Sample records for time-series satellite images

  1. DISCOVERING SIGNIFICANT EVOLUTION PATTERNS FROM SATELLITE IMAGE TIME SERIES

    E-print Network

    Paris-Sud XI, Université de

    pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which. The next generation of satellites (e.g., V enµs, Sentinel-2) will actually be able to acquire image time

  2. Sesso Temtica: Tcnicas para anlise de sries temporais de imagens de satlite (SITS). (Techniques for analyzing satellite images time series (SITS))

    E-print Network

    ). (Techniques for analyzing satellite images time series (SITS)) Coordenadoras: Dra. Luciana Alvim S. Romani e images time series, focusing on data mining, including the integration and analysis of large volumes, Campinas, Brazil) 09:10 Satellite Image Time Series applied to Agricultural Monitoring Dr. Jurandir Zullo

  3. Connectivity constraint-based sequential pattern extraction from Satellite Image Time Series (SITS)

    NASA Astrophysics Data System (ADS)

    Julea, Andreea; Méger, Nicolas

    2013-10-01

    The temporal evolution of pixel values in Satellite Image Time Series (SITS) is considered as criterion for the characterization, discrimination and identification of terrestrial objects and phenomena. Due to the exponential behavior of sequences number with specialization, Sequential Data Mining (SDM) techniques need to be applied. The huge search and solution spaces imply the use of constraints according to the user's knowledge, interest and expectation. The spatial aspect of the data was taken into account by the introduction of connectivity measures that characterize the pixels tendency to form objects. These measures can highlight stratifications in data structure, can be useful for shape recognition and offer a base for post-processing operations similar to those from mathematical morphology (dilation, erosion etc.). The conjunction of corresponding Connectivity Constraints (CC) with the Support Constraint (SC) leads to the extraction of Grouped Frequent Sequential Patterns (GFSP), a concept with proved capability for preliminary description and localization of terrestrial events. This work is focused on efficient SITS extraction of evolutions that fulfill SC and CC. Different types of extractions using anti-monotone constraints are analyzed. Experiments performed on two interferometric SITS are used to illustrate the potential of the approach to find interesting evolution patterns.

  4. Dynamics Monitoring and Disaster Assessment for Watershed Management Using Time-Series Satellite Images

    Microsoft Academic Search

    Jiann-Yeou Rau; Liang-Chien Chen; Jin-King Liu; Tong-Hsiung Wu

    2007-01-01

    This paper presents a mechanism that utilizes intensive multitemporal and multisensor satellite images to monitor land cover dynamics. The proposed approach could be applied for regular dynamics monitoring, disaster monitoring and assessment, and vegetation recovery after natural disasters. The disaster monitoring and assessment are the most important issues imbedded in the program. This paper gives an example using the proposed

  5. Flood hazard and flood risk assessment using a time series of satellite images: a case study in Namibia.

    PubMed

    Skakun, Sergii; Kussul, Nataliia; Shelestov, Andrii; Kussul, Olga

    2014-08-01

    In this article, the use of time series of satellite imagery to flood hazard mapping and flood risk assessment is presented. Flooded areas are extracted from satellite images for the flood-prone territory, and a maximum flood extent image for each flood event is produced. These maps are further fused to determine relative frequency of inundation (RFI). The study shows that RFI values and relative water depth exhibit the same probabilistic distribution, which is confirmed by Kolmogorov-Smirnov test. The produced RFI map can be used as a flood hazard map, especially in cases when flood modeling is complicated by lack of available data and high uncertainties. The derived RFI map is further used for flood risk assessment. Efficiency of the presented approach is demonstrated for the Katima Mulilo region (Namibia). A time series of Landsat-5/7 satellite images acquired from 1989 to 2012 is processed to derive RFI map using the presented approach. The following direct damage categories are considered in the study for flood risk assessment: dwelling units, roads, health facilities, and schools. The produced flood risk map shows that the risk is distributed uniformly all over the region. The cities and villages with the highest risk are identified. The proposed approach has minimum data requirements, and RFI maps can be generated rapidly to assist rescuers and decisionmakers in case of emergencies. On the other hand, limitations include: strong dependence on the available data sets, and limitations in simulations with extrapolated water depth values. PMID:24372226

  6. A robust approach for phenological change detection within satellite image time series

    Microsoft Academic Search

    Jan Verbesselt; Martin Herold; Rob Hyndman; Achim Zeileis; Darius Culvenor

    2011-01-01

    The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as startof-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt,

  7. Temporal Signatures of Mediterranean Irrigated Crops Using Satellite Image Time Series

    Microsoft Academic Search

    P. Serra; X. Pons

    2007-01-01

    This work summarizes the methodology applied for monitoring the temporal signatures of some Mediterranean irrigated crops using 26 Landsat-5&7 TM&ETM+ images, from 2002 to 2005. The obtained four crop maps, one for each year, allows monitoring dynamics of maize, alfalfa and fruit trees with a thematic accuracy high than 90%.

  8. Quantifying post-fire recovery of forest canopy structure and its environmental drivers using satellite image time-series

    NASA Astrophysics Data System (ADS)

    Khanal, Shiva; Duursma, Remko; Boer, Matthias

    2014-05-01

    Fire is a recurring disturbance in most of Australia's forests. Depending on fire severity, impacts on forest canopies vary from light scorching to complete defoliation, with related variation in the magnitude and duration of post-fire gas exchange by that canopy. Estimates of fire impacts on forest canopy structure and carbon uptake for south-eastern Australia's forests do not exist. Here, we use 8-day composite measurements of the fraction of Absorbed Photosynthetically Active radiation (FPAR) as recorded by the Moderate-resolution Imaging Spectroradiometer (MODIS) to characterise forest canopies before and after fire and to compare burnt and unburnt sites. FPAR is a key biophysical canopy variable and primary input for estimating Gross Primary Productivity (GPP). Post-fire FPAR loss was quantified for all forest areas burnt between 2001 and 2010, showing good agreement with independent assessments of fire severity patterns of 2009 Black Saturday fires. A new method was developed to determine the duration of post-fire recovery from MODIS-FPAR time-series. The method involves a spatial-mode principal component analysis on full FPAR time series followed by a K-means clustering to group pixels based on similarity in temporal patterns. Using fire history data, time series of FPAR for burnt and unburnt pixels in each cluster were then compared to quantify the duration of the post-fire recovery period, which ranged from less than 1 to 8 years. The results show that time series of MODIS FPAR are well suited to detect and quantify disturbances of forest canopy structure and function in large areas of highly variable climate and phenology. Finally, the role of post-fire climate conditions and previous fire history on the duration of the post-fire recovery of the forest canopy was examined using generalized additive models.

  9. Using Time-Series Satellite Imaging Radar Data to Monitor Inundation Patterns and Hydroperiod in Herbaceous Wetlands of Southern Florida

    NASA Astrophysics Data System (ADS)

    Bourgeau-Chavez, L. L.; Kasischke, E.

    2002-05-01

    Knowledge of the components of the hydrologic cycle, including spatial and temporal distribution of water, is critical for regional hydrologic applications. However, at a regional scale, the variations of hydrologic condition are often too great to be easily quantified with ground-based observations alone. We developed methods to use satellite imaging radar data to monitor changes in hydrologic condition of regional scale wetland ecosystems in south Florida. Satellite imaging radar data have been shown to be sensitive to soil moisture variations and to flood conditions in a variety of wetland ecosystems. Initial observations of south Florida imagery from the European Space Agency's C-band microwave sensor onboard the European Remote Sensing Satellite (ERS) showed dynamic variations in backscatter between wet and dry seasons. Further studies revealed how fluctuations in water level influenced ERS radar backscatter for several different herbaceous vegetation cover types. Unfortunately, the C-band wavelength is incapable of penetrating dense forested canopies, thus, our research was focused on the vast herbaceous wetland ecosystems of southern Florida. The ERS synthetic aperture radar (SAR) sensor is a C-band, 5.7 cm wavelength imaging radar with vertical transmit and receive polarization (C-VV). The ERS sensor has a resolution of 30 m and a footprint of 100 by 100 km. SARs have the unique capability to collect data independent of cloud cover and solar illumination. This provides an advantage in areas typically covered by clouds such as tropical and sub-tropical regions like south Florida. In this study, several techniques were developed to utilize SAR data to detect, monitor, and map spatial and temporal changes in wetland hydrology. This study shows that radar imagery can be used to create innundation maps of relative soil moisture and flooding in herbaceous wetlands. Using C-band SAR imagery collected between 1997 and 1999, hydropattern maps were created at approximately bi-monthly periods for the south Florida region. In addition, a methodology for creating hydroperiod (the time period of flooding) maps was developed and examples from wet and dry years are presented. Principal component Analysis (PCA) was the basis of our hydroperiod maps and was linked to rainfall patterns of the south Florida region. Validation of the maps was conducted with in situ data and review by experts in the region.

  10. A radar image time series

    NASA Technical Reports Server (NTRS)

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

    1981-01-01

    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.

  11. Monitoring vegetation recovery in fire-affected areas using temporal profiles of spectral signal from time series MODIS and LANDSAT satellite images

    NASA Astrophysics Data System (ADS)

    Georgopoulou, Danai; Koutsias, Nikos

    2015-04-01

    Vegetation phenology is an important element of vegetation characteristics that can be useful in vegetation monitoring especially when satellite remote sensing observations are used. In that sense temporal profiles extracted from spectral signal of time series MODIS and LANDSAT satellite images can be used to characterize vegetation phenology and thus to be helpful for monitoring vegetation recovery in fire-affected areas. The aim of this study is to explore the vegetation recovery pattern of the catastrophic wildfires that occurred in Peloponnisos, southern Greece, in 2007. These fires caused the loss of 67 lives and were recognized as the most extreme natural disaster in the country's recent history. Satellite remote sensing data from MODIS and LANDSAT satellites in the period from 2000 to 2014 were acquired and processed to extract the temporal profiles of the spectral signal for selected areas within the fire-affected areas. This dataset and time period analyzed together with the time that these fires occurred gave the opportunity to create temporal profiles seven years before and seven years after the fire. The different scale of the data used gave us the chance to understand how vegetation phenology and therefore the recovery patterns are influenced by the spatial resolution of the satellite data used. Different metrics linked to key phenological events have been created and used to assess vegetation recovery in the fire-affected areas. Our analysis was focused in the main land cover types that were mostly affected by the 2007 wildland fires. Based on CORINE land-cover maps these were agricultural lands highly interspersed with large areas of natural vegetation followed by sclerophyllous vegetation, transitional woodland shrubs, complex cultivation patterns and olive groves. Apart of the use of the original spectral data we estimated and used vegetation indices commonly found in vegetation studies as well as in burned area mapping studies. In this study we explore the strength and the use of these time series satellite data to characterize vegetation phenology as an a aid to monitor vegetation recovery in fire affected-areas. In a recent study we found that the original spectral channels, based on which these indices are estimated, are sensitive to external vegetation parameters such as the spectral reflectance of the background soil. In such cases, the influence of the soil in the reflectance values is different in the various spectral regions depending on its type. The use of such indices is also justified according to a recent study on the sensitivity of spectral reflectance values to different burn and vegetation ratios, who concluded that the Near Infrared (NIR) and Short-Wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas. Additionally, it has been found that semi-burned classes are spectrally more consistent to their different fractions of scorched and non-scorched vegetation, than the original spectral channels based on which these indices are estimated.

  12. Incorporating Satellite Time-Series Data into Modeling

    NASA Technical Reports Server (NTRS)

    Gregg, Watson

    2008-01-01

    In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

  13. Drought Monitoring by Time Series Analysis of Satellite Land Surface Temperature

    Microsoft Academic Search

    J. Li; L. Jia

    2009-01-01

    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,

  14. Monitoring Seasonal Evapotranspiration in Vulnerable Agriculture using Time Series VHSR Satellite Data

    NASA Astrophysics Data System (ADS)

    Dalezios, Nicolas; Spyropoulos, Nicos V.; Tarquis, Ana M.

    2015-04-01

    The research work stems from the hypothesis that it is possible to perform an estimation of seasonal water needs of olive tree farms under drought periods by cross correlating high spatial, spectral and temporal resolution (~monthly) of satellite data, acquired at well defined time intervals of the phenological cycle of crops, with ground-truth information simultaneously applied during the image acquisitions. The present research is for the first time, demonstrating the coordinated efforts of space engineers, satellite mission control planners, remote sensing scientists and ground teams to record at specific time intervals of the phenological cycle of trees from ground "zero" and from 770 km above the Earth's surface, the status of plants for subsequent cross correlation and analysis regarding the estimation of the seasonal evapotranspiration in vulnerable agricultural environment. The ETo and ETc derived by Penman-Montieth equation and reference Kc tables, compared with new ETd using the Kc extracted from the time series satellite data. Several vegetation indices were also used especially the RedEdge and the chlorophyll one based on WorldView-2 RedEdge and second NIR bands to relate the tree status with water and nutrition needs. Keywords: Evapotransipration, Very High Spatial Resolution - VHSR, time series, remote sensing, vulnerability, agriculture, vegetation indeces.

  15. Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

    Microsoft Academic Search

    W. C. Koeppen; E. Pilger; R. Wright

    2011-01-01

    We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify\\u000a the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed\\u000a MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust\\u000a Satellite Techniques (RST)

  16. De-noising of microwave satellite soil moisture time series

    NASA Astrophysics Data System (ADS)

    Su, Chun-Hsu; Ryu, Dongryeol; Western, Andrew; Wagner, Wolfgang

    2013-04-01

    The use of satellite soil moisture data for scientific and operational hydrologic, meteorological and climatological applications is advancing rapidly due to increasing capability and temporal coverage of current and future missions. However evaluation studies of various existing remotely-sensed soil moisture products from these space-borne microwave sensors, which include AMSR-E (Advanced Microwave Scanning Radiometer) on Aqua satellite, SMOS (Soil Moisture and Ocean Salinity) mission and ASCAT (Advanced Scatterometer) on MetOp-A satellite, found them to be significantly different from in-situ observations, showing large biases and different dynamic ranges and temporal patterns (e.g., Albergel et al., 2012; Su et al., 2012). Moreover they can have different error profiles in terms of bias, variance and correlations and their performance varies with land surface characteristics (Su et al., 2012). These severely impede the effort to use soil moisture retrievals from multiple sensors concurrently in land surface modelling, cross-validation and multi-satellite blending. The issue of systematic errors present in data sets should be addressed prior to renormalisation of the data for blending and data assimilation. Triple collocation estimation technique has successfully yielded realistic error estimates (Scipal et al., 2008), but this method relies on availability of large number of coincident data from multiple independent satellite data sets. In this work, we propose, i) a conceptual framework for distinguishing systematic periodic errors in the form of false spectral resonances from non-systematic errors (stochastic noise) in remotely-sensed soil moisture data in the frequency domain; and ii) the use of digital filters to reduce the variance- and correlation-related errors in satellite data. In this work, we focus on the VUA-NASA (Vrije Universiteit Amsterdam with NASA) AMSR-E, CATDS (Centre National d'Etudes Spatiales, CNES) SMOS and TUWIEN (Vienna University of Technology) ASCAT data sets to identify two types of errors that are spectrally distinct. Based on a semi-empirical model of soil moisture dynamics, we consider possible digital filter designs to improve the accuracy of their soil moisture products by reducing systematic periodic errors and stochastic noise. We describe a methodology to design bandstop filters to remove artificial resonances, and a Wiener filter to remove stochastic white noise present in the satellite data. Utility of these filters is demonstrated by comparing de-noised data against in-situ observations from ground monitoring stations in the Murrumbidgee Catchment (Smith et al., 2012), southeast Australia. Albergel, C., de Rosnay, P., Gruhier, C., Muñoz Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y. H., & Wagner, W. (2012). Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations. Remote Sensing of Environment, 118, 215-226. Scipal, K., Holmes, T., de Jeu, R., Naeimi, V., & Wagner, W. (2008), A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophysical Research Letters, 35, L24403. Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M., Pipunic, R. C., Grayson, R. B., Siriwardena, L., Chiew, F. H. S., & Richter, H. (2012). The Murrumbidgee soil moisture network data set. Water Resources Research, 48, W07701. Su, C.-H., Ryu, D., Young, R., Western, A. W., & Wagner, W. (2012). Inter-comparison of microwave satellite soil moisture retrievals over Australia. Submitted to Remote Sensing of Environment.

  17. The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series

    NASA Technical Reports Server (NTRS)

    Verger, Aleixandre; Baret, F.; Weiss, M.; Kandasamy, S.; Vermote, E.

    2013-01-01

    Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction.

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

    Microsoft Academic Search

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

    2002-01-01

    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

  19. The impact of terrain on NDVI dynamics of corn field using Generalized Estimating Equations and time-series MODIS images

    Microsoft Academic Search

    Meng-Lung Lin; Cheng-Hwang Perng

    2011-01-01

    In this study, we used the generalized estimating equation (GEE) method to examine the relationship between terrain factors (height, slope and aspect) and NDVI in the western Liaoning, northeast China. Time-series data of NDVI derived from satellite images of the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to show the growing curve of corn field and the impact of terrain

  20. D City Transformations by Time Series of Aerial Images

    NASA Astrophysics Data System (ADS)

    Adami, A.

    2015-02-01

    Recent photogrammetric applications, based on dense image matching algorithms, allow to use not only images acquired by digital cameras, amateur or not, but also to recover the vast heritage of analogue photographs. This possibility opens up many possibilities in the use and enhancement of existing photos heritage. The research of the original figuration of old buildings, the virtual reconstruction of disappeared architectures and the study of urban development are some of the application areas that exploit the great cultural heritage of photography. Nevertheless there are some restrictions in the use of historical images for automatic reconstruction of buildings such as image quality, availability of camera parameters and ineffective geometry of image acquisition. These constrains are very hard to solve and it is difficult to discover good dataset in the case of terrestrial close range photogrammetry for the above reasons. Even the photographic archives of museums and superintendence, while retaining a wealth of documentation, have no dataset for a dense image matching approach. Compared to the vast collection of historical photos, the class of aerial photos meets both criteria stated above. In this paper historical aerial photographs are used with dense image matching algorithms to realize 3d models of a city in different years. The models can be used to study the urban development of the city and its changes through time. The application relates to the city centre of Verona, for which some time series of aerial photographs have been retrieved. The models obtained in this way allowed, right away, to observe the urban development of the city, the places of expansion and new urban areas. But a more interesting aspect emerged from the analytical comparison between models. The difference, as the Euclidean distance, between two models gives information about new buildings or demolitions. As considering accuracy it is necessary point out that the quality of final observations from model comparison depends on several aspects such as image quality, image scale and marker accuracy from cartography.

  1. MITRA Virtual laboratory for operative application of satellite time series for land degradation risk estimation

    NASA Astrophysics Data System (ADS)

    Nole, Gabriele; Scorza, Francesco; Lanorte, Antonio; Manzi, Teresa; Lasaponara, Rosa

    2015-04-01

    This paper aims to present the development of a tool to integrate time series from active and passive satellite sensors (such as of MODIS, Vegetation, Landsat, ASTER, COSMO, Sentinel) into a virtual laboratory to support studies on landscape and archaeological landscape, investigation on environmental changes, estimation and monitoring of natural and anthropogenic risks. The virtual laboratory is composed by both data and open source tools specifically developed for the above mentioned applications. Results obtained for investigations carried out using the implemented tools for monitoring land degradation issues and subtle changes ongoing on forestry and natural areas are herein presented. In detail MODIS, SPOT Vegetation and Landsat time series were analyzed comparing results of different statistical analyses and the results integrated with ancillary data and evaluated with field survey. The comparison of the outputs we obtained for the Basilicata Region from satellite data analyses and independent data sets clearly pointed out the reliability for the diverse change analyses we performed, at the pixel level, using MODIS, SPOT Vegetation and Landsat TM data. Next steps are going to be implemented to further advance the current Virtual Laboratory tools, by extending current facilities adding new computational algorithms and applying to other geographic regions. Acknowledgement This research was performed within the framework of the project PO FESR Basilicata 2007/2013 - Progetto di cooperazione internazionale MITRA "Remote Sensing tecnologies for Natural and Cultural heritage Degradation Monitoring for Preservation and valorization" funded by Basilicata Region Reference 1. A. Lanorte, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance International Journal of Applied Earth Observation and Geoinformation 2441-446 2. G Calamita, A Lanorte, R Lasaponara, B Murgante, G Nole 2013 Analyzing urban sprawl applying spatial autocorrelation techniques to multi-temporal satellite data. Urban and Regional Data Management: UDMS Annual 2013, 161 3. R Lasaponara 2013 Geospatial analysis from space: Advanced approaches for data processing, information extraction and interpretation International Journal of Applied Earth Observations and Geoinformation 20 . 1-3 4. R Lasaponara, A Lanorte 2011 Satellite time-series analysis International Journal of Remote Sensing 33 (15), 4649-4652 5. G Nolè, M Danese, B Murgante, R Lasaponara, A Lanorte Using spatial autocorrelation techniques and multi-temporal satellite data for analyzing urban sprawl Computational Science and Its Applications-ICCSA 2012, 512-527

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

    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.

  3. Mesoscale variability in time series data: Satellite-based estimates for the U.S. JGOFS Bermuda Atlantic

    E-print Network

    Mesoscale variability in time series data: Satellite-based estimates for the U.S. JGOFS Bermuda TOPEX/Poseidon­ERS-1/2) are used to characterize, statistically, the mesoscale variability about the U to better understand the contribution of mesoscale eddies to the time series record and the model- data

  4. Correcting the hooking effect in satellite altimetry data for time series estimation over smaller rivers

    NASA Astrophysics Data System (ADS)

    Boergens, Eva; Dettmering, Denise; Schwatke, Christian

    2015-04-01

    Since many years the numbers of in-situ gauging stations are declining. Satellite altimetry can be used as a gap-filler even over smaller inland waters like rivers. However, since altimetry measurements are not designed for inland water bodies a special data handling is necessary in order to estimate reliable water level heights over inland waters. We developed a new routine for estimating water level heights over smaller inland waters with satellite altimetry by correcting the hooking effect. The hooking effect occurs when the altimeter is not measuring in nadir before and after passing a water body due to the stronger reflectance of the water than the surrounding land surface. These off-nadir measurements, together with the motion of the satellite, lead to overlong ranges and heights declining in a parabolic shape. The vertex of this parabola is on the water surface. Therefore, by estimating the parabola we are able to determine the water level height without the need of any point over the water body itself. For estimating the parabola we only use selected measurements which are effected by the hooking effect. The applied search approach is based on the RANSAC algorithm (random sample consensus) which is a non-deterministic algorithm especially designed for finding geometric entities in point clouds with many outliers. With the hooking effect correction we are able to retrieve water level height time series from the Mekong River from Envisat and Saral/Altika high frequency data. It is possible to determine reliable time series even if the river has only a width of 500m or less. The expected annual variations are clearly depicted and the comparison of the time series with available in-situ gauging data shows a very good agreement.

  5. Multi-satellite time series of inherent optical properties in the California Current

    NASA Astrophysics Data System (ADS)

    Kahru, Mati; Lee, Zhongping; Kudela, Raphael M.; Manzano-Sarabia, Marlenne; Greg Mitchell, B.

    2015-02-01

    Satellite ocean color radiometry is a powerful method to study ocean biology but the relationships between satellite measurements and the in situ ocean properties are not well understood. Moreover, the measurements made with one satellite sensor may not be directly compatible with similar measurements from another sensor. We estimate inherent optical properties (IOPs) in the California Current by applying empirically optimized versions of the Quasi-Analytical Algorithm (QAA) of Lee et al. (2002) to satellite remote sensing reflectance (Rrs) from four ocean color sensors (OCTS, SeaWiFS, MODISA and MERIS). The set of estimated IOPs includes the total absorption coefficient at 490 nm (a490), phytoplankton absorption coefficient at 440 nm (aph440), absorption by dissolved and detrital organic matter at 440 nm (adg440) and particle backscattering coefficient at 490 nm (bbp490). The empirical inversion models are created by minimizing the deviations between satellite match-ups with in situ measurements and between the estimates of individual overlapping satellite sensors. The derived empirical algorithms were then applied to satellite Level-3 daily Rrs to create merged multi-sensor time series of the near-surface optical characteristics in the California Current region for a time period of over 16 years (November 1996-December 2012). Due to the limited number of in situ match-ups and their uneven distribution as well as the large errors in the satellite-derived Rrs, the uncertainty in the retrieved IOPs is still significant and difficult to quantify. The merged time series show the dominant annual cycle but also significant variability at interannual time scales. The ratio of adg440 to aph440 is around 1 in the transition zone, is >1 in the coastal zone and generally <1 offshore. adg440 decreases towards south and towards offshore. The long-term (~16 years) trend in aph440, representative of phytoplankton biomass, shows a significant (P<0.01) increasing trend in a wide band (~500 km) along the coast and a significant decreasing trends in the oligotrophic North Pacific gyre. The trend of increasing aph440 in the upwelling areas off California is positively correlated with the increasing wind speed along the coast.

  6. Satellite Images

    NSDL National Science Digital Library

    In this online, interactive module, students learn about the three types of satellite images (visible, infrared, and water vapor); how to identify basic cloud types and storm systems in satellite images; and demonstrate the basic knowledge necessary to interpret satellite observations. The module is part of an online course for grades 7-12 in satellite meteorology, which includes 10 interactive modules. The site also includes lesson plans developed by teachers and links to related resources. Each module is designed to serve as a stand-alone lesson, however, a sequential approach is recommended. Designed to challenge students through the end of 12th grade, middle school teachers and students may choose to skim or skip a few sections.

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

    USGS Publications Warehouse

    U.S. Geological Survey

    2008-01-01

    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).

  8. Trend analysis of time-series phenology derived from satellite data

    USGS Publications Warehouse

    Reed, B.C.; Brown, J.F.

    2005-01-01

    Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface for the past 15 years. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land surface processes over large areas. Repeat observations from satellite-borne multispectral sensors provide a mechanism to move from plant-specific to regional scale studies of phenology. In addition, we now have sufficient time-series (since 1982 at 8-km resolution covering the globe and since 1989 at 1-km resolution over the conterminous US) to study seasonal and interannual trends from satellite data. Phenology metrics including start of season, end of season, duration of season, and seasonally integrated greenness were derived from 8 km AVHRR data over North America spanning the years 1982-2003. Trend analysis was performed on the annual summaries of the metrics to determine areas with increasing or decreasing trends for the time period under study. Results show only small areas of changing start of season, but the end of season is coming later over well defined areas of New England and SE Canada, principally as a result of land use changes. The total greenness metric is most striking at the shrub/tundra boundary of North America, indicating increasing vegetation vigor or possible vegetation conversion as a result of warming. ?? 2005 IEEE.

  9. A robust anomaly based change detection method for time-series remote sensing images

    NASA Astrophysics Data System (ADS)

    Shoujing, Yin; Qiao, Wang; Chuanqing, Wu; Xiaoling, Chen; Wandong, Ma; Huiqin, Mao

    2014-03-01

    Time-series remote sensing images record changes happening on the earth surface, which include not only abnormal changes like human activities and emergencies (e.g. fire, drought, insect pest etc.), but also changes caused by vegetation phenology and climate changes. Yet, challenges occur in analyzing global environment changes and even the internal forces. This paper proposes a robust Anomaly Based Change Detection method (ABCD) for time-series images analysis by detecting abnormal points in data sets, which do not need to follow a normal distribution. With ABCD we can detect when and where changes occur, which is the prerequisite condition of global change studies. ABCD was tested initially with 10-day SPOT VGT NDVI (Normalized Difference Vegetation Index) times series tracking land cover type changes, seasonality and noise, then validated to real data in a large area in Jiangxi, south of China. Initial results show that ABCD can precisely detect spatial and temporal changes from long time series images rapidly.

  10. Development of satellite green vegetation fraction time series for use in mesoscale modeling: application to the European heat wave 2006

    NASA Astrophysics Data System (ADS)

    Refslund, Joakim; Dellwik, Ebba; Hahmann, Andrea N.; Barlage, Michael J.; Boegh, Eva

    2014-08-01

    A method is presented for development of satellite green vegetation fraction (GVF) time series for use in the Weather Research and Forecasting (WRF) model. The GVF data is in the WRF model used to describe the temporal evolution of many land surface parameters, in addition to the evolution of vegetation. Several high-resolution GVF products, derived from high-quality satellite retrievals from Moderate Resolution Imaging Spectroradiometer images, were produced and their performance was evaluated in long-term WRF simulations. The atmospheric conditions during the 2006 heat wave year over Europe were simulated since significant interannual variability in vegetation seasonality was found. Such interannual variability is expected to increase in the coming decades due to climatic changes. The simulation using a quadratic normalized difference vegetation index to GVF relationship resulted in consistent improvements of modeled temperatures. The model mean temperature cold bias was reduced by 10 % for the whole domain and by 20-45 % in areas affected by the heat wave. The study shows that WRF simulations during heat waves and droughts, when vegetation conditions deviate from the climatology, require concurrent land surface properties in order to produce accurate results.

  11. Reconstruction of SO2 emission height time-series and plume age using a combination of satellite imagery, volcanic tremor and back trajectory modelling at Mt. Etna

    NASA Astrophysics Data System (ADS)

    Pardini, Federica; Burton, Mike; Salerno, Giuseppe; Merucci, Luca; Corradini, Stefano; Barsotti, Sara; de'Michieli Vitturi, Mattia; di Grazia, Giuseppe

    2015-04-01

    While much work has focused on detection of volcanic gas emissions from space, relatively little progress has been made on examining volcanic processes using satellite measurements of volcanic plumes. In theory, much information can be derived regarding the temporal evolution of an eruption from a single image of an eruption plume. This information could be used to constrain models of magma chamber emptying, and comparison with InSAR measurements of syn-eruptive deflation. The over-arching goal of the work presented here therefore is SO2 flux time-series reconstruction using satellite imagery of SO2 in volcanic plumes. One of the major sources of uncertainty in the determination of SO2 abundances from satellite imagery is the plume height, and so we have focused on the development of a robust procedure that allows us to make accurate reconstructions of plume height time series. Starting from satellite images of SO2 emitted from Mt. Etna, Italy, we identified specific pixels where SO2 was detected and utilized the HYSPLIT Lagrangian back-trajectory model in order to retrieve the emission height and time of the eruption column over the volcano. The results have been refined using a probabilistic approach that allows calculation of the most probable emission height range. Previous work has highlighted that volcanic tremor is strongly connected to eruption intensity, and therefore, potentially to plume height. We therefore examined the relationship between volcanic tremor measured on Etna with our derived plume height time series. We discovered a relatively good agreement between the time series, suggesting that the physical processes controlling both the distribution of SO2 in the atmosphere and the intensity of volcanic tremor are strongly coupled, through the explosivity of the eruptive activity. The synthesis of volcanic tremor and derived plume heights is a novel new approach, and opens the possibility of more quantitative analysis of SO2 amounts in satellite imagery, and deeper insights into the volcanological processes driving eruptive activity.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    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.)

  13. A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data

    E-print Network

    Bradley, Bethany

    A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite variability and land cover change. However, the utility of using NDVI-derived phenology to detect change is employed: first, a harmonic approach models the average annual phenology; second, a spline-based approach

  14. Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite remote sensing

    E-print Network

    Radeloff, Volker C.

    Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite: AVHRR MODIS RESURS Landsat Burned area mapping Southern Russia Arid grasslands Grazing Fire, assessing fire regime changes is challenging, especially in grasslands because of high intra- and inter

  15. Automated approaches for displaying spatially oriented time?series data through image processing techniques

    Microsoft Academic Search

    Ian E. Von Essen; Stephen J. Walsh

    1989-01-01

    This study demonstrated the cartographic implications of automated image processing and computer graphics for the study of time?series data. Automated statistical and image processing techniques were applied to a case study data set consisting of weekly Crop Moisture Index (CMI) values summarized at 174 state cooperative weather stations within Oklahoma for the time period between February and October, 1980. Computer

  16. Analysing, classifying and displaying time series of images pixel- by-pixel: the package ADDAPIX

    Microsoft Academic Search

    Silvio Griguolo; Paolo Santacroce

    1995-01-01

    SUMMARY ADDAPIX is a menu-driven collection of programs that carries out the various steps of an analysis aiming at clustering pixel-by-pixel a time series of regularly collected images. Each pixel is represented by the series of its values: pixels with globally similar cycles are assigned to the same class. The output is an IDA compatible classified image, where all pixels

  17. A non-stationary time-series modeling approach for CT image reconstruction from truncated data

    Microsoft Academic Search

    K. P. Anoop; Kasi Rajgopal

    2009-01-01

    Truncated data problems are encountered in computed tomographic (CT) scanning scenarios where it is desirable to restrict the radiation dosage to a region-of-interest (ROI) of the object cross-section being imaged. In this paper, we propose a new image reconstruction technique for handling truncated data based on projection data extrapolation using a non-stationary time-series modeling approach. A special case of the

  18. Kalman filter approach for estimating water level time series over inland water using multi-mission satellite altimetry

    NASA Astrophysics Data System (ADS)

    Schwatke, C.; Dettmering, D.; Bosch, W.; Seitz, F.

    2015-05-01

    Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, since some years, this technology is also used for observing inland water levels of lakes and rivers. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series available through the web service "Database for Hydrological Time Series over Inland Water" (DAHITI). The method is based on a Kalman filter approach incorporating multi-mission altimeter observations and their uncertainties. As input data, cross-calibrated altimeter data from Envisat, ERS-2, Jason-1, Jason-2, Topex/Poseidon, and SARAL/AltiKa are used. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparison with in-situ gauge data and results from external inland altimeter databases. The new approach yields RMS differences with respect to in-situ data between 4 and 38 cm for lakes and 12 and 139 cm for rivers, respectively. For most study cases, more accurate height information than from available other altimeter data bases can be achieved.

  19. Use of spectral channels and vegetation indices from satellite VEGETATION time series for the Post-Fire vegetation recovery estimation

    NASA Astrophysics Data System (ADS)

    Coluzzi, Rosa; Lasaponara, Rosa; Montesano, Tiziana; Lanorte, Antonio; de Santis, Fortunato

    2010-05-01

    Satellite data can help monitoring the dynamics of vegetation in burned and unburned areas. Several methods can be used to perform such kind of analysis. This paper is focused on the use of different satellite-based parameters for fire recovery monitoring. In particular, time series of single spectral channels and vegetation indices from SPOT-VEGETATION have investigated. The test areas is the Mediterranean ecosystems of Southern Italy. For this study we considered: 1) the most widely used index to follow the process of recovery after fire: normalized difference vegetation index (NDVI) obtained from the visible (Red) and near infrared (NIR) by using the following formula NDVI = (NIR_Red)/(NIR + Red), 2) moisture index MSI obtained from the near infrared and Mir for characterization of leaf and canopy water content. 3) NDWI obtained from the near infrared and Mir as in the case of MSI, but with the normalization (as the NDVI) to reduce the atmospheric effects. All analysis for this work was performed on ten-daily normalized difference vegetation index (NDVI) image composites (S10) from the SPOT- VEGETATION (VGT) sensor. The final data set consisted of 279 ten-daily, 1 km resolution NDVI S1O composites for the period 1 April 1998 to 31 December 2005 with additional surface reflectance values in the blue (B; 0.43-0.47,um), red (R; 0.61-0.68,um), near-infrared (NIR; 0.78-0.89,um) and shortwave-infrared (SWIR; 1.58-1.75,um) spectral bands, and information on the viewing geometry and pixel status. Preprocessing of the data was performed by the Vlaamse Instelling voor Technologisch Onderzoek (VITO) in the framework of the Global Vegetation Monitoring (GLOVEG) preprocessing chain. It consisted of the Simplified Method for Atmospheric Correction (SMAC) and compositing at ten-day intervals based on the Maximum Value Compositing (MVC) criterion. All the satellite time series were analysed using the Detrended Fluctuation Analysis (DFA) to estimate post fire vegetation recovery. The DFA is a well-known methodology, which allows the detectin of long-range power-law correlations in signals possibly characterized by non-stationarity, which features most of the observational and experimental signals. We analyzed time variation of both single channels and spectral indices from 1998 to 2005 of fire- affected and fire unaffected areas. In order to eliminate the seasonal and/or phenological fluctuations, for each decadal composition, we focused on the normalized departure: 1) NDVI; 2) NDWId, 3) MSId. Results from our analysis point out that the persistence of vegetation dynamics is significantly increased by the occurrence of fires. In particular, a scaling behavior of two classes of vegetation (burned and unburned) has been best revealed by NDVI. The estimated scaling exponents of both classes suggest a persistent character of the vegetation dynamics. But, the burned sites show much larger exponents than those calculated for the unburned sites. Small variations have been observed between the estimated scaling exponents of both fire-affected and fire-unaffected areas.

  20. IMAGE Satellite Scaling

    NSDL National Science Digital Library

    2012-08-03

    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.

  1. Automatic Burned Land Mapping From MODIS Time Series Images: Assessment in Mediterranean Ecosystems

    Microsoft Academic Search

    Aitor Bastarrika; Emilio Chuvieco; M. Pilar Martin

    2011-01-01

    A novel automatic burned area mapping algorithm for Mediterranean ecosystems based on Moderate-Resolution Imaging Spectroradiometer (MODIS) time series data is presented in this paper. This algorithm is based on a two-phase approach. The first phase detects the most severely burned areas, using spectral\\/temporal rules computed from dynamic temporal win- dows. The second phase improves the discrimination of burned areas around

  2. Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

    Microsoft Academic Search

    Tomislav Hengl; Gerard B. M. Heuvelink; Melita Per?ec Tadi?; Edzer J. Pebesma

    2012-01-01

    A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product\\u000a Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from\\u000a the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of\\u000a daily temperature for the year 2008. Temperature was modeled as a

  3. Satellite Imaging Corporation: IKONOS Satellite Images

    NSDL National Science Digital Library

    Satellite Imaging Corporation

    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.

  4. Shifts in Arctic phenology in response to climate and anthropogenic factors as detected from multiple satellite time series

    NASA Astrophysics Data System (ADS)

    Zeng, Heqing; Jia, Gensuo; Forbes, Bruce C.

    2013-09-01

    There is an urgent need to reduce the uncertainties in remotely sensed detection of phenological shifts of high latitude ecosystems in response to climate changes in past decades. In this study, vegetation phenology in western Arctic Russia (the Yamal Peninsula) was investigated by analyzing and comparing Normalized Difference Vegetation Index (NDVI) time series derived from the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and SPOT-Vegetation (VGT) during the decade 2000-2010. The spatial patterns of key phenological parameters were highly heterogeneous along the latitudinal gradients based on multi-satellite data. There was earlier SOS (start of the growing season), later EOS (end of the growing season), longer LOS (length of the growing season), and greater MaxNDVI from north to south in the region. The results based on MODIS and VGT data showed similar trends in phenological changes from 2000 to 2010, while quite a different trend was found based on AVHRR data from 2000 to 2008. A significantly delayed EOS (p < 0.01), thus increasing the LOS, was found from AVHRR data, while no similar trends were detected from MODIS and VGT data. There were no obvious shifts in MaxNDVI during the last decade. MODIS and VGT data were considered to be preferred data for monitoring vegetation phenology in northern high latitudes. Temperature is still a key factor controlling spatial phenological gradients and variability, while anthropogenic factors (reindeer husbandry and resource exploitation) might explain the delayed SOS in southern Yamal. Continuous environmental damage could trigger a positive feedback to the delayed SOS.

  5. Anomalous transient uplift observed at the Lop Nor, China nuclear test site using satellite radar interferometry time-series analysis

    NASA Astrophysics Data System (ADS)

    Vincent, P.; Buckley, S. M.; Yang, D.; Carle, S. F.

    2011-12-01

    Anomalous uplift is observed at the Lop Nor, China nuclear test site using ERS satellite SAR data. Using an InSAR time-series analysis method, we show that an increase in absolute uplift with time is observed between 1997 and 1999. The signal is collocated with past underground nuclear tests. Due to the collocation in space with past underground tests we postulate a nuclear test-related hydrothermal source for the uplift signal. A possible mechanism is presented that can account for the observed transient uplift and is consistent with documented thermal regimes associated with underground nuclear tests conducted at the Nevada National Security Site (NNSS) (formerly the Nevada Test Site).

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

    NASA Astrophysics Data System (ADS)

    Legeckis, Richard

    1986-11-01

    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 reveals annual and interannual variations of the SST which are related to the 1982-1983 El Niño and Southern Oscillation as well as to coastal and equatorial upwelling events. The El Niño was preceded by a weak equatorial cooling pattern from May to August 1982. The subsequent equatorial warming persisted until June 1983. From mid-1983 to early 1984, the equatorial SST pattern was dominated by pronounced upwelling and westward advection of cold water by the South Equatorial Current. North of the equator the zonal equatorial SST front was distorted by a succession of westward moving equatorial long waves which extended nearly to the date line. During both 1984 and 1985 the equatorial upwelling began to intensify during May and lasted until the following February. The transition to seasonal equatorial warming occurred from February to March and was marked by abrupt increases of SST on time scales of less than 1 week. The zonal equatorial SST fronts disappeared at this time, but the upwelling off the coast of South America persisted. From December to April of each year, short-term upwelling events appeared intermittently along the western coast of Central America south of the Gulf of Tehuantepec and southwest of Lake Nicaragua and the Gulf of Panama. These upwelling events were weak from 1982 to 1984 and were more intense during 1985 and 1986. During February and March of 1985, an extreme case of upwelling was observed south of the Gulf of Panama. The SST was depressed by at least 9°C, and cooler water appeared to be advected southwestward to the equator and the Galapagos Islands. Time series of the sea surface temperatures at fixed locations and color images of the SST distribution are used to illustrate these events.

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

    NASA Astrophysics Data System (ADS)

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

    2001-12-01

    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.

  8. IMAGE Satellite Scale Model

    NSDL National Science Digital Library

    2012-08-03

    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.

  9. RESEARCH ARTICLE Time series analysis of infrared satellite data for detecting

    E-print Network

    Wright, Robert

    : a hybrid approach W. C. Koeppen & E. Pilger & R. Wright Received: 3 March 2010 /Accepted: 5 November 2010) (e.g., Harris et al. 1995; Dehn et al. 2000; Webley et al. 2008), the Along-Track Scanning Radiometer (ATSR) (e.g., Wooster et al. 1997), Geostationary Operational Environmental Satellites (GOES) (e.g

  10. Registration of 4D time-series of cardiac images with multichannel Diffeomorphic Demons.

    PubMed

    Peyrat, Jean-Marc; Delingette, Hervé; Sermesant, Maxime; Pennec, Xavier; Xu, Chenyang; Ayache, Nicholas

    2008-01-01

    In this paper, we propose a generic framework for intersubject non-linear registration of 4D time-series images. In this framework, spatio-temporal registration is defined by mapping trajectories of physical points as opposed to spatial registration that solely aims at mapping homologous points. First, we determine the trajectories we want to register in each sequence using a motion tracking algorithm based on the Diffeomorphic Demons algorithm. Then, we perform simultaneously pairwise registrations of corresponding time-points with the constraint to map the same physical points over time. We show this trajectory registration can be formulated as a multichannel registration of 3D images. We solve it using the Diffeomorphic Demons algorithm extended to vector-valued 3D images. This framework is applied to the inter-subject non-linear registration of 4D cardiac CT sequences. PMID:18982699

  11. Utilizing a Multi-sensor Satellite Time Series in Real-time Drought Monitoring Across the United States

    NASA Astrophysics Data System (ADS)

    Brown, J. F.; Miura, T.; Gu, Y.; Jenkerson, C.; Wardlow, B.

    2009-05-01

    Drought events frequently occur in the United States and result in billions of dollars of damage, often exceeding the costs of other weather-related hazards. Monitoring drought conditions is a necessary function of government agencies at State, Federal, and local levels as part of decision support for planning, risk management, and hazard mitigation activities. In partnership with the National Drought Mitigation Center, the National Aeronautics and Space Administration, the U.S. Department of Agriculture Risk Management Agency, and the High Plains Regional Climate Center, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center is developing an operational drought decision support tool with relatively higher spatial resolution (1 km2) than traditional drought monitoring maps. The Vegetation Drought Response Index (VegDRI) is a geospatial model that integrates in-situ climate, satellite, and biophysical data, providing an indicator of canopy vegetation condition (or stress). The satellite data ingested into VegDRI are collected from daily polar-orbiting earth observing systems including the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS). These instruments provide regular synoptic measurements of land surface conditions in near-real time. In VegDRI, remote sensing data provide proxy information about the vegetation status (or health) related to climate-induced changes and are integrated with traditional drought indices based on in-situ climate observations. When merged, the two complementary sources of drought-related data provide a comprehensive and detailed picture of drought impacts across the landscape. A 20-year history of AVHRR time-series data produced over the U.S. at a 1 km2 resolution provides a historical context for monitoring drought conditions. However, the MODIS instrument has improved sensor characteristics designed for land surface monitoring. To seamlessly extend the multi-sensor NDVI data record, inter-sensor NDVI continuity/compatibility has been examined and appropriate adjustments, or multi- sensor translations, have been made to datasets by means of cross-calibration. The data translation equations were derived from an overlapping period of observations with geometric mean regressions to treat variations in both AVHRR and MODIS datasets equally. Since standard MODIS products are often not delivered quickly enough to aid operational decisions, USGS has designed a system based on a direct broadcast model called eMODIS. The eMODIS system at EROS provides the near-real time MODIS vegetation index data needed to supply VegDRI products on a schedule that meets the needs of the U.S. drought monitoring community, largely driven by the U.S. Drought Monitor and the National Integrated Drought Information System. The requirements of the community include providing synoptic indicators in a timely fashion and in an easy-to-interpret format for incorporation into the weekly U.S. Drought Monitor map process.

  12. Constraints on surface deformation in the Seattle, WA, urban corridor from satellite radar interferometry time-series analysis

    NASA Astrophysics Data System (ADS)

    Finnegan, Noah J.; Pritchard, Mathew E.; Lohman, Rowena B.; Lundgren, Paul R.

    2008-07-01

    We apply differential InSAR (DInSAR) time-series techniques to the urban corridor between Tacoma, Seattle and Everett, WA, using 93 interferograms from three satellites (ERS 1, ERS 2 and RADARSAT-1) between 1992 and 2007. Our goal is to study local tectonic, geomorphic and groundwater processes. Consequently, we remove long-wavelength (>50-100 km) deformation signals from unwrapped interferograms. By comparing surface velocities generated via the time-series technique at more than two million points within the overlapping region between two independent ERS tracks, we estimate the uncertainty of relative surface velocity measurements to be ~0.5 mm yr-1 in the vertical. We estimate the uncertainty of relative displacement measurements to be ~5.4 mm, given our comparisons of DInSAR-derived time-series to GPS data, a result that is consistent both with previous DInSAR time-series analysis and with the uncertainty expected from GPS displacements projected onto the radar line-of-sight. Active tectonic deformation at shallow depths on the region's numerous east-west structures is absent over the ~11.5 yr of SAR data examined. Assuming that the south-dipping thrust beneath Seattle and Tacoma takes up 3 mm yr-1 of north-south shortening, our data indicate that the fault must be currently locked to a depth of greater than 10 km. We also document extensive groundwater-related deformation throughout much of the study region. Most notably, we identify sharp, linear deformation gradients near Federal Way, WA, and running between Sumner, WA, and Steilacoom, WA. These features may mark the locations of previously unmapped fault splays that locally control groundwater movement. We find no slow landslide deformation on any of the numerous mapped slide complexes within Seattle, although regions of known active landsliding, such as along Perkins Lane in Seattle, exhibit radar phase de-correlation. These observations are consistent with relatively infrequent and rapid landslide deformation within Seattle.

  13. Quality analysis in N-dimensional lossy compression of multispectral remote sensing time series images

    NASA Astrophysics Data System (ADS)

    Pesquer, L.; Zabala, A.; Pons, X.; Serra-Sagristà, J.

    2010-08-01

    This work aims to determine an efficient procedure (balanced between quality and compression ratio) for compressing multispectral remote sensing time series images in a 4-dimensional domain (2 spatial, 1 spectral and 1 temporal dimension). The main factors studied were: spectral and temporal aggregation, landscape type, compression ratio, cloud cover, thermal segregation and nodata regions. In this study, the authors used three-dimensional Discrete Wavelet Transform (3d-DWT) as the compression methodology, implemented in the Kakadu software with the JPEG2000 standard. This methodology was applied to a series of 2008 Landsat-5 TM images that covered three different landscapes, and to one scene (19-06-2007) from a hyperspectral CASI sensor. The results show that 3d-DWT significantly improves the quality of the results for the hyperspectral images; for example, it obtains the same quality as independently compressed images at a double compression ratio. The differences between the two compression methodologies are smaller in the Landsat spectral analysis than in the CASI analysis, and the results are more irregular depending on the factor analyzed. The time dimensional analysis for the Landsat series images shows that 3d-DWT does not improve on band-independent compression.

  14. ASTER's Satellite Image Gallery

    NSDL National Science Digital Library

    NASA Jet Propulsion Laboratory

    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.

  15. Automated in vivo change analysis of tumor vasculature from two-photon confocal image time series

    NASA Astrophysics Data System (ADS)

    Abdul-Karim, Muhammad-Amri; Al-Kofahi, Omar; Brown, Edward B., III; Jain, Rakesh K.; Al-Kofahi, Khalid; Roysam, Badrinath

    2003-07-01

    Automated methods are described for in vivo quantitation of changes in tumor vasculature. The tumor subsurface is imaged non-invasively over time with two-photon confocal microscopy aided by a variety of chronic animal window preparations. This results in time series of three-dimensional (3-D) image stacks for each specimen at high resolution (768x512x32 voxels, 8 bits/voxel, every 24 hours for 7 days), imaging depth and signal-to-background ratio. Next, automated image analysis allows detection and quantitation of vascular changes in a rapid and objective manner without manual tedium. We describe a fast new algorithm for fully automated 3-D tracing (50 seconds to trace a 10 MB stack on a Dell 1 GHz Pentium III personal computer). A variety of measurements including tortuosity, length, thickness, and branching order are generated and analyzed. Quantitative validation of the performance of the tracing algorithm against manual tracing resulted in 81% concordance. This enables a broader set of change analysis studies including testing the efficacy of anti-angiogenic therapies and deriving vessel growth parameters that may be correlated with physiological and gene expression profiles in tumor.

  16. Characterization of horizontal flows around solar pores from high-resolution time series of images

    NASA Astrophysics Data System (ADS)

    Vargas Domínguez, S.; de Vicente, A.; Bonet, J. A.; Martínez Pillet, V.

    2010-06-01

    Context. Though there is increasing evidence linking the moat flow and the Evershed flow along the penumbral filaments, there is not a clear consensus regarding the existence of a moat flow around umbral cores and pores, and the debate is still open. Solar pores appear to be a suitable scenario to test the moat-penumbra relation as they correspond to a direct interaction between the umbra and the convective plasma in the surrounding photosphere without any intermediate structure in between. Aims: We study solar pores based on high-resolution ground-based and satellite observations. Methods: Local correlation tracking techniques were applied to different-duration time series to analyze the horizontal flows around several solar pores. Results: Our results establish that the flows calculated from different solar pore observations are coherent among each other and show the determining and overall influence of exploding events in the granulation around the pores. We do not find any sign of moat-like flows surrounding solar pores, but a clearly defined region of inflows surrounding them. Conclusions: The connection between moat flows and flows associated to penumbral filaments is hereby reinforced.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    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.

  18. Dynamic changes of ecosystem service value of water conversation based on time series Landsat images

    NASA Astrophysics Data System (ADS)

    Gu, Xiaohe; Guo, Wei; Long, Huiling; Song, Xiaoyu

    2014-11-01

    Water conservation is one of the important ecological service functions of ecosystem. Time series LandSat images were used to analyze the change of spatial pattern of ecosystem in recent thirty years. Four types of ecosystems including farmland, forest, grassland and water were mapped, which had the function of water conservation. The water conservation function of ecosystem is similar to storing water of reservoir. The expense substitution method was used to calculate the service value of water conservation of ecosystem. The average cost of constructing the reservoir was substituted to evaluate the service value of water conservation function of ecosystem. Results showed that the ecological value of water conservation in Beijing area in 1978 was highest among four years, while that in 2000 was lowest. The fluctuation of water storage of ecosystem was consistent with the precipitation. The main contributors of ecological value of water conservation were forest and farmland. Because the government was committed to promoting the percentage of forest covering, the forest was the stable contributor for water conservation in Beijing area.

  19. Comprehensive mathematical simulation of functional magnetic resonance imaging time series including motion-related image distortion and spin saturation effect.

    PubMed

    Kim, Boklye; Yeo, Desmond T B; Bhagalia, Roshni

    2008-02-01

    There has been vast interest in determining the feasibility of functional magnetic resonance imaging (fMRI) as an accurate method of imaging brain function for patient evaluations. The assessment of fMRI as an accurate tool for activation localization largely depends on the software used to process the time series data. The performance evaluation of different analysis tools is not reliable unless truths in motion and activation are known. Lack of valid truths has been the limiting factor for comparisons of different algorithms. Until now, currently available phantom data do not include comprehensive accounts of head motion. While most fMRI studies assume no interslice motion during the time series acquisition in fMRI data acquired using a multislice and single-shot echo-planar imaging sequence, each slice is subject to a different set of motion parameters. In this study, in addition to known three-dimensional motion parameters applied to each slice, included in the time series computation are geometric distortion from field inhomogeneity and spin saturation effect as a result of out-of-plane head motion. We investigated the effect of these head motion-related artifacts and present a validation of the mapping slice-to-volume (MSV) algorithm for motion correction and activation detection against the known truths. MSV was evaluated, and showed better performance in comparison with other widely used fMRI data processing software, which corrects for head motion with a volume-to-volume realignment method. Furthermore, improvement in signal detection was observed with the implementation of the geometric distortion correction and spin saturation effect compensation features in MSV. PMID:17662548

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

    NASA Technical Reports Server (NTRS)

    Menenti, M.; Azzali, S.; Verhoef, W.; Van Swol, R.

    1993-01-01

    Examples are presented of applications of a fast Fourier transform algorithm to analyze time series of images of Normalized Difference Vegetation Index 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 using the Fourier analysis.

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

    Microsoft Academic Search

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

    2007-01-01

    Summary  In this study, the trends of annual and seasonal precipitation time series were examined on the basis of measurements of 22\\u000a surface stations in Greece for the period 1955–2001, and satellite data during the period 1980–2001. For this purpose, two\\u000a statistical tests based on the least square method and one based on the Mann-Kendall test, which is also capable of

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

    Microsoft Academic Search

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

    2004-01-01

    Summary 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

  3. The Time Series Toolbox

    NASA Astrophysics Data System (ADS)

    Boži?, Bojan; Havlik, Denis

    2010-05-01

    Many applications commonly used in sensor service networks operate on the same type of data repeatedly over time. This kind of data is most naturally represented in the form of "time series". In its simplest form, a time series may consist of a single floating point number (e.g. temperature), that is recorded at regular intervals. More complex forms of time series include time series of complex observations (e.g. aggregations of related measurements, spectra, 2D coverages/images, ...), and time series recorded at irregular intervals. In addition, the time series may contain meta-information describing e.g. the provenance, uncertainty, and reliability of observations. The Time Series Toolbox (TS Toolbox) provides a set of software components and application programming interfaces that simplify recording, storage, processing and publishing of time series. This includes (1) "data connector" components implementing access to data using various protocols and data formats; (2) core components interfacing with the connector components and providing specific additional functionalities like data processing or caching; and (3) front-end components implementing interface functionality (user interfaces or software interfaces). The functionalities implemented by TS Toolbox components provide application developers with higher-level building blocks than typical general purpose libraries, and allow rapid development of fully fledged applications. The TS Toolbox also includes example applications that can be either used as they are, or as a basis for developing more complex applications. The TS-Toolbox, which was initially developed by the Austrian Institute of Technology in the scope of SANY "Sensors Anywhere", is written in Java, published under the terms of the GPL, and available for download on the SANY web site.

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

    NASA Technical Reports Server (NTRS)

    Jasinski, Michael F.; Borak, Jordan S.

    2008-01-01

    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.

  5. 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.

  6. Grassland analysis using satellite image time Mathieu Fauvel, Annie Ouin et David Sheeren

    E-print Network

    Dobigeon, Nicolas

    Grassland analysis using satellite image time series Mathieu Fauvel, Annie Ouin et David Sheeren of grassland. By monitoring, it means that some properties have to be inferred from the SITS. In this work

  7. Height Estimation and Error Assessment of Inland Water Level Time Series calculated by a Kalman Filter Approach using Multi-Mission Satellite Altimetry

    NASA Astrophysics Data System (ADS)

    Schwatke, Christian; Dettmering, Denise; Boergens, Eva

    2015-04-01

    Originally designed for open ocean applications, satellite radar altimetry can also contribute promising results over inland waters. Its measurements help to understand the water cycle of the system earth and makes altimetry to a very useful instrument for hydrology. In this paper, we present our methodology for estimating water level time series over lakes, rivers, reservoirs, and wetlands. Furthermore, the error estimation of the resulting water level time series is demonstrated. For computing the water level time series multi-mission satellite altimetry data is used. The estimation is based on altimeter data from Topex, Jason-1, Jason-2, Geosat, IceSAT, GFO, ERS-2, Envisat, Cryosat, HY-2A, and Saral/Altika - depending on the location of the water body. According to the extent of the investigated water body 1Hz, high-frequent or retracked altimeter measurements can be used. Classification methods such as Support Vector Machine (SVM) and Support Vector Regression (SVR) are applied for the classification of altimeter waveforms and for rejecting outliers. For estimating the water levels we use a Kalman filter approach applied to the grid nodes of a hexagonal grid covering the water body of interest. After applying an error limit on the resulting water level heights of each grid node, a weighted average water level per point of time is derived referring to one reference location. For the estimation of water level height accuracies, at first, the formal errors are computed applying a full error propagation within Kalman filtering. Hereby, the precision of the input measurements are introduced by using the standard deviation of the water level height along the altimeter track. In addition to the resulting formal errors of water level heights, uncertainties of the applied geophysical correction (e.g. wet troposphere, ionosphere, etc.) and systematic error effects are taken into account to achieve more realistic error estimates. For validation of the time series, we compare our results with gauges and external inland altimeter databases (e.g. Hydroweb). We yield very high correlations between absolute water level height time series from altimetry and gauges. Moreover, the comparisons of water level heights are also used for the validation of the error assessment. More than 200 water level time series were already computed and made public available via the "Database for Hydrological Time Series of Inland Waters" (DAHITI) which is available via http://dahiti.dgfi.tum.de .

  8. Construction of merged satellite total O3 and NO2 time series in the tropics for trend studies and evaluation by comparison to NDACC SAOZ measurements

    NASA Astrophysics Data System (ADS)

    Pastel, M.; Pommereau, J.-P.; Goutail, F.; Richter, A.; Pazmiño, A.; Ionov, D.; Portafaix, T.

    2014-10-01

    Long time series of ozone and NO2 total column measurements in the southern tropics are available from two ground-based SAOZ (Système d'Analyse par Observation Zénithale) UV-visible spectrometers operated within the Network for the Detection of Atmospheric Composition Change (NDACC) in Bauru (22° S, 49° W) in S-E Brazil since 1995 and Reunion Island (21° S, 55° E) in the S-W Indian Ocean since 1993. Although the stations are located at the same latitude, significant differences are observed in the columns of both species, attributed to differences in tropospheric content and equivalent latitude in the lower stratosphere. These data are used to identify which satellites operating during the same period, are capturing the same features and are thus best suited for building reliable merged time series for trend studies. For ozone, the satellites series best matching SAOZ observations are EP-TOMS (1995-2004) and OMI-TOMS (2005-2011), whereas for NO2, best results are obtained by combining GOME version GDP5 (1996-2003) and SCIAMACHY - IUP (2003-2011), displaying lower noise and seasonality in reference to SAOZ. Both merged data sets are fully consistent with the larger columns of the two species above South America and the seasonality of the differences between the two stations, reported by SAOZ, providing reliable time series for further trend analyses and identification of sources of interannual variability in the future analysis.

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

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

    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.

  10. A tool for synthesizing rain attenuation time series in LEO Earth observation satellite downlinks at Ka band

    Microsoft Academic Search

    Pantelis-Daniel Arapoglou; Athanasios D. Panagopoulos

    2011-01-01

    Rapidly increasing requirements for Earth Obser- vation (EO) data via Low Earth Orbit (LEO) satellites push operation of the corresponding systems to higher frequency bands and particularly the Ka band, where more spectrum is available but atmospheric effects combined with the motion of the LEO satellite form a new propagation environment. Unfortunately, due to the lack of available experimental data,

  11. Estimation of the time series of the meridional heat transport across 15°N in the Pacific Ocean from Argo and satellite data

    NASA Astrophysics Data System (ADS)

    Yang, Tingting; Xu, Yongsheng

    2015-04-01

    The time series of the net meridional heat transport (MHT) at 15°N in the Pacific Ocean from 2003 to 2012 is estimated by combining the Argo profiles with the satellite altimeter and scatterometer data. The estimate is validated against the climatological ocean data and the ECCO2 products, and is demonstrated to be reasonable. The MHT has a high degree of variability with a temporal mean of 0.70 ± 0.31 PW, which is concentrated in the upper 800 dbar. The time series of the MHT and Ekman temperature transport have a significant annual cycle which peaks near April and December, whereas the time series of the geostrophic temperature transport have a subannual cycle. The results are consistent with previous estimates and lower than the ECCO2 estimate, which may mainly be caused by the different data sources and processings. The correlation between the air-sea flux and MHT is 0.50 with a 3 month delay. This report describes the first such attempt at a continuous transport of heat at 15°N in the Pacific Ocean from in situ observations.

  12. Mapping double-cropped irrigated rice fields in Taiwan using time-series Satellite Pour I'Observation de la Terre data

    NASA Astrophysics Data System (ADS)

    Chen, Chi-Farn; Huang, Su-Wei; Son, Nguyen-Thanh; Chang, Li-Yu

    2011-01-01

    Rice is the most important food crop in Taiwan. During recent decades, rice production in Taiwan has sharply declined because of industrialization and urbanization. Monitoring the areas of rice cultivation thus becomes important due to the official initiatives to ensure food supply. This study aims to develop a remote sensing classification approach for mapping double-cropped irrigated rice fields in Taiwan using time-series SPOT (Satellite Pour l'Observation de la Terre) data. Three study sites with different farming conditions in Taitung, Chiayi, and Taoyuan counties were chosen to test the new classification method. Data processing steps include: 1. filtering time-series SPOT-based normalized difference vegetation index (NDVI) using empirical mode decomposition (EMD) and wavelet transform, 2. classifying double-cropped irrigated rice fields using statistical methods (i.e., correlation analysis and sign-test statistics), and 3. assessing classification accuracy. The comparisons between the classification maps and ground-truth maps in 2005 indicated that classification using the EMD-based filtered NDVI time-series data yielded more accurate results than did the wavelet transform-based filtered data.

  13. Interpreting Satellite Images

    NSDL National Science Digital Library

    2012-08-03

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

    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.

  15. Satellite Hyperspectral Imaging Simulation

    NASA Technical Reports Server (NTRS)

    Zanoni, Vicki; Stanley, Tom; Blonski, Slawomir; Cao, Changyong; Gasser, Jerry; Ryan, Robert

    1999-01-01

    Simulation of generic pushbroom satellite hyperspectral sensors have been performed to evaluate the potential performance and validation techniques for satellite systems such as COIS(NEMO), Warfighter-1(OrbView-4) and Hyperion(EO-1). The simulations start with a generation of synthetic scenes from material maps of studied terrain. Scene-reflected radiance is corrected for atmospheric effects and convolved with sensor spectral response using MODTRAN 4 radiance and transmissions calculations. Scene images are further convolved with point spread functions derived from Optical Transfer Functions (OTF's) of the sensor system. Photon noise and etectorr/electronics noise are added to the simulated images, which are also finally quantized to the sensor bit resolution. Studied scenes include bridges and straight roads used for evaluation of sensor spatial resolution, as well as fields of minerals, vegetation and manmade materials used for evaluation of sensor radiometric response and sensitivity. The scenes are simulated with various seasons and weather conditions. Signal-to-noise ratios and expected performance are estimated for typical satellite system specifications and are discussed for all the scenes.

  16. Satellite Hyperspectral Imaging Simulation

    NASA Technical Reports Server (NTRS)

    Zanoni, Vicki; Stanley, Tom; Blonski, Slawomir; Cao, Changyong; Gasser, Jerry; Ryan, Robert; Zanoni, Vicki; Stanley, Tom

    1999-01-01

    Simulations of generic pushbroom satellite hyperspetral sensors have been performed to evaluate the potential performance and validation techniques for satellite systems such as COIS (NEMO), Warfighter-1 (OrbView-4), and Hyperion (EO-1). The simulaitons start with a generation of synthetic scenes form material maps of studied terrain. Scene-reflected radiance is corrected for atmospheric effects and convolved with sensor spectral response uwing MODTRAN 4 radiance and transmission calculations. Scene images are further convolved with point spread functions derived from Optical Transfer Functions (OTF's) of the sensor system. Photon noise and detector/electronics noise are added to the simulated images, which are also finally quantized to the sensor bit resolution. Studied scenes include bridges and straight roads used for evaluation of sensor spatial resolution, as well as fields of minerals, vegetation, and manmade materials used for evaluation of sensor radiometric response and sensitivity. The scenes are simulated with various seasons and weather conditions. Signal-to-noise ratos and expected performance are estimated for typica satellite system specifications and are discussed for all the scenes.

  17. The Time Series Image Analysis of the HeLa Cell Using Viscous Fluid Registration

    Microsoft Academic Search

    Soichiro Tokuhisa; Kunihiko Kaneko

    2010-01-01

    \\u000a Optical microscopy image analysis is important in the life science research. To obtain the motion of the cell, we use the\\u000a viscous fluid registration method based on fluid dynamics. Viscous fluid registration deforms an image at time t to the next image at time t+1. In this algorithm, there is a problem that an object cannot be divided into two.

  18. Monitoring irrigation water consumption using high resolution NDVI image time series (Sentinel-2 like). Calibration and validation in the Kairouan plain (Tunisia)

    NASA Astrophysics Data System (ADS)

    Saadi, Sameh; Simonneaux, Vincent; Boulet, Gilles; Mougenot, Bernard; Zribi, Mehrez; Lili Chabaane, Zohra

    2015-04-01

    Water scarcity is one of the main factors limiting agricultural development in semi-arid areas. It is thus of major importance to design tools allowing a better management of this resource. Remote sensing has long been used for computing evapotranspiration estimates, which is an input for crop water balance monitoring. Up to now, only medium and low resolution data (e.g. MODIS) are available on regular basis to monitor cultivated areas. However, the increasing availability of high resolution high repetitivity VIS-NIR remote sensing, like the forthcoming Sentinel-2 mission to be lunched in 2015, offers unprecedented opportunity to improve this monitoring. In this study, regional crops water consumption was estimated with the SAMIR software (Satellite of Monitoring Irrigation) using the FAO-56 dual crop coefficient water balance model fed with high resolution NDVI image time series providing estimates of both the actual basal crop coefficient (Kcb) and the vegetation fraction cover. The model includes a soil water model, requiring the knowledge of soil water holding capacity, maximum rooting depth, and water inputs. As irrigations are usually not known on large areas, they are simulated based on rules reproducing the farmer practices. The main objective of this work is to assess the operationality and accuracy of SAMIR at plot and perimeter scales, when several land use types (winter cereals, summer vegetables…), irrigation and agricultural practices are intertwined in a given landscape, including complex canopies such as sparse orchards. Meteorological ground stations were used to compute the reference evapotranspiration and get the rainfall depths. Two time series of ten and fourteen high-resolution SPOT5 have been acquired for the 2008-2009 and 2012-2013 hydrological years over an irrigated area in central Tunisia. They span the various successive crop seasons. The images were radiometrically corrected, first, using the SMAC6s Algorithm, second, using invariant objects located on the scene, based on visual observation of the images. From these time series, a Normalized Difference Vegetation Index (NDVI) profile was generated for each pixel. SAMIR was first calibrated based on ground measurements of evapotranspiration achieved using eddy-correlation devices installed on irrigated wheat and barley plots. After calibration, the model was run to spatialize irrigation over the whole area and a validation was done using cumulated seasonal water volumes obtained from ground survey at both plot and perimeter scales. The results show that although determination of model parameters was successful at plot scale, irrigation rules required an additional calibration which was achieved at perimeter scale.

  19. Classification of agricultural fields using time series of dual polarimetry TerraSAR-X images

    NASA Astrophysics Data System (ADS)

    Mirzaee, S.; Motagh, M.; Arefi, H.; Nooryazdan, M.

    2014-10-01

    Due to its special imaging characteristics, Synthetic Aperture Radar (SAR) has become an important source of information for a variety of remote sensing applications dealing with environmental changes. SAR images contain information about both phase and intensity in different polarization modes, making them sensitive to geometrical structure and physical properties of the targets such as dielectric and plant water content. In this study we investigate multi temporal changes occurring to different crop types due to phenological changes using high-resolution TerraSAR-X imagers. The dataset includes 17 dual-polarimetry TSX data acquired from June 2012 to August 2013 in Lorestan province, Iran. Several features are extracted from polarized data and classified using support vector machine (SVM) classifier. Training samples and different features employed in classification are also assessed in the study. Results show a satisfactory accuracy for classification which is about 0.91 in kappa coefficient.

  20. Remote Sensing Time Series Product Tool

    NASA Technical Reports Server (NTRS)

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

    2006-01-01

    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.

  1. Accuracy of bathymetry and current retrievals from airborne optical time-series imaging of shoaling waves

    Microsoft Academic Search

    Cynthia C. Piotrowski; John P. Dugan

    2002-01-01

    A sequence of visual images of shoaling ocean waves collected from an aircraft can be used to retrieve maps of water depth and\\/or currents. The data are mapped to rectilinear coordinates on the mean ocean surface, and three-dimensional (3D) cubes of these data (typically 2-min dwell and 256 m × 256 m square area) are Fourier transformed to provide the

  2. Inverting multispectral thermal time series images of volcanic eruptions for lava emplacement models

    E-print Network

    Barnie, T. D.; Oppenheimer, C.

    2015-06-04

    and at wavelengths in the Short Wave 276 Infrared (SWIR), Mid Infrared (MIR) and Thermal Infrared (TIR), a typical sampling interval and set of 277 wavebands for geostationary imagers (e.g. SEVIRI, Aminou 2002). Four kernels are shown for 278 observation 50 in a... Equation 20, and Gaussian noise with a 371 standard deviation of 0.05 times the standard deviation of the radiance signal is added. We invert 372 for combinations of radiances from bands at 1.6 ?m, 3.9 ?m and 10.8 ?m which we refer to as SWIR 373...

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    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.

  4. Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data

    PubMed Central

    Lee, Kuo-Jung; Jones, Galin L.; Caffo, Brian S.; Bassett, Susan Spear

    2014-01-01

    A common objective of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate a Bayesian approach that incorporates spatial and temporal dependence and allows for the task-related change in the BOLD signal to change dynamically over the scanning session. In this way, our model accounts for potential learning effects in addition to other mechanisms of temporal drift in task-related signals. We study the properties of the model through its performance on simulated and real data sets. PMID:25530824

  5. Earth Exploration Toolbook Chapter: 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.

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

    SciTech Connect

    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

    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.

  7. On the evaluation of vegetation resilience in Southern Italy by using satellite VEGETATION, MODIS, TM time series

    NASA Astrophysics Data System (ADS)

    Coluzzi, C.; Didonna, I.

    2009-04-01

    Satellite technologies can be profitably used for investigating the dynamics of vegetation re-growth after disturbance at different temporal and spatial scales. Nevertheless, disturbance -induced dynamical processes are very difficult to study since they affect the complex soil-surface-atmosphere system, due to the existence of feedback mechanisms involving human activity, ecological patterns and different subsystems of climate. The remote sensing of vegetation has been traditionally carried out by using vegetation indices, which are quantitative measures, based on vegetation spectral properties, that attempt to measure biomass or vegetative vigor. The vegetation indices operate by contrasting intense chlorophyll pigment absorption in the red against the high reflectance of leaf mesophyll in the near infrared. The simplest form of vegetation index is simply a ratio between two digital values from these two spectral bands. The most widely used index is the well-known normalized difference vegetation index NDVI = [NIR-R]/ [NIR+R]. The normalization of the NDVI reduces the effects of variations caused by atmospheric contaminations. High values of the vegetation index identify pixels covered by substantial proportions of healthy vegetation. NDVI is indicative of plant photosynthetic activity and has been found to be related to the green leaf area index and the fraction of photosynthetically active radiation absorbed by vegetation. Therefore variations in NDVI values become indicative of variations in vegetation composition and dynamics. In this study, we analyze the mutiscale satellite temporal series ( 1998 to 2008) of NDVI and other vegetation indices from SPOT VEGETATION and Landsat TM data acquired for some significant test areas affetced and unaffected (Southern Italy) by different type of environmenta diturbances (drought, salinity, pollution, etc). Our objective is to characterize quantitatively the resilient effect of vegetation cover at different temporal and spatial scales

  8. On the evaluation of vegetation resilience in Southern Italy by using VEGETATION, MODIS, TM satellite time series

    NASA Astrophysics Data System (ADS)

    Didonna, I.; Coluzzi, R.

    2009-04-01

    Satellite technologies can be profitably used for investigating the dynamics of vegetation re-growth after disturbance at different temporal and spatial scales. Nevertheless, disturbance -induced dynamical processes are very difficult to study since they affect the complex soil-surface-atmosphere system, due to the existence of feedback mechanisms involving human activity, ecological patterns and different subsystems of climate. The remote sensing of vegetation has been traditionally carried out by using vegetation indices, which are quantitative measures, based on vegetation spectral properties, that attempt to measure biomass or vegetative vigor. The vegetation indices operate by contrasting intense chlorophyll pigment absorption in the red against the high reflectance of leaf mesophyll in the near infrared. The simplest form of vegetation index is simply a ratio between two digital values from these two spectral bands. The most widely used index is the well-known normalized difference vegetation index NDVI = [NIR-R]/ [NIR+R]. The normalization of the NDVI reduces the effects of variations caused by atmospheric contaminations. High values of the vegetation index identify pixels covered by substantial proportions of healthy vegetation. NDVI is indicative of plant photosynthetic activity and has been found to be related to the green leaf area index and the fraction of photosynthetically active radiation absorbed by vegetation. Variations in NDVI values become indicative of variations in vegetation composition and dynamics. In this study, we analyze the mutiscale satellite temporal series ( 2000 to 2008) of NDVI and other vegetation indices from SPOT VEGETATION, MODIS and Landsat TM data acquired for some significant test areas affetced and unaffected (Southern Italy) by different types of environmental diturbances (drought, salinity, pollution, etc). Our objective was to characterize quantitatively the resilient effect of vegetation cover at differen temporal and spatial scales.

  9. Imaging transient slip events and their interaction with slow earthquakes in southwest Japan using reanalyzed GEONET GPS time series

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    Geodetic and seismic studies over the past decade have revealed a wide range of slow earthquake phenomena including long- and short-term slow slip events (SSEs), non-volcanic tremor, low and very-low frequency earthquakes (LFEs, V-LFEs) in major subduction zones. Despite much progress, the physical mechanisms of SSEs and seismic slow earthquakes and how these phenomena relate to each other are still not clear. The Japanese GPS network (GEONET) with ~1450 stations provide a unique opportunity to study subduction zone dynamics and to understand these different faulting behaviors at various spatiotemporal scales. We extended our reanalysis of GPS position time series for the entire GEONET from 1996 to 2014 using JPL GIPSY/OASIS-II based GPS Network Processor and raw data provided by Geospatial Information Authority of Japan and Caltech. The reestimated JPL precise GPS orbits, GMF troposphere model, and single receiver phase ambiguity resolution strategy were used in the analysis. The resultant ~18 years position estimates show good consistency and reduced scattering over the entire time period compared to prior analysis. We perform a systematic time series analysis to identify and correct the offsets caused by earthquakes, instrument and other unknown sources. We image the spatiotemporal variation of slip transients and investigate how they interact with seismic slow earthquakes. Our application to recurrent long-term SSEs in Bungo Channel region shows these events share the similar spatial distribution with regard to interplate coupling model, consistent with their recurrent nature. The universal modulation of transient slip rate on downdip LFEs/tremors and their different spatial patterns and moment release suggest that they are not different manifestations of the same physical process. There is considerable difference in space-time details of these slip transients, indicating that these recurrent events are not identical. We find a clear difference in temporal correspondence between transient slip rate and up-dip V-LFEs, possibly reflecting different triggering mode from that of LFEs/tremors. The high quality of GPS position time series also help reveal much smaller events over the "quiet" inter-SSE period, showing that not all SSEs are accompanied by LFE/tremors.

  10. Exploring Time Series Plots

    NSDL National Science Digital Library

    2012-08-03

    Students will explore time series plots and raw data to understand the role of sea surface temperature increases on arctic ice melt. This is part three of a four-part activity on polar science. The activity builds on the knowledge gained in Using Data and Images to Understand Albedo (part 2). Extension activities examining air and sea surface temperature in relation to changing Earth albedo are included. Information is provided on data access using the NOAA Earth System Research Laboratory Web site. This activity is one of several learning activities connected with the 2007 GLOBE Earth system poster.

  11. Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

    PubMed

    Sun, Wenchao; Ishidaira, Hiroshi; Bastola, Satish; Yu, Jingshan

    2015-05-01

    Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins. PMID:25680241

  12. Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

    NASA Astrophysics Data System (ADS)

    Nitze, Ingmar; Barrett, Brian; Cawkwell, Fiona

    2015-02-01

    The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8-10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.

  13. Analysis of Satellite sea surface temperature time series in the Brazil-Malvinas Current confluence region: Dominance of the annual and semiannual periods

    Microsoft Academic Search

    Christine Provost; Omar Garcia; Véronique Garçon

    1992-01-01

    We study the dominant periodic variations of sea surface temperature (SST) in the Brazil-Malvinas Confluence region from a satellite-derived data set compiled by Olson et al. (1988). This data set is composed of 202 sea surface temperature images with a 4×4 km resolution and extends over 3 years (from July 1984 to July 1987). Each image in a 5-day composite.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    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.

  15. Time-series observations of hydrothermal discharge using an acoustic imaging sonar: a NEPTUNE observatory case study

    NASA Astrophysics Data System (ADS)

    Xu, Guangyu; Bemis, Karen; Jackson, Darrell; Light, Russ

    2015-04-01

    One intriguing feature of a mid-ocean ridge hydrothermal system is the intimate interconnections among hydrothermal, geological, oceanic, and biological processes. The advent of the NEPTUNE observatory operated by Ocean Networks Canada at the Endeavour Segment, Juan de Fuca Ridge enables scientists to study these interconnections through multidisciplinary, continuous, real-time observations. In this study, we present the time-series observations of a seafloor hydrothermal vent made using the Cabled Observatory Vent Imaging Sonar (COVIS). COVIS is currently connected to the NEPTUNE observatory to monitor the hydrothermal discharge from the Grotto mound on the Endeavour Segment. Since its deployment in 2010, COVIS has recorded a 3-year long dataset of the shape and outflow fluxes of the buoyant plumes above Grotto along with the areal coverage of its diffuse flow discharge. The interpretation of these data in light of contemporaneous observations of ocean currents, venting temperature, and seismicity made using other NEPTUNE observatory instruments reveals significant impacts of ocean currents and geological events on hydrothermal venting. In this study, we summarize these findings in the hope of forming a more complete understanding of the intricate interconnections among oceanic, geological, and hydrothermal processes.

  16. Astronomical Time Series Analysis

    E-print Network

    Pelt, Jaan

    Astronomical Time Series Analysis Lecture Notes by Jaan Pelt Tartu Observatory Oulu University 23 October - 18 November, 2003 #12;ii #12;Introduction Astronomical time series are somewhat different if to compare with stan- dard time series often used in other branches of science and businesses. The random

  17. Predicting chaotic time series

    Microsoft Academic Search

    J. Doyne Farmer; John J. Sidorowich

    1987-01-01

    A forecasting technique for chaotic data is presented. After a time series has been embedded in a state space using delay coordinates, the induced nonlinear mapping is 'learned' using a local approximation. This makes it possible to make short-term predictions of the future behavior of a time series, using information based only on past values. An error estimate is presented

  18. Time Series Data Library

    NSDL National Science Digital Library

    Hyndman, Robert

    This is a collection of time series datasets covering many application areas, but are all for time series analysis. Some of the topics covered are: agriculture, chemistry, crime, demography, ecology, finance, health, hydrology, industry, labor market, macroeconomics, physics, production, sales, sport, transportation, tourism, tree rings and utilities. The data are in text format, thus they can be used without any additional software.

  19. Meteorology 5323 Time Series Analysis

    E-print Network

    Droegemeier, Kelvin K.

    Meteorology 5323 Time Series Analysis Fall Semester 2012 MWF 11:00 ­ 11:50 am Room 5600 NWC Course of time series. At the same time we want to use time series analyses to gain insight into the physical understanding of basic time series terminology and methodology to better deal with more advanced time series

  20. Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

    NASA Astrophysics Data System (ADS)

    Pan, Zhuokun; Huang, Jingfeng; Zhou, Qingbo; Wang, Limin; Cheng, Yongxiang; Zhang, Hankui; Blackburn, George Alan; Yan, Jing; Liu, Jianhong

    2015-02-01

    With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.

  1. Remote Sensing Time Series Product Tool

    Microsoft Academic Search

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

    2006-01-01

    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

  2. Introduction Nonlinear time series analysis

    E-print Network

    Savicky, Petr

    Introduction Nonlinear time series analysis Summary Quantifying Interactions between Complex Oscillatory Systems: A Topic in Time Series Analysis Thesis defense presentation M. Vejmelka, supervisor M;Introduction Nonlinear time series analysis Summary Overview Weak interactions between pairs of systems Focus

  3. Random time series in astronomy.

    PubMed

    Vaughan, Simon

    2013-02-13

    Progress in astronomy comes from interpreting the signals encoded in the light received from distant objects: the distribution of light over the sky (images), over photon wavelength (spectrum), over polarization angle and over time (usually called light curves by astronomers). In the time domain, we see transient events such as supernovae, gamma-ray bursts and other powerful explosions; we see periodic phenomena such as the orbits of planets around nearby stars, radio pulsars and pulsations of stars in nearby galaxies; and we see persistent aperiodic variations ('noise') from powerful systems such as accreting black holes. I review just a few of the recent and future challenges in the burgeoning area of time domain astrophysics, with particular attention to persistently variable sources, the recovery of reliable noise power spectra from sparsely sampled time series, higher order properties of accreting black holes, and time delays and correlations in multi-variate time series. PMID:23277606

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

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

    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.

  5. Agrometerological study of semi?arid areas: an experiment for analysing the potential of time series of FORMOSAT?2 images (Tensift?Marrakech plain)

    Microsoft Academic Search

    B. Duchemin; O. Hagolle; B. Mougenot; I. Benhadj; R. Hadria; V. Simonneaux; J. Ezzahar; J. Hoedjes; S. Khabba; M. H. Kharrou; G. Boulet; G. Dedieu; R. Escadafal; A. Olioso; A. G. Chehbouni

    2008-01-01

    Earth Observing Systems designed to provide both high spatial resolution (10 m) and high capacity of time revisit (a few days) offer strong opportunities for the management of agricultural water resources. The FORMOSAT?2 satellite is the first and only satellite with the ability to provide daily high?resolution images over a particular area with constant viewing angles. As part of the SudMed

  6. 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.

  7. Reading Time Series Plots

    NSDL National Science Digital Library

    Shelley Olds

    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:

  8. Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

    NASA Technical Reports Server (NTRS)

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

    2008-01-01

    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.

  9. Time Series Hilary Term 2002

    E-print Network

    Time Series Hilary Term 2002 Dr. Gesine Reinert Outline 1. The nature of time series Types of data, examples, objectives, informal analysis, overview of tech- niques for time series analysis 2. Stationary, maximum-likelihood #12;tting, frequency domain 4. Some more advanced topics Multiple time series

  10. Development of an approach for generation of temporally complete daily nadir MODIS reflectance time series

    Microsoft Academic Search

    Junchang Ju; David P. Roy; Yanmin Shuai; Crystal Schaaf

    2010-01-01

    Consistent, spatially and temporally complete reflectance time series are required for reliable terrestrial monitoring. The Moderate Resolution Imaging Spectroradiometer (MODIS), like other polar-orbiting wide field of view satellite sensors, can provide global observations on a nearly daily basis, but the sparseness of valid observations due to cloud, residual atmospheric effects, and sensor anomalies, may result in gaps in the derived

  11. Evaluating the Impact of Abrupt Changes in Forest Policy and Management Practices on Landscape Dynamics: Analysis of a Landsat Image Time Series in the Atlantic Northern Forest

    PubMed Central

    Legaard, Kasey R.; Sader, Steven A.; Simons-Legaard, Erin M.

    2015-01-01

    Sustainable forest management is based on functional relationships between management actions, landscape conditions, and forest values. Changes in management practices make it fundamentally more difficult to study these relationships because the impacts of current practices are difficult to disentangle from the persistent influences of past practices. Within the Atlantic Northern Forest of Maine, U.S.A., forest policy and management practices changed abruptly in the early 1990s. During the 1970s-1980s, a severe insect outbreak stimulated salvage clearcutting of large contiguous tracts of spruce-fir forest. Following clearcut regulation in 1991, management practices shifted abruptly to near complete dependence on partial harvesting. Using a time series of Landsat satellite imagery (1973-2010) we assessed cumulative landscape change caused by these very different management regimes. We modeled predominant temporal patterns of harvesting and segmented a large study area into groups of landscape units with similar harvest histories. Time series of landscape composition and configuration metrics averaged within groups revealed differences in landscape dynamics caused by differences in management history. In some groups (24% of landscape units), salvage caused rapid loss and subdivision of intact mature forest. Persistent landscape change was created by large salvage clearcuts (often averaging > 100 ha) and conversion of spruce-fir to deciduous and mixed forest. In groups that were little affected by salvage (56% of landscape units), contemporary partial harvesting caused loss and subdivision of intact mature forest at even greater rates. Patch shape complexity and edge density reached high levels even where cumulative harvest area was relatively low. Contemporary practices introduced more numerous and much smaller patches of stand-replacing disturbance (typically averaging <15 ha) and a correspondingly large amount of edge. Management regimes impacted different areas to different degrees, producing different trajectories of landscape change that should be recognized when studying the impact of policy and management practices on forest ecology. PMID:26106893

  12. Evaluating the Impact of Abrupt Changes in Forest Policy and Management Practices on Landscape Dynamics: Analysis of a Landsat Image Time Series in the Atlantic Northern Forest.

    PubMed

    Legaard, Kasey R; Sader, Steven A; Simons-Legaard, Erin M

    2015-01-01

    Sustainable forest management is based on functional relationships between management actions, landscape conditions, and forest values. Changes in management practices make it fundamentally more difficult to study these relationships because the impacts of current practices are difficult to disentangle from the persistent influences of past practices. Within the Atlantic Northern Forest of Maine, U.S.A., forest policy and management practices changed abruptly in the early 1990s. During the 1970s-1980s, a severe insect outbreak stimulated salvage clearcutting of large contiguous tracts of spruce-fir forest. Following clearcut regulation in 1991, management practices shifted abruptly to near complete dependence on partial harvesting. Using a time series of Landsat satellite imagery (1973-2010) we assessed cumulative landscape change caused by these very different management regimes. We modeled predominant temporal patterns of harvesting and segmented a large study area into groups of landscape units with similar harvest histories. Time series of landscape composition and configuration metrics averaged within groups revealed differences in landscape dynamics caused by differences in management history. In some groups (24% of landscape units), salvage caused rapid loss and subdivision of intact mature forest. Persistent landscape change was created by large salvage clearcuts (often averaging > 100 ha) and conversion of spruce-fir to deciduous and mixed forest. In groups that were little affected by salvage (56% of landscape units), contemporary partial harvesting caused loss and subdivision of intact mature forest at even greater rates. Patch shape complexity and edge density reached high levels even where cumulative harvest area was relatively low. Contemporary practices introduced more numerous and much smaller patches of stand-replacing disturbance (typically averaging <15 ha) and a correspondingly large amount of edge. Management regimes impacted different areas to different degrees, producing different trajectories of landscape change that should be recognized when studying the impact of policy and management practices on forest ecology. PMID:26106893

  13. Seasonal shifts in satellite time series portend vegetation state change - verification using long-term data in an arid grassland ecosyste

    NASA Astrophysics Data System (ADS)

    Browning, D. M.; Maynard, J. J.; Karl, J.; Peters, D. C.

    2014-12-01

    The frequency and severity of drought is forecasted to increase in the 21st century. The need to understand how managed ecosystems respond to climate is intensified by uncertainty associated with knowing when, where, and how long drought conditions will manifest. Analysis of broad scale patterns in ecosystem productivity can inform our understanding of ecosystem dynamics and improve predictions for responses to climate extremes. We leveraged observations of plant biomass at a long-term ecological research site in southern New Mexico to verify the use of NDVI time-series as a proxy for landscape productivity from 13 years of MODIS data. The period between 2000 and 2013 encompassed years of sustained drought (2000-2003) and record-breaking high rainfall (2006 and 2008) that yielded decreases followed by increases in biomass with a restructuring of plant communities. We decomposed patterns derived from the 250m MODIS NDVI product over this period into contributions from the long-term trend, seasonal cycle, and unexplained variance using the Breaks For Additive Seasonal and Trend (BFAST) model to identify significant deviations from the modelled trend and seasonal components. Observed breakpoints in NDVI trend and seasonal components were verified with field estimates of species-specific biomass data at 15 sites. We found that breaks in the trend reflected large changes in mean biomass and seasonal breaks reflected changes in dominance of perennial grasses, shrubs, and/or annual grasses. The BFAST method proved useful for detecting observed state changes in this arid ecosystem. The ability to distinguish between long-term phenological change and temporal variability is strongly needed in water-limited ecosystems with high inter-annual variability in primary productivity. We demonstrate that time-series analysis of NDVI data holds potential for monitoring landscape condition at spatial scales needed to generate indicators for ecosystem responses to changing climate.

  14. HIGH RESOLUTION SATELLITE IMAGING SYSTEMS - OVERVIEW

    Microsoft Academic Search

    K. Jacobsen

    More and more high and very high resolution optical space sensors are available. Not in any case the systems are well known and the images are distributed over popular distributing channels or are accessible for any user. Several imaging satellites are announced for the near future. Not every announced satellite finally will be launched and some starts are failing. In

  15. 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)

    Bradley, Eliza Swan

    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.

  16. Developing an automated global validation site time series system for VIIRS

    NASA Astrophysics Data System (ADS)

    Wang, Wenhui; Cao, Changyong; Uprety, Sirish; Bai, Yan; Padula, Frank; Shao, Xi

    2014-10-01

    Long-term top of atmosphere (TOA) reflectance, brightness temperature, and band ratio time series over well-established validation sites provide important information for post-launch calibration stability monitoring and analysis. In this study, we present an automated and highly extensible global validation site time series system for the Visible and Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. The system includes 30 globally distributed sites. Validation site database and quality control parameters for each site can be easily modified without software code modification. VIIRS sensor data records over each validation site are automatically identified from NPP product archives. Validation time series plots for all VIIRS Reflective Solar Bands and Thermal Emissive Bands are updated daily and available online. Bands I2/M7 and I3/M10 inter-channel consistency analysis using the validation time series developed in this study is also included.

  17. Denoising Deterministic Time Series

    E-print Network

    Steven P. Lalley; Andrew B. Nobel

    2006-04-21

    This paper is concerned with the problem of recovering a finite, deterministic time series from observations that are corrupted by additive, independent noise. A distinctive feature of this problem is that the available data exhibit long-range dependence and, as a consequence, existing statistical theory and methods are not readily applicable. This paper gives an analysis of the denoising problem that extends recent work of Lalley, but begins from first principles. Both positive and negative results are established. The positive results show that denoising is possible under somewhat restrictive conditions on the additive noise. The negative results show that, under more general conditions on the noise, no procedure can recover the underlying deterministic series.

  18. Estimating the solar meridional flow by normal mode decomposition of long time series of Doppler imaging data

    NASA Astrophysics Data System (ADS)

    Doerr, Hans-Peter; Roth, Markus; Krieger, Lars

    2010-05-01

    Although investigations have been carried out for many decades the solar magnetic cycle is not yet understood in all its basic properties and it still is one of the main research foci of today's solar physics. An important ingredient to most dynamic dynamo models is the solar meridional flow; on the surface of each hemisphere, a polewards flow in the order of 10 - 20 m/s can be measured with different techniques. From mass conservation, one expects a much slower equatorwards return-flow in deeper layers of the solar convection zone which reaches down to about 200 mega meters below the surface. Numerous attempts have been made to derive the depth profile of the flow using a variety of helioseismic techniques (e.g. Giles, P.M., 2000). While most results agree well about the horizontal velocity structures in the upper 20 Mm, sometimes contrary findings have been published for the lower parts of the convection zone. We use a Fourier-Legendre decomposition of the surface wave field generated by the solar normal modes into directly opposed travelling wave fields, corresponding a modification of a method suggested earlier by Braun & Fan (1998). The partition allows for the estimation of the frequency difference, caused by the horizontal meridional flow between waves that propagate polewards and equatorwards respectively. These frequency shifts are used to determine the meridional flow profile as a function of depth and latitude by a SOLA (Subtractive Optimally Localized Averaging) inversion method. Because low-degree modes penetrate deeper into the solar interior than high-degree modes, decomposing the seismic wave field within large patches on the solar surface allows to probe a large fraction of the solar convection zone for the average meridional flow. Smaller patches allow us to study the latitudinal dependence of the flow in higher layers and also a direct comparison of our findings with other methods like ring-diagram analysis. For our analysis, we use Doppler imaging data provided by the ground based instruments of the GONG (Global Oscillation Network Group) network as well as from the MDI (Michelson Doppler Imager) instrument aboard the SOHO (Solar and Heliospheric Observatory) spacecraft. Both observatories now provide data spanning about one decade and thus allow us to study the variation with time of the meridional flow during the past solar cycle. Beside a short but broad overview about the significance of the meridional flow for modelling the solar internal processes, several new results of the ongoing analysis are presented. We are able to extend the seismic probing of the solar interior beyond those shallow regions that were accessible to other methods.

  19. GPS Position Time Series @ JPL

    NASA Technical Reports Server (NTRS)

    Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

    2013-01-01

    Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

  20. A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images

    NASA Astrophysics Data System (ADS)

    Xue, Cunjin; Song, Wanjiao; Qin, Lijuan; Dong, Qing; Wen, Xiaoyang

    2015-06-01

    A spatiotemporal mining framework is a novel tool for the analysis of marine association patterns using multiple remote sensing images. From data pretreatment, to algorithm design, to association rule mining and pattern visualization, this paper outlines a spatiotemporal mining framework for abnormal association patterns in marine environments, including pixel-based and object-based mining models. Within this framework, some key issues are also addressed. In the data pretreatment phase, we propose an algorithm for extracting abnormal objects or pixels over marine surfaces, and construct a mining transaction table with object-based and pixel-based strategies. In the mining algorithm phase, a recursion method to construct a direct association pattern tree is addressed with an asymmetric mutual information table, and a recursive mining algorithm to find frequent items. In the knowledge visualization phase, a "Dimension-Attributes" visualization framework is used to display spatiotemporal association patterns. Finally, spatiotemporal association patterns for marine environmental parameters in the Pacific Ocean are identified, and the results prove the effectiveness and the efficiency of the proposed mining framework.

  1. AMOS Observations of NASA's IMAGE Satellite

    Microsoft Academic Search

    Doyle Hall; John Africano; David Archambeault; Brian Birge; David Witte; Paul Kervin

    2006-01-01

    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

  2. NOTES ON TIME SERIES ANALYSIS

    E-print Network

    Masci, Frank

    NOTES ON TIME SERIES ANALYSIS ARIMA MODELS AND SIGNAL EXTRACTION Regina Kaiser and Agustín Maravall practice in applied time series work, mostly at economic policy or dataproducing agencies, relies heavily on using moving average lters to estimate unobserved components (or signals) in time series

  3. Development of an IUE Time Series Browser

    NASA Technical Reports Server (NTRS)

    Massa, Derck

    2005-01-01

    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.

  4. Image processing for weather satellite cloud segmentation

    Microsoft Academic Search

    I. J. H. Leung; J. E. Jordan

    1995-01-01

    Image segmentation of weather satellite imagery is an important first step in an automated weather forecasting system. Accurate cloud extraction is also important in the determination of solar radiative transfer in atmospheric research, where satellite observations are used as inputs to global climate models to predict climatic change. Most of the current cloud extraction algorithms tend to be quite complicated

  5. 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)

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

    2009-04-01

    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.

  6. Offshore wind resource assessment through satellite images

    E-print Network

    1 Slide no. 4 Offshore wind resource assessment through satellite images Charlotte Bay Hasager images for offshore wind ressource assessment in lieu of in-situ mast observations #12;4 Slide no 2001 i.e. prior to construction of the wind farm 19991019 Footprint averaging per scene #12;1 0 Slide

  7. ENVIRONMENTALLYORIENTED PROCESSING OF MULTISPECTRAL SATELLITE IMAGES

    E-print Network

    Kreinovich, Vladik

    ENVIRONMENTALLY­ORIENTED PROCESSING OF MULTI­SPECTRAL SATELLITE IMAGES: NEW CHALLENGES FOR BAYESIAN METHODS S.A. STARKS AND V. KREINOVICH NASA Pan American Center for Earth and Environmental Studies University of Texas at El Paso El Paso, TX 79968, USA y Abstract. Remotely sensed images from new generation

  8. Animation of Archived Composite Infrared Satellite Images

    NSDL National Science Digital Library

    With this tool, users can build their own animations from infrared satellite imagery superimposed on a world map. Animations are constructed by selecting year, month, date, and time for the archived images. Users can also adjust the animation length, interval between images, and speed of the animation.

  9. Imaging atomic line filter for satellite tracking

    Microsoft Academic Search

    Eric Korevaar; Mike Rivers; C. S. Liu

    1989-01-01

    The operational principles of an active image preserving atomic line filter (ALF), which matches the wavelength of semiconductor diode lasers, are explained, especially as related to satellite tracking for a communications network. Preliminary results showing image preservation at a spatial resolution of 0.5 mm by a cesium atomic line filter at 852 nm with an acceptance bandwidth of 0.002 nm

  10. Time Series of the Biscuit Fire

    NSDL National Science Digital Library

    Cindy Starr

    2003-08-04

    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.

  11. Spot evolution on the red giant star XX Triangulum. A starspot-decay analysis based on time-series Doppler imaging

    NASA Astrophysics Data System (ADS)

    Künstler, A.; Carroll, T. A.; Strassmeier, K. G.

    2015-06-01

    Context. Solar spots appear to decay linearly proportional to their size. The decay rate of solar spots is directly related to magnetic diffusivity, which itself is a key quantity for the length of a magnetic-activity cycle. Is a linear spot decay also seen on other stars, and is this in agreement with the large range of solar and stellar activity cycle lengths? Aims: We investigate the evolution of starspots on the rapidly-rotating (Prot?24 d) K0 giant XX Tri, using consecutive time-series Doppler images. Our aim is to obtain a well-sampled movie of the stellar surface over many years, and thereby detect and quantify a starspot decay law for further comparison with the Sun. Methods: We obtained continuous high-resolution and phase-resolved spectroscopy with the 1.2-m robotic STELLA telescope on Tenerife over six years, and these observations are ongoing. For each observing season, we obtained between 5 to 7 independent Doppler images, one per stellar rotation, making up a total of 36 maps. All images were reconstructed with our line-profile inversion code iMap. A wavelet analysis was implemented for denoising the line profiles. To quantify starspot area decay and growth, we match the observed images with simplified spot models based on a Monte Carlo approach. Results: It is shown that the surface of XX Tri is covered with large high-latitude and even polar spots and with occasional small equatorial spots. Just over the course of six years, we see a systematically changing spot distribution with various timescales and morphology, such as spot fragmentation and spot merging as well as spot decay and formation. An average linear decay of D = -0.022 ± 0.002 SH/day is inferred. We found evidence of an active longitude in phase toward the (unseen) companion star. Furthermore, we detect a weak solar-like differential rotation with a surface shear of ? = 0.016 ± 0.003. From the decay rate, we determine a turbulent diffusivity of ?T = (6.3 ± 0.5) × 1014 cm2/s and predict a magnetic activity cycle of ?26 ± 6 yr. Finally, we present a short movie of the spatially resolved surface of XX Tri. Based on data obtained with the STELLA robotic telescopes in Tenerife, an AIP facility jointly operated with IAC.Appendices and the movie are available in electronic form at http://www.aanda.org

  12. Data compression for satellite images

    NASA Technical Reports Server (NTRS)

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

    1976-01-01

    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.

  13. Geostationary Lightning Imager for FY4 Meteorological Satellite (Invited)

    Microsoft Academic Search

    F. Huang

    2010-01-01

    The FY-4 satellite scheduled to launch in 2015 is a second-generation Chinese geostationary meteorological satellite. The main payloads for FY-4 satellite include Geostationary Lightning Imager (GLI), Advanced Geostationary Visible and Infrared Imager (AGVII), and Geostationary Interfering Infrared Sounder (GIIRS). Since the GLI is the first lightning detection imager without any heritage on a Chinese meteorological satellite, it is a great

  14. Orbit Adjustment for EROS A1 High Resolution Satellite Images

    Microsoft Academic Search

    Liang-Chien CHEN; Tee-Ann TEO

    As the resolution of satellite images is improving, the applications of satellite images become widespread. Orientation modeling is an indispensable step in the processing for satellite. EROS A1 is a high resolution imaging satellite. Its linear array pushbroom imager is with 1.8meter resolution on ground. EROS A1 is a sun-synchronous satellite and sampling with asynchronous mode. The main purpose of

  15. Title: IKONOS Satellite Image of North York, Ontario Data Creator /

    E-print Network

    Title: IKONOS Satellite Image of North York, Ontario Data Creator / Copyright Owner: Geo of approximately 10 metres Original Image ID: 20090901163045200000116082042000046195600THC Image Date: September 11

  16. Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh.

    PubMed

    Wu, Di; Sun, Da-Wen

    2013-07-15

    This study investigated the potential of using time series-hyperspectral imaging (TS-HSI) in visible and near infrared region (400-1700 nm) for rapid and non-invasive determination of surface total viable count (TVC) of salmon flesh during spoilage process. Hyperspectral cubes were acquired at different spoilage stages for salmon chops and their spectral data were extracted. The reference TVC values of the same samples were measured using standard plate count method and then calibrated with their corresponding spectral data based on two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM), respectively. Competitive adaptive reweighted sampling (CARS) was conducted to identify the most important wavelengths/variables that had the greatest influence on the TVC prediction throughout the whole wavelength range. As a result, eight variables representing the wavelengths of 495 nm, 535 nm, 550 nm, 585 nm, 625 nm, 660 nm, 785 nm, and 915 nm were selected, which were used to reduce the high dimensionality of the hyperspectral data. On the basis of the selected variables, the models of PLSR and LS-SVM were established and their performances were compared. The CARS-PLSR model established using Spectral Set I (400-1000 nm) was considered to be the best for the TVC determination of salmon flesh. The model led to a coefficient of determination (rP(2)) of 0.985 and residual predictive deviation (RPD) of 5.127. At last, the best model was used to predict the TVC values of each pixel within the ROI of salmon chops for visualizing the TVC distribution of salmon flesh. The research demonstrated that TS-HSI technique has a potential for rapid and non-destructive determination of bacterial spoilage in salmon flesh during the spoilage process. PMID:23622523

  17. Mapping impervious surface expansion using medium resolution satellite image time series: A case study in Yangtze river delta, China

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Due to the rapid growth of population and economic development in the developing countries, more people are now living in the cities than in the rural areas in the world for the first time in human history. As a result, cities are sprawling rapidly into their surroundings. A characteristic change as...

  18. Automated land cover change detection: the quest for meaningful high temporal time series extraction

    Microsoft Academic Search

    Brian P. Salmon; Jan C. Olivier; Waldo Kleynhans; Konrad J. Wessels; Frans van den Bergh

    2010-01-01

    An automated land cover change detection method is proposed that uses coarse resolution hyper-temporal satellite time series data. The study compared two different unsupervised clustering approaches that operate on the short term Fourier transform coefficients of subsequences of 8-day composite MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance data that were extracted with a temporal sliding window. The method uses a feature

  19. Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images

    USGS Publications Warehouse

    Chen, Xuexia; Vogelmann, James E.; Chander, Gyanesh; Ji, Lei; Tolk, Brian; Huang, Chengquan; Rollins, Matthew

    2013-01-01

    Routine acquisition of Landsat 5 Thematic Mapper (TM) data was discontinued recently and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) has an ongoing problem with the scan line corrector (SLC), thereby creating spatial gaps when covering images obtained during the process. Since temporal and spatial discontinuities of Landsat data are now imminent, it is therefore important to investigate other potential satellite data that can be used to replace Landsat data. We thus cross-compared two near-simultaneous images obtained from Landsat 5 TM and the Indian Remote Sensing (IRS)-P6 Advanced Wide Field Sensor (AWiFS), both captured on 29 May 2007 over Los Angeles, CA. TM and AWiFS reflectances were compared for the green, red, near-infrared (NIR), and shortwave infrared (SWIR) bands, as well as the normalized difference vegetation index (NDVI) based on manually selected polygons in homogeneous areas. All R2 values of linear regressions were found to be higher than 0.99. The temporally invariant cluster (TIC) method was used to calculate the NDVI correlation between the TM and AWiFS images. The NDVI regression line derived from selected polygons passed through several invariant cluster centres of the TIC density maps and demonstrated that both the scene-dependent polygon regression method and TIC method can generate accurate radiometric normalization. A scene-independent normalization method was also used to normalize the AWiFS data. Image agreement assessment demonstrated that the scene-dependent normalization using homogeneous polygons provided slightly higher accuracy values than those obtained by the scene-independent method. Finally, the non-normalized and relatively normalized ‘Landsat-like’ AWiFS 2007 images were integrated into 1984 to 2010 Landsat time-series stacks (LTSS) for disturbance detection using the Vegetation Change Tracker (VCT) model. Both scene-dependent and scene-independent normalized AWiFS data sets could generate disturbance maps similar to what were generated using the LTSS data set, and their kappa coefficients were higher than 0.97. These results indicate that AWiFS can be used instead of Landsat data to detect multitemporal disturbance in the event of Landsat data discontinuity.

  20. Regression quantiles for time series

    E-print Network

    Cai, Zongwu

    2002-02-01

    In this paper we study nonparametric estimation of regression quantiles for time series data by inverting a weighted Nadaraya–Watson (WNW) estimator of conditional distribution function, which was first used by Hall, Wolff, and Yao (1999, Journal...

  1. Rule Discovery from Time Series

    Microsoft Academic Search

    Gautam Das; King-ip Lin; Heikki Mannila; Gopal Renganathan; Padhraic Smyth

    1998-01-01

    We consider the problem of finding rules relating pat- terns in a time series to other patterns in that series, or patterns in one series to patterns in another se- ries. A simple example is a rule such as \\

  2. Satellite image deconvolution based on nonlocal means.

    PubMed

    Zhao, Ming; Zhang, Wei; Wang, Zhile; Hou, Qingyu

    2010-11-10

    The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem, which can be regularized under the Bayesian framework by introducing an appropriate image prior. In this paper, we derive a new image prior based on the state-of-the-art nonlocal means (NLM) denoising approach under Markov random field theory. Inheriting from the NLM, the prior exploits the intrinsic high redundancy of satellite images and is able to encode the image's nonsmooth information. Using this prior, we propose an inhomogeneous deconvolution technique for satellite images, termed nonlocal means-based deconvolution (NLM-D). Moreover, in order to make our NLM-D unsupervised, we apply the L-curve approach to estimate the optimal regularization parameter. Experimentally, NLM-D demonstrates its capacity to preserve the image's nonsmooth structures (such as edges and textures) and outperforms the existing total variation-based and wavelet-based deconvolution methods in terms of both visual quality and signal-to-noise ratio performance. PMID:21068860

  3. Salton Sea Satellite Image Showing Fault Slip

    USGS Multimedia Gallery

    Landsat satellite image (LE70390372003084EDC00) showing location of surface slip triggered along faults in the greater Salton Trough area. Red bars show the generalized location of 2010 surface slip along faults in the central Salton Trough and many additional faults in the southwestern section of t...

  4. Progress on the calibration of channel geometry and friction parameters of the LISFLOOD-FP hydraulic model using time series of SAR flood images

    NASA Astrophysics Data System (ADS)

    Wood, M.; Neal, J. C.; Hostache, R.; Corato, G.; Bates, P. D.; Chini, M.; Giustarini, L.; Matgen, P.; Wagener, T.

    2014-12-01

    The objective of this work is to calibrate channel depth and roughness parameters of the LISFLOOD-FP Sub-Grid 2D hydraulic model using SAR image-derived flood extent maps. The aim is to reduce uncertainty in flood model predictions for those rivers where channel geometry is unknown and/or cannot be easily measured. In particular we consider the effectiveness of using real SAR data for calibration and whether the number and timings of SAR acquisitions is of benefit to the final result. Terrain data are processed from 2m LiDAR images and inflows to the model are taken from gauged data. As a test case we applied the method to the River Severn between Worcester and Tewkesbury. We firstly applied the automatic flood mapping algorithm of Giustarini[1] et al. (2013) to ENVISAT ASAR (wide swath mode) flood images; generating a series of flood maps. We then created an ensemble of flood extent maps with the hydraulic model (each model representing a unique parameter set). Where there is a favourable comparison between the modelled flood map and the SAR obtained flood map we may suggest an optimal parameter set. Applying the method to a sequence of SAR acquisitions provides insight into the advantages, disadvantages and limitations of using series of acquired images. To complete the investigation we simultaneously explore parameter 'identifiabilty' within a sequence of available satellite observations by adopting the DYNIA method proposed by Wagener[2] et al. (2003). We show where we might most easily detect the depth and roughness parameters within the SAR acquisition sequence. [1] Giustarini. 2013. 'A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X'. IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 4. [2] Wagener. 2003. 'Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis'. Hydrol. Process. 17, 455-476.

  5. Robust Copyright Protection of Satellite Images Using a Novel Digital Image-In-Image Watermarking Algorithm

    E-print Network

    Doran, Simon J.

    Robust Copyright Protection of Satellite Images Using a Novel Digital Image-In-Image Watermarking, a novel digital watermarking technique for copyright protection of satellite images is presented, rotation, clipping and filtering to the watermarked image. Conventional digital watermarking techniques use

  6. Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat

    Microsoft Academic Search

    E. H. Helmer; Thomas S. Ruzycki; Joseph M. Wunderle Jr.; Shannon Vogesser; Bonnie Ruefenacht; Charles Kwit; Thomas J. Brandeis; David N. Ewert

    2010-01-01

    Remote sensing of forest vertical structure is possible with lidar data, but lidar is not widely available. Here we map tropical dry forest height (RMSE=0.9m, R2=0.84, range 0.6–7m), and we map foliage height profiles, with a time series of Landsat and Advanced Land Imager (ALI) imagery on the island of Eleuthera, The Bahamas, substituting time for vertical canopy space. We

  7. Change detection in satellite images

    NASA Astrophysics Data System (ADS)

    Thonnessen, U.; Hofele, G.; Middelmann, W.

    2005-05-01

    Change detection plays an important role in different military areas as strategic reconnaissance, verification of armament and disarmament control and damage assessment. It is the process of identifying differences in the state of an object or phenomenon by observing it at different times. The availability of spaceborne reconnaissance systems with high spatial resolution, multi spectral capabilities, and short revisit times offer new perspectives for change detection. Before performing any kind of change detection it is necessary to separate changes of interest from changes caused by differences in data acquisition parameters. In these cases it is necessary to perform a pre-processing to correct the data or to normalize it. Image registration and, corresponding to this task, the ortho-rectification of the image data is a further prerequisite for change detection. If feasible, a 1-to-1 geometric correspondence should be aspired for. Change detection on an iconic level with a succeeding interpretation of the changes by the observer is often proposed; nevertheless an automatic knowledge-based analysis delivering the interpretation of the changes on a semantic level should be the aim of the future. We present first results of change detection on a structural level concerning urban areas. After pre-processing, the images are segmented in areas of interest and structural analysis is applied to these regions to extract descriptions of urban infrastructure like buildings, roads and tanks of refineries. These descriptions are matched to detect changes and similarities.

  8. Spatial Cone Tree: An Index Structure for Correlation-based Similarity Queries on Spatial Time Series Data

    E-print Network

    Huang, Yan

    University of North Texas Email: huangyan@cs.unt.edu October 1, 2003 1 Introduction A spatial time series dataset [15] is a collection of time series [3], each referencing a location in a common spatial framework [14]. Finding highly correlated time series from spatial time series datasets collected by satellites

  9. 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)

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

    2014-05-01

    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

  10. CHEMICAL TIME-SERIES SAMPLING

    EPA Science Inventory

    The rationale for chemical time-series sampling has its roots in the same fundamental relationships as govern well hydraulics. Samples of ground water are collected as a function of increasing time of pumpage. The most efficient pattern of collection consists of logarithmically s...

  11. Observe animated satellite images of water vapor

    NSDL National Science Digital Library

    TERC. Center for Earth and Space Science Education

    2003-01-01

    This animation shows Earth science students how jet streams drive the movement of water vapor in the atmosphere. The introduction explains how the infrared images were taken from a satellite positioned about 8 kilometers above the Earth's surface. Students are instructed to observe how the jet streams (indicated by dark areas) are juxtaposed against areas of dense water vapor (indicated by light areas). Movie controls allow students to repeat, pause, or step through the animation, which can give students more time to analyze the images. Copyright 2005 Eisenhower National Clearinghouse

  12. Satellite high resolution imaging simulation in space field

    Microsoft Academic Search

    Xiaomei Chen; Ting Li; Bo Xue; Xuan Zhang; Gang Chen; Guoqiang Ni

    2009-01-01

    In the paper, a new satellite image simulation method in space field is proposed. According to the path of the satellite imaging transmission, the simulation is divided into three parts: atmosphere transmission simulation, optical system imaging simulation, and CCD sampling, integral and quantizing simulation. The experiment results show that the simulation method in space field can get images closer to

  13. Imaging of the outer planets and satellites.

    NASA Technical Reports Server (NTRS)

    Murray, B. C.

    1973-01-01

    Imaging is the most widely applicable single means of exploring the outer planets and their satellites and also complements other planet-oriented instruments. Photography of Jupiter from terrestrial telescopes has revealed features which were neither predictable or predicted. Close-up imaging from fly-bys and orbiters affords the opportunity for discovery of atmospheric phenomena on the outer planets forever beyond the reach of terrestrial laboratories and intuition. On the other hand, a large number of specific applications of close-up imaging to study the giant planets are suggested by experience in photography from Earth and Mars orbit, and by ground-based telescopic studies of Jupiter and Saturn. The satellites of the outer planets actually constitute three distinct classes: lunar-sized objects, asteroidal-sized objects, and particulate rings. Imaging promises to be the primary observational tool for each category with results that could impact scientific thinking in the late 70's and 80's as significantly as has close-up photography of Mars and the Moon in the last 10 yr.

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

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    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.

  15. Absolute image registration for geosynchronous satellites

    NASA Technical Reports Server (NTRS)

    Nankervis, R.; Koch, D.; Sielski, H.; Hall, D.

    1980-01-01

    A procedure for the absolute registration of earth images acquired by cameras on geosynchronous satellites is described. A conventional least squares process is used to estimate navigational parameters and camera pointing biases from observed minus computed landmark line and element numbers. These estimated parameters along with orbit and attitude dynamic models are used to register images, employing an automated grey-level correlation technique, inside the span represented by the landmark data. Experimental results obtained from processing the SMS-2 observation data base covering May 2, 1979 through May 20, 1979 show registration accuracies with a standard deviation of less than two pixels if the registration is within the landmark data span. It is also found that accurate registration can be expected for images obtained up to 48 hours outside of the landmark data span.

  16. Integration of the GG model with SEBAL to produce time series of evapotranspiration of high spatial resolution at watershed scales

    NASA Astrophysics Data System (ADS)

    Long, Di; Singh, Vijay P.

    2010-11-01

    Lack of good quality satellite images because of cloud contamination or long revisit time severely degrades predictions of evapotranspiration (ET) time series at watershed/regional scales from satellite-based surface flux models. We integrate the feedback model developed by Granger and Gray (the GG model) with the Surface Energy Balance Algorithm for Land (SEBAL), with the objective to generate ET time series of high spatial resolution and reliable temporal distribution at watershed scales. First, SEBAL is employed to yield estimates of ET for the Baiyangdian watershed in a semihumid climatic zone in north China on cloud-free days, where there exists the complementary relationship (CR) between actual ET and pan ET. These estimates constitute input to the GG model to inversely derive the relationship between the relative evaporation and the relative drying power of the air. Second, the modified GG model is used to yield ET time series on a daily basis simply by using routine meteorological data and Moderate Resolution Imaging Spectroradiometer (MODIS) albedo and leaf area index products. Results suggest that the modified GG model that has incorporated remotely sensed ET can effectively extend remote sensing based ET to days without images and improve spatial representation of ET at watershed scales. Utility of the evaporative fraction method and the crop coefficients approaches to extrapolate ET time series depends largely on the number and interval of good quality satellite images. Comparison of ET time series from the two techniques and the proposed integration method for days with daily net radiation larger than 100 W m-2 and corresponding pan ET clearly shows that only the integration method can exhibit an asymmetric CR at the watershed scale and daily time scale. Validation performed using hydrologic budget calculations indicate that the proposed method has the highest accuracy in terms of annual estimates of ET for both watersheds in north China.

  17. Learning Graphical Models for Stationary Time Series

    E-print Network

    Sekhon, Jasjeet S.

    Learning Graphical Models for Stationary Time Series Francis R. Bach Computer Science Division Probabilistic graphical models can be extended to time series by considering probabilistic dependencies between entire time series. For stationary Gaussian time series, the graphical model semantics can be expressed

  18. Multiple Indicator Stationary Time Series Models.

    ERIC Educational Resources Information Center

    Sivo, Stephen A.

    2001-01-01

    Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

  19. Mining fuzzy rules for time series classification

    Microsoft Academic Search

    Wai-Ho Au; Keith C. C. Chan

    2004-01-01

    Time series classification is concerned about discovering classification models in a database of pre-classified time series and using them to classify unseen time series. To better handle the noises and fuzziness in time series data, we propose a new data mining technique to mine fuzzy rules in the data. The fuzzy rules discovered employ fuzzy sets to represent the revealed

  20. Forecasting financial time series with ensemble learning

    Microsoft Academic Search

    Yaohui Bai; Jiancheng Sun; Jianguo Luo; Xiaobin Zhang

    2010-01-01

    The forecasting of financial time series is a challenging problem that has been addressed by many researchers due to the possible profit. We provide an analysis of using classical time series method to create an ensemble of exponential smoothing and ARIMA to solve forecasting tasks of financial time series. The algorithm is tested on several financial time series of different

  1. Algorithm for Compressing Time-Series Data

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

    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").

  2. Automatic identification of oil spills on satellite images

    Microsoft Academic Search

    Iphigenia Keramitsoglou; Constantinos Cartalis; Chris T. Kiranoudis

    2006-01-01

    Abstract A fully automated,system for the identification of possible oil spills present on Synthetic Aperture Radar (SAR) satellite images based on artificial intelligence fuzzy logic has been developed. Oil spills are recognized,by experts as dark patterns of characteristic shape, in particular context. The system analyzes the satellite images and assigns the probability of a dark image shape to be an

  3. Distributed Geo-rectification of Satellite Images using Grid Computing

    E-print Network

    Teo, Yong-Meng

    include wrapping satellite positional data to compensate the earth curvature, and consist of several steps by the earth's curvature in raw satellite images. It establishes the image in the correct spatial location], specifically the geo-rectification of earth images. Geo-rectification is the correction of skew caused

  4. Title: Worldview 2 Satellite Image of York University Data Creator /

    E-print Network

    Title: Worldview 2 Satellite Image of York University Data Creator / Copyright Owner: Digital Globe Publisher: Digital Globe Edition: N/A Versions: N/A Publication Date: 2013 Coverage Date(s): July 25, 2012 Updates: N/A Abstract: Worldview 2 Satellite Image: Orthorectified image of York University Keele campus

  5. United States Forest Disturbance Trends Observed Using Landsat Time Series

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

    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.

  6. Figure 1. Satellite image before neural net Figure 5. Satellite image after

    E-print Network

    Michel, Howard E.

    . Satellite image after processing for identification of land and water (Water is colored as jet black regions for land and roads region identification (roads are blue, land yellow and water region red) Figure 4 and land and roads as greenandpink). Figure 6. Map depicting all the water resources in Boston region

  7. Impact of Sensor Degradation on the MODIS NDVI Time Series

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

    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.

  8. Impact of Sensor Degradation on the MODIS NDVI Time Series

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

    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.

  9. Stochastic Time-Series Spectroscopy

    E-print Network

    Scoville, John

    2015-01-01

    Spectroscopically measuring low levels of non-equilibrium phenomena (e.g. emission in the presence of a large thermal background) can be problematic due to an unfavorable signal-to-noise ratio. An approach is presented to use time-series spectroscopy to separate non-equilibrium quantities from slowly varying equilibria. A stochastic process associated with the non-equilibrium part of the spectrum is characterized in terms of its central moments or cumulants, which may vary over time. This parameterization encodes information about the non-equilibrium behavior of the system. Stochastic time-series spectroscopy (STSS) can be implemented at very little expense in many settings since a series of scans are typically recorded in order to generate a low-noise averaged spectrum. Higher moments or cumulants may be readily calculated from this series, enabling the observation of quantities that would be difficult or impossible to determine from an average spectrum or from prinicipal components analysis (PCA). This meth...

  10. Studies of soundings and imagings measurements from geostationary satellites

    NASA Technical Reports Server (NTRS)

    Suomi, V. E.

    1973-01-01

    Soundings and imaging measurements from geostationary satellites are presented. The subjects discussed are: (1) meteorological data processing techniques, (2) sun glitter, (3) cloud growth rate study, satellite stability characteristics, and (4) high resolution optics. The use of perturbation technique to obtain the motion of sensors aboard a satellite is described. The most conditions, and measurement errors. Several performance evaluation parameters are proposed.

  11. Road Extraction from High Resolution Satellite Images

    NASA Astrophysics Data System (ADS)

    Özkaya, M.

    2012-07-01

    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.

  12. Managing an archive of weather satellite images

    NASA Technical Reports Server (NTRS)

    Seaman, R. L.

    1992-01-01

    The author's experiences of building and maintaining an archive of hourly weather satellite pictures at NOAO are described. This archive has proven very popular with visiting and staff astronomers - especially on windy days and cloudy nights. Given access to a source of such pictures, a suite of simple shell and IRAF CL scripts can provide a great deal of robust functionality with little effort. These pictures and associated data products such as surface analysis (radar) maps and National Weather Service forecasts are updated hourly at anonymous ftp sites on the Internet, although your local Atsmospheric Sciences Department may prove to be a more reliable source. The raw image formats are unfamiliar to most astronomers, but reading them into IRAF is straightforward. Techniques for performing this format conversion at the host computer level are described which may prove useful for other chores. Pointers are given to sources of data and of software, including a package of example tools. These tools include shell and Perl scripts for downloading pictures, maps, and forecasts, as well as IRAF scripts and host level programs for translating the images into IRAF and GIF formats and for slicing & dicing the resulting images. Hints for displaying the images and for making hardcopies are given.

  13. Time series exponential models: theory and methods 

    E-print Network

    Holan, Scott Harold

    2004-09-30

    The exponential model of Bloomfield (1973) is becoming increasingly important due to its recent applications to long memory time series. However, this model has received little consideration in the context of short memory time series. Furthermore...

  14. Daily imaging scheduling of an Earth observation satellite

    Microsoft Academic Search

    Wei-cheng Lin; Da-yin Liao; Chung-yang Liu; Yong-yao Lee

    2005-01-01

    This work presents the development of a daily imaging scheduling system for a low-orbit, Earth observation satellite. The daily imaging scheduling problem of satellite considers various imaging requests with different reward opportunities, changeover efforts between two consecutive imaging tasks, cloud-coverage effects, and the availability of the spacecraft resource. It belongs to a class of single-machine scheduling problems with salient features

  15. Accurate mapping of forest types using dense seasonal Landsat time-series

    NASA Astrophysics Data System (ADS)

    Zhu, Xiaolin; Liu, Desheng

    2014-10-01

    An accurate map of forest types is important for proper usage and management of forestry resources. Medium resolution satellite images (e.g., Landsat) have been widely used for forest type mapping because they are able to cover large areas more efficiently than the traditional forest inventory. However, the results of a detailed forest type classification based on these images are still not satisfactory. To improve forest mapping accuracy, this study proposed an operational method to get detailed forest types from dense Landsat time-series incorporating with or without topographic information provided by DEM. This method integrated a feature selection and a training-sample-adding procedure into a hierarchical classification framework. The proposed method has been tested in Vinton County of southeastern Ohio. The detailed forest types include pine forest, oak forest, and mixed-mesophytic forest. The proposed method was trained and validated using ground samples from field plots. The three forest types were classified with an overall accuracy of 90.52% using dense Landsat time-series, while topographic information can only slightly improve the accuracy to 92.63%. Moreover, the comparison between results of using Landsat time-series and a single image reveals that time-series data can largely improve the accuracy of forest type mapping, indicating the importance of phenological information contained in multi-seasonal images for discriminating different forest types. Thanks to zero cost of all input remotely sensed datasets and ease of implementation, this approach has the potential to be applied to map forest types at regional or global scales.

  16. Revealing glacier flow and surge dynamics from animated satellite image sequences: examples from the Karakoram

    NASA Astrophysics Data System (ADS)

    Paul, F.

    2015-04-01

    Although animated images are very popular on the Internet, they have so far found only limited use for glaciological applications. With long time-series of satellite images becoming increasingly available and glaciers being well recognized for their rapid changes and variable flow dynamics, animated sequences of multiple satellite images reveal glacier dynamics in a time-lapse mode, making the otherwise slow changes of glacier movement visible and understandable for a wide public. For this study animated image sequences were created from freely available image quick-looks of orthorectified Landsat scenes for four regions in the central Karakoram mountain range. The animations play automatically in a web-browser and might help to demonstrate glacier flow dynamics for educational purposes. The animations revealed highly complex patterns of glacier flow and surge dynamics over a 15-year time period (1998-2013). In contrast to other regions, surging glaciers in the Karakoram are often small (around 10 km2), steep, debris free, and advance for several years at comparably low annual rates (a few hundred m a-1). The advance periods of individual glaciers are generally out of phase, indicating a limited climatic control on their dynamics. On the other hand, nearly all other glaciers in the region are either stable or slightly advancing, indicating balanced or even positive mass budgets over the past few years to decades.

  17. 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

    Microsoft Academic Search

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

    2009-01-01

    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

  18. Statistical criteria for characterizing irradiance time series

    Microsoft Academic Search

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

    2010-01-01

    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

  19. Time Series Analysis James D. Hamilton

    E-print Network

    Landweber, Laura

    Time Series Analysis James D. Hamilton Since its publication just over ten years ago, James Hamilton's Time Series Analysis has taken its place in the canon of modern technical eco- nomic literature. Econometrics is the mathematical and statistical analysis of eco- nomic data. Time Series Analysis supplied

  20. Multivariate Time Series Forecasting in Incomplete Environments

    E-print Network

    Roberts, Stephen

    Multivariate Time Series Forecasting in Incomplete Environments Technical Report PARG 08-03 Seung Time Series Forecasting in Incomplete Environments Summary We consider the problem of predicting missing observations and forecasting future values in incomplete multivariate time series data. We study

  1. Time Series Prediction Competition: The CATS Benchmark

    E-print Network

    Verleysen, Michel

    Time Series Prediction Competition: The CATS Benchmark Amaury Lendasse, Erkki Oja and Olli Simula and the time series have become widely used benchmarks. The goal of these competitions is the prediction of the following values of a given time series (30 to 100 values to predict). Unfortunately, the long

  2. From time series to superstatistics Christian Beck

    E-print Network

    Texas at Austin. University of

    From time series to superstatistics Christian Beck School of Mathematical Sciences, Queen Mary of a statistics", in short, a "superstatistics". We describe how to proceed from a given experimental time series data. We illustrate our approach by applying it to velocity time series measured in turbulent Taylor

  3. Subsequence Time Series Clustering Jason R. Chen*

    E-print Network

    Chen, Jason

    Subsequence Time Series Clustering Jason R. Chen* Department of Information Engineering Research.chen@anu.edu.au (* Corresponding author) #12;Subsequence Time Series Clustering Jason Chen, Australian National University want to cluster is a time series data set. Such data has a temporal ordering on its elements

  4. ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES

    E-print Network

    Ide, Kayo

    ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES M. Ghil,1,2 M. R. Allen,3 M. D. Dettinger,4 K 2002. [1] The analysis of univariate or multivariate time series provides crucial information of novel methods for extract- ing useful information from time series has recently revitalized

  5. Spectral Analysis of Economic Time Series

    E-print Network

    Landweber, Laura

    Spectral Analysis of Economic Time Series C.W.J. Granger, in association with M. Hatanaka In Spectral Analysis of Economic Time Series, Clive Granger showed that then-established statisti- cal methods by members of the Time Series Project of the Econometric Re- search Program of Princeton University, Granger

  6. Time Series 14.1 Study Suggestions

    E-print Network

    Wardrop, Robert L.

    Chapter 14 Time Series 14.1 Study Suggestions Chapter 14 has a very modest goal, namely, to pro- vide a gentle introduction to the ideas of time series analysis. It is important to remember that autocorrelation and smoothing simply are descriptive techniques. In addition, since a time series is observational

  7. ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES

    E-print Network

    Ghil, Michael

    ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES M. Ghil,1 M. R. Allen,2 M. D. Dettinger,3 K. Ide] The analysis of univariate or multivariate time series provides crucial information to describe, understand- ing useful information from time series has recently revitalized this classical field of study

  8. Forecasting Time-Series by Kohonen Classification

    E-print Network

    Verleysen, Michel

    221 Forecasting Time-Series by Kohonen Classification Amaury Lendasse1 , Michel Verleysen1 , Eric, Belgium. Abstract. In this paper, we propose a generic non-linear approach for time series forecasting algorithm. The method is then applied to a widely known time-series from the SantaFe competition

  9. DATA MINING OF MULTIPLE NONSTATIONARY TIME SERIES

    Microsoft Academic Search

    RICHARD J. POVINELLI; XIN FENG

    1999-01-01

    A data mining method for synthesizing multiple time series is presented. Based on a single time series algorithm, the method embeds multiple time series into a phase space. The reconstructed state space allows temporal pattern extraction and local model development. Using an a priori data mining objective, an optimal local model is chosen for short-term forecasting. For the same sampling

  10. Characterization and Correction of Interpolation Effects in the Realignment of fMRI Time Series

    E-print Network

    Gabrieli, John

    Characterization and Correction of Interpolation Effects in the Realignment of fMRI Time Series S time series. The nature of these artifacts is characterized using simulated displacements of an f interpo- lation errors from the image time series on a voxel-by- voxel basis is proposed. The artifacts

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

    Microsoft Academic Search

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

    2011-01-01

    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

  12. Benchmarking of energy time series

    SciTech Connect

    Williamson, M.A.

    1990-04-01

    Benchmarking consists of the adjustment of time series data from one source in order to achieve agreement with similar data from a second source. The data from the latter source are referred to as the benchmark(s), and often differ in that they are observed at a lower frequency, represent a higher level of temporal aggregation, and/or are considered to be of greater accuracy. This report provides an extensive survey of benchmarking procedures which have appeared in the statistical literature, and reviews specific benchmarking procedures currently used by the Energy Information Administration (EIA). The literature survey includes a technical summary of the major benchmarking methods and their statistical properties. Factors influencing the choice and application of particular techniques are described and the impact of benchmark accuracy is discussed. EIA applications and procedures are reviewed and evaluated for residential natural gas deliveries series and coal production series. It is found that the current method of adjusting the natural gas series is consistent with the behavior of the series and the methods used in obtaining the initial data. As a result, no change is recommended. For the coal production series, a staged approach based on a first differencing technique is recommended over the current procedure. A comparison of the adjustments produced by the two methods is made for the 1987 Indiana coal production series. 32 refs., 5 figs., 1 tab.

  13. Multivariate Time Series Similarity Searching

    PubMed Central

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

    2014-01-01

    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. PMID:24895665

  14. Optoelectronic image processing system for satellite landmark navigation

    Microsoft Academic Search

    Sergey V. Dyblenko; Klaus Janschek; Anton Kisselev; Albert H. Sultanov; Valerij Tchernykh

    2004-01-01

    Information system determining the satellite navigation parameters on the base of landmark image processing is considered. The concept of the optoelectronic navigation is based on the onboard optical correlator application for real time matching of the Earth images and prerecorded images of landmarks with known coordinates. The system is suitable for the low-orbit Earth imaging missions with 3-axis attitude stabilization

  15. Time-series Bitmaps: A Practical Visualization Tool for working with Large Time Series Databases

    E-print Network

    Lonardi, Stefano

    Time-series Bitmaps: A Practical Visualization Tool for working with Large Time Series Databases, eamonn, stelo, ratana}@cs.ucr.edu Abstract The increasing interest in time series data mining in the last world practitioners who work with time series on a daily basis rarely take advantage of the wealth

  16. BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences

    E-print Network

    West, Mike

    BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Abstract. This articles discusses developments in Bayesian time series mod­ elling and analysis relevant in studies of time series in the physical and engineer­ ing sciences. With illustrations and references, we

  17. An autoregressive model for analysis of ice sheet elevation change time series

    Microsoft Academic Search

    Adam C. Ferguson; Curt H. Davis; Joseph E. Cavanaugh

    2004-01-01

    We present an autoregressive (AR) model that can effectively characterize both seasonal and interannual variations in ice sheet elevation change time series constructed from satellite radar or laser altimeter data. The AR model can be used in conjunction with weighted least squares regression to accurately estimate any longer term linear trend present in the cyclically varying elevation change time series.

  18. Nonlinear time-series analysis of Hyperion's lightcurves

    NASA Astrophysics Data System (ADS)

    Tarnopolski, M.

    2015-06-01

    Hyperion is a satellite of Saturn that was predicted to remain in a chaotic rotational state. This was confirmed to some extent by Voyager 2 and Cassini series of images and some ground-based photometric observations. The aim of this article is to explore conditions for potential observations to meet in order to estimate a maximal Lyapunov Exponent (mLE), which being positive is an indicator of chaos and allows to characterise it quantitatively. Lightcurves existing in literature as well as numerical simulations are examined using standard tools of theory of chaos. It is found that existing datasets are too short and undersampled to detect a positive mLE, although its presence is not rejected. Analysis of simulated lightcurves leads to an assertion that observations from one site should be performed over a year-long period to detect a positive mLE, if present, in a reliable way. Another approach would be to use 2-3 telescopes spread over the world to have observations distributed more uniformly. This may be achieved without disrupting other observational projects being conducted. The necessity of time-series to be stationary is highly stressed.

  19. Nonlinear time-series analysis of Hyperion's lightcurves

    E-print Network

    Mariusz Tarnopolski

    2015-06-24

    Hyperion is a satellite of Saturn that was predicted to remain in a chaotic rotational state. This was confirmed to some extent by Voyager 2 and Cassini series of images and some ground-based photometric observations. The aim of this aticle is to explore conditions for potential observations to meet in order to estimate a maximal Lyapunov Exponent (mLE), which being positive is an indicator of chaos and allows to characterise it quantitatively. Lightcurves existing in literature as well as numerical simulations are examined using standard tools of theory of chaos. It is found that existing datasets are too short and undersampled to detect a positive mLE, although its presence is not rejected. Analysis of simulated lightcurves leads to an assertion that observations from one site should be performed over a year-long period to detect a positive mLE, if present, in a reliable way. Another approach would be to use 2---3 telescopes spread over the world to have observations distributed more uniformly. This may be achieved without disrupting other observational projects being conducted. The necessity of time-series to be stationary is highly stressed.

  20. Structural Breaks in Financial Time Series

    Microsoft Academic Search

    Elena Andreou; Eric Ghysels

    This paper reviews the literature on structural breaks in financial time series. The second section discusses the implications\\u000a of structural breaks in financial time series for statistical inference purposes. In the third section we discuss change-point\\u000a tests in financial time series, including historical and sequential tests as well as single and multiple break tests. The\\u000a fourth section focuses on structural

  1. Time-Series Models in Marketing

    Microsoft Academic Search

    Marnik G. Dekimpe; Philip Hans Franses; Dominique M. Hanssens; Prasad A. Naik

    2006-01-01

    Marketing data appear in a variety of forms. An often-seen form is time-series data, like sales per month, prices over the last few years, market shares per week. Time-series data can be summarized in time-series models. In this chapter we review a few of these, focusing in particular on domains that have received considerable attention in the marketing literature. These

  2. Fast orthorectification for satellite images using patch backprojection

    Microsoft Academic Search

    Liang-Chien Chen; Tee-Ann Teo; Jiann-Yeou Rau

    2003-01-01

    The most rigorous way to register a remotely sensed image with a topomap or a relevant spatial data layer is performing orthorectification for the image. The focus of this investigation is to establish a fast orthorectification procedure for satellite images using a proposed \\

  3. Evaluation of Urban Environmental Quality with High Resolution Satellite Images

    Microsoft Academic Search

    Meichun Yan; Liliang Ren; Xiufeng He; Wengang Sang

    2008-01-01

    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

  4. Geostationary Lightning Imager for FY-4 Meteorological Satellite (Invited)

    NASA Astrophysics Data System (ADS)

    Huang, F.

    2010-12-01

    The FY-4 satellite scheduled to launch in 2015 is a second-generation Chinese geostationary meteorological satellite. The main payloads for FY-4 satellite include Geostationary Lightning Imager (GLI), Advanced Geostationary Visible and Infrared Imager (AGVII), and Geostationary Interfering Infrared Sounder (GIIRS). Since the GLI is the first lightning detection imager without any heritage on a Chinese meteorological satellite, it is a great challenge to implement this mission. The GLI covers the most part of China, land and ocean and nearby areas. The continuous and real time lightning imaging products from GLI will be applied to weather forecasting, convection event monitoring, and typhoon tracking. The instrument formulation studies started 4 years ago, and now it is at implementation stage of making prototype models. A working group has begun to develop the L1 and L2 algorithms for lightning imaging data processing. At present, we are focusing on resolving several critical issues for GLI. The first one is how to make sure the Real Time Event Processor (RTEP) works well in orbit, which relates whether or not the lightning information could be picked up correctly. The second is how to make best uses of lightning imaging products from GLI in all kinds of application fields. Since the Geostationary Lightning Mapper (GLM) and Lighting Imager (LI) are lighting imagers on geostationary satellites with similar instrument structure and working principles to GLI, we welcome international collaboration on GLI lightning products: algorithm development, lightning imaging applications, and other relative topics.

  5. A satellite imager for atmospheric x-rays

    Microsoft Academic Search

    W. Calvert; T. C. Sanders; H. D. Voss

    1985-01-01

    A high-sensitivity X-Ray Imaging Spectrometer (XRIS) was developed for measurements of atmospheric bremsstrahlung X-rays. The XRIS instrument flown on a 3-axis stabilized polar orbiting satellite (S81-1) employed a one-dimensional pinhole camera to acquire a 2-dimensional X-ray image as the satellite passed over an auroral scene. Using a position sensitive gas proportional counter, with an active area of 1200 cmS divided

  6. A satellite imager for atmospheric X-rays

    Microsoft Academic Search

    W. Calvert; H. D. Voss; T. C. Sanders

    1985-01-01

    A high-sensitivity X-Ray Imaging Spectrometer (XRIS) was developed for measurements of atmospheric bremsstrahlung X-rays. The XRIS instrument flown on a 3-axis stabilized polar orbiting satellite (S81-1) employed a one-dimensional pinhole camera to acquire a 2-dimensional X-ray image as the satellite passed over an auroral scene. Using a position sensitive gas proportional counter, with an active area of 1200 sq cm

  7. Distributed Geo-Rectification of Satellite Images Using Grid Computing

    Microsoft Academic Search

    Yong Meng Teo; Sok Chay Low; Seng Chuan Tay; Johan Prawira Gozali

    2003-01-01

    Grid computing seeks to aggregate computing resources within an enterprise and leverage on resources you don't own for compute-intensive applications. Geo-rectification is a process for correcting spatial location and orientation of a satellite image. This paper focuses on the parallelization of the compute- intensive satellite image geo-rectification problem on a cluster grid. We discuss our approach to data and task

  8. Wavelet Analysis of Variance for Time Series with Missing Values

    E-print Network

    Washington at Seattle, University of

    methods have been used to study a large number of problems in signal and image processing including variance is a scale-based decomposition of the process variance for a time series and has been used to analyze, for example, time deviations in atomic clocks, variations in soil properties in agricultural

  9. Time Series of the Biscuit Fire with Smoke

    NSDL National Science Digital Library

    Cindy Starr

    2003-08-04

    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.

  10. Nonlinear dynamical models from time series

    E-print Network

    Jose-Maria Fullana

    2014-07-30

    We present an optimization process to estimate parameters in systems of ordinary differential equations from chaotic time series. The optimization technique is based on a variational approach, and numerical studies on noisy time series demonstrate that it is very robust and appropriate to reduce the complexity of the model. The proposed process also allows to discard the parameters with scanty influence on the dynamic.

  11. Rule discovery from time series Department of

    E-print Network

    Lin, King-Ip "David"

    stock price goes up and Intel falls, then IBM goes up the next day,'' and ``if Microsoft goes up. We first form subsequences by sliding a window through the time series, and then cluster is obtained by taking the cluster identifiers correspond­ ing to the subsequence. Once the time­series is dis

  12. Cluster financial time series for portfolio

    Microsoft Academic Search

    He-Shan Guan; Qing-Shan Jiang

    2007-01-01

    Stocks are a common kind of financial time series. In this paper we present a new similarity measure for time series clustering, and then select a set of stocks to create efficient portfolio, which is of crucial importance in the process of creating efficient portfolio. We largely reduce the efficient times of portfolio using clustering-based selection, and only select a

  13. Efficient Time Series Matching by Wavelets

    Microsoft Academic Search

    Kin-pong Chan; Ada Wai-chee Fu

    1999-01-01

    Time series stored as feature vectors can be indexed by multi- dimensional index trees like R-Trees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vec- tors. Different transformations like Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Karhunen-Loeve (K- L) transform or Singular Value

  14. Macintosh Program performs time-series analysis

    Microsoft Academic Search

    Didier Paillard; Laurent Labeyrie; Pascal Yiou

    1996-01-01

    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

  15. Heterogeneous Time Series Learning for Crisis Monitoring

    Microsoft Academic Search

    William H. Hsu; Nathan D. Gettings; Victoria E. Lease; Yu Pan; David C. Wilkins

    A very important application of time series learning is online diagnosis, or monitoring, to detect and classify hazardous conditions in a physical system. Examples of crisis monitoring in the industrial, military, agricultural and environmental sciences are numerous. This paper first defines heterogeneous time series, those containing different types of embedded, statistical patterns. Next, it surveys basic techniques for acquiring several

  16. Building Chaotic Model From Incomplete Time Series

    Microsoft Academic Search

    Michael Siek; Dimitri Solomatine

    2010-01-01

    This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general,

  17. Statictical Models for Unequally Spaced Time Series

    Microsoft Academic Search

    Alina Beygelzimer; Emre Erdogan; Sheng Ma; Irina Rish

    2005-01-01

    Irregularly observed time series and their analysis are fun- damental for any application in which data are collected in a distributed or asynchronous manor. We propose a theoretical framework for analyzing both stationary and non-stationary irregularly spaced time series. Our mod- els can be viewed as extensions of the well known auto- regression (AR) model. We provide experiments suggest- ing

  18. Linear Relations in Time Series Models. I.

    ERIC Educational Resources Information Center

    Villegas, C.

    1976-01-01

    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…

  19. Moments of power transformed time series

    Microsoft Academic Search

    Richard W. Katz

    1999-01-01

    SUMMARY A simple recursion is presented for calculating moments (e.g., mean, variance, and autocorrelation func- tion) of a time series that has been power transformed to normality. Its derivation is elementary, relying on the moment-generating function for a bivariate normal distribution. To make clear the distinction between the moments of the transformed and original time series, the special case of

  20. Advanced Spectral Methods for Climatic Time Series

    Microsoft Academic Search

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

    2002-01-01

    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

  1. Evolutionary computation and economic time series forecasting

    Microsoft Academic Search

    Dipti Srinivasan; Vishal Sharma

    2007-01-01

    This paper summarizes the collective work done in the application of evolutionary computation for financial time series forecasting. These are mainly stock market indices and foreign exchange rate prediction. The time series corresponding to these indices is a non-linear dynamic stochastic system different from other static patterns which are independent of time. Evolutionary techniques have capabilities of efficient search space

  2. Statistical criteria for characterizing irradiance time series.

    SciTech Connect

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

    2010-10-01

    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.

  3. RIGOROUS GENERATION OF DIGITAL ORTHOPHOTOS FROM EROS A HIGH RESOLUTION SATELLITE IMAGES

    Microsoft Academic Search

    Liang-Chien CHEN; Tee-Ann TEO

    As the resolution of satellite images is improving, the applications of satellite images become widespread. Orthorectification is an indispensable step in the processing for satellite images. EROS A is a high resolution imaging satellite. Its linear array pushbroom imager is with 1.8meter resolution on ground. The satellite is sun-synchronous and sampling with asynchronous mode. The main purpose of this investigation

  4. Texture Features for Segmentation of Satellite Images

    Microsoft Academic Search

    Mariana Tsaneva

    To be able to find different textures in an image, a simple strategy is to perform texture measurements on a moving window and assign scalar features to each of the image pixels corresponding to window centers. This operation is similar to filtering. It transforms an image into a feature image. Three novel texture features for image segmentation based on gray

  5. Burnt area mapping from ERS-SAR time series using the principal components transformation

    NASA Astrophysics Data System (ADS)

    Gimeno, Meritxell; San-Miguel Ayanz, Jesus; Barbosa, Paulo M.; Schmuck, Guido

    2003-03-01

    Each year thousands of hectares of forest burnt across Southern Europe. To date, remote sensing assessments of this phenomenon have focused on the use of optical satellite imagery. However, the presence of clouds and smoke prevents the acquisition of this type of data in some areas. It is possible to overcome this problem by using synthetic aperture radar (SAR) data. Principal component analysis (PCA) was performed to quantify differences between pre- and post- fire images and to investigate the separability over a European Remote Sensing (ERS) SAR time series. Moreover, the transformation was carried out to determine the best conditions to acquire optimal SAR imagery according to meteorological parameters and the procedures to enhance burnt area discrimination for the identification of fire damage assessment. A comparative neural network classification was performed in order to map and to assess the burnts using a complete ERS time series or just an image before and an image after the fire according to the PCA. The results suggest that ERS is suitable to highlight areas of localized changes associated with forest fire damage in Mediterranean landcover.

  6. Adaptive split test for multivariate time series classification trees Adaptive split test for multivariate time series

    E-print Network

    Paris-Sud XI, Université de

    Adaptive split test for multivariate time series classification trees Adaptive split test for multivariate time series classification trees Ahlame Douzal Chouakria1 , Cécile Amblard1 LIG (Lab. d : This paper proposes an extension of the classification trees to time series input variables. A new split

  7. Forecasting daily time series using periodic unobserved components time series models

    Microsoft Academic Search

    Siem Jan Koopman; Marius Ooms

    2006-01-01

    We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend, seasonal and irregular. Periodic time series models allow dynamic characteristics such as auto- covariances to depend on the period of the year, month, week or day. In the standard multivariate approach one can interpret periodic

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    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.

  9. Space telemetric panomorph imaging system for micro/nano satellite

    NASA Astrophysics Data System (ADS)

    Thibault, Simon; Harvey, Eric

    2008-04-01

    For many years, many microsatellites (satellites in the 10-100 kg mass range) and nanosatellites (in the 1-10 kg mass range) missions have been designed, built and launched having the objective of technology demonstration. Recently, due to the advance of technologies over the past decade, a new trend is to use them in more demanding space missions such as space science, earth observation, flying formation and space surveillance. In micro/nano satellites applications, the need for size, mass, power consumption and cost reduction is critical. This is why there is an effort toward the development of specialized and integrated hardware. Among space hardware for satellites, the development of optical imaging payload and miniaturized attitude sensors are of great interest for space surveillance and space science applications. We proposed the development of a panomorph lens optical module designed to record wide and broadband images of a panoramic scene around the satellite. A key requirement of the optical module is therefore to be able to manage the field coverage properties to distinguish true element that can be used for star tracking, earth horizon sensing and related tracking functionalities. The optical module must provide all usable telemetric information for the satellite. The proposed technology consists of a concept of space telemetric imaging system, which will combine optically imaging for surveillance/visual monitoring of space and attitude determination capabilities in one compact and low-power consumption device for micro/nano satellite applications.

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

    NASA Technical Reports Server (NTRS)

    1982-01-01

    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.

  11. Time Series Clustering Based on ICA for Stock Data Analysis

    Microsoft Academic Search

    Chonghui Guo; Hongfeng Jia; Na Zhang

    2008-01-01

    Time series clustering is an important task in time series data mining. Compared to traditional clustering problems, time series clustering poses additional difficulties. The unique structure of time series makes many traditional clustering methods unable to apply directly. This paper presents a novel feature-based approach to time series clustering, which first converts the raw time series data into feature vectors

  12. Geospatial Visualization of Global Satellite Images with Vis-EROS

    SciTech Connect

    Standart, G. D.; Stulken, K. R.; Zhang, Xuesong; Zong, Ziliang

    2011-04-13

    The Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey is currently managing and maintaining the world largest satellite images distribution system, which provides 24/7 free download service for researchers all over the globe in many areas such as Geology, Hydrology, Climate Modeling, and Earth Sciences. A large amount of geospatial data contained in satellite images maintained by EROS is generated every day. However, this data is not well utilized due to the lack of efficient data visualization tools. This software implements a method for visualizing various characteristics of the global satellite image download requests. More specifically, Keyhole Markup Language (KML) files are generated which can be loaded into an earth browser such as Google Earth. Colored rectangles associated with stored satellite scenes are painted onto the earth browser; and the color and opacity of each rectangle is varied as a function of the popularity of the corresponding satellite image. An analysis of the geospatial information obtained relative to specified time constraints provides an ability to relate image download requests to environmental, political, and social events.

  13. Effect of Ground Cover on Satellite Images

    NSDL National Science Digital Library

    Tom Whittaker

    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.

  14. Optimal Waypoint Scheduling of an Imaging Satellite

    E-print Network

    West, Matthew

    -cost satellite application, attitude control is accomplished solely through reaction wheels. Solving for the optimal solution is complicated by the fact that attitude control dynamics are nonlinear of this study is to determine time optimal control policies to slew between scheduled waypoint views

  15. Advanced spectral methods for climatic time series

    USGS Publications Warehouse

    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

    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.

  16. Detecting nonlinear structure in time series

    SciTech Connect

    Theiler, J.

    1991-01-01

    We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs.

  17. Wavelet Transforms in Time Series Andrew Tangborn

    E-print Network

    Kalnay, Eugenia

    . What is a Wavelet? 3. Continuous and Discrete Wavelet Transforms 4. Construction of Wavelets through compression, efficient representation. 8. Soft Thresholding. 9. Continuous Transform - Morlet Wavelet 10Wavelet Transforms in Time Series Analysis Andrew Tangborn Global Modeling and Assimilation Office

  18. Modeling Time Series Data of Real Systems

    E-print Network

    Dilip P. Ahalpara; Jitendra C. Parikh

    2006-07-14

    Dynamics of complex systems is studied by first considering a chaotic time series generated by Lorenz equations and adding noise to it. The trend (smooth behavior) is separated from fluctuations at different scales using wavelet analysis and a prediction method proposed by Lorenz is applied to make out of sample predictions at different regions of the time series. The prediction capability of this method is studied by considering several improvements over this method. We then apply this approach to a real financial time series. The smooth time series is modeled using techniques of non linear dynamics. Our results for predictions suggest that the modified Lorenz method gives better predictions compared to those from the original Lorenz method. Fluctuations are analyzed using probabilistic considerations.

  19. FATS: Feature Analysis for Time Series

    E-print Network

    Nun, Isadora; Sim, Brandon; Zhu, Ming; Dave, Rahul; Castro, Nicolas; Pichara, Karim

    2015-01-01

    In this paper, we present the FATS (Feature Analysis for Time Series) library. FATS is a Python library which facilitates and standardizes feature extraction for time series data. In particular, we focus on one application: feature extraction for astronomical light curve data, although the library is generalizable for other uses. We detail the methods and features implemented for light curve analysis, and present examples for its usage.

  20. Pattern Modelling in Time-series Forecasting

    Microsoft Academic Search

    Sameer Singh

    2000-01-01

    Pattern modelling in time-series prediction refers to the process of identifying pastrelationships and trends in historical data for predicting future values. This paper describesthe development of a new pattern matching technique for univariate time-series forecasting.The pattern modelling technique out-performs frequently used statistical methods such asExponential Smoothing on different error measures and predicting the direction of change intime-series. The paper discusses

  1. Vehicle Detection and Classification from High Resolution Satellite Images

    NASA Astrophysics Data System (ADS)

    Abraham, L.; Sasikumar, M.

    2014-11-01

    In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

  2. Indexing of satellite images with different resolutions by wavelet features.

    PubMed

    Luo, Bin; Aujol, Jean-François; Gousseau, Yann; Ladjal, Saïd

    2008-08-01

    Space agencies are rapidly building up massive image databases. A particularity of these databases is that they are made of images with different, but known, resolutions. In this paper, we introduce a new scheme allowing us to compare and index images with different resolutions. This scheme relies on a simplified acquisition model of satellite images and uses continuous wavelet decompositions. We establish a correspondence between scales which permits us to compare wavelet decompositions of images having different resolutions. We validate the approach through several matching and classification experiments, and we show that taking the acquisition process into account yields better results than just using scaling properties of wavelet features. PMID:18632354

  3. Multisensor Satellite Image Sub-pixel Registration

    Microsoft Academic Search

    Shuai Xing; Qing Xu; Dongyang Ma

    2007-01-01

    One way of geocoding a remote sensing image is to register it to an existing orthoimage. A multi-sensor and multi-resolution remote sensing image sub-pixel registration scheme is proposed in this paper. In this registration scheme, point feature, three image matching algorithms (ICM, PRM, ISM) and three constraints (APC, CDM, ICC) are used. They guarantee that the precision of point matching

  4. Simultaneous Fusion and Denoising of Panchromatic and Multispectral Satellite Images

    NASA Astrophysics Data System (ADS)

    Ragheb, Amr M.; Osman, Heba; Abbas, Alaa M.; Elkaffas, Saleh M.; El-Tobely, Tarek A.; Khamis, S.; Elhalawany, Mohamed E.; Nasr, Mohamed E.; Dessouky, Moawad I.; Al-Nuaimy, Waleed; Abd El-Samie, Fathi E.

    2012-12-01

    To identify objects in satellite images, multispectral (MS) images with high spectral resolution and low spatial resolution, and panchromatic (Pan) images with high spatial resolution and low spectral resolution need to be fused. Several fusion methods such as the intensity-hue-saturation (IHS), the discrete wavelet transform, the discrete wavelet frame transform (DWFT), and the principal component analysis have been proposed in recent years to obtain images with both high spectral and spatial resolutions. In this paper, a hybrid fusion method for satellite images comprising both the IHS transform and the DWFT is proposed. This method tries to achieve the highest possible spectral and spatial resolutions with as small distortion in the fused image as possible. A comparison study between the proposed hybrid method and the traditional methods is presented in this paper. Different MS and Pan images from Landsat-5, Spot, Landsat-7, and IKONOS satellites are used in this comparison. The effect of noise on the proposed hybrid fusion method as well as the traditional fusion methods is studied. Experimental results show the superiority of the proposed hybrid method to the traditional methods. The results show also that a wavelet denoising step is required when fusion is performed at low signal-to-noise ratios.

  5. Detecting chaos in irregularly sampled time series.

    PubMed

    Kulp, C W

    2013-09-01

    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

  6. Impacts of Reprojection and Sampling of MODIS Satellite Images on Estimating Crop Evapotranspiration Using METRIC model

    NASA Astrophysics Data System (ADS)

    Pun, M.; Kilic, A.; Allen, R.

    2014-12-01

    Landsat satellite images have been used frequently to map evapotranspiration (ET) andbiophysical variables at the field scale with surface energy balance algorithms. Although Landsat images have high spatial resolution with 30m cell size, it has limitations for real time monitoring of crop ET by providing only two to four images per month for an area, which, when encountered with cloudy days, further deteriorates the availability of images and snapshots of ET behavior. Therefore real time monitoring essentially has to include near-daily thermal satellites such as MODIS/VIIRS into the time series. However, the challenge with field scale monitoring with these systems is the large size of the thermal band which is 375 m with VIIRS and 1000 meter with MODIS. To maximize the accuracy of ET estimates during infusion of MODIS products into land surface models for monitoring field scale ET, it is important to assess the geometric accuracy of the various MODIS products, for example, spatial correspondence among the 250 m red and near-infrared bands, the 500 m reflectance bands; and the 1000 m thermal bands and associated products. METRIC model was used with MODIS images to estimate ET from irrigated and rainfed fields in Nebraska. Our objective was to assess geometric accuracy of MODIS image layers and how to correctly handle these data for highest accuracy of estimated ET at the individual field scale during the extensive drought of 2012. For example, the particular tool used to subset and reproject MODIS swath images from level-1 and level-2 products (e.g., using the MRTSwath and other tools), the initial starting location (upper left hand corner), and the projection system all effect how pixel corners of the various resolution bands align. Depending on the approach used, origin of pixel corners can vary from image to image date and therefore impacts the pairing of ET information from multiple dates the consistency and accuracy of sampling ET from within field interiors. Higher level MODIS products, including multi-day products, can have more consistent registration, but may suffer some compromisation of spatial fidelity due to the resampling required during various reprocessing steps.

  7. HJ-1-A/B optical satellite image geometric correction

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Wu, Wei; Cui, Yan; Wang, Wei

    2014-03-01

    Small satellite constellation of environment and disaster's monitoring and predicting (shorted for HJ-1) is not a mapping satellite, and its parameters of attitude and orbit cannot satisfy the requirement of geometric correction using strict imaging model. On the other hand, due to the 12000 CCD detectors and large overlay of multispectral payload named CCD carried by HJ-1 satellite, the error caused by CCD distortion cannot be ignored. Aiming at these problems of HJ-1, this paper proposes a strict orbit model algorithm based on Ground Control Point (GCP) and collinear condition equations. Through the robust estimation of parameters, this algorithm can effectively set up imaging geometric model of CCD, and satisfy the requirement of high precision geometric correction.

  8. EarthShots: Satellite Images of Environmental Change

    NSDL National Science Digital Library

    2001-01-12

    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.

  9. Comparison of Harmonic, Geometric and Arithmetic Means for Change Detection in SAR Time Series

    E-print Network

    Paris-Sud XI, Université de

    Comparison of Harmonic, Geometric and Arithmetic Means for Change Detection in SAR Time Series The amplitude distribution in a SAR image can present a heavy tail. Indeed, very high­valued outliers can, Geometric and Arithmetic means, enables a change detection method along SAR time series. 1 Different means

  10. Wavelet-based multifractal analysis of fMRI time series

    Microsoft Academic Search

    Yu Shimizu; Markus Barth; Christian Windischberger; Ewald Moser; Stefan Thurner

    2004-01-01

    Functional magnetic resonance imaging (fMRI) time series are investigated with a multifractal method based on the Wavelet Modulus Maxima (WTMM) method to extract local singularity (“fractal”) exponents. The spectrum of singularity exponents of each fMRI time series is quantified by spectral characteristics including its maximum and the corresponding dimension. We found that the range of Hölder exponents in voxels with

  11. Partial unmixing as a tool for single surface class detection and time series analysis

    Microsoft Academic Search

    C. Kuenzer; M. Bachmann; A. Mueller; L. Lieckfeld; W. Wagner

    2008-01-01

    In this paper we present the results of time series analysis for a coal mining region based on partial unmixing. We test the method also known as mixture tuned matched filtering on an eight image Landsat 5 TM and Landsat 7 ETM+ time series covering the period from 1987 to 2003. Common change detection methods often include the comparison of

  12. High-resolution satellite image segmentation using Hölder exponents

    Microsoft Academic Search

    Debasish Chakraborty; Gautam Kumar Sen; Sugata Hazra

    2009-01-01

    Texture in high-resolution satellite images requires substantial amendment in the conventional segmentation algorithms. A\\u000a measure is proposed to compute the Hölder exponent (HE) to assess the roughness or smoothness around each pixel of the image.\\u000a The localized singularity information is incorporated in computing the HE. An optimum window size is evaluated so that HE\\u000a reacts to localized singularity. A two-step

  13. Numbers to Pictures: How Satellite Images are Created

    NSDL National Science Digital Library

    2012-11-15

    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.

  14. Learning time series for intelligent monitoring

    NASA Technical Reports Server (NTRS)

    Manganaris, Stefanos; Fisher, Doug

    1994-01-01

    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.

  15. Space-efficient Online Approximation of Time Series Data

    E-print Network

    California at Santa Barbara, University of

    Space-efficient Online Approximation of Time Series Data: Streams, Amnesia, and Out-of-order Luca Luca Foschini (UCSB) Time Series Approximation ICDE 2010 1 / 21 #12;Outline 1 Time Series Approximation and Future Work Luca Foschini (UCSB) Time Series Approximation ICDE 2010 2 / 21 #12;Time Series

  16. Infrastructure Inventory Compilation Using Single High Resolution Satellite Images

    Microsoft Academic Search

    Pooya Sarabandi; Anne S. Kiremidjian; Ronald T. Eguchi

    This paper introduces a methodological approach for rapidly obtaining spatial and structural information from a single high-resolution satellite image, using rational polynomial coefficients (RPCs) as a camera replacement model. Geometric information defining the sensor's orientation is used in conjunction with the RPC projection model to generate an accurate digital elevation model (DEM). This paper describes how the location (longitude and

  17. 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.

  18. SEGMENTATION OF SATELLITE IMAGES BY MODIFIED MOUNTAIN CLUSTERING

    Microsoft Academic Search

    M. Hanmandlu; Devendra Jha; Delhi Rochak Sharma

    Segmentation of satellite images is an important issue in various applications. Though clustering techniques have been in vogue for many years, they have not been too effective because of several problems such as selection of the number of clusters. This proposed work tackles this problem by having a validity measure coupled with the new clustering technique. This method treats each

  19. Automatic Digital Elevation Model Extraction Using SPOT Satellite Image

    Microsoft Academic Search

    Jeong-kee Kim; Chan-eung Park; Kwae-hi Lee

    1992-01-01

    The purpose of this papcr is to extract automatic DEM (Digital Elevation Model) us in^ SPOT satellib stereo imams. DEM extraction pmccss consists of satellite mdeling, image match in^ and elevation find in^. This papcr presents the unifid hierarchical mtchin~ technique and analyzes DRh4 error accordia~ to matching accuracy. The --based matchinn is adopted for 'hnage matching and DEM is

  20. Detecting smoothness in noisy time series

    SciTech Connect

    Cawley, R.; Hsu, G.; Salvino, L.W. [Information Sciences and Systems Branch, Naval Surface Warfare Center, Dahlgren Division, White Oak, 10901 New Hamsphire Avenue, Silver Spring, Maryland 20903-5640 (United States)

    1996-06-01

    We describe the role of chaotic noise reduction in detecting an underlying smoothness in a dataset. We have described elsewhere a general method for assessing the presence of determinism in a time series, which is to test against the class of datasets producing smoothness (i.e., the null hypothesis is determinism). In order to reduce the likelihood of a false call, we recommend this kind of analysis be applied first to a time series whose deterministic origin is at question. We believe this step should be taken before implementing other methods of dynamical analysis and measurement, such as correlation dimension or Lyapounov spectrum. {copyright} {ital 1996 American Institute of Physics.}

  1. Digital Image-in-Image Watermarking For Copyright Protection Of Satellite Images Using the Fast Hadamard Transform

    E-print Network

    Doran, Simon J.

    Digital Image-in-Image Watermarking For Copyright Protection Of Satellite Images Using the Fast-In this paper, a robust and efficient digital image watermarking algorithm using the fast Hadamard transform an entire image or pattern as a watermark such as a company's logo or trademark directly into the original

  2. Spectrally Consistent Satellite Image Fusion with Improved Image Priors

    E-print Network

    -dependent image smoothing. I. INTRODUCTION Image fusion is the subset of data fusion dealing with merging images]. Image fusion can be done at several levels: Pixel level, feature level, object level and decision level, depending on the intended use of the fused image. This paper is only concerned with pixel level fusion

  3. Reconstruction of the land surface temperature time series using harmonic analysis

    NASA Astrophysics Data System (ADS)

    Xu, Yongming; Shen, Yan

    2013-12-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

    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.

  5. Validating Long-term Consistency of MODIS EVI Time Series Using Ground-based Radiation Flux Data

    NASA Astrophysics Data System (ADS)

    Kato, A.; Miura, T.

    2014-12-01

    The Enhanced Vegetation Index (EVI) time series from Moderate Resolution Imaging Spectrometer (MODIS) has exceeded a decade in length. It is, thus, desirable to evaluate how well the time series captures inter-annual variability of vegetation phenology. Previous studies calculated a two-band version of the EVI (EVI2) from tower radiation flux data and used it to validate satellite VI time series. Differences in view angle, bandpass, and spatial representativeness between flux and satellite data, however, may lead to landcover-dependent biases when they are compared directly. The objective of this study was to validate long-term consistency of MODIS EVI time series with radiation flux-derived EVI2 time series by comparing phenological metrics derived from these datasets. Ten years of MODIS EVI and ground-based EVI2 (Tower EVI2) were obtained for 10 AmeriFlux sites. Asymmetric double logistic functions were fitted to each of VIs, from which SOSs were derived. After the Gram-Schmidt orthogonalization of the derived SOSs, the standard deviation (SD) in horizontal direction (inter-annual variability) was compared with SD in perpendicular direction (differences) to assess consistency of MODIS EVI in tracking vegetation dynamics. Temporal profiles of MODIS EVI showed analogous patterns with those of tower EVI2 across five biomes although site specific differences were seen in the VI amplitude. Cross plots of SOS from MODIS and Tower VIs closely aligned to the 1:1 line (slope > 0.865, R2>0.896). The SD in inter-annual variability (? 20 days) was more than twice larger than the SD of SOS difference averaged for five biomes (? 9 days). MODIS consistently captured SOSs with 2.7-4.9-day differences at deciduous broad leaf forest and clopland sites, and also agreed well at a wooded savanna site (< 6 days). Grassland sites showed more than a week difference due to a failure in model fitting of the year with subtle VI amplitude and the year with multiple growing seasons. These results indicate that MODIS EVI time series was capable to capture long-term variability in surface vegetation dynamics at four biomes other than arid grassland in North America. Further efforts on data screening and fitting on multiple growing seasons could improve results on arid grassland sites.

  6. 1MaPhySto Workshop 9/04 Nonlinear Time Series ModelingNonlinear Time Series Modeling

    E-print Network

    . Examples 3. "Stylized facts" concerning financial time series 4. ARCH and GARCH models 5. Forecasting1MaPhySto Workshop 9/04 Nonlinear Time Series ModelingNonlinear Time Series Modeling Part II: Time Series Models in FinancePart II: Time Series Models in Finance Richard A. Davis Colorado State University

  7. Can biomass time series be reliably assessed from CPUE time series data Francis Lalo1

    E-print Network

    Hawai'i at Manoa, University of

    1 Can biomass time series be reliably assessed from CPUE time series data only? Francis Laloë1 to abundance. This means (i) that catchability is constant and (ii) that all the biomass is catchable. If so, relative variations in CPUE indicate the same relative variations in biomass. Myers and Worm consider

  8. Time Series of Suspended-Solids Concentration, Salinity, Temperature, and

    E-print Network

    Time Series of Suspended-Solids Concentration, Salinity, Temperature, and Total Mercury.................................................................................................................................. 3 Time Series Data................................................................................................................................... 15 #12;3 San Francisco Estuary Institute Time Series of Suspended-Solids Concentration, Salinity

  9. Handbook of Time Series Analysis Recent Theoretical Developments and Applications

    E-print Network

    Bressler, Steven L.

    Handbook of Time Series Analysis Recent Theoretical Developments and Applications ion Theory Edited time series analysis provides the basic framework for analyzing the patterns of neural interactions in the form of multiple simultaneous time series. To evaluatethe statistical interdependenceamong

  10. DETECTING THE ENVIRONMENTAL CHANGES FROM SATELLITE IMAGE

    Microsoft Academic Search

    Nang Mya Mya Nwe

    ABSTRACT: The goal of this paper is detecting the environmental,changes that could,affect the globe in the future conditions like pollution, global climate change, natural resource management, urban growth, and much more and trend across large geographic areas from a subset of a Landsat Thematic Mapper (TM) multispectral image to use in GIS. In this paper, Environmental Changes are detected from

  11. Unsupervised Feature Learning for High-Resolution Satellite Image Classification

    SciTech Connect

    Cheriyadat, Anil M [ORNL

    2013-01-01

    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.

  12. A New System to Perform Unsupervised and Supervised Classification of Satellite Images from Google Maps

    E-print Network

    Plaza, Antonio J.

    A New System to Perform Unsupervised and Supervised Classification of Satellite Images from Google of satellite images from Google Maps. The system has been developed using the SwingX-WS library Systems ENVI package. Keywords: Satellite image classification, Google Maps. 1. INTRODUCTION The wealth

  13. An Adaptive System to Diminish the Influence of Clouds in Satellite Images for Texture Segmentation

    E-print Network

    Lewiner, Thomas (Thomas Lewiner)

    of clouds in satellite images, allowing the computer vision system to check in an easier way whatAn Adaptive System to Diminish the Influence of Clouds in Satellite Images for Texture an approach to reduce the influence of clouds in satellite images. The developed implementation

  14. Semi-supervised time series classification

    Microsoft Academic Search

    Li Wei; Eamonn J. Keogh

    2006-01-01

    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

  15. Fast time series classification using numerosity reduction

    Microsoft Academic Search

    Xiaopeng Xi; Eamonn J. Keogh; Christian R. Shelton; Li Wei; Chotirat Ann Ratanamahatana

    2006-01-01

    Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is exceptionally difficult to beat. This approach has one weakness, however; it is computationally too demanding for many real- time applications. One way to mitigate this problem is to speed up the DTW calculations. Nonetheless, there

  16. Nonlinear time-series analysis revisited

    E-print Network

    Elizabeth Bradley; Holger Kantz

    2015-03-25

    In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-series analysis: the analysis of observed data---typically univariate---via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-series analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-series analysis can be helpful in understanding, characterizing, and predicting dynamical systems.

  17. Time Series Regression with a Unit Root

    Microsoft Academic Search

    P. C. B. Phillips

    1987-01-01

    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

  18. SLEX Analysis of Multivariate Nonstationary Time Series

    Microsoft Academic Search

    Hernando Ombao; Rainer von Sachs; Wensheng Guo

    2005-01-01

    We propose to analyze a multivariate non-stationary time series using the SLEX (Smooth Localized Complex EXponentials) library. The SLEX library is a collection of bases; each basis consists of the SLEX waveforms which are orthogonal localized versions of the Fourier complex exponentials. In our procedure, we first build a family of multivariate SLEX models such that every model has a

  19. SO2 EMISSIONS AND TIME SERIES MODELS

    EPA Science Inventory

    The paper describes a time series model that permits the estimation of the statistical properties of pounds of SO2 per million Btu in stack emissions. It uses measured values for this quantity provided by coal sampling and analysis (CSA), by a continuous emissions monitor (CEM), ...

  20. Some recent progress in count time series

    Microsoft Academic Search

    Konstantinos Fokianos

    2011-01-01

    We review some regression models for the analysis of count time series. These models have been the focus of several investigations over the last years, but only recently simple conditions for stationarity and ergodicity were worked out in detail. This advancement makes possible the development of the maximum-likelihood estimation theory under minimal assumptions.

  1. Modeling Time Series with Calendar Variation

    Microsoft Academic Search

    W. R. Bell; S. C. Hillmer

    1983-01-01

    The modeling of time series data that include calendar variation is considered. Autocorrelation, trends, and seasonality are modeled by ARIMA models. Trading day variation and Easter holiday variation are modeled by regression-type models. The overall model is a sum of ARIMA and regression models. Methods of identification, estimation, inference, and diagnostic checking are discussed. The ideas are illustrated through actual

  2. Nonlinear Time Series Analysis via Neural Networks

    NASA Astrophysics Data System (ADS)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  3. Time-series models in marketing

    Microsoft Academic Search

    Marnik G. Dekimpe; Dominique M. Hanssens

    2000-01-01

    Time-series methods have been available to explain and forecast the behavior of longitudinal variables for several decades. We first discuss why, at first, these methods received relatively little attention from marketing model builders and users. We then show how a number of obstacles to their more widespread use have recently been attenuated. Finally, we identify four developments that may significantly

  4. Circulant Matrices and Time-Series Analysis

    ERIC Educational Resources Information Center

    Pollock, D. S. G.

    2002-01-01

    This paper sets forth some salient results in the algebra of circulant matrices which can be used in time-series analysis. It provides easy derivations of some results that are central to the analysis of statistical periodograms and empirical spectral density functions. A statistical test for the stationarity or homogeneity of empirical processes…

  5. Modeling Time Series Data for Supervised Learning

    ERIC Educational Resources Information Center

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  6. Three Analysis Examples for Time Series Data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    With improvements in instrumentation and the automation of data collection, plot level repeated measures and time series data are increasingly available to monitor and assess selected variables throughout the duration of an experiment or project. Records and metadata on variables of interest alone o...

  7. Integrated method for chaotic time series analysis

    DOEpatents

    Hively, Lee M. (Philadelphia, TN); Ng, Esmond G. (Concord, TN)

    1998-01-01

    Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. 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. Predicting Time Series with Support Vector Machines

    Microsoft Academic Search

    Klaus-robert Müller; Alex J. Smola; Gunnar Rätsch; Bernhard Schölkopf; Jens Kohlmorgen; Vladimir Vapnik

    1997-01-01

    . Support Vector Machines are used for time series predictionand compared to radial basis function networks. We make use of twodierent cost functions for Support Vectors: training with (i) an insensitiveloss and (ii) Huber's robust loss function and discuss how to choosethe regularization parameters in these models. Two applications are considered:data from (a) a noisy (normal and uniform noise) Mackey

  9. Modeling Multiple Time Series with Applications

    Microsoft Academic Search

    G. C. Tiao; G. E. P. Box

    1981-01-01

    An approach to the modeling and analysis of multiple time series is proposed. Properties of a class of vector autoregressive moving average models are discussed. Modeling procedures consisting of tentative specification, estimation, and diagnostic checking are outlined and illustrated by three real examples.

  10. Intelligent techniques for forecasting multiple time series

    E-print Network

    Michalewicz, Zbigniew

    Intelligent techniques for forecasting multiple time series in real-world systems Neal Wagner Technology, Warsaw, Poland, and Sven Schellenberg, Constantin Chiriac and Arvind Mohais SolveIT Software Pty-world system developed for a large food distribution company which requires forecasting demand for thousands

  11. Aerosolic time series and stochastic resonance

    Microsoft Academic Search

    S. de Martino; M. Falanga; L. Mona

    2003-01-01

    Fifteen years long time series (November 1978-May 1993) of Aerosol Index data provided by the TOMS have been analysed. In the power spectrum of these signals, there is a principal peak characterized by an annual periodicity. Dynamical analysis reveals the high dimensionality of this system and an important stochastic component in the observed signals. The ICA Independent Component Analysis was

  12. Integrated method for chaotic time series analysis

    DOEpatents

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

    1998-09-29

    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.

  13. Regent developments in time series forecasting

    Microsoft Academic Search

    R. Fildes

    1988-01-01

    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

  14. Evolving Time Series Forecasting ARMA Models

    Microsoft Academic Search

    Paulo Cortez; Miguel Rocha; José Neves

    2004-01-01

    Time Series Forecasting (TSF) allows the modeling of complex systems as “black-boxes”, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level

  15. Offset detection in GPS coordinate time series

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    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

  16. The Features of the Geodetic Reference of Satellite Images

    NASA Astrophysics Data System (ADS)

    Gojamanov, M. H.

    2012-07-01

    Space information is increasingly the main source of spatial data for many projects. Modern methods of processing of satelite images and remote sensing data can get maps, showing the distribution of varios objects and phenomena on the earth's surface. An important feature of future space vehicles is the high precision gridded images. And it allows creating maps of scale 1:10,000 without using ground control points. As usual orthophotos and vector plans a large scale in the Countries of Independent States, created in the local area (local) coordinate system based on the coordinates in the coordinate system of 1942 (SF-42) to Gauss project. However, the spatial position of the satellite images is given in the WGS-84. Therefore there is a need to translate the raw materials of the WGS-84 in local coordinate system of the project, in other words, the geodesic linking satelite images. Keys are needed for this conversion of coordinates from the local to the state or directly to the WGS-84. To determine the key of the transition there are used the different methods. This article describes the features of geodetic reference satellite images with the use of software and hardware for "DPS-4.0 Talca." This software has the task of linking images with rational polynomial coefficients in the local coordinate sytem in the unknown parameters of translation to the coordinate system WGS-84. To do this, "DPS-Talca 4.0" has special tool "linkimg images RPC in the local coordinate sytem."

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

    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.

  18. Automatic Crowd Analysis from Very High Resolution Satellite Images

    NASA Astrophysics Data System (ADS)

    Sirmacek, B.; Reinartz, P.

    2011-04-01

    Recently automatic detection of people crowds from images became a very important research field, since it can provide crucial information especially for police departments and crisis management teams. Due to the importance of the topic, many researchers tried to solve this problem using street cameras. However, these cameras cannot be used to monitor very large outdoor public events. In order to bring a solution to the problem, herein we propose a novel approach to detect crowds automatically from remotely sensed images, and especially from very high resolution satellite images. To do so, we use a local feature based probabilistic framework. We extract local features from color components of the input image. In order to eliminate redundant local features coming from other objects in given scene, we apply a feature selection method. For feature selection purposes, we benefit from three different type of information; digital elevation model (DEM) of the region which is automatically generated using stereo satellite images, possible street segment which is obtained by segmentation, and shadow information. After eliminating redundant local features, remaining features are used to detect individual persons. Those local feature coordinates are also assumed as observations of the probability density function (pdf) of the crowds to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding pdf which gives us information about dense crowd and people locations. We test our algorithm usingWorldview-2 satellite images over Cairo and Munich cities. Besides, we also provide test results on airborne images for comparison of the detection accuracy. Our experimental results indicate the possible usage of the proposed approach in real-life mass events.

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

    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.

  20. High temperature superconducting infrared imaging satellite

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

    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.

  1. Analysis of Decadal Vegetation Dynamics Using Multi-Scale Satellite Images

    NASA Astrophysics Data System (ADS)

    Chiang, Y.; Chen, K.

    2013-12-01

    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.

  2. Graph - Based High Resolution Satellite Image Segmentation for Object Recognition

    NASA Astrophysics Data System (ADS)

    Ravali, K.; Kumar, M. V. Ravi; Venugopala Rao, K.

    2014-11-01

    Object based image processing and analysis is challenging research in very high resolution satellite utilisation. Commonly ei ther pixel based classification or visual interpretation is used to recognize and delineate land cover categories. The pixel based classification techniques use rich spectral content of satellite images and fail to utilise spatial relations. To overcome th is drawback, traditional time consuming visual interpretation methods are being used operational ly for preparation of thematic maps. This paper addresses computational vision principles to object level image segmentation. In this study, computer vision algorithms are developed to define the boundary between two object regions and segmentation by representing image as graph. Image is represented as a graph G (V, E), where nodes belong to pixels and, edges (E) connect nodes belonging to neighbouring pixels. The transformed Mahalanobis distance has been used to define a weight function for partition of graph into components such that each component represents the region of land category. This implies that edges between two vertices in the same component have relatively low weights and edges between vertices in different components should have higher weights. The derived segments are categorised to different land cover using supervised classification. The paper presents the experimental results on real world multi-spectral remote sensing images of different landscapes such as Urban, agriculture and mixed land cover. Graph construction done in C program and list the run time for both graph construction and segmentation calculation on dual core Intel i7 system with 16 GB RAM, running 64bit window 7.

  3. Optimized satellite image compression and reconstruction via evolution strategies

    NASA Astrophysics Data System (ADS)

    Babb, Brendan; Moore, Frank; Peterson, Michael

    2009-05-01

    This paper describes the automatic discovery, via an Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), of vectors of real-valued coefficients representing matched forward and inverse transforms that outperform the 9/7 Cohen-Daubechies-Feauveau (CDF) discrete wavelet transform (DWT) for satellite image compression and reconstruction under conditions subject to quantization error. The best transform evolved during this study reduces the mean squared error (MSE) present in reconstructed satellite images by an average of 33.78% (1.79 dB), while maintaining the average information entropy (IE) of compressed images at 99.57% in comparison to the wavelet. In addition, this evolved transform achieves 49.88% (3.00 dB) average MSE reduction when tested on 80 images from the FBI fingerprint test set, and 42.35% (2.39 dB) average MSE reduction when tested on a set of 18 digital photographs, while achieving average IE of 104.36% and 100.08%, respectively. These results indicate that our evolved transform greatly improves the quality of reconstructed images without substantial loss of compression capability over a broad range of image classes.

  4. Time-series animation techniques for visualizing urban growth

    USGS Publications Warehouse

    Acevedo, W.; Masuoka, P.

    1997-01-01

    Time-series animation is a visually intuitive way to display urban growth. Animations of landuse change for the Baltimore-Washington region were generated by showing a series of images one after the other in sequential order. Before creating an animation, various issues which will affect the appearance of the animation should be considered, including the number of original data frames to use, the optimal animation display speed, the number of intermediate frames to create between the known frames, and the output media on which the animations will be displayed. To create new frames between the known years of data, the change in each theme (i.e. urban development, water bodies, transportation routes) must be characterized and an algorithm developed to create the in-between frames. Example time-series animations were created using a temporal GIS database of the Baltimore-Washington area. Creating the animations involved generating raster images of the urban development, water bodies, and principal transportation routes; overlaying the raster images on a background image; and importing the frames to a movie file. Three-dimensional perspective animations were created by draping each image over digital elevation data prior to importing the frames to a movie file. ?? 1997 Elsevier Science Ltd.

  5. Spacecraft design project: High temperature superconducting infrared imaging satellite

    NASA Technical Reports Server (NTRS)

    1991-01-01

    The High Temperature Superconductor Infrared Imaging Satellite (HTSCIRIS) is designed to perform the space based infrared imaging and surveillance mission. The design of the satellite follows the black box approach. The payload is a stand alone unit, with the spacecraft bus designed to meet the requirements of the payload as listed in the statement of work. Specifications influencing the design of the spacecraft bus were originated by the Naval Research Lab. A description of the following systems is included: spacecraft configuration, orbital dynamics, radio frequency communication subsystem, electrical power system, propulsion, attitude control system, thermal control, and structural design. The issues of testing and cost analysis are also addressed. This design project was part of the course Advanced Spacecraft Design taught at the Naval Postgraduate School.

  6. Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

    NASA Technical Reports Server (NTRS)

    Forkel, Matthias; Carvalhais, Nuno; Verbesselt, Jan; Mahecha, Miguel D.; Neigh, Christopher S.R.; Reichstein, Markus

    2013-01-01

    Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. 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. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

  7. Modelling population change from time series data

    USGS Publications Warehouse

    Barker, R.J.; Sauer, J.R.

    1992-01-01

    Information on change in population size over time is among the most basic inputs for population management. Unfortunately, population changes are generally difficult to identify, and once identified difficult to explain. Sources of variald (patterns) in population data include: changes in environment that affect carrying capaciyy and produce trend, autocorrelative processes, irregular environmentally induced perturbations, and stochasticity arising from population processes. In addition. populations are almost never censused and many surveys (e.g., the North American Breeding Bird Survey) produce multiple, incomplete time series of population indices, providing further sampling complications. We suggest that each source of pattern should be used to address specific hypotheses regarding population change, but that failure to correctly model each source can lead to false conclusions about the dynamics of populations. We consider hypothesis tests based on each source of pattern, and the effects of autocorrelated observations and sampling error. We identify important constraints on analyses of time series that limit their use in identifying underlying relationships.

  8. Intrinsic superstatistical components of financial time series

    NASA Astrophysics Data System (ADS)

    Vamo?, C?lin; Cr?ciun, Maria

    2014-12-01

    Time series generated by a complex hierarchical system exhibit various types of dynamics at different time scales. A financial time series is an example of such a multiscale structure with time scales ranging from minutes to several years. In this paper we decompose the volatility of financial indices into five intrinsic components and we show that it has a heterogeneous scale structure. The small-scale components have a stochastic nature and they are independent 99% of the time, becoming synchronized during financial crashes and enhancing the heavy tails of the volatility distribution. The deterministic behavior of the large-scale components is related to the nonstationarity of the financial markets evolution. Our decomposition of the financial volatility is a superstatistical model more complex than those usually limited to a superposition of two independent statistics at well-separated time scales.

  9. Time series analysis with the VSAA method

    NASA Astrophysics Data System (ADS)

    Tsantilas, S.; Kolenberg, K.; Rovithis-Livaniou, H.

    2009-03-01

    Time series analysis is a common task in many scientific fields, and so it is in astronomy, too. Fourier Transform and Wavelet Analysis are usually applied to handle the majority of the cases. Even so, problems arise when the time series signal presents modulation in the frequency under inspection. The Variable Sine Algorithmic Analysis (VSAA) is a new method focused exactly on this type of signals. It is based on a single sine function with variable coefficients and it is powered by the simplex algorithm. In cases of phenomena triggered by a single mechanism - that Fourier Transform and Wavelet Analysis fail to describe practically and efficiently - VSAA provides a straightforward solution. The method has already been applied to orbital period changes and magnetic field variations of binary stars, as well as to the Blazhko effect of the pulsating RR Lyrae stars and to sunspot activity.

  10. Structure and Nonrigid Motion Analysis of Satellite Cloud Images

    Microsoft Academic Search

    Lin Zhou; Chandra Kambhamettu

    1998-01-01

    This paper proposes a new method for recoveringnonrigid motion and structure of clouds under affineconstraints using time-varying cloud images obtainedfrom meteorological satellites. This problem is challengingnot only due to the correspondence problembut also due to the lack of depth cues in the 2D cloudimages (scaled orthographic projection). In this paper,affine motion is chosen as a suitable model forsmall local cloud

  11. Delay differential analysis of time series.

    PubMed

    Lainscsek, Claudia; Sejnowski, Terrence J

    2015-03-01

    Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis. PMID:25602777

  12. Detecting Breaths in Capnography Time Series

    Microsoft Academic Search

    Markus Nilsson; Mattias Karlsson; Andreas Selenwall; Peter Funk

    2005-01-01

    Finding individual breaths is essential in the classification of respiratory sinus arrhythmia. The identification task may become quite difficult if the sampling rates of available physiological measurements are low, as in the HR3Modul decision support system. We introduce an improved respiration analysis of the HR3Modul system that uses an Euclidian distance based Nearest-Neighbour classification of low resolu- tion capnography time-series

  13. Univariate time series forecasting with fuzzy CMAC

    Microsoft Academic Search

    Da-Ming Shi; Jun-Bin Gao; R. Tilani

    2004-01-01

    In financial and business areas, forecasting is a necessary tool that enables decision makers to predict changes in demands, plans and sales. This work applies a novel fuzzy cerebellar-model-articulation-controller (FCMAC) into univariate time-series forecasting and investigates its performance in comparison to established techniques such as single exponential smoothing, Holt's linear trend, Holt-Winter's additive and multiplicative methods and the Box-Jenkin's ARIMA

  14. A satellite imager for atmospheric x-rays

    SciTech Connect

    Calvert, W.; Sanders, T.C.; Voss, H.D.

    1985-02-01

    A high-sensitivity X-Ray Imaging Spectrometer (XRIS) was developed for measurements of atmospheric bremsstrahlung X-rays. The XRIS instrument flown on a 3-axis stabilized polar orbiting satellite (S81-1) employed a one-dimensional pinhole camera to acquire a 2-dimensional X-ray image as the satellite passed over an auroral scene. Using a position sensitive gas proportional counter, with an active area of 1200 cmS divided into sixteen cross-track pixels, the instrument had a geometric factor of about 0.4 cmS-steradian per pixel (6 cmS-sr total) for X-rays of 4 to 40 keV. At an orbital altitude of 250 km, it provided a spatial resolution of 30 km and the temporal resolution was one-eighth of a second. Designed primarily to detect artificial electron precipitation at lower latitudes, the instrument also produced the first satellite X-ray images of the aurora during May and June 1982. Special features of the instrument included a quadrupole broom magnet to reject energetic electrons, a multilayer plastic-on-tantalum shielding to suppress the bremsstrahlung X-rays generated from electrons which impact the instrument surface, and a new technique for position sensing within the detector, using signal division in a resistor array.

  15. TIME SERIES PREDICTABILITY Minglei Duan, B.S.

    E-print Network

    Povinelli, Richard J.

    TIME SERIES PREDICTABILITY By Minglei Duan, B.S. A Thesis Submitted to the Faculty of the Graduate that quantifies the predictability of a time series is introduced. This new time series predictability metric the predictability changes over different subsequences in a time series. The new metric can be built on top of many

  16. Lectures for STAT280/380 Applied Time Series

    E-print Network

    Ravishanker, Nalini

    Lectures for STAT280/380 Applied Time Series Nalini Ravishanker Department of Statistics University - (iv) Cochrane-Orcutt Procedure III. Determinsitic Time Series Regression Meth- ods: - (i) Structural) Steps in Structural Time Series Modeling 1 #12;IV. Time Series Smoothing Methods: - (i) Simple

  17. Wavelet-Based Bootstrapping for Non-Gaussian Time Series

    E-print Network

    Percival, Don

    Wavelet-Based Bootstrapping for Non-Gaussian Time Series Don Percival Applied Physics Laboratory time series) · review one wavelet-based approach to bootstrapping (Percival, Sardy and Davison, 2001-Gaussian time series · demonstrate methodology on time series related to BMW stock · conclude with some remarks

  18. TIME SERIES DATAMINING: IDENTIFYING TEMPORAL PATTERNS FOR CHARACTERIZATIONAND

    E-print Network

    Povinelli, Richard J.

    TIME SERIES DATAMINING: IDENTIFYING TEMPORAL PATTERNS FOR CHARACTERIZATIONAND PREDICTION OF TIME favorite topic, Time Series Data Mining, and on his, Fuzzy Optimal Control. I am indebted to him. #12; iv Abstract A new framework for analyzing time series data called Time Series Data Mining (TSDM

  19. Clustering of Unevenly Sampled Gene Expression Time-Series Data

    E-print Network

    Rostock, Universität

    Clustering of Unevenly Sampled Gene Expression Time-Series Data C. S. M¨oller-Levet a , F. Klawonn in clustering time-series. However, the shortness of gene expression time-series data limits the use of conventional statistical models and techniques for time-series analysis. To address this problem, this paper

  20. Analysis of Time Series Using Compact Model-Based Descriptions

    E-print Network

    Kriegel, Hans-Peter

    Analysis of Time Series Using Compact Model-Based Descriptions Hans-Peter Kriegel, Peer Kr for the compres- sion of time series based on mathematical models that explore dependen- cies between different time series. This representation models each time series by a combination of a set of specific

  1. AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series

    E-print Network

    AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series Ori Rosen Department a method for analyzing possibly nonstationary time series by adaptively dividing the time series Markov chain Monte Carlo (RJMCMC) meth- ods. For a given segmentation of the time series, the likelihood

  2. Singular spectrum analysis for time series with missing data

    USGS Publications Warehouse

    Schoellhamer, D.H.

    2001-01-01

    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.

  3. Methods for the Estimation of Missing Values in Time Series

    Microsoft Academic Search

    David S. Fung

    2006-01-01

    Time Series is a sequential set of data measured over time. Examples of time series arise in a variety of areas, ranging from engineering to economics. The analysis of time series data constitutes an important area of statistics. Since, the data are records taken through time, missing observations in time series data are very common. This occurs because an observation

  4. Singular spectrum analysis for time series with missing data

    Microsoft Academic Search

    David H. Schoellhamer

    2001-01-01

    Geophysical time series often contain missing data, which prevents analysis with many signal processing and multivariate tools. A modication of singular spec- trum analysis for time series with missing data is developed andsuccessfullytestedwithsyntheticandactualincomplete time series of suspended-sediment concentration from San Francisco Bay. This method also can be used to low pass lter incomplete time series.

  5. GOES-R ABI: Next Generation Satellite Imaging

    NSDL National Science Digital Library

    COMET

    2013-02-19

    This extension of the COMET module “GOES-R: Benefits of Next Generation Environmental Monitoring” focuses on the ABI instrument, the satellite's 16-channel imager. With increased spectral coverage, greater spatial resolution, more frequent imaging, and improved image pixel geolocation and radiometric performance, the ABI will bring significant advancements to forecasting, numerical weather prediction, and climate and environmental monitoring. The first part of the module introduces the ABI's key features and improvements over earlier GOES imagers. The second section lets users interactively explore the ABI's 16 channels. The third section contains movies that show the advancements that the ABI will bring to the following application areas: convection, flooding, wildfires, land cover, hurricanes, climate, air quality, aviation, fog and low visibility, and coastal and marine. The final section contains additional resources pertaining to the ABI. The module has numerous takeaways, including ten application movies and an interactive spectrum.

  6. State estimation and absolute image registration for geosynchronous satellites

    NASA Technical Reports Server (NTRS)

    Nankervis, R.; Koch, D. W.; Sielski, H.

    1980-01-01

    Spacecraft state estimation and the absolute registration of Earth images acquired by cameras onboard geosynchronous satellites are described. The basic data type of the procedure consists of line and element numbers of image points called landmarks whose geodetic coordinates, relative to United States Geodetic Survey topographic maps, are known. A conventional least squares process is used to estimate navigational parameters and camera pointing biases from observed minus computed landmark line and element numbers. These estimated parameters along with orbit and attitude dynamic models are used to register images, using an automated grey level correlation technique, inside the span represented by the landmark data. In addition, the dynamic models can be employed to register images outside of the data span in a near real time mode. An important application of this mode is in support of meteorological studies where rapid data reduction is required for the rapid tracking and predicting of dynamic phenomena.

  7. The Research of Satellite Cloud Image Recognition Base on Variational Method and Texture Feature Analysis

    Microsoft Academic Search

    Wei Shangguan; Yanling Hao; Zhizhong Lu; Peng Wu

    2007-01-01

    Recently, the development of satellite cloud image processing technology has become very quick; the research aspects concentrate on judge the cloud type and classify the cloud mainly. These image processing methods relate to the subject category like image processing and pattern recognition etc; it has become one of the fields of most quickly development in the research of satellite image

  8. An image-based retrieval algorithm of aerosol characteristics and surface reflectance for satellite images

    Microsoft Academic Search

    C. H. LIU; A. J. CHEN; G. R. LIU

    1996-01-01

    An image-based retrieval algorithm of aerosol characteristics and surface reflectance for satellite images is proposed. By assuming the Junge size distributed aerosol in the atmosphere and feeding back the new Junge parameter, not only the aerosol optical depth but also the Junge parameter, single-scattering albedo and phase function can be iteratively derived and converged from digital counts of dense dark

  9. Spatial, Temporal and Spectral Satellite Image Fusion via Sparse Representation

    NASA Astrophysics Data System (ADS)

    Song, Huihui

    Remote sensing provides good measurements for monitoring and further analyzing the climate change, dynamics of ecosystem, and human activities in global or regional scales. Over the past two decades, the number of launched satellite sensors has been increasing with the development of aerospace technologies and the growing requirements on remote sensing data in a vast amount of application fields. However, a key technological challenge confronting these sensors is that they tradeoff between spatial resolution and other properties, including temporal resolution, spectral resolution, swath width, etc., due to the limitations of hardware technology and budget constraints. To increase the spatial resolution of data with other good properties, one possible cost-effective solution is to explore data integration methods that can fuse multi-resolution data from multiple sensors, thereby enhancing the application capabilities of available remote sensing data. In this thesis, we propose to fuse the spatial resolution with temporal resolution and spectral resolution, respectively, based on sparse representation theory. Taking the study case of Landsat ETM+ (with spatial resolution of 30m and temporal resolution of 16 days) and MODIS (with spatial resolution of 250m ~ 1km and daily temporal resolution) reflectance, we propose two spatial-temporal fusion methods to combine the fine spatial information of Landsat image and the daily temporal resolution of MODIS image. Motivated by that the images from these two sensors are comparable on corresponding bands, we propose to link their spatial information on available Landsat- MODIS image pair (captured on prior date) and then predict the Landsat image from the MODIS counterpart on prediction date. To well-learn the spatial details from the prior images, we use a redundant dictionary to extract the basic representation atoms for both Landsat and MODIS images based on sparse representation. Under the scenario of two prior Landsat-MODIS image pairs, we build the corresponding relationship between the difference images of MODIS and ETM+ by training a low- and high-resolution dictionary pair from the given prior image pairs. In the second scenario, i.e., only one Landsat- MODIS image pair being available, we directly correlate MODIS and ETM+ data through an image degradation model. Then, the fusion stage is achieved by super-resolving the MODIS image combining the high-pass modulation in a two-layer fusion framework. Remarkably, the proposed spatial-temporal fusion methods form a unified framework for blending remote sensing images with phenology change or land-cover-type change. Based on the proposed spatial-temporal fusion models, we propose to monitor the land use/land cover changes in Shenzhen, China. As a fast-growing city, Shenzhen faces the problem of detecting the rapid changes for both rational city planning and sustainable development. However, the cloudy and rainy weather in region Shenzhen located makes the capturing circle of high-quality satellite images longer than their normal revisit periods. Spatial-temporal fusion methods are capable to tackle this problem by improving the spatial resolution of images with coarse spatial resolution but frequent temporal coverage, thereby making the detection of rapid changes possible. On two Landsat-MODIS datasets with annual and monthly changes, respectively, we apply the proposed spatial-temporal fusion methods to the task of multiple change detection. Afterward, we propose a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning and sparse non-negative matrix factorization. By combining the spectral information from hyperspectral image, which is characterized by low spatial resolution but high spectral resolution and abbreviated as LSHS, and the spatial information from multispectral image, which is featured by high spatial resolution but low spectral resolution and abbreviated as HSLS, this method aims to generate the fused data with both high spa

  10. Time-Series Analysis of Supergranule Characterstics at Solar Minimum

    NASA Technical Reports Server (NTRS)

    Williams, Peter E.; Pesnell, W. Dean

    2013-01-01

    Sixty days of Doppler images from the Solar and Heliospheric Observatory (SOHO) / Michelson Doppler Imager (MDI) investigation during the 1996 and 2008 solar minima have been analyzed to show that certain supergranule characteristics (size, size range, and horizontal velocity) exhibit fluctuations of three to five days. Cross-correlating parameters showed a good, positive correlation between supergranulation size and size range, and a moderate, negative correlation between size range and velocity. The size and velocity do exhibit a moderate, negative correlation, but with a small time lag (less than 12 hours). Supergranule sizes during five days of co-temporal data from MDI and the Solar Dynamics Observatory (SDO) / Helioseismic Magnetic Imager (HMI) exhibit similar fluctuations with a high level of correlation between them. This verifies the solar origin of the fluctuations, which cannot be caused by instrumental artifacts according to these observations. Similar fluctuations are also observed in data simulations that model the evolution of the MDI Doppler pattern over a 60-day period. Correlations between the supergranule size and size range time-series derived from the simulated data are similar to those seen in MDI data. A simple toy-model using cumulative, uncorrelated exponential growth and decay patterns at random emergence times produces a time-series similar to the data simulations. The qualitative similarities between the simulated and the observed time-series suggest that the fluctuations arise from stochastic processes occurring within the solar convection zone. This behavior, propagating to surface manifestations of supergranulation, may assist our understanding of magnetic-field-line advection, evolution, and interaction.

  11. Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data Thanawin Rakthanmanon Eamonn J. Keogh Stefano Lonardi Scott Evans

    E-print Network

    Lonardi, Stefano

    Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data Thanawin@ge.com Abstract--Given the pervasiveness of time series data in all human endeavors, and the ubiquity of clustering as a data mining application, it is somewhat surprising that the problem of time series clustering

  12. Shape and Topography of Saturn's Satellites from Imaging Data

    NASA Astrophysics Data System (ADS)

    Gaskell, R. W.; Mastrodemos, N.; Rizk, B.

    2010-12-01

    Detailed global and local digital topographies of eight of Saturn's satellites are being constructed from ensembles of overlapping maplets which completely cover the visible surfaces. Each maplet is a digital representation of a piece of the surface topography and albedo constructed from imaging data with stereophotoclinometry. Multiple images projected onto the maplet provide brightness values at each pixel which are used in a least-squares estimation for slope and relative albedo. The slopes are then integrated to produce the topography solution. The central pixel of each maplet represents a control point, and the ensemble of these points is used in an estimation for their body-fixed locations, the rotational state of the body, and the position and attitude of the spacecraft. Applications of these data products include studies of cratering of icy bodies and the subsequent relaxation of the surface, while detailed shapes for the small, irregular satellites can be used to predict the surface gravity and local slope at high resolution. For a larger satellite, a precise shape determination is important because often the shape was frozen in when the body was in a different rotational state. This enables an analysis of the rotational and orbital histories of these bodies. The high resolution topography yields surface roughness, slopes, overall elevation variations, and fractal character of the surface.

  13. Land Cover Classification of Satellite Images Using Contextual Information

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

    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.

  14. Assessing nonstationary time series using wavelets

    NASA Astrophysics Data System (ADS)

    Whitcher, Brandon J.

    1998-08-01

    The discrete wavelet transform has be used extensively in the field of Statistics, mostly in the area of "denoising signals" or nonparametric regression. This thesis provides a new application for the discrete wavelet transform, assessing nonstationary events in time series-especially long memory processes. Long memory processes are those which exhibit substantial correlations between events separated by a long period of time. Departures from stationarity in these heavily autocorrelated time series, such as an abrupt change in the variance at an unknown location or "bursts" of increased variability, can be detected and accurately located using discrete wavelet transforms-both orthogonal and overcomplete. A cumulative sum of squares method, utilizing a Kolomogorov-Smirnov-type test statistic is applied to this problem. By analyzing a time series on a scale by scale basis each scale corresponding to a range of frequencies, the ability to detect and locate a sudden change in the variance in the time series is introduced. Using this same procedure to detect a change in the long memory parameter, when the process variance remains constant, is also briefly investigated. Applications involve Nile River minimum water levels and vertical ocean shear measurements. In the atmospheric sciences, broadband features in the spectrum of recorded time series have been hypothesized to be nonstationary events; e.g., the Madden-Julian oscillation. The Madden-Julian oscillation is a result of large-scale circulation cells oriented in the equatorial plane from the Indian Ocean to the central Pacific. The oscillation has been noted to have higher frequencies during warm events in El Nino-Southern Oscillation (ENSO) years. The concepts of wavelet covariance and wavelet correlation are introduced and applied to this problem as an alternative to cross-spectrum analysis. The wavelet covariance is shown to decompose the covariance between two stationary processes on a scale by scale basis. Asymptotic normality of estimators of the wavelet covariance and correlation is shown in order to construct approximate confidence intervals. Both quantities are generalized into the wavelet cross-covariance and cross-correlation in order to investigate possible lead/lag relations in bivariate time series on a scale by scale basis. Atmospheric measurements (such as station pressure and zonal wind speeds) from a single station at Canton Island (2.8sp°S, 171.7sp°W) are put through a wavelet analysis of covariance and are shown to provide similar results to those found in Madden and Julian (1971) and multitaper spectral techniques. To investigate the possible interaction between ENSO activity and the Madden-Julian oscillation, a daily "Southern Oscillation Index" and station pressure series collected from Truk Island (7.4sp°N, 151.8sp°W) are analyzed. The wavelet cross-covariance nicely decomposes the usual cross-covariance into scales which are more easily associated with atmospheric phenomena. The time-varying wavelet variance and covariance are used to investigate possible seasonal effects and changes due to ENSO activity.

  15. The 30-year TAMSAT African Rainfall Climatology1 And Time-series (TARCAT) Dataset

    E-print Network

    Allan, Richard P.

    Page 1 The 30-year TAMSAT African Rainfall Climatology1 And Time-series (TARCAT) Dataset 2 Authors 2 Key points1 Development of a satellite based 30 year rainfall dataset for Africa2 The dataset has been designed to be temporally consistency3 The dataset skilfully captures interannual

  16. Time series analysis of PIXE aerosol measurements

    NASA Astrophysics Data System (ADS)

    Annegarn, H. J.

    1987-03-01

    Airborne particle sampling networks using instruments optimized for subsequent PIXE analysis are now in routine use in numerous environmental and industrial monitoring programs. Interpretation of the resulting large multielemental data sets has not always kept pace with the rapid rate at which PIXE can generate such data. In this contribution application of Box-Jenkins ARIMA time series analysis to PIXE aerosol data is investigated. Data were from streaker sampling in an underground mine, consisting of 142 sequential 1-h steps analyzed for Al, Si, S, Cl, K, Ca, Ti, Fe, Zn and Pb. This data had strong diurnal variations. Results of interpretive and diagnostic tests used to verify the selected ARIMA models are presented. Derived models, of the form SARMA(1,1) × (1,0) 24, gave a parsimonious representation of the time structure. Multivariate factor analyses were applied to the derived residual white noise shocks. Substantive differences in the derived factors resulted from removal of serial correlations by time series modeling. It is concluded that erroneous conclusions may result from failure to consider serial correlations, and that ARIMA modeling should become one of the standard techniques used for interpretation of sequential PIXE aerosol data.

  17. Path planning on satellite images for unmanned surface vehicles

    NASA Astrophysics Data System (ADS)

    Yang, Joe-Ming; Tseng, Chien-Ming; Tseng, P. S.

    2015-01-01

    In recent years, the development of autonomous surface vehicles has been a field of increasing research interest. There are two major areas in this field: control theory and path planning. This study focuses on path planning, and two objectives are discussed: path planning for Unmanned Surface Vehicles (USVs) and implementation of path planning in a real map. In this paper, satellite thermal images are converted into binary images which are used as the maps for the Finite Angle A* algorithm (FAA*), an advanced A* algorithm that is used to determine safer and suboptimal paths for USVs. To plan a collision-free path, the algorithm proposed in this article considers the dimensions of surface vehicles. Furthermore, the turning ability of a surface vehicle is also considered, and a constraint condition is introduced to improve the quality of the path planning algorithm, which makes the traveled path smoother. This study also shows a path planning experiment performed on a real satellite thermal image, and the path planning results can be used by an USV.

  18. Cloud Thickness and Satellite Images (title provided or enhanced by cataloger)

    NSDL National Science Digital Library

    Tom Whittaker

    This applet explores how the thickness of a cloud changes the way it looks from a satellite. The image is in the visible part of the spectrum, and the radiant energy is a function of not just temperature, as in the case of infrared images. The cloud thickness, its effective brightness, and the surface temperature can be modified while observing the satellite image.

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

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    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.

  20. Time Series Analysis of SOLSTICE Measurements

    NASA Astrophysics Data System (ADS)

    Wen, G.; Cahalan, R. F.

    2003-12-01

    Solar radiation is the major energy source for the Earth's biosphere and atmospheric and ocean circulations. Variations of solar irradiance have been a major concern of scientists both in solar physics and atmospheric sciences. A number of missions have been carried out to monitor changes in total solar irradiance (TSI) [see Fröhlich and Lean, 1998 for review] and spectral solar irradiance (SSI) [e.g., SOLSTICE on UARS and VIRGO on SOHO]. Observations over a long time period reveal the connection between variations in solar irradiance and surface magnetic fields of the Sun [Lean1997]. This connection provides a guide to scientists in modeling solar irradiances [e.g., Fontenla et al., 1999; Krivova et al., 2003]. Solar spectral observations have now been made over a relatively long time period, allowing statistical analysis. This paper focuses on predictability of solar spectral irradiance using observed SSI from SOLSTICE . Analysis of predictability is based on nonlinear dynamics using an artificial neural network in a reconstructed phase space [Abarbanel et al., 1993]. In the analysis, we first examine the average mutual information of the observed time series and a delayed time series. The time delay that gives local minimum of mutual information is chosen as the time-delay for phase space reconstruction [Fraser and Swinney, 1986]. The embedding dimension of the reconstructed phase space is determined using the false neighbors and false strands method [Kennel and Abarbanel, 2002]. Subsequently, we use a multi-layer feed-forward network with back propagation scheme [e.g., Haykin, 1994] to model the time series. The predictability of solar irradiance as a function of wavelength is considered. References Abarbanel, H. D. I., R. Brown, J. J. Sidorowich, and L. Sh. Tsimring, Rev. Mod. Phys. 65, 1331, 1993. Fraser, A. M. and H. L. Swinney, Phys. Rev. 33A, 1134, 1986. Fontenla, J., O. R. White, P. Fox, E. H. Avrett and R. L. Kurucz, The Astrophysical Journal, 518, 480-499, 1999. Fröhlich, C. and J. Lean, IAU Symposium 185: New Eyes to See Inside the Sun and Stars, edited by F. L. Deubner, 82-102, Kluwer Academic Publ., Dordrecht, The Netherland, 1998. Haykin, S., 696 pp, Macmillan, New York, 1994. Kennel, M. B. and H. D. I. Abarbanel, Phys. Rev. E 66, 026209, 2002. Krivova, N. A., S. K. Solanki, M. Fligge, and Y. C. Unruh, 399, L1-L4, 2003. Lean, J., Annu. Rev. Astron. Astrophys., 35, 33-67, 1997.

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

    NASA Astrophysics Data System (ADS)

    Vermeesch, P.

    2012-04-01

    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.

  2. Scaling laws from geomagnetic time series

    USGS Publications Warehouse

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

    1998-01-01

    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.

  3. Fractal fluctuations in cardiac time series

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

    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.

  4. Modeling of aggregated hydrologic time series

    NASA Astrophysics Data System (ADS)

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

    1986-10-01

    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.

  5. Concepts for on-board satellite image registration, volume 1

    NASA Technical Reports Server (NTRS)

    Ruedger, W. H.; Daluge, D. R.; Aanstoos, J. V.

    1980-01-01

    The NASA-NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. One very specific area of interest is the preprocessing required to register imaging sensor data which have been distorted by anomalies in subsatellite-point position and/or attitude control. The concepts and considerations involved in using state-of-the-art positioning systems such as the Global Positioning System (GPS) in concert with state-of-the-art attitude stabilization and/or determination systems to provide the required registration accuracy are discussed with emphasis on assessing the accuracy to which a given image picture element can be located and identified, determining those algorithms required to augment the registration procedure and evaluating the technology impact on performing these procedures on-board the satellite.

  6. Advanced DTM Generation from Very High Resolution Satellite Stereo Images

    NASA Astrophysics Data System (ADS)

    Perko, R.; Raggam, H.; Gutjahr, K. H.; Schardt, M.

    2015-03-01

    This work proposes a simple filtering approach that can be applied to digital surface models in order to extract digital terrain models. The method focusses on robustness and computational efficiency and is in particular tailored to filter DSMs that are extracted from satellite stereo images. It represents an evolution of an existing DTM generation method and includes distinct advancement through the integration of multi-directional processing as well as slope dependent filtering, thus denoted "MSD filtering". The DTM generation workflow is fully automatic and requires no user interaction. Exemplary results are presented for a DSM generated from a Pléiades tri-stereo image data set. Qualitative and quantitative evaluations with respect to highly accurate reference LiDAR data confirm the effectiveness of the proposed algorithm.

  7. Control of satellite imaging arrays in multi-body regimes

    NASA Astrophysics Data System (ADS)

    Millard, Lindsay Demoore

    In the current study, control strategies are investigated for spacecraft imaging formations in multi-body regimes. The specific focus of the analysis is spacecraft motion as modeled in the circular restricted three-body problem, where two large gravitational bodies affect the motion of spacecraft in their vicinity. Five equilibrium points, or libration points, exist as solutions to the differential equations of motion in the circular restricted three-body problem. A specific periodic solution to these equations is an orbit in the vicinity of a libration point, i.e., a halo orbit. Halo orbits are ideal locations for spacecraft imaging arrays as they remain at a nearly fixed distance from the larger, or primary, bodies in the system. For example, if the Sun and Earth are considered the primary bodies, a spacecraft array can be placed near a libration point on the far side of the Earth, protected from the harsh radiation of the Sun at all times. A model of image reconstruction is developed for two common satellite imaging platform designs: an interferometric sparse aperture array and an occulter-telescope formation. The resolution of an image produced by an array is largely determined by the corresponding coverage of the (u, v) plane. The (u, v) plane is not a physical plane, but rather a relationship between frequencies and amplitudes in the Fourier expansion of the electromagnetic signal from the object of interest. Coverage of the (u, v) plane is derived based on several characteristics of the spacecraft configuration and the motion in physical space. Therefore, to determine formation motion history that may be advantageous to imaging, a mathematical model relating spacecraft motion in physical space to coverage of the (u, v) plane, and thus image reconstruction, is necessary. From these models, two control algorithms are developed that increase the resolution of the images produced by the formation while exploiting multi-body dynamics to reduce satellite fuel usage. The first method incorporates nonlinear optimal control techniques to determine constellation motion that maximizes resolution of an image while minimizing fuel. Specifically, the problem is formulated using an augmented Lagrange multiplier method and numerically solved using a sequential quadratic programming algorithm. The second approach is a geometric control algorithm that is developed based on the characteristics of the dynamical phase space near periodic orbits in the circular restricted three-body problem. This algorithm incorporates natural quasi-periodic motion in the problem to reduce control costs and produce relative spacecraft motion advantageous for imaging arrays. These two new methods are compared and contrasted with more traditional methods, including time-varying linear quadratic regulators, impulsive targeting, and input feedback linearization. Methods for state estimation are also explored. The control algorithms are implemented (numerically) on satellite constellations of differing size and function, including examples similar to the following National Aeronautics and Space Administration missions: Terrestrial Planet Finder, the Micro-Arcsecond X-ray Interferometry Mission, and the Terrestrial Planet Finder-Occulter. Notional image reconstruction is demonstrated for varying formation size, maximum baseline, distance to the object of interest, and wavelength of electromagnetic radiation.

  8. Use of satellite images for the monitoring of water systems

    NASA Astrophysics Data System (ADS)

    Hillebrand, Gudrun; Winterscheid, Axel; Baschek, Björn; Wolf, Thomas

    2015-04-01

    Satellite images are a proven source of information for monitoring ecological indicators in coastal waters and inland river systems. This potential of remote sensing products was demonstrated by recent research projects (e.g. EU-funded project Freshmon - www.freshmon.eu) and other activities by national institutions. Among indicators for water quality, a particular focus was set on the temporal and spatial dynamics of suspended particulate matter (SPM) and Chlorophyll-a (Chl-a). The German Federal Institute of Hydrology (BfG) was using the Weser and Elbe estuaries as test cases to compare in-situ measurements with results obtained from a temporal series of automatically generated maps of SPM distributions based on remote sensing data. Maps of SPM and Chl-a distributions in European inland rivers and alpine lakes were generated by the Freshmon Project. Earth observation based products are a valuable source for additional data that can well supplement in-situ monitoring. For 2015, the BfG and the Institute for Lake Research of the State Institute for the Environment, Measurements and Nature Conservation of Baden-Wuerttemberg, Germany (LUBW) are in the process to start implementing an operational service for monitoring SPM and Chl-a based on satellite images (Landsat 7 & 8, Sentinel 2, and if required other systems with higher spatial resolution, e.g. Rapid Eye). In this 2-years project, which is part of the European Copernicus Programme, the operational service will be set up for - the inland rivers of Rhine and Elbe - the North Sea estuaries of Elbe, Weser and Ems. Furthermore - Lake Constance and other lakes located within the Federal State of Baden-Wuerttemberg. In future, the service can be implemented for other rivers and lakes as well. Key feature of the project is a data base that holds the stock of geo-referenced maps of SPM and Chl-a distributions. Via web-based portals (e.g. GGInA - geo-portal of the BfG; UIS - environmental information system of the Federal State of Baden-Wuerttemberg; BOWIS - information system for the Lake Constance) the maps will be made accessible to the public. The aim of the project is to implement a service that automatically recognizes new satellite images covering the area of selected water systems (lake, river or estuary) and therefore is able to continually update the data base. Furthermore, the service includes a procedure to analyse newly available data with the highest possible degree of automatization. It is planned to add new maps of SPM and Chl-a distributions to the data base within a couple of days after the satellite image was taken. A high degree of automatization is the essential condition to process a large number of satellite images each year at reasonable costs. It could be demonstrated by the Freshmon Project that there are simplified but robust algorithms and procedures existing. For the successful implementation of the service, it is important to further validate the results obtained by the service line as well as the used procedure and algorithms. Therefore, several test cases will be set up. Each case is going to include an analysis of the uncertainties to describe the expected deviation between values derived from earth observation data and the in-situ data obtained from the BfG and LUBW monitoring networks. Furthermore, it will include a description of possible sources of error and the boundary conditions which are most sensitive to the analysis. Test cases are planned to be made public with all necessary data. The scientific community is invited to use the data as a benchmark test case to develop their own algorithms and procedures.

  9. Early fire detection system based on multi-temporal images of geostationary and polar satellites

    Microsoft Academic Search

    Evaristo Cisbani; Antonio Bartoloni; Marco Marchese; G. Efisei; Antonello Salvati

    2002-01-01

    A new, early fire automated alarm technique based on multi-temporal and multi-spectral analysis of satellite data is presented; it combines the multi-spectral capabilities of the TERRA\\/MODIS polar satellite to the high temporal frequency of the GOES\\/IMAGER geostationary satellite.

  10. TASAT Simulations of NASA Image Satellite to Predict the Spin Rate

    Microsoft Academic Search

    V. S. Rao Gudimetla; Eric Reinhart; Chris L. Hart

    2007-01-01

    TASAT simulations of an approximate model of NASA Image Satellite have been conducted and the resulting time domain data of the passive cross section was collected. FFT of this data shows a clear significant peak at the frequency corresponding to the spin rate of the satellite. The spin rate of the satellite has been varied in the simulations and the

  11. DETECTION OF OUTLIER PATCHES IN AUTOREGRESSIVE TIME SERIES1

    E-print Network

    Justel Eusebio, Ana

    measures based on data deletion (or equivalently, using techniques of missing value in time series analysisDETECTION OF OUTLIER PATCHES IN AUTOREGRESSIVE TIME SERIES1 Ana Justel? 2 Daniel Pe~na?? and Ruey S, sequential learning, time series. 1Short running title: Outlier patches in autoregressive processes. 2

  12. Fast Algorithms for Mining Co-evolving Time Series

    E-print Network

    to handle large data sets on modern computing hardware? We develop models to mine time series with missing center energy efficiency #12;to my parents #12;iv #12;Abstract Time series data arise in many series: 1. Forecasting and imputation: How to do forecasting and to recover missing values in time series

  13. Negative binomial time series models based on expectation thinning operators

    Microsoft Academic Search

    Rong Zhu; Harry Joe

    2010-01-01

    The study of count data time series has been active in the past decade, mainly in theory and model construction. There are different ways to construct time series models with a geometric autocorrelation function, and a given univariate margin such as negative binomial. In this paper, we investigate negative binomial time series models based on the binomial thinning and two

  14. Timeline hidden Markov experts for time series prediction

    Microsoft Academic Search

    Xin Wang; Peter Whigham; Da Deng; Martin Purvis

    2003-01-01

    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

  15. Signal versus noise in glaciological GPS time series

    Microsoft Academic Search

    M. A. King

    2006-01-01

    Noise characteristics of geodetic GPS time series have received significant attention over recent years. However, because of the different behaviour of glaciers\\/ice streams\\/ice shelves, and the generally shorter GPS time series, understanding the noise spectrum of glaciological GPS time series warrants separate investigation. It has been previously shown that incorrectly processed GPS data may result in spurious ice movement time

  16. Improved FMRI Time-series Registration Using Probability Density Priors

    E-print Network

    Fessler, Jeffrey A.

    Improved FMRI Time-series Registration Using Probability Density Priors R. Bhagalia, J. A. Fessler University of Michigan, Ann Arbor Feburary 8, 2009 (University of Michigan) Improved fMRI Time-series Registration Feburary 8, 2009 1 / 15 #12;1 Existing FMRI Time-series Registration Approaches 2 Improving SV

  17. Time Series Photometry Data: Standard Access, Standard Formats

    E-print Network

    Holl, András

    Time Series Photometry Data: Standard Access, Standard Formats Andr#19; as Holl Konkoly Observatory should be able to locate time series photometry data on a given object stored at di#11;erent locations: Data stored at CDS Strasbourg, plotted with a software tool Standard access Time series photometric

  18. Clinical time series prediction: Towards a hierarchical dynamical system framework

    E-print Network

    Hauskrecht, Milos

    Clinical time series prediction: Towards a hierarchical dynamical system framework Zitao Liu temporal models of clinical time series is important for understand- ing of the patient condition. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data

  19. Meteorological Time Series Modeling Using an Adaptive Gene Expression Programming

    E-print Network

    Fernandez, Thomas

    Meteorological Time Series Modeling Using an Adaptive Gene Expression Programming ALINA BRBULESCU of an adaptive evolutionary technique that give promising results for the development of non-linear time series models. Key-Words: time series modeling, gene expression programming, adaptive algorithm, precipitation 1

  20. Financial Time Series Rutgers Business School 26:960:576

    E-print Network

    Lin, Xiaodong

    Financial Time Series Rutgers Business School 26:960:576 Instructor: Xiaodong Lin. Email: lin time series, with an emphasis on model building and accurate prediction. Completion of this course will equip students with insights and modeling tools to analyze real world financial and business time series

  1. TEStool : A Visual Interactive Environment for Modeling Autocorrelated Time Series

    E-print Network

    TEStool : A Visual Interactive Environment for Modeling Autocorrelated Time Series Jon R. Hill TEStool is a visual interactive software environment for modeling autocorrelated time series, using time series adopted in TEStool, as well as TEStool's graphical user interface (GUI). Special emphasis

  2. Time series analysis in R D G Rossiter

    E-print Network

    Rossiter, D G "David"

    Time series analysis in R D G Rossiter Department of Earth Systems Analysis University of Twente Introduction 1 2 Loading and examining a time series 2 2.1 Example 1: Groundwater level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3 Analysis of a single time series 25 3.1 Summaries

  3. RisI1228(EN) Time Series Analysis

    E-print Network

    Risø­I­1228(EN) Time Series Analysis Dealing with Imperfect Data Leif Kristensen Risø National recording and analyzing turbulent time series in geophysics a number of practical problems are usually with trends in the time series. The effect of two types of noise, from counting and from signal quantization

  4. Priors and Component Structures in Autoregressive Time Series Models

    E-print Network

    West, Mike

    Priors and Component Structures in Autoregressive Time Series Models Gabriel Huerta and Mike West to prior speci cation and structuring in autoregressive time series models are introduced and developed. We components of an autoregressive model for an observed time series. These new priors naturally permit

  5. 9 Graphical modelling of dynamic relationships in multivariate time series

    E-print Network

    Eichler, Michael

    9 Graphical modelling of dynamic relationships in multivariate time series Michael Eichler Institut of interactions among multiple simultaneously recorded time series is an important problem in many scientific and investigating causal relationships in multivariate time series. The key role in this graphical approach

  6. Wavelet-Based Bootstrapping for Non-Gaussian Time Series

    E-print Network

    Percival, Don

    Wavelet-Based Bootstrapping for Non-Gaussian Time Series Don Percival Applied Physics Laboratory the sampling variability in statistics computed from a time series X0, X1, . . . , XN-1? · start with some correlated time series) · review one wavelet-based approach to bootstrapping (Percival, Sardy and Davison

  7. Wavelet-Based Bootstrapping for Non-Gaussian Time Series

    E-print Network

    Percival, Don

    Wavelet-Based Bootstrapping for Non-Gaussian Time Series Don Percival Applied Physics Laboratory the sampling variability in statistics computed from a time series X0, X1, . . . , XN-1? · start with some correlated time series) · review previously proposed wavelet-based approach to boot- strapping (Percival

  8. Normal-form Based Analysis of Climate Time Series

    E-print Network

    Mumby, Peter J.

    Normal-form Based Analysis of Climate Time Series Jan Sieber Department of Mathematics in collaboration with J.M.T. Thompson, FRS, School of Engineering, Aberdeen #12;Outline Time series analysis Saddle-node induced tipping Estimate of normal form parameters from time series #12;Tipping in palaeoclimate time

  9. A simple feature to characterize oscillatory nonlinear stochastic time series

    E-print Network

    Timmer, Jens

    A simple feature to characterize oscillatory nonlinear stochastic time series J. Timmer Freiburger series. For measured data, we show that the feature allows for a segmentation of time series. 05.45.+b dynamics underlying measured time series. Thus, some type of a nonlinear stochastic differential equation

  10. A TEMPORAL PATTERN APPROACH FOR PREDICTING WEEKLY FINANCIAL TIME SERIES

    E-print Network

    Povinelli, Richard J.

    A TEMPORAL PATTERN APPROACH FOR PREDICTING WEEKLY FINANCIAL TIME SERIES DAVID H. DIGGS Department time. Times series such as the stock market are often seen as non-stationary which present challenges. The method is designed to analyze non-stationary time series and provides the basis for this work. The paper

  11. Time Series Forecasting using Distribution Enhanced Linear Regression

    E-print Network

    Bailey, James

    Time Series Forecasting using Distribution Enhanced Linear Regression Goce Ristanoski1,2 , Wei Liu2}@unimelb.edu.au Abstract. Amongst the wealth of available machine learning algorithms for forecasting time series, linear. In particular, we propose segmenting the input time series into groups and simultaneously optimizing both

  12. Priors and Component Structures in Autoregressive Time Series Models

    E-print Network

    West, Mike

    Priors and Component Structures in Autoregressive Time Series Models Gabriel Huerta and Mike West to prior specification and structuring in autoregressive time series models are introduced and developed to latent components of an autoregressive model for an observed time series. These new priors naturally

  13. Competitive Principal Component Analysis for Locally Stationary Time Series

    E-print Network

    Slatton, Clint

    1 Competitive Principal Component Analysis for Locally Stationary Time Series Craig L. Fancourt algorithm is proposed that performs competitive principal component analysis (PCA) of a time series. A set-product, the time series is both segmented and identified according to stationary regions. Examples showing

  14. Comparison of time series with unequal length Jorge Caiadoa

    E-print Network

    Crato, Nuno

    1 Comparison of time series with unequal length Jorge Caiadoa Nuno Cratob and Daniel Peñac with classi...cation and clustering analysis for indepen- dent time series with unequal length. A periodogram-based statistic is used to determine whether the time series at hand are generated by the same stochastic

  15. Time Series Retrieval Using Multiple Reduced Muhammad Marwan Muhammad Fuad

    E-print Network

    Boyer, Edmond

    Time Series Retrieval Using Multiple Reduced Spaces Muhammad Marwan Muhammad Fuad Université de is one of the main problems in time series data mining. Traditionally, this problem has been tackled by sequentially comparing the given query against all the time series in the database and returning the time

  16. Streaming Time Series Summarization Using User-Defined Amnesic Functions

    E-print Network

    Palpanas, Themis

    Streaming Time Series Summarization Using User-Defined Amnesic Functions Themis Palpanas, Michail 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

  17. Progressive Horizon Graphs: Improving Small Multiples Visualization of Time Series

    E-print Network

    Paris-Sud XI, Université de

    Progressive Horizon Graphs: Improving Small Multiples Visualization of Time Series Charles Perin horizon graphs with the best baseline and a zoom factor of ten Figure 1: Three time series visualization task: "find the time series having the highest value of the three marked points." ABSTRACT Many

  18. An Association Framework to Analyze Dependence Structure in Time Series

    E-print Network

    Slatton, Clint

    An Association Framework to Analyze Dependence Structure in Time Series: Bilal H. Fadlallah1) framework that reduces the effect of temporal structure in time series; second, to assess the reliability of using associ- ation methods to capture dependence between pairs of EEG channels using their time series

  19. BIOINFORMATICS Inferring Gene Regulatory Networks from Time Series

    E-print Network

    Babu, M. Madan

    BIOINFORMATICS Inferring Gene Regulatory Networks from Time Series Data Using the Minimum of inferring genetic regulatory networks from time series gene-expression profi- les. By adopting exceptionally excels in effi- ciency, accuracy, robustness and scalability. Given a time series data set

  20. Combinatorial detection of determinism in noisy time series

    E-print Network

    Rey Juan Carlos, Universidad

    OFFPRINT Combinatorial detection of determinism in noisy time series J. M. Amig´o, S. Zambrano.epljournal.org doi: 10.1209/0295-5075/83/60005 Combinatorial detection of determinism in noisy time series J. M. Amig in final form 31 July 2008 published online 9 September 2008 PACS 05.45.Tp ­ Time series analysis PACS 05

  1. Robust detection of periodic time series measured from biological systems

    Microsoft Academic Search

    Miika Ahdesmäki; Harri Lähdesmäki; Ronald K. Pearson; Heikki Huttunen; Olli Yli-harja

    2005-01-01

    BACKGROUND: Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from

  2. Singular spectrum analysis for time series with missing data

    Microsoft Academic Search

    David H. Schoellhamer

    2001-01-01

    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

  3. Characteristic-Based Clustering for Time Series Data

    Microsoft Academic Search

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

    2006-01-01

    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

  4. Financial Time Series Forecasting Using KNearest Neighbors Classification \\Lambda

    E-print Network

    Giles, C. Lee

    Financial Time Series Forecasting Using K­Nearest Neighbors Classification \\Lambda M. Maggini a , C. This is particularly true for the analysis and for all the attempts in forecasting financial time series. It seems terms: Nearest­neighbors, Financial time series. 1 Introduction The forecasting of the future values

  5. Advanced Time Series Analysis: MATH5805, Special Topic in Mathematics

    E-print Network

    Blennerhassett, Peter

    and forecasting. The topics covered are applicable to modelling a single response time series with covariates, particularly policy changes, relevant to policy evaluation, and modelling financial time series at variousAdvanced Time Series Analysis: MATH5805, Special Topic in Mathematics Course Outline: This course

  6. Quarterly Time-Series Forecasting With Neural Networks

    Microsoft Academic Search

    G. Peter Zhang; Douglas M. Kline

    2007-01-01

    Abstract—Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach to forecasting quarterly time series. With a large data set of 756 quarterly time series from the M3 forecasting competition, we conduct a comprehensive investigation of the effectiveness of several data preprocessing and modeling approaches. We

  7. A decadal observation of vegetation dynamics using multi-resolution satellite images

    NASA Astrophysics Data System (ADS)

    Chiang, Yang-Sheng; Chen, Kun-Shan; Chu, Chang-Jen

    2012-10-01

    Vegetation cover not just affects the habitability of the earth, but also provides potential terrestrial mechanism for mitigation of greenhouse gases. This study aims at quantifying such green resources by incorporating multi-resolution satellite images from different platforms, including Formosat-2(RSI), SPOT(HRV/HRG), and Terra(MODIS), to investigate vegetation fractional cover (VFC) and its inter-/intra-annual variation in Taiwan. Given different sensor capabilities in terms of their spatial coverage and resolution, infusion of NDVIs at different scales was used to determine fraction of vegetation cover based on NDVI. Field campaign has been constantly conducted on a monthly basis for 6 years to calibrate the critical NDVI threshold for the presence of vegetation cover, with test sites covering IPCC-defined land cover types of Taiwan. Based on the proposed method, we analyzed spatio- temporal changes of VFC for the entire Taiwan Island. A bimodal sequence of VFC was observed for intra-annual variation based on MODIS data, with level around 5% and two peaks in spring and autumn marking the principal dual-cropping agriculture pattern in southwestern Taiwan. Compared to anthropogenic-prone variation, the inter-annual VFC (Aug.-Oct.) derived from HRV/HRG/RSI reveals that the moderate variations (3%) and the oscillations were strongly linked with regional climate pattern and major disturbances resulting from extreme weather events. Two distinct cycles (2002-2005 and 2005-2009) were identified in the decadal observations, with VFC peaks at 87.60% and 88.12% in 2003 and 2006, respectively. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

    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.

  9. Accuracy assessment of topographic mapping using UAV image integrated with satellite images

    NASA Astrophysics Data System (ADS)

    Azmi, S. M.; Ahmad, Baharin; Ahmad, Anuar

    2014-02-01

    Unmanned Aerial Vehicle or UAV is extensively applied in various fields such as military applications, archaeology, agriculture and scientific research. This study focuses on topographic mapping and map updating. UAV is one of the alternative ways to ease the process of acquiring data with lower operating costs, low manufacturing and operational costs, plus it is easy to operate. Furthermore, UAV images will be integrated with QuickBird images that are used as base maps. The objective of this study is to make accuracy assessment and comparison between topographic mapping using UAV images integrated with aerial photograph and satellite image. The main purpose of using UAV image is as a replacement for cloud covered area which normally exists in aerial photograph and satellite image, and for updating topographic map. Meanwhile, spatial resolution, pixel size, scale, geometric accuracy and correction, image quality and information contents are important requirements needed for the generation of topographic map using these kinds of data. In this study, ground control points (GCPs) and check points (CPs) were established using real time kinematic Global Positioning System (RTK-GPS) technique. There are two types of analysis that are carried out in this study which are quantitative and qualitative assessments. Quantitative assessment is carried out by calculating root mean square error (RMSE). The outputs of this study include topographic map and orthophoto. From this study, the accuracy of UAV image is ± 0.460 m. As conclusion, UAV image has the potential to be used for updating of topographic maps.

  10. Detection of cavity migration risks using radar interferometric time series

    NASA Astrophysics Data System (ADS)

    Chang, L.; Hanssen, R. F.

    2012-12-01

    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.

  11. One goal of examing satellite images is to derive maps of earth Digital Image Classification Has Two Components

    E-print Network

    Frank, Thomas D.

    One goal of examing satellite images is to derive maps of earth #12;Digital Image Classification Image Classification The objective of digital image classification is to partition feature space into decision regions, then to assign pixels in an image to the most likely feature category. #12;By selecting

  12. Special Sensor Ultraviolet Limb Imager: An ionospheric and neutral density profiler for the Defense Meteorological Satellite Program satellites

    Microsoft Academic Search

    Robert P. McCoy; K. F. Dymond; G. G. Fritz; S. E. Thonnard; R. R. Meier; P. A. Regeon

    1994-01-01

    The Naval Research Laboratory is developing a series of far-and extreme-ultraviolet spectrographs (800 to 1,700 [angstrom]) to measure altitude profiles of the ionospheric and thermospheric airglow from the US Air Force Defense Meteorological Satellite Program's Block 5D3 satellites. These spectrographs, which comprise the Special Sensor Ultraviolet Limb Imager (SSULI), use a near-Wadsworth optical configuration with a mechanical grid collimator, concave

  13. Special Sensor Ultraviolet Limb Imager: an ionospheric and neutral density profiler for the Defense Meteorological Satellite Program satellites

    Microsoft Academic Search

    Robert P. McCoy; Kenneth F. Dymond; Gilbert G. Fritz; Stefan E. Thonnard; Robert R. Meier; Paul A. Regeon

    1994-01-01

    The Naval Research Laboratory is developing a series of far- and extreme-ultraviolet spectrographs (800 to 1700 angstroms) to measure altitude profiles of the ionospheric and thermospheric airglow from the U.S. Air Force Defense Meteorological Satellite Program's Block 5D3 satellites. These spectrographs, **** the Special Sensor Ultraviolet Limb Imager (SSULI), use a near-Wadsworth optical configuration with a mechanical grid collimator, concave

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    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.

  15. NORCAMA: Change Analysis in SAR Time Series by Likelihood Ratio Change Matrix , Charles-Alban Deledalle2

    E-print Network

    Paris-Sud XI, Université de

    NORCAMA: Change Analysis in SAR Time Series by Likelihood Ratio Change Matrix Clustering Xin Su1 and classification for synthetic aperture radar (SAR) time series, namely NORmalized Cut on chAnge criterion MAtrix on both synthetic and real SAR image series show the effective performance of the proposed framework

  16. A FRAMEWORK FOR AUTOMATIC LOW-RESOLUTION SATELLITE IMAGE INTERPRETATION BASED ON SPECTRAL, CONTEXTUAL

    E-print Network

    . Although such packages offer digital image processing and pattern recognition tools, they lackA FRAMEWORK FOR AUTOMATIC LOW-RESOLUTION SATELLITE IMAGE INTERPRETATION BASED ON SPECTRAL, Agriculture, Automation, Knowledge base, Fuzzy Logic, Multitemporal ABSTRACT: This work proposes a framework

  17. Spectrophotometric Time Series of ? Carinae's Great Eruption

    NASA Astrophysics Data System (ADS)

    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

    ? 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.

  18. Evolutionary factor analysis of replicated time series.

    PubMed

    Motta, Giovanni; Ombao, Hernando

    2012-09-01

    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

  19. A New SBUV Ozone Profile Time Series

    NASA Technical Reports Server (NTRS)

    McPeters, Richard

    2011-01-01

    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.

  20. Time series for blind biosignal classification model.

    PubMed

    Wong, Derek F; Chao, Lidia S; Zeng, Xiaodong; Vai, Mang-I; Lam, Heng-Leong

    2014-11-01

    Biosignals such as electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG), are important noninvasive measurements useful for making diagnostic decisions. Recently, considerable research has been conducted in order to potentially automate signal classification for assisting in disease diagnosis. However, the biosignal type (ECG, EEG, EMG or other) needs to be known prior to the classification process. If the given biosignal is of an unknown type, none of the existing methodologies can be utilized. In this paper, a blind biosignal classification model (B(2)SC Model) is proposed in order to identify the source biosignal type automatically, and thus ultimately benefit the diagnostic decision. The approach employs time series algorithms for constructing the model. It uses a dynamic time warping (DTW) algorithm with clustering to discover the similarity between two biosignals, and consequently classifies disease without prior knowledge of the source signal type. The empirical experiments presented in this paper demonstrate the effectiveness of the method as well as the scalability of the approach. PMID:25199847

  1. Proxy-based handheld device access to live NASA satellite weather images

    Microsoft Academic Search

    Min Wu; Jianfei Cai; Chang W. Chen

    2003-01-01

    We present a new proxy-based system to allow public use handheld devices to access instantly to the Low Resolution Picture Taking (LRPT) satellite weather image data and display regional weather image. First the location-based transcoding is done from non frame based satellite image to frame based CIF\\/QCIF image for handheld device. GPS information from expansion module is used in the

  2. Hybrid Image Segmentation based on Fuzzy Clustering Algorithm for Satellite Imagery Searching and Retrieval

    Microsoft Academic Search

    W. S. Ooi; C. P. Lim

    2004-01-01

    Satellite image processing is a complex task that has received considerable attention from many researchers. In this paper,\\u000a an interactive image query system for satellite imagery searching and retrieval is proposed. Like most image retrieval systems,\\u000a extraction of image features is the most important step that has a great impact on the retrieval performance. Thus, a new\\u000a technique that fuses

  3. SATELLITE AND AERIAL IMAGE DECONVOLUTION USING AN EM METHOD WITH COMPLEX WAVELETS

    E-print Network

    Figueiredo, Mário A. T.

    SATELLITE AND AERIAL IMAGE DECONVOLUTION USING AN EM METHOD WITH COMPLEX WAVELETS André Jalobeanu step is a Landweber update iteration, and the M step consists of denoising the image, which is achieved by wavelet coefficient thresholding. The new algorithm has been applied to high resolution satellite

  4. Feature Selection in AVHRR Ocean Satellite Images by Means of Filter Methods

    Microsoft Academic Search

    Jose A. Piedra-Fernández; Manuel Cantón-Garbín; James Z. Wang

    2010-01-01

    Automatic retrieval and interpretation of satellite images is critical for managing the enormous volume of environmental remote sensing data available today. It is particularly useful in oceanography and climate studies for examination of the spatio-temporal evolution of mesoscalar ocean structures appearing in the satellite images taken by visible, infrared, and radar sensors. This is because they change so quickly and

  5. Lossless Compression of Satellite Image Sets Using Spatial Area Overlap Compensation

    E-print Network

    Cheng, Howard

    satellite [3]. These images are taken in a cloud-free environment and each pixel represents a 10m × 10m areaLossless Compression of Satellite Image Sets Using Spatial Area Overlap Compensation Vivek Trivedi and Howard Cheng Department of Mathematics and Computer Science University of Lethbridge Lethbridge, Alberta

  6. THE ROLE OF EPHEMERIDES AND GCPs DISTRIBUTION IN HIGH RESOLUTION SATELLITE IMAGES MODELLING

    Microsoft Academic Search

    M. Fiani; P. Pistillo; S. Troisi; L. Turturici

    The geometric resolution of the images coming from the new generation satellites is almost competitive with that found in the trational aerial photograms. The aim of this work is to define the role of satellite ephemerides and to optimise the number and distribution of Ground Control Points (GCPs) for the image registration. A zone of the Campania region in Italy

  7. Satellite image analysis for surveillance, vegetation and climate change

    SciTech Connect

    Cai, D Michael [Los Alamos National Laboratory

    2011-01-18

    Recently, many studies have provided abundant evidence to show the trend of tree mortality is increasing in many regions, and the cause of tree mortality is associated with drought, insect outbreak, or fire. Unfortunately, there is no current capability available to monitor vegetation changes, and correlate and predict tree mortality with CO{sub 2} change, and climate change on the global scale. Different survey platforms (methods) have been used for forest management. Typical ground-based forest surveys measure tree stem diameter, species, and alive or dead. The measurements are low-tech and time consuming, but the sample sizes are large, running into millions of trees, covering large areas, and spanning many years. These field surveys provide powerful ground validation for other survey methods such as photo survey, helicopter GPS survey, and aerial overview survey. The satellite imagery has much larger coverage. It is easier to tile the different images together, and more important, the spatial resolution has been improved such that close to or even higher than aerial survey platforms. Today, the remote sensing satellite data have reached sub-meter spatial resolution for panchromatic channels (IKONOS 2: 1 m; Quickbird-2: 0.61 m; Worldview-2: 0.5 m) and meter spatial resolution for multi-spectral channels (IKONOS 2: 4 meter; Quickbird-2: 2.44 m; Worldview-2: 2 m). Therefore, high resolution satellite imagery can allow foresters to discern individual trees. This vital information should allow us to quantify physiological states of trees, e.g. healthy or dead, shape and size of tree crowns, as well as species and functional compositions of trees. This is a powerful data resource, however, due to the vast amount of the data collected daily, it is impossible for human analysts to review the imagery in detail to identify the vital biodiversity information. Thus, in this talk, we will discuss the opportunities and challenges to use high resolution satellite imagery and machine learning theory to monitor tree mortality at the level of individual trees.

  8. InSAR time series shows multiple deformation and interaction of gravitational spreading, intrusion and compaction on Hawaii Island

    NASA Astrophysics Data System (ADS)

    Shirzaei, M.; Walter, T. R.

    2009-04-01

    Hawaii Island comprising some of the world most active volcanoes shows a complex deformation field. The two active volcanoes Mauna Loa and Kilauea are subject to magma emplacement and the islands' flanks are subject to gravitational spreading and landslide process. An island wide deformation analysis, however, and potential interaction of different sources was not systematically elaborated, yet. Using a newly developed Wavelet based InSAR time series (WAB-InSAR) approach we mapped the spatio-temporal deformation field in the period of 2003 and 2008. In this study we have utilized 30 radar images acquired by ENVISAT satellite in descending mode. The deformation time series shows different episodes of the uplift and subsidence over Mauna Loa and Kilauea volcano. At last to investigate the source of deformation we applied a time dependent non linear multiple source modeling approach based on continuous genetic algorithm. The result of this research show that volcano-tectonic signals are co-occurring, implying complex interaction of various process on Hawaii Island.

  9. Geostatistical Analysis of Surface Temperature and In-Situ Soil Moisture Using LST Time-Series from Modis

    NASA Astrophysics Data System (ADS)

    Sohrabinia, M.; Rack, W.; Zawar-Reza, P.

    2012-07-01

    The objective of this analysis is to provide a quantitative estimate of the fluctuations of land surface temperature (LST) with varying near surface soil moisture (SM) on different land-cover (LC) types. The study area is located in the Canterbury Plains in the South Island of New Zealand. Time series of LST from the MODerate resolution Imaging Spectro-radiometer (MODIS) have been analysed statistically to study the relationship between the surface skin temperature and near-surface SM. In-situ measurements of the skin temperature and surface SM with a quasi-experimental design over multiple LC types are used for validation. Correlations between MODIS LST and in-situ SM, as well as in-situ surface temperature and SM are calculated. The in-situ measurements and MODIS data are collected from various LC types. Pearson's r correlation coefficient and linear regression are used to fit the MODIS LST and surface skin temperature with near-surface SM. There was no significant correlation between time-series of MODIS LST and near-surface SM from the initial analysis, however, careful analysis of the data showed significant correlation between the two parameters. Night-time series of the in-situ surface temperature and SM from a 12 hour period over Irrigated-Crop, Mixed-Grass, Forest, Barren and Open- Grass showed inverse correlations of -0.47, -0.68, -0.74, -0.88 and -0.93, respectively. These results indicated that the relationship between near-surface SM and LST in short-terms (12 to 24 hours) is strong, however, remotely sensed LST with higher temporal resolution is required to establish this relationship in such time-scales. This method can be used to study near-surface SM using more frequent LST observations from a geostationary satellite over the study area.

  10. 3D displacement time series in the Afar rift zone computed from SAR phase and amplitude information

    NASA Astrophysics Data System (ADS)

    Casu, Francesco; Manconi, Andrea

    2013-04-01

    Large and rapid deformations, such as those caused by earthquakes, eruptions, and landslides cannot be fully measured by using standard DInSAR applications. Indeed, the phase information often degrades and some areas of the interferograms are affected by high fringe rates, leading to difficulties in the phase unwrapping, and/or to complete loss of coherence due to significant misregistration errors. This limitation can be overcome by exploiting the SAR image amplitude information instead of the phase, and by calculating the Pixel-Offset (PO) field SAR image pairs, for both range and azimuth directions. Moreover, it is possible to combine the PO results by following the same rationale of the SBAS technique, to finally retrieve the offset-based deformation time series. Such technique, named PO-SBAS, permits to retrieve the deformation field in areas affected by very large displacements at an accuracy that, for ENVISAT data, correspond to 30 cm and 15 cm for the range and azimuth, respectively [1]. Moreover, the combination of SBAS and PO-SBAS time series can help to better study and model deformation phenomena characterized by spatial and temporal heterogeneities [2]. The Dabbahu rift segment of the Afar depression has been active since 2005 when a 2.5 km3 dyke intrusion and hundreds of earthquakes marked the onset a rifting episode which continues to date. The ENVISAT satellite has repeatedly imaged the Afar depression since 2003, generating a large SAR archive. In this work, we study the Afar rift region deformations by using both the phase and amplitude information of several sets of SAR images acquired from ascending and descending ENVISAT tracks. We combined sets of small baseline interferograms through the SBAS algorithm, and we generate both ground deformation maps and time series along the satellite Line-Of-Sight (LOS). In areas where the deformation gradient causes loss of coherence, we retrieve the displacement field through the amplitude information. Furthermore, we could also retrieve the full 3D deformation field, by considering the North-South displacement component obtained from the azimuth PO information. The combination of SBAS and PO-SBAS information permits to better retrieve and constrain the full deformation field due to repeated intrusions, fault movements, as well as the magma movements from individual magma chambers. [1] Casu, F., A. Manconi, A. Pepe and R. Lanari, 2011. Deformation time-series generation in areas characterized by large displacement dynamics: the SAR amplitude Pixel-Offset SBAS technique, IEEE Transaction on Geosciences and Remote Sensing. [2] Manconi, A. and F. Casu, 2012. Joint analysis of displacement time series retrieved from SAR phase and amplitude: impact on the estimation of volcanic source parameters, Geophysical Research Letters, doi:10.1029/2012GL052202.

  11. Intercomparison of six Mediterranean zooplankton time series

    NASA Astrophysics Data System (ADS)

    Berline, Léo; Siokou-Frangou, Ioanna; Marasovi?, Ivona; Vidjak, Olja; Fernández de Puelles, M.a. Luz; Mazzocchi, Maria Grazia; Assimakopoulou, Georgia; Zervoudaki, Soultana; Fonda-Umani, Serena; Conversi, Alessandra; Garcia-Comas, Carmen; Ibanez, Frédéric; Gasparini, Stéphane; Stemmann, Lars; Gorsky, Gabriel

    2012-05-01

    We analyzed and compared Mediterranean mesozooplankton time series spanning 1957-2006 from six coastal stations in the Balearic, Ligurian, Tyrrhenian, North and Middle Adriatic and Aegean Sea. Our analysis focused on fluctuations of major zooplankton taxonomic groups and their relation with environmental and climatic variability. Average seasonal cycles and interannual trends were derived. Stations spanned a large range of trophic status from oligotrophic to moderately eutrophic. Intra-station analyses showed (1) coherent multi-taxa trends off Villefranche sur mer that diverge from the previous results found at species level, (2) in Baleares, covariation of zooplankton and water masses as a consequence of the boundary hydrographic regime in the middle Western Mediterranean, (3) decrease in trophic status and abundance of some taxonomic groups off Naples, and (4) off Athens, an increase of zooplankton abundance and decrease in chlorophyll possibly caused by reduction of anthropogenic nutrient input, increase of microbial components, and more efficient grazing control on phytoplankton. (5) At basin scale, the analysis of temperature revealed significant positive correlations between Villefranche, Trieste and Naples for annual and/or winter average, and synchronous abrupt cooling and warming events centered in 1987 at the same three sites. After correction for multiple comparisons, we found no significant correlations between climate indices and local temperature or zooplankton abundance, nor between stations for zooplankton abundance, therefore we suggest that for these coastal stations local drivers (climatic, anthropogenic) are dominant and that the link between local and larger scale of climate should be investigated further if we are to understand zooplankton fluctuations.

  12. High Resolution Imaging of Satellites with Ground-Based 10-m Astronomical Telescopes

    SciTech Connect

    Marois, C

    2007-01-04

    High resolution imaging of artificial satellites can play an important role in current and future space endeavors. One such use is acquiring detailed images that can be used to identify or confirm damage and aid repair plans. It is shown that a 10-m astronomical telescope equipped with an adaptive optics system (AO) to correct for atmospheric turbulence using a natural guide star can acquire high resolution images of satellites in low-orbits using a fast shutter and a near-infrared camera even if the telescope is not capable of tracking satellites. With the telescope pointing towards the satellite projected orbit and less than 30 arcsec away from a guide star, multiple images of the satellite are acquired on the detector using the fast shutter. Images can then be shifted and coadded by post processing to increase the satellite signal to noise ratio. Using the Keck telescope typical Strehl ratio and anisoplanatism angle as well as a simple diffusion/reflection model for a satellite 400 km away observed near Zenith at sunset or sunrise, it is expected that such system will produced > 10{sigma} K-band images at a resolution of 10 cm inside a 60 arcsec diameter field of view. If implemented, such camera could deliver the highest resolution satellite images ever acquired from the ground.

  13. On studying relations between time series in climatology

    NASA Astrophysics Data System (ADS)

    Privalsky, V.

    2015-06-01

    Relationships between time series are often studied on the basis of cross-correlation coefficients and regression equations. This approach is generally incorrect for time series, irrespective of the cross-correlation coefficient value, because relations between time series are frequency-dependent. Multivariate time series should be analyzed in both time and frequency domains, including fitting a parametric (preferably, autoregressive) stochastic difference equation to the time series and then calculating functions of frequency such as spectra and coherent spectra, coherences, and frequency response functions. The example with a bivariate time series "Atlantic Multidecadal Oscillation (AMO) - sea surface temperature in Niño area 3.4 (SST3.4)" proves that even when the cross correlation is low, the time series' components can be closely related to each other. A full time and frequency domain description of this bivariate time series is given. The AMO-SST3.4 time series is shown to form a closed-feedback loop system with a 2-year memory. The coherence between AMO and SST3.4 is statistically significant at intermediate frequencies where the coherent spectra amount up to 55 % of the total spectral densities. The gain factors are also described. Some recommendations are offered regarding time series analysis in climatology.

  14. On studying relations between time series in climatology

    NASA Astrophysics Data System (ADS)

    Privalsky, V.

    2015-04-01

    In climatology, relationships between time series are often studied on the basis of crosscorrelation coefficients and regression equations. This approach is generally incorrect for time series irrespective of the crosscorrelation coefficient value because relations between time series are frequency-dependent. Multivariate time series should be analyzed in both time and frequency domains, including fitting a parametric (preferably, autoregressive) stochastic difference equation to the time series and then calculating functions of frequency such as spectra and coherent spectra, coherences, and frequency response functions. The example with a bivariate time series "Atlantic Multidecadal Oscillation (AMO) - sea surface temperature in Niño area 3.4 (SST3.4)" proves that even when the crosscorrelation is low, the time series' components can be closely related to each other. A full time and frequency domain description of this bivariate time series is given. The AMO - SST3.4 time series is shown to form a closed feedback loop system. The coherence between AMO and SST3.4 is statistically significant at intermediate frequencies where the coherent spectra amount up to 55% of the total spectral densities. The gain factors are also described. Some recommendations are offered regarding time series analysis in climatology.

  15. Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm

    Microsoft Academic Search

    Diofantos G. Hadjimitsis

    2009-01-01

    The aim of this study is to present and apply the proposed algorithm on archived time series Landsat TM over an urban area\\u000a in the vicinity of Heathrow Airport (UK) acquired in 1984–1986 and to two up-to-dated Landsat TM images in the vicinity of\\u000a Paphos Airport in Cyprus acquired in July–August 2008. The monitoring of aerosol concentrations becomes a high

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

    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.

  17. Territorial development using GIS and satellite images in southeast Spain

    NASA Astrophysics Data System (ADS)

    Bienvenido, Fernando; Flores-Parra, Isabel M.; Diaz-Alvarez, Jose R.

    2003-03-01

    Almeria, holding the only dessert area of continental Europe, is a semiarid province located southeast the Iberian Peninsula. The development status of the different counties of the province is widely different. Near the coast, greenhouse growing and tourism have generated an extremely active economic area. On the other side, inner areas of the province have gone down economically, with the reduction of mining and classic grows. In order to analyze new development alternatives, we built ESTIARA-Sig for the Ministry of Agriculture of Andalusia; our main objective developing this GIS was to catalog the different resources of the whole province to support development decisions. It includes four types of information: a) alphanumeric data (36 groups/tables, relative to statistics and resources), b) vectorial data (including the cartography), c) raster geographical data (obtained from satellite images, they let us to differentiate specifically greenhouse growing, populated and other areas), and d) photographic images (including types of constructions or special locations). A dynamic user interface was added to facilitate its use. In this work, we present main characteristics of the system and analyze their use along last six years, presenting as conclusions the experience obtained in order to develop a new version.

  18. Coseismic and Postseismic Deformation from the August 2014 Mw 6.0 South Napa Earthquake Measured with InSAR Time Series

    NASA Astrophysics Data System (ADS)

    Fielding, E. J.; Milillo, P.; Yun, S. H.; Burgmann, R.; Samsonov, S. V.

    2014-12-01

    The Mw 6.0 South Napa Earthquake struck Napa County of California on 24 August 2014 with extensive surface ruptures mapped in the field and with satellite and airborne interferometric synthetic aperture radar (InSAR). The Italian Space Agency's (ASI) has been acquiring COSMO-SkyMed™ (CSK) synthetic aperture radar (SAR) images of the Napa area since June 2013 with two different look directions (satellite moving south looking west, and satellite moving north and looking east). The Canadian Space Agency and partner MDA have been acquiring RADARSAT-2 (RS2) SAR images over the San Francisco area for several years, with partial coverage of the southern part of Napa County. Preliminary time series analysis of the CSK and RS2 SAR image time series before the 2014 earthquake shows moderate rates of surface deformation likely related to variations in ground water levels. By the end of September 2014, there were 6 CSK scenes on the descending track (first on 27 August 02:08 UTC) and two scenes on the ascending track (first on 3 September 13:55 UTC) acquired after the earthquake. We use GIAnT to extend the time series analysis across the time of the earthquake and calculate a better estimate of the coseismic deformation as a step function in the time interval between the date of the last pre-quake scene and first post-quake scene, plus a postseismic deformation time function. With the 6 CSK descending track scenes, the time series estimate of the coseismic deformation has much less of the atmospheric effects that are present in single interferograms, but the 2 CSK ascending track scenes do not provide as much improvement. The postseismic single interferograms are useful for seeing the early postseismic deformation. We observe rapid afterslip on both the ascending and descending interferograms, concentrated at shallow depth on the southern part of the main coseismic rupture, extending about 4 km north of the epicenter. We also observe what appears to be poroelastic rebound in the area west of the southern part of the rupture, which needs confirmation from additional observations and modeling.

  19. Automatic Registration of Satellite Images LEILA M. G. FONSECA 1 MAX H. M. COSTA 2

    E-print Network

    registration is one of the basic image processing operations in remote sensing. With the increase in the number maxima. The algorithm was tested on SPOT and TM images from forest, urban and agricultural areas. In all, satellite image. 1 Introduction Image registration is the process of matching two im­ ages so

  20. Automatic Registration of Satellite Images LEILA M. G. FONSECA1 MAX H. M. COSTA2

    E-print Network

    registration is one of the basic image processing operations in remote sensing. With the increase in the number maxima. The algorithm was tested on SPOT and TM images from forest, urban and agricultural areas. In all, satellite image. 1 Introduction Image registration is the process of matching two im- ages so

  1. Satellite image segmentation using Self Organizing Maps and Fuzzy C-Means

    Microsoft Academic Search

    Mohamad M. Awad; Ahmad Nasri

    2009-01-01

    The quality of image interpretation depends strongly on the segmentation process which is an important step in image processing. Most of the segmentation methods and approaches are not suitable for noisy environments such as satellite images of high resolution. Sometime they require a priori knowledge, and another time they do not work on all types of images. Self-organizing maps (SOMs)

  2. Satellite images geometric correction based on non-parametric algorithms and self-extracted GCPs

    Microsoft Academic Search

    Marco Gianinetto; Marco Scaioni; Enrico Borgogno Mondino; Fabio Giulio Tonolo

    2004-01-01

    The geometric correction of high resolution satellite images can be carried out through generic non-parametric models that relates image to terrain coordinates. Traditional approaches to image geocoding rely on the measurement of a sufficient number of GCPs in both the ground and the image reference systems. Non-parametric models require a large number of GCPs well distributed on the whole scene,

  3. Design of an Image Motion Compenstaion (IMC) Algorithm for Image Registration of the Communication, Ocean, Meteorolotical Satellite (COMS)-1

    NASA Astrophysics Data System (ADS)

    Jung, Taek Seo; Park, Sang-Young; Lee, Un-Seob; Ju, Gwanghyeok; Yang, Koon Ho

    2006-03-01

    This paper presents an Image Motion Compensation (IMC) algorithm for the Korea's Communication, Ocean, and Meteorological Satellite (COMS)-1. An IMC algorithm is a priority component of image registration in Image Navigation and Registration (INR) system to locate and register radiometric image data. Due to various perturbations, a satellite has orbit and attitude errors with respect to a reference motion. These errors cause depointing of the imager aiming direction, and in consequence cause image distortions. To correct the depointing of the imager aiming direction, a compensation algorithm is designed by adapting different equations from those used for the GOES satellites. The capability of the algorithm is compared with that of existing algorithm applied to the GOES's INR system. The algorithm developed in this paper improves pointing accuracy by 40%, and efficiently compensates the depointings of the imager aiming direction.

  4. General properties of transcriptional time series in Escherichia coli

    Microsoft Academic Search

    Lok-hang So; Anandamohan Ghosh; Chenghang Zong; Leonardo A Sepúlveda; Ronen Segev; Ido Golding

    2011-01-01

    Gene activity is described by the time series of discrete, stochastic mRNA production events. This transcriptional time series shows intermittent, bursty behavior. One consequence of this temporal intricacy is that gene expression can be tuned by varying different features of the time series. Here we quantify copy-number statistics of mRNA from 20 Escherichia coli promoters using single-molecule fluorescence in situ

  5. Non-parametric causal inference for bivariate time series

    E-print Network

    McCracken, James M

    2015-01-01

    We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.

  6. Spectral estimation for locally stationary time series with missing observations

    Microsoft Academic Search

    Marina I. Knight; Matthew A. Nunes; Guy P. Nason

    Time series arising in practice often have an inherently irregular sampling structure or missing values, that can arise for\\u000a example due to a faulty measuring device or complex time-dependent nature. Spectral decomposition of time series is a traditionally\\u000a useful tool for data variability analysis. However, existing methods for spectral estimation often assume a regularly-sampled\\u000a time series, or require modifications to

  7. Linguistic time series forecasting using fuzzy recurrent neural network

    Microsoft Academic Search

    Rafik A. Aliev; Bijan Fazlollahi; R. R. Aliev; Babek Guirimov

    2008-01-01

    It is known that one of the most spread forecasting methods is the time series analysis. A weakness of traditional crisp time\\u000a series forecasting methods is that they process only measurement based numerical information and cannot deal with the perception-based\\u000a historical data represented by linguistic values. Application of a new class of time series, a fuzzy time series whose values

  8. Using GARCH-GRNN Model to Forecast Financial Time Series

    Microsoft Academic Search

    Weimin Li; Jianwei Liu; Jiajin Le

    2005-01-01

    \\u000a Recent researches in forecasting with generalized regression neural network (GRNN) suggest that GRNN can be a promising alternative\\u000a to the linear and nonlinear time series models. It has shown great abilities in modeling and forecasting nonlinear time series.\\u000a Generalized autoregressive conditional heteroscedastic (GARCH) model is a popular time series model in forecasting volatility\\u000a of financial returns. In this paper, a

  9. Two algorithms to fill cloud gaps in LST time series

    NASA Astrophysics Data System (ADS)

    Frey, Corinne; Kuenzer, Claudia

    2013-04-01

    Cloud contamination is a challenge for optical remote sensing. This is especially true for the recording of a fast changing radiative quantity like land surface temperature (LST). The substitution of cloud contaminated pixels with estimated values - gap filling - is not straightforward but possible to a certain extent, as this research shows for medium-resolution time series of MODIS data. Area of interest is the Upper Mekong Delta (UMD). The background for this work is an analysis of the temporal development of 1-km LST in the context of the WISDOM project. The climate of the UMD is characterized by peak rainfalls in the summer months, which is also the time where cloud contamination is highest in the area. Average number of available daytime observations per pixel can go down to less than five for example in the month of June. In winter the average number may reach 25 observations a month. This situation is not appropriate to the calculation of longterm statistics; an adequate gap filling method should be used beforehand. In this research, two different algorithms were tested on an 11 year time series: 1) a gradient based algorithm and 2) a method based on ECMWF era interim re-analysis data. The first algorithm searches for stable inter-image gradients from a given environment and for a certain period of time. These gradients are then used to estimate LST for cloud contaminated pixels in each acquisition. The estimated LSTs are clear-sky LSTs and solely based on the MODIS LST time series. The second method estimates LST on the base of adapted ECMWF era interim skin temperatures and creates a set of expected LSTs. The estimated values were used to fill the gaps in the original dataset, creating two new daily, 1 km datasets. The maps filled with the gradient based method had more than the double amount of valid pixels than the original dataset. The second method (ECMWF era interim based) was able to fill all data gaps. From the gap filled data sets then monthly mean, anomaly, and trend maps were calculated. The accuracy of these two gap filling methods was assessed calculating RMS, mean absolute differences (MAD), and r2 of modelled values versus original MODIS LST values for clear-sky pixels only. These first statistical values showed that the adapted era interim data suites well to fill the data gaps. The gradient based method however should be used more carefully.

  10. Change detection for visual satellite inspection using pose estimation and image synthesis

    NASA Astrophysics Data System (ADS)

    Buffington, Ryan L.; McInroy, John E.

    2011-06-01

    Satellites are subject to harsh lighting conditions which make visual inspection difficult. Automated systems which detect changes in the appearance of a satellite can generate false positives in the presence of intense shadows and specular reflections. This paper presents a new algorithm which can detect visual changes to a satellite in the presence of these lighting conditions. The position and orientation of the satellite with respect to the camera, or pose, is estimated using a new algorithm. Unlike many other pose estimation algorithms which attempt to reduce image reprojection error, this algorithm minimizes the sum of the weighted 3-dimensional error of the points in the image. Each inspection image is compared to many different views of the satellite, so that pose may be estimated regardless of which side of the satellite is facing the camera. The features in the image used to generate the pose estimate are chosen automatically using the scale-invariant feature transform. It is assumed that a good 3-dimensional model of the satellite was recorded prior to launch. Once the pose between the camera and the satellite have been estimated, the expected appearance of the satellite under the current lighting conditions is generated using a raytracing system and the 3-dimensional model. Finally, this estimate is compared with the image obtained from the camera. The ability of the algorithm to detect changes in the external appearance of satellites was evaluated using several test images exhibiting varying lighting and pose conditions. The test images included images containing shadows and bright specular reflections.

  11. Hidden Markov model segmentation of hydrological and enviromental time series

    E-print Network

    Ath. Kehagias

    2002-06-25

    Motivated by Hubert's segmentation procedure 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 computationally feasible for even longer time series. The segmentation algorithm computes the Maximum Likelihood segmentation by use of an expectation / maximization iteration. We rigorously prove algorithm convergence and use numerical experiments, involving temperature and river discharge time series, to show that the algorithm usually converges to the globally optimal segmentation. The relation of the proposed algorithm to Hubert's segmentation procedure is also discussed.

  12. Scene Context Dependency of Pattern Constancy of Time Series Imagery

    NASA Technical Reports Server (NTRS)

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

    2008-01-01

    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.

  13. Time series photometry of faint cataclysmic variables with a CCD

    NASA Astrophysics Data System (ADS)

    Abbott, Timothy Mark Cameron

    1992-08-01

    I describe a new hardware and software environment for the practice of time-series stellar photometry with the CCD systems available at McDonald Observatory. This instrument runs suitable CCD's in frame transfer mode and permits windowing on the CCD image to maximize the duty cycle of the photometer. Light curves may be extracted and analyzed in real time at the telescope and image data are stored for later, more thorough analysis. I describe a star tracking algorithm, which is optimized for a timeseries of images of the same stellar field. I explore the extraction of stellar brightness measures from these images using circular software apertures and develop a complete description of the noise properties of this technique. I show that scintillation and pixelization noise have a significant effect on high quality observations. I demonstrate that optimal sampling and profile fitting techniques are unnecessarily complex or detrimental methods of obtaining stellar brightness measures under conditions commonly encountered in timeseries CCD photometry. I compare CCD's and photomultiplier tubes as detectors for timeseries photometry using light curves of a variety of stars obtained simultaneously with both detectors and under equivalent conditions. A CCD can produce useful data under conditions when a photomultiplier tube cannot, and a CCD will often produce more reliable results even under photometric conditions. I prevent studies of the cataclysmic variables (CV's) AL Com, CP Eri, V Per, and DO Leo made using the time series CCD photometer. AL Com is a very faint CV at high Galactic latitude and a bona fide Population II CV. Some of the properties of AL Com are similar to the dwarf nova WZ Sge and others are similar to the intermediate polar EX Hya, but overall AL Com is unlike any other well-studied cataclysmic variable. CP Eri is shown to be the fifth known interacting binary white dwarf. V Per was the first CV found to have an orbital period near the middle of the gap in the orbital period distribution of CV's. DO Leo is an eclipsing CV which can reasonably be included in a sample of Population II CV candidates.

  14. Ultrasound RF time series for tissue typing: first in vivo clinical results

    NASA Astrophysics Data System (ADS)

    Moradi, Mehdi; Mahdavi, S. Sara; Nir, Guy; Jones, Edward C.; Goldenberg, S. Larry; Salcudean, Septimiu E.

    2013-03-01

    The low diagnostic value of ultrasound in prostate cancer imaging has resulted in an effort to enhance the tumor contrast using ultrasound-based technologies that go beyond traditional B-mode imaging. Ultrasound RF time series, formed by echo samples originating from the same location over a few seconds of imaging, has been proposed and experimentally used for tissue typing with the goal of cancer detection. In this work, for the first time we report the preliminary results of in vivo clinical use of spectral parameters extracted from RF time series in prostate cancer detection. An image processing pipeline is designed to register the ultrasound data to wholemount histopathology references acquired from prostate specimens that are removed in radical prostatectomy after imaging. Support vector machine classification is used to detect cancer in 524 regions of interest of size 5×5 mm, each forming a feature vector of spectral RF time series parameters. Preliminary ROC curves acquired based on RF time series analysis for individual cases, with leave-one-patient-out cross validation, are presented and compared with B-mode texture analysis.

  15. LINKING IN SITU TIME SERIES FOREST CANOPY LAI AND PHENOLOGY METRICS WITH MODIS AND LANDSAT NDVI AND LAI PRODUCTS

    EPA Science Inventory

    The subject of this presentation is forest vegetation dynamics as observed by the TERRA spacecraft's Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper, and complimentary in situ time series measurements of forest canopy metrics related to Leaf Area...

  16. Mackenzie River Delta morphological change based on Landsat time series

    NASA Astrophysics Data System (ADS)

    Vesakoski, Jenni-Mari; Alho, Petteri; Gustafsson, David; Arheimer, Berit; Isberg, Kristina

    2015-04-01

    Arctic rivers are sensitive and yet quite unexplored river systems to which the climate change will impact on. Research has not focused in detail on the fluvial geomorphology of the Arctic rivers mainly due to the remoteness and wideness of the watersheds, problems with data availability and difficult accessibility. Nowadays wide collaborative spatial databases in hydrology as well as extensive remote sensing datasets over the Arctic are available and they enable improved investigation of the Arctic watersheds. Thereby, it is also important to develop and improve methods that enable detecting the fluvio-morphological processes based on the available data. Furthermore, it is essential to reconstruct and improve the understanding of the past fluvial processes in order to better understand prevailing and future fluvial processes. In this study we sum up the fluvial geomorphological change in the Mackenzie River Delta during the last ~30 years. The Mackenzie River Delta (~13 000 km2) is situated in the North Western Territories, Canada where the Mackenzie River enters to the Beaufort Sea, Arctic Ocean near the city of Inuvik. Mackenzie River Delta is lake-rich, productive ecosystem and ecologically sensitive environment. Research objective is achieved through two sub-objectives: 1) Interpretation of the deltaic river channel planform change by applying Landsat time series. 2) Definition of the variables that have impacted the most on detected changes by applying statistics and long hydrological time series derived from Arctic-HYPE model (HYdrologic Predictions for Environment) developed by Swedish Meteorological and Hydrological Institute. According to our satellite interpretation, field observations and statistical analyses, notable spatio-temporal changes have occurred in the morphology of the river channel and delta during the past 30 years. For example, the channels have been developing in braiding and sinuosity. In addition, various linkages between the studied explanatory variables, such as land cover, precipitation, evaporation, discharge, snow mass and temperature, were found. The significance of this research is emphasised by the growing population, increasing tourism, and economic actions in the Arctic mainly due to the ongoing climate change and technological development.

  17. Image positioning accuracy analysis for the super low altitude remote sensing satellite

    NASA Astrophysics Data System (ADS)

    Xu, Ming; Zhou, Nan

    2012-01-01

    Super low altitude remote sensing satellite maintains lower flight altitude by means of ion propulsion to improve resolution and positioning accuracy of imaging images. The design of engineering data for achieving image positioning accuracy is discussed in this paper based on principles of photogrammetry theory. The line-of-sight exact rebuilding of each detection element and this direction intersects with the earth's elliptical precisely are ensured by the joint design of key parameters when the camera on satellite is imaging. These parameters include orbit determination accuracy, attitude determination accuracy, exposure time of camera, synchronizing time of getting ephemeris and attitude data accuracy, geometric calibration and checking precise on orbit. Simulation calculation of precision proves that image positioning accuracy of super low altitude remote sensing satellites is not improved obviously. Attitude determination error of satellite still restricts positioning accuracy.

  18. Improving multispectral satellite image compression using onboard subpixel registration

    NASA Astrophysics Data System (ADS)

    Albinet, Mathieu; Camarero, Roberto; Isnard, Maxime; Poulet, Christophe; Perret, Jokin

    2013-09-01

    Future CNES earth observation missions will have to deal with an ever increasing telemetry data rate due to improvements in resolution and addition of spectral bands. Current CNES image compressors implement a discrete wavelet transform (DWT) followed by a bit plane encoding (BPE) but only on a mono spectral basis and do not profit from the multispectral redundancy of the observed scenes. Recent CNES studies have proven a substantial gain on the achievable compression ratio, +20% to +40% on selected scenarios, by implementing a multispectral compression scheme based on a Karhunen Loeve transform (KLT) followed by the classical DWT+BPE. But such results can be achieved only on perfectly registered bands; a default of registration as low as 0.5 pixel ruins all the benefits of multispectral compression. In this work, we first study the possibility to implement a multi-bands subpixel onboard registration based on registration grids generated on-the-fly by the satellite attitude control system and simplified resampling and interpolation techniques. Indeed bands registration is usually performed on ground using sophisticated techniques too computationally intensive for onboard use. This fully quantized algorithm is tuned to meet acceptable registration performances within stringent image quality criteria, with the objective of onboard real-time processing. In a second part, we describe a FPGA implementation developed to evaluate the design complexity and, by extrapolation, the data rate achievable on a spacequalified ASIC. Finally, we present the impact of this approach on the processing chain not only onboard but also on ground and the impacts on the design of the instrument.

  19. Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil

    Microsoft Academic Search

    Damien Arvor; Milton Jonathan; Margareth Simões Penello Meirelles; Vincent Dubreuil; Laurent Durieux

    2011-01-01

    Agriculture in Brazilian Amazonia is going through a period of intensification. Crop mapping is important in understanding the way this intensification is occurring and the impact it is having. Two successive classifications based on MODIS (MODerate Resolution Imaging Spectroradiometer)-TERRA\\/EVI (Enhanced Vegetation Index) time series are applied (1) to map agricultural areas and (2) to identify five crop classes. These classes

  20. A MODEL-FREE TIME SERIES SEGMENTATION APPROACH FOR LAND COVER CHANGE DETECTION

    E-print Network

    Minnesota, University of

    resource managers and researchers to address the issues related to global environmental changes. A large images: one before and one after a change [8]. However, such techniques are usually domain or regionA MODEL-FREE TIME SERIES SEGMENTATION APPROACH FOR LAND COVER CHANGE DETECTION ASHISH GARG*, LYDIA

  1. APPARENT WATER OPTICAL PROPERTIES AT THE CARIBBEAN TIME SERIES STATION

    E-print Network

    Gilbes, Fernando

    APPARENT WATER OPTICAL PROPERTIES AT THE CARIBBEAN TIME SERIES STATION Roy A. Armstrong, Jose M of Puerto Rico Mayagüez, Puerto Rico 00681 ABSTRACT The Caribbean Time Series, located 28 nautical miles in near- surface waters of the northeastern Caribbean Basin. Apparent optical properties such as, remote

  2. Forecasting, Structural Time Series Models and the Kalman Filter

    Microsoft Academic Search

    Andrew C. Harvey

    1989-01-01

    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.

  3. Improved singular spectrum analysis for time series with missing data

    NASA Astrophysics Data System (ADS)

    Shen, Y.; Peng, F.; Li, B.

    2014-12-01

    Singular spectrum analysis (SSA) is a powerful technique for time series analysis. Based on the property that the original time series can be reproduced from its principal components, this contribution will develop an improved SSA (ISSA) for processing the incomplete time series and the modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach was evaluated with the synthetic and real incomplete time series data of suspended-sediment concentration from San Francisco Bay. The result from the synthetic time series with missing data shows that the relative errors of the principal components reconstructed by ISSA are much smaller than those reconstructed by SSAM. Moreover, when the percentage of the missing data over the whole time series reaches 60%, the improvements of relative errors are up to 19.64, 41.34, 23.27 and 50.30% for the first four principal components, respectively. Besides, both the mean absolute errors and mean root mean squared errors of the reconstructed time series by ISSA are also much smaller than those by SSAM. The respective improvements are 34.45 and 33.91% when the missing data accounts for 60%. The results from real incomplete time series also show that the SD derived by ISSA is 12.27 mg L-1, smaller than 13.48 mg L-1 derived by SSAM.

  4. Instant Trend-Seasonal Decomposition of Time Series with Splines

    E-print Network

    Krivobokova, Tatyana

    Instant Trend-Seasonal Decomposition of Time Series with Splines Luis Francisco Rosales1 Tatyana to decompose a times series into trend, seasonal and remainder components. This fully data-driven technique is based on penalized splines and makes an explicit characterization of the varying seasonality

  5. Simulation of time series by distorted Gaussian processes

    NASA Technical Reports Server (NTRS)

    Greenhall, C. A.

    1977-01-01

    Distorted stationary Gaussian process can be used to provide computer-generated imitations of experimental time series. A method of analyzing a source time series and synthesizing an imitation is shown, and an example using X-band radiometer data is given.

  6. Modeling Internet Traffic Using Nongaussian Time Series Models

    Microsoft Academic Search

    Zikuan Liu; Jalal Almhana; Vartan Choulakian; Robert Mcgorman

    2005-01-01

    Internet traffic is usually represented by a time series of number of packets or number of bits received in each time slot. There exists a class of Internet traffic traces that have slowly decreasing autocorrelation, their marginal distributions of the number of packets are fit by negative binomial distributions and the time series of number of bits are fit by

  7. STRUCTURAL DAMAGE DETECTION USING CHAOTIC TIME SERIES EXCITATION

    Microsoft Academic Search

    Lillian Y. Chang; Karl A. Erickson; Kenton G. Lee; Michael D. Todd

    This study explores a structural damage detection strategy employing the novel use of chaotic time series excitation. Chaotic time series have several useful properties such as determinism and controllable dimension. Therefore, these series are attractive candidates for probing a structure's dynamics for the subtle changes that could occur because of damage. This approach is applied to a metal frame structure

  8. Practical chaos time series analysis with financial applications

    Microsoft Academic Search

    Ikuo Matsuba; Hiroki Suyari; Sekjun Weon; D. Sato

    2000-01-01

    We describe the practical implementation of the nonlinear (chaos) time series analysis based on the paradigm of deterministic chaos. Some important techniques of statistical test for nonlinearity, phase space reconstruction, and nonlinear prediction are discussed with some applications to finance. The use of the nonlinear time series analysis is illustrated with particular emphasis on issues of choices of time delay

  9. Making Subsequence Time Series Clustering Meaningful Jason R. Chen

    E-print Network

    Chen, Jason

    in the stock market, or the value returned by a sensor on a mobile robot, etc. Clustering is a techniqueMaking Subsequence Time Series Clustering Meaningful Jason R. Chen Department of Information that sequential time series clustering is meaningless. This has important consequences for a significant amount

  10. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

    ERIC Educational Resources Information Center

    Price, Larry R.

    2012-01-01

    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…

  11. A Computer Evolution in Teaching Undergraduate Time Series

    ERIC Educational Resources Information Center

    Hodgess, Erin M.

    2004-01-01

    In teaching undergraduate time series courses, we have used a mixture of various statistical packages. We have finally been able to teach all of the applied concepts within one statistical package; R. This article describes the process that we use to conduct a thorough analysis of a time series. An example with a data set is provided. We compare…

  12. The application of antigenic search techniques to time series forecasting

    Microsoft Academic Search

    Ian Nunn; Tony White

    2005-01-01

    Time series have been a major topic of interest and analysis for hundreds of years, with forecasting a central problem. A large body of analysis techniques has been developed, particularly from methods in statistics and signal processing. Evolutionary techniques have only recently have been applied to time series problems. To date, applications of artificial immune system (AIS) techniques have been

  13. Crisis Monitoring: Methods for Heterogeneous Time Series Learning

    Microsoft Academic Search

    William H. Hsu; Nathan D. Gettings; Victoria E. Lease; Yu Pan; David C. Wilkins

    A very important application of time series learning is online diagnosis, or monitoring, to detect and classify hazardous conditions in a physical system. Examples of crisis monitoring in the industrial, military, agricultural and environmental sciences are numerous. This paper first defines heterogeneous time series, those containing different types of embedded, statistical patterns. Next, it surveys basic techniques for acquiring several

  14. A Symbolic Approach to Gene Expression Time Series Analysis

    Microsoft Academic Search

    Ivan G. Costa; Francisco De A. T. De Carvalho; Marcílio Carlos Pereira De Souto

    2002-01-01

    In the analysis of gene expression time series, emphasis has been given on the capture of shape similarity (or dissimilarity). A number of proximity functions have been proposed for this task. However, none of them will suitably measure shape similarity (or dissimilarity) with data containing multiple gene expression time series, unless special data handling is made. In this paper, a

  15. Estimation of time series spectra with randomly missing data

    Microsoft Academic Search

    P. M. T. Broersen; R. Bos

    2004-01-01

    Maximum likelihood theory presents an elegant asymptomatic solution for the estimation of the parameters of time series models. Unfortunately, the performance of algorithms is often disappointing in finite samples with missing data. The likelihood function for the estimated zeros of time series models is symmetric with respect to the unit circle. As a consequence, the unit circle is either a

  16. Spectrum estimation of time series with missing data

    Microsoft Academic Search

    Henry M. Dante

    1985-01-01

    In several practical situations involving the estimation of sinusoids from time series, the data available is not complete due to missing data points. The Gerschberg-Papoulis extrapolation algorithm, originally used for the extrapolation of band-limited signals is used for the estimation of the spectrum from incomplete time series. The use of this algorithm is studied for cases where the spectrum of

  17. Long-memory analysis of time series with missing values

    Microsoft Academic Search

    P. Wilson; A. Tomsett; R. Toumi

    2003-01-01

    The estimation of long memory is often restricted by missing data. We examine the effects on the estimation of long memory of three simple gap-filling techniques: interpolation, random, and mean filling. Numerical simulations show that the gap-filling techniques introduce significant deviations from the expected scaling behavior for both persistent and antipersistent time series. For persistent time series the interpolation method

  18. Recent developments of time series analysis in environmental impact studies

    Microsoft Academic Search

    Chung Chen

    1991-01-01

    Time series analysis, particularly intervention analysis, is commonly employed in impact studies of environmental data. Environmental time series are susceptible to exogenous variations and often contain various types of outliers. Outliers, depending upon the time of their occurrences and nature, can have substantial impact on the estimates of intervention effects and their test statistics. Hence, outlier detection and adjustment should

  19. Two Fractal Overlap Time Series: Earthquakes and Market Crashes

    E-print Network

    Chakrabarti, Bikas K; Bhattacharyya, Pratip

    2007-01-01

    We find prominent similarities in the features of the time series for the (model earthquakes or) overlap of two Cantor sets when one set moves with uniform relative velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations.

  20. Bayesian Time Series Modelling with LongRange Dependence

    E-print Network

    of a climatological time series. These models are of interest to address the questions of existence and extent We develop a class of Bayesian time series models for data that may exhibit both structured trends been high in recent years in connection with problems of assessing observed patterns in climatological

  1. Dynamic time-series forecasting using local approximation

    Microsoft Academic Search

    Sameer Singh; Paul McAtackney

    1998-01-01

    Pattern recognition techniques for time-series forecasting are beginning to be realised as an important tool for predicting chaotic behaviour of dynamic systems. In this paper we develop the concept of a pattern modelling and recognition system which is used for predicting future behaviour of time-series using local approximation. In this paper we compare this forecasting tool with neural networks. We

  2. Hybrid differential evolutionary system for financial time series forecasting

    Microsoft Academic Search

    Ricardo De A. Araújo; Germano C. Vasconcelos; Tiago A. E. Ferreira

    2007-01-01

    This paper proposes a hybrid differential evolutionary system (HDES) for financial time series forecasting, which performs a differential evolutionary search for the minimum dimension to determining the characteristic phase space that generates the time series phenomenon. It consists of an intelligent hybrid model composed of an artificial neural network (ANN) combined with the improved differential evolution (IDE). The proposed IDE

  3. Time Dependent Directional Profit Model for Financial Time Series Forecasting

    Microsoft Academic Search

    Jingtao Yao; Chew Lim Tan

    2000-01-01

    Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for

  4. Kernel-SOM Based Visualization of Financial Time Series Forecasting

    Microsoft Academic Search

    Dong-jun Yu; Yong Qi; Yong-hong Xu; Jing-yu Yang

    2006-01-01

    Visualizing the forecasting results of financial time series provides significant convenience to the user. In this paper, the disadvantages of the traditional SOM for the visualization of financial time series forecasting are discussed first and then the Kernel-SOM is proposed for better visualization performance. Experimental results demonstrated that compared with the traditional SOM, Kernel-SOM is more suitable for the visualization

  5. Multivariate time series forecasting using independent component analysis

    Microsoft Academic Search

    Theodor D. Popescu

    2003-01-01

    The paper presents a method for multivariate time series forecasting using independent component analysis, as a preprocessing tool. The idea of this approach is to do the forecasting in the space of independent components (sources), and then transforming back to the original time series. The forecasting can be done separately and with a different method for each component, depending on

  6. Application of support vector machines in financial time series forecasting

    Microsoft Academic Search

    Francis E. H. Tay; Lijuan Cao

    2001-01-01

    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

  7. Financial Time Series Forecasting Using K-Nearest Neighbors

    Microsoft Academic Search

    Abstract: Deriving a relationship that allows to predict future values of a time series is a challengingtask when the underlying law is highly non linear. Usually, when facing with a problem ofnon-linear prediction, we are provided with the past history of the time series and we wantto extract from that set of data a mathematical function that relates a certain

  8. The extraction of multiple cropping index of China based on NDVI time-series

    NASA Astrophysics Data System (ADS)

    Huang, Haitao; Gao, Zhiqiang

    2011-09-01

    Multiple cropping index reflects the intensity of arable land been used by a certain planting system. The bond between multiple cropping index and NDVI time-series is the crop cycle rule, which determines the crop process of seeding, jointing, tasseling, ripeness and harvesting and so on. The cycle rule can be retrieved by NDVI time-series for that peaks and valleys on the time-series curve correspond to different periods of crop growth. In this paper, we aim to extract the multiple cropping index of China from NDVI time-series. Because of cloud contamination, some NDVI values are depressed. MVC (Maximum Value Composite) synthesis is used to SPOT-VGT data to remove the noise, but this method doesn't work sufficiently. In order to accurately extract the multiple cropping index, the algorithm HANTS (Harmonic Analysis of Time Series) is employed to remove the cloud contamination. The reconstructed NDVI time-series can explicitly characterize the biophysical process of planting, seedling, elongating, heading, harvesting of crops. Based on the reconstructed curve, we calculate the multiple cropping index of arable land by extracting the number of peaks of the curve for that one peak represents one season crop. This paper presents a method to extracting the multiple cropping index from remote sensing image and then the multiple cropping index of China is extracted from VEGETATION decadal composites NDVI time series of year 2000 and 2009. From the processed data, we can get the spatial distribution of tillage system of China, and then further discussion about cropping index change between the 10 years is conducted.

  9. EXPLORATION OF SATELLITE IMAGES IN THE DYNAMICALLY LINKED ARCVIEW XGOBI XPLORE ENVIRONMENT1

    E-print Network

    Symanzik, Jürgen

    EXPLORATION OF SATELLITE IMAGES IN THE DYNAMICALLY LINKED ARCVIEW XGOBI XPLORE ENVIRONMENT1 J successfully used to explore and analyze allkinds of spatially referenced data | fromforest health data over precipitation data to precision agricultural data. In this paper, we will focus on the exploration of satellite

  10. Automatic Road Network Extraction using High Resolution Multi-temporal Satellite Images

    Microsoft Academic Search

    Vinay Pandit; Sudhir Gupta; Krishnan S. Rajan

    2009-01-01

    Automated road network extraction from remotely sensed imagery is of importance in the context of road databases creation, refinement and updating. Substantial amount of research has been carried out to extract road network from satellite imagery in the photogrammetric and computer vision communities. However, little research has been conducted on utility of multi-temporal satellite images in the context of road

  11. Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image

    E-print Network

    Boyer, Edmond

    1 Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite on a coastal site using a Worldview-2 satellite image Identification and mapping of natural vegetation" DOI : 10.1016/j.jenvman.2014.05.027 #12;2 Identification and mapping of natural vegetation

  12. Short time-series microarray analysis: methods and challenges.

    PubMed

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

    2008-01-01

    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. PMID:18605994

  13. Functional and stochastic models estimation for GNSS coordinates time series

    NASA Astrophysics Data System (ADS)

    Galera Monico, J. F.; Silva, H. A.; Marques, H. A.

    2014-12-01

    GNSS has been largely used in Geodesy and correlated areas for positioning. The position and velocity of terrestrial stations have been estimated using GNSS data based on daily solutions. So, currently it is possible to analyse the GNSS coordinates time series aiming to improve the functional and stochastic models what can help to understand geodynamic phenomena. Several sources of errors are mathematically modelled or estimated in the GNSS data processing to obtain precise coordinates what in general is carried out by using scientific software. However, due to impossibility to model all errors some kind of noises can remain contaminating the coordinate time series, especially those related with seasonal effects. The noise affecting GNSS coordinate time series can be composed by white and coloured noises what can be characterized from Variance Component Estimation technique through Least Square Method. The methodology to characterize noise in GNSS coordinates time series will be presented in this paper so that the estimated variance can be used to reconstruct stochastic and functional models of the times series providing a more realistic and reliable modeling of time series. Experiments were carried out by using GNSS time series for few Brazilian stations considering almost ten years of daily solutions. The noises components were characterized as white, flicker and random walk noise and applied to estimate the times series functional model considering semiannual and annual effects. The results show that the adoption of an adequate stochastic model considering the noises variances of time series can produce more realistic and reliable functional model for GNSS coordinate time series. Such results may be applied in the context of the realization of the Brazilian Geodetic System.

  14. ONLINE satellite images and educational material: the Danish Galathea 3 world expedition under and after

    Microsoft Academic Search

    Charlotte Bay Hasager; Peter Brøgger Sørensen; Ole Baltazar Andersen; Merete Badger; Niels Kristian Højerslev; Jacob L. Høyer; Bo Løkkegaard; Jürg Lichtenegger; Lotte Nyborg; Roberto Saldo

    2010-01-01

    Students and teachers may use ONLINE satellite image in the classroom. Images have been archived since August 2006 and the archive is updated every day since. This means that series of nearly four years of daily global images are available online. The parameters include ocean surface temperature, sea level anomaly, ocean wave height, ocean winds, global ozone in the atmosphere

  15. The Ability of Grade 5 Students To Use Radarsat Satellite Images.

    ERIC Educational Resources Information Center

    Kirman, Joseph M.; Busby, Stephanie

    2000-01-01

    A study examined the ability of 32 grade-5 students in Alberta (Canada) to interpret Radarsat satellite radar images. The students were able to interpret most elements of the images, but working directly with the CD-ROM proved too difficult for them. The Radarsat images have limited value as a geographic resource at the grade-5 level. (TD)

  16. The Ability of Sixth Grade Children to Use Radarsat Satellite Images.

    ERIC Educational Resources Information Center

    Kirman, Joseph M.; Nyitrai, Lorna

    1998-01-01

    Argues that Radarsat satellite images should be used in elementary classrooms to teach geography. Studies the abilities of children to interpret features of a Radarsat image. Shows that they can interpret major geographical features on the image but had problems finding Radarsat locations on road maps. (DSK)

  17. Matching conjugate points between multi-resolution satellite images using geometric and radiometric properties

    Microsoft Academic Search

    Ahmed F. Elaksher; Abdel-Latif A. Alharthy

    2011-01-01

    Remotely sensed images are the main source for a variety of mapping and change detection applications. Images from different satellites are employed in several of these applications. However, each type of these images has different resolution and orientation. Hence, they need to be co-registered before any meaningful utilization. The first step in the registration process is to find conjugate points

  18. A method of using commercial virtual satellite image to check the pattern painting spot effect

    NASA Astrophysics Data System (ADS)

    Wang, Zheng-gang; Kang, Qing; Shen, Zhi-qiang; Cui, Chang-bin

    2014-02-01

    A method of using commercial virtual satellite image to check the pattern painting spot effect contrast with the satellite images before painting and after painting have been discussed. Using a housetop as the testing platform analyses and discusses the factors' influence such as resolution of satellite image, spot size and color of pattern painting spot and pattern painting camouflage method choosing to the plan implement. The pattern painting design and spot size used in the testing has been ensured, and housetop pattern painting has been painted. Finally, the small spot pattern painting camouflage effect of engineering using upon painting pattern size, color and texture have been checked, contrasting with the satellite image before painting and after painting.

  19. Pastures from Space: What can we learn from satellite images?

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Satellites such as the Landsat platform record both visible light and near infrared radiation. These can be combined to produce estimates of standing plant biomass. Satellite estimates of plant production have been widely used in rangelands and forests where large areas are studied. The square Lands...

  20. Identification of environmental anomaly hot spots in West Africa from time series of NDVI and rainfall

    NASA Astrophysics Data System (ADS)

    Boschetti, Mirco; Nutini, Francesco; Brivio, Pietro Alessandro; Bartholomé, Etienne; Stroppiana, Daniela; Hoscilo, Agata

    2013-04-01

    Studies of the impact of human activity on vegetation dynamics of the Sahelian belt of Africa have been recently re-invigorated by new scientific findings that highlighted the primary role of climate in the drought crises of the 1970s-1980s. Time series of satellite observations revealed a re-greening of the Sahelian belt that indicates no noteworthy human effect on vegetation dynamics at sub continental scale from the 1980s to late 1990s. However, several regional/local crises related to natural resources occurred in the last decades despite the re-greening thus underlying that more detailed studies are needed. In this study we used time-series (1998-2010) of SPOT-VGT NDVI and FEWS-RFE rainfall estimates to analyse vegetation - rainfall correlation and to map areas of local environmental anomalies where significant vegetation variations (increase/decrease) are not fully explained by seasonal changes of rainfall. Some of these anomalous zones (hot spots) were further analysed with higher resolution images Landsat TM/ETM+ to evaluate the reliability of the identified anomalous behaviour and to provide an interpretation of some example hot spots. The frequency distribution of the hot spots among the land cover classes of the GlobCover map shows that increase in vegetation greenness is mainly located in the more humid southern part and close to inland water bodies where it is likely to be related to the expansion/intensification of irrigated agricultural activities. On the contrary, a decrease in vegetation greenness occurs mainly in the northern part (12°-15°N) in correspondence with herbaceous vegetation covers where pastoral and cropping practices are often critical due to low and very unpredictable rainfall. The results of this study show that even if a general positive re-greening due to increased rainfall is evident for the entire Sahel, some local anomalous hot spots exist and can be explained by human factors such as population growth whose level reaches the ecosystem carrying capacity as well as population displacement leading to vegetation recovery.

  1. Time Series of North Pacific Volcanic Eruptions

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    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.

  2. Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection

    PubMed Central

    Chávez, Roberto O.; Clevers, Jan G. P. W.; Verbesselt, Jan; Naulin, Paulette I.; Herold, Martin

    2014-01-01

    Heliotropic leaf movement or leaf ‘solar tracking’ occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (?NDVImo-mi) and between winter and summer (?NDVIW-S). In this paper, we showed that the ?NDVImo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ?NDVIW-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ?NDVImo-mi and ?NDVIW-S. For an 11-year time series without rainfall events, Landsat ?NDVIW-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ?NDVImo-mi and ?NDVIW-S have potential to detect early water stress of paraheliotropic vegetation. PMID:25188305

  3. Structure and Semi-Fluid Motion Analysis of Stereoscopic Satellite Images for Cloud Tracking

    Microsoft Academic Search

    Kannappan Palaniappan; Chandra Kambhamettu; Frederick Hasler; Dmitry B. GoldgofS

    1995-01-01

    Time-varying multispectral observations of cloudsfrom meteorological satellites are used to estimatecloud-top heights (structure) and cloud winds (semifluidmotion). Stereo image pairs over several timesteps were acquired by two geostationary satellites withsynchronized scanning instruments. Cloud-top heightestimation from these image pairs is performed usingan improved automatic stereo analysis algorithm on amassively parallel Maspar computer with 16K processors.A new category of...

  4. Spatiotemporal Characterization of Aquifers Using InSAR Time Series and Time-dependent Poroelastic Modeling in Phoenix, Arizona

    NASA Astrophysics Data System (ADS)

    Miller, M. M.; Shirzaei, M.

    2014-12-01

    Alluvial basins in Phoenix experience surface deformation due to large volumes of fluid withdrawn and added to aquifers. The spatiotemporal pattern of deformation is controlled by pumping and recharge rates, hydraulic boundaries, and properties such as diffusivity, transmissivity, and hydraulic conductivity. Land subsidence can cause damages to structures, earth fissures, and a permanent loss of aquifer storage; effects are often apparent after the onset of sustained events. Improving our understanding of the source and mechanisms of deformation is important for risk management and future planning. Monitoring subsidence and uplift using InSAR allows for detailed, dense spatial coverage with less than one cm measurement precision. Envisat data acquired from 2003-11 includes 38 ascending and 53 descending SAR images forming 239 and 423 coherent interferograms respectively. Displacement is separated into vertical and horizontal components by accounting for the satellite look angle and combining ascending and descending line of sight (LOS) data. Vertical velocity from Envisat reveals subsidence reaching -1.84 cm/yr and 0.60 cm/yr uplift. ERS 1&2 satellites delivered useful data from 1992-97, comprised of 6 ascending and 12 descending SAR images. Ascending images form 7 interferograms with LOS velocity from -1.23 to 1.65 cm/yr; descending images produce 25 interferograms with LOS velocity rates from -1.40 to 0.75 cm/yr. InSAR time series are compared with hydraulic head levels from 33 observation wells. Wavelet decomposition is used to separate the long-term, inelastic components from cyclic, elastic signals in InSAR and well level data. The specific storage coefficient, a parameter used in poroelastic models, is estimated as the ratio of cyclic vertical deformation to the equivalent component of the well level time series. Poroelastic theory assumes that pore pressure and fluid mass within the aquifer change during fluid withdrawal, while the relatively impermeable aquifer boundaries remain unchanged; thus, long-term aquifer compaction varies linearly with the decline in pore pressure. Performing inversion analysis, subsurface pressure changes are decoupled and calculated from deformation and well level data. The pressure front can then be used in forward modeling to project future patterns.

  5. Earth from Above: Using Color -Coded Satellite Images to Examine the Global Environment

    NSDL National Science Digital Library

    This product provides an introduction to understanding and interpreting satellite images. Beginning with two short chapters on visible satellite images and radiation, the book then covers six key Earth-atmosphere variables on topics including the Antarctic ozone hole, El Nino, deforestation, the missing carbon dilemma, and the effects of sea ice, snow cover and volcanoes on atmospheric temperatures. A final chapter broadens the discussion to consider satellite Earth observations in general. Each section concludes with a list of questions; answers are provided at the back of the book.

  6. NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering

    NASA Astrophysics Data System (ADS)

    Su, Xin; Deledalle, Charles-Alban; Tupin, Florence; Sun, Hong

    2015-03-01

    This paper presents a likelihood ratio test based method of change detection and classification for synthetic aperture radar (SAR) time series, namely NORmalized Cut on chAnge criterion MAtrix (NORCAMA). This method involves three steps: (1) multi-temporal pre-denoising step over the whole image series to reduce the effect of the speckle noise; (2) likelihood ratio test based change criteria between two images using both the original noisy images and the denoised images; (3) change classification by a normalized cut based clustering-and-recognizing method on change criterion matrix (CCM). The experiments on both synthetic and real SAR image series show the effective performance of the proposed framework.

  7. High performance biomedical time series indexes using salient segmentation.

    PubMed

    Woodbridge, Jonathan; Mortazavi, Bobak; Bui, Alex A T; Sarrafzadeh, Majid

    2012-01-01

    The advent of remote and wearable medical sensing has created a dire need for efficient medical time series databases. Wearable medical sensing devices provide continuous patient monitoring by various types of sensors and have the potential to create massive amounts of data. Therefore, time series databases must utilize highly optimized indexes in order to efficiently search and analyze stored data. This paper presents a highly efficient technique for indexing medical time series signals using Locality Sensitive Hashing (LSH). Unlike previous work, only salient (or interesting) segments are inserted into the index. This technique reduces search times by up to 95% while yielding near identical search results. PMID:23367072

  8. Factorizing Markov Models for Categorical Time Series Prediction

    NASA Astrophysics Data System (ADS)

    Freudenthaler, Christoph; Rendle, Steffen; Schmidt-Thieme, Lars

    2011-09-01

    During the last decade, recommender systems became a popular class of models for many commercial websites. One of the best state-of-the-art methods for recommender systems are Matrix and Tensor Factorization models. Besides, Markov Chain models are common for representing sequential data problems (e.g. categorical time series data). The item recommendation problem of recommender systems in fact is a categorical time series problem where each user represents an individual categorical time series. In this paper we combine factorization models with Markov Chain models. To increase efficiency of parameter estimation we introduce our generalized Factorized Markov Chain model.

  9. Updated model of RazakSAT's attitude during sun tracking mode using time series

    NASA Astrophysics Data System (ADS)

    Hamzah, Nor Hazadura; Yaacob, Sazali; Muthusamy, Hariharan; Hamzah, Norhizam

    2015-05-01

    The accuracy of control and estimation tasks can strongly depend on the accuracy of the underlying model. In space, there are many sources that contribute to the uncertainty in the dynamics model of satellite attitude. Hence, the aim of this paper is to update the dynamical attitude model using grey modeling technique. In this paper, the residual error between the nominal dynamics model and in-flight attitude data is modeled using time series data analysis. Then the time series model of the residual error is augmented in the nominal dynamics model. The updated model is simulated and its performance is analyzed. The results show that the updated model is adequate describing the data.

  10. Cloudsat Satellite Images of Amanda - Duration: 30 seconds.

    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...

  11. Satellite Image Structure Analysis with the GRID Technologies

    Microsoft Academic Search

    A. I. Alexanin; M. G. Alexanina; P. V. Babyak; S. E. Diyakov; G. V. Tarasov

    2010-01-01

    \\u000a A problem considered is to increase computational capacity of Satellite Data Processing Center on the base on external computational\\u000a resources. As the problem solution, the facilities of the Satellite Center and Supercomputing Center were integrated by means\\u000a of the GRID technologies. The developed GRID-services network infrastructure make the possibility to considerably increase\\u000a the speed of data processing and provide feasible

  12. High Resolution Soil Water from Regional Databases and Satellite Images

    NASA Technical Reports Server (NTRS)

    Morris, Robin D.; Smelyanskly, Vadim N.; Coughlin, Joseph; Dungan, Jennifer; Clancy, Daniel (Technical Monitor)

    2002-01-01

    This viewgraph presentation provides information on the ways in which plant growth can be inferred from satellite data and can then be used to infer soil water. There are several steps in this process, the first of which is the acquisition of data from satellite observations and relevant information databases such as the State Soil Geographic Database (STATSGO). Then probabilistic analysis and inversion with the Bayes' theorem reveals sources of uncertainty. The Markov chain Monte Carlo method is also used.

  13. DEM time series of an agricultural watershed

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

    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.

  14. Autonomous Sub-Pixel Satellite Track Endpoint Determination for Space Based Images

    SciTech Connect

    Simms, L M

    2011-03-07

    An algorithm for determining satellite track endpoints with sub-pixel resolution in spaced-based images is presented. The algorithm allows for significant curvature in the imaged track due to rotation of the spacecraft capturing the image. The motivation behind the subpixel endpoint determination is first presented, followed by a description of the methodology used. Results from running the algorithm on real ground-based and simulated spaced-based images are shown to highlight its effectiveness.

  15. Quantifying morphology changes in time series data with skew

    E-print Network

    Sung, Phil

    This paper examines strategies to quantify differences in the morphology of time series while accounting for time skew in the observed data. We adapt four measures originally designed for signal shape comparison: Dynamic ...

  16. Recurrence networks - A novel paradigm for nonlinear time series analysis

    E-print Network

    Reik V. Donner; Y. Zou; Jonathan F. Donges; Norbert Marwan; Juergen Kurths

    2009-08-24

    This paper presents a new approach for analysing structural properties of time series from complex systems. Starting from the concept of recurrences in phase space, the recurrence matrix of a time series is interpreted as the adjacency matrix of an associated complex network which links different points in time if the evolution of the considered states is very similar. A critical comparison of these recurrence networks with similar existing techniques is presented, revealing strong conceptual benefits of the new approach which can be considered as a unifying framework for transforming time series into complex networks that also includes other methods as special cases. It is demonstrated that there are fundamental relationships between the topological properties of recurrence networks and the statistical properties of the phase space density of the underlying dynamical system. Hence, the network description yields new quantitative characteristics of the dynamical complexity of a time series, which substantially complement existing measures of recurrence quantification analysis.

  17. An Adaptive Approach to Filter a Time Series Data

    E-print Network

    Koushik Ghosh; Probhas Raychaudhuri

    2007-01-30

    A physical data (such as astrophysical, geophysical, meteorological etc.) may appear as an output of an experiment or it may come out as a signal from a dynamical system or it may contain some sociological, economic or biological information. Whatever be the source of a time series data some amount of noise is always expected to be embedded in it. Analysis of such data in presence of noise may often fail to give accurate information. The method of filtering a time series data is a tool to clean these errors as possible as we can just to make the data compatible for further analysis. Here we made an attempt to develop an adaptive approach of filtering a time series and we have shown analytically that the present model can fight against the propagation of error and can maintain the positional importance in the time series very efficiently.

  18. Gene clustering methods for time series microarray data Laney Kuenzel

    E-print Network

    being studied into smaller sets of genes with similar expression patterns, where the definition such as Alzheimer's [18], HIV [37], and cancer [44]. Fourth and finally, researchers can use time series ex

  19. Times Series Study of Effects of Petroleum Production on GDP 

    E-print Network

    Ballinger, Leslie 1991-

    2012-05-02

    development. The countries studied include: Argentina, Canada, Colombia, the United States, Mexico, Venezuela, Peru, and Indonesia. The dates of analysis are different for every country due to data reliability. This paper focuses mainly on a time series...

  20. 14.384 Time Series Analysis, Fall 2007

    E-print Network

    Mikusheva, Anna, 1976-

    The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain ...