means for diagnosing the variability of precipitation in Greece and detecting trends provided that they have been adjusted by surface measurements in the wider area of interest. The relationship between precipitation variability in Greece and atmospheric circulation was also examined using correlation analysis with three circulation indices: the well-known North Atlantic Oscillation Index (NAOI), a Mediterranean Oscillation Index (MOI) and a new Mediterranean Circulation Index (MCI). NAOI is the index that presented the most interesting correlation with winter, summer and annual precipitation in Greece, whereas the MOI and MCI were found to explain a significant proportion of annual and summer precipitation variability, respectively. The observed downward trend in winter and annual precipitation in Greece is linked mainly to a rising trend in the hemispheric circulation modes of the NAO, which are connected with the Mediterranean Oscillation Index.

Feidas, H.; Noulopoulou, Ch.; Makrogiannis, T.; Bora-Senta, E.

2007-01-01

82

Monitoring of Ground Deformation in the Haoud Berkaoui Oil Field (Sahara, Algeria) Using Time Series Analysis of SAR Images  

NASA Astrophysics Data System (ADS)

We investigate the surface displacement in the Haoud Berkaoui (Algerian Sahara) area, a locus of an oil well accident since 1978, using an advanced MT-InSAR analysis. The Haoud Berkaoui area also includes numerous wells that served for oil extraction starting from 1970s. Among all wells, OKN32 and OKN32bis collapsed due to dissolution of evaporitic rocks inducing rapid ground subsidence and eventually a spectacular 320-m-diameter crater and ~80-m-depth as per today. We apply the small baseline (SB) and the PS-InSAR (Persistent Scatterer) methods to retrieve deformation maps and displacement time series from ESA - SAR images (ERS1 and ERS2) acquired between 1992 and 2002. Our analysis delimits the subsidence area and shows an average 1.5 mm/year subsidence located around the OKN32 (oil well) and in the direction of Ouargla city. We also evaluate the possible propagation and the direction of subsidence by studying the spatial temporal variation of subsidence together with the distribution of the other oil wells in the same area. An elastic model with volume decrease is calculated to correlate the surface subsidence with the dissolution-collapse at depth. The study of this incident helps in the understanding of the subsidence process and the mitigation of further underground collapse that may affect neighboring urban areas.

Meghraoui, M.; Bouraoui, S.; Bougdal, R.; Cakir, Z.

2012-12-01

83

Interpreting Satellite Images  

NSDL National Science Digital Library

In this activity, students Identify differences between photography and satellite imagery, and identify the features in true color and false color images. A worksheet, data sheet, answer key, and Web links are included. This is Lesson 5 in the unit Remote Sensing, part of IMAGERS, Interactive Media Adventures for Grade School Education using Remote Sensing. The website provides hands-on activities in the classroom supporting the science content in two interactive media books, The Adventures of Echo the Bat and Amelia the Pigeon.

84

Trend analysis of air temperature time series in Greece and their relationship with circulation using surface and satellite data: 1955 2001  

NASA Astrophysics Data System (ADS)

In this study, trends of annual and seasonal surface air temperature time series were examined for 20 stations in Greece for the period 1955 2001, and satellite data for the period 1980 2001. Two statistical tests based on the least square method and one based on the Mann-Kendall test, which is also capable of detecting the starting year of possible climatic discontinuities or changes, were used for the analysis. Greece, in general, shows a cooling trend in winter for the period 1955 2001, whereas, summer shows an overall warming trend, however, neither is statistically significant. As a result, the overall trend of the annual values is nearly zero. Comparison with corresponding trends in the Northern Hemisphere (NH) shows that temperatures in Greece do not follow the intense warming trends. Satellite data indicate a remarkable warming trend in mean annual, winter and summer in Greece for the period 1980 2001, and a slight warming trend in annual, spring and autumn for the NH. Comparison with the respective trends detected in the surface air temperature for the same period (1980 2001) shows they match each other quite well in both Greece and the NH. The relationship between temperature variability in Greece and atmospheric circulation was also examined using correlation analysis with three circulation indices: the well-known North Atlantic Oscillation Index (NAOI), a Mediterranean Oscillation Index (MOI) and a new Mediterranean Circulation Index (MCI). The MOI and MCI indices show the most interesting correlation with winter temperatures in Greece. The behaviour of pressure and the height of the 500 hPa surface over the Mediterranean region supports these results.

Feidas, H.; Makrogiannis, T.; Bora-Senta, E.

2004-12-01

85

Multi-temporal land cover classification of the Konya Basin, south-central Turkey, based on a LANDSAT TM-derived NDVI/NDMI time series: satellite remote sensing in support of landscape-scale soil biogeochemistry research  

NASA Astrophysics Data System (ADS)

Recently, terrestrial biogeochemists and soil scientists have called for new approaches to study human impacts on soil biogeochemical properties at landscape-wide, 100-1000 km2 spatial scales (Trumbore and Czimczik 2008). Here, we use satellite remote sensing to map land cover across a 16,000 km2 region in the Konya Basin, south-central Turkey, in support of research into agricultural and pastoral land use impacts on soil biogeochemistry. Our land cover classification is based on time series analysis of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) data, derived from eight LANDSAT TM images spanning the 2006-2007 growing seasons. Using a hierarchical, binary-split classification approach and a support vector machine (SVM) algorithm, we map five land cover classes that correspond with the following dominant land-use categories: 1) annual cultivated row-crops, 2) perennial orchards/cultivated woody vegetation, 3) fallow fields, 4) uncultivated woody vegetation, 5) steppe vegetation/rangeland. The final map has an overall classification accuracy of 87.4% (kappa = 0.842), determined via traditional confusion-matrix analysis and over 150 site visits during summer 2010. Classes 1 and 2, which have the highest per-pixel NDVI and NDMI sums across image dates, attain the highest producer and consumer accuracies (>95%). We also compare the relative contributions and efficacy of NDVI and NDMI in separating land cover classes, and the influence of radiometric correction and calibration across image dates on classification accuracies. Our results support previous research showing that NDVI time series can effectively classify agricultural landscapes in semi-arid to arid environments (Simonneaux et al. 2008; Pax-Lenny et al. 1996). By combining our land cover map with other geospatial information in a GIS, we demonstrate how satellite remote sensing can help expand spatial scales of terrestrial biogeochemistry research from experimental plots to landscapes. As an example, our final map proved to be a very practical tool for locating sites for soil sampling in the Konya Basin during the summer 2010 field season.

Mayes, M. T.; Ozdogan, M.; Marin-Spiotta, E.

2010-12-01

86

Predicting Nonlinear Time Series.  

National Technical Information Service (NTIS)

Predicting future values of a time series has many practical uses in real-time signal processing and understanding. This thesis implements an Adaptive Time Delay Neural Network (ATNN) capable of user-defined degeneration to the more common Time Delay Neur...

J. C. Gainey

1993-01-01

87

Reading Time Series Plots  

NSDL National Science Digital Library

This activity provides a brief introduction to GPS and provides a student activity to practice creating and reading time series plots with simplified GPS data. Students graph how a tectonic plate (and the GPS unit attached to it) has moved over a five year time period by moving a GPS model across a North-East coordinate graph. Students practice these skills by analyzing GPS time series from two GPS stations in Iceland. Teaching Tips Adaptations that allow this activity to be successful in an online environment Need to really transform to an online environment. I did have one participant draw the vectors on an online map of Iceland - however, only one person gets to do this, so I'd like to figure out other techniques for this. Elements of this activity that are most effective Recommendations for other faculty adapting this activity to their own course:

Olds, Shelley

88

Analysis of Forest Fire Disturbance in the Western United States Using Landsat Time Series Images: 1985 to 2005  

NASA Astrophysics Data System (ADS)

In this study we used two different disturbance maps (both utilizing 30 m resolution Landsat imagery) to assess disturbance trends in Western US forests. The first are maps developed by the NAFD project (North American Forest Dynamics). Each NAFD data cube contains an annual-biennial record of forest disturbance events from 1984-2005. We complimented the NAFD maps with MTBS maps (Monitoring Trends in Burn Severity). MTBS solely maps fire disturbance, recording historical (1985-2005) and contemporary burn severity and fire perimeter across the United States. We used Landsat time series stacks for four locations: Oregon (Landsat path 45 row 29), California (p43r33), Idaho (p41r29), and Utah (p32r37). In all four stacks, fire was a relatively small percentage of the total forest disturbance (ranging from 8% in Utah to 27% in Oregon for the entire 20 year period). We also found that the years with greatest burned area were years with a high aridity index (lower precipitation and higher temperatures), a condition necessary, but not sufficient for fire activity. To assess post-disturbance vegetation regrowth we used two spectral indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR). Both indices are sensitive to well-defined spectral paths that forests follow during and after disturbance. As expected, NDVI and NBR were lowest (highest) for the highest (lowest) severity class burned area. However, NBR and NDVI only appear to respond to vegetative reflectance in the first decade after a burn. Therefore, they give useful information on location, timing, and magnitude of disturbance, but direct measurement of biomass with other sensors would be necessary to obtain additional ecological information.

Wicklein, H. F.; Collatz, G. J.; Masek, J.; Williams, C.

2008-12-01

89

Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier  

NASA Astrophysics Data System (ADS)

Rice is the most important economic crop in Vietnam's Mekong Delta (MD). It is the main source of employment and income for rural people in this region. Yearly estimates of rice growing areas and delineation of spatial distribution of rice crops are needed to devise agricultural economic plans and to ensure security of the food supply. The main objective of this study is to map rice cropping systems with respect to monitoring agricultural practices in the MD using time-series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250-m data. These time-series NDVI data were derived from the 8-day MODIS 250-m data acquired in 2008. Various spatial and nonspatial data were also used for accuracy verification. The method used in this study consists of the following three main steps: 1. filtering noise from the time-series NDVI data using wavelet transformation (Coiflet 4); 2. classification of rice cropping systems using parametric and nonparametric classification algorithms: the maximum likelihood classifier (MLC) and support vector machines (SVMs); and 3. verification of classification results using ground truth data and government rice statistics. Good results can be found using wavelet transformation for cleaning rice signatures. The results of classification accuracy assessment showed that the SVMs outperformed the MLC. The overall accuracy and Kappa coefficient achieved by the SVMs were 89.7% and 0.86, respectively, while those achieved by the MLC were 76.2% and 0.68, respectively. Comparison of the MODIS-derived areas obtained by the SVMs with the government rice statistics at the provincial level also demonstrated that the results achieved by the SVMs (R2 = 0.95) were better than the MLC (R2 = 0.91). This study demonstrates the effectiveness of using a nonparametric classification algorithm (SVMs) and time-series MODIS NVDI data for rice crop mapping in the Vietnamese MD.

Chen, Chi-Farn; Son, Nguyen-Thanh; Chen, Cheng-Ru; Chang, Ly-Yu

2011-01-01

90

Simultaneous and Time Series Measurements of Two-Dimensional Velocity and Temperature Fields Using an Image Processing Technique.  

National Technical Information Service (NTIS)

The spatial and simultaneous qualification of velocity and temperature field of flow has been made using image processing for understanding large scale structures of thermal mixing and a time dependent movement of vorticies. The velocity vectors were obta...

J. Sakakibara K. Hishida M. Maeda

1992-01-01

91

Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia.  

PubMed

Fuzzy C-means clustering and principal components analysis were used to analyze a temporal series of near-IR images taken of a human forearm during periods of venous outflow restriction and complete forearm ischemia. The principal component eigen-time course analysis provided no useful information and the principal component eigen-image analysis gave results that correlated poorly with anatomical features. The fuzzy C-means clustering analysis, on the other hand, showed distinct regional differences in the hemodynamic response and scattering properties of the tissue, which correlated well with the anatomical features of the forearm. PMID:9475436

Mansfield, J R; Sowa, M G; Scarth, G B; Somorjai, R L; Mantsch, H H

1997-01-01

92

Contrail Detection in Satellite Images  

NASA Astrophysics Data System (ADS)

Methods for detecting linear contrail pixels in satellite infrared images are described. An objective contrail detection algorithm has been developed and extensively applied to data from various polar and geostationary satellite sensors. The method uses the contrast in brightness temperatures near 11 and 12 ?m wavelengths and detects linear contrails using image processing techniques. The paper discusses the development of the algorithms, detection efficiency, false alarm rate, some of the results, and their validation. The contrail detection algorithm detects only a fraction of all contrail cirrus. Progress is expected from combining spatiotemporal satellite data in correlation with traffic and meteorological data.

Mannstein, Hermann; Vázquez-Navarro, Margarita; Graf, Kaspar; Duda, David P.; Schumann, Ulrich

93

Simulation scheme of dusk scene using piece-wise multiple regression based on time-series color-block images  

NASA Astrophysics Data System (ADS)

Dusk and dawn are usually the most beautiful moments of the day, and are almost always too short for busy people nowadays to witness their coming. In this work, an efficient strategy for simulating a dusk scene of an outdoor scene image taken at other times before the sunset is presented. The strategy is a hybrid approach combining the piece-wise multiple regression (PMR) of data, discrete cosine transformation (DCT), and a look-up table algorithm. The process begins using a series of color-block images taken in the afternoon of a day. The best fitting functions of PMR for these color block images exist on separate planes (red, green, and blue) in the DCT domain individually. The reference databases of the DCT coefficients varying with respect to time are then established according to the best fitting functions of PMR. Finally, the dusk scene of an outdoor scene taken in the afternoon is synthesized by querying the reference database. The experiment results show that the presented algorithm can precisely simulate the desired dusk scene from a scene image taken in the afternoon.

Liu, Chen-Chung; Yang, Chih-Chao

2010-09-01

94

Geomatics techniques applied to time series of aerial images for multitemporal geomorphological analysis of the Miage Glacier (Mont Blanc).  

NASA Astrophysics Data System (ADS)

The Miage glacier is the major one in the Italian side of the Mont Blanc Massif, the third by area and the first by longitudinal extent among Italian glaciers. It is a typical debris covered glacier, since the end of the L.I.A. The debris coverage reduces ablation, allowing a relative stability of the glacier terminus, which is characterized by a wide and articulated moraine apparatus. For its conservative landforms, the Miage Glacier has a great importance for the analysis of the geomorphological response to recent climatic changes. Thanks to an organized existing archive of multitemporal aerial images (1935 to present) a photogrammetric approach has been applied to detect recent geomorphological changes in the Miage glacial basin. The research team provided: a) to digitize all the available images (still in analogic form) through photogrammetric scanners (very low image distortions devices) taking care of correctly defining the resolution of the acquisition compared to the scale mapping images are suitable for; b) to import digitized images into an appropriate digital photogrammetry software environment; c) to manage images in order, where possible, to carried out the stereo models orientation necessary for 3D navigation and plotting of critical geometric features of the glacier. Recognized geometric feature, referring to different periods, can be transferred to vector layers and imported in a GIS for further comparisons and investigations; d) to produce multi-temporal Digital Elevation Models for glacier volume changes; e) to perform orthoprojection of such images to obtain multitemporal orthoimages useful for areal an planar terrain evaluation and thematic analysis; f) to evaluate both planimetric positioning and height determination accuracies reachable through the photogrammetric process. Users have to known reliability of the measures they can do over such products. This can drive them to define the applicable field of this approach and this can help them to better program future flights for glacier survey; All produced data, differently from the original ones, can be considered as map products. All of them represent geocoded entity and maps that can be easily imported in a GIS for assessment and management. The operational workflow allowed to the definition of changes occurred over the Miage glacier area and to the interpretation of related significant geomorphological processes. Particular attention has been paid to the identification of changes in the debris cove pattern, to the differences calculation of glacial mass volumes, to the natural instability phenomena (landslides, debris flows, glacier lakes). Short-term climate trend has been evoked to the glacial expansion of mid 1980s quantified by remote sensing interpretation; contemporary activation of local glacial risks on the outer moraines has been mapped too. Glacial mass contraction of 1990-2000 has been traced and repeated rock falls accumulation over the Miage Glacier have been individualized. Later differential uplifts and subsidences of glacier topography have been interpreted as local intense differential ablation processes, recently associated to ephemeral epiglacial lakes formation.

Perotti, Luigi; Carletti, Roberto; Giardino, Marco; Mortara, Giovanni

2010-05-01

95

Modelling bursty time series  

NASA Astrophysics Data System (ADS)

Many human-related activities show power-law decaying interevent time distribution with exponents usually varying between 1 and 2. We study a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. The model is characterized by a priority distribution as an input parameter, which describes the choice procedure from the list. We give exact results on the asymptotic behaviour of the model and we show that the interevent time distribution is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tunable between 1 and 2. The model satisfies a scaling law between the exponents of interevent time distribution (?) and autocorrelation function (?): ? + ? = 2. This law is general for renewal processes with power-law decaying interevent time distribution. We conclude that slowly decaying autocorrelation function indicates long-range dependence only if the scaling law is violated.

Vajna, Szabolcs; Tóth, Bálint; Kertész, János

2013-10-01

96

Characterizing Methane Emissions at Local Scales with a 20 Year Total Hydrocarbon Time Series, Imaging Spectrometry, and Web Facilitated Analysis  

NASA Astrophysics Data System (ADS)

Methane is an important greenhouse gas for which uncertainty in local emission strengths necessitates improved source characterizations. Although CH4 plume mapping did not motivate the NASA Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) design and municipal air quality monitoring stations were not intended for studying marine geological seepage, these assets have capabilities that can make them viable for studying concentrated (high flux, highly heterogeneous) CH4 sources, such as the Coal Oil Point (COP) seep field (˜0.015 Tg CH4 yr-1) offshore Santa Barbara, California. Hourly total hydrocarbon (THC) data, spanning 1990 to 2008 from an air pollution station located near COP, were analyzed and showed geologic CH4 emissions as the dominant local source. A band ratio approach was developed and applied to high glint AVIRIS data over COP, resulting in local-scale mapping of natural atmospheric CH4 plumes. A Cluster-Tuned Matched Filter (CTMF) technique was applied to Gulf of Mexico AVIRIS data to detect CH4 venting from offshore platforms. Review of 744 platform-centered CTMF subsets was facilitated through a flexible PHP-based web portal. This dissertation demonstrates the value of investigating municipal air quality data and imaging spectrometry for gathering insight into concentrated methane source emissions and highlights how flexible web-based solutions can help facilitate remote sensing research.

Bradley, Eliza Swan

97

Satellite Imaging Corporation  

NSDL National Science Digital Library

Satellite imagery and aerial photography incorporated with geographic information systems GIS can give coastal resource managers and emergency officials a wealth of information for assessment analysis and monitoring of natural disasters such as hurricane,s tornadoes and cyclone damage from small to large regions around the globe.

Romeijn, Monique; Corporation, Satellite I.

98

Infrared Satellite Images  

NSDL National Science Digital Library

This site from the University of Illinois presents an explanation of how infrared imaging provides information about clouds. Images and commentary show how altitude, temperature, and humidity can be inferred from the infrared images. The site also contains links to descriptions of other kinds of remote sensing.

2008-03-28

99

Aerial Photographs and Satellite Images  

USGS Publications Warehouse

Photographs and other images of the Earth taken from the air and from space show a great deal about the planet's landforms, vegetation, and resources. Aerial and satellite images, known as remotely sensed images, permit accurate mapping of land cover and make landscape features understandable on regional, continental, and even global scales. Transient phenomena, such as seasonal vegetation vigor and contaminant discharges, can be studied by comparing images acquired at different times. The U.S. Geological Survey (USGS), which began using aerial photographs for mapping in the 1930's, archives photographs from its mapping projects and from those of some other Federal agencies. In addition, many images from such space programs as Landsat, begun in 1972, are held by the USGS. Most satellite scenes can be obtained only in digital form for use in computer-based image processing and geographic information systems, but in some cases are also available as photographic products.

U.S. Geological Survey

1997-01-01

100

Ordinal analysis of time series  

NASA Astrophysics Data System (ADS)

In order to develop fast and robust methods for extracting qualitative information from non-linear time series, Bandt and Pompe have proposed to consider time series from the pure ordinal viewpoint. On the basis of counting ordinal patterns, which describe the up-and-down in a time series, they have introduced the concept of permutation entropy for quantifying the complexity of a system behind a time series. The permutation entropy only provides one detail of the ordinal structure of a time series. Here we present a method for extracting the whole ordinal information.

Keller, K.; Sinn, M.

2005-10-01

101

From Networks to Time Series  

NASA Astrophysics Data System (ADS)

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

102

Impact of Sensor Degradation on the MODIS NDVI Time Series.  

National Technical Information Service (NTIS)

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor...

A. Wu D. Morton D. Wang E. Vermote J. Masek J. Nagol R. Levy R. Wolfe X. Xiong

2011-01-01

103

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool  

NASA Technical Reports Server (NTRS)

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

104

Non-invasive detection of optical changes elicited by seizure activity using time-series analysis of light scattering images in a rat model of generalized seizure.  

PubMed

For the first time, we detected optical changes elicited by seizure activity in pentylenetetrazol (PTZ)-treated rats (n=6) versus saline controls (n=2) over a 30min recording session using a novel time-series analysis of scattering images obtained non-invasively with a real-time multispectral diffuse optical tomography (DOT) system. Spatio-temporal images of absorption and scattering coefficients were recovered from PTZ- and saline-treated rats' brains using a finite element-based DOT image reconstruction algorithm. After pulse artifacts were eliminated, an independent component (IC) analysis was conducted for blind-source separation of the optical signals. The retrieved ICs were compared with concurrently measured EEG signals, and the selected components were further refined using K-means clustering and spectrum analysis tools. The results revealed that changes in absorption and scattering coefficients emerge sooner than changes in the EEG signal and a low frequency peak signal of ?0.3Hz in the spectra of light scattering images after PTZ injection. This low frequency caused by slow volume changes in CNS cells was not detected in control animals. Brain regions that we detected early changes in optical signals and activation maps were confirmed in an additional 3 PTZ-treated rats using the DOT system and concurrent EEG recordings obtained from multiple brain regions. Our results show that the analysis of scattered diffuse light is a sensitive and reliable modality for detecting changes in neural activity associated with generalized seizure and other CNS disorders with the additional benefit of providing access to physiological parameters that other modalities cannot access. PMID:24530435

Hajihashemi, M Reza; Zhang, Tao; Ormerod, Brandi K; Jiang, Huabei

2014-04-30

105

Large scale variability, long-term trends and extreme events in total ozone over the northern mid-latitudes based on satellite time series  

NASA Astrophysics Data System (ADS)

Various generations of satellites (e.g. TOMS, GOME, OMI) made spatial datasets of column ozone available to the scientific community. This study has a special focus on column ozone over the northern mid-latitudes. Tools from geostatistics and extreme value theory are applied to analyze variability, long-term trends and frequency distributions of extreme events in total ozone. In a recent case study (Rieder et al., 2009) new tools from extreme value theory (Coles, 2001; Ribatet, 2007) have been applied to the world's longest total ozone record from Arosa, Switzerland (e.g. Staehelin 1998a,b), in order to describe extreme events in low and high total ozone. Within the current study this analysis is extended to satellite datasets for the northern mid-latitudes. Further special emphasis is given on patterns and spatial correlations and the influence of changes in atmospheric dynamics (e.g. tropospheric and lower stratospheric pressure systems) on column ozone. References: Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and Davison, A.C.: From ozone mini holes and mini highs towards extreme value theory: New insights from extreme events and non stationarity, submitted to J. Geophys. Res., 2009. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998a. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998b.

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Ribatet, M.; Davison, A. C.

2009-04-01

106

Development of an IUE Time Series Browser  

NASA Technical Reports Server (NTRS)

The International Ultraviolet Explorer (IUE) satellite operated successfully for more than 17 years. Its archive of more than 100,000 science exposures is widely acknowledged as an invaluable scientific resource that will not be duplicated in the foreseeable future. We have searched this archive for objects which were observed 10 or more times with the same spectral dispersion and wavelength coverage over the lifetime of IUE. Using this definition of a time series, we find that roughly half of the science exposures are members of such time series. This paper describes a WEB-based IUE time series browser which enables the user to visually inspect the repeated observations for variability and to examine each member spectrum individually. Further, if the researcher determines that a specific data set is worthy of further investigation, it can be easily downloaded for further, detailed analysis.

Massa, Derck

2005-01-01

107

Novelty detection in time series of ULF magnetic and electric components obtained from DEMETER satellite experiments above Samoa (29 September 2009) earthquake region  

NASA Astrophysics Data System (ADS)

Using ULF (ultra low frequency) measurements of magnetometer and ICE (Instrument Champ Electrique) experiments on board the DEMETER satellite, possible irregularities in ULF magnetic and electric components have been surveyed in the vicinity of Samoa (29 September 2009) earthquake region. The data used in this paper cover the period from 1 August 2009 to 11 October 2009. The anomalous variations in magnetic components (Bx, By and Bz) were clearly observed on 1 and 3 days before the event. It is seen that the periodic patterns of the magnetic components obviously changed prior to the earthquake. These unusual variations have been also observed in the variations of polarization index obtained from the magnetic components during the whole period at ~10:30 and ~22:30 LT. It is concluded that the polarization exhibits an apparent increase on 1 and 3 days preceding the earthquake. These observed unusual disturbances in ULF magnetic components were acknowledged using the detected perturbations in ULF electric components (Ex, Ey and Ez) in the geomagnetic coordinate system. Finally, the results reported in this paper were compared with previous results for this Samoa earthquake. Hence, the detected anomalies resulting from the magnetometer and ICE ULF waveforms in quiet geomagnetic conditions could be regarded as seismo-ionospheric precursors.

Akhoondzadeh, M.

2013-01-01

108

Earth Exploration Toolbook: Annotating Change in Satellite Images  

NSDL National Science Digital Library

This chapter walks users through a technique for documenting change in before-and-after sets of satellite images. The technique can be used for any set of time-series images that are spatially registered to show the exact same area at the same scale. In the chapter, users examine three Landsat images of the Pearl River delta in southeastern China. In these images, users observe changes in land use, then identify and outline areas of new land that were created by dredging sediments from the river bottom. The final product is an annotated image that highlights new land and indicates when it was created. The chapter is part of the Earth Exploration Toolbook, which provides teachers and/or students with direct practice for using scientific tools to analyze Earth science data. Students should begin on the Case Study page.

109

FROG: Time-series analysis  

NASA Astrophysics Data System (ADS)

FROG performs time series analysis and display. It provides a simple user interface for astronomers wanting to do time-domain astrophysics but still offers the powerful features found in packages such as PERIOD (ascl:1406.005). FROG includes a number of tools for manipulation of time series. Among other things, the user can combine individual time series, detrend series (multiple methods) and perform basic arithmetic functions. The data can also be exported directly into the TOPCAT (ascl:1101.010) application for further manipulation if needed.

Allan, Alasdair

2014-06-01

110

Clustering of financial time series  

NASA Astrophysics Data System (ADS)

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

111

Time Series of the Biscuit Fire  

NSDL National Science Digital Library

This animation contains a time series of print resolution still images showing the progression of the Biscuit fire in Oregon during 2002. Fire locations are represented as particles that change color as the fire ages. The Biscuit fire burned 500,000 acres of forest in Oregon and Northern California during the summer and fall of 2002.

Starr, Cindy; Justice, Chris; Sohlberg, Robert; Gunther, Fred; North, William

2003-08-04

112

IMAGE Satellite 1/4-scale Model  

NSDL National Science Digital Library

This is an activity about scale model building. Learners will use mathematics to determine the scale model size, construct a pattern, and build a one-fourth size scale model of the IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) satellite, the first satellite mission to image the Earth's magnetosphere. This is the third activity in the Solar Storms and You: Exploring Satellite Design educator guide.

113

Seasonal variabilty of surface velocities and ice discharge of Columbia Glacier, Alaska using high-resolution TanDEM-X satellite time series and NASA IceBridge data  

NASA Astrophysics Data System (ADS)

Columbia Glacier is a grounded tidewater glacier located on the south coast of Alaska. It has lost half of its volume during 1957-2007, more rapidly after 1980. It is now split into two branches, known as Main/East and West branch due to the dramatic retreat of ~ 23 km and calving of iceberg from its terminus in past few decades. In Alaska, a majority of the mass loss from glaciers is due to rapid ice flow and calving icebergs into tidewater and lacustrine environments. In addition, submarine melting and change in the frontal position can accelerate the ice flow and calving rate. We use time series of high-resolution TanDEM-X stripmap satellite imagery during 2011-2013. The active image of the bistatic TanDEM-X acquisitions, acquired over 11 or 22 day repeat intervals, are utilized to derive surface velocity fields using SAR intensity offset tracking. Due to the short temporal baselines, the precise orbit control and the high-resolution of the data, the accuracies of the velocity products are high. We observe a pronounce seasonal signal in flow velocities close to the glacier front of East/Main branch of Columbia Glacier. Maximum values at the glacier front reach up to 14 m/day were recorded in May 2012 and 12 m/day in June 2013. Minimum velocities at the glacier front are generally observed in September and October with lowest values below 2 m/day in October 2012. Months in between those dates show corresponding increase or deceleration resulting a kind of sinusoidal annual course of the surface velocity at the glacier front. The seasonal signal is consistently decreasing with the distance from the glacier front. At a distance of 17.5 km from the ice front, velocities are reduced to 2 m/day and almost no seasonal variability can be observed. We attribute these temporal and spatial variability to changes in the basal hydrology and lubrification of the glacier bed. Closure of the basal drainage system in early winter leads to maximum speeds while during a fully developed basal drainage system speeds are at their minimum. We also analyze the variation in conjunction with the prevailing meteorological conditions as well as changes in calving front position in order to exclude other potential influencing factors. In a second step, we also exploit TanDEM-X data to generate various digital elevation models (DEMs) at different time steps. The multi-temporal DEMs are used to estimate the difference in surface elevation and respective ice thickness changes. All TanDEM-X DEMs are well tied with a SPOT reference DEM. Errors are estimated over ice free moraines and rocky areas. The quality of the TanDEM-X DEMs on snow and ice covered areas are further assessed by a comparison to laser scanning data from NASA Icebridge campaigns. The time wise closest TanDEM-X DEMs were compared to the Icebridge tracks from winter and summer surveys in order to judge errors resulting from the radar penetration of the x/band radar signal into snow, ice and firn. The average differences between laser scanning and TanDEM-X in August, 2011 and March, 2012 are observed to be 8.48 m and 14.35 m respectively. Retreat rates of the glacier front are derived manually by digitizing the terminus position. By combining the data sets of ice velocity, ice thickness and the retreat rates at different time steps, we estimate the seasonal variability of the ice discharge of Columbia Glacier.

Vijay, Saurabh; Braun, Matthias

2014-05-01

114

Providing web-based tools for time series access and analysis  

NASA Astrophysics Data System (ADS)

Time series information is widely used in environmental change analyses and is also an essential information for stakeholders and governmental agencies. However, a challenging issue is the processing of raw data and the execution of time series analysis. In most cases, data has to be found, downloaded, processed and even converted in the correct data format prior to executing time series analysis tools. Data has to be prepared to use it in different existing software packages. Several packages like TIMESAT (Jönnson & Eklundh, 2004) for phenological studies, BFAST (Verbesselt et al., 2010) for breakpoint detection, and GreenBrown (Forkel et al., 2013) for trend calculations are provided as open-source software and can be executed from the command line. This is needed if data pre-processing and time series analysis is being automated. To bring both parts, automated data access and data analysis, together, a web-based system was developed to provide access to satellite based time series data and access to above mentioned analysis tools. Users of the web portal are able to specify a point or a polygon and an available dataset (e.g., Vegetation Indices and Land Surface Temperature datasets from NASA MODIS). The data is then being processed and provided as a time series CSV file. Afterwards the user can select an analysis tool that is being executed on the server. The final data (CSV, plot images, GeoTIFFs) is visualized in the web portal and can be downloaded for further usage. As a first use case, we built up a complimentary web-based system with NASA MODIS products for Germany and parts of Siberia based on the Earth Observation Monitor (www.earth-observation-monitor.net). The aim of this work is to make time series analysis with existing tools as easy as possible that users can focus on the interpretation of the results. References: Jönnson, P. and L. Eklundh (2004). TIMESAT - a program for analysing time-series of satellite sensor data. Computers and Geosciences 30, 833-845. Verbesselt, J., R. Hyndman, G. Newnham and D. Culvenor (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115. DOI: 10.1016/j.rse.2009.08.014 Forkel, M., N. Carvalhais, J. Verbesselt, M. Mahecha, C. Neigh and M. Reichstein (2013). Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology. Remote Sensing 5, 2113-2144.

Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane

2014-05-01

115

Finding semantics in time series  

Microsoft Academic Search

In order to understand a complex system, we analyze its output or its log data. For example, we track a system's resource consumption (CPU, memory, message queues of different types, etc) to help avert system failures; we examine economic indicators to assess the severity of a recession; we monitor a patient's heart rate or EEG for disease diagnosis. Time series

Peng Wang; Haixun Wang; Wei Wang

2011-01-01

116

Use of time series of optical and SAR images in the estimation of snow cover for the optimization of water use in the Andes of Argentina and Chile  

NASA Astrophysics Data System (ADS)

This paper describes the progress in the bilateral cooperation project between academic and water resources management institutions from the Andes region of Argentina and Chile. The study zone is located in fragile ecosystems and mountain areas of the Andes (limit zone between the Province of San Juan, Argentina, and the IV Region of Coquimbo, Chile), with arid climate, where snow precipitates in the headwaters of watershed feed the rivers of the region by melting, which are the only source of water for human use, productive and energetic activities, as well as the native flora and fauna. CONAE, the Argentine Space Agency, participates in the Project through the provision of satellite data to the users and by this it contributes to ensuring the continuity of the results of the project. Also, it provides training in digital image processing. The project also includes the participation of water resource management institutions like Secretaria de Recursos Hidricos of Argentina and the Centro de Información de Recursos Naturales de Chile (CIREN), and of academic institution like the University of San Juan (Argentina) and University of La Serena (Chile). These institutions benefit from the incorporation of new methodologies advanced digital image processing and training of staff (researcher, lecturers, PhD Students and technical). Objectives: 1-Improve water distribution incorporating space technology for application in the prediction models of the stream flow. 2- Conduct an inventory of glaciers as well as studies in selected watersheds in the Andean region, aiming to know the water resource, its availability and potential risks to communities in the region. 3. Contribute to vulnerability studies in biodiversity Andean watersheds. Results: For estimation Snow cover Area, the MODIS images are appropriate due their high temporal resolution and allows for monitoring large areas (greater than 10 km) The proposed methodology (Use of snow index, NSDI) is appropriate for operational application because it is simple and easy to implement. From the analysis of multitemporal study in the region using COSMO SkyMed images, it is observed that the values of wet snow coverage, obtained along the 2012 hydrological cycle, are consistent with the dynamics of the same: The study area has a high rise and steep relief (up to 6400m), therefore the shadows loom large, processing optical and SAR images improve the results. The behavior of the accumulation process (winter) and snowmelt (summer), is influenced by the elevation of the different study areas. A high percentage (49%) of surface snow at higher elevations to 3000 m. This is due to the accumulation of snow increases with elevation, by the combined effect of low temperatures and increased precipitation snowy orographic effect. In studies of wet meadows with optical images, a high correspondence between the spectral classes and vigor of vegetation and soil moisture (seen in the field) so are considered as indicators of degradation of these ecosystems was observed

Salinas de Salmuni, Graciela; Cabezas Cartes, Ricardo; Menicocci, Felix

2014-05-01

117

Benefits from Homogeneously Reprocessed GPS Time Series  

NASA Astrophysics Data System (ADS)

The transition from relative to absolute phase center variations (PCVs), which is planed by the International GNSS Service (IGS) for the beginning of 2006, will cause significant changes in the time series of estimated parameters, especially in coordinate time series and reference frame realization. But most of the GPS time series are also inhomogeneous due to changes in modeling and parameterization during their generation and their interpretation is therefore very difficult. To overcome this problem, the IGS reanalysis project was initiated in the mid of 2005. The Technical Universities in Dresden and Munich already performed a successful reprocessing of more than eleven years of GPS data (1994 till mid of 2005). The benefits from the homogeneously reprocessed GPS solution will be demonstrated by selected examples like orbit consistency and coordinate repeatability. In the framework of this reprocessing also several more sophisticated modeling techniques were tested: (1) absolute satellite antenna PCVs, (2) consideration of higher-order ionosphere terms, (3) mapping of the troposphere zenith delay with the isobaric mapping function (IMF) using weather model data as input information. The influence of this advanced modeling will be demonstrated by comparing the "advanced" solutions with "standard" solutions using (1) relative PCVs, (2) only the first-order ionospheric term, (3) the widely used Niell mapping function. For inter-technique comparisons a VLBI solution generated by a DGFI reprocessing was used: the biases for common parameters of the two space geodetic techniques can serve as a quality indicator to assess the effects of the advanced modeling.

Steigenberger, P.; Rothacher, M.; Dietrich, R.; Fritsche, M.; Rülke, A.; Tesmer, V.

2005-12-01

118

Optimizing time series discretization for knowledge discovery  

Microsoft Academic Search

Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the temporal order of values. This often leads to symbols that do not correspond to states of the process generating the time series and cannot be interpreted meaningfully. We propose a new method for meaningful unsupervised discretization

Fabian Mörchen; Alfred Ultsch

2005-01-01

119

Inductive time series modeling program  

SciTech Connect

A number of features that comprise environmental time series share a common mathematical behavior. Analysis of the Mauna Loa carbon dioxide record and other time series is aimed at constructing mathematical functions which describe as many major features of the data as possible. A trend function is fit to the data, removed, and the resulting residuals analyzed for any significant behavior. This is repeated until the residuals are driven to white noise. In the following discussion, the concept of trend will include cyclic components. The mathematical tools and program packages used are VARPRO (Golub and Pereyra 1973), for the least squares fit, and a modified version of our spectral analysis program (Kirk et al. 1979), for spectrum and noise analysis. The program is written in FORTRAN. All computations are done in double precision, except for the plotting calls where the DISSPLA package is used. The core requirement varies between 600 K and 700 K. The program is implemented on the IBM 360/370. Currently, the program can analyze up to five different time series where each series contains no more than 300 points. 12 refs.

Kirk, B.L.; Rust, B.W.

1985-10-01

120

Satellite image analysis using neural networks  

NASA Technical Reports Server (NTRS)

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Sheldon, Roger A.

1990-01-01

121

Performance assessment of fire-sat monitoring system based on satellite time series for fire danger estimation : the experience of the pre-operative application in the Basilicata Region (Italy)  

NASA Astrophysics Data System (ADS)

This paper presents the results we obtained in the context of the FIRE-SAT project during the 2012 operative application of the satellite based tools for fire monitoring. FIRE_SAT project has been funded by the Civil Protection of the Basilicata Region in order to set up a low cost methodology for fire danger monitoring and fire effect estimation based on satellite Earth Observation techniques. To this aim, NASA Moderate Resolution Imaging Spectroradiometer (MODIS), ASTER, Landsat TM data were used. Novel data processing techniques have been developed by researchers of the ARGON Laboratory of the CNR-IMAA for the operative monitoring of fire. In this paper we only focus on the danger estimation model which has been fruitfully used since 2008 to 2012 as an reliable operative tool to support and optimize fire fighting strategies from the alert to the management of resources including fire attacks. The daily updating of fire danger is carried out using satellite MODIS images selected for their spectral capability and availability free of charge from NASA web site. This makes these data sets very suitable for an effective systematic (daily) and sustainable low-cost monitoring of large areas. The preoperative use of the integrated model, pointed out that the system properly monitor spatial and temporal variations of fire susceptibility and provide useful information of both fire severity and post fire regeneration capability.

Lanorte, Antonio; Desantis, Fortunato; Aromando, Angelo; Lasaponara, Rosa

2013-04-01

122

Prediction for discrete time series  

Microsoft Academic Search

Let {Xn} be a stationary and ergodic time series taking values from a finite or countably infinite set \\u000a\\u000a\\u0009 Assume that the distribution of the process is otherwise unknown. We propose a sequence of stopping times ?n along which we will be able to estimate the conditional probability P(\\u000a\\u000a\\u0009=x|X0,...,\\u000a\\u000a\\u0009) from data segment (X0,...,\\u000a\\u000a\\u0009) in a pointwise consistent way

Gusztáv Morvai; Benjamin Weiss

2005-01-01

123

Threshold Detection of Geodetic Satellite Images.  

National Technical Information Service (NTIS)

A literature survey and study has been made on the photographic detection of stellar images. Photographic detection theory in the literature was extended as required by the special conditions of geodetic satellite photography. These conditions include the...

S. Ackerman

1966-01-01

124

Cloudsat Satellite Images of Amanda  

NASA Video Gallery

NASA's CloudSat satellite flew over Hurricane Amanda on May 25, at 5 p.m. EDT and saw a deep area of moderate to heavy-moderate precipitation below the freezing level (where precipitation changes f...

125

Comparisons of zooplankton time series  

NASA Astrophysics Data System (ADS)

Evidence for climate-correlated low frequency variability of various components of marine ecosystems has accumulated rapidly over the past 2 decades. There has also been a growing recognition that society needs to learn how the fluctuations of these various components are linked, and to predict the likely amplitude and steepness of future changes. Demographic characteristics of marine zooplankton make them especially suitable for examining variability of marine ecosystems at interannual to decadal time scales. Their life cycle duration is short enough that there is little carryover of population membership from year to year, but long enough that variability can be tracked with monthly-to-seasonal sampling. Because zooplankton are rarely fished, comparative analysis of changes in their abundance can greatly enhance our ability to evaluate the importance of and interaction between physical environment, food web, and fishery harvest as causal mechanisms driving ecosystem level changes. A number of valuable within-region analyses of zooplankton time series have been published in the past decade, covering a variety of modes of variability including changes in total biomass, changes in size structure and species composition, changes in spatial distribution, and changes in seasonal timing. But because most zooplankton time series are relatively short compared to the time scales of interest, the statistical power of local analyses is often low, and between-region and between-variable comparisons are also needed. In this paper, we review the results of recent within- and between-region analyses, and suggest some priorities for future work.

Mackas, David L.; Beaugrand, Gregory

2010-02-01

126

Clustering Time Series Data: An Evolutionary Approach  

Microsoft Academic Search

Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising\\u000a in bioinformatics, in marketing research, software engineering and management. This chapter discusses the state-of-the-art\\u000a methodology for some mining time series databases and presents a new evolutionary algorithm for times series clustering an\\u000a input time series data set. The data mining methods

Monica Chis; Soumya Banerjee; Aboul Ella Hassanien

2009-01-01

127

Albedo Pattern Recognition and Time-Series Analyses in Malaysia  

NASA Astrophysics Data System (ADS)

Pattern recognition and time-series analyses will enable one to evaluate and generate predictions of specific phenomena. The albedo pattern and time-series analyses are very much useful especially in relation to climate condition monitoring. This study is conducted to seek for Malaysia albedo pattern changes. The pattern recognition and changes will be useful for variety of environmental and climate monitoring researches such as carbon budgeting and aerosol mapping. The 10 years (2000-2009) MODIS satellite images were used for the analyses and interpretation. These images were being processed using ERDAS Imagine remote sensing software, ArcGIS 9.3, the 6S code for atmospherical calibration and several MODIS tools (MRT, HDF2GIS, Albedo tools). There are several methods for time-series analyses were explored, this paper demonstrates trends and seasonal time-series analyses using converted HDF format MODIS MCD43A3 albedo land product. The results revealed significance changes of albedo percentages over the past 10 years and the pattern with regards to Malaysia's nebulosity index (NI) and aerosol optical depth (AOD). There is noticeable trend can be identified with regards to its maximum and minimum value of the albedo. The rise and fall of the line graph show a similar trend with regards to its daily observation. The different can be identified in term of the value or percentage of rises and falls of albedo. Thus, it can be concludes that the temporal behavior of land surface albedo in Malaysia have a uniform behaviours and effects with regards to the local monsoons. However, although the average albedo shows linear trend with nebulosity index, the pattern changes of albedo with respects to the nebulosity index indicates that there are external factors that implicates the albedo values, as the sky conditions and its diffusion plotted does not have uniform trend over the years, especially when the trend of 5 years interval is examined, 2000 shows high negative linear trend relationship (R2 = 0.8017), while in 2005 the R2 is 0.4428 of positive linear trend relationship and in 2009 its negative relationship has remarkably change when the R2 is 0.9663 according to the second order polynomial trend line.

Salleh, S. A.; Abd Latif, Z.; Mohd, W. M. N. Wan; Chan, A.

2012-07-01

128

Multivariate Time Series Similarity Searching  

PubMed Central

Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches.

Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng

2014-01-01

129

Algorithm for Compressing Time-Series Data  

NASA Technical Reports Server (NTRS)

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

130

Data compression for satellite images  

NASA Technical Reports Server (NTRS)

An efficient data compression system is presented for satellite pictures and two grey level pictures derived from satellite pictures. The compression techniques take advantages of the correlation between adjacent picture elements. Several source coding methods are investigated. Double delta coding is presented and shown to be the most efficient. Both predictive differential quantizing technique and double delta coding can be significantly improved by applying a background skipping technique. An extension code is constructed. This code requires very little storage space and operates efficiently. Simulation results are presented for various coding schemes and source codes.

Chen, P. H.; Wintz, P. A.

1976-01-01

131

Hydrodynamic analysis of time series  

NASA Astrophysics Data System (ADS)

It was proved that balance equations for systems with corpuscular structure can be derived if a kinematic description by piece-wise analytic functions is available [1]. For example, the hydrodynamic equations for one-dimensional systems of inelastic particles, derived in [2], were used to prove the inconsistency of the Fourier law of heat with the microscopic structure of the system. The hydrodynamic description is also possible for single particle systems. In this case, averages of physical quantities associated with the particle, over a space-time window, generalizing the usual ``moving averages'' which are performed on time intervals only, were shown to be almost everywhere continuous space-time functions. Moreover, they obey balance partial differential equations (continuity equation for the 'concentration', Navier-Stokes equation, a. s. o.) [3]. Time series can be interpreted as trajectories in the space of the recorded parameter. Their hydrodynamic interpretation is expected to enable deterministic predictions, when closure relations can be obtained for the balance equations. For the time being, a first result is the estimation of the probability density for the occurrence of a given parameter value, by the normalized concentration field from the hydrodynamic description. The method is illustrated by hydrodynamic analysis of three types of time series: white noise, stock prices from financial markets and groundwater levels recorded at Krauthausen experimental field of Forschungszentrum Jülich (Germany). [1] C. Vamo?, A. Georgescu, N. Suciu, I. Turcu, Physica A 227, 81-92, 1996. [2] C. Vamo?, N. Suciu, A. Georgescu, Phys. Rev E 55, 5, 6277-6280, 1997. [3] C. Vamo?, N. Suciu, W. Blaj, Physica A, 287, 461-467, 2000.

Suciu, N.; Vamos, C.; Vereecken, H.; Vanderborght, J.

2003-04-01

132

Principal component analysis based classification of settlements in satellite images  

Microsoft Academic Search

The objective of this research is to use satellite images for the classification and identification of settlements. Satellite images are used in this research. A wide area is covered in a single satellite image and it contains enormous information therefore satellite images can be used for many useful purposes, like classification of objects and land cover classes, change detection and

Abida Najab; Irshad Khan; Farooq Ahmad

2009-01-01

133

It's time for a crisper image of the Face of the Earth: Landsat and climate time series for massive land cover & climate change mapping at detailed resolution.  

NASA Astrophysics Data System (ADS)

Combining climate dynamics and land cover at a relative coarse resolution allows a very interesting approach to global studies, because in many cases these studies are based on a quite high temporal resolution, but they may be limited in large areas like the Mediterranean. However, the current availability of long time series of Landsat imagery and spatially detailed surface climate models allow thinking on global databases improving the results of mapping in areas with a complex history of landscape dynamics, characterized by fragmentation, or areas where relief creates intricate climate patterns that can be hardly monitored or modeled at coarse spatial resolutions. DinaCliVe (supported by the Spanish Government and ERDF, and by the Catalan Government, under grants CGL2012-33927 and SGR2009-1511) is the name of the project that aims analyzing land cover and land use dynamics as well as vegetation stress, with a particular emphasis on droughts, and the role that climate variation may have had in such phenomena. To meet this objective is proposed to design a massive database from long time series of Landsat land cover products (grouped in quinquennia) and monthly climate records (in situ climate data) for the Iberian Peninsula (582,000 km2). The whole area encompasses 47 Landsat WRS2 scenes (Landsat 4 to 8 missions, from path 197 to 202 and from rows 30 to 34), and 52 Landsat WRS1 scenes (for the previous Landsat missions, 212 to 221 and 30 to 34). Therefore, a mean of 49.5 Landsat scenes, 8 quinquennia per scene and a about 6 dates per quinquennium , from 1975 to present, produces around 2376 sets resulting in 30 m x 30 m spatial resolution maps. Each set is composed by highly coherent geometric and radiometric multispectral and multitemporal (to account for phenology) imagery as well as vegetation and wetness indexes, and several derived topographic information (about 10 Tbyte of data). Furthermore, on the basis on a previous work: the Digital Climatic Atlas of the Iberian Peninsula, spatio-temporal surface climate data has been generated with a monthly resolution (from January 1950 to December 2010) through a multiple regression model and residuals spatial interpolation using geographic variables (altitude, latitude and continentality) and solar radiation (only in the case of temperatures). This database includes precipitation, mean minimum and mean maximum air temperature and mean air temperature, improving the previous one by using the ASTER GDEM at 30 m spatial resolution, by deepening to a monthly resolution and by increasing the number of meteorological stations used, representing a total amount of 0.7 Tbyte of data. An initial validation shows accuracies higher than 85 % for land cover maps and an RMS of 1.2 ºC, 1.6 ºC and 22 mm for mean and extreme temperatures, and for precipitation, respectively. This amount of new detailed data for the Iberian Peninsula framework will be used to study the spatial direction, velocity and acceleration of the tendencies related to climate change, land cover and tree line dynamics. A global analysis using all these datasets will try to discriminate the climatic signal when interpreted together with anthropogenic driving forces. Ultimately, getting ready for massive database computation and analysis will improve predictions for global models that will require of the growing high-resolution information available.

Pons, Xavier; Miquel, Ninyerola; Oscar, González-Guerrero; Cristina, Cea; Pere, Serra; Alaitz, Zabala; Lluís, Pesquer; Ivette, Serral; Joan, Masó; Cristina, Domingo; Maria, Serra Josep; Jordi, Cristóbal; Chris, Hain; Martha, Anderson; Juanjo, Vidal

2014-05-01

134

Measuring Earthquakes from Optical Satellite Images  

Microsoft Academic Search

Syst me pour l Observation de la Terre images are used to map ground displacements induced by earthquakes. Deformations (offsets) induced by stereoscopic effect and roll, pitch, and yaw of satellite and detector artifacts are estimated and compensated. Images are then resampled in a cartographic projection with a low-bias interpolator. A subpixel correlator in the Fourier domain provides two-dimensional offset

Nadége van Puymbroeck; Rémi Michel; Renaud Binet; Jean-Philippe Avouac; Jean Taboury

2000-01-01

135

Radar imaging of satellites at meter wavelengths  

Microsoft Academic Search

Earth-based radar imaging of orbiting satellites at frequencies below about 1 GHz requires compensation of the dispersive effects of the ionosphere. Without the appropriate compensation, image resolution is limited to about 10--100 m at radar frequencies of 300 MHz, depending on ionospheric conditions. With compensation, the resolution expected in the absence of ionospheric dispersion can be achieved. For stable ionospheric

Arlen Dale Schmidt

2000-01-01

136

Integrated Ground-Based LiDAR and Global Fiducials Program Satellite Imagery Time Series Analysis of the Terminus of Bering Glacier, Alaska During the 2008-2011 Surge  

NASA Astrophysics Data System (ADS)

Satellite imagery from the Global Fiducials Program (GFP: classified satellite imagery released to the general public for science use: http://gfl.usgs.gov) tracked the 2008-2011 surge of the Bering Glacier, the largest and longest glacier in North America. The terminus displacement began in late 2010, with maximum velocities of greater than 20 meters per day by late January 2011, as measured using feature tracking with GFP imagery. By July, the velocities had decreased to less than 10 m/d. We used the GFP imagery to locate three helicopter accessible targets on the terminus of the Bering Glacier to collect high-resolution (0.5-4 cm spot spacing) 4D time-series tripod/terrestrial LiDAR (T-LiDAR) data. During the week of July 24, 2011 we collected hourly and daily T-LiDAR data to resolve spatially and temporally varied advancement rates at each of the sites. The first site was located on the west side of Tashalich arm on the western side of the Bering Lobe terminus proximal to the region where the maximum GFP velocities had previously been measured. Using the T-LiDAR data, we found that the terminus advanced 5.4 m over 76 hours of observation. The hourly advancement rates for the same location are a very consistent 4.2 cm/h during our daylight hours of observation (0900-1800 local) and when daily rate are extrapolated to the full 76 hours, we should have measured 3.2 m of horizontal displacement: this is a discrepancy between the total and hourly measured displacements of an additional 2.2 m of motion during the night and early morning hours (1800-0900 local). The additional motion may be explained by accelerated terminus velocity associated with daily thermal heating and resulting melt. Motion may also be explained by rain on the second day of the survey that "lubricated" the glacier bed thereby allowing it to advance at a faster velocity. The second site was on Arrowhead Island, located on the eastern side of the terminus where the vertical relieve of the glacier terminus was significantly lower than at the fist site that had the elevated velocities. Here the T-LiDAR data measured a much slower advancement rate of 0-2.0 cm/h. The third T-LiDAR site also on Arrowhead Island, approximately 300 meters to the west, measured 7.3 cm/h of motion where the Bering Glacier entered Vitus Lake. We will compare the T-LiDAR and GFP velocities with GPS hourly data collected within the nearby Glacier Ablation Sensor Systems that are deployed on the glacier's surface to assess if the daily variations at the terminus are observed elsewhere during this surge event.

Bawden, G. W.; Molnia, B. F.; Howle, J.; Bond, S.; Angeli, K.; Shuchman, R. A.

2012-12-01

137

The MROI's capabilities for imaging geosynchronous satellites  

NASA Astrophysics Data System (ADS)

Interferometry provides the only practicable way to image meter-scale structure in geosynchronous satellites. This capability represents a unique commercial opportunity for astronomical interferometry, but to date no interferometer has been able to make an image of such a satellite. We discuss the challenges of imaging these objects and present results of sensitivity calculations and imaging simulations which show that the Magdalena Ridge Observatory Interferometer is likely to be well-suited to this application. Our preliminary results suggest that a significant proportion of GEO targets may be accessible and that it may be possible to routinely extract key satellite diagnostics with an imaging capability that would be able to distinguish, for example, 70 cm features on a 5-meter satellite bus and payload, 30 cm features on a 2-meter satellite bus or similarly sized structure, as well as precise quantitative information on much larger structures such as 10 m long solar panels. Optimised observation and data reduction strategies are likely to allow these limits to be improved in due course.

Young, John; Haniff, Christopher; Buscher, David; Creech-Eakman, Michelle; Payne, Ifan; Jurgenson, Colby; Romero, Van

2012-07-01

138

High Resolution Satellite Image Orientation Models  

Microsoft Academic Search

\\u000a A few years ago high resolution satellite imagery became available to a limited number of government and defense agencies\\u000a that managed such imagery with highly sophisticated software and hardware tools. Such images became available to civil users\\u000a in 1999 with the launch of Ikonos, the first civil satellite offering a spatial resolution of 1 m. Since then other high resolution

Mattia Crespi; Francesca Fratarcangeli; Francesca Giannone; Francesca Pieralice

139

Aerial photographs and satellite images  

USGS Publications Warehouse

Because photographs and images taken from the air or from space are acquired without direct contact with the ground, they are referred to as remotely sensed images. The U.S. Geological Survey (USGS) has used remote sensing from the early years of the 20th century to support earth science studies and for mapping purposes.

U.S. Geological Survey

1995-01-01

140

Estimation of vertical surface directions in outdoor environments on changes of the incident angle of sunlight in time series of observation images  

NASA Astrophysics Data System (ADS)

This paper proposes a method to estimate the directions of vertical surfaces in outdoor environments based on the changes of the incident angle of the sunlight in a series of observation images caught with a fixed camera. This method uses an interaction between a time when an incidence angle of the sunlight for every direction of a vertical surface is at the minimum and a time when a brightness of every pixel in the series of observation image is at the maximum. This method is not robust about the weather changes. This paper introduces the method integrating multi-day estimations. With this multi-day integration, the proposed direction estimation method is robust about the weather changes. And then, this paper shows experiments on real out-door images.

Aoki, Kyota; Yamamura, Teppei

2014-04-01

141

Exploration of Subsidence Estimation by Persistent Scatterer InSAR on Time Series of High Resolution TerraSAR-X Images  

Microsoft Academic Search

Ground subsidence is a major concern for land use planning and engineering risk assessment. This paper explores subsidence detection by the persistent scatterer (PS) interfer- ometric synthetic aperture radar (InSAR) technique using the multitemporal high resolution spaceborne SAR images. We first describe the mathematical models and the data reduction proce- dures of the PS solution. The experiments of subsidence detection

Guoxiang Liu; Hongguo Jia; Rui Zhang; Huixin Zhang; Hongliang Jia; Bing Yu; Mingzhi Sang

2011-01-01

142

Analysis of Galileo Style Geostationary Satellite Imaging: Image Reconstruction.  

National Technical Information Service (NTIS)

Earlier this year DARPA announced the Galileo project, with the basic conceptual idea of using optical interferometry to combine the light from two telescopes, with one of them being movable, to image geostationary satellites. This project aims at obtaini...

A. M. Jorgensen D. Mozurkewich H. R. Schmitt J. T. Armstrong S. R. Restaino

2012-01-01

143

Reducing uncertainty on satellite image classification through spatiotemporal reasoning  

NASA Astrophysics Data System (ADS)

The natural habitat constantly endures both inherent natural and human-induced influences. Remote sensing has been providing monitoring oriented solutions regarding the natural Earth surface, by offering a series of tools and methodologies which contribute to prudent environmental management. Processing and analysis of multi-temporal satellite images for the observation of the land changes include often classification and change-detection techniques. These error prone procedures are influenced mainly by the distinctive characteristics of the study areas, the remote sensing systems limitations and the image analysis processes. The present study takes advantage of the temporal continuity of multi-temporal classified images, in order to reduce classification uncertainty, based on reasoning rules. More specifically, pixel groups that temporally oscillate between classes are liable to misclassification or indicate problematic areas. On the other hand, constant pixel group growth indicates a pressure prone area. Computational tools are developed in order to disclose the alterations in land use dynamics and offer a spatial reference to the pressures that land use classes endure and impose between them. Moreover, by revealing areas that are susceptible to misclassification, we propose specific target site selection for training during the process of supervised classification. The underlying objective is to contribute to the understanding and analysis of anthropogenic and environmental factors that influence land use changes. The developed algorithms have been tested upon Landsat satellite image time series, depicting the National Park of Ainos in Kefallinia, Greece, where the unique in the world Abies cephalonica grows. Along with the minor changes and pressures indicated in the test area due to harvesting and other human interventions, the developed algorithms successfully captured fire incidents that have been historically confirmed. Overall, the results have shown that the use of the suggested procedures can contribute to the reduction of the classification uncertainty and support the existing knowledge regarding the pressure among land-use changes.

Partsinevelos, Panagiotis; Nikolakaki, Natassa; Psillakis, Periklis; Miliaresis, George; Xanthakis, Michail

2014-05-01

144

Traffic Flow Estimation from Single Satellite Images  

NASA Astrophysics Data System (ADS)

Exploiting a special focal plane assembly of most satellites allows for the extraction of moving objects from only one multispectral satellite image. Push broom scanners as used on most earth observation satellites are composed of usually more than one CCD line - mostly one for multispectral and one for panchromatic acquisistion. Some sensors even have clearly separated CCD lines for different multispectral channels. Such satellites are for example WorldView-2 or RapidEye. During the Level-0-processing of the satellite data these bands get coregistered on the same ground level which leads to correct multispectral and exactly fitting pan images. But if objects are very high above the coregistering plane or are moving significantly in between the short acquisition time gap these objects get registered on different points in different channels. Measuring relative distances of these objects between these channels and knowing the acquisition time gap allows retrieving the speed of the objects or the height above the coregistering plane. In this paper we present our developed method in general for different satellite systems - namely RapidEye, WorldView-2 and the new Pléiades system. The main challenge in most cases is nevertheless the missing knowledge of the acquisition time gap between the different CCD lines and often even of the focal plane assembly. So we also present our approach to receive a coarse focal plane assembly model together with a most likely estimation of the acqusition time gaps for the different systems.

Krauß, T.; Stätter, R.; Philipp, R.; Bräuninger, S.

2013-09-01

145

United States Forest Disturbance Trends Observed Using Landsat Time Series  

NASA Technical Reports Server (NTRS)

Disturbance events strongly affect the composition, structure, and function of forest ecosystems; however, existing U.S. land management inventories were not designed to monitor disturbance. To begin addressing this gap, the North American Forest Dynamics (NAFD) project has examined a geographic sample of 50 Landsat satellite image time series to assess trends in forest disturbance across the conterminous United States for 1985-2005. The geographic sample design used a probability-based scheme to encompass major forest types and maximize geographic dispersion. For each sample location disturbance was identified in the Landsat series using the Vegetation Change Tracker (VCT) algorithm. The NAFD analysis indicates that, on average, 2.77 Mha/yr of forests were disturbed annually, representing 1.09%/yr of US forestland. These satellite-based national disturbance rates estimates tend to be lower than those derived from land management inventories, reflecting both methodological and definitional differences. In particular the VCT approach used with a biennial time step has limited sensitivity to low-intensity disturbances. Unlike prior satellite studies, our biennial forest disturbance rates vary by nearly a factor of two between high and low years. High western US disturbance rates were associated with active fire years and insect activity, while variability in the east is more strongly related to harvest rates in managed forests. We note that generating a geographic sample based on representing forest type and variability may be problematic since the spatial pattern of disturbance does not necessarily correlate with forest type. We also find that the prevalence of diffuse, non-stand clearing disturbance in US forests makes the application of a biennial geographic sample problematic. Future satellite-based studies of disturbance at regional and national scales should focus on wall-to-wall analyses with annual time step for improved accuracy.

Masek, Jeffrey G.; Goward, Samuel N.; Kennedy, Robert E.; Cohen, Warren B.; Moisen, Gretchen G.; Schleeweis, Karen; Huang, Chengquan

2013-01-01

146

Visualizing Multivariate Time Series Data to Detect Specific Medical Conditions  

PubMed Central

Efficient unsupervised algorithms for the detection of patterns in time series data, often called motifs, have been used in many applications, such as identifying words in different languages, detecting anomalies in ECG readings, and finding similarities between images. We present a process that creates a personalized multivariate time series representation—a Multivariate Time Series Amalgam (MTSA) — of physiological data and laboratory results that physicians can visually interpret. We then apply a technique that has demonstrated success with the interpretation of univariate data, named Symbolic Aggregate Approximation (SAX), to visualize patterns in the MTSAs that may differentiate between medical conditions such as renal and respiratory failure.

Ordonez, Patricia; desJardins, Marie; Feltes, Carolyn; Lehmann, Christoph U.; Fackler, James

2008-01-01

147

Visualizing multivariate time series data to detect specific medical conditions.  

PubMed

Efficient unsupervised algorithms for the detection of patterns in time series data, often called motifs, have been used in many applications, such as identifying words in different languages, detecting anomalies in ECG readings, and finding similarities between images. We present a process that creates a personalized multivariate time series representation a Multivariate Time Series Amalgam (MTSA) of physiological data and laboratory results that physicians can visually interpret. We then apply a technique that has demonstrated success with the interpretation of univariate data, named Symbolic Aggregate Approximation (SAX), to visualize patterns in the MTSAs that may differentiate between medical conditions such as renal and respiratory failure. PMID:18999033

Ordóñez, Patricia; DesJardins, Marie; Feltes, Carolyn; Lehmann, Christoph U; Fackler, James

2008-01-01

148

Nonlinear multivariate and time series analysis by neural network methods  

Microsoft Academic Search

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series

William W. Hsieh

2004-01-01

149

Impact of Sensor Degradation on the MODIS NDVI Time Series  

NASA Technical Reports Server (NTRS)

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, the impact of sensor degradation on trend detection was evaluated using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470 nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004 yr-1 decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends in MODIS NDVI over North America were consistentwith simulated results,with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in detection of NDVI trends.

Wang, Dongdong; Morton, Douglas Christopher; Masek, Jeffrey; Wu, Aisheng; Nagol, Jyoteshwar; Xiong, Xiaoxiong; Levy, Robert; Vermote, Eric; Wolfe, Robert

2012-01-01

150

Impact of Sensor Degradation on the MODIS NDVI Time Series  

NASA Technical Reports Server (NTRS)

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor degradation on trend detection using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004/yr decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in NDVI trends over vegetation.

Wang, Dongdong; Morton, Douglas; Masek, Jeffrey; Wu, Aisheng; Nagol, Jyoteshwar; Xiong, Xiaoxiong; Levy, Robert; Vermote, Eric; Wolfe, Robert

2011-01-01

151

Detecting weathered oil from the Deepwater Horizon incident in the wetlands of Barataria Bay, Louisiana, using a time series of AVIRIS imaging spectrometer data (Invited)  

NASA Astrophysics Data System (ADS)

From April to July 2010 oil flowed from the Macondo well into the Gulf of Mexico. The USA shoreline was contaminated along hundreds of kilometers of beach and coastal wetland ecosystems. As part of the emergency response to the incident, data from the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) were collected on several data in 2010, 2011, and 2012. These collections were made over impacted coasts from high altitude and low altitude aircraft, acquiring data with 4.4 to 20 meter pixel size, dependent on flight altitude. Spectroscopic methods were applied to these data to delineate the most heavily impacted areas, characterize the physical and chemical impacts of the oil on the ecosystem, and evaluate short-term vegetation response. The challenges to detecting oil contamination in this heavily vegetated area will be discussed, including requirements for atmospheric correction, impacts of clouds, discrimination of oil from spectrally similar materials, and the observed limit of detection.

Kokaly, R. F.; Couvillion, B. R.; Holloway, J. M.; Roberts, D. A.; Ustin, S.; Peterson, S. H.; Khanna, S.; Piazza, S.

2013-12-01

152

Knowledge-based analysis of satellite images  

NASA Astrophysics Data System (ADS)

This paper shows several working steps for updating the `Digitale Landschaftsmodell 200 (DLM 200)' using satellite images. It is based on a two-step approach: verification and classification. First the existing semantic model (DLM 200) is used for the knowledge based object oriented analysis of the satellite images. At the second stage the information gained from the first step serves to prove and update the DLM 200. Since the DLM 200 is produced by digitizing the map layers of the `Topographische Ubersichtskarte (TUK 200),' typical cartographic aspects have to be considered. Some examples illustrating these effects on a representative class of the DLM 200 are shown. After the determination of these geometric relations between the DLM 200 and the images the `knowledge,' based on the DLM 200, backs up the object based analysis of the satellite images. Image areas which do not fit the DLM 200 are examined at the second stage. The classification has to assign the changes detected in the course of the verification to appropriate classes of the DLM 200. This process uses the parameters of the image analysis as additional information.

Schilling, Klaus-Juergen; Voegtle, Thomas; Muessig, Peter

1994-08-01

153

Wavelet Analysis of the Bivariate Time Series of Transmittance and Reflectance of an Atmospheric Column  

NASA Astrophysics Data System (ADS)

In this investigation, collocated time series of narrowband 0.6 m atmospheric flux transmittance at the surface and bidirectional reflectance at the top-of-atmosphere are decomposed into distinct frequency bands, to investigate the time scale dependences of their variance and correlation. To this goal, we apply a multiresolution analysis based on the maximum overlap discrete wavelet transform and the Haar wavelet to 5 minute resolution measurements from two multifilter rotating shadowband radiometers operated at Cabauw, the Netherlands, and Heselbach, Germany, and to observations of the geostationary METEOSAT8 SEVIRI satellite imager operating in rapid scan mode. Both time series are best correlated when the satellite data are shifted by about 1 pixel or 6 km to the North, which is likely attributable to the parallax effect caused by the location of cloud tops above the surface and the slant satellite viewing geometry. While variations in transmittance and reflectance with periods longer than an hour are found to be highly anti-correlated, the correlation breaks down for shorter periods. For periods below one hour, the transmittance time series also exhibits significantly higher variance than the reflectance. The larger extent of the satellite pixel (6 Ã- 3km2) versus the point-nature of the ground measurements is proposed as an explanation. Due to the small contributions of high frequency variability to the total variance of the reflectance, aliasing effects due to the 5 minute repeat cycle of SEVIRI are expected to be small. Our findings have important implications for the evaluation of satellite estimates of surface solar irradiance with surface measurements. Temporal averaging of the surface measurements over a period of at least 40 minutes is recommended to exclude frequencies with higher variance in transmittance than in reflectance. Estimates from geostationary satellites should be averaged over an period equal to that used for averaging the surface measurement to obtain an optimal agreement.

Deneke, H. M.; Knap, W. H.; Simmer, C.

2009-04-01

154

Quantifying the Physical Composition of Urban Morphology throughout Wales by analysing a Time Series (1989-2011) of Landsat TM/ETM+ images and Supporting GIS data  

NASA Astrophysics Data System (ADS)

Knowledge of impervious surface areas (ISA) and on their changes in magnitude, location, geometry and morphology over time is significant for a range of practical applications and research alike from local to global scale. It is a key indicator of global environmental change and is also important parameter for urban planning and environmental resources management, especially within a European context due to the policy recommendations given to the European Commission by the Austrian Environment Agency in 2011. Despite this, use of Earth Observation (EO) technology in mapping ISAs within the European Union (EU) and in particular in the UK is inadequate. In the present study, selected study sites across Wales have been used to test the use of freely distributed EO data from Landsat TM/ETM+ sensors in retrieving ISA for improving the current European estimations of international urbanization and soil sealing. A traditional classifier and a linear spectral mixture analysis (LSMA) were both applied to a series of Landsat TM/ETM+ images acquired over a period spanning 22 years to extract ISA. Aerial photography with a spatial resolution of 0.4m, acquired over the summer period in 2005 was used for validation purposes. The Welsh study areas provided a unique chance to detect largely dispersed urban morphology within an urban-rural frontier context. The study also presents an innovative method for detecting clouds and cloud shadow layers, detected with an overall accuracy of around 97%. The process tree built and presented in this study is important in terms of moving forward into a biennial program for the Welsh Government and is comparable to currently existing products. This EO-based product also offers a much less subjectively static and more objectively dynamic estimation of ISA cover. Our methodology not only inaugurates the local retrieval of ISA for Wales but also meliorates the existing EU international figures, and expands relatively stationary 'global' US/China-centric ISA research. With the recent launch of Landsat 8, our study can also provide important input to efforts focusing towards the development of a global scale operational cost-effective and consistent long term monitoring of ISA based on EO technology.

Scott, Douglas; Petropoulos, George

2014-05-01

155

Satellite Imaging in the Study of Pennsylvania's Environmental Issues.  

ERIC Educational Resources Information Center

This document focuses on using satellite images from space in the classroom. There are two types of environmental satellites routinely broadcasting: (1) Polar-Orbiting Operational Environmental Satellites (POES), and (2) Geostationary Operational Environmental Satellites (GOES). Imaging and visualization techniques provide students with a better…

Nous, Albert P.

156

A neuromorphic approach to satellite image understanding  

NASA Astrophysics Data System (ADS)

Remote sensing satellite imagery provides high altitude, top viewing aspects of large geographic regions and as such the depicted features are not always easily recognizable. Nevertheless, geoscientists familiar to remote sensing data, gradually gain experience and enhance their satellite image interpretation skills. The aim of this study is to devise a novel computational neuro-centered classification approach for feature extraction and image understanding. Object recognition through image processing practices is related to a series of known image/feature based attributes including size, shape, association, texture, etc. The objective of the study is to weight these attribute values towards the enhancement of feature recognition. The key cognitive experimentation concern is to define the point when a user recognizes a feature as it varies in terms of the above mentioned attributes and relate it with their corresponding values. Towards this end, we have set up an experimentation methodology that utilizes cognitive data from brain signals (EEG) and eye gaze data (eye tracking) of subjects watching satellite images of varying attributes; this allows the collection of rich real-time data that will be used for designing the image classifier. Since the data are already labeled by users (using an input device) a first step is to compare the performance of various machine-learning algorithms on the collected data. On the long-run, the aim of this work would be to investigate the automatic classification of unlabeled images (unsupervised learning) based purely on image attributes. The outcome of this innovative process is twofold: First, in an abundance of remote sensing image datasets we may define the essential image specifications in order to collect the appropriate data for each application and improve processing and resource efficiency. E.g. for a fault extraction application in a given scale a medium resolution 4-band image, may be more effective than costly, multispectral, very high resolution imagery. Second, we attempt to relate the experienced against the non-experienced user understanding in order to indirectly assess the possible limits of purely computational systems. In other words, obtain the conceptual limits of computation vs human cognition concerning feature recognition from satellite imagery. Preliminary results of this pilot study show relations between collected data and differentiation of the image attributes which indicates that our methodology can lead to important results.

Partsinevelos, Panagiotis; Perakakis, Manolis

2014-05-01

157

Antarctica: measuring glacier velocity from satellite images  

SciTech Connect

Many Landsat images of Antarctica show distinctive flow and crevasse features in the floating part of ice streams and outlet glaciers immediately below their grounding zones. Some of the features, which move with the glacier or ice stream, remain visible over many years and thus allow time-lapse measurements of ice velocities. Measurements taken from Landsat images of features on Byrd Glacier agree well with detailed ground and aerial observations. The satellite-image technique thus offers a rapid and cost-effective method of obtaining average velocities, to a first order of accuracy, of many ice streams and outlet glaciers near their termini.

Lucchitta, B.K.; Ferguson, H.M.

1986-11-28

158

Antarctica: Measuring glacier velocity from satellite images  

USGS Publications Warehouse

Many Landsat images of Antarctica show distinctive flow and crevasse features in the floating part of ice streams and outlet glaciers immediately below their grounding zones. Some of the features, which move with the glacier or ice stream, remain visible over many years and thus allow time-lapse measurements of ice velocities. Measurements taken from Landsat images of features on Byrd Glacier agree well with detailed ground and aerial observations. The satellite-image technique thus offers a rapid and cost-effective method of obtaining average velocities, to a first order of accuracy, of many ice streams and outlet glaciers near their termini.

Lucchitta, B. K.; Ferguson, H. M.

1986-01-01

159

Application of Ray-Tracing to Satellite Image Understanding.  

National Technical Information Service (NTIS)

A Monte Carlo ray tracing scheme was developed for the simulation of image formation processes in satellite images. It consists of an object-oriented ray tracer which includes satellite-specific models for exterior and interior orientation, empirical mode...

J. Muller M. Dalton

1988-01-01

160

Using MODIS time series for burn area mapping  

NASA Astrophysics Data System (ADS)

Wildfire significantly impacts forest ecosystems throughout the world. At the regional scale, wildfire affects a wide range of ecological, economic and social values related to forests. At the global scale, forest fire emissions of greenhouse gases, particulates and aerosols emissions into the atmosphere, direct and significantly impacts on atmospheric and biogeochemical cycles and the Earth radiative budget. The assessment of the timing and spatial extent of biomass burning, as needed for different tasks, is a mission that nowadays is only affordable using remote sensing techniques. Since forest fires are a major cause of surface change mainly due to vegetation combustion, burn are mapping is a task that can be achieved as a change detection process. The present study describes an algorithm developed to map fire-affected areas at regional scale (Spain) using MODIS (MODerate resolution Imaging Spectroradiometer) time series data. In particular, we used MODIS surface reflectance data (MOD09A product) as well as MODIS hotspot data for two fires seasons. Burned area maps as resulted from this work were compared to official fire statistics and perimeters from the Spanish Ministry of Environment. Results were also tested against burns perimeters as derived from finer spatial resolution satellite images. Reached results showed that this method would be of great interest at regional to national scales, since it was proved to be quick, accurate and cost-effective.

Huesca, M.; Palacios-Orueta, A.; Merino-de-Miguel, S.; Litago, J.

2009-04-01

161

Time Series Datamining: Identifying Temporal Patterns For Characterizationand Prediction Of Time Series Events  

Microsoft Academic Search

A new framework for analyzing time series data called Time Series Data Mining(TSDM) is introduced. This framework adapts and innovates data mining concepts toanalyzing time series data. In particular, it creates a set of methods that reveal hiddentemporal patterns that are characteristic and predictive of time series events. Traditionaltime series analysis methods are limited by the requirement of stationarity of

Richard J. Povinelli

1999-01-01

162

Source positions time series generation and analysis  

NASA Astrophysics Data System (ADS)

Time series for more than 600 sources were calculated with using QUASAR software from VLBI data processing. Source positions for every sources were obtained from single series analysis by two ways - with fixed coordinates of all another sources with and without EOP estimation.Time series analysis is performed with covariation analysis technique. The attempt was made to propose the parameter which can be used for selection of stable and unstable sources from analysis of source positions time series.

Kurdubov, S. L.; Skurikhina, E.

2008-04-01

163

AMOS Observations of NASA's IMAGE Satellite  

NASA Astrophysics Data System (ADS)

NASA's Imager for Magnetopause-to-Aurora Global Exploration (IMAGE) satellite stopped transmitting telemetry to ground stations in December 2005, after functioning for more than 5 years on Earth orbit. Before this loss of telemetry, the IMAGE satellite actively maintained a spin-stabilized attitude with spin axis perpendicular to the orbital plane and a nominal rotation rate of about 0.5 rpm. The spinning action served to both stabilize the satellite and keep the 250 m-long radial wire antennas of the satellite's Radio Plasma Imager under tension perpendicular to the satellite spin axis. After loss of telemetry, it was unclear whether the spacecraft remained in this spin-stabilized configuration, or whether it could continue to receive and execute up- linked commands. In late January and early February of 2006 the AMOS 3.6m Advanced Electro Optical System (AEOS) conducted an initial set of observations in an effort to help diagnose the state of the unresponsive spacecraft. The AEOS observations employed the Visible Imager (VisIm) instrument in the photometric I-band as well as the long-wavelength infrared (LWIR) imager. The wide field-of-view VisIm images clearly show the long radial wire antennas glinting in reflected sunlight during each revolution of the spinning spacecraft, creating a photometric signature characterized by large amplitude periodic variations. Analysis of concurrent AEOS LWIR observations indicates radiometric temperatures ranging from 250 to 310 Kelvin, with the higher temperatures occurring when more of the continuously-sunlit portions of the spacecraft were observable from AMOS. A detailed periodic analysis of the VisIm photometric signatures acquired on 2006 day-of-year (DOY) 028, 031 and 034 indicates a spin axis orientation consistent with that reported in the last telemetry down-linked from the satellite approximately seven weeks earlier. However, the periodic variations indicate a satellite spin rate of 0.4741 ° 0.0005 rpm, measurably slower than the last known spin rate from down-linked telemetry. Shortly after these initial AEOS observations were conducted, the NASA IMAGE satellite team up-linked commands to the spacecraft to increase the spin rate up to 0.52 rpm in order to test if the spacecraft could receive and execute such commands. Subsequent AMOS observations conducted 2006 DOY 150, however, did not show evidence of an increased spin rate, but instead indicated a further reduction down to 0.4709 ° 0.0004 rpm. The AEOS observations therefore confirm that the IMAGE spacecraft has lost the ability to receive and/or execute up-linked commands, and indicates that, between 2005-DEC-12 and 2006-MAY-30, the spacecraft's spin axis orientation remained stable to within detection limits but the spin rate declined at a rate of (3.1 ± 0.3) × 10-5 rpm/day, a deceleration most likely caused by magnetically-induced environmental torques.

Hall, D.; Africano, J.; Archambeault, D.; Birge, B.; Witte, D.; Kervin, P.

164

Measuring statistical dependences in a time series  

Microsoft Academic Search

We propose two methods to measure all (linear and nonlinear) statistical dependences in a stationary time series. Presuming ergodicity, the measures can be obtained from efficient numerical algorithms.

Bernd Pompe

1993-01-01

165

Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information  

NASA Astrophysics Data System (ADS)

The accurate and timely information of crop area is vital for crop production and food security. In this study, the Enhanced Vegetation Index (EVI) data from MODerate resolution Imaging Spectroradiometer (MODIS) integrated crop phenological information was used to estimate the maize cultivated area over a large scale in Northeast China. The fine spatial resolution China's Environment Satellite (HJ-1 satellite) images and the support vector machine (SVM) algorithm were employed to discriminate distribution of maize in the reference area. The mean MODIS-EVI time series curve of maize was extracted in the reference area by using multiple periods MODIS-EVI data. By analysing the temporal shift of crop calendars from northern to southern parts in Northeast China, the lag value was derived from phenological data of twenty-one agro-meteorological stations; here integrating with the mean MODIS-EVI time series image of maize, a standard MODIS-EVI time series image of maize was obtained in the whole study area. By calculating mean absolute distances (MAD) map between standard MODIS-EVI image and mean MODIS-EVI time series images, and setting appropriate thresholds in three provinces, the maize cultivated area was extracted in Northeast China. The results showed that the overall classification accuracy of maize cultivated area was approximately 79%. At the county level, the MODIS-derived maize cultivated area and statistical data were well correlated (R2 = 0.82, RMSE = 283.98) over whole Northeast China. It demonstrated that MODIS-EVI time series data integrated with crop phenological information can be used to improve the extraction accuracy of crop cultivated area over a large scale.

Zhang, Jiahua; Feng, Lili; Yao, Fengmei

2014-08-01

166

Data Mining of Modis Time-Series to Investigate Land Use Conversion to Sugarcane  

NASA Astrophysics Data System (ADS)

Human activities can cause land use and land cover changes (LULCC) that vary over space and time. Remote sensing satellite imagery provides a valuable tool to monitor global environmental changes. The MODIS sensors on board of Terra and Aqua satellites have provided high quality images that have been widely used in LULCC studies throughout the world. The MODIS sensors acquire images over the entire Earth on almost daily bases generating a huge amount of data that require powerful computational tools provided with adequate statistical methods for information extraction and analyses. Recently, metrics extracted from time-series of remotely sensed data have been used to describe the behavior of these time-series (mean, minimum and maximum values, and amplitude). These metrics are then used to define the input attributes in the process of data mining (classification) of the time-series. Within this context the objective of the present work is to apply data mining techniques to automatically classify time-series of MODIS vegetation index (EVI-2) in order to provide the history of land use conversion of sugarcane expansion in the South-Central region of Brazil, over a period of nine crop years from 2000 to 2009. Firstly, 1035 MODIS pixels were randomly selected over recent sugarcane expansion fields. The time-series for each of these pixels were visually interpreted assigning, to each crop year, one of the following classes: pasture, annual crop, citrus, forest and sugarcane. Multitemporal Landsat images were used to assist the visual interpretation procedure. After visual interpretation two thirds of the pixels were used to train the decision tree classifier implemented in WEKA software. The remaining third of pixels was used for accuracy assessment. Considering the crop year 2000/01, the classification result showed that about 70% and 25% of sugarcane expansion was over pasture and annual crop land, respectively. The overall accuracy index was greater than 80% indicating that data mining is a promising tool to analyses MODIS time-series for land use and land cover change studies.

Mello, M. P.; Alves de Aguiar, D.; Rudorff, B.

2011-12-01

167

Monitoring of wetlands Ecosystems using satellite images  

NASA Astrophysics Data System (ADS)

Wetlands are very sensitive ecosystems, functioning as habitat for many organisms. Protection and regeneration of wetlands has been the crucial importance in ecological research and in nature conservation. Knowledge on biophysical properties of wetlands vegetation retrieved from satellite images will enable us to improve monitoring of these unique areas, very often impenetrable. The study covers Biebrza wetland situated in the Northeast part of Poland and is considered as Ramsar Convention test site. The research aims at establishing of changes in biophysical parameters as the scrub encroachment, lowering of the water table, and changes of the farming activity caused ecological changes at these areas. Data from the optical and microwave satellite images collected for the area of Biebrza marshland ecosystem have been analysed and compared with the detailed soil-vegetation ground measurements conducted in conjunction with the overflights. Satellite data include Landsat ETM, ERS-2 ATSR and SAR, SPOT VEGETATION, ENVISAT MERIS and ASAR, and NOAA AVHRR. From the optical data various vegetation indices have been calculated, which characterize the vegetation surface roughness, its moisture conditions and stage of development. Landsat ETM image has been used for classification of wetlands vegetation. For each class of vegetation various moisture indices have been developed. Ground data collected include wet and dry biomass, LAI, vegetation height, and TDR soil moisture. The water cloud model has been applied for retrieval of soil vegetation parameters taking into account microwave satellite images acquired at VV, HV and HH polarisations at different viewing angles. The vegetation parameters have been used for to distinguish changes, which occurred at the area. For each of the vegetation class the soil moisture was calculated from microwave data using developed algorithms. Results of this study will help mapping and monitoring wetlands with the high spatial and temporal resolution for better management and protection of this ecosystems. The research has been conducted under AO ID-122 ESA Project

Dabrowska-Zielinska, K.; Gruszczynska, M.; Yesou, H.; Hoscilo, A.

168

Estimating seasonal evapotranspiration from temporal satellite images  

USGS Publications Warehouse

Estimating seasonal evapotranspiration (ET) has many applications in water resources planning and management, including hydrological and ecological modeling. Availability of satellite remote sensing images is limited due to repeat cycle of satellite or cloud cover. This study was conducted to determine the suitability of different methods namely cubic spline, fixed, and linear for estimating seasonal ET from temporal remotely sensed images. Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model in conjunction with the wet METRIC (wMETRIC), a modified version of the METRIC model, was used to estimate ET on the days of satellite overpass using eight Landsat images during the 2001 crop growing season in Midwest USA. The model-estimated daily ET was in good agreement (R2 = 0.91) with the eddy covariance tower-measured daily ET. The standard error of daily ET was 0.6 mm (20%) at three validation sites in Nebraska, USA. There was no statistically significant difference (P > 0.05) among the cubic spline, fixed, and linear methods for computing seasonal (July–December) ET from temporal ET estimates. Overall, the cubic spline resulted in the lowest standard error of 6 mm (1.67%) for seasonal ET. However, further testing of this method for multiple years is necessary to determine its suitability.

Singh, Ramesh K.; Liu, Shuguang; Tieszen, Larry L.; Suyker, Andrew E.; Verma, Shashi B.

2012-01-01

169

Image-Based Attitude Control of a Remote Sensing Satellite  

Microsoft Academic Search

This paper deals with the image-based control of a satellite for remote sensing. Approach is demonstrated by simulation where\\u000a the position of the satellite is obtained with the Simplified General Perturbations Version 3 model and its orientation by\\u000a simulating its dynamic and kinematic models. For a known position and orientation of the satellite the images are obtained\\u000a using the satellite’s

Gregor Klan?ar; Sašo Blaži?; Drago Matko

170

Trends and Cycles in Macroeconomic Time Series  

Microsoft Academic Search

Two structural time series models for annual observations are constructed in terms of trend, cycle, and irregular components. The models are then estimated via the Kalman filter using data on five U.S. macroeconomic time series. The results provide some interesting insights into the dynamic structure of the series, particularly with respect to cyclical behavior. At the same time, they illustrate

A. C. Harvey

1985-01-01

171

ARMA Model identification of hydrologic time series  

Microsoft Academic Search

In recent years, ARMA models have become popular for modeling geophysical time series in general and hydrologic time series in particular. The identification of the appropriate order of the model is an important stage in ARMA modeling. Such model identification is generally based on the autocorrelation and partial autocorrelation functions, although recently improvements have been obtained using the inverse autocorrelation

J. D. Salas; J. T. B. Obeysekera

1982-01-01

172

Linear Relations in Time Series Models. II  

ERIC Educational Resources Information Center

An asymptotic theory is developed for a new time series model introduced in TM 502 289. An algorithm for computing estimates of the parameters of this time series model is given, and it is shown that these estimators are asymptotically efficient in that they have the same asymptotic distribution as the maximum likelihood estimators. (Author/RC)

Rennie, Robert R.; Villegas, C.

1976-01-01

173

Linear Relations in Time Series Models. I.  

ERIC Educational Resources Information Center

A multiple time series is defined as the sum of an autoregressive process on a line and independent Gaussian white noise or a hyperplane that goes through the origin and intersects the line at a single point. This process is a multiple autoregressive time series in which the regression matrices satisfy suitable conditions. For a related article…

Villegas, C.

1976-01-01

174

Time Series Modeling, Spectral Analysis, and Forecasting.  

National Technical Information Service (NTIS)

A strategy for building models for an observed time series is presented in this paper. We seek to fit time domain models which can be interpreted in terms of trend and seasonal components, provide forecasts, and provide spectral estimators. Our time serie...

E. Parzen

1979-01-01

175

Generalized Relevance LVQ for Time Series  

Microsoft Academic Search

An application of the recently proposed generalized relevance learn- ing vector quantization (GRLVQ) to the analysis and modeling of time series data is presented. We use GRLVQ for two tasks: first, for obtaining a phase space embedding of a scalar time series, and second, for short term and long term data prediction. The proposed embedding method is tested with a

Marc Strickert; Thorsten Bojer; Barbara Hammer

2001-01-01

176

Current Trends in Time Series Representation  

Microsoft Academic Search

Time series data generation has been exploded in almost every domain such as in business, industry, medicine, science or entertainment. Consequently, there is an increasing need for analysing efficiently the huge amount of this information either online or offline. The inherent characteristics of time series data, specifically, the high dimensionality, the high feature correlation and the large amounts of noise

Leonidas Karamitopoulos; Georgios Evangelidis

177

Time series anomaly detection using recessive subsequence  

Microsoft Academic Search

Time series arise frequently in many sciences and engineering application, including finance, digital audio, motion capture, network security, and transportation. In this work, we propose a technique for discovering anomalies in time series that takes advantages of the Symbolic Aggregate approXimation (SAX) technique and inspiration from a motif discovery algorithm. We use SAX to reduce the dimension of the time

Yonchanok Khaokaew; Sirikarn Pukkawanna

2012-01-01

178

Macintosh Program performs time-series analysis  

Microsoft Academic Search

A Macintosh computer program that can perform many time-series analysis procedures is now available on the Internet free of charge. Although AnalySeries was originally designed for paleoclimatic time series, it can be useful for most fields of Earth sciences. The program's graphical user interface allows easy access even for people unfamiliar with computer calculations. Previous versions of the program are

Didier Paillard; Laurent Labeyrie; Pascal Yiou

1996-01-01

179

Research perspectives for time series management systems  

Microsoft Academic Search

Empirical research based on time series is a data intensive activity that needs a data base management system (DBMS). We investigate the special properties a time series management system (TSMS) should have. We then show that currently available solutions and related research directions are not well suited to handle the existing problems. Therefore, we propose the development of a special

Werner Dreyer; Angelika Kotz Dittrich; Duri Schmidt

1994-01-01

180

Heuristic Analysis of Time Series Internal Structure  

Microsoft Academic Search

A method of analysis of Time Series Internal Structures based on Singular Spectrum Analysis is discussed. It has been shown that in the case when the Time Series contains deterministic additive components rank of the trajectory matrices equal to number of parameters of the components. Also it was proved that both eigen and factor vectors repeat shapes of the additive

Cihan Mert; Alexander Milnikov

2010-01-01

181

Finding Stationary Subspaces in Multivariate Time Series  

Microsoft Academic Search

Identifying temporally invariant components in complex multivariate time series is key to understanding the underlying dynamical system and predict its future behavior. In this Letter, we propose a novel technique, stationary subspace analysis (SSA), that decomposes a multivariate time series into its stationary and nonstationary part. The method is based on two assumptions: (a) the observed signals are linear superpositions

Paul von Bünau; Frank C. Meinecke; Franz C. Király; Klaus-Robert Müller

2009-01-01

182

Grouping Multivariate Time Series: A Case Study  

Microsoft Academic Search

We present a case study to demonstrate a process for grouping massive multivariate time series based on nonpara- metric statistical summaries aided by information visualization. We want a method that allows us to quickly find approximate groups in time series, both to identify typical aggregate behaviors and to find aberrant outliers. We use simple statistical summaries to capture the temporal

Tamraparni Dasu; Deborah F. Swayne; David Poole

183

Statistical criteria for characterizing irradiance time series.  

SciTech Connect

We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

2010-10-01

184

Coupling between time series: A network view  

NASA Astrophysics Data System (ADS)

Recently, the visibility graph has been introduced as a novel method for analyzing time series, which maps a time series to a complex network. In this paper we introduce a new algorithm of visibility, “cross-visibility”, which reveals the conjugation of two coupled time series. The correspondence between the two time series is mapped to a network, “the cross-visibility graph”, to demonstrate the correlation between them. We have applied the algorithm to several correlated and uncorrelated time series, generated by the linear stationary ARFIMA process, in order to better understand the results of the cross-visibility of empirical series. The comparison between the degree distribution of coupled and uncoupled (shuffled) series' networks demonstrates the emergence of super nodes (extremely high-degree nodes) in the uncoupled ones. Furthermore, we have applied the algorithm to real-world data from the financial trades of two companies and oil, and observed significant small-scale coupling in their dynamics.

Mehraban, S.; Shirazi, A. H.; Zamani, M.; Jafari, G. R.

2013-09-01

185

River flood events as natural tracer tests for investigating a coupled river-aquifer system: improved time-lapse 3D imaging of flow patterns by deconvolving ERT time-series  

NASA Astrophysics Data System (ADS)

We are investigating how temporal fluctuations in 3D apparent resistivity data can be used to image freshly infiltrated river water in an aquifer. To this end, we have installed 18 wells within a gravel aquifer in the vicinity of the losing river Thur in Switzerland. A sequence of ˜15,000 crosswell apparent resistivity measurements is acquired every 7 h. A neighboring river gauge and 14 loggers also record water table, temperature, and electrical resistivity of water. Following precipitation events, the river stage increases quickly (e.g., 2 m in 6 h) and the salinity of the river water decreases (e.g., 30%). The changing electrical characteristics of the infiltrating water can thus be used as a natural conservative tracer that we can track in space and time in the aquifer. The time-lapse ERT data are sensitive to variations in salinity and watertable height, with the relative contributions of these two opposing effects depending on time and electrode configuration. Initial time-lapse inversions of the raw data display strong artifacts related to the watertable fluctuations. Here, we focus on correcting the apparent resistivity data to avoid these effects. We assume that variations in the apparent resistivity for each electrode configuration can be predicted at all times through a convolution of unknown smoothly varying finite linear impulse responses (to be determined) with variations in the river stage and the electrical resistivity of the river water. Prior to deconvolution, the apparent resistivities of each time-lapse sequence are resampled to a common time. We also filter out the effects of long-term variations in temperature on the apparent resistivities. The transfer functions estimated through deconvolution allow us to estimate accurately the variations in the apparent resistivity data (the mean correlation coefficient cc is 0.92). The ERT data filtered for the watertable effect have an increased correlation with the time-series of the groundwater electrical resistivity (cc from 0.75 to 0.81) and are practically uncorrelated with those of the watertable (cc = -0.16). To test the generality of the estimated transfer functions, we use input signals outside the calibration period to predict the apparent resistivity time-series with overall satisfactory results. From data recorded within the calibration period, we extract time-series corresponding to a specific flooding event and perform time-lapse inversion of the filtered data. Time-series of the inverted electrical resistivity at different locations within the aquifer display reduced correlation with water table fluctuations (cc from -0.77 to -0.49 similar to the correlation of the input signals) and much higher correlation with the groundwater resistivity data (cc from 0.77 to 0.86). The time-lapse inversions reveal that a central 2-m-thick high resistivity part of the aquifer displays the largest resistivity time variations. We observe resistivity increases up to 10%, with the arrival peak moving at approximately 10 m/day.

Coscia, I.; Linde, N.; Greenhalgh, S. A.; Vogt, T.; Doetsch, J. A.; Green, A. G.

2010-12-01

186

Remote sensing of grazing effects on vegetation in northern Senegal using MODIS time series  

NASA Astrophysics Data System (ADS)

The effect of grazing on vegetation development in the Sahel is a much debated and researched issue. For monitoring grazing intensity remote sensing could be a powerful tool, providing ways to fill some of the unavoidable gaps in information for research in this field. Especially as instruments with moderate spatial resolution capabilities onboard polar orbiting platforms, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), are providing time series of data which are now long enough to allow statistically valid per-pixel examinations. A key issue for proper application of remote sensing observations is an in depth understanding of how variations in surface properties are captured by satellite sensors. In this context, the sensitivity of remote sensing data to the effects of grazing, or lack of grazing, has been little examined. This study examines time series of in situ vegetation measurements sampled under different grazing regimes at the Widou Thiengoly site in semi-arid Northern Senegal and compares these with several growing season parameters. These parameters are derived using the TIMESAT software from time series of MODIS Normalized Difference Vegetation Index (NDVI) product, with 16 day temporal and 250 meters spatial resolution. Areas excluded from grazing are found on average to produce more herbaceous biomass and experience a change in annual species composition, with a greater presence of higher fodder quality species. Furthermore the biomass production in these areas presents a higher correlation to annual rainfall than that observed in adjacent grazed areas. These noticeable differences are only subtly reflected in the growing season parameters derived from MODIS data; e.g. the higher herbaceous biomass and different species composition are not well captured by commonly used parameters such as amplitude or integral of the NDVI time series. In light of these results, conclusions should be made with caution if time series of NDVI metrics are used to examine grazing or the effects of grazing on biomass.

Olsen, J. L.; Miehe, S.; Ceccato, P.; Fensholt, R.

2013-12-01

187

Automatic satellite image georeferencing using a contour-matching approach  

Microsoft Academic Search

Multitemporal and multisatellite studies or comparisons between satellite data and local ground measurements require nowadays precise and automatic geometric correction of satellite images. This paper presents a fully automatic geometric correction system capable of georeferencing satellite images with high accuracy. An orbital prediction model, which provides initial earth locations, is combined with the proposed automatic contour-matching technique. This combination allows

Francisco Eugenio; Ferran Marqués

2003-01-01

188

Time Series Analysis of Data With Gaps  

NASA Astrophysics Data System (ADS)

The presence of gaps in the time series means that some of the standard methods of analysis cannot be used, but do not necessarily mean that the full information content cannot be retrieved. In addition, there exist analysis methods that effectively account for not only gaps in otherwise evenly sampled data, but also for the case where the sampling is at arbitrary times. I will review methods for computing correlation function, power spectra (both Fourier and Wavelet), structure functions, and time-scale and time-frequency distributions - all of these can be computed in but auto- (single time series) as well as cross- (two or more time series) modes.

Scargle, J.

2010-12-01

189

Medical image transmission via communication satellite: evaluation of ultrasonographic images.  

PubMed

As compared with terrestrial circuits, communication satellites possess superior characteristics such as wide area coverage, broadcasting functions, high capacity, and resistance to disasters. Utilizing the narrow band channel (64 kbps) of the stationary communication satellite JCSAT1 located at an altitude of 36,000 km above the equator, we investigated satelliterelayed dynamic medical images transmitted by video signals, using hepatic ultrasonography as a model. We conclude that the "variable playing speed transmission scheme" proposed by us is effective for the transmission of dynamic images in the narrow band channel. This promises to permit diverse utilization and applications for purposes such as the transmission of other types of ultrasonic images as well as remotely directed medical diagnosis and treatment. PMID:8916265

Suzuki, H; Horikoshi, H; Shiba, H; Shimamoto, S

1996-01-01

190

Transforming Time Series into Complex Networks  

NASA Astrophysics Data System (ADS)

We introduce transformations from time series data to the domain of complex networks which allow us to characterise the dynamics underlying the time series in terms of topological features of the complex network. We show that specific types of dynamics can be characterised by a specific prevalence in the complex network motifs. For example, low-dimensional chaotic flows with one positive Lyapunov exponent form a single family while noisy non-chaotic dynamics and hyper-chaos are both distinct. We find that the same phenomena is also true for discrete map-like data. These algorithms provide a new way of studying chaotic time series and equip us with a wide range of statistical measures previously not available in the field of nonlinear time series analysis.

Small, Michael; Zhang, Jie; Xu, Xiaoke

191

Time Series Model Identification by Estimating Information.  

National Technical Information Service (NTIS)

Statisticians, economists, and system engineers are becoming aware that to identify models for time series and dynamic systems, information theoretic ideas can plan a valuable (and unifying) role. This paper discusses how models for a univariate or multiv...

E. Parzen

1982-01-01

192

Advanced spectral methods for climatic time series  

USGS Publications Warehouse

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal- to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

Ghil, M.; Allen, M. R.; Dettinger, M. D.; Ide, K.; Kondrashov, D.; Mann, M. E.; Robertson, A. W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

2002-01-01

193

Time Series of the Biscuit Fire with Smoke  

NSDL National Science Digital Library

This animation contains a time series of print resolution still images showing the progression of the Biscuit fire with smoke plumes in Oregon during 2002. Fire locations are represented as particles that change color as the fire ages. The Biscuit fire burned 500,000 acres of forest in Oregon and Northern California during the summer and fall of 2002.

Starr, Cindy; Justice, Chris; Sohlberg, Robert; Gunther, Fred; North, William

2003-08-04

194

Time-Series-Cross-Section Methods  

Microsoft Academic Search

Time-series-cross-section (TSCS) data consist of comparable time series data observed on a variety of units. The paradigmatic applications are to the study of comparative politi-cal economy, where the units are countries (often the advanced industrial democracies) and where for each country we observe annual data on a variety of political and economic vari-ables. A standard question for such studies relates

Nathaniel Beck

195

Time series irreversibility: a visibility graph approach  

Microsoft Academic Search

We propose a method to measure real-valued time series irreversibility which combines two differ- ent tools: the horizontal visibility algorithm and the Kullback-Leibler divergence. This method maps a time series to a directed network according to a geometric criterion. The degree of irreversibility of the series is then estimated by the Kullback-Leibler divergence (i.e. the distinguishability) between the in and

Lucas Lacasa; Ángel M. Núñez; Édgar Roldán; Juan M. R. Parrondo; Bartolo Luque

2011-01-01

196

PCA-based Time Series Similarity Search  

Microsoft Academic Search

\\u000a We propose a novel approach in multivariate time series similarity search for the purpose of improving the efficiency of data\\u000a mining techniques without substantially affecting the quality of the obtained results. Our approach includes a representation\\u000a based on principal component analysis (PCA) in order to reduce the intrinsically high dimensionality of time series and utilizes\\u000a as a distance measure a

Leonidas Karamitopoulos; Georgios Evangelidis; Dimitris Dervos

197

InSAR time-series: Results from Kilauea volcano, Hawaii, and the Eastern California Shear Zone.  

NASA Astrophysics Data System (ADS)

With up to 100-150 SAR images acquired for many places since 1992 the InSAR technique has developed in the past years from the typical one-interferogram approach to time-series approaches relying on the simultaneous analysis of all available acquisitions. The advantage of the time-series methods are that (1) the variability of ground deformation with time can be resolved, (2) subtle deformation of the order of a few mm/yr can be recovered by averaging over long time periods, (3) phase contributions due to errors associated with the satellite orbits used for the processing can be largely eliminated. This is particularly important for imagery acquired by the Radarsat-1 satellite for which no precise orbit information is available. We present example of time-series analysis for the Eastern California Shear zone and for Hawaii. In the ECSZ we use InSAR to resolve the deformation across the Hunter Mountain fault. In Hawaii we use InSAR time series to illuminate the shallow plumbing system of Kilauea volcano. These examples illustrate the importance of frequent SAR acquisitions.

Amelung, F.; Gourmelen, N.; Baker, S.

2008-12-01

198

Double regions growing algorithm for automated satellite image mosaicking  

NASA Astrophysics Data System (ADS)

Feathering is a most widely used method in seamless satellite image mosaicking. A simple but effective algorithm - double regions growing (DRG) algorithm, which utilizes the shape content of images' valid regions, is proposed for generating robust feathering-line before feathering. It works without any human intervention, and experiment on real satellite images shows the advantages of the proposed method.

Tan, Yihua; Chen, Chen; Tian, Jinwen

2011-11-01

199

A new impulse-response method for estimating the distribution of global OH using satellite measurements of CO, a multi-year time series of fire emissions, and a chemical transport model  

NASA Astrophysics Data System (ADS)

Estimating the global abundance and regional distribution of hydroxyl radical (OH) is challenging. Past empirical approaches have used methyl chloroform, methane, 14CO and other alternatives as atmospheric tracers, drawing upon their well-quantified reaction rates with OH. Here we propose a new method that draws upon multi-year time series of fire emissions and column carbon monoxide (CO) observations. Several recent findings provide the foundation for the development of our method. First, global fires are sporadic in nature, with high emissions events distributed widely in northern, tropical, and southern regions as a result of year-to-year variability in both climate and land use processes. Second, these fire-induced ‘impulses’ in CO explain most of the interannual CO anomalies in both surface and column observations. For example, using the GEOS-CHEM model and the Global Fire Emissions Database (GFEDv2), we found that fires explained 93% of CO variability at surface stations between 90°N and 30°N, 69% between 30°N and 30°S, 74% between 30°S and 90°S. Third, the sensitivity of CO levels to changes in OH is low over source regions and increases toward remote ocean regions. CO is relatively insensitive to OH over source regions because diffusive and advective atmospheric transport occurs more rapidly than reaction with OH. In contrast, in remote ocean areas, the path length from source regions is large, and so air parcels have been exposed to the cumulative effects of OH oxidation for relatively long time intervals (weeks to months). Our optimization approach draws upon these finding in two steps. First, we perform a Bayesian inversion to optimize fire emissions using MOPITT 3 observations over source regions (where the sensitivity to emissions is high, but the sensitivity to OH is low). In a second step using the optimized fire fluxes, we conducted a series of GEOS-CHEM simulations with prescribed OH levels that varied between 0.1 and 2.0 times the predicted OH distribution from a full chemistry run. We then calculated Taylor scores and RMSE estimates at remote locations for each OH level. The OH level corresponding to the highest Taylor score (or lowest RMSE) was identified as the optimal OH for each model grid cell. Our results confirm that in many areas, the chemical transport model overestimated OH levels. A limitation of our approach is that errors in atmospheric transport will influence the OH optimization.

Mu, M.; Randerson, J. T.; Kasibhatla, P. S.; van der Werf, G.

2009-12-01

200

Road Extraction from High Resolution Satellite Images  

NASA Astrophysics Data System (ADS)

Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas. In this paper, the road extraction approach is based on Active Contour Models for 1-meter resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was separated as salient-roads, non-salient roads and crossings and extraction of these is provided by using Ribbon Snake and Ziplock Snake methods. These methods are derived from traditional snake model. Finally, various experimental results were presented. Ribbon and Ziplock Snake methods were compared for both salient and non-salient roads. Also these methods were used to extract roads in an image. While Ribbon snake is described for extraction of salient roads in an image, Ziplock snake is applied for extraction of non-salient roads. Beside these, some constant variables in literature were redefined and expressed in a formula as depending on snake approach and a new approach for extraction of crossroads were described and tried.

Özkaya, M.

2012-07-01

201

Highly comparative time-series analysis: the empirical structure of time series and their methods  

PubMed Central

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Fulcher, Ben D.; Little, Max A.; Jones, Nick S.

2013-01-01

202

Detecting chaos in irregularly sampled time series.  

PubMed

Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars. PMID:24089946

Kulp, C W

2013-09-01

203

Time series of the northeast Pacific  

NASA Astrophysics Data System (ADS)

In July 2006, the North Pacific Marine Science Organization (PICES) and Fisheries & Oceans Canada sponsored the symposium “Time Series of the Northeast Pacific: A symposium to mark the 50th anniversary of Line P”. The symposium, which celebrated 50 years of oceanography along Line P and at Ocean Station Papa (OSP), explored the scientific value of the Line P and other long oceanographic time series of the northeast Pacific (NEP). Overviews of the principal NEP time-series were presented, which facilitated regional comparisons and promoted interaction and exchange of information among investigators working in the NEP. More than 80 scientists from 8 countries attended the symposium. This introductory essay is a brief overview of the symposium and the 10 papers that were selected for this special issue of Progress in Oceanography.

Peña, M. Angelica; Bograd, Steven J.

2007-10-01

204

ARMA Model Identification of Hydrologic Time Series  

NASA Astrophysics Data System (ADS)

In recent years, ARMA models have become popular for modeling geophysical time series in general and hydrologic time series in particular. The identification of the appropriate order of the model is an important stage in ARMA modeling. Such model identification is generally based on the autocorrelation and partial autocorrelation functions, although recently improvements have been obtained using the inverse autocorrelation and the inverse partial autocorrelation functions. This paper demonstrates the use of the generalized partial autocorrelation function (GPAF) and the R and S functions of Gray et al. (1978) for ARMA model identification of hydrologic time series. These functions are defined, and some recursive relations are given for ease of computation. All three functions, when presented in tabular form, have certain characteristic patterns that are useful in ARMA model identification. Several examples are included to demonstrate the usefulness of the proposed identification technique. Actual applications are made using the Saint Lawrence River and Nile River annual streamflow series.

Salas, J. D.; Obeysekera, J. T. B.

1982-08-01

205

Learning time series for intelligent monitoring  

NASA Technical Reports Server (NTRS)

We address the problem of classifying time series according to their morphological features in the time domain. In a supervised machine-learning framework, we induce a classification procedure from a set of preclassified examples. For each class, we infer a model that captures its morphological features using Bayesian model induction and the minimum message length approach to assign priors. In the performance task, we classify a time series in one of the learned classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. We report results from experiments in a monitoring domain of interest to NASA.

Manganaris, Stefanos; Fisher, Doug

1994-01-01

206

Geometric Cloud Top Height Assignment by Geosynchronous Meteorological Satellite Images  

Microsoft Academic Search

In this research, the biases for Geometric Cloud Top Height (CTH) assignment are simulated for the current operational geostationary satellite constellation. The simulation shows that the geometric CTHs are best retrieved when the two satellites are separated by 60 degrees and presents CTHs properties for various satellite configurations. In addition, a case study based on GOES-10\\/12 images is shown to

Feng Lu; Jianmin Xu; W. Paul Menzel; Christopher S. Velden

2009-01-01

207

Intermittent estimation of stationary time series  

Microsoft Academic Search

Let {X\\u000a \\u000a n\\u000a }\\u000a \\u000a n=0\\u000a \\u000a ?\\u000a be a stationary real-valued time series with unknown distribution. Our goal is to estimate the conditional expectation ofX\\u000a n+1 based on the observations,X\\u000a \\u000a i\\u000a , 0?i?n in a strongly consistent way. Bailey and Ryabko proved that this is not possible even for ergodic binary time series if one\\u000a estimates at all values ofn. We

Gusztfiv Morvai; Benjamin Weiss

2004-01-01

208

Clustering streamflow time series for regional classification  

NASA Astrophysics Data System (ADS)

SummaryThe article aims to show how some dissimilarity criteria, the Mahalanobis distance between regression coefficients and the Euclidean distance between Autoregressive weights, can be applied to hydrologic time series clustering. Specifically, the temporal dynamics of streamflow time series are compared through the estimated parameters of the corresponding linear models which may include both short and long memory components. The performance of the proposed technique is assessed by means of an empirical study concerning a set of daily streamflow series recorded at sites in Oregon and Washington State.

Corduas, Marcella

2011-09-01

209

Multivariate principal component analysis of SST-pigments 2D vector field from a time series of satellite images of the Alboran Sea  

Microsoft Academic Search

Multivariate Principal Component Analysis (MPCA) is used to decompose a series of AVHRR SST maps and SeaWiFS phytoplankton pigment concentration maps relative to the Alboran Sea area (Western Mediterranean Sea) acquired during the period from November 1997 to October 1998. The results of MPCA decomposition are presented and discussed.

G. Corsini; M. Diani; R. Grasso

2002-01-01

210

Trend change detection in vegetation greenness time series: Contrasting methodologies, data sets and global vegetation models  

NASA Astrophysics Data System (ADS)

Newly developed satellite datasets and time series analysis methods allow the quantification of changes in vegetation greenness. However, the estimation of trends and trend changes depend often on the applied time series analysis method and the used satellite dataset. Thus, the environmental plausibility of the estimated trends and trend breakpoints is often questionable. We compared four trend and trend change detection methods to assess their performance. We applied the methods to NDVI and FAPAR time series from global satellite datasets and from global vegetation models. We generated surrogate time series with known trends and breakpoints and applied the methods to re-detect the known trends and trend changes. Our results demonstrate that the performance of methods decrease with increasing inter-annual variability of the time series. An overestimation of breakpoints in NDVI time series can result in wrong or even opposite trend estimates. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. The application of the trend change detection methods to real time series allows assessing the multi-method ensemble of trend estimates. Nevertheless, the interpretation of the environmental plausibility of these trend estimates is challenging. For example, some methods suggest a weakening of greening trends in the Tundra after the early 2000s while other methods suggest an ongoing greening. Comparison with vegetation model simulations suggest that this weakening is not an artefact of the satellite dataset or of the applied trend change detection method but might be caused by real changes in environmental conditions. Our results demonstrate the need for a critical appraisal of trend change detection methods. All methods require a careful assessment of the environmental plausibility of detected trend changes in vegetation greenness time series.

Forkel, Matthias; Carvalhais, Nuno; Verbesselt, Jan; Mahecha, Miguel; Neigh, Christopher; Thonicke, Kirsten; Reichstein, Markus

2014-05-01

211

FORTRAN subroutines for time series data reduction  

Microsoft Academic Search

For several years the author has been concerned with time series data reduction of guided missile data derived from tracking, telemetry and static test instrumentation. The data, which is acquired from many sources and comes in a great variety of formats and coding systems, must commonly be manipulated in a number of general ways before the calculation of functions specific

Roger A. MacGowan; Redstone Arsenal

1964-01-01

212

Wavelet analysis of radon time series  

NASA Astrophysics Data System (ADS)

Radon is a radioactive noble gas with a half-life of 3.8 days ubiquitous in both natural and indoor environments. Being produced in uranium-bearing materials by decay from radium, radon can be easily and accurately measured by nuclear methods, making it an ideal proxy for time-varying geophysical processes. Radon time series exhibit a complex temporal structure and large variability on multiple scales. Wavelets are therefore particularly suitable for the analysis on a scale-by-scale basis of time series of radon concentrations. In this study continuous and discrete wavelet analysis is applied to describe the variability structure of hourly radon time series acquired both indoors and on a granite site in central Portugal. A multi-resolution decomposition is performed for extraction of sub-series associated to specific scales. The high-frequency components are modeled in terms of stationary autoregressive / moving average (ARMA) processes. The amplitude and phase of the periodic components are estimated and tidal features of the signals are assessed. Residual radon concentrations (after removal of periodic components) are further examined and the wavelet spectrum is used for estimation of the corresponding Hurst exponent. The results for the several radon time series considered in the present study are very heterogeneous in terms of both high-frequency and long-term temporal structure indicating that radon concentrations are very site-specific and heavily influenced by local factors.

Barbosa, Susana; Pereira, Alcides; Neves, Luis

2013-04-01

213

Haar Wavelet Analysis of Climatic Time Series  

NASA Astrophysics Data System (ADS)

In order to extract the intrinsic information of climatic time series from background red noise, we will first give an analytic formula on the distribution of Haar wavelet power spectra of red noise in a rigorous statistical framework. The relation between scale aand Fourier period T for the Morlet wavelet is a= 0.97T . However, for Haar wavelet, the corresponding formula is a= 0.37T . Since for any time series of time step ?t and total length N?t, the range of scales is from the smallest resolvable scale 2?t to the largest scale N?t in wavelet-based time series analysis, by using the Haar wavelet analysis, one can extract more low frequency intrinsic information. Finally, we use our method to analyze Arctic Oscillation which is a key aspect of climate variability in the Northern Hemisphere, and discover a great change in fundamental properties of the AO,-commonly called a regime shift or tripping point. Our partial results have been published as follows: [1] Z. Zhang, J.C. Moore and A. Grinsted, Haar wavelet analysis of climatic time series, Int. J. Wavelets, Multiresol. & Inf. Process., in press, 2013 [2] Z. Zhang, J.C. Moore, Comment on "Significance tests for the wavelet power and the wavelet power spectrum", Ann. Geophys., 30:12, 2012

Zhang, Zhihua; Moore, John; Grinsted, Aslak

2014-05-01

214

An Online Algorithm for Segmenting Time Series  

Microsoft Academic Search

In recent years, there has been an explosion of interest in mining time series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule

Eamonn J. Keogh; Selina Chu; David Hart; Michael J. Pazzani

2001-01-01

215

Flood prediction using Time Series Data Mining  

NASA Astrophysics Data System (ADS)

SummaryThis paper describes a novel approach to river flood prediction using Time Series Data Mining which combines chaos theory and data mining to characterize and predict events in complex, nonperiodic and chaotic time series. Geophysical phenomena, including earthquakes, floods and rainfall, represent a class of nonlinear systems termed chaotic, in which the relationships between variables in a system are dynamic and disproportionate, however completely deterministic. Chaos theory provides a structured explanation for irregular behavior and anomalies in systems that are not inherently stochastic. While nonlinear approaches such as Artificial Neural Networks, Hidden Markov Models and Nonlinear Prediction are useful in forecasting of daily discharge values in a river, the focus of these approaches is on forecasting magnitudes of future discharge values rather than the prediction of floods. The described Time Series Data Mining methodology focuses on the prediction of events where floods constitute the events in a river daily discharge time series. The methodology is demonstrated using data collected at the St. Louis gauging station located on the Mississippi River in the USA. Results associated with the impact of earliness of prediction and the acceptable risk-level vs. prediction accuracy are presented.

Damle, Chaitanya; Yalcin, Ali

2007-02-01

216

Flood forecasting using time series data mining  

Microsoft Academic Search

Earthquakes, floods, rainfall represent a class of nonlinear systems termed chaotic, in which the relationships between variables in a system are dynamic and disproportionate, however completely deterministic. Classical linear time series models have proved inadequate in analysis and prediction of complex geophysical phenomena. Nonlinear approaches such as Artificial Neural Networks, Hidden Markov Models and Nonlinear Prediction are useful in forecasting

Chaitanya Damle

2005-01-01

217

Detecting time series motifs under uniform scaling  

Microsoft Academic Search

Time series motifs are approximately repeated patterns found within the data. Such motifs have utility for many data min- ing algorithms, including rule-discovery, novelty-detection, summarization and clustering. Since the formalization of the problem and the introduction of ecient linear time al- gorithms, motif discovery has been successfully applied to many domains, including medicine, motion capture, robotics and meteorology. In this

Dragomir Yankov; Eamonn J. Keogh; Jose Medina; Bill Chiu; Victor B. Zordan

2007-01-01

218

Reducing noise in discretized time series.  

PubMed

We show that applying a noise-reduction algorithm to a discretized time series increases its average error, compared to the original series. We find that adding external noise comparable to the discretization step before noise reduction limits the increase of the average error and improves the estimation of Lyapunov exponents. PMID:11690129

Cuéllar, M C; Binder, P M

2001-10-01

219

Confidence interval estimation using standardized time series  

Microsoft Academic Search

Observations of a stationary stochastic process can be transformed into a standardized time series. This paper presents a lemma giving the asymptotic properties of this standardized series under quite general conditions. In particular, the conditions are satisfied by stationary discrete-event simulations. Confidence intervals can be constructed using this lemma. For illustration, we develop two easily computed interval estimators for the

Lee W. Schruben

1983-01-01

220

Online Amnesic Approximation of Streaming Time Series  

Microsoft Academic Search

The past decade has seen a wealth of research on time se- ries representations, because the manipulation, storage, and indexing of large volumes of raw time series data is imprac- tical. The vast majority of research has concentrated on rep- resentations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the in- creasing deployment

Themistoklis Palpanas; Michail Vlachos; Eamonn J. Keogh; Dimitrios Gunopulos; Wagner Truppel

2004-01-01

221

Nonparametric inference for ergodic, stationary time series  

Microsoft Academic Search

The setting is a stationary, ergodic time series. The challenge is to construct a sequence of functions, each based on only finite segments of the past, which together provide a strongly consistent estimator for the\\u000aconditional probability of the next observation, given the infinite past. Ornstein gave such a construction for the case that the values are from a finite

Gusztáv Morvai; Sidney Yakowitz; László Györfi

1996-01-01

222

Nonparametric inference for ergodic, stationary time series  

Microsoft Academic Search

The setting is a stationary, ergodic time series. The challenge is to construct a sequence of functions, each based on only finite segments of the past, which together provide a strongly consistent estimator for the conditional probability of the next observation, given the infinite past. Ornstein gave such a construction for the case that the values are from a finite

G. Morvai; S. Yakowitz; L. Gyorfi

2007-01-01

223

Regent developments in time series forecasting  

Microsoft Academic Search

Summary Time series (extrapolative) forecasting procedures are widely used in business. Their importance has led to the development of new methods of forecasting and research into the evaluation of well-established methods. This paper presents an overview of the major methods of extrapolative forecasting. Secondly it considers the evidence on the relative accuracy of these methods, highlighting the unexpected conclusions arrived

R. Fildes

1988-01-01

224

Power Laws in Financial Time Series  

Microsoft Academic Search

We attempt empirical detection and characterization of power laws in financial time series. Fractional Brownian motion is defined. After testing for multifractality we calculate the multifractal spectrum of the series. The multifractal nature of stock prices leads to volatility clus- tering (conditional heteroscedasticity) and long memory (slowly decaying autocorrelation). Wavelet Transform Modulus Maxima approach to mul- tifractal spectrum estimation proved

Rossitsa Yalamova; Liu Qi; Yudong Chen; Chenwei Wang; Lin Wang; Qiao Wang

2007-01-01

225

Interdisciplinary application of nonlinear time series methods  

Microsoft Academic Search

This paper reports on the application to field measurements of time series methods developed on the basis of the theory of deterministic chaos. The major difficulties are pointed out that arise when the data cannot be assumed to be purely deterministic and the potential that remains in this situation is discussed. For signals with weakly nonlinear structure, the presence of

Thomas Schreiber

1998-01-01

226

Interdisciplinary application of nonlinear time series methods  

Microsoft Academic Search

This paper reports on the application to field measurements of time series methods developed on the basis of the theory of deterministic chaos. The major difficulties are pointed out that arise when the data cannot be assumed to be purely deterministic and the potential that remains in this situation is discussed. For signals with weakly nonlinear structure, the presence of

Thomas Schreiber

1999-01-01

227

Time Series Modeling of Urban Pollution Levels.  

National Technical Information Service (NTIS)

Research was conducted to find enough time series data of various types of signals to display the versatility of the modeling technique called Autoregressive-Moving Average (ARMA) (p,q). This was done by obtaining several 24-hour average air pollutant mea...

T. S. Lee R. Bethke

1974-01-01

228

Time Series Regression with a Unit Root  

Microsoft Academic Search

This paper studies the random walk in a general time series setting that allows for weakly dependent and heterogeneously distributed innovations. It is shown that simple least squares regression consistently estimates a unit root under very general conditions in spite of the presence of autocorrelated errors. The limiting distribution of the standardized estimator and the associated regression t statistic are

P. C. B. Phillips

1987-01-01

229

Semi-supervised time series classification  

Microsoft Academic Search

The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other domain specialists. As in many

Li Wei; Eamonn J. Keogh

2006-01-01

230

Multifractal geometry in stock market time series  

Microsoft Academic Search

It has been recently noticed that time series of returns in stock markets are of multifractal (multiscaling) character. In that context, multifractality has been always evidenced by its statistical signature (i.e., the scaling exponents associated to a related variable). However, a direct geometrical framework, much more revealing about the underlying dynamics, is possible. In this paper, we present the techniques

Antonio Turiel; Conrad J. Pérez-Vicente

2003-01-01

231

Integrated method for chaotic time series analysis  

DOEpatents

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data are disclosed. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated. 8 figs.

Hively, L.M.; Ng, E.G.

1998-09-29

232

Offset detection in GPS coordinate time series  

NASA Astrophysics Data System (ADS)

Global Positioning System (GPS) time series are commonly affected by offsets of unknown magnitude and the large volume of data globally warrants investigation of automated detection approaches. The Detection of Offsets in GPS Experiment (DOGEx) showed that accuracy of Global Positioning System (GPS) time series can be significantly improved by applying statistical offset detection methods (see Gazeaux et al. (2013)). However, the best of these approaches did not perform as well as manual detection by expert analysts. Many of the features of GPS coordinates time series have not yet been fully taken into account in existing methods. Here, we apply Bayesian theory in order to make use of prior knowledge of the site noise characteristics and metadata in an attempt to make the offset detection more accurate. In the past decades, Bayesian theory has shown relevant results for a widespread range of applications, but has not yet been applied to GPS coordinates time series. Such methods incorporate different inputs such as a dynamic model (linear trend, periodic signal..) and a-priori information in a process that provides the best estimate of parameters (velocity, phase and amplitude of periodic signals...) based on all the available information. We test the new method on the DOGEx simulated dataset and compare it to previous solutions, and to Monte-Carlo method to test the accuracy of the procedure. We make a preliminary extension of the DOGEx dataset to introduce metadata information, allowing us to test the value of this data type in detecting offsets. The flexibility, robustness and limitations of the new approach are discussed. Gazeaux, J. Williams, S., King, M., Bos, M., Dach, R., Deo, M.,Moore, A.W., Ostini, L., Petrie, E., Roggero, M., Teferle, F.N., Olivares, G.,Webb, F.H. 2013. Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment. Journal of Geophysical Research: Solid Earth 118. 5. pp:2169-9356. Keywords : GPS, offsets detection, homogenization, velocity

Gazeaux, J.; King, M. A.; Williams, S. D.

2013-12-01

233

A time-series analysis of flood disaster around Lena river using Landsat TM/ETM+  

NASA Astrophysics Data System (ADS)

Landsat satellite has provided a continuous record of earth observation since 1972, gradually improving sensors (i.e. MSS, TM and ETM+). Already processed archives of Landsat image are now available free of charge from the internet. The Landsat image of 30 m spatial resolution with multiple spectral bands between 450 and 2350 nm is appropriate for detailed mapping of natural resource at wide geographical areas. However, one of the biggest concerns in the use of Landsat image is the uncertainty in the timing of acquisitions. Although detection of land cover change usually requires acquisitions before and after the change, the Landsat image is often unavailable because of the long-term intervals (16 days) and variation in atmosphere. Nearly cloud-free image is acquired at least once per year (total of 22 or 23 scenes per year). Therefore, it may be difficult to acquire appropriate images for monitoring natural disturbances caused at short-term intervals (e.g., flood, forest fire and hurricanes). Our objectives are: (1) to examine whether a time-series of Landsat image is available for monitoring a flood disaster, and (2) to evaluate the impact and timing of the flood disaster around Lena river in Siberia. A set of Landsat TM/ETM+ satellite images was used to enable acquisition of cloud-free image, although Landsat ETM+ images include failure of the Scan Line Corrector (SLC) from May 2003. The overlap area of a time series of 20 Landsat TM/ETM+ images (path 120-122, row 17) from April 2007 to August 2007 was clipped (approximately 33 km × 90 km), and the other area was excluded from the analyses. Image classification was performed on each image separately using an unsupervised ISODATA method, and each Landsat TM/ETM+ image was classified into three land cover types: (1) ice, (2) water, and (3) land. From three land cover types, the area of Lena river was estimated. The area of Lena river dramatically changed after spring breakup. The middle part of Lena river around Tabaga (61.83°N, 129.60°E) was frozen hard until early May 2007. River-ice breakup began in patches on 13 May 2007. Then, the area of Lena river rapidly increased due to overhead flooding on 14 May 2007, and reached the peak on 15 May 2007. In the brief period of one or two days, the area of Lena river was more than twice. After this, the area of Lena river exponentially decreased over three months, and it was quite stable in late August 2007. A time-series of Landsat TM/ETM+ images could detect these large temporal variations. In addition, the temporal variations in the area of Lena river synchronized with water stage measured in the field. These results indicate that a time-series of Landsat TM/ETM+ images enables to monitor natural disturbances caused at short-term intervals, although significantly limited to local scales. The requirement of spatial and temporal resolution is often application specific in the context of the desired measurement goals. This type of research and resultant information is critical for the utilization of remote sensing data to the fullest extent.

Sakai, Toru; Hatta, Shigemi; Okumura, Makoto; Takeuchi, Wataru; Hiyama, Tetsuya; Inoue, Gen

2010-05-01

234

Tissue typing with ultrasound RF time series: phantom studies  

NASA Astrophysics Data System (ADS)

We report phantom studies on a new approach to ultrasound-based tissue typing. In the proposed approach, we continuously record RF echo signals backscattered from tissue, while the imaging probe and the tissue are fixed in position. The continuously recorded RF data generates a time series of echoes for each spatial sample of the RF signal. We use the spectral and fractal features of ultrasound RF time series averaged over a region of interest, along with support vector machine classifiers, for tissue typing. In this paper, the effects of two properties of tissue on RF time series are investigated: cell size and elasticity. We show that RF time series acquired from agar-gelatin based tissue mimicking phantoms, with difference only in the size of cell-mimicking glass beads, are distinguishable with statistically reliable accuracies up to 82.2%. Similar experiments using phantoms with different elastic properties did not result in consistently high classification accuracies. The results of this study confirm that the evident differences in microstructure of the cancerous versus normal tissue could play a role in the success of the proposed tissue typing method in detection of prostate cancer.

Moradi, Mehdi; Mousavi, Parvin; Rohling, Robert; Abolmaesumi, Purang

2009-02-01

235

Sea state variability observed by high resolution satellite radar images  

NASA Astrophysics Data System (ADS)

The spatial variability of the wave parameters is measured and investigated using new TerraSAR-X (TS-X) satellite SAR (Synthetic Aperture Radar) images. Wave groupiness, refraction and breaking of individual wave are studied. Space borne SAR is a unique sensor providing two dimensional information of the ocean surface. Due to its daylight, weather independency and global coverage, the TS-X radar is particularly suitable for many ocean and coastal observations and it acquires images of the sea surface with up to 1m resolution; individual ocean waves with wavelength below 30m are detectable. Two-dimensional information of the ocean surface, retrieved using TS-X data, is validated for different oceanographic applications: derivation of the fine resolved wind field (XMOD algorithm) and integrated sea state parameters (XWAVE algorithm). The algorithms are capable to take into account fine-scale effects in the coastal areas. This two-dimensional information can be successfully applied to validate numerical models. For this, wind field and sea state information retrieved from SAR images are given as input for a spectral numerical wave model (wind forcing and boundary condition). The model runs and sensitivity studies are carried out at a fine spatial horizontal resolution of 100m. The model results are compared to buoy time series at one location and with spatially distributed wave parameters obtained from SAR. The comparison shows the sensitivity of waves to local wind variations and the importance of local effects on wave behavior in coastal areas. Examples for the German Bight, North Sea and Rottenest Island, Australia are shown. The wave refraction, rendered by high resolution SAR images, is also studied. The wave ray tracking technique is applied. The wave rays show the propagation of the peak waves in the SAR-scenes and are estimated using image spectral analysis by deriving peak wavelength and direction. The changing of wavelength and direction in the rays allows detecting underwater structures (banks, reefs, shallows) and to obtain bathymetry in case a well-developed swell is imaged. Further, wave energy flux propagation towards the coast and its dissipation are obtained using the wave ray technique: wave height and wavelength are derived from TS-X image spectrum. The height of individual breaking waves is obtained from SAR-image signatures and it is compared to the model results and the buoy measurements. The results show some lower amplitude of the breaking waves, when compared to model results in the shoaling zone. This effect could be explained by an actual stronger dissipation than the one given by the model in the investigated area (coral reefs). Wave groups are detected for a cross sea and in storm condition in the ocean. The parameters of the wave groups are investigated and the conditions, which are responsible for their origin, are studied by numerical simulation using spectral wave model.

Pleskachevsky, A.; Lehner, S.

2012-04-01

236

A nonparametric Bayesian approach to time series alignment  

Microsoft Academic Search

We propose a nonparametric Bayesian approach to time series alignment. Time series alignment is a technique often required when we analyze a set of time series in which there exists a typical structural pattern common to all the time series. Such a set of time series is usually obtained by repeated measurements of a biological, chemical or physical process. In

Shinji Akimoto; Nobuo Suematsu

2010-01-01

237

Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications  

Microsoft Academic Search

In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly

Eamonn J. Keogh; Jessica Lin; Ada Wai-chee Fu; Helga Van Herle

2006-01-01

238

Tracking Large Area Mangrove Deforestation with Time-Series of High Fidelity MODIS Imagery  

NASA Astrophysics Data System (ADS)

Mangrove forests are important coastal ecosystems of the tropical and subtropical regions. These forests provide critical ecosystem services, fulfill important socio-economic and environmental functions, and support coastal livelihoods. But these forest are also among the most vulnerable ecosystems, both to anthropogenic disturbance and climate change. Yet, there exists no map or published study showing detailed spatiotemporal trends of mangrove deforestation at local to regional scales. There is an immediate need of producing such detailed maps to further study the drivers, impacts and feedbacks of anthropogenic and climate factors on mangrove deforestation, and to develop local and regional scale adaptation/mitigation strategies. In this study we use a time-series of high fidelity imagery from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) for tracking changes in the greenness of mangrove forests of Kalimantan Island of Indonesia. A novel method of filtering satellite data for cloud, aerosol, and view angle effects was used to produce high fidelity MODIS time-series images at 250-meter spatial resolution and three-month temporal resolution for the period of 2000-2010. Enhanced Vegetation Index 2 (EVI2), a measure of vegetation greenness, was calculated from these images for each pixel at each time interval. Temporal variations in the EVI2 of each pixel were tracked as a proxy to deforestaton of mangroves using the statistical method of change-point analysis. Results of these change detection were validated using Monte Carlo simulation, photographs from Google-Earth, finer spatial resolution images from Landsat satellite, and ground based GIS data.

Rahman, A. F.; Dragoni, D.; Didan, K.

2011-12-01

239

Spartan random processes in time series modeling  

NASA Astrophysics Data System (ADS)

A Spartan random process (SRP) is used to estimate the correlation structure of time series and to predict (interpolate and extrapolate) the data values. SRPs are motivated from statistical physics, and they can be viewed as Ginzburg-Landau models. The temporal correlations of the SRP are modeled in terms of ‘interactions’ between the field values. Model parameter inference employs the computationally fast modified method of moments, which is based on matching sample energy moments with the respective stochastic constraints. The parameters thus inferred are then compared with those obtained by means of the maximum likelihood method. The performance of the Spartan predictor (SP) is investigated using real time series of the quarterly S&P 500 index. SP prediction errors are compared with those of the Kolmogorov-Wiener predictor. Two predictors, one of which is explicit, are derived and used for extrapolation. The performance of the predictors is similarly evaluated.

Žukovi?, M.; Hristopulos, D. T.

2008-06-01

240

Future profiling of time series behavior  

Microsoft Academic Search

The study of time-dependent univariate systems plays an important role in severalphysical and applied sciences. Time-series behaviour of such systems is mostly complexin nature and sophisticated mathematical modelling tools are needed for making accurateforecasts. These forecasts can be used for specific purposes in different domains, forexample, to plan resources, develop market strategies or control\\/understand complexsystems. In the majority of successful

Sameer Singh

2000-01-01

241

Hidden Markov Models for Individual Time Series  

Microsoft Academic Search

This chapter introduces hidden Markov models to study and characterize (individual) time series such as observed in psychological\\u000a experiments of learning, repeated panel data, repeated observations comprising a developmental trajectory etc. Markov models\\u000a form a broad and flexible class of models with many possible extensions, while at the same time allowing for relatively easy\\u000a analysis and straightforward interpretation. Here we

Ingmar Visser; Maartje E. J. Raijmakers

2009-01-01

242

Forecasting for stationary binary time series  

Microsoft Academic Search

The forecasting problem for a stationary and ergodic binary time series $\\\\{X_n\\\\}_{n=0}^{\\\\infty}$ is to estimate the probability that $X_{n+1}=1$ based on the observations $X_i$, $0\\\\le i\\\\le n$ without prior knowledge of the distribution of the process $\\\\{X_n\\\\}$. It is known that this is not possible if one estimates at all values of $n$. We present a simple procedure which will

Gusztav Morvai; Benjamin Weiss

2007-01-01

243

Forward estimation for ergodic time series  

Microsoft Academic Search

The forward estimation problem for stationary and ergodic time series {Xn}n=0? taking values from a finite alphabet X is to estimate the probability that Xn+1=x based on the observations Xi, 0?i?n without prior knowledge of the distribution of the process {Xn}. We present a simple procedure gn which is evaluated on the data segment (X0,…,Xn) and for which, error(n)=|gn(x)?P(Xn+1=x|X0,…,Xn)|?0 almost

Gusztáv Morvai; Benjamin Weiss

2005-01-01

244

Forecasting for Stationary Binary Time Series  

Microsoft Academic Search

The forecasting problem for a stationary and ergodic binary time series {Xn}n=08 is to estimate the probability that Xn+1=1 based on the observations Xi, 0=i=n without prior knowledge of the distribution of the process {Xn}. It is known that this is not possible if one estimates at all values of n. We present a simple procedure which will attempt to

Gusztáv Morvai; Benjamin Weiss

2003-01-01

245

Time series regression studies in environmental epidemiology.  

PubMed

Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed ('lagged') associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model. PMID:23760528

Bhaskaran, Krishnan; Gasparrini, Antonio; Hajat, Shakoor; Smeeth, Liam; Armstrong, Ben

2013-08-01

246

Unevenly sampled time series analysis in astrophysics  

NASA Astrophysics Data System (ADS)

Much astrophysics time series are unevenly spaced in time which abate the power of ordinary Fourier transform A method to find the true Fourier spectrum for unevenly spaced time series is developed It is found that the true Fourier spectrum associates with the conventional Fourier spectrum by a system of linear equations so it can be obtained by a method of iterative process It is an effective method for detecting and describing the true multiperiodic signals even in the case that some strong peaks in a conventional Fourier spectrum occur at spurious frequencies For the true Fourier spectrum composed of finite isolated harmonic components this method gives a better estimation of the frequencies and amplitudes This method is tested by simulated time series and the published data for servel blazars Then it is applied to some radio variabilities of a sample of blazars In some cases typical timescale of several decades are found indicating that this method is of high capability even in finding some very low frequency signals

Liu, Y.; Fan, J.-H.; Wang, H.-G.

247

Time series irreversibility: a visibility graph approach  

NASA Astrophysics Data System (ADS)

We propose a method to measure real-valued time series irreversibility which combines two different tools: the horizontal visibility algorithm and the Kullback-Leibler divergence. This method maps a time series to a directed network according to a geometric criterion. The degree of irreversibility of the series is then estimated by the Kullback-Leibler divergence (i.e. the distinguishability) between the in and out degree distributions of the associated graph. The method is computationally efficient and does not require any ad hoc symbolization process. We find that the method correctly distinguishes between reversible and irreversible stationary time series, including analytical and numerical studies of its performance for: (i) reversible stochastic processes (uncorrelated and Gaussian linearly correlated), (ii) irreversible stochastic processes (a discrete flashing ratchet in an asymmetric potential), (iii) reversible (conservative) and irreversible (dissipative) chaotic maps, and (iv) dissipative chaotic maps in the presence of noise. Two alternative graph functionals, the degree and the degree-degree distributions, can be used as the Kullback-Leibler divergence argument. The former is simpler and more intuitive and can be used as a benchmark, but in the case of an irreversible process with null net current, the degree-degree distribution has to be considered to identify the irreversible nature of the series.

Lacasa, L.; Nuñez, A.; Roldán, É.; Parrondo, J. M. R.; Luque, B.

2012-06-01

248

Monitoring Forest Regrowth Using a Multi-Platform Time Series  

NASA Technical Reports Server (NTRS)

Over the past 50 years, the forests of western Washington and Oregon have been extensively harvested for timber. This has resulted in a heterogeneous mosaic of remaining mature forests, clear-cuts, new plantations, and second-growth stands that now occur in areas that formerly were dominated by extensive old-growth forests and younger forests resulting from fire disturbance. Traditionally, determination of seral stage and stand condition have been made using aerial photography and spot field observations, a methodology that is not only time- and resource-intensive, but falls short of providing current information on a regional scale. These limitations may be solved, in part, through the use of multispectral images which can cover large areas at spatial resolutions in the order of tens of meters. The use of multiple images comprising a time series potentially can be used to monitor land use (e.g. cutting and replanting), and to observe natural processes such as regeneration, maturation and phenologic change. These processes are more likely to be spectrally observed in a time series composed of images taken during different seasons over a long period of time. Therefore, for many areas, it may be necessary to use a variety of images taken with different imaging systems. A common framework for interpretation is needed that reduces topographic, atmospheric, instrumental, effects as well as differences in lighting geometry between images. The present state of remote-sensing technology in general use does not realize the full potential of the multispectral data in areas of high topographic relief. For example, the primary method for analyzing images of forested landscapes in the Northwest has been with statistical classifiers (e.g. parallelepiped, nearest-neighbor, maximum likelihood, etc.), often applied to uncalibrated multispectral data. Although this approach has produced useful information from individual images in some areas, landcover classes defined by these techniques typically are not consistent for the same scene imaged under different illumination conditions, especially in the mountainous regions. In addition, it is difficult to correct for atmospheric and instrumental differences between multiple scenes in a time series. In this paper, we present an approach for monitoring forest cutting/regrowth in a semi-mountainous portion of the southern Gifford Pinchot National Forest using a multisensor-time series composed of MSS, TM, and AVIRIS images.

Sabol, Donald E., Jr.; Smith, Milton O.; Adams, John B.; Gillespie, Alan R.; Tucker, Compton J.

1996-01-01

249

Using Image Tour to Explore Multiangle, Multispectral Satellite Image  

NASA Technical Reports Server (NTRS)

This viewgraph presentation reviews the use of Image Tour to explore the multiangle, multispectral satellite imagery. Remote sensing data are spatial arrays of p-dimensional vectors where each component corresponds to one of p variables. Applying the same R(exp p) to R(exp d) projection to all pixels creates new images, which may be easier to analyze than the original because d < p. Image grand tour (IGT) steps through the space of projections, and d=3 outputs a sequence of RGB images, one for each step. In this talk, we apply IGT to multiangle, multispectral data from NASA's MISR instrument. MISR views each pixel in four spectral bands at nine view angles. Multiple views detect photon scattering in different directions and are indicative of physical properties of the scene. IGT allows us to explore MISR's data structure while maintaining spatial context; a key requirement for physical interpretation. We report results highlighting the uniqueness of multiangle data and how IGT can exploit it.

Braverman, Amy; Wegman, Edward J.; Martinez, Wendy; Symanzik, Juergen; Wallet, Brad

2006-01-01

250

Analysis of time series geospatial data for seismic precursors detection in Vrancea zone  

NASA Astrophysics Data System (ADS)

Rock microfracturing in the Earth's crust preceding a seismic rupture may cause local surface deformation fields, rock dislocations, charged particle generation and motion, electrical conductivity changes, gas emission, fluid diffusion, electrokinetic, piezomagnetic and piezoelectric effects. Space-time anomalies of Earth's emitted radiation (radon in underground water and soil , thermal infrared in spectral range measured from satellite months to weeks before the occurrence of earthquakes etc.), ionospheric and electromagnetic anomalies are considered as pre-seismic signals. Satellite remote sensing data provides a systematic, synoptic framework for advancing scientific knowledge of the Earth complex system of geophysical phenomena which often lead to seismic hazards. The GPS data provides exciting prospects in seismology including detecting, imaging and analyzing signals in regions of seismo-active areas. This paper aims at investigating thermal seismic precursors for some major earthquakes in Romania in Vrancea area, occurred in 1977, 1986, 1990 and 2004, based on time series satellite data provided by NOAA and MODIS. Quantitative analysis of land surface temperature (LST) and ongoing long wave radiation (OLR) data extracted from satellite and in-situ monitoring available data recorded before and during the occurrence of earthquake events shows the consistent increasing in the air and land surface in the epicentral locations several days before earthquake, and at different distances of hypocenters function of registered earthquake moment magnitude.

Zoran, M. A.; Savastru, R. S.; Savastru, D. M.

2012-10-01

251

On Fire regime modelling using satellite TM time series  

Microsoft Academic Search

Wildfires can cause an environment deterioration modifying vegetation dynamics because they have the capacity of changing vegetation diversity and physiognomy. In semiarid regions, like the northwestern Patagonia, fire disturbance is also important because it could impact on the potential productivity of the ecosystem. There is reduction plant biomass and with that reducing the animal carrying capacity and\\/or the forest site

F. Oddi; L.. Ghermandi; A. Lanorte; R. Lasaponara

2009-01-01

252

Time Series Search Using Hidden Markov Models  

NASA Astrophysics Data System (ADS)

Instances of unusual behavior are often difficult to locate in large volumes of data. In a typical case, a single instance of the unusual behavior is located and additional instances are desired. We propose a method for performing this search using hidden Markov models (HMMs). This approach has the advantage that it generalizes to the detection of any signal whose behavior exhibits statistically distinguishable modes. Our method employs the following approach: first, a time series snippet containing an instance of the target behavior is used to train an HMM; this also results in a classification of the snippet observations. Second, the trained HMM is used to classify observations in the time series we are searching. Third, we calculate matches between the snippet classification and the search series classification. These matches can be ranked and returned according to a quality metric. Optimal fitting of hidden Markov models to generalized data is a difficult problem. In some cases, sufficient a priori information is available to constrain the problem and reduce the number of free parameters. Most often in exploratory data analysis such constraints are not available and standard optimization techniques are likely to become unstable. We solve this problem by employing a robust model fitting algorithm that uses regularization and annealing to stabilize the optimization procedure. This robust HMM procedure allows the method to work on a first try basis, making it applicable to real time interactive data analysis. We examine the performance of this method on selected engineering and science time series, including data from the Southern California Integrated GPS Network (SCIGN) and the Southern California Seismic Network.

Granat, R.

2005-12-01

253

Evaluation of Urban Environmental Quality with High Resolution Satellite Images  

Microsoft Academic Search

It can serve for the city planning scientifically and improve the people's daily life level actively to evaluate the urban environment status efficiently from the meter resolution satellite images such as IKONOS images and QUICKBIRD images. We can obtain the environment information such as vegetation type and its cover area, water body area and water pollution status, the air pollution

Meichun Yan; Liliang Ren; Xiufeng He; Wengang Sang

2008-01-01

254

Human Visual System Enhancement of Reconstructed Satellite Images.  

National Technical Information Service (NTIS)

This research investigated the enhancement of satellite images. The goal was to develop and test a suite of image enhancement software routines to improve the quality of reconstructed images for the human visual system. The primary focus was to enhance sa...

J. E. Treleaven

1993-01-01

255

Identification of satellite images based on mel frequency cepstral coefficients  

Microsoft Academic Search

MFCC technique is an efficient technique which can be used for speech signals' classification as MFCC can be applied for 1-D signals. This paper suggests a new application for MFCC technique as it can be used for classification of satellite images, which are 2-D objects. Applying MFCC to images, through transforming images to 1-D vectors is an innovation in its

T. M. Talal; A. El-Sayed

2009-01-01

256

RECENT DEVELOPMENTS IN HYDROWEB DATABASE Water level time series on lakes and reservoirs (Invited)  

NASA Astrophysics Data System (ADS)

We present the current state of the Hydroweb database as well as developments in progress. It provides offline water level time series on rivers, reservoirs and lakes based on altimetry data from several satellites (Topex/Poseidon, ERS, Jason-1&2, GFO and ENVISAT). The major developments in Hydroweb concerns the development of an operational data centre with automatic acquisition and processing of IGDR data for updating time series in near real time (both for lakes & rivers) and also use of additional remote sensing data, like satellite imagery allowing the calculation of lake's surfaces. A lake data centre is under development at the Legos in coordination with Hydrolare Project leaded by SHI (State Hydrological Institute of the Russian Academy of Science). It will provide the level-surface-volume variations of about 230 lakes and reservoirs, calculated through combination of various satellite images (Modis, Asar, Landsat, Cbers) and radar altimetry (Topex / Poseidon, Jason-1 & 2, GFO, Envisat, ERS2, AltiKa). The final objective is to propose a data centre fully based on remote sensing technique and controlled by in situ infrastructure for the Global Terrestrial Network for Lakes (GTN-L) under the supervision of WMO and GCOS. In a longer perspective, the Hydroweb database will integrate data from future missions (Jason-3, Jason-CS, Sentinel-3A/B) and finally will serve for the design of the SWOT mission. The products of hydroweb will be used as input data for simulation of the SWOT products (water height and surface variations of lakes and rivers). In the future, the SWOT mission will allow to monitor on a sub-monthly basis the worldwide lakes and reservoirs bigger than 250 * 250 m and Hydroweb will host water level and extent products from this

Cretaux, J.; Arsen, A.; Calmant, S.

2013-12-01

257

Calibration of infrared satellite images using high altitude aircraft measurements  

NASA Technical Reports Server (NTRS)

The use of infrared radiance measurements made from high altitude aircraft for satellite image validation is discussed. Selected examples are presented to illustrate the techniques and the potentials of such validation studies.

Hammer, Philip D.; Gore, Warren J. Y.; Valero, Francisco P. J.

1989-01-01

258

Land Surface Models as Collateral Data in Satellite Image Interpretation.  

National Technical Information Service (NTIS)

An analytic scheme for the human interpretation process of satellite images is proposed. Two types of collateral data are used: ecological and socioeconomic (land use) systems and models; and the differentiation of the real space (topographic and thematic...

M. Seger P. Mandl

1986-01-01

259

Satellite Image Processing Applications in MedioGRID  

Microsoft Academic Search

This paper presents a high level architectural specification of MedioGRID, a research project aiming at implementing a real-time satellite image processing system for extracting relevant environmental and meteorological parameters on a grid system. The presentation focuses on the key architectural decisions of the GRID-aware satellite image processing system, highlighting the technologies for each of the major components. An essential part

Ovidiu Muresan; textbfFlorin Pop; Dorian Gorgan; Valentin Cristea

2006-01-01

260

Near-real-time satellite image processing: metacomputing in CC++  

Microsoft Academic Search

Metacomputing combines heterogeneous system elements in a seamless computing service. In this case study, we introduce the elements of metacomputing and describe an application for cloud detection and visualization of infrared and visible-light satellite images. The application processes the satellite images by using Compositional C++ (CC++)-a simple, yet powerful extension of C++-and its runtime system, Nexus, to integrate specialized resources,

Craig A. Lee; Carl Kesselman; Stephen Schwab

1996-01-01

261

Structure-Based Statistical Features and Multivariate Time Series Clustering  

Microsoft Academic Search

We propose a new method for clustering multivariate time series. A univariate time series can be represented by a fixed-length vector whose components are statistical features of the time series, capturing the global structure. These descriptive vectors, one for each component of the multivariate time series, are concatenated, before being clustered using a standard fast clustering algorithm such as k-means

Xiaozhe Wang; Anthony Wirth; Liang Wang

2007-01-01

262

Identifying distinctive subsequences in multivariate time series by clustering  

Microsoft Academic Search

Most time series comparison algorithms attempt to discover what themembers of a set of time series have in common. We investigate a differentproblem, determining what distinguishes time series in that setfrom other time series obtained from the same source. In both casesthe goal is to identify shared patterns, though in the latter case thosepatterns must be distinctive as well. An

Tim Oates

1999-01-01

263

Singular spectrum analysis for time series with missing data  

USGS Publications Warehouse

Geophysical time series often contain missing data, which prevents analysis with many signal processing and multivariate tools. A modification of singular spectrum analysis for time series with missing data is developed and successfully tested with synthetic and actual incomplete time series of suspended-sediment concentration from San Francisco Bay. This method also can be used to low pass filter incomplete time series.

Schoellhamer, D. H.

2001-01-01

264

Reconstruction of the land surface temperature time series using harmonic analysis  

NASA Astrophysics Data System (ADS)

Satellite remote sensing is an important approach for obtaining land surface temperature (LST) over wide temporal and spatial ranges. However, the presence of clouds generates numerous missing and abnormal values that affect the application of LST data. To fill data gaps and improve data quality, the Harmonic ANalysis of Time Series (HANTS) algorithm was employed to remove cloud-affected observations and reconstruct the Moderate Resolution Imaging Spectroradiometer (MODIS) LST data taken in the year 2005 for the Yangtze River Delta region of China. Analysis of MODIS data quality indicated that the yearly proportion of high-quality LST data in this region was less than 50% with numerous missing and low-quality data points. To reconstruct 8-day LST via the removal of cloud-contaminated observations, we applied pixel-by-pixel harmonic fitting to the time series and used fitted values to replace the missing and abnormal values in the original LST data. To evaluate the reconstruction performance, a simulated dataset was generated according to the percentage of cloud coverage in each 8-day period. Satisfactory validation results indicate that the harmonic method can effectively fit the NA Values caused by cloud cover and fill data gaps in the LST data, which can significantly improve the practical value of the MODIS LST dataset.

Xu, Yongming; Shen, Yan

2013-12-01

265

Longest time series of glacier mass changes in the Himalaya based on stereo imagery  

NASA Astrophysics Data System (ADS)

Mass loss of Himalayan glaciers has wide-ranging consequences such as declining water resources, sea level rise and an increasing risk of glacial lake outburst floods (GLOFs). The assessment of the regional and global impact of glacier changes in the Himalaya is, however, hampered by a lack of mass balance data for most of the range. Multi-temporal digital terrain models (DTMs) allow glacier mass balance to be calculated since the availability of stereo imagery. Here we present the longest time series of mass changes in the Himalaya and show the high value of early stereo spy imagery such as Corona (years 1962 and 1970) aerial images and recent high resolution satellite data (Cartosat-1) to calculate a time series of glacier changes south of Mt. Everest, Nepal. We reveal that the glaciers are significantly losing mass with an increasing rate since at least ~1970, despite thick debris cover. The specific mass loss is 0.32 ± 0.08 m w.e. a-1, however, not higher than the global average. The spatial patterns of surface lowering can be explained by variations in debris-cover thickness, glacier velocity, and ice melt due to exposed ice cliffs and ponds.

Bolch, T.; Pieczonka, T.; Benn, D. I.

2010-12-01

266

IMAGE-BASED ESTIMATION AND VALIDATION OF NIIRS FOR HIGH-RESOLUTION SATELLITE IMAGES  

Microsoft Academic Search

As high resolution satellite images are being used widely in many applications more and more users are demanding images of good quality. The 'quality' of satellite images are expressed by many technical terms such as ground sampling distance, modular transfer function, and signal to noise ration and by NIIRS (National Imagery Interpretability Rating Scale) in user community. The purpose of

Taejung Kim; Hyunsuk Kim; HeeSeob Kim

267

Time series analyses of global change data.  

PubMed

The hypothesis that statistical analyses of historical time series data can be used to separate the influences of natural variations from anthropogenic sources on global climate change is tested. Point, regional, national, and global temperature data are analyzed. Trend analyses for the period 1901-1987 suggest mean annual temperatures increased (in degrees C per century) globally at the rate of about 0.5, in the USA at about 0.3, in the south-western USA desert region at about 1.2, and at the Walnut Gulch Experimental Watershed in south-eastern Arizona at about 0.8. However, the rates of temperature change are not constant but vary within the 87-year period. Serial correlation and spectral density analysis of the temperature time series showed weak periodicities at various frequencies. The only common periodicity among the temperature series is an apparent cycle of about 43 years. The temperature time series were correlated with the Wolf sunspot index, atmospheric CO(2) concentrations interpolated from the Siple ice core data, and atmospheric CO(2) concentration data from Mauna Loa measurements. Correlation analysis of temperature data with concurrent data on atmospheric CO(2) concentrations and the Wolf sunspot index support previously reported significant correlation over the 1901-1987 period. Correlation analysis between temperature, atmospheric CO(2) concentration, and the Wolf sunspot index for the shorter period, 1958-1987, when continuous Mauna Loa CO(2) data are available, suggest significant correlation between global warming and atmospheric CO(2) concentrations but no significant correlation between global warming and the Wolf sunspot index. This may be because the Wolf sunspot index apparently increased from 1901 until about 1960 and then decreased thereafter, while global warming apparently continued to increase through 1987. Correlation of sunspot activity with global warming may be spurious but additional analyses are required to test this hypothesis. Given the inconclusive correlation between temperature and solar activity, the significant intercorrelation between time, temperature, and atmospheric CO(2) concentrations, and the suggestion of weak periodicity in the temperature data, additional research is needed to separate the anthropogenic component from the natural variability in temperature when assessing local, regional, and global warming trends. PMID:15091751

Lane, L J; Nichols, M H; Osborn, H B

1994-01-01

268

Temporal registration of multispectral digital satellite images using their edge images  

NASA Technical Reports Server (NTRS)

An algorithm is described which will form an edge image by detecting the edges of features in a particular spectral band of a digital satellite image. It is capable also of forming composite multispectral edge images. In addition, an edge image correlation algorithm is presented which performs rapid automatic registration of the edge images and, consequently, the grey level images.

Nack, M. L.

1975-01-01

269

A Novel Change Detection Method for Unregistered Optical Satellite Images  

NASA Astrophysics Data System (ADS)

In this letter, we propose a novel method for change detection in multitemporal optical satellite images. Unlike the tradition methods, the proposed method is able to detect changed region even from unregistered images. In order to obtain the change detection map from the unregistered images, we first compute the sum of the color difference (SCD) of a pixel to all pixels in an input image. Then we calculate the SCD of this pixel to all pixels in the other input image. Finally, we use the difference of the two SCDs to represent the change detection map. Experiments on the multitemporal images demonstrates the good performance of the proposed method on the unregistered images.

Luo, Wang; Li, Hongliang; Liu, Guanghui; Gui, Guan

270

Multifractal analysis of polyalanines time series  

NASA Astrophysics Data System (ADS)

Multifractal properties of the energy time series of short ?-helix structures, specifically from a polyalanine family, are investigated through the MF-DFA technique ( multifractal detrended fluctuation analysis). Estimates for the generalized Hurst exponent h(q) and its associated multifractal exponents ?(q) are obtained for several series generated by numerical simulations of molecular dynamics in different systems from distinct initial conformations. All simulations were performed using the GROMOS force field, implemented in the program THOR. The main results have shown that all series exhibit multifractal behavior depending on the number of residues and temperature. Moreover, the multifractal spectra reveal important aspects of the time evolution of the system and suggest that the nucleation process of the secondary structures during the visits on the energy hyper-surface is an essential feature of the folding process.

Figueirêdo, P. H.; Nogueira, E.; Moret, M. A.; Coutinho, Sérgio

2010-05-01

271

Fractal fluctuations in cardiac time series  

NASA Technical Reports Server (NTRS)

Human heart rate, controlled by complex feedback mechanisms, is a vital index of systematic circulation. However, it has been shown that beat-to-beat values of heart rate fluctuate continually over a wide range of time scales. Herein we use the relative dispersion, the ratio of the standard deviation to the mean, to show, by systematically aggregating the data, that the correlation in the beat-to-beat cardiac time series is a modulated inverse power law. This scaling property indicates the existence of long-time memory in the underlying cardiac control process and supports the conclusion that heart rate variability is a temporal fractal. We argue that the cardiac control system has allometric properties that enable it to respond to a dynamical environment through scaling.

West, B. J.; Zhang, R.; Sanders, A. W.; Miniyar, S.; Zuckerman, J. H.; Levine, B. D.; Blomqvist, C. G. (Principal Investigator)

1999-01-01

272

Modelling high-frequency economic time series  

NASA Astrophysics Data System (ADS)

The minute-by-minute move of the Hang Seng index (HSI) data over a 4-yr period is analysed and shown to possess similar statistical features as those of other markets. Based on a mathematical theorem (Pope, Ching, Phys. Fluids A 5 (1993) 1529), we derive an analytic form for the probability distribution function (PDF) of index moves from fitted functional forms of certain conditional averages of the time series. Furthermore, following a recent work by Stolovitzky and Ching (Phys. Lett. A 255 (1999) 11), we show that the observed PDF can be reproduced by a Langevin process with a move-dependent noise amplitude. The form of the Langevin equation can be determined directly from the market data.

Tang, Lei-Han; Huang, Zhi-Feng

2000-12-01

273

Modeling of aggregated hydrologic time series  

NASA Astrophysics Data System (ADS)

The concept of aggregation of the most commonly used models of seasonal hydrologic time series is the main subject discussed herein. The PAR(1) and PARMA(1, 1) models are assumed for representing the seasonal series and their equivalent stationarity and invertibility conditions are given. Likewise explicit expressions are given for determining the periodic covariance structure of such models and the concept of aggregation is illustrated by deriving the model of the corresponding annual series. Since the models of the seasonal series dictate the type of model of the annual series, then a unique structural linkage in the usual linear disaggregation model may be obtained in closed form. Seasonal and annual flows of the Niger River are used to illustrate some of the estimation procedures based on the foregoing aggregation approach.

Obeysekera, J. T. B.; Salas, J. D.

1986-10-01

274

Generalized redundancies for time series analysis  

NASA Astrophysics Data System (ADS)

Extensions to various information-theoretic quantities (such as entropy, redundancy, and mutual information) are discussed in the context of their role in nonlinear time series analysis. We also discuss “linearized” versions of these quantities and their use as benchmarks in tests for nonlinearity. Many of these quantities can be expressed in terms of the generalized correlation integral, and this expression permits us to more clearly exhibit the relationships of these quantities to each other and to other commonly used nonlinear statistics (such as the BDS and Green-Savit statistics). Further, numerical estimation of these quantities is found to be more accurate and more efficient when the the correlation integral is employed in the computation. Finally, we consider several “local” versions of these quantities, including a local Kolmogorov-Sinai entropy, which gives an estimate of variability of the short-term predictability.

Prichard, Dean; Theiler, James

275

Scaling laws from geomagnetic time series  

USGS Publications Warehouse

The notion of extended self-similarity (ESS) is applied here for the X - component time series of geomagnetic field fluctuations. Plotting nth order structure functions against the fourth order structure function we show that low-frequency geomagnetic fluctuations up to the order n = 10 follow the same scaling laws as MHD fluctuations in solar wind, however, for higher frequencies (f > l/5[h]) a clear departure from the expected universality is observed for n > 6. ESS does not allow to make an unambiguous statement about the non triviality of scaling laws in "geomagnetic" turbulence. However, we suggest to use higher order moments as promising diagnostic tools for mapping the contributions of various remote magnetospheric sources to local observatory data. Copyright 1998 by the American Geophysical Union.

Voros, Z.; Kovacs, P.; Juhasz, A.; Kormendi, A.; Green, A. W.

1998-01-01

276

Using entropy to cut complex time series  

NASA Astrophysics Data System (ADS)

Using techniques from statistical physics, physicists have modeled and analyzed human phenomena varying from academic citation rates to disease spreading to vehicular traffic jams. The last decade's explosion of digital information and the growing ubiquity of smartphones has led to a wealth of human self-reported data. This wealth of data comes at a cost, including non-uniform sampling and statistically significant but physically insignificant correlations. In this talk I present our work using entropy to identify stationary sub-sequences of self-reported human weight from a weight management web site. Our entropic approach--inspired by the infomap network community detection algorithm--is far less biased by rare fluctuations than more traditional time series segmentation techniques.

Mertens, David; Poncela Casasnovas, Julia; Spring, Bonnie; Amaral, L. A. N.

2013-03-01

277

Potential for calibration of geostationary meteorological satellite imagers using the Moon  

USGS Publications Warehouse

Solar-band imagery from geostationary meteorological satellites has been utilized in a number of important applications in Earth Science that require radiometric calibration. Because these satellite systems typically lack on-board calibrators, various techniques have been employed to establish "ground truth", including observations of stable ground sites and oceans, and cross-calibrating with coincident observations made by instruments with on-board calibration systems. The Moon appears regularly in the margins and corners of full-disk operational images of the Earth acquired by meteorological instruments with a rectangular field of regard, typically several times each month, which provides an excellent opportunity for radiometric calibration. The USGS RObotic Lunar Observatory (ROLO) project has developed the capability for on-orbit calibration using the Moon via a model for lunar spectral irradiance that accommodates the geometries of illumination and viewing by a spacecraft. The ROLO model has been used to determine on-orbit response characteristics for several NASA EOS instruments in low Earth orbit. Relative response trending with precision approaching 0.1% per year has been achieved for SeaWiFS as a result of the long time-series of lunar observations collected by that instrument. The method has a demonstrated capability for cross-calibration of different instruments that have viewed the Moon. The Moon appears skewed in high-resolution meteorological images, primarily due to satellite orbital motion during acquisition; however, the geometric correction for this is straightforward. By integrating the lunar disk image to an equivalent irradiance, and using knowledge of the sensor's spectral response, a calibration can be developed through comparison against the ROLO lunar model. The inherent stability of the lunar surface means that lunar calibration can be applied to observations made at any time, including retroactively. Archived geostationary imager data that contains the Moon can be used to develop response histories for these instruments, regardless of their current operational status.

Stone, T. C.; Kieffer, H. H.; Grant, I. F.

2005-01-01

278

Digital image-in-image watermarking for copyright protection of satellite images using the fast Hadamard transform  

Microsoft Academic Search

In this paper, a robust and efficient digital image watermarking algorithm using the fast Hadamard transform (FFIT) is proposed for the copyright protection of satellite images. This algorithm can embed or hide an entire image or pattern as a watermark such as a company's logo or trademark directly into the original satellite image. The performance of the proposed algorithm is

Anthony T. S. Ho; Jun Shen; Soon Hie Tan; Alex C. Kot

2002-01-01

279

Advances in time-series InSAR  

NASA Astrophysics Data System (ADS)

The need to measure temporally-evolving geophysical processes has spurred the development of precise geodetic Earth surface imaging methods using InSAR time series. The sequences of interferograms analyzed with these techniques not only reveal how the surface deforms with time, but permit unprecendented (1 mm/yr or less) accuracy over wide areas. Here we highlight several recent directions in the continuing improvement of the technology: a more comprehensive model of decorrelation that includes partially coherent pixels used in persistent scattering (PS) analysis, the use of information-theoretic tools to describe and optimize PS selection, phase retrieval, and extrapolation, and the development of 3D displacement imaging with PS and SBAS (small baseline subset analysis) methods. In addition, InSAR data collected as time series can be processed spatially for volume imaging of the surface cover. Such applications as forest canopy structure or ice volume effects benefit from this approach. Here we present each of these developments, both in theory and with examples from the current constellation of airborne and spaceborne radar systems to show the accuracies achievable today.

Zebker, H. A.; Wortham, C.; Lien, J.; Agram, P. S.

2011-12-01

280

Effect of Ground Cover on Satellite Images  

NSDL National Science Digital Library

This applet explores how the thickness of a cloud changes the way it looks from a satellite, as well as how modifying the surface characteristics (ground cover type) alters the brightness of the scene. Students may alter cloud thickness and vary the ground cover to include snow/ice, forests, oceans and crops.

Whittaker, Tom; Ackerman, Steve

281

Observe animated satellite images of water vapor  

NSDL National Science Digital Library

This Flash animation points out water vapor content 6-10 km above Earth's surface measured by infrared sensors on satellites. Lighter areas represent high moisture content, darker areas, little water vapor. Jet streams are viewed as elongated dark regions bordered by lighter sections.

Goes; Noaa; Earth, Exploring

282

Monitoring of Illegal Dumping Using Satellite Images  

Microsoft Academic Search

Illegal dumping of industrial wastes is recently becoming one of the serious social and environmental problems in Japan. Illegal dumping is currently monitored with airplane patrol or ground inspection by local government. However, it becomes more difficult to discover illegal dumping sites with conventional methods since dumping itself is getting crafty. Satellite remote sensing can provide an efficient tool to

Nobuaki ISHIHARA; Shiro OCHI; Yoshifumi YASUOKA; Masayuki TAMURA

283

Retrieving Multispectral Satellite Images Using Physics-Based Invariant Representations  

Microsoft Academic Search

We present a set of algorithms and a search strategy for the robust content-based retrieval of multispectral satellite images. Since the property of interest in these images is usually the physical characteristics of ground cover, we use representations and methods that are invariant to illumination and atmospheric conditions. The representations and algorithms are derived for this application from a physical

Glenn Healey; Amit Jain

1996-01-01

284

Timber age verification using historical satellite image analysis  

Microsoft Academic Search

Timber inventory data is important for estimating the net present value (NPV) of timberland sales among paper companies, timber investment and management organizations (TIMO), institutional investors, and individual investors. Timber age in an inventory GIS database can be quickly verified through a series of historical satellite image analyses based on image differencing techniques. The normalized difference vegetation index (NDVI), tassel-cap

Joon Heo; Jong Hong Kim; Ji Sang Park; Hong-Gyoo Sohn

2006-01-01

285

Downscaling of satellite hyperspectral images for monitoring croplands  

Microsoft Academic Search

Remote sensing has potential to provide a cost-efficient and fast tool to map soil properties across large areas. Especially, hyperspectral image can potentially discriminate between crop residues and soils as well as vegetation. Satellite hyperspectral image has very narrow spectral bands but a coarse spatial resolution to detect soil properties and vegetation in small parcels of croplands. This study focused

Eunyoung Choe; SukYoung Hong; YiHyun Kim

2010-01-01

286

Cassini imaging of Jupiter's atmosphere, satellites, and rings.  

PubMed

The Cassini Imaging Science Subsystem acquired about 26,000 images of the Jupiter system as the spacecraft encountered the giant planet en route to Saturn. We report findings on Jupiter's zonal winds, convective storms, low-latitude upper troposphere, polar stratosphere, and northern aurora. We also describe previously unseen emissions arising from Io and Europa in eclipse, a giant volcanic plume over Io's north pole, disk-resolved images of the satellite Himalia, circumstantial evidence for a causal relation between the satellites Metis and Adrastea and the main jovian ring, and information on the nature of the ring particles. PMID:12624258

Porco, Carolyn C; West, Robert A; McEwen, Alfred; Del Genio, Anthony D; Ingersoll, Andrew P; Thomas, Peter; Squyres, Steve; Dones, Luke; Murray, Carl D; Johnson, Torrence V; Burns, Joseph A; Brahic, Andre; Neukum, Gerhard; Veverka, Joseph; Barbara, John M; Denk, Tilmann; Evans, Michael; Ferrier, Joseph J; Geissler, Paul; Helfenstein, Paul; Roatsch, Thomas; Throop, Henry; Tiscareno, Matthew; Vasavada, Ashwin R

2003-03-01

287

The McIDAS system. [for meteorological satellite image processing  

NASA Technical Reports Server (NTRS)

The man-computer interactive data access system (McIDAS) hardware and software design is outlined, with emphasis on meteorological applications. The McIDAS system features a flexible digital image enhancement device. Theory of operation, the McIDAS language, the McIDAS executive monitor, image acquisition, image parameters, image display, graphics, image navigation, and image massaging are explained. The WINDCO/CLDHGT system, developed to align time sequences of geosynchronous satellite pictures and to determine the motion and height of selected cloud targets, is also described.

Smith, E. A.

1975-01-01

288

Time series modeling for syndromic surveillance  

PubMed Central

Background Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Methods Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Results Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Conclusions Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization.

Reis, Ben Y; Mandl, Kenneth D

2003-01-01

289

Workshop on Satellite Meteorology. Part 2; Satellite Image Analysis and Interpretation  

NASA Technical Reports Server (NTRS)

The Workshop on Satellite Meteorology is co-sponsored by the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University and the American Meteorological Society's Committee on Meteorological Aspects of Aerospace Systems. The workshop covers uses of satellite data in atmospheric science. It provides state-of-the-art information to those in Universities, research groups, and other users. One area of primary focus is to provide source material to university personnel for developing and augmenting courses in satellite meteorology and the atmospheric sciences. The items in the program include information on meteorological satellites and data sources, uses of satellite imagery for all scales of weather analysis and forecasting, uses of sounding data and other radiance information and research opportunities on interactive systems. Each session is presented by a group of experts in the field and includes an open discussion of the state-of-the-art and promising areas for future development. This pre-print volume is one of three parts on the workshop. The three parts are: PART I. Satellites and Their Data; PART II. Satellite Image Analysis and Interpretation; PART III. Satellite Soundings and Their Uses.

1982-01-01

290

Phase correlation of foreign exchange time series  

NASA Astrophysics Data System (ADS)

Correlation of foreign exchange rates in currency markets is investigated based on the empirical data of USD/DEM and USD/JPY exchange rates for a period from February 1 1986 to December 31 1996. The return of exchange time series is first decomposed into a number of intrinsic mode functions (IMFs) by the empirical mode decomposition method. The instantaneous phases of the resultant IMFs calculated by the Hilbert transform are then used to characterize the behaviors of pricing transmissions, and the correlation is probed by measuring the phase differences between two IMFs in the same order. From the distribution of phase differences, our results show explicitly that the correlations are stronger in daily time scale than in longer time scales. The demonstration for the correlations in periods of 1986-1989 and 1990-1993 indicates two exchange rates in the former period were more correlated than in the latter period. The result is consistent with the observations from the cross-correlation calculation.

Wu, Ming-Chya

2007-03-01

291

Extraction of stochastic dynamics from time series  

NASA Astrophysics Data System (ADS)

We present a method for the reconstruction of the dynamics of processes with discrete time. The time series from such a system is described by a stochastic recurrence equation, the continuous form of which is known as the Langevin equation. The deterministic f and stochastic g components of the stochastic equation are directly extracted from the measurement data with the assumption that the noise has finite moments and has a zero mean and a unit variance. No other information about the noise distribution is needed. This is contrary to the usual Langevin description, in which the additional assumption that the noise is Gaussian (?-correlated) distributed as necessary. We test the method using one dimensional deterministic systems (the tent and logistic maps) with Gaussian and with Gumbel noise. In addition, results for human heart rate variability are presented as an example of the application of our method to real data. The differences between cardiological cases can be observed in the properties of the deterministic part f and of the reconstructed noise distribution.

Petelczyc, M.; ?ebrowski, J. J.; Gac, J. M.

2012-07-01

292

A New SBUV Ozone Profile Time Series  

NASA Technical Reports Server (NTRS)

Under NASA's MEaSUREs program for creating long term multi-instrument data sets, our group at Goddard has re-processed ozone profile data from a series of SBUV instruments. We have processed data from the Nimbus 7 SBUV instrument (1979-1990) and data from SBUV/2 instruments on NOAA-9 (1985-1998), NOAA-11 (1989-1995), NOAA-16 (2001-2010), NOAA-17 (2002-2010), and NOAA-18 (2005-2010). This reprocessing uses the version 8 ozone profile algorithm but now uses the Brion, Daumont, and Malicet (BMD) ozone cross sections instead of the Bass and Paur cross sections. The new cross sections have much better resolution, and extended wavelength range, and a more consistent temperature dependence. The re-processing also uses an improved cloud height climatology based on the Raman cloud retrievals of OMI. Finally, the instrument-to-instrument calibration is set using matched scenes so that ozone diurnal variation in the upper stratosphere does not alias into the ozone trands. Where there is no instrument overlap, SAGE and MLS are used to estimate calibration offsets. Preliminary analysis shows a more coherent time series as a function of altitude. The net effect on profile total column ozone is on average an absolute reduction of about one percent. Comparisons with ground-based systems are significantly better at high latitudes.

McPeters, Richard

2011-01-01

293

Spectrophotometric Time Series of ? Carinae's Great Eruption  

NASA Astrophysics Data System (ADS)

? Car serves as our most important template for understanding non-SN transients from massive stars in external galaxies. However, until recently, no spectra were available because its historic ``Great Eruption'' (GE) occurred from 1838-1858, before the invention of the astronomical spectrograph, and only visual estimates of its brightness were recorded teSF11. Now we can also obtain a spectral sequence of the eruption through its light echoes we discovered, which will be of great value since spectra are our most important tool for inferring physical properties of extragalactic transients. Subsequent spectroscopic follow-up revealed that its outburst was most similar to those of G-type supergiants, rather than reported LBV outburst spectral types of F-type (or earlier) teRest12_eta. These differences between the GE and the extragalactic transients presumed to be its analogues raise questions about traditional scenarios for the outburst. We propose to obtain a spectrophotometric time series of the GE from different directions, allowing the original eruption of ? Car to be studied as a function of time as well as latitude, something only possible with light echoes. This unique detailed spectroscopic study of the light echoes of ? Car will help us understand (episodic) mass- loss in the most massive evolved stars and their connection to the most energetic core-collapse SNe.

Rest, Armin; Bianco, Federica; Chornock, Ryan; Clocchiatti, Alejandro; James, David; Margheim, Steve; Matheson, Thomas; Prieto, Jose Luis; Smith, Chris; Smith, Nathan; Walborn, Nolan; Welch, Doug; Zenteno, Alfredo

2014-08-01

294

Spectrophotometric Time Series of ? Carinae's Great Eruption  

NASA Astrophysics Data System (ADS)

? Carinae (? Car) serves as our most important template for understanding non-SN transients from massive stars in external galaxies. However, until recently, no spectra were available because its historic ``Great Eruption'' (GE) occurred before the invention of the astronomical spectrograph. Now we can also obtain a spectral sequence of the eruption through echoes, which will be of great value since spectra are our most important tool for inferring physical properties of extragalactic transients. ? Car was seen as the second brightest star in the sky during its 1800s GE, but only visual estimates of its brightness were recorded teSF11. In 2011 we discovered several light echoes (LEs) which appear to be from the 1838- 1858 ? Car eruptions teRest12_eta. Subsequent spectroscopic follow-up revealed that its outburst spectral type was most similar to those of G-type supergiants, rather than reported LBV outburst spectral types of F-type (or earlier) teRest12_eta. These differences between the GE and the extragalactic transients presumed to be its analogues raise questions about traditional scenarios for the outburst. We propose to obtain a spectrophotometric time series of the GE from different directions, allowing the original eruption of ? Car to be studied as a function of time as well as latitude. A detailed spectroscopic study of the LEs of ? Car would help us understand (episodic) mass-loss in the most massive evolved stars and their connection to the most energetic core-collapse SNe.

Rest, Armin; Bianco, Federica; Chornock, Ryan; Matheson, Thomas; Prieto, Jose Luis; Smith, Chris; Smith, Nathan; Walborn, Nolan; Welch, Doug

2014-02-01

295

Evolutionary factor analysis of replicated time series.  

PubMed

In this article, we develop a novel method that explains the dynamic structure of multi-channel electroencephalograms (EEGs) recorded from several trials in a motor-visual task experiment. Preliminary analyses of our data suggest two statistical challenges. First, the variance at each channel and cross-covariance between each pair of channels evolve over time. Moreover, the cross-covariance profiles display a common structure across all pairs, and these features consistently appear across all trials. In the light of these features, we develop a novel evolutionary factor model (EFM) for multi-channel EEG data that systematically integrates information across replicated trials and allows for smoothly time-varying factor loadings. The individual EEGs series share common features across trials, thus, suggesting the need to pool information across trials, which motivates the use of the EFM for replicated time series. We explain the common co-movements of EEG signals through the existence of a small number of common factors. These latent factors are primarily responsible for processing the visual-motor task which, through the loadings, drive the behavior of the signals observed at different channels. The estimation of the time-varying loadings is based on the spectral decomposition of the estimated time-varying covariance matrix. PMID:22364516

Motta, Giovanni; Ombao, Hernando

2012-09-01

296

Information Contents of High Resolution Satellite Images  

Microsoft Academic Search

Large scale topographic maps do include more details like small scale maps. Corresponding to this, the required details have to be visible in the used images. Not only the pixel size, also the image quality, the spectral range and the number of spectral bands and the sensor type are important for the object identification. Even if space photos are scanned

H. Topan; K. Jacobsen

297

Ground-Truthing Moderate Resolution Satellite Imagery with Near-Surface Canopy Images in Hawai'i's Tropical Cloud Forests  

NASA Astrophysics Data System (ADS)

Phenological studies are gaining importance globally as the onset of climate change is impacting the timing of green up and senescence in forest canopies and agricultural regions. Many studies use and analyze land surface phenology (LSP) derived from satellite vegetation index time series (VI's) such as those from Moderate Resolution Imaging Spectroradiometer (MODIS) to monitor changes in phenological events. Seasonality is expected in deciduous temperate forests, while tropical regions are predicted to show more static reflectance readings given their stable and steady state. Due to persistent cloud cover and atmospheric interference in tropical regions, satellite VI time series are often subject to uncertainties and thus require near surface vegetation monitoring systems for ground-truthing. This study has been designed to assess the precision of MODIS phenological signatures using above-canopy, down-looking digital cameras installed on flux towers on the Island of Hawai'i. The cameras are part of the expanding Phenological Eyes Network (PEN) which has been implementing a global network of above-canopy, hemispherical digital cameras for forest and agricultural phenological monitoring. Cameras have been installed at two locations in Hawaii - one on a flux tower in close proximity to the Thurston Lave Tube (HVT) in Hawai'i Volcanoes National Park and the other on a weather station in a section of the Hawaiian Tropical Experimental Forest in Laupaphoehoe (LEF). HVT consists primarily of a single canopy species, ohi'a lehua (Metrosideros polymorpha), with an understory of hapu'u ferns (Cibotium spp), while LEF is similarly comprised with an additional dominant species, Koa (Acacia Koa), included in the canopy structure. Given these species' characteristics, HVT is expected to show little seasonality, while LEF has the potential to deviate slightly during periods following dry and wet seasons. MODIS VI time series data are being analyzed and will be compared to images from the cameras which will have VI's extracted from their RGB image planes and will be normalized to be comparable with MODIS VI's. Given Hawai'i's susceptibility to invasion and delicacy of its endemic species, results from this study will provide necessary site specific detail in determining the reliability of satellite based inference in similar tropical phenology studies. Should satellite images provide adequate information, results from this study will allow for extrapolation across similar understudied tropical forests.

Bergstrom, R.; Miura, T.; Lepczyk, C.; Giambelluca, T. W.; Nullet, M. A.; Nagai, S.

2012-12-01

298

Modeling Multivariate Covariance Nonstationary Time Series and Their Dependency Structure.  

National Technical Information Service (NTIS)

The parametric modeling of covariance nonstationary time series and the computation of their changing interdependency structure from the fitted model are treated. The nonstationary time series are modeled by a multivariate time varying autoregressive (AR)...

W. Gersch

1985-01-01

299

TSAP - A Time Series Analysis Package for Terminal Use.  

National Technical Information Service (NTIS)

The time series analysis package, TSAP, is an on-line system of interactive, functionally dependent programs for the analysis of time series. This analysis usually consists of a long sequence of statistical computations which requires decision making as t...

R. R. Singers

1973-01-01

300

AVHRR, MODIS and Landsat Time Series for the Monitoring of Vegetation Changes Around the World (Invited)  

NASA Astrophysics Data System (ADS)

A confluence of computing power, cost of storage, ease of access to data, and ease of product delivery make it possible to harness the power of multiple remote sensing data streams to monitor land surface dynamics. Change detection has always been a fundamental remote sensing task, and there are myriad ways to perceive differences. From a statistical viewpoint, image time series of the vegetated land surface are complicated data to analyze. The time series are often seasonal and have high temporal autocorrelation. These characteristics result in the failure of the data to meet the assumption of most standard parametric statistical tests. Failure of statistical assumptions is not trivial and the use of inappropriate statistical methods may lead to the detection of spurious trends, while any actual trends and/or step changes might be overlooked. While the analysis of messy data, which can be influenced by discontinuity, missing observation, non-linearity and seasonality, is still developing within the remote sensing community, other scientific research areas routinely encounter similar problems and have developed statistically appropriate ways to deal with them. In this talk we describe the process of change analysis as a sequence of tasks: (1) detection of changes; (2) quantification of changes; (3) assessment of changes; (4) attribution of changes; and (5) projection of the potential consequences of changes. To detect, quantify, and assess the significance of broad scale land surface changes, we will first apply the nonparametric Seasonal Kendall (SK) trend test corrected for first-order temporal autocorrelation to MODIS image time series. We will then discuss three case studies, situated in the USA, Russia, and New Zealand in which we combine or fuse satellite data at two spatial resolutions (30m Landsat and 500m MODIS) to assess and attribute changes at fine spatial and temporal scales. In the USA we will investigate changes as a result of urban development, in Russia we will map cropping intensity between 2002 and 2012 to get a better understanding of the activity occurring on arable lands. In New Zealand, we demonstrate a fused image product to detect fine temporal and spatial resolution disturbances in plantation forests and grazing lands.

de Beurs, K.; Owsley, B.; Julian, J.; Henebry, G. M.

2013-12-01

301

Copula–Based Models for Financial Time Series  

Microsoft Academic Search

This paper presents an overview of the literature on applications of copulas in the modelling of financial time series. Copulas\\u000a have been used both in multivariate time series analysis, where they are used to characterize the (conditional) cross-sectional\\u000a dependence between individual time series, and in univariate time series analysis, where they are used to characterize the\\u000a dependence between a sequence

Andrew J. Patton

2008-01-01

302

APCAS: An Approximate Approach to Adaptively Segment Time Series Stream  

Microsoft Academic Search

We study the problem of segmenting time series stream. Existing segmenting methods for time series mainly focus on the static\\u000a data, and may be infeasible under the circumstance of time series stream. We propose an approximate method of APCAS(Adaptive Piecewise Constant Approximate Segmentation) to adaptively segment time series stream, which works in linear time. Extensive experiments, both on synthetic and

Li Junkui; Wang Yuanzhen

2007-01-01

303

Timeline hidden Markov experts for time series prediction  

Microsoft Academic Search

A modularised connectionist model, based on the mixture of experts (ME) algorithm for time series prediction, is introduced. A group of connectionist modules learn to be local experts over some commonly appeared states in a time series. The dynamics for combining the experts is a hidden Markov process, in which the states of a time series are regarded as states

Xin Wang; Peter Whigham; Da Deng; Martin Purvis

2003-01-01

304

Hidden Markov model segmentation of hydrological and enviromental time series  

Microsoft Academic Search

Motivated by Hubert's segmentation procedure (16, 17), we discuss the application of hidden Markov models (HMM) to the segmentation of hydrological and enviromental time series. We use a HMM algorithm which segments time series of several hundred terms in a few seconds and is compu- tationally feasible for even longer time series. The segmentation algorithm computes the Maximum Likelihood segmentation

Athanasios Kehagias

2002-01-01

305

Multi-step Time Series Prediction in Complex Instrumented Domains  

Microsoft Academic Search

Time series prediction algorithms are widely used for applications such as demand forecasting, weather forecasting and many others to make well informed decisions. In this paper, we compare the most prevalent of these methods as well as suggest our own, where the time series are generated from highly complex industrial processes. These time series are non-stationary and the relationships between

Amit Dhurandhar

2010-01-01

306

Characteristic-Based Clustering for Time Series Data  

Microsoft Academic Search

With the growing importance of time series clustering research, particularly for similarity searches amongst long time series such as those arising in medicine or finance, it is critical for us to find a way to resolve the outstanding problems that make most clustering methods impractical under certain circumstances. When the time series is very long, some clustering algorithms may fail

Xiaozhe Wang; Kate A. Smith; Rob J. Hyndman

2006-01-01

307

Level change detection in time series using higher order statistics  

Microsoft Academic Search

Changes in the level of a time series are usually attributed to an intervention that interrupts its evolution. The resulting time series are referred to as interrupted time series and they are studied in order to measure, e.g. the impact of new laws or medical treatments. In the present paper a heuristic method for level change detection in non-stationary time

C. S. Hilas; I. T. Rekanos; S. K. Goudos; P. A. Mastorocostas; J. N. Sahalos

2009-01-01

308

Notes on Time Series Analysis. Special Report No. 722.  

ERIC Educational Resources Information Center

A research project developed a computer program for analyzing time series quasi-experimental data. The program generates a nonstationary, integrated moving average time series model; it is used to estimate a parameter which indexes the instantaneous change in level of the time series due to a predesignated treatment. The entire method is based on…

Pruzek, Robert; Bond, Jack H.

309

An observed 20 yr time-series of Agulhas leakage  

NASA Astrophysics Data System (ADS)

We provide a time series of Agulhas leakage anomalies over the last twenty years from satellite altimetry. Until now, measuring the interannual variability of Indo-Atlantic exchange has been the major barrier in the investigation of the dynamics and large scale impact of Agulhas leakage. We compute the difference of transport between Agulhas Current and Agulhas Return Current which allows to deduce Agulhas leakage. The main difficulty is to separate the Agulhas Return Current from the southern limb of the subtropical "supergyre" south of Africa. For this purpose, an algorithm that uses absolute dynamic topography data is developed. The algorithm is applied to a state-of-the-art ocean model. The comparison with a Lagrangian method to measure the leakage allows to validate the new method. An important result is that it is possible to measure Agulhas leakage in this model using the velocity field along a section that crosses both the Agulhas Current and the Agulhas Return Current. In the model a good correlation is found between measuring leakage using the full depth velocities and using only the surface geostrophic velocities. This allows us to extend the method to along-track absolute dynamic topography from satellites. It is shown that the accuracy of the mean dynamic topography does not allow to determine the mean leakage but that leakage anomalies can be accurately computed.

Le Bars, D.; Durgadoo, J. V.; Dijkstra, H. A.; Biastoch, A.; De Ruijter, W. P. M.

2014-01-01

310

Imaging of Geostationary Satellites with the MRO Interferometer  

NASA Astrophysics Data System (ADS)

The emerging field of optical interferometery will enable imaging of geo-stationary satellites at a height of 36,000 meter with a resolution of less than 1 meter. The current generation of optical interferometers has baselines up to 300 meters, which is a factor 100 larger than a 3 meter single dish telescope. Since the spatial resolution scales with wavelength over lambda, the increase in baseline translates directly in an increase in resolving power to see smaller details. The Magdalena Ridge Observatory Interferometer (MROI) will be a 10 element optical interferometer. Each telescope will have a 1.4 meter primary mirror and the maximum distance within the array is close to 400 meters. MROI is currently under construction in the heart of New Mexico and is designed to meet a dual purpose: provide imaging capabilities for space situational awareness, and to provide science capabilities to astronomers. This paper is specifically aimed to demonstrate the capabilities of MROI for imaging geostationary satellites. We have performed simulations of the performance of MROI to image geostationary satellites. These simulations start with a real image of a satellite and a model which uses simple geometric shapes to best represent the real image. This model is fed to a simulator that takes into account the interferometer array configuration and computes the observables using estimates of the errors in the observations and due to the intervening atmosphere. For an imaging interferometer these are the visibilities and closures phases for each available baseline. Finally we use exiting imaging reconstruction algorithms to compute a reconstructed image. To complete the paper with present a discussion on the limitations of these simulations and optical interferometry in general, but will also point to specific issues of interest to the community that these simulations have identified. A separate paper will be presented by the MRO program office that provides an overview of the status of the MRO project.

Bakker, E.; Cormier, C.; Romero, V.

311

Surface reconstruction and landslide displacement measurements with Pléiades satellite images  

NASA Astrophysics Data System (ADS)

Recent advances in image-matching techniques and VHR satellite imaging at submeter resolution theoretically offer the possibility to measure Earth surface displacements with decimetric precision. However, this possibility has yet not been explored and requirements of ground control and external topographic datasets are considered as important bottlenecks that hinder a more common application of optical image correlation for displacement measurements. This article describes an approach combining spaceborne stereo-photogrammetry, orthorectification and sub-pixel image correlation to measure the horizontal surface displacement of landslides from Pléiades satellite images. The influence of the number of ground-control points on the accuracy of the image orientation, the extracted surface models and the estimated displacement rates is quantified through comparisons with airborne laser scan and in situ global navigation satellite measurements at permanent stations. The comparison shows a maximum error of 0.13 m which is one order of magnitude more accurate than what has been previously reported with spaceborne optical images from other sensors. The obtained results indicate that the approach can be applied without significant loss in accuracy when no ground control points are available. It could, therefore, greatly facilitate displacement measurements for a broad range of applications.

Stumpf, A.; Malet, J.-P.; Allemand, P.; Ulrich, P.

2014-09-01

312

Hydroxyl time series and recirculation in turbulent nonpremixed swirling flames  

SciTech Connect

Time-series measurements of OH, as related to accompanying flow structures, are reported using picosecond time-resolved laser-induced fluorescence (PITLIF) and particle-imaging velocimetry (PIV) for turbulent, swirling, nonpremixed methane-air flames. The [OH] data portray a primary reaction zone surrounding the internal recirculation zone, with residual OH in the recirculation zone approaching chemical equilibrium. Modeling of the OH electronic quenching environment, when compared to fluorescence lifetime measurements, offers additional evidence that the reaction zone burns as a partially premixed flame. A time-series analysis affirms the presence of thin flamelet-like regions based on the relation between swirl-induced turbulence and fluctuations of [OH] in the reaction and recirculation zones. The OH integral time-scales are found to correspond qualitatively to local mean velocities. Furthermore, quantitative dependencies can be established with respect to axial position, Reynolds number, and global equivalence ratio. Given these relationships, the OH time-scales, and thus the primary reaction zone, appear to be dominated by convection-driven fluctuations. Surprisingly, the OH time-scales for these nominally swirling flames demonstrate significant similarities to previous PITLIF results in nonpremixed jet flames. (author)

Guttenfelder, Walter A.; Laurendeau, Normand M.; Ji, Jun; King, Galen B.; Gore, Jay P. [School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907-1288 (United States); Renfro, Michael W. [Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269-3139 (United States)

2006-10-15

313

Detection of cavity migration risks using radar interferometric time series  

NASA Astrophysics Data System (ADS)

The upward migration of near-surface underground cavities can pose a major hazard for people and infrastructure. Being the major cause of sudden collapse-sinkholes, or causing a sudden lack of support of building foundations, a migrating cavity can cause the collapse of buildings, water defense systems, drainage of water bodies, or transport infrastructure. Cavity migration can occur naturally, e.g. in karst-massifs, but could also be caused by anthropogenic activities such as mining. The chief difficulty in the assessment of sinkhole risk is the lack of prior knowledge on the location of the cavity. Although in situ measurements such as gravimetry, seismic or EM-surveying or GPR are in principle able to detect an underground void, it is generally not economically possible to use these techniques over vast areas. Moreover, the risk of casualties is highest for urbanized areas, in which it is difficult to get close enough to perform these measurements. The second problem is that there is usually no data available prior to the collapse, to understand whether there is for example precursory motion, and how far ahead in time critical levels can be detected. Here we report on the catastrophic collapse of the foundation of an underground parking garage in Heerlen, the Netherlands. In December 2011, some pillars supporting the roof of the garage and the shopping mall above it suddenly subsided more than one meter. This caused the near collapse of a part of the shopping mall, the immediate evacuation of the building, and the decision of the authorities to eliminate the building. In the analysis of the event, several hypotheses were formulated on the driving mechanisms, such as subsurface water flows and karst. However, as the region was subject to coal mining in the last century, alternative hypotheses were cavity migration due to the mining, or rebound of the surface due to mine water. Our study jointly exploits the data archives of four imaging radar satellites, ERS-1, ERS-2, Envisat, and Radarsat-2, to investigate the dynamics (deformation) of the area. In particular we show, for the first time, shear-stress change distribution patterns within the structure of a building, over a period of close to 20 years. Time series analysis shows that deformation rates of ~4 mm/a could be detected for about 18 years, followed by a dramatic increase of up to 20 mm/a in the last period. These results imply that the driving mechanisms of the 2011 catastrophe have a very long lead time and are therefore likely due to a long-lasting gradual motion, such as the upward migration of a cavity. The analysis shows the collocation of the deformation location with relatively shallow near-horizontal mine shafts, suggesting that cavity migration has a high likelihood to be the driving mechanism of the collapse-sinkhole.

Chang, L.; Hanssen, R. F.

2012-12-01

314

EarthShots: Satellite Images of Environmental Change  

NSDL National Science Digital Library

EarthShots is an e-book of images (1972-present) showing recent environmental events through remotely sensed images while also introducing remote sensing techniques. Place-specific case studies offer before-and-after satellite imagery as well as descriptive text. Examples of case studies include agriculture along the Nile River Delta, urban development as it impacts the hydrology of the Imperial Valley in California, desertification in Southern Mauritania, the disastrous effects of the Mount St. Helens eruption in Washington, and glacial activity in Hubbard Glacier, Alaska. A world map allows users to access these instances of environmental change by geographic area. Information on remote sensing technology and an overview of the site is provided by a Garden City, Kansas case study. Each set of images and text are accompanied by a political/topographic map of the area, detailed information pertaining to the source of the satellite images and maps, and relevant references.

2001-01-12

315

Numbers to Pictures: How Satellite Images are Created  

NSDL National Science Digital Library

This activity introduces the primary colors of light. Satellites transmit images to us as a series of numbers, and this activity is designed to show how numbers are combined to create images using the primary colors of light. Students work in groups to create different colors using flashlights with red, blue, and green theatrical gels. Students create a numerical code to represent colors of light, experiment with building colors using the code, and complete a color mixing table.

316

High spatial resolution water level time series in the Florida Everglades wetlands using multi-track ALOS PALSAR data  

NASA Astrophysics Data System (ADS)

Wetland InSAR (Interferometric Synthetic Aperture Radar) observations provide very high-resolution maps of water level changes that cannot be obtained by any terrestrial technique. We recently developed the Small Temporal Baseline Subset (STBAS) approach, which combines single-track InSAR and stage (water level) observations to generate high-resolution absolute water level time series maps. However, the temporal resolution of produced time series is coarse compared with in-situ stage observation and, hence, the usefulness of these maps is rather limited. To compensate for the low temporal resolution weakness of space-based water level time series, we propose using a multi-track STBAS technique, which utilizes all available Synthetic Aperture Radar (SAR) observations acquired over a certain wetland area. We use a four-year long L-band ALOS PALSAR dataset acquired during 2007-2011 to test the proposed method over the Water Conservation Area 1 (WCA1) in the Everglades wetlands, south Florida (USA). A total of 37 images acquired with four tracks were collected. Daily water level data at 12 stage stations, which are monitored by the Everglades Depth Estimation Network (EDEN) in WCA1 area, were used to calibrate the InSAR-derived water level data. The proposed multi-track approach yielded a significant improvement of temporal resolution, which is dependent on the SAR satellite revisit cycle. Instead of the 46-day repeat orbit of ALOS, the multi-track method produces water level maps with temporal resolution of only 7 days. A quality control analysis of the methods indicates that the average root mean square error (RMSE) of the differences between stage water level and retrieved water level by InSAR technique is 4.0 cm. The end products of absolute water level time series with improved temporal and very high spatial resolutions can be used as excellent constraints for high-resolution wetland flow models. Furthermore, the next generation of SAR satellites has been designed with shorter revisit cycles, which will provide temporally denser maps of water level changes. Fig. 1. Comparison between stage (solid line) and InSAR (circle: 148 track, cross: 149 track, diamond: 464 track and square: 465 track) determined water level time series.

Hong, S.; Wdowinski, S.

2013-05-01

317

What do Satellite Images Tell Us About Mars?  

NSDL National Science Digital Library

Learners will compare satellite images of Mars and Earth to look for similar features. Then they brainstorm a list of forces or events that could have caused some of these features to form on Mars. This is activity 3 of 9 in Mars and Earth: Science Learning Activities for After School.

318

Change detection with 1 m resolution satellite and aerial images  

Microsoft Academic Search

We propose an optimization of a computer based change detection technique based on Iterative Principal Component Analysis (IPCA). We determine and evaluate the changes between an airborne and a spaceborne multispectral image data set, the latter recorded by the commercial satellite IKONOS-2. The change detection algorithm proved to be applicable to large remotely sensed data sets. A vegetation filter, a

Hartwig Spitzer; Ramon Franck; Martin Kollewe; Niklas Rega; A. Rothkirch; R. Wiemker

2001-01-01

319

Automatic Red Tide Detection using MODIS Satellite Images  

Microsoft Academic Search

Red tides pose a significant economic and environmental threat in the Gulf of Mexico. Detecting red tide is important for understanding this phenomenon. In this thesis, machine learning approaches based on Random Forests, Support Vector Machines and K-Nearest Neighbors have been evaluated for red tide detection from MODIS satellite images. Detection results using machine learning algorithms were compared to ship

Wijian Cheng

2009-01-01

320

Automatic traffic monitoring using neural networks from satellite images  

Microsoft Academic Search

Considering the widespread problems of road transport, approach of the paper is a system to automatically control the roads by using images from satellite in night and day. Although no coherent system with appropriate performance has been yet introduced to achieve this goal, some methods has been proposed to estimate the road or recognize objects on the road, which have

Mehrad Eslami; Karim Faez

2010-01-01

321

Satellite Image Analysis for Disaster and Crisis-Management Support  

Microsoft Academic Search

This paper describes how multisource satellite data and efficient image analysis may successfully be used to conduct rapid-mapping tasks in the domain of disaster and crisis-management support. The German Aerospace Center (DLR) has set up a dedicated crosscutting service, which is the so-called \\

Stefan Voigt; Thomas Kemper; Torsten Riedlinger; Ralph Kiefl; Klaas Scholte; Harald Mehl

2007-01-01

322

Singular spectrum analysis and forecasting of hydrological time series  

NASA Astrophysics Data System (ADS)

The singular spectrum analysis (SSA) technique is applied to some hydrological univariate time series to assess its ability to uncover important information from those series, and also its forecast skill. The SSA is carried out on annual precipitation, monthly runoff, and hourly water temperature time series. Information is obtained by extracting important components or, when possible, the whole signal from the time series. The extracted components are then subject to forecast by the SSA algorithm. It is illustrated the SSA ability to extract a slowly varying component (i.e. the trend) from the precipitation time series, the trend and oscillatory components from the runoff time series, and the whole signal from the water temperature time series. The SSA was also able to accurately forecast the extracted components of these time series.

Marques, C. A. F.; Ferreira, J. A.; Rocha, A.; Castanheira, J. M.; Melo-Gonçalves, P.; Vaz, N.; Dias, J. M.

323

Imaging Geosynchronous Satellites with the AEOS Telescope  

Microsoft Academic Search

The USA has significant civilian and military assets in geostationary orbit. High-resolution, ground-based imaging of these assets enables us to monitor in detail their health and safety and to detect the presence of any foreign microsatellites that might be in proximity. Although adaptive optics compensation of ground-based imagery imparts some level of mitigation of the deleterious effects due to the

Douglas A. Hope; Stuart M. Jefferies; Cindy Giebink

2008-01-01

324

Approximate Entropies for Stochastic Time Series and EKG Time Series of Patients with Epilepsy and Pseudoseizures  

NASA Astrophysics Data System (ADS)

A wide range of heart rate irregularities have been reported in small studies of patients with temporal lobe epilepsy [TLE]. We hypothesize that patients with TLE display cardiac dysautonomia in either a subclinical or clinical manner. In a small study, we have retrospectively identified (2003-8) two groups of patients from the epilepsy monitoring unit [EMU] at the Cleveland Clinic. No patients were diagnosed with cardiovascular morbidities. The control group consisted of patients with confirmed pseudoseizures and the experimental group had confirmed right temporal lobe epilepsy through a seizure free outcome after temporal lobectomy. We quantified the heart rate variability using the approximate entropy [ApEn]. We found similar values of the ApEn in all three states of consciousness (awake, sleep, and proceeding seizure onset). In the TLE group, there is some evidence for greater variability in the awake than in either the sleep or proceeding seizure onset. Here we present results for mathematically-generated time series: the heart rate fluctuations ? follow the ? statistics i.e., p(?)=?-1(k) ?^k exp(-?). This probability function has well-known properties and its Shannon entropy can be expressed in terms of the ?-function. The parameter k allows us to generate a family of heart rate time series with different statistics. The ApEn calculated for the generated time series for different values of k mimic the properties found for the TLE and pseudoseizure group. Our results suggest that the ApEn is an effective tool to probe differences in statistics of heart rate fluctuations.

Vyhnalek, Brian; Zurcher, Ulrich; O'Dwyer, Rebecca; Kaufman, Miron

2009-10-01

325

IDS plot tools for time series of DORIS station positions and orbit residuals  

NASA Astrophysics Data System (ADS)

DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite) is a Doppler satellite tracking system developed for precise orbit determination and precise ground location. It is onboard the Cryosat-2, Jason-1, Jason-2 and HY-2A altimetric satellites and the remote sensing satellites SPOT-4 and SPOT-5. It also flew with SPOT-2, SPOT-3, TOPEX/POSEIDON and ENVISAT. Since 1994 and thanks to its worldwide distributed network of more than fifty permanent stations, DORIS contributes to the realization and maintenance of the ITRS (International Terrestrial Reference System). 3D positions and velocities of the reference sites at a cm and mm/yr accuracy lead to scientific studies in geodesy and geophysics. The primary objective of the International DORIS Service (IDS) is to provide a support, through DORIS data and products, to research and operational activities. In order to promote the use of the DORIS products, the IDS has made available on its web site (ids-doris.org) a new set of tools, called Plot tools, to interactively build and display graphs of DORIS station coordinates time series and orbit residuals. These web tools are STCDtool providing station coordinates time series (North, East, Up position evolution) from the IDS Analysis Centers, and POEtool providing statistics time series (orbit residuals and number of measurements for the DORIS stations) from CNES (the French Space Agency) Precise Orbit Determination processing. Complementary data about station and satellites events can also be displayed (e.g. antenna changes, system failures, degraded data...). Information about earthquakes obtained from USGS survey service can also be superimposed on the position time series. All these events can help in interpreting the discontinuities in the time series. The purpose of this presentation is to show the functionalities of these tools and their interest for the monitoring of the crustal deformation at DORIS sites.

Soudarin, L.; Ferrage, P.; Moreaux, G.; Mezerette, A.

2012-12-01

326

Generation of long-term time series of remote sensing data using ESA's GPOD system  

NASA Astrophysics Data System (ADS)

Several authors have shown that the analysis of time series of remote sensing data is able to characterize the properties of different earth systems. They have used different techniques, ARIMA, Fourier Analysis and others, to estimate cyclical and trend variations from the information stored in the time series. Most systems in earth cycle due to seasonal changes in the flow of energy and/or matter, ecosystems cycle due to seasonal changes in evapotranspiration, day length and temperature. Time series analysis can characterized the period, amplitude and offset of the cycle. Trend analysis, on the other hand, can be used to detect permanent changes in certain systems. Changes in sea surface temperature or the artic ice pack due to climate change would belong to this category. Performing this analysis requires data with a good temporal resolution, usually in the range of days and a good temporal extent, usually in the multiyear timescale. As a result users have to process a big set of satellite images, sometimes in the order of thousands, to extract the information required. This processing consumes a great amount of time and resources due to the big amount of data and computer power involved. To facilitate the generation of long-term time series of remote sensing data the European Space Agency has developed a system able to extract all the information available for a small area from the MEdium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT in an easily accessible format. This service allows the user to specify an area of interest of rectangular or circular shape giving its geographical location and its size, specify the period of time that he is interested in and obtain several files with all the information available from the MERIS sensor for this location. For each MERIS product the information is given in two formats. One is highly portable following the XML standard. Output is also given in Google Earth and Excel formats allowing for a fast and easy analysis of the data recovered. The service generates also several summaries of all the products processed and stores them in easily usable formats. This service is able to generate year long time series processing several terabytes of data in the order of a couple of hours. It has already been used by several research groups proving its utility. The European Southern Observatory (ESO) used it to survey potential sites for the deploying of ESO's next generation of very large telescopes (E-ELT). Currently it's being used by the Max Planck Institute to characterize their two telescope sites at Mount Graham (US) and Calar Alto (Spain) The system is powered by ESA's GRID Processing on Demand infrastructure. This is a GRID-based operational environment able to process large amounts of remote sensing data in an efficient way. The access to ESA data catalogue coupled with high-performance and sizeable computing resources managed by GRID technologies, enables the user to develop applications that were not feasible till now.

Rubio, M. A.; Colin, O.; Mathot, E.

2009-04-01

327

Estimating vegetation phenological trends using MODIS NDVI time series  

NASA Astrophysics Data System (ADS)

The method to extract phenological information for different land cover types is presented. Phenological features are two different start dates of growing season, date of maximum growth, end of growing season and two growing season lengths. Also, quality indicators are estimated for some phenological features. The method is based on NDVI-time series extracted from MODIS-images. The errors between extracted dates and in-situ measurements are reasonably small. For example, the residuals of the estimation of the start of Flux Growing Season are on only 2 days for broadleaf forest in one Southern Finland hydrological drainage basin. The method has been tested on Northern Boreal forest zone, where there are freezing temperatures and snow during winter.

Törmä, Markus; Kervinen, Mikko; Anttila, Saku

2011-10-01

328

Satellite image GIS for rapid damage assessment  

NASA Astrophysics Data System (ADS)

A system architecture for rapid and reliable emergency response in consequence of suddenly occurring natural disaster is conceived and described throughout this paper. The final goal of the developed methodology is the integration, within a single user interface environment, of data access and standardization techniques, image processing tools, GIS technology, analytical modeling and communication tools. This would allow to sensibly reduce the effects of the earthquake disaster by providing an immediate estimate of the extent and location of the suffered area and making this knowledge available to the responsible agencies. In particular, attention is focused on the implementation of the above system architecture distinguishing between local and central nodes in order to gain effectiveness and to optimize the reaction time for an efficient and rapid emergency response. The local nodes are located in the single municipalities and consists of personal computers where the spatial and tabular databases are collected and stored. The central node consists of a Unix workstation where the GIS software and the image processing tools are resident.

Casciati, Fabio; Faravelli, L.; Giorgi, F.

1998-04-01

329

A 45-year time series of Saharan dune mobility from remote sensing  

NASA Astrophysics Data System (ADS)

Decadal trends in the aeolian dust record of the Sahara affect the global climate system and the nutrient budget of the Atlantic Ocean. One proposed cause of these trends are changes in the frequency and intensity of dust storms, which have hitherto been hard to quantify. Because sand flux scales with the cube of wind speed, dune migration rates can be used as a proxy for storminess. Relative changes in the storminess of the Sahara can thus be monitored by tracking the migration rates of individual sand dunes over time. The Bodélé Depression of northern Chad was selected as a target area for this method, because it is the most important point-source of aeolian dust on the planet and features the largest and fastest dunes on Earth. A collection of co-registered Landsat, SPOT, and ASTER scenes, combined with declassified American spy satellite images was used to construct a 45 year record of dune migration in the Bodélé Depression. One unexpected outcome of the study was the observation of binary dune interactions in the imagery sequence, which reveals that when two barchan dunes collide, a transfer of mass occurs so that one dune appears to travel through the other unscathed, like a solitary wave. This confirms a controversial numerical model prediction and settles a decade-old debate in aeolian geomorphology. The COSI-Corr change detection method was used to measure the dune migration rates from 1984 until 1987, 1990, 1996, 2000, 2003, 2005, 2007, 2008, 2009, and 2010. An algorithm was developed to automatically warp the resulting displacement fields back to a common point in time. Thus, individual image pixels of a dune field were tracked over time, allowing the extraction of a time series from the co-registered satellite images without further human intervention. The automated analysis was extended further back into the past by comparison of the 1984 image with declassified American spy satellite (Corona) images from 1965 and 1970. Due to the presence of specks of dust as well as image distortions caused by shrinking of the photographic film, it was not possible to automatically measure the dune displacements of these scenes with COSI-Corr. Instead, the image was georeferenced and coregistered to the 1984 Landsat imagery by third order polynomial fits to 531 tie points, and the displacements of ten large barchan dunes were measured by hand. Thanks to the 19-year time lapse between the two images used for these 'analog' measurements, their precision is better than 5%, which is comparable with that of the automated COSI-Corr analysis. The resulting dune celerities are identical to the automated measurements, which themselves show little or no temporal variability over the subsequent 26 years. The lack of any trend in the time series of dune celerity paints a picture of remarkably stable dune mobility over the past 45 years. None of the distributions fall outside the overall average of 25m/yr. The constant dune migration rates resulting from our study indicate that there has been no change in the storminess of the Sahara over the past 45 years. The observed dust trends are therefore caused by changes in vegetation cover, which in turn reflect changes in precipitation and land usage. This work highlights the importance of the hyper-arid Bodélé Depression, which provides a steady but finite supply of aeolian dust to the atmosphere without which nutrient fluxes and terrestrial albedo would be more variable than they are today.

Vermeesch, P.

2012-04-01

330

Kinetic temperature image modeling from thermal infrared satellite images  

Microsoft Academic Search

A long wave infrared radiance model has been defined that permits modeling of the radiance reaching an aerial or satellite sensor. Given the kinetic temperature and emissivity characteristics of the target and background and the prevailing atmospheric data (e.g., radiosonde) atmospheric transmission (the LOWTRAN transmission code is used to compute integrated transmission as a function of altitude), upwelled and downwelled

J. R. Schott; J. D. Biegel

1985-01-01

331

Orthorectification model research of Beijing-1 small satellite image  

NASA Astrophysics Data System (ADS)

Beijing-1 small satellite was launched Oct.27 2005 and has taken part in the plan of China high-performance earth observation after finishing on-orbit test period. Two kinds of sensors were carried on the satellite. One is 3-band multi-spectral senor whose spatial resolution was 32m, the other panchromatic sensor whose spatial resolution was 4m. In order to ensure truly utility for small satellite data, preliminary deep processing system had been developed for receiving, preprocessing, and data-distribution. Meanwhile, several key questions must be deal with including radiometric calibration, geometric precise rectification, orthographic rectification, image fusion and application demonstration. The paper will focus on the works of the second part including RPC orthographic rectification model and how to optimize algorithms of orthographic rectification which consider the feature of 4m high spatial resolution. RFM is a generalized sensor model, which uses RPC parameters to perform orthographic rectification in no need of orbit parameters and sensor imaging parameters. It is independent on sensors or platforms and supports any object space coordinate system with a variable coordinate system. Compared to linear transformation and polynomial transform, RFM has the highest positioning accuracy. Because RPC is determined by applying the least squares principle to GCP data, approximate error can be evenly distributed through RFM rectification. Based on the experiment on the Beijing-1 high resolution small satellite data using RFM and improved RFM, a generalized model of orthographic rectification of high resolution small satellite data can be developed. The experiment proves: Using second-order improved RFM to rectify the Beijing-1 small satellite image has a sub-pixel positioning accuracy that is close to the accuracy of the rigorous sensor model based on the collinearity equation when the GCPs are evenly distributed.

Gong, Jianming; Yang, Xiaomei; Zhou, Chenghu; Zhang, Dandan

2007-08-01

332

Laboratory Imaging of Satellites and Orbital Appearance Estimation  

NASA Astrophysics Data System (ADS)

For an increasingly cluttered space environment, having detailed pre-launch image information that can be used to predict space object appearance is essential. Both laboratory and extrapolated imagery may provide important diagnostic information in the event of a satellite malfunction or assist in space object discrimination . In the visible and NIR wavelength ranges, simple setups that reduce unwanted background light and that mimic solar glint and diffuse earth shine are described. Numerical methods for extrapolating either high resolution laboratory satellite imagery or unresolved spectral data to space-like scenarios are presented. Image extrapolation, which is performed in the spatial frequency and spectral domains, requires that the camera modulation transfer function (MTF), and that source and sensor characteristics be known. Image data would be referenced to a known reflectance standard and realistic laboratory illumination geometries would be investigated.

Wellems, D.; Bowers, D.; Boger, J.; Kleinschmidt, N.

333

Mapping giant salvinia with satellite imagery and image analysis.  

PubMed

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer's and user's accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat. PMID:17516139

Everitt, J H; Fletcher, R S; Elder, H S; Yang, C

2008-04-01

334

Quantitation of satellite cell proliferation in vivo using image analysis.  

PubMed

A nonisotopic, double fluorescence technique was developed to study myogenic satellite cell proliferation in posthatch turkey skeletal muscle. Labeled satellite cell nuclei were identified on enzymatically isolated myofiber segments using a mouse monoclonal antibody (anti-BrdU) followed by fluorescein-5-isothiocyanate (FITC) conjugated goat anti-mouse IgG secondary antibody. Myofiber nuclei (myonuclei+satellite cell nuclei) were counterstained with propidium iodide (PI). The myofiber segment length, myofiber segment diameter, and the number of PI and FITC labeled nuclei contained in each segment was determined using a Nikon fluorescence microscope, a SIT video camera and Image-1 software. Data collected by three different operators of the image analysis system revealed 5.0 +/- 1.4 satellite cell nuclei per 1000 myofiber nuclei and 5284 +/- 462 microns3 of cytoplasm surrounding each myofiber nucleus in the pectoralis thoracicus of 9-week-old tom turkeys. BrdU immunohistochemistry coupled with the new approach of PI staining of whole myofiber mounts is an effective combination to allow the use of an efficient semi-automated image analysis protocol. PMID:7819418

Mozdziak, P E; Fassel, T; Gregory, R; Schultz, E; Greaser, M L; Cassens, R G

1994-09-01

335

Using Progressive Resolution to Visualize large Satellite Image dataset  

NASA Astrophysics Data System (ADS)

Unidata's Integrated Data Viewer (IDV) is a Java-based software application that provides new and innovative ways of displaying satellite imagery, gridded data, and surface, upper air, and radar data within a unified interface. Progressive Resolution (PR) is a advanced feature newly developed in the IDV. When loading a large satellite dataset with PR turned on, the IDV calculates the resolution of the view window, sets the magnification factors dynamically, and loads a sufficient amount of the data to generate an image at the correct resolution. A rubber band box (RBB) interface allows the user to zoom in/out or change the projection, forcing the IDV to recalculate the magnification factors and get higher/lower resolution data. This new feature improves the IDV memory usage significantly. In the preliminary test, loading 100 time steps of GOES-East 1 km 0.65 visible image data (100 X 10904 X 6928) with PR, both memory and CPU usage are comparable to generating a single time-step display at full resolution (10904 X 6928), and the quality of the resulting image is not compromised. The PR feature is currently available for both satellite imagery and gridded datasets, and will be expanded to other datasets. In this presentation we will present examples of PR usage with large satellite datasets for academic investigations and scientific discovery.

ho, yuan; ramanmurthy, mohan

2014-05-01

336

Unsupervised Feature Learning for High-Resolution Satellite Image Classification  

SciTech Connect

The rich data provided by high-resolution satellite imagery allow us to directly model geospatial neighborhoods by understanding their spatial and structural patterns. In this paper we explore an unsupervised feature learning approach to model geospatial neighborhoods for classification purposes. While pixel and object based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper we extract low-level features to characterize the local neighborhood patterns. We exploit the unlabeled feature measurements in a novel way to learn a set of basis functions to derive new features. The derived sparse feature representation obtained by encoding the measured features in terms of the learned basis function set yields superior classification performance. We applied our technique on two challenging image datasets: ORNL dataset representing one-meter spatial resolution satellite imagery representing five land-use categories and, UCMERCED dataset consisting of 21 different categories representing sub-meter resolution overhead imagery. Our results are highly promising and, in the case of UCMERCED dataset we outperform the best results obtained for this dataset. We show that our feature extraction and learning methods are highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large-facility.

Cheriyadat, Anil M [ORNL

2013-01-01

337

High temperature superconducting infrared imaging satellite  

NASA Technical Reports Server (NTRS)

A low earth orbiting platform for an infrared (IR) sensor payload is examined based on the requirements of a Naval Research Laboratory statement of work. The experiment payload is a 1.5-meter square by 0.5-meter high cubic structure equipped with the imaging system, radiators, and spacecraft mounting interface. The orbit is circular at 509 km (275 nmi) altitude and 70 deg. inclination. The spacecraft is three-axis stabilized with pointing accuracy of plus or minus 0.5 deg. in each axis. The experiment payload requires two 15-minute sensing periods over two contiguous orbit periods for 30 minutes of sensing time per day. The spacecraft design is presented for launch via a Delta 2 rocket. Subsystem designs include attitude control, propulsion, electric power, telemetry, tracking and command, thermal design, structure, and cost analysis.

Angus, B.; Covelli, J.; Davinic, N.; Hailey, J.; Jones, E.; Ortiz, V.; Racine, J.; Satterwhite, D.; Spriesterbach, T.; Sorensen, D.

1992-01-01

338

MISR Browse Images: Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS)  

... Images: Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) These MISR Browse images provide ... the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaign. CLAMS focused on understanding pieces of the ...

2013-03-22

339

The Mount Wilson CaK Plage Index Time Series  

NASA Astrophysics Data System (ADS)

The Mount Wilson solar photographic archive digitization project makes available to the scientific community in digital form a selection of the solar images in the archives of the Carnegie Observatories. This archive contains over 150,000 images of the Sun which were acquired over a time span in excess of 100 years. The images include broad-band images called White Light Directs, ionized CaK line spectroheliograms and Hydrogen Balmer alpha spectroheliograms. This project will digitize essentially all of the CaK and broad-band direct images out of the archive with 12 bits of significant precision and up to 3000 by 3000 spatial pixels. The analysis of this data set will permit a variety of retrospective analyzes of the state of the solar magnetism and provide a temporal baseline of about 100 years for many solar properties. We have already completed the digitization of the CaK series and we are currently working on the broad-band direct images. Solar images have been extracted and identified with original logbook parameters of observation time and scan format, and they are available from the project web site at www.astro.ucla.edu/~ulrich/MW_SPADP. We present preliminary results on a CaK plage index time series derived from the analysis of 70 years of CaK observations, from 1915 to 1985. One of the main problem we encountered during the calibration process of these images is the presence of a vignetting function. This function is linked to the relative position between the pupil and the grating. As a result of this effect the intensity and its gradient are highly variable from one image to another. We currently remove this effect by using a running median filter to determine the background of the image and divide the image by this background to obtain a flat image. A plage index value is then computed from the intensity distribution of this flat image. We show that the temporal variability of our CaK plage index agrees very well with the behavior of the international sunspot number series.

Bertello, L.; Ulrich, R. K.; Boyden, J. E.; Javaraiah, J.

2008-05-01

340

Time Series of North Pacific Volcanic Eruptions  

NASA Astrophysics Data System (ADS)

The record of volcanic eruptions was gathered from the 1986 eruption of Augustine Volcano to present for Alaska, Kamchatka and the Kuriles Islands. In this time over 400 ash producing eruptions were noted, and many more events that produced some other activity, e.g. lava, lahar, small explosion, seismic crisis. This represents a minimum for the volcanic activity in this region. It is thought that the records for Alaska are complete for this time period, but it is possible that activity in the Kuriles and Kamchatka could have been overlooked, particularly smaller events. For the Alaska region, 19 different volcanoes have been active in this time. Mt. Cleveland shows the most activity over the time period (40 % likely to have activity in a 3 month period), followed closely by Pavlof (34% likely)volcano. In Kamchatka only 7 volcanoes have been active, Shiveluch is the most active (83% likely) followed by Bezymianny and Kliuchevskoi volcanoes (tied at 60%). The Kuriles only has had 4 active volcanoes, and only 6 known eruptions. Overall this region is one of the most active in the world, in any 3 month period there is a 77% likelihood of volcano activity. For well instrumented volcanoes, the majority of activity is preceded by significant seismicity. For just over half of the events, explosive activity is preceded by thermal signals in infrared satellite data. Rarely (only about 5% of the time) is a stand alone thermal signal not followed within 3 months by an explosive eruption. For remaining events where an ash plume begins the activity, over 90% of the cases show a thermal signal the eruption. The volcanoes with the most activity are the least likely to produce large ash plumes. Conversely the volcanoes that erupt rarely often begin with larger ash producing events. Though there appears to be a recurrent progression of volcanic activity down the chain from east to west, this may be an artifact of several independent systems, each working at their own rate, that briefly coincide in a perceived pattern. To see if there is an arc-wide linkage to eruption rates many more decades of data are needed.

Dehn, J.; Worden, A. K.; Webley, P. W.

2011-12-01

341

Local to Global Scale Time Series Analysis of US Dryland Degradation Using Landsat, AVHRR, and MODIS  

NASA Astrophysics Data System (ADS)

Drylands cover 41% of the terrestrial land surface and annually generate $1 trillion in ecosystem goods and services for 38% of the global population, yet estimates of the global extent of Dryland degradation is uncertain with a range of 10 - 80%. It is currently understood that Drylands exhibit topological complexity including self-organization of parameters of different levels-of-organization, e.g., ecosystem and landscape parameters such as soil and vegetation pattern and structure, that gradually or discontinuously shift to multiple basins of attraction in response to herbivory, fire, and climatic drivers at multiple spatial and temporal scales. Our research has shown that at large geographic scales, contemporaneous time series of 10 to 20 years for response and driving variables across two or more spatial scales is required to replicate and differentiate between the impact of climate and land use activities such as commercial grazing. For example, the Pacific Decadal Oscillation (PDO) is a major driver of Dryland net primary productivity (NPP), biodiversity, and ecological resilience with a 10-year return interval, thus 20 years of data are required to replicate its impact. Degradation is defined here as a change in physiognomic composition contrary to management goals, a persistent reduction in vegetation response, e.g., NPP, accelerated soil erosion, a decline in soil quality, and changes in landscape configuration and structure that lead to a loss of ecosystem function. Freely available Landsat, Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradimeter (MODIS) archives of satellite imagery exist that provide local to global spatial coverage and time series between 1972 to the present from which proxies of land degradation can be derived. This paper presents time series assessments between 1972 and 2011 of US Dryland degradation including early detection of dynamic regime shifts in the Mojave and landscape pattern and erosion state changes in the Intermountain region in response to the "Great North American Drought" in 1988, PDO and El Niño Southern Oscillation (ENSO) and commercial grazing. Additionally, we will show the discoveries in the last 10-years that US Drylands are "greening" despite the severe Southwestern drought and that commercial livestock are a driver of this response with an annual appropriation of some 58% of NPP.

Washington-Allen, R. A.; Ramsey, R. D.; West, N. E.; Kulawardhana, W.; Reeves, M. C.; Mitchell, J. E.; Van Niel, T. G.

2011-12-01

342

Deformation in the Basin & Range Province and Rio Grande Rift using InSAR Time Series  

NASA Astrophysics Data System (ADS)

High heat flow in the Basin and Range Province and Rio Grande Rift has been attributed to partial melting in the crust and upper mantle as a result of ongoing extension (e.g. Lachenbruch 1978). We would then expect to observe surface deformation in areas with actively moving magmatic fluids. The distribution of these magmatic fluids has implications for the rheology of the crust and upper mantle. For this study, we use InSAR to locate deformation due to magmatic sources as well as localized hydrologic deformation. While our focus is magmatic deformation, hydrologic signals are important for correcting geodetic data used to monitor tectonic activity. InSAR is a suitable technique for a large study in the Basin and Range and Rio Grande Rift since SAR acquisitions are both numerous and temporally extensive in these regions. We use ERS-1, ERS-2, and ENVISAT SAR images from 1992-2010 to create time series' with interferograms up to 1800km long from both ascending and descending satellite tracks. Each time series has an average of 100 interferograms reducing the atmospheric noise that masks small deformation signals in single interferograms. The time series' results are validated using overlapping tracks and are further compared to signals identified in previous geophysical studies (e.g. Reilinger and Brown 1980, Massonnet et al 1997, Finnegan and Pritchard 2009). We present results for several areas of deformation in the Basin & Range Province and Rio Grande Rift. An agricultural area near Roswell, NM exhibits seasonal uplift and subsidence of ±3.5cm/yr between 1992 and 1999. Results indicate subsidence on the order of 1cm/yr and uplift of 2cm/yr at the Raft River power plant, ID that is likely related to the start of geothermal fluid production and injection. Just north of the Raft River plant, we detect what appears to be rapid agricultural subsidence in an area extending for 50km. We discuss subsidence of ~2cm/yr in Escalante Valley, UT that is comparable to deformation observed in an earlier InSAR study on subsidence caused by ground-water withdrawal (Forster, 2006).

Taylor, H.; Pisaniello, M.; Pritchard, M. E.

2012-12-01

343

Flood Identification from Satellite Images Using Artificial Neural Networks  

NASA Astrophysics Data System (ADS)

Typhoons and storms hit Taiwan several times every year and they cause serious flood disasters. Because the rivers are short and steep, and their flows are relatively fast with floods lasting only few hours and usually less than one day. Flood identification can provide the flood disaster and extent information to disaster assistance and recovery centers. Due to the factors of the weather, it is not suitable for aircraft or traditional multispectral satellite; hence, the most appropriate way for investigating flooding extent is to use Synthetic Aperture Radar (SAR) satellite. In this study, back-propagation neural network (BPNN) model and multivariate linear regression (MLR) model are built to identify the flooding extent from SAR satellite images. The input variables of the BPNN model are Radar Cross Section (RCS) value and mean of the pixel, standard deviation, minimum and maximum of RCS values among its adjacent 3×3 pixels. The MLR model uses two images of the non-flooding and flooding periods, and The inputs are the difference between the RCS values of two images and the variances among its adjacent 3×3 pixels. The results show that the BPNN model can perform much better than the MLR model. The correct percentages are more than 80% and 73% in training and testing data, respectively. Many misidentified areas are very fragmented and unrelated. In order to reinforce the correct percentage, morphological image analysis is used to modify the outputs of these identification models. Through morphological operations, most of the small, fragmented and misidentified areas can be correctly assigned to flooding or non-flooding areas. The final results show that the flood identification of satellite images has been improved a lot and the correct percentages increases up to more than 90%.

Chang, L.; Kao, I.; Shih, K.

2011-12-01

344

Time Series Subsequence Searching in Specialized Binary Tree  

Microsoft Academic Search

Subsequence searching is a non-trivial task in time series data analysis and mining. In recent years, different approaches\\u000a are published to improve the performance of subsequence searching which based on index the time series and lower bound the\\u000a Euclidean distance. In this paper, the problem of applying Euclidean distance on time series similarity measure is first reviewed.\\u000a Previous approaches to

Tak-chung Fu; Hak-pun Chan; Fu-lai Chung; Ng Chak-man

2006-01-01

345

Two satellite image sets for the training and validation of image processing systems for defense applications  

NASA Astrophysics Data System (ADS)

Many image processing algorithms utilize the discrete wavelet transform (DWT) to provide efficient compression and near-perfect reconstruction of image data. Defense applications often require the transmission of data at high levels of compression over noisy channels. In recent years, evolutionary algorithms (EAs) have been utilized to optimize image transform filters that outperform standard wavelets for bandwidth-constrained compression of satellite images. The optimization of these filters requires the use of training images appropriately chosen for the image processing system's intended applications. This paper presents two robust sets of fifty images each intended for the training and validation of satellite and unmanned aerial vehicle (UAV) reconnaissance image processing algorithms. Each set consists of a diverse range of subjects consisting of cities, airports, military bases, and landmarks representative of the types of images that may be captured during reconnaissance missions. Optimized algorithms may be "overtrained" for a specific problem instance and thus exhibit poor performance over a general set of data. To reduce the risk of overtraining an image filter, we evaluate the suitability of each image as a training image. After evolving filters using each image, we assess the average compression performance of each filter across the entire set of images. We thus identify a small subset of images from each set that provide strong performance as training images for the image transform optimization problem. These images will also provide a suitable platform for the development of other algorithms for defense applications. The images are available upon request from the contact author.

Peterson, Michael R.; Aldridge, Shawn; Herzog, Britny; Moore, Frank

2010-04-01

346

Apparatus for statistical time-series analysis of electrical signals  

NASA Technical Reports Server (NTRS)

An apparatus for performing statistical time-series analysis of complex electrical signal waveforms, permitting prompt and accurate determination of statistical characteristics of the signal is presented.

Stewart, C. H. (inventor)

1973-01-01

347

Scene context dependency of pattern constancy of time series imagery  

NASA Astrophysics Data System (ADS)

A fundamental element of future generic pattern recognition technology is the ability to extract similar patterns for the same scene despite wide ranging extraneous variables, including lighting, turbidity, sensor exposure variations, and signal noise. In the process of demonstrating pattern constancy of this kind for retinex/visual servo (RVS) image enhancement processing, we found that the pattern constancy performance depended somewhat on scene content. Most notably, the scene topography and, in particular, the scale and extent of the topography in an image, affects the pattern constancy the most. This paper will explore these effects in more depth and present experimental data from several time series tests. These results further quantify the impact of topography on pattern constancy. Despite this residual inconstancy, the results of overall pattern constancy testing support the idea that RVS image processing can be a universal front-end for generic visual pattern recognition. While the effects on pattern constancy were significant, the RVS processing still does achieve a high degree of pattern constancy over a wide spectrum of scene content diversity, and wide ranging extraneousness variations in lighting, turbidity, and sensor exposure.

Woodell, Glenn; Jobson, Daniel J.; Rahman, Zia-ur

2008-05-01

348

On the Character and Mitigation of Atmospheric Noise in InSAR Time Series Analysis (Invited)  

NASA Astrophysics Data System (ADS)

Time series analysis of interferometric synthetic aperture radar (InSAR) data, with its broad spatial coverage and ability to image regions that are sometimes very difficult to access, is a powerful tool for characterizing continental surface deformation and its temporal variations. With the impending launch of dedicated SAR missions such as Sentinel-1, ALOS-2, and the planned NASA L-band SAR mission, large volume data sets will allow researchers to further probe ground displacement processes with increased fidelity. Unfortunately, the precision of measurements in individual interferograms is impacted by several sources of noise, notably spatially correlated signals caused by path delays through the stratified and turbulent atmosphere and ionosphere. Spatial and temporal variations in atmospheric water vapor often introduce several to tens of centimeters of apparent deformation in the radar line-of-sight, correlated over short spatial scales (<10 km). Signals resulting from atmospheric path delays are particularly problematic because, like the subsidence and uplift signals associated with tectonic deformation, they are often spatially correlated with topography. In this talk, we provide an overview of the effects of spatially correlated tropospheric noise in individual interferograms and InSAR time series analysis, and we highlight where common assumptions of the temporal and spatial characteristics of tropospheric noise fail. Next, we discuss two classes of methods for mitigating the effects of tropospheric water vapor noise in InSAR time series analysis and single interferograms: noise estimation and characterization with independent observations from multispectral sensors such as MODIS and MERIS; and noise estimation and removal with weather models, multispectral sensor observations, and GPS. Each of these techniques can provide independent assessments of the contribution of water vapor in interferograms, but each technique also suffers from several pitfalls that we outline. The multispectral near-infrared (NIR) sensors provide high spatial resolution (~1 km) estimates of total column tropospheric water vapor by measuring the absorption of reflected solar illumination and provide may excellent estimates of wet delay. The Online Services for Correcting Atmosphere in Radar (OSCAR) project currently provides water vapor products through web services (http://oscar.jpl.nasa.gov). Unfortunately, such sensors require daytime and cloudless observations. Global and regional numerical weather models can provide an additional estimate of both the dry and atmospheric delays with spatial resolution of (3-100 km) and time scales of 1-3 hours, though these models are of lower accuracy than imaging observations and are benefited by independent observations from independent observations of atmospheric water vapor. Despite these issues, the integration of these techniques for InSAR correction and uncertainty estimation may contribute substantially to the reduction and rigorous characterization of uncertainty in InSAR time series analysis - helping to expand the range of tectonic displacements imaged with InSAR, to robustly constrain geophysical models, and to generate a-priori assessments of satellite acquisitions goals.

Barnhart, W. D.; Fielding, E. J.; Fishbein, E.

2013-12-01

349

The determination of physical and dynamical parameters of Pluto/Charon and binary asteroids by least-square formation of a matched filter for a time series of images. I - The operational theory  

NASA Technical Reports Server (NTRS)

A theory is derived for the determination of the masses, radii, and orbital elements of the Pluto/Charon, or similar, system based on the prediction of an image distribution over space and time and its comparison with observation. The comparison may be ultimately through the theory of least squares or the application of a matched filter to the observations as a three-dimensional signal stream at an initial or intermediate state. The theory is an approximation correct to fifth order in the diameters of celestial bodies. The theory of astronomical seeing that is used is based on Kolmogorov turbulence in the long-exposure limit. The images must be photometric. Linear tracking errors that can be removed are preferable to either automatic or manual guiding, in the collection of candidate observations.

Wildey, R. L.

1985-01-01

350

A Multi-Scale Detection Technique for Anomaly on Ocean Surface Using Optical Satellite Images  

Microsoft Academic Search

Using satellite images for monitoring oceanic surface has become popular recently. One of the striking feature can be detected from satellite image is the anomalous phenomenon on oceanic surface. In general, it is easy to observe the diversified anomalies, caused by abrupt change of the reflectance on oceanic surface, on the optical satellite images. Among them, the anomaly caused by

Chi-Farn Chen; Li-Yu Chang

351

Analysis of the Effects of Image Quality on Digital Map Generation from Satellite Images  

NASA Astrophysics Data System (ADS)

High resolution satellite images are widely used to produce and update a digital map since they became widely available. It is well known that the accuracy of digital map produced from satellite images is decided largely by the accuracy of geometric modelling. However digital maps are made by a series of photogrammetric workflow. Therefore the accuracy of digital maps are also affected by the quality of satellite images, such as image interpretability. For satellite images, parameters such as Modulation Transfer Function(MTF), Signal to Noise Ratio(SNR) and Ground Sampling Distance(GSD) are used to present images quality. Our previous research stressed that such quality parameters may not represent the quality of image products such as digital maps and that parameters for image interpretability such as Ground Resolved Distance(GRD) and National Imagery Interpretability Rating Scale(NIIRS) need to be considered. In this study, we analyzed the effects of the image quality on accuracy of digital maps produced by satellite images. QuickBird, IKONOS and KOMPSAT-2 imagery were used to analyze as they have similar GSDs. We measured various image quality parameters mentioned above from these images. Then we produced digital maps from the images using a digital photogrammetric workstation. We analyzed the accuracy of the digital maps in terms of their location accuracy and their level of details. Then we compared the correlation between various image quality parameters and the accuracy of digital maps. The results of this study showed that GRD and NIIRS were more critical for map production then GSD, MTF or SNR.

Kim, H.; Kim, D.; Kim, S.; Kim, T.

2012-07-01

352

An Active Testing Model for Tracking Roads in Satellite Images  

Microsoft Academic Search

We present a new approach for tracking roads from satellite images, and therebyillustrate a general computational strategy ("active testing") for tracking 1D structuresand other recognition tasks in computer vision. Our approach is related to recent workin active vision on "where to look next" and motivated by the "divide-and-conquer"strategy of parlor games such as "Twenty Questions." We choose "tests" (matchedfilters for

Donald Geman; Bruno Jedynak

1996-01-01

353

An Image Registration Approach for Accurate Satellite Attitude Estimation  

Microsoft Academic Search

Satellites are controlled by an autonomous guidance system that corrects in real time their attitude according to information\\u000a coming from ensemble of sensors and star trackers. The latter estimate the attitude by continuously comparing acquired image\\u000a of the sky with a star atlas stored on board. Beside being expensive, star trackers undergo the problem of Sun and Moon blinding,\\u000a thus

Alessandro Bevilacqua; Ludovico Carozza; Alessandro Gherardi

2009-01-01

354

Seasonal signals in the reprocessed GPS coordinate time series  

NASA Astrophysics Data System (ADS)

The global (IGS) and regional (EPN) CGPS time series have already been studied in detail by several authors to analyze the periodic signals and noise present in the long term displacement series. The comparisons indicated that the amplitude and phase of the CGPS derived seasonal signals mostly disagree with the surface mass redistribution models. The CGPS results are highly overestimating the seasonal term, only about 40% of the observed annual amplitude can be explained with the joint contribution of the geophysical models (Dong et al. 2002). Additionally the estimated amplitudes or phases are poorly coherent with the models, especially at sites close to coastal areas (van Dam et al, 2007). The conclusion of the studies was that the GPS results are distorted by analysis artifacts (e.g. ocean tide loading, aliasing of unmodeled short periodic tidal signals, antenna PCV models), monument thermal effects and multipath. Additionally, the GPS series available so far are inhomogeneous in terms of processing strategy, applied models and reference frames. The introduction of the absolute phase center variation (PCV) models for the satellite and ground antennae in 2006 and the related reprocessing of the GPS precise orbits made a perfect ground and strong argument for the complete re-analysis of the GPS observations from global to local level of networks. This enormous work is in progress within the IGS and a pilot analysis was already done for the complete EPN observations from 1996 to 2007 by the MUT group (Military University of Warsaw). The quick analysis of the results proved the expectations and the superiority of the reprocessed data. The noise level (weekly coordinate repeatability) was highly reduced making ground for the later analysis on the daily solution level. We also observed the significant decrease of the seasonal term in the residual coordinate time series, which called our attention to perform a repeated comparison of the GPS derived annual periodicity and the surface mass redistribution models. We expect that using the reprocessed EPN data we can exclude several analysis related artifacts and we get a more clear view on the real physical information content of the data. In this paper we present a general overview and results of the EPN reprocessing and we show the detailed results of the harmonic analysis.

Kenyeres, A.; van Dam, T.; Figurski, M.; Szafranek, K.

2008-12-01

355

DEM time series of an agricultural watershed  

NASA Astrophysics Data System (ADS)

In agricultural landscape soil surface evolves notably due to erosion and deposition phenomenon. Even if most of the field data come from plot scale studies, the watershed scale seems to be more appropriate to understand them. Currently, small unmanned aircraft systems and images treatments are improving. In this way, 3D models are built from multiple covering shots. When techniques for large areas would be to expensive for a watershed level study or techniques for small areas would be too time consumer, the unmanned aerial system seems to be a promising solution to quantify the erosion and deposition patterns. The increasing technical improvements in this growth field allow us to obtain a really good quality of data and a very high spatial resolution with a high Z accuracy. In the center of Belgium, we equipped an agricultural watershed of 124 ha. For three years (2011-2013), we have been monitoring weather (including rainfall erosivity using a spectropluviograph), discharge at three different locations, sediment in runoff water, and watershed microtopography through unmanned airborne imagery (Gatewing X100). We also collected all available historical data to try to capture the "long-term" changes in watershed morphology during the last decades: old topography maps, soil historical descriptions, etc. An erosion model (LANDSOIL) is also used to assess the evolution of the relief. Short-term evolution of the surface are now observed through flights done at 200m height. The pictures are taken with a side overlap equal to 80%. To precisely georeference the DEM produced, ground control points are placed on the study site and surveyed using a Leica GPS1200 (accuracy of 1cm for x and y coordinates and 1.5cm for the z coordinate). Flights are done each year in December to have an as bare as possible ground surface. Specific treatments are developed to counteract vegetation effect because it is know as key sources of error in the DEM produced by small unmanned aircraft systems. The poster will present the older and more recent changes of relief in this intensely exploited watershed and notably show how unmanned airborne imagery might be of help in DEM dynamic modelling to support soil conservation research.

Pineux, Nathalie; Lisein, Jonathan; Swerts, Gilles; Degré, Aurore

2014-05-01

356

Digital filtering of APT images from NOAA series satellites  

NASA Astrophysics Data System (ADS)

Comparative tests of various filters have been conducted on NOAA/APT meteorological satellite images. It is demonstrated that the best results in terms of noise reduction and blurring effect have been obtained by weighted mean and extremal filters, while other techniques show a nonuniform behavior. It is concluded that these techniques are very useful for 'restoring' noisy received images in connection with the efficient utilization of APT analog data for many applications in which low resolution is acceptable and low cost is required.

Baronti, Stefano; Carla, Roberto; Sacco, Vincenzo M.

1986-12-01

357

Satellite image registration based on the geometrical arrangement of objects  

NASA Astrophysics Data System (ADS)

The knowledge of the geometrical relationship between images is a prerequisite for registration. Assuming a conformal affine transformation, 4 transformation parameters have to be determined. This is done on the basis of the geometrical arrangement of characteristic objects extracted from images in a preprocessing step, for example a land use classification yielding forest, pond, or urban regions. The algorithm introduced establishes correspondence between (centers of gravity of) objects by building and matching so-called ANGLE CHAINS, a linear structure for representing a geometric (2D) arrangement. An example with satellite imagery illustrates the usefulness of the algorithm.

Bartl, Renate; Schneider, Werner

1995-11-01

358

Analysis of Decadal Vegetation Dynamics Using Multi-Scale Satellite Images  

NASA Astrophysics Data System (ADS)

This study aims at quantifying vegetation fractional cover (VFC) by incorporating multi-resolution satellite images, including Formosat-2(RSI), SPOT(HRV/HRG), Landsat (MSS/TM) and Terra/Aqua(MODIS), to investigate long-term and seasonal vegetation dynamics in Taiwan. We used 40-year NDVI records for derivation of VFC, with field campaigns routinely conducted to calibrate the critical NDVI threshold. Given different sensor capabilities in terms of their spatial and spectral properties, translation and infusion of NDVIs was used to assure NDVI coherence and to determine the fraction of vegetation cover at different spatio-temporal scales. Based on the proposed method, a bimodal sequence of intra-annual VFC which corresponds to the dual-cropping agriculture pattern was observed. Compared to seasonal VFC variation (78~90%), decadal VFC reveals moderate oscillations (81~86%), which were strongly linked with landuse changes and several major disturbances. This time-series mapping of VFC can be used to examine vegetation dynamics and its response associated with short-term and long-term anthropogenic/natural events.

Chiang, Y.; Chen, K.

2013-12-01

359

Aligning gene expression time series with time warping algorithms  

Microsoft Academic Search

Motivation: Increasingly, biological processes are being studied through time series of RNA expression data col- lected for large numbers of genes. Because common pro- cesses may unfold at varying rates in different experiments or individuals, methods are needed that will allow corre- sponding expression states in different time series to be mapped to one another. Results: We present implementations of

John Aach; George M. Church

2001-01-01

360

CRUSTAL MOVEMENT OF THE GREAT LAKES—TIME SERIES ANALYSIS  

Microsoft Academic Search

Time series techniques were employed to determine rates of vertical crustal movement within the Great Lakes region of North America. Observations of water level elevations as recorded at gauges around the lakes, and differences in elevations between pairs of gauges were analysed for linear trends, periodicities and stochastic components. It was found that the variance of time series of elevations

K. ADAMOWSKI; G. W. KITE

1973-01-01

361

Exception Mining on Multiple Time Series in Stock Market  

Microsoft Academic Search

This paper presents our research on exception mining on multiple time series data which aims to assist stock market surveillance by identifying market anomalies. Traditional technologies on stock market surveillance have shown their limitations to handle large amount of complicated stock market data. In our research, the outlier mining on multiple time series (OMM) is proposed to improve the effectiveness

Chao Luo; Yanchang Zhao; Longbing Cao; Yuming Ou; Chengqi Zhang

2008-01-01

362

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models  

ERIC Educational Resources Information Center

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Price, Larry R.

2012-01-01

363

A nearest neighbor bootstrap for resampling hydrologic time series  

Microsoft Academic Search

A nonparametric method for resampling scalar or vector-valued time series is introduced. Multivariate nearest neighbor probability density estimation provides the basis for the resampling scheme developed. The motivation for this work comes from a desire to preserve the dependence structure of the time series while bootstrapping (resampling it with replacement). The method is data driven and is preferred where the

Upmanu Lall; Ashish Sharma

1996-01-01

364

Detecting Gene Regulation Relations from Microarray Time Series Data  

Microsoft Academic Search

Microarrays are important tools in the quest to map the gene regulation networks of cells. A common use of microarrays result in time series pairs that indicates how the output of one gene affects another. Substantial efforts have been made towards identifying pairs of microarray time series that indicate that one gene is a regulator for another. However, most approaches

Nawar Malhis; Arden Ruttan

2006-01-01

365

Streaming Time Series Summarization Using User-Defined Amnesic Functions  

Microsoft Academic Search

The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractical. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the increasing deployment of mobile devices and

Themis Palpanas; Michail Vlachos; Eamonn J. Keogh; Dimitrios Gunopulos

2008-01-01

366

Experiencing SAX: a novel symbolic representation of time series  

Microsoft Academic Search

Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc. Many researchers have also considered symbolic rep- resentations of time series, noting that such representations would potentiality allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities. While many

Jessica Lin; Eamonn J. Keogh; Li Wei; Stefano Lonardi

2007-01-01

367

Perception-based approach to time series data mining  

Microsoft Academic Search

Time series data mining (TSDM) techniques permit exploring large amounts of time series data in search of consistent patterns and\\/or interesting relationships between variables. TSDM is becoming increasingly important as a knowledge management tool where it is expected to reveal knowledge structures that can guide decision making in conditions of limited certainty. Human decision making in problems related with analysis

Ildar Z. Batyrshin; Leonid Sheremetov

2008-01-01

368

Application of support vector machines in financial time series forecasting  

Microsoft Academic Search

This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial time series forecasting. The objective of this paper is to examine the feasibility of SVM in financial time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market

Francis E. H. Tay; Lijuan Cao

2001-01-01

369

Decision templates for the classification of bioacoustic time series  

Microsoft Academic Search

Time series classification based on decision templates is the topic of this paper. The decision templates are built over several local feature vectors which are extracted from local time windows of the time series. To learn characteristic classifier outputs a set of decision templates is determined for each class. In the classification phase class memberships based on the decision templates

Christian Dietrich; Günther Palm; Friedhelm Schwenker

2003-01-01

370

Forecasting, Structural Time Series Models and the Kalman Filter  

Microsoft Academic Search

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology.

Andrew C. Harvey

1989-01-01

371

Horizontal visibility graphs: Exact results for random time series  

Microsoft Academic Search

The visibility algorithm has been recently introduced as a mapping between time series and complex networks. This procedure allows us to apply methods of complex network theory for characterizing time series. In this work we present the horizontal visibility algorithm, a geometrically simpler and analytically solvable version of our former algorithm, focusing on the mapping of random series (series of

Bartolo Luque; Lucas Lacasa; Fernando Ballesteros; Jordi Luque

2009-01-01

372

Examination of Time Series through Randomly Broken Windows.  

National Technical Information Service (NTIS)

In order to determine the Fourier transform of a quasi-periodic time series (linear problem), or the power spectrum of a stationary random time series (quadratic problem), it is desirable that data be recorded without interruption over a long time interva...

P. A. Sturrock E. C. Shoub

1981-01-01

373

Using Time-Series Regression to Predict Academic Library Circulations.  

ERIC Educational Resources Information Center

Four methods were used to forecast monthly circulation totals in 15 midwestern academic libraries: dummy time-series regression, lagged time-series regression, simple average (straight-line forecasting), monthly average (naive forecasting). In tests of forecasting accuracy, dummy regression method and monthly mean method exhibited smallest average…

Brooks, Terrence A.

1984-01-01

374

Mapping Canopy Damage from Understory Fires in Amazon Forests Using Annual Time Series of Landsat and MODIS Data  

NASA Technical Reports Server (NTRS)

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars less than 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (greater than 500 ha) burn scars that accounted for the majority of all fire-damaged forest in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 square kilometers) were an order of magnitude higher than during the 1997-1998 El Nino event (124 square kilometers and 39 square kilometers, respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.

Morton, Douglas C.; DeFries, Ruth S.; Nagol, Jyoteshwar; Souza, Carlos M., Jr.; Kasischke, Eric S.; Hurtt, George C.; Dubayah, Ralph

2011-01-01

375

Short time-series microarray analysis: Methods and challenges  

PubMed Central

The detection and analysis of steady-state gene expression has become routine. Time-series microarrays are of growing interest to systems biologists for deciphering the dynamic nature and complex regulation of biosystems. Most temporal microarray data only contain a limited number of time points, giving rise to short-time-series data, which imposes challenges for traditional methods of extracting meaningful information. To obtain useful information from the wealth of short-time series data requires addressing the problems that arise due to limited sampling. Current efforts have shown promise in improving the analysis of short time-series microarray data, although challenges remain. This commentary addresses recent advances in methods for short-time series analysis including simplification-based approaches and the integration of multi-source information. Nevertheless, further studies and development of computational methods are needed to provide practical solutions to fully exploit the potential of this data.

Wang, Xuewei; Wu, Ming; Li, Zheng; Chan, Christina

2008-01-01

376

Monitoring NPP VIIRS on-orbit radiometric performance from TOA reflectance time series  

NASA Astrophysics Data System (ADS)

The recently launched (October 28, 2011) Suomi NPP (National Polar-orbiting Partnership) satellite has been operating nominally to daily collect global data. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key NPP sensor onboard the spacecraft. Similar to the heritage sensor MODIS, VIIRS has on-board calibration components including a solar diffuser (SD) and a solar diffuser stability monitor (SDSM) for the reflective solar bands (RSB), a V-groove blackbody for the thermal emissive bands (TEB), and a space view (SV) port for background. This study examines VIIRS reflective solar bands (RSB) calibration stability and performance using observed top-of-atmosphere (TOA) reflectance time series collected from two approaches. The first is from comparison with a well-calibrated Aqua MODIS and the second is from overpasses over the widely used Liby-4 desert site. The VIIRS and MODIS comparison data is obtained from simultaneous nadir overpasses (SNO) for their spectrally matched bands. The reflectance trends over the Libya-4 site are extracted from 16-day repeatable orbits so each data point has the same viewing geometry relative to the site. The impact due to the band spectral differences between the two instruments is corrected based on MODTRAN5 simulations. Results of this study provide useful information on NPP VIIRS post-launch calibration assessment and preliminary analysis of its calibration stability and consistency for the first 1.5 years.

Wu, A.; Xiong, X.; Cao, C.; Sun, C.

2013-09-01

377

MPEG-7 Descriptors for Earth Observation Satellite Images  

NASA Astrophysics Data System (ADS)

The amount of digital multimedia information has experienced a spectacular growth during the last years thanks to the advances on digital systems of image, video and audio acquisition. As a response to the need of organising all this information, ISO/IEC has developed a new standard for multimedia content description called MPEG-7. Among other topics, MPEG-7 defines a set of multimedia descriptors that can be automatically generated using signal processing techniques. Earth Observation Satellites generate large quantities of images stored on enormous databases that can take advantage of the new standard. An automatic indexation of these images using MPEG-7 metadata can improve their contents management as well as simplify interaction between independent databases. This paper gives an overall description on MPEG-7 standard focusing on the low-level Visual Descriptors. These descriptors can be grouped into four categories: color, texture, shape and motion. Visual Color Descriptors represent the colour distribution of an image in terms of a specified colour space. Visual Texture Descriptors define the visual pattern of an image according to its homogenities and non-homogenities. Visual Shape Descriptors describe the shape of 2D and 3D objects being, at the same time, invariant to scaling, rotation and translation. Motion Descriptors give the essential characteristics of objects and camera motions. These descriptors can be used individually or in combination to index and retrieve satellite images of the Earth from a database. For example, oceans and glaciars can be discerned based on their Colour Descriptors, also cities and deserts based on the Texture Descriptors, island images can be grouped using the Shape Descriptors, and cyclone trajectories studied and compared using the Motion Descriptors.

Nieto, X. Giro I.; Marques Acosta, F.

378

Ice Sheet Change Detection by Satellite Image Differencing  

NASA Technical Reports Server (NTRS)

Differencing of digital satellite image pairs highlights subtle changes in near-identical scenes of Earth surfaces. Using the mathematical relationships relevant to photoclinometry, we examine the effectiveness of this method for the study of localized ice sheet surface topography changes using numerical experiments. We then test these results by differencing images of several regions in West Antarctica, including some where changes have previously been identified in altimeter profiles. The technique works well with coregistered images having low noise, high radiometric sensitivity, and near-identical solar illumination geometry. Clouds and frosts detract from resolving surface features. The ETM(plus) sensor on Landsat-7, ALI sensor on EO-1, and MODIS sensor on the Aqua and Terra satellite platforms all have potential for detecting localized topographic changes such as shifting dunes, surface inflation and deflation features associated with sub-glacial lake fill-drain events, or grounding line changes. Availability and frequency of MODIS images favor this sensor for wide application, and using it, we demonstrate both qualitative identification of changes in topography and quantitative mapping of slope and elevation changes.

Bindschadler, Robert A.; Scambos, Ted A.; Choi, Hyeungu; Haran, Terry M.

2010-01-01

379

Land Cover Classification of Satellite Images Using Contextual Information  

NASA Astrophysics Data System (ADS)

This paper presents a method for the classification of satellite images into multiple predefined land cover classes. The proposed approach results in a fully automatic segmentation and classification of each pixel, using a small amount of training data. Therefore, semantic segmentation techniques are used, which are already successful applied to other computer vision tasks like facade recognition. We explain some simple modifications made to the method for the adaption of remote sensing data. Besides local features, the proposed method also includes contextual properties of multiple classes. Our method is flexible and can be extended for any amount of channels and combinations of those. Furthermore, it is possible to adapt the approach to several scenarios, different image scales, or other earth observation applications, using spatially resolved data. However, the focus of the current work is on high resolution satellite images of urban areas. Experiments on a QuickBird-image and LiDAR data of the city of Rostock show the flexibility of the method. A significant better accuracy can be achieved using contextual features.

Fröhlich, B.; Bach, E.; Walde, I.; Hese, S.; Schmullius, C.; Denzler, J.

2013-05-01

380

Calibration of the ERS-1 SAR Fast-Delivery Images.  

National Technical Information Service (NTIS)

The possibility of performing absolute radiometric calibration of ERS-1 SAR (Synthetic Aperture Radar) Fast Delivery (FD) images is investigated. A 1.5 month time series during the first ice phase included fourteen satellite overpasses and showed that the...

L. M. H. Ulander

1993-01-01

381

Use of Geostationary Satellite Images for Interactive Meteorological Analysis.  

NASA Astrophysics Data System (ADS)

The southern oceans are data-sparse regions and this is especially true for the middle-latitude and sub -Antarctic zones. To obtain a better meteorological data coverage full use must be made of available geostationary satellite data. Data received from Meteosat II, which views the Atlantic Ocean has been available since June 1981 and is available for analysis of the charts of the tropical and middle latitude zones of this ocean. The Man Computer Interactive Data Access System (MCIDAS) provides the means to display and manipulate Meteosat II images. This motivated the development of the Bogus Using Meteosat MCIDAS System (BUMMS). The BUMMS is capable of displaying a meteorological field superimposed over a Meteosat II image, both being transformed to polar stereographic coordinates. Bogus (pseudo) data are entered via the video display, followed by execution of a revised 1000-300 mb thickness analysis and the corresponding field of omega values obtained from a two-level omega equation model. The 1000-300 mb thickness field is interactively modified by the BUMMS until agreement is obtained with the cloud features displayed by the satellite image. Omega equation vertical velocities are used to verify the fit between the thickness field and the cloud features. The BUMMS operate well within the time constraints imposed by the operational procedure. Modifications to the thickness field are introduced by applying Satellite Image Analysis Rules (SIAR) consisting of 10 guidelines based on sound meteorological theory. Seven case studies are discussed. In each case the thickness field is modified using the SIAR. The modified thickness field forms the basis of a new 10-level analysis which becomes the input to a Primitive Equation Nested Model (PENEST). Prognostic 36-hour output from this model is compared with a verification analysis as well as the original prognoses. Results indicate positive improvement in the prognoses using the BUMMS procedure and the SIAR. Full operational implementation of the BUMMS can be recommended.

van Heerden, Johan

382

Biomass Accumulation Rates of Amazonian Secondary Forest and Biomass of Old-Growth Forests from Landsat Time Series and GLAS  

NASA Astrophysics Data System (ADS)

We estimate the age of humid lowland tropical forests in Rondônia, Brazil, from a somewhat densely spaced time series of Landsat images (1975-2003) with an automated procedure, the Threshold Age Mapping Algorithm (TAMA), first described here. We then estimate a landscape-level rate of aboveground woody biomass accumulation of secondary forest by combining forest age mapping with biomass estimates from the Geoscience Laser Altimeter System (GLAS). Though highly variable, the estimated average biomass accumulation rate of 8.4 Mg ha-1 yr-1 agrees well with ground-based studies for young secondary forests in the region. In isolating the lowland forests, we map land cover and general types of old-growth forests with decision tree classification of Landsat imagery and elevation data. We then estimate aboveground live biomass for seven classes of old-growth forest. TAMA is simple, fast, and self-calibrating. By not using between-date band or index differences or trends, it requires neither image normalization nor atmospheric correction. In addition, it uses an approach to map forest cover for the self-calibrations that is novel to forest mapping with satellite imagery; it maps humid secondary forest that is difficult to distinguish from old-growth forest in single-date imagery; it does not assume that forest age equals time since disturbance; and it incorporates Landsat Multispectral Scanner (MSS) imagery. Variations on the work that we present here can be applied to other forested landscapes. Applications that use image time series will be helped by the free distribution of coregistered Landsat imagery, which began in December 2008, and of the Ice Cloud and land Elevation Satellite (ICESat) Vegetation Product, which simplifies the use of GLAS data. Finally, we demonstrate here for the first time how the optical imagery of fine spatial resolution that is viewable on Google Earth provides a new source of reference data for remote sensing applications related to land cover. Reference: Helmer, E. H., M. A. Lefsky and D. A. Roberts. 2009. Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System. Journal of Applied Remote Sensing 3:033505.

Helmer, E.; Lefsky, M. A.; Roberts, D.

2009-12-01

383

Using long time series of Landsat data to monitor impervious surface dynamics: a case study in the Zhoushan Islands  

NASA Astrophysics Data System (ADS)

Islands are an important part of the marine ecosystem. Increasing impervious surfaces in the Zhoushan Islands due to new development and increased population have an ecological impact on the runoff and water quality. Based on time-series classification and the complement of vegetation fraction in urban regions, Landsat thematic mapper and other high-resolution satellite images were applied to monitor the dynamics of impervious surface area (ISA) in the Zhoushan Islands from 1986 to 2011. Landsat-derived ISA results were validated by the high-resolution Worldview-2 and aerial photographs. The validation shows that mean relative errors of these ISA maps are <15 %. The results reveal that the ISA in the Zhoushan Islands increased from 19.2 km2 in 1986 to 86.5 km2 in 2011, and the period from 2006 to 2011 had the fastest expansion rate of 5.59 km2 per year. The major land conversions to high densities of ISA were from the tidal zone and arable lands. The expansions of ISA were unevenly distributed and most of them were located along the periphery of these islands. Time-series maps revealed that ISA expansions happened continuously over the last 25 years. Our analysis indicated that the policy and the topography were the dominant factors controlling the spatial patterns of ISA and its expansions in the Zhoushan Islands. With continuous urbanization processes, the rapid ISA expansions may not be stopped in the near feature.

Zhang, Xiaoping; Pan, Delu; Chen, Jianyu; Zhan, Yuanzeng; Mao, Zhihua

2013-01-01

384

Sensor-Generated Time Series Events: A Definition Language  

PubMed Central

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

Anguera, Aurea; Lara, Juan A.; Lizcano, David; Martinez, Maria Aurora; Pazos, Juan

2012-01-01

385

Clustering Financial Time Series by Network Community Analysis  

NASA Astrophysics Data System (ADS)

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

386

Outliers detection in multivariate time series by independent component analysis.  

PubMed

In multivariate time series, outlying data may be often observed that do not fit the common pattern. Occurrences of outliers are unpredictable events that may severely distort the analysis of the multivariate time series. For instance, model building, seasonality assessment, and forecasting may be seriously affected by undetected outliers. The structure dependence of the multivariate time series gives rise to the well-known smearing and masking phenomena that prevent using most outliers' identification techniques. It may be noticed, however, that a convenient way for representing multiple outliers consists of superimposing a deterministic disturbance to a gaussian multivariate time series. Then outliers may be modeled as nongaussian time series components. Independent component analysis is a recently developed tool that is likely to be able to extract possible outlier patterns. In practice, independent component analysis may be used to analyze multivariate observable time series and separate regular and outlying unobservable components. In the factor models framework too, it is shown that independent component analysis is a useful tool for detection of outliers in multivariate time series. Some algorithms that perform independent component analysis are compared. It has been found that all algorithms are effective in detecting various types of outliers, such as patches, level shifts, and isolated outliers, even at the beginning or the end of the stretch of observations. Also, there is no appreciable difference in the ability of different algorithms to display the outlying observations pattern. PMID:17521286

Baragona, Roberto; Battaglia, Francesco

2007-07-01

387

Simulations of Non-resolved, Infrared Imaging of Satellites  

NASA Astrophysics Data System (ADS)

Simulations of near-infrared, non-resolved imaging of earth-orbiting satellites during nighttime and daytime were created to consider the feasibility of such observations. By using the atmospheric radiative transfer code MODTRAN (MODerate resolution atmospheric TRANsmission), we incorporate site-specific mean weather conditions for several possible locations. In general, the dominant effect to be modeled is the sky radiance, which has a strong dependence upon the solar angle and the nature of the distribution of aerosols. Other significant effects included in the model are telescope design, camera design, and detector selection. The simulations are used in turn to predict the signal to noise ratio (SNR) in standard astronomical filter bands for several test cases of satellite-sun-observer geometries. The SNR model is then used to devise a method to design an optimal filter band for these observations.

Jim, K.; Kuluhiwa, K.; Scott, B. Knox, R.; Frith, J.; Gibson, B.

388

Satellite Images of Israel and the Middle East  

NSDL National Science Digital Library

The Israel Science and Technology Homepage is the national database and directory of science and technology related sites in Israel. Within this site is the Satellite Images of Israel and the Middle East page. Visitors will find a list of stunning images (acquired from the NASA Johnson Space Center) of the area during the day, at night, its physical features, a topographical view, specific country images, and links to a map of Israel and its cities. Other areas of the site provide lists of organizations; databases; and additional information regarding chemistry, earth and environment, physics, and more of the area. Although limited in scope, the site does give some interesting resources within a specific subject area that some may find helpful.

1999-01-01

389

Multi sensor satellite imagers for commercial remote sensing  

NASA Astrophysics Data System (ADS)

This paper will discuss and compare recent refractive and catodioptric imager designs developed and manufactured at SunSpace for Multi Sensor Satellite Imagers with Panchromatic, Multi-spectral, Area and Hyperspectral sensors on a single Focal Plane Array (FPA). These satellite optical systems were designed with applications to monitor food supplies, crop yield and disaster monitoring in mind. The aim of these imagers is to achieve medium to high resolution (2.5m to 15m) spatial sampling, wide swaths (up to 45km) and noise equivalent reflectance (NER) values of less than 0.5%. State-of-the-art FPA designs are discussed and address the choice of detectors to achieve these performances. Special attention is given to thermal robustness and compactness, the use of folding prisms to place multiple detectors in a large FPA and a specially developed process to customize the spectral selection with the need to minimize mass, power and cost. A refractive imager with up to 6 spectral bands (6.25m GSD) and a catodioptric imager with panchromatic (2.7m GSD), multi-spectral (6 bands, 4.6m GSD), hyperspectral (400nm to 2.35?m, 200 bands, 15m GSD) sensors on the same FPA will be discussed. Both of these imagers are also equipped with real time video view finding capabilities. The electronic units could be subdivided into the Front-End Electronics and Control Electronics with analogue and digital signal processing. A dedicated Analogue Front-End is used for Correlated Double Sampling (CDS), black level correction, variable gain and up to 12-bit digitizing and high speed LVDS data link to a mass memory unit.

Cronje, T.; Burger, H.; Du Plessis, J.; Du Toit, J. F.; Marais, L.; Strumpfer, F.

2005-10-01

390

Achieving EMC in high frequency and high power switching environment on Radar Imaging Satellite  

Microsoft Academic Search

Satellite deck provides a challenging electro magnetic (EM) environment as the overall volume available is limited and a number of DC-DC converters and clocks are present. Add to this high frequency and high power switching the electro magnetic interference (EMI) scenario couldn't be worse. Radar imaging satellite (RISAT) India's first satellite with day night imaging capability, slated for launch by

G. V. C. Rajan; V. B. Pramod

2008-01-01

391

Genetic programming-based chaotic time series modeling.  

PubMed

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling. PMID:15495338

Zhang, Wei; Wu, Zhi-ming; Yang, Gen-ke

2004-11-01

392

Characterizing time series: when Granger causality triggers complex networks  

NASA Astrophysics Data System (ADS)

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

393

A Dimensionality Reduction Technique for Efficient Time Series Similarity Analysis  

PubMed Central

We propose a dimensionality reduction technique for time series analysis that significantly improves the efficiency and accuracy of similarity searches. In contrast to piecewise constant approximation (PCA) techniques that approximate each time series with constant value segments, the proposed method--Piecewise Vector Quantized Approximation--uses the closest (based on a distance measure) codeword from a codebook of key-sequences to represent each segment. The new representation is symbolic and it allows for the application of text-based retrieval techniques into time series similarity analysis. Experiments on real and simulated datasets show that the proposed technique generally outperforms PCA techniques in clustering and similarity searches.

Wang, Qiang; Megalooikonomou, Vasileios

2008-01-01

394

Analysis of complex time series using refined composite multiscale entropy  

NASA Astrophysics Data System (ADS)

Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series that has been applied in many fields successfully. However, MSE may yield an inaccurate estimation of entropy or induce undefined entropy because the coarse-graining procedure reduces the length of a time series considerably at large scales. Composite multiscale entropy (CMSE) was recently proposed to improve the accuracy of MSE, but it does not resolve undefined entropy. Here we propose a refined composite multiscale entropy (RCMSE) to improve CMSE. For short time series analyses, we demonstrate that RCMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy.

Wu, Shuen-De; Wu, Chiu-Wen; Lin, Shiou-Gwo; Lee, Kung-Yen; Peng, Chung-Kang

2014-04-01

395

Testing time series irreversibility using complex network methods  

NASA Astrophysics Data System (ADS)

The absence of time-reversal symmetry is a fundamental property of many nonlinear time series. Here, we propose a new set of statistical tests for time series irreversibility based on standard and horizontal visibility graphs. Specifically, we statistically compare the distributions of time-directed variants of the common complex network measures degree and local clustering coefficient. Our approach does not involve surrogate data and is applicable to relatively short time series. We demonstrate its performance for paradigmatic model systems with known time-reversal