Science.gov

Sample records for time-series satellite images

  1. Satellite image time series simulation for environmental monitoring

    NASA Astrophysics Data System (ADS)

    Guo, Tao

    2014-11-01

    The performance of environmental monitoring heavily depends on the availability of consecutive observation data and it turns out an increasing demand in remote sensing community for satellite image data in the sufficient resolution with respect to both spatial and temporal requirements, which appear to be conflictive and hard to tune tradeoffs. Multiple constellations could be a solution if without concerning cost, and thus it is so far interesting but very challenging to develop a method which can simultaneously improve both spatial and temporal details. There are some research efforts to deal with the problem from various aspects, a type of approaches is to enhance the spatial resolution using techniques of super resolution, pan-sharpen etc. which can produce good visual effects, but mostly cannot preserve spectral signatures and result in losing analytical value. Another type is to fill temporal frequency gaps by adopting time interpolation, which actually doesn't increase informative context at all. In this paper we presented a novel method to generate satellite images in higher spatial and temporal details, which further enables satellite image time series simulation. Our method starts with a pair of high-low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and the temporal change is then projected onto high resolution data plane and assigned to each high resolution pixel referring the predefined temporal change patterns of each type of ground objects to generate a simulated high resolution data. A preliminary experiment shows that our method can simulate a high resolution data with a good accuracy. We consider the contribution of our method is to enable timely monitoring of temporal changes through analysis of low resolution images time series only, and usage of costly high resolution data can be reduced as much as possible, and it presents an efficient solution with great cost performance to build up an economically operational monitoring service for environment, agriculture, forest, land use investigation, and other applications.

  2. Towards Time-Series Processing of Vhr Satellite Images for Surface Deformation Detecton and Measurements

    NASA Astrophysics Data System (ADS)

    Stumpf, A.; Delacourt, C.; Malet, J. P.

    2015-08-01

    The increasing fleet of VHR optical satellites (e.g. Pléiades, Spot 6/7, WorldView-3) offers new opportunities for the monitoring of surface deformation resulting from gravitational (e.g. glaciers, landslides) or tectonic forces (coseismic slip). Image correlation techniques have been developed and successfully employed in many geoscientific studies to quantify horizontal surface deformation at sub-pixel precision. The analysis of time-series, however, has received less attention in this context and there is still a lack of techniques that fully exploit archived image time-series and the increasing flux of incoming data. This study targets the development of an image correlation processing chain that relies on multiple pair-wise matching to exploit the redundancy of deformation measurements recorded at different view angles and over multiple time steps. The proposed processing chain is based on a hierarchical image correlation scheme that readily uses parallel processing. Since pair-wise matching can be performed independently the distribution of individual tasks is straightforward and yields to significant reductions of the overall runtime scaling with the available HPC infrastructure. We find that it is more convenient to implement experimental analytical tasks in a high-level programming language (i.e. R) and explore the use of parallel programming to compensate for performance bottlenecks of the interpreted language. Preliminary comparisons against maps from domain expert suggest that the proposed methodology is suitable to eliminate false detections and, thereby, enhances the reliability of correlation-based detections of surface deformation.

  3. A method for monitoring land-cover disturbance using satellite time series images

    NASA Astrophysics Data System (ADS)

    Zhou, Zengguang; Tang, Ping; Zhang, Zheng

    2014-11-01

    Land cover disturbance is an abrupt ecosystem change that occurs over a short time period, such as flood, fire, drought and deforestation. It is crucial to monitor disturbances for rapid response. In this paper, we propose a time series analysis method for monitoring of land-cover disturbance with high confidence level. The method integrates procedures including (1) modeling of a piece of history time series data with season-trend model and (2) forecasting with the fitted model and monitoring disturbances based on significance of prediction errors. The method is tested using 16-day MODIS NDVI time series to monitor abnormally inundated areas of the Tongjiang section of Heilongjiang River of China, where had extreme floods and bank break in summer 2013. The test results show that the method could detect the time and areas of disturbances for each image with no detection delay and with high specified confidence level. The method has few parameters to be specified and less computation complexity so that it could be developed for monitoring of land-cover disturbance on large scales.

  4. Evaluating nitrogen removal by vegetation uptake using satellite image time series in riparian catchments.

    PubMed

    Wang, Xuelei; Wang, Qiao; Yang, Shengtian; Zheng, Donghai; Wu, Chuanqing; Mannaerts, C M

    2011-06-01

    Nitrogen (N) removal by vegetation uptake is one of the most important functions of riparian buffer zones in preventing non-point source pollution (NSP), and many studies about N uptake at the river reach scale have proven the effectiveness of plants in controlling nutrient pollution. However, at the watershed level, the riparian zones form dendritic networks and, as such, may be the predominant spatially structured feature in catchments and landscapes. Thus, assessing the functions of riparian system at the basin scale is important. In this study, a new method coupling remote sensing and ecological models was used to assess the N removal by riparian vegetation on a large spatial scale. The study site is located around the Guanting reservoir in Beijing, China, which was abandoned as the source water system for Beijing due to serious NSP in 1997. SPOT 5 data was used to map the land cover, and Landsat-5 TM time series images were used to retrieve land surface parameters. A modified forest nutrient cycling and biomass model (ForNBM) was used to simulate N removal, and the modified net primary productivity (NPP) module was driven by remote sensing image time series. Besides the remote sensing data, the necessary database included meteorological data, soil chemical and physical data and plant nutrient data. Pot and plot experiments were used to calibrate and validate the simulations. Our study has proven that, by coupling remote sensing data and parameters retrieval techniques to plant growth process models, catchment scale estimations of nitrogen uptake rates can be improved by spatial pixel-based modelling. PMID:21496878

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

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

  6. IDP camp evolvement analysis in Darfur using VHSR optical satellite image time series and scientific visualization on virtual globes

    NASA Astrophysics Data System (ADS)

    Tiede, Dirk; Lang, Stefan

    2009-09-01

    In this paper we focus on the application of transferable, object-based image analysis algorithms for dwelling extraction in a camp for internally displaced people (IDP) in Darfur, Sudan along with innovative means for scientific visualisation of the results. Three very high spatial resolution satellite images (QuickBird: 2002, 2004, 2008) were used for: (1) extracting different types of dwellings and (2) calculating and visualizing added-value products such as dwelling density and camp structure. The results were visualized on virtual globes (Google Earth and ArcGIS Explorer) revealing the analysis results (analytical 3D views,) transformed into the third dimension (z-value). Data formats depend on virtual globe software including KML/KMZ (keyhole mark-up language) and ESRI 3D shapefiles streamed as ArcGIS Server-based globe service. In addition, means for improving overall performance of automated dwelling structures using grid computing techniques are discussed using examples from a similar study.

  7. IDP camp evolvement analysis in Darfur using VHSR optical satellite image time series and scientific visualization on virtual globes

    NASA Astrophysics Data System (ADS)

    Tiede, Dirk; Lang, Stefan

    2010-11-01

    In this paper we focus on the application of transferable, object-based image analysis algorithms for dwelling extraction in a camp for internally displaced people (IDP) in Darfur, Sudan along with innovative means for scientific visualisation of the results. Three very high spatial resolution satellite images (QuickBird: 2002, 2004, 2008) were used for: (1) extracting different types of dwellings and (2) calculating and visualizing added-value products such as dwelling density and camp structure. The results were visualized on virtual globes (Google Earth and ArcGIS Explorer) revealing the analysis results (analytical 3D views,) transformed into the third dimension (z-value). Data formats depend on virtual globe software including KML/KMZ (keyhole mark-up language) and ESRI 3D shapefiles streamed as ArcGIS Server-based globe service. In addition, means for improving overall performance of automated dwelling structures using grid computing techniques are discussed using examples from a similar study.

  8. Monitoring vegetation change and dynamics on U.S. Army training lands using satellite image time series analysis.

    PubMed

    Hutchinson, J M S; Jacquin, A; Hutchinson, S L; Verbesselt, J

    2015-03-01

    Given the significant land holdings of the U.S. Department of Defense, and the importance of those lands to support a variety of inherently damaging activities, application of sound natural resource conservation principles and proactive monitoring practices are necessary to manage military training lands in a sustainable manner. This study explores a method for, and the utility of, analyzing vegetation condition and trends as sustainability indicators for use by military commanders and land managers, at both the national and local levels, in identifying when and where vegetation-related environmental impacts might exist. The BFAST time series decomposition method was applied to a ten-year MODIS NDVI time series dataset for the Fort Riley military installation and Konza Prairie Biological Station (KPBS) in northeastern Kansas. Imagery selected for time-series analysis were 16-day MODIS NDVI (MOD13Q1 Collection 5) composites capable of characterizing vegetation change induced by human activities and climate variability. Three indicators related to gradual interannual or abrupt intraannual vegetation change for each pixel were calculated from the trend component resulting from the BFAST decomposition. Assessment of gradual interannual NDVI trends showed the majority of Fort Riley experienced browning between 2001 and 2010. This result is supported by validation using high spatial resolution imagery. The observed versus expected frequency of linear trends detected at Fort Riley and KPBS were significantly different and suggest a causal link between military training activities and/or land management practices. While both sites were similar with regards to overall disturbance frequency and the relative spatial extents of monotonic or interrupted trends, vegetation trajectories after disturbance were significantly different. This suggests that the type and magnitude of disturbances characteristic of each location result in distinct post-disturbance vegetation responses. Using a remotely-sensed vegetation index time series with BFAST and the indicators outlined here provides a consistent and relatively rapid assessment of military training lands with applicability outside of grassland biomes. Characterizing overall trends and disturbance responses of vegetation can promote sustainable use of military lands and assist land managers in targeting specific areas for various rehabilitation activities. PMID:25441663

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  10. 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.

  11. Crop growth dynamics modeling using time-series satellite imagery

    NASA Astrophysics Data System (ADS)

    Zhao, Yu

    2014-11-01

    In modern agriculture, remote sensing technology plays an essential role in monitoring crop growth and crop yield prediction. To monitor crop growth and predict crop yield, accurate and timely crop growth information is significant, in particularly for large scale farming. As the high cost and low data availability of high-resolution satellite images such as RapidEye, we focus on the time-series low resolution satellite imagery. In this research, NDVI curve, which was retrieved from satellite images of MODIS 8-days 250m surface reflectance, was applied to monitor soybean's yield. Conventional model and vegetation index for yield prediction has problems on describing the growth basic processes affecting yield component formation. In our research, a novel method is developed to well model the Crop Growth Dynamics (CGD) and generate CGD index to describe the soybean's yield component formation. We analyze the standard growth stage of soybean and to model the growth process, we have two key calculate process. The first is normalization of the NDVI-curve coordinate and division of the crop growth based on the standard development stages using EAT (Effective accumulated temperature).The second is modeling the biological growth on each development stage through analyzing the factors of yield component formation. The evaluation was performed through the soybean yield prediction using the CGD Index in the growth stage when the whole dataset for modeling is available and we got precision of 88.5% which is about 10% higher than the conventional method. The validation results showed that prediction accuracy using our CGD modeling is satisfied and can be applied in practice of large scale soybean yield monitoring.

  12. Assessing land-use and carbon stock in slash-and-burn ecosystems in tropical mountain of Laos based on time-series satellite images

    NASA Astrophysics Data System (ADS)

    Inoue, Yoshio; Kiyono, Yoshiyuki; Asai, Hidetoshi; Ochiai, Yukihito; Qi, Jiaguo; Olioso, Albert; Shiraiwa, Tatsuhiko; Horie, Takeshi; Saito, Kazuki; Dounagsavanh, Linkham

    2010-08-01

    In the tropical mountains of Southeast Asia, slash-and-burn (S/B) agriculture is a widely practiced and important food production system. The ecosystem carbon stock in this land-use is linked not only to the carbon exchange with the atmosphere but also with food and resource security. The objective of this study was to provide quantitative information on the land-use and ecosystem carbon stock in the region as well as to infer the impacts of alternative land-use and ecosystem management scenarios on the carbon sequestration potential at a regional scale. The study area was selected in a typical slash-and-burn region in the northern part of Laos. The chrono-sequential changes of land-use such as the relative areas of community age and cropping (C) + fallow (F) patterns were derived from the analysis of time-series satellite images. The chrono-sequential analysis showed that a consistent increase of S/B area during the past three decades and a rapid increase after 1990. Approximately 37% of the whole area was with the community age of 1-5 years, whereas 10% for 6-10 years in 2004. The ecosystem carbon stock at a regional scale was estimated by synthesizing the land-use patterns and semi-empirical carbon stock model derived from in situ measurements where the community age was used as a clue to the linkage. The ecosystem carbon stock in the region was strongly affected by the land-use patterns; the temporal average of carbon stock in 1C + 10F cycles, for example, was greater by 33 MgC ha -1 compared to that in 1C + 2F land-use pattern. The amount of carbon lost from the regional ecosystems during 1990-2004 periods was estimated to be 42 MgC ha -1. The study approach proved to be useful especially in such regions with low data-availability and accessibility. This study revealed the dynamic change of land-use and ecosystem carbon stock in the tropical mountain of Laos as affected by land-use. Results suggest the significant potential of carbon sequestration through changing land-use and ecosystem management scenarios. These quantitative estimates would be useful to better understand and manage the land-use and ecosystem carbon stock towards higher sustainability and food security in similar ecosystems.

  13. IMAGE GUIDED RESPIRATORY MOTION TIME SERIES AND IMAGE REGISTRATION

    E-print Network

    Fessler, Jeffrey A.

    IMAGE GUIDED RESPIRATORY MOTION ANALYSIS: TIME SERIES AND IMAGE REGISTRATION by Dan Ruan (Electrical Engineering: Systems) in The University of Michigan 2008 Doctoral Committee: Professor Jeffrey A Steve B. Jiang (UCSC), Dr. Gregory C. Sharp (Mass General Hospital) and many other people over the years

  14. Automated construction of generative models from time series cell images

    E-print Network

    Murphy, Robert F.

    #12;Movie Analysis via Object Type Changes · HeLa cells expressing GFP-tagged growth factor receptor, Univ. Pittsburgh School Medicine #12;HeLa cells expressing growth factor receptor-bound protein 2(Grb2Automated construction of generative models from time series cell images: Tools for more complete

  15. 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.

  16. Improved Time Series Reconstruction for Dynamic Magnetic Resonance Imaging

    PubMed Central

    Sümbül, Uygar; Santos, Juan M.; Pauly, John M.

    Time series of in-vivo magnetic resonance images exhibit high levels of temporal correlation. Higher temporal resolution reconstructions are obtained by acquiring data at a fraction of the Nyquist rate and resolving the resulting aliasing using the correlation information. The dynamic imaging experiment is modeled as a linear dynamical system. A Kalman filter based unaliasing reconstruction is described for accelerated dynamic magnetic resonance imaging (MRI). The algorithm handles arbitrary readout trajectories naturally. The reconstruction is causal and very fast, making it applicable to real-time imaging. In-vivo results are presented for cardiac MRI of healthy volunteers. PMID:19150785

  17. Classification of satellite time series-derived land surface phenology focused on the northern Fertile Crescent

    NASA Astrophysics Data System (ADS)

    Bunker, Brian

    Land surface phenology describes events in a seasonal vegetation cycle and can be used in a variety of applications from predicting onset of future drought conditions, to revealing potential limits of historical dry farming, to guiding more accurate dating of archeological sites. Traditional methods of monitoring vegetation phenology use data collected in situ. However, vegetation health indices derived from satellite remote sensor data, such as the normalized difference vegetation index (NDVI), have been used as proxy for vegetation phenology due to their repeated acquisition and broad area coverage. Land surface phenology is accessible in the NDVI satellite record when images are processed to be intercomparable over time and temporally ordered to create a time series. This study utilized NDVI time series to classify areas of similar vegetation phenology in the northern Fertile Crescent, an area from the middle Mediterranean coast to southern/south-eastern Turkey to western Iran and northern Iraq. Phenological monitoring of the northern Fertile Crescent is critical due to the area's minimal water resources, susceptibility to drought, and understanding ancient historical reliance on precipitation for subsistence dry farming. Delineation of phenological classes provides areal and temporal synopsis of vegetation productivity time series. Phenological classes were developed from NDVI time series calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) imagery with 8 × 8 km spatial resolution over twenty-five years, and by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) with 250 × 250 m spatial resolution over twelve years. Both AVHRR and MODIS time series were subjected to data reduction techniques in spatial and temporal dimensions. Optimized ISODATA clusters were developed for both of these data reduction techniques in order to compare the effects of spatial versus temporal aggregation. Within the northern Fertile Crescent study area, the spatial reduction technique showed increased cluster cohesion over the temporal reduction method. The latter technique showed an increase in temporal smoothing over the spatial reduction technique. Each technique has advantages depending on the desired spatial or temporal granularity. Additional work is required to determine optimal scale size for the spatial data reduction technique.

  18. 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.

  19. Change detection from very high resolution satellite time series with variable off-nadir angle

    NASA Astrophysics Data System (ADS)

    Barazzetti, Luigi; Brumana, Raffaella; Cuca, Branka; Previtali, Mattia

    2015-06-01

    Very high resolution (VHR) satellite images have the potential for revealing changes occurred overtime with a superior level of detail. However, their use for metric purposes requires accurate geo-localization with ancillary DEMs and GCPs to achieve sub-pixel terrain correction, in order to obtain images useful for mapping applications. Change detection with a time series of VHS images is not a simple task because images acquired with different off-nadir angles have a lack of pixel-to-pixel image correspondence, even after accurate geo-correction. This paper presents a procedure for automatic change detection able to deal with variable off-nadir angles. The case study concerns the identification of damaged buildings from pre- and post-event images acquired on the historic center of L'Aquila (Italy), which was struck by an earthquake in April 2009. The developed procedure is a multi-step approach where (i) classes are assigned to both images via object-based classification, (ii) an initial alignment is provided with an automated tile-based rubber sheeting interpolation on the extracted layers, and (iii) change detection is carried out removing residual mis-registration issues resulting in elongated features close to building edges. The method is fully automated except for some thresholds that can be interactively set to improve the visualization of the damaged buildings. The experimental results proved that damages can be automatically found without additional information, such as digital surface models, SAR data, or thematic vector layers.

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

    NASA Astrophysics Data System (ADS)

    Stewart, Chris

    2014-05-01

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

  1. 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.

  2. 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.

  3. Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series

    PubMed Central

    Bajocco, Sofia; Dragoz, Eleni; Gitas, Ioannis; Smiraglia, Daniela; Salvati, Luca; Ricotta, Carlo

    2015-01-01

    Traditionally fuel maps are built in terms of ‘fuel types’, thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000–2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies. PMID:25822505

  4. A Least Square Approach for Joining Persistent Scatterer InSAR Time Series Acquired by Different Satellites

    NASA Astrophysics Data System (ADS)

    Caro Cuenca, Miguel; Esfahany, Sami Samiei; Hanssen, Ramon F.

    2010-12-01

    Persistent scatterer Radar Interferometry (PSI) can provide with a wealth of information on surface motion. These methods overcome the major limitations of the antecessor technique, interferometric SAR (InSAR), such as atmospheric disturbances, by detecting the scatterers which are slightly affected by noise. The time span that surface deformation processes are observed is limited by the satellite lifetime, which is usually less than 10 years. However most of deformation phenomena last longer. In order to fully monitor and comprehend the observed signal, acquisitions from different sensors can be merged. This is a complex task for one main reason. PSI methods provide with estimations that are relative in time to one of the acquisitions which is referred to as master or reference image. Therefore, time series acquired by different sensors will have different reference images and cannot be directly compared or joint unless they are set to the same time reference system. In global terms, the operation of translating from one to another reference systems consist of calculating a vertical offset, which is the total deformation that occurs between the two master times. To estimate this offset, different strategies can be applied, for example, using additional data such as leveling or GPS measurements. In this contribution we propose to use a least squares to merge PSI time series without any ancillary information. This method treats the time series individually, i.e. per PS, and requires some knowledge of the deformation signal, for example, if a polynomial would fairly describe the expected behavior. To test the proposed approach, we applied it to the southern Netherlands, where the surface is affected by ground water processes in abandoned mines. The time series were obtained after processing images provided by ERS1/2 and Envisat. The results were validated using in-situ water measurements, which show very high correlation with deformation time series.

  5. Spatial and temporal Amazon vegetation dynamics and phenology using time series satellite data

    NASA Astrophysics Data System (ADS)

    Ratana, Piyachat

    Improved knowledge of landscape seasonal variations and phenology at the regional scale is needed for carbon and water flux studies, and biogeochemical, hydrological, and climate models. Amazon vegetation mechanisms and dynamics controlling biosphere-atmosphere interactions are not entirely understood. To better understand these processes, vegetation photosynthetic activity and canopy water and temperature dynamics were analyzed over various types of vegetation in Amazon using satellite data from the Terra-Moderate Resolution Imaging Spectroradiometer (MODIS). The objectives of this dissertation were to (1) assess the spatial and temporal variations of satellite data over the Amazon as a function of vegetation physiognomies for monitoring and discrimination, (2) investigate seasonal vegetation photosynthetic activity and phenology across the forest-cerrado ecotone and conversion areas, and (3) investigate seasonal variations of satellite-based canopy water and land surface temperature in relation to photosynthetic activity over the Amazon basin. The results of this study showed the highly diverse and complex cerrado biome and associated cerrado conversions could be monitored and analyzed with MODIS vegetation index (VI) time series data. The MODIS enhanced vegetation index (EVI) seasonal profiles were found useful in characterizing the spatial and temporal variability in landscape phenology across a climatic gradient of rainfall and sunlight conditions through the rainforest-cerrado ecotone. Significant trends in landscape phenology were observed across the different biomes with strong seasonal shifts resulting from differences in vegetation physiognomic responses to rainfall and sunlight. We also found unique seasonal and temporal patterns of the land surface water index (LSWI) and land surface temperature (LST), which in combination with the EVI provided improved information for monitoring the seasonal ecosystem dynamics of the Amazon rainforest, cerrado, ecotone, and conversion areas. In conclusion, satellite-based, regional scale studies were found to aid in understanding land surface processes and mechanisms at the ecosystem level, providing a "big picture" of landscape dynamics. Coupling this with ground, in-situ measurements, such as from flux towers, can greatly improve the estimation of carbon and water fluxes, and our understanding of the biogeochemistry and climate in very dynamic and changing landscapes.

  6. 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

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

    NASA Astrophysics Data System (ADS)

    Zoran, Maria; Savastru, Roxana; Savastru, Dan

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  9. On the use of satellite VEGETATION time series for vegetation disturbance recovery assessment

    NASA Astrophysics Data System (ADS)

    Lanorte, A.; Coluzzi, R.; de Santis, F.; Didonna, I.

    2009-04-01

    The characterization of vegetation reaction to disturbance is of primary importance since changes in the status or types of vegetation play an active role in ecological processes (such as productivity level, creation of altered patches, modification in vegetation structure and shifts in vegetation cover composition), as well as in land surface processes (such as surface energy, water balance, carbon cycle). The assessment of disturbance impacts on ecological resources requires investigations performed at different temporal and spatial scales, from local up to regional level. In such a context, satellite technologies can be profitably used for investigating the dynamics of vegetation after disturbance at different temporal and spatial scales; although, dynamical processes induced by disturbance 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. In this study, a time series of normalized difference vegetation index (NDVI) data derived from SPOT-VEGETATION was used to examine the recovery characteristics of drought and fire affected vegetation in some test areas of the Mediterranean ecosystems of Southern Italy. 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 temporal series from 1998 to 2005 of NDVI satellite SPOT VEGETATION data acquired for a shrubland test site In order to eliminate the phenological fluctuations, for each decadal composition of each pixel, we focused on the departure NDVId = [NDVI - ]/?, where is the decadal mean and ? is the decadal standard deviation. The decadal mean and the standard deviation were calculated for each decade, e.g. 1st decade of January, by averaging over all years in the record. We analyzed both: 1) Post-disturbance NDVI spatial patterns on each image date were compared to the pre-disturbace pattern to determine the extent to which the pre-disturbance pattern was re-established, and the rate of this recovery. 2) time variation of NDVI from 1998 to 2005 of two pixels for the disturbance affected and disturbance unaffected areas. Results show the ability of NDVI time series to capture the different impacts/effects of different disturbances (drought and fire in the current case) and the capability of VEGETATION-NDVI data set to monitoring vegetation status from local up to a global scale.

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

    NASA Astrophysics Data System (ADS)

    Hashiba, Hideki; Nakayama, Yasunori; Sugimura, Toshiro

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

  11. 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.

  12. Assessment and surveillance of active seismic regions through time series satellite data

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

    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. Cumulative stress energy in seismic active regions under operating tectonic force manifests various earthquakes' precursors. Space-time anomalies of Earth's emitted radiation (radon in underground water and soil, thermal infrared in spectral range measured from satellite months to weeks before the occurrence of earthquakes etc.), and electromagnetic anomalies are considered as pre-seismic signals. This energy transformation may result in enhanced transient thermal infrared (TIR) emission, which can be detected through satellites equipped with thermal sensors like AVHRR (NOAA), MODIS (Terra/Aqua). This paper presents observations made using time series NOAA-AVHRR and MODIS satellite data-derived land surface temperature (LST) and outgoing longwave radiation (OLR) values in case of 27th 2004 earthquake recorded in seismic Vrancea region, Romania, using anomalous TIR signals as reflected in LST rise and high OLR values which followed similar growth pattern spatially and temporally. In all analyzed cases, starting with almost one week prior to a moderate or strong earthquake a transient thermal infrared rise in LST of several Celsius degrees (°C) and the increased OLR values higher than the normal have been recorded around epicentral areas, function of the magnitude and focal depth, which disappeared after the main shock. As Vrancea area has a significant regional tectonic activity in Romania and Europe, the joint analysis of geospatial and in-situ geophysical information is revealing new insights in the field of hazard assessment.

  13. Correcting incompatible DN values and geometric errors in nighttime lights time series images

    SciTech Connect

    Zhao, Naizhuo; Zhou, Yuyu; Samson, Eric

    2015-04-01

    The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multi-year quantitative research. In this study we extend and improve previous studies on inter-calibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time series (NLT) image data for China and the United States (US) through four steps: inter-calibration, geometric correction, steady increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992-1997 and 2001-2008 respectively and the US suffered from nighttime lights decay in large areas after 2001.

  14. Intercomparison of Satellite Derived Gravity Time Series with Inferred Gravity Time Series from TOPEX/POSEIDON Sea Surface Heights and Climatological Model Output

    NASA Technical Reports Server (NTRS)

    Cox, C.; Au, A.; Klosko, S.; Chao, B.; Smith, David E. (Technical Monitor)

    2001-01-01

    The upcoming GRACE mission promises to open a window on details of the global mass budget that will have remarkable clarity, but it will not directly answer the question of what the state of the Earth's mass budget is over the critical last quarter of the 20th century. To address that problem we must draw upon existing technologies such as SLR, DORIS, and GPS, and climate modeling runs in order to improve our understanding. Analysis of long-period geopotential changes based on SLR and DORIS tracking has shown that addition of post 1996 satellite tracking data has a significant impact on the recovered zonal rates and long-period tides. Interannual effects such as those causing the post 1996 anomalies must be better characterized before refined estimates of the decadal period changes in the geopotential can be derived from the historical database of satellite tracking. A possible cause of this anomaly is variations in ocean mass distribution, perhaps associated with the recent large El Nino/La Nina. In this study, a low-degree spherical harmonic gravity time series derived from satellite tracking is compared with a TOPEX/POSEIDON-derived sea surface height time series. Corrections for atmospheric mass effects, continental hydrology, snowfall accumulation, and ocean steric model predictions will be considered.

  15. Time series satellite and ground-based data for detecting earthquake anomalies

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

    Earthquake science has entered a new era with the development of space-based technologies to measure surface geophysical parameters and deformation at the boundaries of tectonic plates and large faults. 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. Cumulative stress energy in seismic active regions under operating tectonic force manifests various earthquakes' precursors. Space-time anomalies of Earth's emitted radiation (thermal infrared in spectral range measured from satellite months to weeks before the occurrence of earthquakes, radon in underground water and soil, etc.), and electromagnetic anomalies are considered as pre-seismic signals. Vrancea tectonic active zone in Romania is characterized by a high seismic hazard in European- Mediterranean region, being responsible of intermediate depth and normal earthquakes generation on a confined epicentral area.Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. This paper presents observations made using in-situ data and time series MODIS and NOAA-AVHRR satellite data for derived multi geophysical parameters (land surface temperature -LST, outgoing long-wave radiation- OLR, net surface latent heat flux (LHF) and mean air temperature- AT for some seismic events recorded in Vrancea region in Romania, which is one of the most active intracontinental seismic areas in Europe. Starting with almost one week prior to a moderate or strong earthquake a transient thermal infrared rise in LST of several Celsius degrees (°C) and the increased OLR values higher than the normal have been recorded around epicentral areas, function of the magnitude and focal depth, which disappeared after the main shock.

  16. Space Monitoring of air pollution using satellite time series: from a global view down to local scale

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    Assessment of air pollution has been performed by different means over the years and, recently, the use of satellite data for detecting and monitoring atmospheric pollution has received considerable attention especially for application in industrial and urban areas. Methods based on satellite data (such as Landsat TM, SPOT MODIS images) are focused on the estimation of aerosol optical thickness (AOT) that is a measure of aerosol loading in the atmosphere, and therefore, it is considered as the main significant parameter of the presence/absence of atmospheric pollutants. A higher AOT value expresses the degree to which aerosols prevent the transmission of light, therefore, higher columnar of aerosol loading means lower visibility and higher aerosol concentration Several state-of-art aerosol retrieval techniques provide aerosol properties in global scale, as for example products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System (EOS) Terra and Aqua satellites. The current aerosol optical thickness (AOT) products from MODIS (available free of charge by the NASA web site) is 10 km. This product is suitable for global research, but it faces difficulty in local area research, especially in urban areas However, new aerosol retrieval algorithm for the (MODIS) 500m resolution data have been developed to retrieve aerosol properties over land, which helps on addressing the aerosol climatic issues in local/urban scale. Over the years, several algorithms for determining the aerosol optical thickness have been developed using several approaches and satellite sensors including medium (Landsat; ASTER) and high resolution imagery (IKONOS and Quickbird). A comparison of results from these methods and independent data sets has been performed in the Basilicata region in the framework of the MITRA project (ref). This research activity was conducted in order to analyze their temporal dynamics and reliability for systematically using them in operative applications. Next step of the project is oriented towards the identification, on the basis of satellite time series, of critical levels of the major atmospheric pollutants

  17. 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).

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

    PubMed

    Demir, Begum; Bovolo, Francesca; Bruzzone, Lorenzo

    2013-08-01

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

  19. Using satellite time series for remote sensing based investigations of ancient acqueduct systems: the case of the Nasca puquios

    NASA Astrophysics Data System (ADS)

    Lasaponara, R.; Masini, N.

    2012-04-01

    Satellite time series can provide valuable information to reconstruct ancient environmental changes, still fossilized in the present landscape. In particular, satellite derived moisture content and moisture patter variations over the seasons and years may facilitate the identification of areas involved in early environmental manipulation. Up to now, only a few number of archaeological studies on spatial patterns of moisture have been carried out through the world using satellite optical data. We focus on Landsat and ASTER multitemporal data acquired for some areas near Nasca basin (Peru) to extract information on ancient irrigation systems and artificial wet agro-ecosystems. The study area is particularly interesting mainly because it was populated since millennia ago despite its drought and critical environment conditions presented serious obstacles to human occupation. Considering this extreme drought, which characterizes this area today as several centuries ago, ancient populations of the Nasca River valley devised an efficient system for retrieval water and to face the drought conditions. This system was based on underground aqueducts called puquios, which in part are still used today. Archaeological record put in evidence that during the Nasca flourishing period, the number and spatial distribution of puquios were larger than today. On the basis of satellite multitemporal moisture maps, Unknown puquios were identified and confirmed by ground survey. This information can be a basic The successful results achieved in the Nasca Basin area may be also rejoined in similar environmental conditions (in Meso-America, Middle East, North Africa, Asia) where ancient populations devised aqueducts to face drought and retrieve water for domestic, ritual and agricultural needs. Reference Lasaponara R., Masini N., Following the Ancient Nasca Puquios from Space, in Lasaponara R. and Masini N. (Eds), Satellite Remote Sensing: A New Tool for Archaeology (Remote Sensing and Digital Image Processing), Springer, ISBN: 9048188008, pp. 269-290.

  20. Spatiotemporal Mining of Time-Series Remote Sensing Images Based on Sequential Pattern Mining

    NASA Astrophysics Data System (ADS)

    Liu, H. C.; He, G. J.; Zhang, X. M.; Jiang, W.; Ling, S. G.

    2015-07-01

    With the continuous development of satellite techniques, it is now possible to acquire a regular series of images concerning a given geographical zone with both high accuracy and low cost. Research on how best to effectively process huge volumes of observational data obtained on different dates for a specific geographical zone, and to exploit the valuable information regarding land cover contained in these images has received increasing interest from the remote sensing community. In contrast to traditional land cover change measures using pair-wise comparisons that emphasize the compositional or configurational changes between dates, this research focuses on the analysis of the temporal sequence of land cover dynamics, which refers to the succession of land cover types for a given area over more than two observational periods. Using a time series of classified Landsat images, ranging from 2006 to 2011, a sequential pattern mining method was extended to this spatiotemporal context to extract sets of connected pixels sharing similar temporal evolutions. The resultant sequential patterns could be selected (or not) based on the range of support values. These selected patterns were used to explore the spatial compositions and temporal evolutions of land cover change within the study region. Experimental results showed that continuous patterns that represent consistent land cover over time appeared as quite homogeneous zones, which agreed with our domain knowledge. Discontinuous patterns that represent land cover change trajectories were dominated by the transition from vegetation to bare land, especially during 2009-2010. This approach quantified land cover changes in terms of the percentage area affected and mapped the spatial distribution of these changes. Sequential pattern mining has been used for string mining or itemset mining in transactions analysis. The expected novel significance of this study is the generalization of the application of the sequential pattern mining method for capturing the spatial variability of landscape patterns, and their trajectories of change, to reveal information regarding process regularities with satellite imagery.

  1. 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.

  2. Time Series Satellite And In-SITU Monitoring Data For Climate Fluctuations And Seismic Precursors Assessment

    NASA Astrophysics Data System (ADS)

    Zoran, Maria; Savstru, Roxana; Savastru, Dan

    2013-12-01

    Results of recent investigations suggest that climate change tends to exacerbate geo-disasters like as earthquake events. Earthquake science has entered a new era with the development of space-based technologies to measure surface geophysical parameters and deformation at the boundaries of tectonic plates and large faults. This paper presents observations made using time series NOAA-AVHRR and MODIS satellite data-derived land surface temperature (LST) and outgoing long-wave radiation (OLR) values in case of 27th 2004 earthquake recorded in seismic active Vrancea region, Romania, using anomalous TIR signals as reflected in LST rise and high OLR values which followed similar growth pattern spatially and temporally. Starting with almost one week prior to a moderate or strong earthquake a transient thermal infrared rise in LST of several Celsius degrees (oC) and the increased OLR values higher than the normal have been recorded around epicentral areas, function of the magnitude and focal depth, which disappeared after the main shock.

  3. 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.)

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

    E-print Network

    Turner, Monica G.

    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

  5. Tools for Generating Useful Time-series Data from PhenoCam Images

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  6. Satellite time-series data for vegetation phenology detection and environmental assessment in Southeast Asia

    NASA Astrophysics Data System (ADS)

    Suepa, Tanita

    The relationship between temporal and spatial data is considered the major advantage of remote sensing in research related to biophysical characteristics. With temporally formatted remote sensing products, it is possible to monitor environmental changes as well as global climate change through time and space by analyzing vegetation phenology. Although a number of different methods have been developed to determine the seasonal cycle using time series of vegetation indices, these methods were not designed to explore and monitor changes and trends of vegetation phenology in Southeast Asia (SEA). SEA is adversely affected by impacts of climate change, which causes considerable environmental problems, and the increase in agricultural land conversion and intensification also adds to those problems. Consequently, exploring and monitoring phenological change and environmental impacts are necessary for a better understanding of the ecosystem dynamics and environmental change in this region. This research aimed to investigate inter-annual variability of vegetation phenology and rainfall seasonality, analyze the possible drivers of phenological changes from both climatic and anthropogenic factors, assess the environmental impacts in agricultural areas, and develop an enhanced visualization method for phenological information dissemination. In this research, spatio-temporal patterns of vegetation phenology were analyzed by using MODIS-EVI time series data over the period of 2001-2010. Rainfall seasonality was derived from TRMM daily rainfall rate. Additionally, this research assessed environmental impacts of GHG emissions by using the environmental model (DNDC) to quantify emissions from rice fields in Thailand. Furthermore, a web mapping application was developed to present the output of phenological and environmental analysis with interactive functions. The results revealed that satellite time-series data provided a great opportunity to study regional vegetation variability and internal climatic fluctuation. The EVI and phenological patterns varied spatially according to climate variations and human management. The overall regional mean EVI value in SEA from 2001 to 2010 has gradually decreased and phenological trends appeared to shift towards a later and slightly longer growing season. Regional vegetation dynamics over SEA exhibited patterns associated with major climate events such as El Nino in 2005. The rainy season tended to start early and end late and the length of rainy season was slightly longer. However, the amount of rainfall has decreased from 2001 to 2010. The relationship between phenology and rainfall varied among different ecosystems. Additionally, the local scale results indicated that rainfall is a dominant force of phenological changes in naturally vegetated areas and rainfed croplands, whereas human management is a key factor in heavily agricultural areas with irrigated systems. The results of estimating GHG emissions from rice fields in Thailand demonstrated that human management, climate variation, and physical geography had a significant influence on the change in GHG emissions. In addition, the complexity of spatio-temporal patterns in phenology and related variables were displayed on the visualization system with effective functions and an interactive interface. The information and knowledge in this research are useful for local and regional environmental management and for identifying mitigation strategies in the context of climate change and ecosystem dynamics in this region.

  7. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter

    NASA Technical Reports Server (NTRS)

    Boys, B. L.; Martin, R. V.; von Donkelaar, A.; MacDonnell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W.

    2014-01-01

    Ambient fine particulate matter (PM2.5) is a leading environmental risk factor for premature mortality. We use aerosol optical depth (AOD) retrieved from two satellite instruments, MISR and SeaWiFS, to produce a unified 15-year global time series (1998-2012) of ground-level PM2.5 concentration at a resolution of 1deg x 1deg. The GEOS-Chem chemical transport model (CTM) is used to relate each individual AOD retrieval to ground-level PM2.5. Four broad areas showing significant, spatially coherent, annual trends are examined in detail: the Eastern U.S. (-0.39 +/- 0.10 micro-g/cu m/yr), the Arabian Peninsula (0.81 +/- 0.21 micro-g/cu m/yr), South Asia (0.93 +/- 0.22 micro-g/cu m/yr) and East Asia (0.79 +/- 0.27 micro-g/cu m/yr). Over the period of dense in situ observation (1999- 2012), the linear tendency for the Eastern U.S. (-0.37 +/- 0.13 micro-g/cu m/yr) agrees well with that from in situ measurements (-0.38 +/- 0.06 micro-g/cu m/yr). A GEOS-Chem simulation reveals that secondary inorganic aerosols largely explain the observed PM2.5 trend over the Eastern U.S., South Asia, and East Asia, while mineral dust largely explains the observed trend over the Arabian Peninsula.

  8. Image/Time Series Mining Algorithms: Applications to Developmental Biology, Document Processing and Data Streams

    ERIC Educational Resources Information Center

    Tataw, Oben Moses

    2013-01-01

    Interdisciplinary research in computer science requires the development of computational techniques for practical application in different domains. This usually requires careful integration of different areas of technical expertise. This dissertation presents image and time series analysis algorithms, with practical interdisciplinary applications…

  9. Mapping the extent of abandoned farmland in Central and Eastern Europe using MODIS time series satellite data

    NASA Astrophysics Data System (ADS)

    Alcantara, Camilo; Kuemmerle, Tobias; Baumann, Matthias; Bragina, Eugenia V.; Griffiths, Patrick; Hostert, Patrick; Knorn, Jan; Müller, Daniel; Prishchepov, Alexander V.; Schierhorn, Florian; Sieber, Anika; Radeloff, Volker C.

    2013-09-01

    The demand for agricultural products continues to grow rapidly, but further agricultural expansion entails substantial environmental costs, making recultivating currently unused farmland an interesting alternative. The collapse of the Soviet Union in 1991 led to widespread abandonment of agricultural lands, but the extent and spatial patterns of abandonment are unclear. We quantified the extent of abandoned farmland, both croplands and pastures, across the region using MODIS NDVI satellite image time series from 2004 to 2006 and support vector machine classifications. Abandoned farmland was widespread, totaling 52.5 Mha, particularly in temperate European Russia (32 Mha), northern and western Ukraine, and Belarus. Differences in abandonment rates among countries were striking, suggesting that institutional and socio-economic factors were more important in determining the amount of abandonment than biophysical conditions. Indeed, much abandoned farmland occurred in areas without major constraints for agriculture. Our map provides a basis for assessing the potential of Central and Eastern Europe’s abandoned agricultural lands to contribute to food or bioenergy production, or carbon storage, as well as the environmental trade-offs and social constraints of recultivation.

  10. DAHITI - an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

    Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, for some years, this technology has also been used to retrieve water levels from reservoirs, wetlands and in general any inland water body, although the radar altimetry technique has been especially applied to rivers and lakes. 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 of rivers and lakes available through the web service "Database for Hydrological Time Series over Inland Waters" (DAHITI). The new method is based on an extended outlier rejection and a Kalman filter approach incorporating cross-calibrated multi-mission altimeter data from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, including their uncertainties. 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 comparisons 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 36 cm for lakes and 8 and 114 cm for rivers. For most study cases, more accurate height information than from other available altimeter databases can be achieved.

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

    PubMed Central

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

    2013-01-01

    Large lipid pools in vulnerable plaques, in principle, can be detected using US based thermal strain imaging (US-TSI). One practical challenge for in vivo cardiovascular application of US-TSI is that the thermal strain is masked by the mechanical strain caused by cardiac pulsation. ECG gating is a widely adopted method for cardiac motion compensation, but it is often susceptible to electrical and physiological noise. In this paper, we present an alternative time series analysis approach to separate thermal strain from the mechanical strain without using ECG. The performance and feasibility of the time-series analysis technique was tested via numerical simulation as well as in vitro water tank experiments using a vessel mimicking phantom and an excised human atherosclerotic artery where the cardiac pulsation is simulated by a pulsatile pump. PMID:24808628

  12. 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.

  13. Measuring Mars Sand Flux Seasonality from a Time Series of Hirise Images

    NASA Astrophysics Data System (ADS)

    Ayoub, F.; Avouac, J.; Bridges, N. T.; Leprince, S.; Lucas, A.

    2012-12-01

    The volumetric transport rate of sand, or flux, is a fundamental quantity that relates to the rate of landscape evolution through surface deposition and erosion. Measuring this quantity on Mars is particularly relevant as wind is the dominant geomorphic agent active at present on the planet. Measuring sand flux on Mars has been made possible thanks to the availability of times series of high resolution images acquired by the High Resolution Imaging Science Experiment (HiRISE) and precise image registration and correlation methods which permits the quantification of movement to sub-pixel precision. In this study, focused on the Nili Patera dune field, we first measured the migration rate of sand ripples and dune lee fronts over 105 days, using a pair of HiRISE images acquired in 2007, correlated and co-registered with COSI-Corr. From these measurements and the estimation of the ripple and dune heights, we derived the reptation and saltation sand fluxes. We next applied the same methodology to a time-series of eight images acquired in 2010-2011 covering one Mars year. Pairs of sequential images, were processed with COSI-Corr yielding a times series of 8 displacement maps. A Principal Component Analysis (PCA) was applied to the time-series to quantify more robustly the time evolution of the signal and filter out noise, in particular due to misalignment of CCDs. Using the first two components, which account for 84% of the variance, the seasonal variation of the ripple migration rate was estimated. We clearly observe continuously active migration throughout the year with a strong seasonal quasi-sinusoidal variation which peaks at perihelion. Ripple displacement orientation is stable in time, toward ~N115°E. The wind direction is thus relatively constant in this area, a finding consistent with the barchan morphology and orientation of the dunes. The dataset require that sand moving winds must occur daily to weekly throughout the year. The amplitude of the seasonal variation is about twice the mean signal.

  14. 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).

  15. Comparison of time-series registration methods in breast dynamic infrared imaging

    NASA Astrophysics Data System (ADS)

    Riyahi-Alam, S.; Agostini, V.; Molinari, F.; Knaflitz, M.

    2015-03-01

    Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons' registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons' registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation.

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

    NASA Astrophysics Data System (ADS)

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

    2013-07-01

    Satellite microwave retrievals and in situ measurements of surface soil moisture are usually compared in the time domain. This paper examines their differences in the conjugate frequency domain to develop a spectral description of the satellite data, suggesting the presence of stochastic random and systematic periodic errors. Based on a semiempirical model of the observed power spectral density, we describe systematic designs of causal and noncausal filters to remove these erroneous signals. The filters are applied to the retrievals from active and passive satellite sensors and evaluated against field data from the Murrumbidgee Basin, southeast Australia, to show substantive increase in linear correlations.

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1994-01-01

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

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

    EPA Science Inventory

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

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

    NASA Astrophysics Data System (ADS)

    Müller, B.; Bernhardt, M.; Schulz, K.

    2014-06-01

    The identification of catchment functional behavior with regard to water and energy balance is an important step during the parameterization of land surface models. An approach based on time series of thermal infrared (TIR) data from remote sensing is developed and investigated to identify land surface functioning as is represented in the temporal dynamics of land surface temperature (LST). For the meso-scale Attert catchment in midwestern Luxembourg, a time series of 28 TIR images from ASTER was extracted and analyzed. The application mathematical-statistical pattern analysis techniques demonstrated a strong degree of pattern persistency in the data. Dominant LST patterns over a period of 12 years were extracted by a principal component analysis. Component values of the 2 most dominant components could be related for each land surface pixel to vegetation/land use data, and geology, respectively. A classification of the landscape by introducing "binary word", representing distinct differences in LST dynamics, allowed the separation into functional units under radiation driven conditions. It is further outlined that both information, component values from PCA as well as the functional units from "binary words" classification, will highly improve the conceptualization and parameterization of land surface models and the planning of observational networks within a catchment.

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

    NASA Astrophysics Data System (ADS)

    Müller, Benjamin; Bernhardt, Matthias; Schulz, Karsten

    2014-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Müller, B.; Bernhardt, M.; Schulz, K.

    2014-12-01

    The identification of catchment functional behavior with regards to water and energy balance is an important step during the parameterization of land surface models. An approach based on time series of thermal infrared (TIR) data from remote sensing is developed and investigated to identify land surface functioning as is represented in the temporal dynamics of land surface temperature (LST). For the mesoscale Attert catchment in midwestern Luxembourg, a time series of 28 TIR images from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) was extracted and analyzed, applying a novel process chain. First, the application of mathematical-statistical pattern analysis techniques demonstrated a strong degree of pattern persistency in the data. Dominant LST patterns over a period of 12 years were then extracted by a principal component analysis. Component values of the two most dominant components could be related for each land surface pixel to land use data and geology, respectively. The application of a data condensation technique ("binary words") extracting distinct differences in the LST dynamics allowed the separation into landscape units that show similar behavior under radiation-driven conditions. It is further outlined that both information component values from principal component analysis (PCA), as well as the functional units from the binary words classification, will highly improve the conceptualization and parameterization of land surface models and the planning of observational networks within a catchment.

  3. Estimating the population local wavelet spectrum with application to non-stationary functional magnetic resonance imaging time series.

    PubMed

    Gott, Aimee N; Eckley, Idris A; Aston, John A D

    2015-12-20

    Functional magnetic resonance imaging (fMRI) is a dynamic four-dimensional imaging modality. However, in almost all fMRI analyses, the time series elements of this data are assumed to be second-order stationary. In this paper, we examine, using time series spectral methods, whether such stationary assumptions can be made and whether estimates of non-stationarity can be used to gain understanding into fMRI experiments. A non-stationary version of replicated stationary time series analysis is proposed that takes into account the replicated time series that are available from nearby voxels in a region of interest (ROI). These are used to investigate non-stationarities in both the ROI itself and the variations within the ROI. The proposed techniques are applied to simulated data and to an anxiety-inducing fMRI experiment. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26310288

  4. Novel approaches in Extended Principal Components Analysis to compare spatio-temporal patterns among multiple image time series

    NASA Astrophysics Data System (ADS)

    Neeti, N.; Eastman, R.

    2012-12-01

    Extended Principal Components Analysis (EPCA) aims to examine the patterns of variability shared among multiple image time series. Conventionally, this is done by virtually extending the spatial dimension of the time series by spatially concatenating the different time series and then performing S-mode PCA. In S-mode analysis, samples in space are the statistical variables and samples in time are the statistical observations. This paper introduces the concept of temporal concatenation of multiple image time series to perform EPCA. EPCA can also be done with T-mode orientation in which samples in time are the statistical variables and samples in space are the statistical observations. This leads to a total of four orientations in which EPCA can be carried out. This research explores these four orientations and their implications in investigating spatio-temporal relationships among multiple time series. This research demonstrates that EPCA carried out with temporal concatenation of the multiple time series with T-mode (tT) is able to identify similar spatial patterns among multiple time series. The conventional S-mode EPCA with spatial concatenation (sS) identifies similar temporal patterns among multiple time series. The other two modes, namely T-mode with spatial concatenation (sT) and S-mode with temporal concatenation (tS), are able to identify patterns which share consistent temporal phase relationships and consistent spatial phase relationships with each other, respectively. In a case study using three sets of precipitation time series data from GPCP, CMAP and NCEP-DOE, the results show that examination of all four modes provides an effective basis comparison of the series.

  5. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  7. Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series

    NASA Astrophysics Data System (ADS)

    Lê, Thu Trang; Atto, Abdourrahmane M.; Trouvé, Emmanuel; Solikhin, Akhmad; Pinel, Virginie

    2015-09-01

    This paper presents a method for analyzing Synthetic Aperture Radar (SAR) and polarimetric SAR (PolSAR) image time series based on change detection matrices (CDM) containing information on changed and unchanged pixels. These matrices are constructed for each spatial position over the time series by implementing similarity cross tests. The proposed matrix is then exploited for adaptive temporal filtering, analysis of change dynamics and multitemporal change detection. The proposed approach is illustrated on the three following data sets: 25 ascending TerraSAR-X images and 7 descending RADARSAT 2 full polarization images over Chamonix-MontBlanc, France, where the seasonal evolution of glaciers and mountains can be observed, and a time series of 11 ascending ALOS-PALSAR dual polarization images over Merapi volcano, Indonesia during a period including the 2010 eruption.

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

    NASA Astrophysics Data System (ADS)

    Ku, Taeyun; Lee, Jungsul; Choi, Chulhee

    2010-02-01

    Measurement of cerebral perfusion is important for study of various brain disorders such as stroke, epilepsy, and vascular dementia; however, efficient and convenient methods which can provide quantitative information about cerebral blood flow are not developed. Here we propose an optical imaging method using time-series analysis of dynamics of indocyanine green (ICG) fluorescence to generate cerebral blood flow maps. In scalp-removed mice, ICG was injected intravenously, and 740nm LED light was illuminated for fluorescence emission signals around 820nm acquired by cooled-CCD. Time-lapse 2-dimensional images were analyzed by custom-built software, and the maximal time point of fluorescent influx in each pixel was processed as a blood flow-related parameter. The generated map exactly reflected the shape of the brain without any interference of the skull, the dura mater, and other soft tissues. This method may be further applicable for study of other disease models in which the cerebral hemodynamics is changed either acutely or chronically.

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

    PubMed

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

    2014-06-01

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

  10. Remote sensing evaluation of ecosystem service value of gas regulation with time series Landsat images

    NASA Astrophysics Data System (ADS)

    Gu, Xiaohe; Guo, Wei; Wang, Yancang; Yang, Guijun

    2014-10-01

    Gas regulation is one of the important ecological service functions of ecosystem. Plants transform solar energy into biotic energy through photosynthesis, fixing CO2 and releasing O2, which plays an irreplaceable role in maintaining the CO2/O2 balance and mitigating greenhouse gases emissions. The ecosystem service value of gas regulation can be evaluated from the amount of CO2 and releasing O2. Taken the net primary productivity (NPP) of ecosystem as transition parameter, the value of gas regulation service in Beijing city in recent 30 years was evaluated and mapped with time series LandSat images, which was used to analyze the spatial patterns and driving forces. Results showed that he order of ecosystem service value of gas regulation in Beijing area was 1978 < 1992 < 2000 < 2010, which was consistent with the order of NPP. The contribution order for gas regulation service of six ecosystems from1978 to 2010 was basically stable. The forest and farmland played important roles of gas regulation, of which the proportion reached 80% and varied with the area from 1978 to 2010. It indicated that increasing the area of forest and farmland was helpful for enhance the ecosystem service value of gas regulation.

  11. 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.

  12. Characterising volcanic cycles at Soufriere Hills Volcano, Montserrat: Time series analysis of multi-parameter satellite data

    NASA Astrophysics Data System (ADS)

    Flower, Verity J. B.; Carn, Simon A.

    2015-10-01

    The identification of cyclic volcanic activity can elucidate underlying eruption dynamics and aid volcanic hazard mitigation. Whilst satellite datasets are often analysed individually, here we exploit the multi-platform NASA A-Train satellite constellation to cross-correlate cyclical signals identified using complementary measurement techniques at Soufriere Hills Volcano (SHV), Montserrat. In this paper we present a Multi-taper (MTM) Fast Fourier Transform (FFT) analysis of coincident SO2 and thermal infrared (TIR) satellite measurements at SHV facilitating the identification of cyclical volcanic behaviour. These measurements were collected by the Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) (respectively) in the A-Train. We identify a correlating cycle in both the OMI and MODIS data (54-58 days), with this multi-week feature attributable to episodes of dome growth. The ~ 50 day cycles were also identified in ground-based SO2 data at SHV, confirming the validity of our analysis and further corroborating the presence of this cycle at the volcano. In addition a 12 day cycle was identified in the OMI data, previously attributed to variable lava effusion rates on shorter timescales. OMI data also display a one week (7-8 days) cycle attributable to cyclical variations in viewing angle resulting from the orbital characteristics of the Aura satellite. Longer period cycles possibly relating to magma intrusion were identified in the OMI record (102-, 121-, and 159 days); in addition to a 238-day cycle identified in the MODIS data corresponding to periodic destabilisation of the lava dome. Through the analysis of reconstructions generated from cycles identified in the OMI and MODIS data, periods of unrest were identified, including the major dome collapse of 20th May 2006 and significant explosive event of 3rd January 2009. Our analysis confirms the potential for identification of cyclical volcanic activity through combined analysis of satellite data, which would be of particular value at poorly monitored volcanic systems.

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  14. 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.

  15. 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 .

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

    USGS Publications Warehouse

    Eldenshink, J.

    2006-01-01

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

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

    E-print Network

    Tourneret, Jean-Yves

    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

  18. 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.

  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. Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images.

    PubMed

    Wang, Jing; Huang, Jing-Feng; Wang, Xiu-Zhen; Jin, Meng-Ting; Zhou, Zhen; Guo, Qiao-Ying; Zhao, Zhe-Wen; Huang, Wei-Jiao; Zhang, Yao; Song, Xiao-Dong

    2015-10-01

    Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of HJ-1 CCD and Landsat-8 operational land imager (OLI) by using the ordinary least-squares (OLS), and construct higher temporal resolution vegetation indices (VIs) time-series data to extract the phenological parameters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2), were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay (S-G) filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error (RMSE), and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available. PMID:26465131

  1. Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images*

    PubMed Central

    Wang, Jing; Huang, Jing-Feng; Wang, Xiu-Zhen; Jin, Meng-Ting; Zhou, Zhen; Guo, Qiao-Ying; Zhao, Zhe-Wen; Huang, Wei-Jiao; Zhang, Yao; Song, Xiao-Dong

    2015-01-01

    Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of HJ-1 CCD and Landsat-8 operational land imager (OLI) by using the ordinary least-squares (OLS), and construct higher temporal resolution vegetation indices (VIs) time-series data to extract the phenological parameters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2), were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay (S-G) filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error (RMSE), and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available. PMID:26465131

  2. 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.

  3. Satellite camera image navigation

    NASA Technical Reports Server (NTRS)

    Kamel, Ahmed A. (Inventor); Graul, Donald W. (Inventor); Savides, John (Inventor); Hanson, Charles W. (Inventor)

    1987-01-01

    Pixels within a satellite camera (1, 2) image are precisely located in terms of latitude and longitude on a celestial body, such as the earth, being imaged. A computer (60) on the earth generates models (40, 50) of the satellite's orbit and attitude, respectively. The orbit model (40) is generated from measurements of stars and landmarks taken by the camera (1, 2), and by range data. The orbit model (40) is an expression of the satellite's latitude and longitude at the subsatellite point, and of the altitude of the satellite, as a function of time, using as coefficients (K) the six Keplerian elements at epoch. The attitude model (50) is based upon star measurements taken by each camera (1, 2). The attitude model (50) is a set of expressions for the deviations in a set of mutually orthogonal reference optical axes (x, y, z) as a function of time, for each camera (1, 2). Measured data is fit into the models (40, 50) using a walking least squares fit algorithm. A transformation computer (66 ) transforms pixel coordinates as telemetered by the camera (1, 2) into earth latitude and longitude coordinates, using the orbit and attitude models (40, 50).

  4. Prostate cancer detection from model-free T1-weighted time series and diffusion imaging

    NASA Astrophysics Data System (ADS)

    Haq, Nandinee F.; Kozlowski, Piotr; Jones, Edward C.; Chang, Silvia D.; Goldenberg, S. Larry; Moradi, Mehdi

    2015-03-01

    The combination of Dynamic Contrast Enhanced (DCE) images with diffusion MRI has shown great potential in prostate cancer detection. The parameterization of DCE images to generate cancer markers is traditionally performed based on pharmacokinetic modeling. However, pharmacokinetic models make simplistic assumptions about the tissue perfusion process, require the knowledge of contrast agent concentration in a major artery, and the modeling process is sensitive to noise and fitting instabilities. We address this issue by extracting features directly from the DCE T1-weighted time course without modeling. In this work, we employed a set of data-driven features generated by mapping the DCE T1 time course to its principal component space, along with diffusion MRI features to detect prostate cancer. The optimal set of DCE features is extracted with sparse regularized regression through a Least Absolute Shrinkage and Selection Operator (LASSO) model. We show that when our proposed features are used within the multiparametric MRI protocol to replace the pharmacokinetic parameters, the area under ROC curve is 0.91 for peripheral zone classification and 0.87 for whole gland classification. We were able to correctly classify 32 out of 35 peripheral tumor areas identified in the data when the proposed features were used with support vector machine classification. The proposed feature set was used to generate cancer likelihood maps for the prostate gland.

  5. 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.

  6. 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.

  7. Creation of Normalized Time Series Data for Microwave Radiometer AMSR2 Loaded on GCOM-W1 Satellite Launched Soon

    NASA Astrophysics Data System (ADS)

    Maeda, T.

    2012-04-01

    The operation of the Advanced Microwave Scanning Radiometer for Earth-Observation System (AMSR-E) loaded on Aqua satellite stopped in October, 2011 after more than 9-years observation. But, JAXA plans to launch GCOM-W1 (Global Change Observation Mission 1st - Water) satellite carrying the successor of AMSR-E (AMSR2), and this satellite will be launched in 2012. AMSR2 is a microwave radiometer to observe microwave signals at 6.9, 7.3, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz emitted from the Earth almost twice a day because GCOM-W1 satellite is deployed into a sun-synchronous sub-recurrent orbit. Generally, measuring points of a spaceborne instrument differently distribute according to satellite tracks, eventually, observation times. The location and size of a receiver's footprint for one measurement also differ among frequencies. Therefore, as long as some kind of resampling method is applied, the data of different times and frequencies cannot be compared. As for a microwave radiometer, because the footprint size is large, and the main mission is to globally retrieve physical quantities, it has not been sufficiently considered what kind of resampling method is most suitable, which deteriorated the qualities of retrieved physical quantities. In contrast, because brightness temperature data observed by AMSR2 are resampled by Backus-Gilbert method, footprint size and location among frequencies are unified. These data are provided as the standard product (L1R product). However, because resampling by Backus-Gilbert method requires high amount of calculation, the process to make the L1R product is simplified, and the data of different times and frequencies still cannot be compared at an arbitrary point. We have developed a method to extract local and faint changes from AMSR-E data, and the idea of Backus-Gilbert method is important to use AMSR2 data for our purpose. So, we implemented Backus-Gilbert method without simplifying to our method to compare the data of different times at an arbitrary point. This paper presents the overview of AMSR2, the details of our method improved by Backus-Gilbert method.

  8. 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.

  9. 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.

  10. 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

  11. Reduction of Influences of the Earth's Surface Fluid Loads on GPS Site Coordinate Time Series and Global Satellite Laser Ranging Analysis

    NASA Astrophysics Data System (ADS)

    Takiguchi, H.; Otsubo, T.; Fukuda, Y.

    2006-12-01

    Temporal change of surface loadings due to the mass redistribution of the fluid envelope of the Earth, deform the Earth and cause the coordinate changes of the observation sites. We estimated the crustal displacements due to the atmospheric load (AL), the non-tidal ocean load (NTOL), the continental water load (CWL) and the snow load (SL) influences using the several meteorological data and model. And then, we tried to eliminate the load influences from the GPS site coordinate time series and global Satellite Laser Ranging (SLR) analysis. As the time series of GPS site coordinates, we employed a solution of IGS which was calculated by using GIPSY-OASIS II (Heflin et al., 2002) by the Jet Propulsion Laboratory (JPL) and the routine solution of GEONET called F2 solution which was calculated by Bernese version 4.2 software (Hatanaka et al., 2003) by the Geographical Survey Institute. To eliminate periodic signals of the loading effects, we calculated Corrected GPS = GPS - (Load1 + Load2 + . . . . . + Loadn). The results show that a combination of atmospheric, non-tidal ocean, continental water, and snow loads can eliminate about 20% of the annual signal in the coordinate time series for vertical components. We applied the loading correction to the data of the 1997 Bungo channel slow slip event and showed that the correction can benefit the analysis of such a non-periodic event. Next, we applied the time series of NTOL and CWL to precise SLR analysis that used the "concerto" program version 4 developed by the National Institute of Information and Communications Technology (NICT). The LAGEOS orbit analysis reveals that the Estimating the Circulation and Climate of the Ocean (ECCO) model makes the root mean square (RMS) of the range residual 0.2% smaller, and that the CWL makes it 0.8% smaller, compared with the case where loading displacement is neglected. On the other hand, with the NTOL derived from Topex/Poseidon altimetry data, the SLR orbit fit is not improved.

  12. Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

    NASA Astrophysics Data System (ADS)

    Bindhu, V. M.; Narasimhan, B.

    2015-03-01

    Normalized Difference Vegetation Index (NDVI), a key parameter in understanding the vegetation dynamics, has high spatial and temporal variability. However, continuous monitoring of NDVI is not feasible at fine spatial resolution (<60 m) owing to the long revisit time needed by the satellites to acquire the fine spatial resolution data. Further, the study attains significance in the case of humid tropical regions of the earth, where the prevailing atmospheric conditions restrict availability of fine resolution cloud free images at a high temporal frequency. As an alternative to the lack of high resolution images, the current study demonstrates a novel disaggregation method (DisNDVI) which integrates the spatial information from a single fine resolution image and temporal information in terms of crop phenology from time series of coarse resolution images to generate estimates of NDVI at fine spatial and temporal resolution. The phenological variation of the pixels captured at the coarser scale provides the basis for relating the temporal variability of the pixel with the NDVI available at fine resolution. The proposed methodology was tested over a 30 km × 25 km spatially heterogeneous study area located in the south of Tamil Nadu, India. The robustness of the algorithm was assessed by an independent comparison of the disaggregated NDVI and observed NDVI obtained from concurrent Landsat ETM+ imagery. The results showed good spatial agreement across the study area dominated with agriculture and forest pixels, with a root mean square error of 0.05. The validation done at the coarser scale showed that disaggregated NDVI spatially averaged to 240 m compared well with concurrent MODIS NDVI at 240 m (R2 > 0.8). The validation results demonstrate the effectiveness of DisNDVI in improving the spatial and temporal resolution of NDVI images for utility in fine scale hydrological applications such as crop growth monitoring and estimation of evapotranspiration.

  13. 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

  14. 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.

  15. 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

  16. Satellite Retrievals of Vegetation Optical Depth Using Time-Series of Dual-Polarized and Single Look-Angle Global Microwave Observations

    NASA Astrophysics Data System (ADS)

    Piles, M.; Konings, A. G.; Mccoll, K. A.; Chan, S.; Entekhabi, D.

    2014-12-01

    Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate depends critically on knowing where, when and how carbon dioxide is exchanged. Vegetation biomass is an important carbon sink that varies significantly over annual and inter-annual timescales. At global scales, the only feasible approach for monitoring vegetation biomass is satellite remote sensing. In this regard, existing passive microwave missions have the potential of estimating Vegetation Optical Depth (VOD), an indicator of total aboveground vegetation water content, closely related to vegetation biomass. Present approaches provide VOD as a soil moisture inversion residual at every time step and are therefore highly contaminated by residuals from model error. This work presents a novel technique for retrieving VOD using time-series of dual-polarized microwave observations. Taking advantage of the slow-time dynamics of VOD, a number of consecutive observations are used to estimate a single VOD. The soil dielectric constant of each observation is also retrieved simultaneously and later used as a consistency check. The method has been applied to two years of L-band passive observations from the NASA's Aquarius sensor. Results show global VOD distribution follows general gradients of climate and canopy biomass conditions, with characteristic seasonal variability among the major land cover classes. Satellite retrievals of microwave VOD provide independent but complementary information to other remote sensing vegetation metrics such as fluorescence and optical-infrared indices. The method presented here could be used in satellite missions such as SMOS and SMAP to decouple soil effects from vegetation, for the benefit of soil moisture retrievals. Also, it could be used to generate a new observational record of vegetation water content for a more comprehensive view of land surface phenology and terrestrial ecology.

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

    NASA Astrophysics Data System (ADS)

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

    2004-12-01

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

  18. Aerial Photographs and Satellite Images

    USGS Publications Warehouse

    U.S. Geological Survey

    1997-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-01-01

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

  20. Satellite imagery meets prepress - Producing image maps

    SciTech Connect

    Audrain, V.; Fehrenbach, J.; Reading, M.; Stauffer, R. )

    1993-07-01

    Satellite imagery provided by Landsat, SPOT, AVHRR, and ERS-1 is being exploited for production of satellite image maps which incorporate images. Satellite image maps produced by transforming digital imagery acquired by spaceborn platforms into lithographic printed maps are used in two primary areas: large scale orthophoto maps for the engineering and construction industries and small-scale satellite image maps for the military and environmental resource organizations.

  1. 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

  2. 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.

  3. Collecting and Animating Online Satellite Images.

    ERIC Educational Resources Information Center

    Irons, Ralph

    1995-01-01

    Describes how to generate automated classroom resources from the Internet. Topics covered include viewing animated satellite weather images using file transfer protocol (FTP); sources of images on the Internet; shareware available for viewing images; software for automating image retrieval; procedures for animating satellite images; and storing…

  4. Pattern Recognition in Time Series

    NASA Astrophysics Data System (ADS)

    Lin, Jessica; Williamson, Sheri; Borne, Kirk D.; DeBarr, David

    2012-03-01

    Perhaps the most commonly encountered data types are time series, touching almost every aspect of human life, including astronomy. One obvious problem of handling time-series databases concerns with its typically massive size—gigabytes or even terabytes are common, with more and more databases reaching the petabyte scale. For example, in telecommunication, large companies like AT&T produce several hundred millions long-distance records per day [Cort00]. In astronomy, time-domain surveys are relatively new—these are surveys that cover a significant fraction of the sky with many repeat observations, thereby producing time series for millions or billions of objects. Several such time-domain sky surveys are now completed, such as the MACHO [Alco01],OGLE [Szym05], SDSS Stripe 82 [Bram08], SuperMACHO [Garg08], and Berkeley’s Transients Classification Pipeline (TCP) [Star08] projects. The Pan-STARRS project is an active sky survey—it began in 2010, a 3-year survey covering three-fourths of the sky with ˜60 observations of each field [Kais04]. The Large Synoptic Survey Telescope (LSST) project proposes to survey 50% of the visible sky repeatedly approximately 1000 times over a 10-year period, creating a 100-petabyte image archive and a 20-petabyte science database (http://www.lsst.org/). The LSST science database will include time series of over 100 scientific parameters for each of approximately 50 billion astronomical sources—this will be the largest data collection (and certainly the largest time series database) ever assembled in astronomy, and it rivals any other discipline’s massive data collections for sheer size and complexity. More common in astronomy are time series of flux measurements. As a consequence of many decades of observations (and in some cases, hundreds of years), a large variety of flux variations have been detected in astronomical objects, including periodic variations (e.g., pulsating stars, rotators, pulsars, eclipsing binaries, planetary transits), quasi-periodic variations (e.g., star spots, neutron star oscillations, active galactic nuclei), outburst events (e.g., accretion binaries, cataclysmic variable stars, symbiotic stars), transient events (e.g., gamma-ray bursts (GRB), flare stars, novae, supernovae (SNe)), stochastic variations (e.g., quasars, cosmic rays, luminous blue variables (LBVs)), and random events with precisely predictable patterns (e.g., microlensing events). Several such astrophysical phenomena are wavelength-specific cases, or were discovered as a result of wavelength-specific flux variations, such as soft gamma ray repeaters, x-ray binaries, radio pulsars, and gravitational waves. Despite the wealth of discoveries in this space of time variability, there is still a vast unexplored region, especially at low flux levels and short time scales (see also the chapter by Bloom and Richards in this book). Figure 28.1 illustrates the gap in astronomical knowledge in this time-domain space. The LSST project aims to explore phenomena in the time gap. In addition to flux-based time series, astronomical data also include motion-based time series. These include the trajectories of planets, comets, and asteroids in the Solar System, the motions of stars around the massive black hole at the center of the Milky Way galaxy, and the motion of gas filaments in the interstellar medium (e.g., expanding supernova blast wave shells). In most cases, the motions measured in the time series correspond to the actual changing positions of the objects being studied. In other cases, the detected motions indirectly reflect other changes in the astronomical phenomenon, such as light echoes reflecting across vast gas and dust clouds, or propagating waves.

  5. Disaggregating times series data

    SciTech Connect

    Joubert, S.B.; Burr, T.; Scovel, J.C.

    1997-05-01

    This report describes our experiences with disaggregating time series data. Suppose we have gathered data every two seconds and want to guess the data at one-second intervals. Under certain assumptions, there are several reasonable disaggregation methods as well as several performance measures to judge their performance. Here we present results for both simulated and real data for two methods using several performance criteria.

  6. MODIS Vegetation Indices time series improvement considering real acquisition dates

    NASA Astrophysics Data System (ADS)

    Testa, S.; Borgogno Mondino, E.

    2013-12-01

    Satellite Vegetation Indices (VI) time series images are widely used for the characterization phenology, which requires a high temporal accuracy of the satellite data. The present work is based on the MODerate resolution Imaging Spectroradiometer (MODIS) MOD13Q1 product - Vegetation Indices 16-Day L3 Global 250m, which is generated through a maximum value compositing process that reduces the number of cloudy pixels and excludes, when possible, off-nadir ones. Because of its 16-days compositing period, the distance between two adjacent-in-time values within each pixel NDVI time series can range from 1 to 32 days, thus not acceptable for phenologic studies. Moreover, most of the available smoothing algorithms, which are widely used for phenology characterization, assume that data points are equidistant in time and contemporary over the image. The objective of this work was to assess temporal features of NDVI time series over a test area, composed by Castanea sativa (chestnut) and Fagus sylvatica (beech) pure pixels within the Piemonte region in Northwestern Italy. Firstly, NDVI, Pixel Reliability (PR) and Composite Day of the Year (CDOY) data ranging from 2000 to 2011 were extracted from MOD13Q1 and corresponding time series were generated (in further computations, 2000 was not considered since it is not complete because acquisition began in February and calibration is unreliable until October). Analysis of CDOY time series (containing the actual reference date of each NDVI value) over the selected study areas showed NDVI values to be prevalently generated from data acquired at the centre of each 16-days period (the 9th day), at least constantly along the year. This leads to consider each original NDVI value nominally placed to the centre of its 16-days reference period. Then, a new NDVI time series was generated: a) moving each NDVI value to its actual "acquisition" date, b) interpolating the obtained temporary time series through SPLINE functions, c) sampling such function to the 9th day of each 16-days period. TIMESAT 3.1 was used to filter and smooth both NDVI time series, with the PR time series used to underweight cloudy pixels, and to extract from each one a set of three seasonality parameters (starting season and end season dates, length of season). Lastly, differences between seasonality parameters were calculated for each considered year. Results showed a negative bias in starting season date estimation through the not-improved NDVI time series (starting season is anticipated) and a positive bias when estimating the end season date (end season occurrence is postponed). The combination of such biases affects the estimated length of the growing season, which results longer in the original, uncorrected MOD13Q1 time series than in the improved time series.

  7. NASA SSTI CLARK 3-meter imaging satellite

    NASA Technical Reports Server (NTRS)

    Sebestyen, George; Hayduk, Robert J.

    1996-01-01

    Within the framework of NASA's Small Satellite Technology Initiative (SSTI) CLARK program, the development of a high technology small satellite is reported on. The satellite will provide 3 m resolution panchromatic and 15 m resolution multispectral image capabilities. The satellite, programmed for launch in 1996, will be in a sun-synchronous orbit and includes the following systems: three-axis zero momentum attitude control; hydrazine propulsion for stationkeeping; Global Positioning System satellite position and star tracker attitude determination; and onboard image storage capability. The objectives of the SSTI CLARK program, the technical characteristics of the satellite, the techniques employed to reduce costs, and the image processing, archiving and distribution are described.

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

    NASA Astrophysics Data System (ADS)

    Liu, Chen-Chung; Yang, Chih-Chao

    2010-09-01

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

  10. 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

  11. 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.

  12. Modelling bursty time series

    NASA Astrophysics Data System (ADS)

    Vajna, Szabolcs; Tóth, Bálint; Kertész, János

    2013-10-01

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

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. A self-documenting source-independent data format for computer processing of tensor time series. [for filing satellite geophysical data

    NASA Technical Reports Server (NTRS)

    Mcpherron, R. L.

    1976-01-01

    The UCLA Space Science Group has developed a fixed format intermediate data set called a block data set, which is designed to hold multiple segments of multicomponent sampled data series. The format is sufficiently general so that tensor functions of one or more independent variables can be stored in the form of virtual data. This makes it possible for the unit data records of the block data set to be arrays of a single dependent variable rather than discrete samples. The format is self-documenting with parameter, label and header records completely characterizing the contents of the file. The block data set has been applied to the filing of satellite data (of ATS-6 among others).

  18. 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.

  19. Providing web-based tools for time series access and analysis

    NASA Astrophysics Data System (ADS)

    Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane

    2014-05-01

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

  20. Satellite image classification using convolutional learning

    NASA Astrophysics Data System (ADS)

    Nguyen, Thao; Han, Jiho; Park, Dong-Chul

    2013-10-01

    A satellite image classification method using Convolutional Neural Network (CNN) architecture is proposed in this paper. As a special case of deep learning, CNN classifies classes of images without any feature extraction step while other existing classification methods utilize rather complex feature extraction processes. Experiments on a set of satellite image data and the preliminary results show that the proposed classification method can be a promising alternative over existing feature extraction-based schemes in terms of classification accuracy and classification speed.

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

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2013-01-01

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

  2. 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

  3. Satellite image analysis using neural networks

    NASA Technical Reports Server (NTRS)

    Sheldon, Roger A.

    1990-01-01

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

  4. ENVIRONMENTALLYORIENTED PROCESSING OF MULTISPECTRAL SATELLITE IMAGES

    E-print Network

    Kreinovich, Vladik

    satellites present an op­ portunity for scientists to investigate problems in environmental and earth scienceENVIRONMENTALLY­ORIENTED 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

  5. Time Series Analysis 1 Time series in astronomy

    E-print Network

    Babu, G. Jogesh

    in astronomy Periodic phenomena: binary orbits (stars, extrasolar planets); stellar rotation (radio pulsars star. Highly variable X-rays are produced in the inner accretion disk. X-ray binary time series often

  6. FROG: Time-series analysis

    NASA Astrophysics Data System (ADS)

    Allan, Alasdair

    2014-06-01

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

  7. Time series with tailored nonlinearities

    NASA Astrophysics Data System (ADS)

    Räth, C.; Laut, I.

    2015-10-01

    It is demonstrated how to generate time series with tailored nonlinearities by inducing well-defined constraints on the Fourier phases. Correlations between the phase information of adjacent phases and (static and dynamic) measures of nonlinearities are established and their origin is explained. By applying a set of simple constraints on the phases of an originally linear and uncorrelated Gaussian time series, the observed scaling behavior of the intensity distribution of empirical time series can be reproduced. The power law character of the intensity distributions being typical for, e.g., turbulence and financial data can thus be explained in terms of phase correlations.

  8. Time series modeling for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Sokolnikov, Andre

    2012-05-01

    Time series modeling is proposed for identification of targets whose images are not clearly seen. The model building takes into account air turbulence, precipitation, fog, smoke and other factors obscuring and distorting the image. The complex of library data (of images, etc.) serving as a basis for identification provides the deterministic part of the identification process, while the partial image features, distorted parts, irrelevant pieces and absence of particular features comprise the stochastic part of the target identification. The missing data approach is elaborated that helps the prediction process for the image creation or reconstruction. The results are provided.

  9. 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...

  10. 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.

  11. 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.

  12. Determining the type and starting time of land cover and land use change in Ghana based on discrete analysis of dense Landsat image time series

    NASA Astrophysics Data System (ADS)

    Shih, Hsiao-chien

    Accra, Ghana and environs have experienced extensive land cover and land use change, which warrants more frequent monitoring. In the study I develop and test approaches for semi-automatically identifying the type and date of land cover and land use change from multi-temporal series of Landsat ETM+ imagery from 2000 to 2014. Clouds, cloud shadows, and scan line corrector-off creates missing or null data in the ETM+ images. Forty-one dates of ETM+ images that partially contain missing data were used in this study. The general approach is to conduct a per-pixel supervised classification on each date of image after masking null data based on stable training sites. Spatial, temporal, and logical filters are applied to correct for misclassification and missing data. Each image is classified into three general classes: Built, Natural Vegetation, and Agricultural, with expansion of Built being our main focus. Reference data for Change-to-Built were independently selected from all available high spatial resolution satellite images, and the beginning time of change was recorded. The change product was used to characterize the urban expansion around Accra. The result shows that the temporal-filtered product identified both the location and the start of Change-to-Built more precisely and accurately. Based on reference data derived from visual imagery analysis, 40% of the Change-to-Built samples were correctly identified without filtering, whereas 80% were correctly identified when applying temporal filter with low amounts of false positive Change-to-Built pixels. The temporal-filtered products have the highest precision and accuracy in identifying the start of Change-to-Built: the identified Change-to-Built samples are on average 2.1 years difference with the reference data. Under the limitation of frequent cloud effect and limited historical archives of high resolution images, a discrete classification approach to LCLUC mapping is shown to be successful by this study, but continuous index approaches needs to be evaluated in future research.

  13. Detecting Sea-Ice Ridges Using Satellite Imaging

    E-print Network

    Detecting Sea-Ice Ridges Using Satellite Imaging Aim: Sea ice radar backscatter model The aim satellite borne radar (SAR) data? And if so, to es- tablish the best SAR sensor parameters pressure ridge diagram.2 References: Image of satellite, Allos 2 Satellite, image -global

  14. Aerial photographs and satellite images

    USGS Publications Warehouse

    U.S. Geological Survey

    1995-01-01

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

  15. 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

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

    ERIC Educational Resources Information Center

    Nous, Albert P.

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

  17. 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...

  18. 4-D display of satellite cloud images

    NASA Technical Reports Server (NTRS)

    Hibbard, William L.

    1987-01-01

    A technique has been developed to display GOES satellite cloud images in perspective over a topographical map. Cloud heights are estimated using temperatures from an infrared (IR) satellite image, surface temperature observations, and a climatological model of vertical temperature profiles. Cloud levels are discriminated from each other and from the ground using a pattern recognition algorithm based on the brightness variance technique of Coakley and Bretherton. The cloud regions found by the pattern recognizer are rendered in three-dimensional perspective over a topographical map by an efficient remap of the visible image. The visible shades are mixed with an artificial shade based on the geometry of the cloud-top surface, in order to enhance the texture of the cloud top.

  19. Inductive time series modeling program

    SciTech Connect

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

    1985-10-01

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

  20. Antarctica: measuring glacier velocity from satellite images

    SciTech Connect

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

    1986-11-28

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

  1. Antarctica: Measuring glacier velocity from satellite images

    USGS Publications Warehouse

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

    1986-01-01

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

  2. A neuromorphic approach to satellite image understanding

    NASA Astrophysics Data System (ADS)

    Partsinevelos, Panagiotis; Perakakis, Manolis

    2014-05-01

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

  3. Segmentation of Infrared Satellite Images V. Lakshmanan1,2

    E-print Network

    Lakshmanan, Valliappa

    Segmentation of Infrared Satellite Images V. Lakshmanan1,2 , R. Rabin1,3 , V. DeBrunner2 1 National/NSSL, ICIP 2000 Introduction Image Description · Satellite infrared image collected every 15 minutes. · 11µ of an infrared image and contouring the same image based on watersheds. #12;V Lakshmanan, OU/NSSL, ICIP 2000

  4. 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").

  5. Introduction to Time Series Analysis

    NASA Technical Reports Server (NTRS)

    Hardin, J. C.

    1986-01-01

    The field of time series analysis is explored from its logical foundations to the most modern data analysis techniques. The presentation is developed, as far as possible, for continuous data, so that the inevitable use of discrete mathematics is postponed until the reader has gained some familiarity with the concepts. The monograph seeks to provide the reader with both the theoretical overview and the practical details necessary to correctly apply the full range of these powerful techniques. In addition, the last chapter introduces many specialized areas where research is currently in progress.

  6. Monitoring of wetlands Ecosystems using satellite images

    NASA Astrophysics Data System (ADS)

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

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

  7. Estimating seasonal evapotranspiration from temporal satellite images

    USGS Publications Warehouse

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

    2012-01-01

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

  8. 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.

  9. 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.

  10. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  13. 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.

  14. Star sightings by satellite for image navigation

    NASA Technical Reports Server (NTRS)

    Kamel, Ahmed A. (Inventor); Ekman, Donald E. (Inventor); Savides, John (Inventor); Zwirn, Gerald J. (Inventor)

    1988-01-01

    Stars are sensed by one or more instruments (1, 2) on board a three-axis stabilized satellite, for purposes of assisting in image navigation. A star acquistion computer (64), which may be located on the earth, commands the instrument mirror (33, 32) to slew just outside the limb of the earth or other celestial body around which the satellite is orbiting, to look for stars that have been cataloged in a star map stored within the computer (64). The instrument (1, 2) is commanded to dwell for a period of time equal to a star search window time, plus the maximum time the instrument (1, 2) takes to complete a current scan, plus the maximum time it takes for the mirror (33, 32) to slew to the star. When the satellite is first placed in orbit, and following first stationkeeping and eclipse, a special operation is performed in which the star-seeking instrument (1, 2) FOV is broadened. The elevation dimension can be broadened by performing repetitive star seeks; the azimuth dimension can be broadened by lengthening the commanded dwell times.

  15. A review of subsequence time series clustering.

    PubMed

    Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

    2014-01-01

    Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

  16. A Review of Subsequence Time Series Clustering

    PubMed Central

    Teh, Ying Wah

    2014-01-01

    Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  18. 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.

  19. Radiometric normalization and cloud detection of optical satellite images using invariant pixels

    NASA Astrophysics Data System (ADS)

    Lin, Chao-Hung; Lin, Bo-Yi; Lee, Kuan-Yi; Chen, Yi-Chen

    2015-08-01

    Clouds in optical satellite images can be a source of information for water measurement or viewed as contaminations that obstruct landscape observations. Thus, the use of a cloud detection method that discriminates cloud and clear-sky pixels in images is necessary in remote sensing applications. With the aid of radiometric correction/normalization, previous methods utilized temporal and spectral information as well as cloud-free reference images to develop threshold-based cloud detection filters. Although this strategy can effectively identify cloud pixels, the detection accuracy mainly relies on the successful radiometric correction/normalization and reference image quality. Relative radiometric normalization generally suffers from cloud covers, while multi-temporal cloud detection is sensitive to the radiometric normalization quality. Thus, the current study proposes a method based on weighted invariant pixels for both processes. A set of invariant pixels is extracted from a time series of cloud-contaminated images by using the proposed weighted principle component analysis, after which multi-temporal images are normalized with the selected invariant pixels. In addition, a reference image is generated for each cloud-contaminated image using invariant pixels with a weighting scheme. In the experiments, image sequences acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor are analyzed qualitatively and quantitatively to evaluate the proposed method. Experimental results indicate that F-measures of cloud detections are improved by 1.1-6.9% using the generated reference images.

  20. 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.

  1. 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.

  2. Fundamental Constraints on Imaging Geosynchronous Satellites

    NASA Astrophysics Data System (ADS)

    Mozurkewich, D.; Schmitt, H.; Armstrong, T.

    Imaging objects in geosynchronous orbit is becoming an increasingly important topic in space situational awareness as evidenced by DARPA recently funding the Galileo program and sponsoring a "deep-space imaging workshop". Because of the required high-angular-resolution, an interferometer is the instrument of choice; there is, however, little consensus about what that system should look like. The DARPA workshop expanded the discussion to include heterodyne interferometry, telescopes mounted on steerable platforms and many more telescopes. However, it is not obvious how performance of these systems varies as a function of design parameters. This paper presents quantitative relationships between system parameters and performance. Sensitivity, how faint an object can be imaged, can be improved by increasing the telescope diameters and the quality of the adaptive optics. Increasing the number of telescopes also helps because shorter baselines, which have higher fringe contrast, can be used to phase the array. Once fringes can be measured, the imaging time is determined by how many times the system has to be reconfigured to make observations at all the required spatial frequencies. The relationships presented here have been validated using detailed numerical models. They constrain the parameter space of workable designs and provide a basis for comparing the cost and feasibility of various designs. Low resolution interferometric observations of such satellites is needed in order to further refine the assumptions used in the calculations presented here.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  4. 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.

  5. 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.

  6. Landslide monitoring using airphotos time series and GIS

    NASA Astrophysics Data System (ADS)

    Kavoura, Katerina; Nikolakopoulos, Konstantinos G.; Sabatakakis, Nikolaos

    2014-10-01

    Western Greece is suffering by landslides. The term landslide includes a wide range of ground movement, such as slides, falls, flows etc. mainly based on gravity with the aid of many conditioning and triggering factors. Landslides provoke enormous changes to the natural and artificial relief. The annual cost of repairing the damage amounts to millions of euros. In this paper a combined use of airphotos time series, high resolution remote sensing data and GIS for the landslide monitoring is presented. Analog and digital air-photos used covered a period of almost 70 years from 1945 until 2012. Classical analog airphotos covered the period from 1945 to 2000, while digital airphotos and satellite images covered the 2008-2012 period. The air photos have been orthorectified using the Leica Photogrammetry Suite. Ground control points and a high accuracy DSM were used for the orthorectification of the air photos. The 2008 digital air photo mosaic from the Greek Cadastral with a spatial resolution of 25 cm and the respective DSM was used as the base map for all the others data sets. The RMS error was less than 0.5 pixel. Changes to the artificial constructions provoked by the landslideswere digitized and then implemented in an ARCGIS database. The results are presented in this paper.

  7. An approach to constructing a homogeneous time series of soil mositure using SMOS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Overlapping soil moisture time series derived from two satellite microwave radiometers (SMOS, Soil Moisture and Ocean Salinity; AMSR-E, Advanced Microwave Scanning Radiometer - Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies f...

  8. Forecasting Enrollments with Fuzzy Time Series.

    ERIC Educational Resources Information Center

    Song, Qiang; Chissom, Brad S.

    The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

  9. Event Discovery in Time Series Dan Preston

    E-print Network

    Masci, Frank

    Event Discovery in Time Series Dan Preston Pavlos Protopapas Carla Brodley Abstract The discovery of events in time series can have important im- plications, such as identifying microlensing events in astro- nomical surveys, or changes in a patient's electrocardiogram. Current methods for identifying events

  10. Generation of artificial helioseismic time-series

    NASA Technical Reports Server (NTRS)

    Schou, J.; Brown, T. M.

    1993-01-01

    We present an outline of an algorithm to generate artificial helioseismic time-series, taking into account as much as possible of the knowledge we have on solar oscillations. The hope is that it will be possible to find the causes of some of the systematic errors in analysis algorithms by testing them with such artificial time-series.

  11. 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.

  12. Time series of a CME blasting out from the Sun

    E-print Network

    Christian, Eric

    #12;Time series of a CME blasting out from the Sun Composite image of the Sun in UV light with the naked eye, the Sun seems static, placid, constant. From the ground, the only notice- able variations in the Sun are its location (where will it rise and set today?) and its color (will clouds cover

  13. Network structure of multivariate time series.

    PubMed

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-01-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail. PMID:26487040

  14. Network structure of multivariate time series

    E-print Network

    Lucas Lacasa; Vincenzo Nicosia; Vito Latora

    2015-10-26

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range of tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  15. Network structure of multivariate time series

    PubMed Central

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-01-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail. PMID:26487040

  16. Network structure of multivariate time series

    NASA Astrophysics Data System (ADS)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  17. Mapping afforestation and forest biomass using time-series Landsat stacks

    NASA Astrophysics Data System (ADS)

    Liu, Liangyun; Peng, Dailiang; Wang, Zhihui; Hu, Yong

    2014-11-01

    Satellite data can adequately capture forest dynamics over larger areas. Firstly, the Landsat ground surface reflectance (GSR) images from 1974 to 2013 were collected and processed based on 6S atmospheric transfer code and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the dense time-series Landsat GSR images, and the afforestation age was successfully retrieved from the Landsat time-series stacks in the last forty years and shown to be consistent with the surveyed tree ages. Then, the above ground biomass (AGB) regression models were greatly improved by integrating the simple ratio vegetation index (SR) and tree age. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in six counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360%. For the forest area, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 1 t/ha. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.

  18. Assessment of temporal variations of water quality in inland water bodies using atmospheric corrected satellite remotely sensed image data.

    PubMed

    Hadjimitsis, Diofantos G; Clayton, Chris

    2009-12-01

    Although there have been many studies conducted on the use of satellite remote sensing for water quality monitoring and assessment in inland water bodies, relatively few studies have considered the problem of atmospheric intervention of the satellite signal. The problem is especially significant when using time series multi-spectral satellite data to monitor water quality surveillance in inland waters such as reservoirs, lakes, and dams because atmospheric effects constitute the majority of the at-satellite reflectance over water. For the assessment of temporal variations of water quality, the use of multi-date satellite images is required so atmospheric corrected image data must be determined. The aim of this study is to provide a simple way of monitoring and assessing temporal variations of water quality in a set of inland water bodies using an earth observation- based approach. The proposed methodology is based on the development of an image-based algorithm which consists of a selection of sampling area on the image (outlet), application of masking and convolution image processing filter, and application of the darkest pixel atmospheric correction. The proposed method has been applied in two different geographical areas, in UK and Cyprus. Mainly, the method has been applied to a series of eight archived Landsat-5 TM images acquired from March 1985 up to November 1985 of the Lower Thames Valley area in the West London (UK) consisting of large water treatment reservoirs. Finally, the method is further tested to the Kourris Dam in Cyprus. It has been found that atmospheric correction is essential in water quality assessment studies using satellite remotely sensed imagery since it improves significantly the water reflectance enabling effective water quality assessment to be made. PMID:19067211

  19. 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.

  20. Complex network approach to fractional time series

    NASA Astrophysics Data System (ADS)

    Manshour, Pouya

    2015-10-01

    In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

  1. 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.

  2. Complex network approach to fractional time series.

    PubMed

    Manshour, Pouya

    2015-10-01

    In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series. PMID:26520071

  3. On reconstruction of time series in climatology

    NASA Astrophysics Data System (ADS)

    Privalsky, V.; Gluhovsky, A.

    2015-10-01

    The approach to time series reconstruction in climatology based upon cross-correlation coefficients and regression equations is mathematically incorrect because it ignores the dependence of time series upon their past. The proper method described here for the bivariate case requires the autoregressive time- and frequency domains modeling of the time series which contains simultaneous observations of both scalar series with subsequent application of the model to restore the shorter one into the past. The method presents further development of previous efforts taken by a number of authors starting from A. Douglass who introduced some concepts of time series analysis into paleoclimatology. The method is applied to the monthly data of total solar irradiance (TSI), 1979-2014, and sunspot numbers (SSN), 1749-2014, to restore the TSI data over 1749-1978. The results of the reconstruction are in statistical agreement with observations.

  4. Potential for calibration of geostationary meteorological satellite imagers using the Moon

    USGS Publications Warehouse

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

    2005-01-01

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

  5. Temporal registration of multispectral digital satellite images using their edge images

    NASA Technical Reports Server (NTRS)

    Nack, M. L.

    1975-01-01

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

  6. TerraSAR-X dual-pol time-series for mapping of wetland vegetation

    NASA Astrophysics Data System (ADS)

    Betbeder, Julie; Rapinel, Sébastien; Corgne, Samuel; Pottier, Eric; Hubert-Moy, Laurence

    2015-09-01

    Mapping vegetation formations at a fine scale is crucial for assessing wetland functions and for better landscape management. Identification and characterization of vegetation formations is generally conducted at a fine scale using ecological ground surveys, which are limited to small areas. While optical remotely sensed imagery is limited to cloud-free periods, SAR time-series are used more extensively for wetland mapping and characterization using the relationship between distribution of vegetation formations and flood duration. The aim of this study was to determine the optimal number and key dates of SAR images to be classified to map wetland vegetation formations at a 1:10,000 scale. A series of eight dual-polarization TerraSAR-X images (HH/VV) was acquired in 2013 during dry and wet seasons in temperate climate conditions. One polarimetric parameter was extracted first, the Shannon entropy, which varies with wetland flooding status and vegetation roughness. Classification runs of all the possible combinations of SAR images using different k (number of images) subsets were performed to determine the best combinations of the Shannon entropy images to identify wetland vegetation formations. The classification runs were performed using Support Vector Machine techniques and were then analyzed using the McNemar test to investigate significant differences in the accuracy of all classification runs based on the different image subsets. The results highlight the relevant periods (i.e. late winter, spring and beginning of summer) for mapping vegetation formations, in accordance with ecological studies. They also indicate that a relationship can be established between vegetation formations and hydrodynamic processes with a short time-series of satellite images (i.e. 5 dates). This study introduces a new approach for herbaceous wetland monitoring using SAR polarimetric imagery. This approach estimates the number and key dates required for wetland management (e.g. restoration) and biodiversity studies using remote sensing data.

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

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  8. Visibility Graph Based Time Series Analysis

    PubMed Central

    Stephen, Mutua; Gu, Changgui; Yang, Huijie

    2015-01-01

    Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it’s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. PMID:26571115

  9. 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

  10. 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.

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

    NASA Astrophysics Data System (ADS)

    Paul, F.

    2015-11-01

    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 to the wider public. For this study, animated image sequences were created for four regions in the central Karakoram mountain range over a 25-year time period (1990-2015) from freely available image quick-looks of orthorectified Landsat scenes. The animations play automatically in a web browser and reveal highly complex patterns of glacier flow and surge dynamics that are difficult to obtain by other methods. In contrast to other regions, surging glaciers in the Karakoram are often small (10 km2 or less), steep, debris-free, and advance for several years to decades at relatively low annual rates (about 100 m a-1). These characteristics overlap with those of non-surge-type glaciers, making a clear identification difficult. However, as in other regions, the surging glaciers in the central Karakoram also show sudden increases of flow velocity and mass waves travelling down glacier. The surges 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 decades.

  12. Cassini Imaging of Jupiter's Atmosphere, Satellites, and Rings

    E-print Network

    Cassini Imaging of Jupiter's Atmosphere, Satellites, and Rings Carolyn C. Porco,1 * Robert A. West Science Subsystem acquired about 26,000 images of the Jupiter system as the spacecraft encountered the giant planet en route to Saturn. We report findings on Jupiter's zonal winds, convective storms, low

  13. 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.

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

    PubMed

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

    2003-03-01

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

  15. 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.

  16. The McIDAS system. [for meteorological satellite image processing

    NASA Technical Reports Server (NTRS)

    Smith, E. A.

    1975-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  18. Wavelet Analysis of Satellite Images for Coastal Watch

    NASA Technical Reports Server (NTRS)

    Liu, Antony K.; Peng, Chich Y.; Chang, Steve Y.-S.

    1997-01-01

    The two-dimensional wavelet transform is a very efficient bandpass filter, which can be used to separate various scales of processes and show their relative phase/location. In this paper, algorithms and techniques for automated detection and tracking of mesoscale features from satellite imagery employing wavelet analysis are developed. The wavelet transform has been applied to satellite images, such as those from synthetic aperture radar (SAR), advanced very-high-resolution radiometer (AVHRR), and coastal zone color scanner (CZCS) for feature extraction. The evolution of mesoscale features such as oil slicks, fronts, eddies, and ship wakes can be tracked by the wavelet analysis using satellite data from repeating paths. Several examples of the wavelet analysis applied to various satellite Images demonstrate the feasibility of this technique for coastal monitoring.

  19. 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.

  20. Biological Visual Attention Guided Automatic Image Segmentation with Application in Satellite Imaging

    E-print Network

    Payeur, Pierre

    intelligent algorithms capable to interpret large datasets of complex geospatial and satellite images in a timely manner. A particularly challenging application is found in the geospatial software industry, which satellite and aerial images serve to attest the mainstream adoption of the geospatially enabled web

  1. 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

  2. Characterization of Ground Deformation above AN Urban Tunnel by Means of Insar Time Series Analysis

    NASA Astrophysics Data System (ADS)

    Ferretti, A.; Iannacone, J.; Falorni, G.; Berti, M.; Corsini, A.

    2013-12-01

    Ground deformation produced by tunnel excavation in urban areas can cause damage to buildings and infrastructure. In these contexts, monitoring systems are required to determine the surface area affected by displacement and the rates of movement. Advanced multi-image satellite-based InSAR approaches are uniquely suited for this purpose as they provide an overview of the entire affected area and can measure movement rates with millimeter precision. Persistent scatterer approaches such as SqueeSAR™ use reflections off buildings, lampposts, roads, etc to produce a high-density point cloud in which each point has a time series of deformation spanning the period covered by the imagery. We investigated an area of about 10 km2 in North Vancouver, (Canada) where the shaft excavation of the Seymour-Capilano water filtration plant was started in 2004. As part of the project, twin tunnels in bedrock were excavated to transfer water from the Capilano Reservoir to the treatment plant. A radar dataset comprising 58 images (spanning March 2001 - June 2008) acquired by the Radarsat-1 satellite and covering the period of excavation was processed with the SqueeSAR™ algorithm (Ferretti et al., 2011) to assess the ground deformation caused by the tunnel excavation. To better characterize the deformation in the time and space domains and correlate ground movement with excavation, an in-depth time series analysis was carried out. Berti et al. (2013) developed an automatic procedure for the analysis of InSAR time series based on a sequence of statistical tests. The tool classifies time series into six distinctive types (uncorrelated; linear; quadratic; bilinear; discontinuous without constant velocity; discontinuous with change in velocity) which can be linked to different physical phenomena. It also provides a series of descriptive parameters which can be used to characterize the temporal changes of ground motion. We processed the movement time series with PSTime to determine the existence of any relationship between the tunnel excavation and the surface ground deformation. Within the area investigated we found that 56% of measurement points are characterized by a bilinear deformation trend, 25% by a quadratic trend, and 7% by a linear trend, indicating that a change in deformation rate was present for a vast majority of points. In addition, 78% of the points accelerated mostly in 2006, corresponding with the date in which excavation of the tunnels started. In general, total deformation of up to ~ 40 mm was preceded by a phase of stable ground and then followed by a stage with the highest variation of velocities recorded at the end of 2007. The time series analysis allowed the accurate detection of very small variations in velocity over large areas. In particular it highlighted a clear relation between the construction of the Seymour-Capilano water plant and ground surface deformation.

  3. 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.

  4. 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...

  5. 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.

  6. 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.

  7. Nonlinear time-series analysis revisited.

    PubMed

    Bradley, Elizabeth; Kantz, Holger

    2015-09-01

    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. PMID:26428563

  8. 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, Louvain-la-Neuve 1348, Belgium Email: verleysen@dice.ucl.ac.be Abstract ­ This paper presents the CATS to be organized. In the present CATS competition, the goal was the prediction of 100 missing values of the time

  9. Nonlinear time-series analysis revisited

    NASA Astrophysics Data System (ADS)

    Bradley, Elizabeth; Kantz, Holger

    2015-09-01

    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.

  10. Layered Ensemble Architecture for Time Series Forecasting.

    PubMed

    Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

    2016-01-01

    Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods. PMID:25751882

  11. 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.

  12. 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…

  13. Monitoring Forest Regrowth Using a Multi-Platform Time Series

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

  14. 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.

  15. 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.

  16. Characterization of aggressive prostate cancer using ultrasound RF time series

    NASA Astrophysics Data System (ADS)

    Khojaste, Amir; Imani, Farhad; Moradi, Mehdi; Berman, David; Siemens, D. Robert; Sauerberi, Eric E.; Boag, Alexander H.; Abolmaesumi, Purang; Mousavi, Parvin

    2015-03-01

    Prostate cancer is the most prevalently diagnosed and the second cause of cancer-related death in North American men. Several approaches have been proposed to augment detection of prostate cancer using different imaging modalities. Due to advantages of ultrasound imaging, these approaches have been the subject of several recent studies. This paper presents the results of a feasibility study on differentiating between lower and higher grade prostate cancer using ultrasound RF time series data. We also propose new spectral features of RF time series to highlight aggressive prostate cancer in small ROIs of size 1 mm × 1 mm in a cohort of 19 ex vivo specimens of human prostate tissue. In leave-one-patient-out cross-validation strategy, an area under accumulated ROC curve of 0.8 has been achieved with overall sensitivity and specificity of 81% and 80%, respectively. The current method shows promising results on differentiating between lower and higher grade of prostate cancer using ultrasound RF time series.

  17. Jovian satellite positions from Hubble Space Telescope images

    NASA Astrophysics Data System (ADS)

    Mallama, Anthony; Aelion, Halley M.; Mallama, Celeste A.

    2004-02-01

    An accurate technique has been developed for measuring planetocentric positions of Jupiter's satellites from Wide Field/Planetary Camera images. Our method of finding the centers of the satellites and planet is based upon established limb-fitting techniques, but we have adapted those techniques to astrometry. We compare our limb-fitting results with previously published work and discuss its errors. A model ellipse is generated from the physical ephemeris of the planet including its phase defect. Then the planet center coordinates are computed by fitting the model to the limb observations using the method of least squares. A satellite position is determined similarly, and its offset from the planet is calculated. A total of 76 positions of the galileans satellites, the small moon Amalthea, and the shadows of Io and Ganymede cast on Jupiter have been measured on 61 images. Comparison between the observational results and JPL satellite ephemerides demonstrates the validity of this new method of analysis. The accuracy of the galilean satellite measurements is estimated to be 0.04 arcsec in right ascension and in declination.

  18. Surface reconstruction and landslide displacement measurements with Pléiades satellite images

    NASA Astrophysics Data System (ADS)

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

    2014-09-01

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

  19. 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.

  20. Forbidden patterns in financial time series.

    PubMed

    Zanin, Massimiliano

    2008-03-01

    The existence of forbidden patterns, i.e., certain missing sequences in a given time series, is a recently proposed instrument of potential application in the study of time series. Forbidden patterns are related to the permutation entropy, which has the basic properties of classic chaos indicators, such as Lyapunov exponent or Kolmogorov entropy, thus allowing to separate deterministic (usually chaotic) from random series; however, it requires fewer values of the series to be calculated, and it is suitable for using with small datasets. In this paper, the appearance of forbidden patterns is studied in different economical indicators such as stock indices (Dow Jones Industrial Average and Nasdaq Composite), NYSE stocks (IBM and Boeing), and others (ten year Bond interest rate), to find evidence of deterministic behavior in their evolutions. Moreover, the rate of appearance of the forbidden patterns is calculated, and some considerations about the underlying dynamics are suggested. PMID:18377070

  1. Forbidden patterns in financial time series

    NASA Astrophysics Data System (ADS)

    Zanin, Massimiliano

    2008-03-01

    The existence of forbidden patterns, i.e., certain missing sequences in a given time series, is a recently proposed instrument of potential application in the study of time series. Forbidden patterns are related to the permutation entropy, which has the basic properties of classic chaos indicators, such as Lyapunov exponent or Kolmogorov entropy, thus allowing to separate deterministic (usually chaotic) from random series; however, it requires fewer values of the series to be calculated, and it is suitable for using with small datasets. In this paper, the appearance of forbidden patterns is studied in different economical indicators such as stock indices (Dow Jones Industrial Average and Nasdaq Composite), NYSE stocks (IBM and Boeing), and others (ten year Bond interest rate), to find evidence of deterministic behavior in their evolutions. Moreover, the rate of appearance of the forbidden patterns is calculated, and some considerations about the underlying dynamics are suggested.

  2. Regularization of Nutation Time Series at GSFC

    NASA Astrophysics Data System (ADS)

    Le Bail, K.; Gipson, J. M.; Bolotin, S.

    2012-12-01

    VLBI is unique in its ability to measure all five Earth orientation parameters. In this paper we focus on the two nutation parameters which characterize the orientation of the Earth's rotation axis in space. We look at the periodicities and the spectral characteristics of these parameters for both R1 and R4 sessions independently. The study of the most significant periodic signals for periods shorter than 600 days is common for these four time series (period of 450 days), and the type of noise determined by the Allan variance is a white noise for the four series. To investigate methods of regularizing the series, we look at a Singular Spectrum Analysis-derived method and at the Kalman filter. The two methods adequately reproduce the tendency of the nutation time series, but the resulting series are noisier using the Singular Spectrum Analysis-derived method.

  3. Univariate time series forecasting algorithm validation

    NASA Astrophysics Data System (ADS)

    Ismail, Suzilah; Zakaria, Rohaiza; Muda, Tuan Zalizam Tuan

    2014-12-01

    Forecasting is a complex process which requires expert tacit knowledge in producing accurate forecast values. This complexity contributes to the gaps between end users and expert. Automating this process by using algorithm can act as a bridge between them. Algorithm is a well-defined rule for solving a problem. In this study a univariate time series forecasting algorithm was developed in JAVA and validated using SPSS and Excel. Two set of simulated data (yearly and non-yearly); several univariate forecasting techniques (i.e. Moving Average, Decomposition, Exponential Smoothing, Time Series Regressions and ARIMA) and recent forecasting process (such as data partition, several error measures, recursive evaluation and etc.) were employed. Successfully, the results of the algorithm tally with the results of SPSS and Excel. This algorithm will not just benefit forecaster but also end users that lacking in depth knowledge of forecasting process.

  4. 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

  5. Revisiting algorithms for generating surrogate time series

    E-print Network

    C. Raeth; M. Gliozzi; I. E. Papadakis; W. Brinkmann

    2012-08-17

    The method of surrogates is one of the key concepts of nonlinear data analysis. Here, we demonstrate that commonly used algorithms for generating surrogates often fail to generate truly linear time series. Rather, they create surrogate realizations with Fourier phase correlations leading to non-detections of nonlinearities. We argue that reliable surrogates can only be generated, if one tests separately for static and dynamic nonlinearities.

  6. Delay Differential Analysis of Time Series

    PubMed Central

    Lainscsek, Claudia; Sejnowski, Terrence J.

    2015-01-01

    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

  7. Turbulencelike Behavior of Seismic Time Series

    SciTech Connect

    Manshour, P.; Saberi, S.; Sahimi, Muhammad; Peinke, J.; Pacheco, Amalio F.; Rahimi Tabar, M. Reza

    2009-01-09

    We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes.

  8. Random Matrix Spectra as a Time Series

    E-print Network

    Ruben Fossion; Gamaliel Torres Vargas; Juan Carlos López Vieyra

    2013-11-23

    Spectra of ordered eigenvalues of finite Random Matrices are interpreted as a time series. Dataadaptive techniques from signal analysis are applied to decompose the spectrum in clearly differentiated trend and fluctuation modes, avoiding possible artifacts introduced by standard unfolding techniques. The fluctuation modes are scale invariant and follow different power laws for Poisson and Gaussian ensembles, which already during the unfolding allows to distinguish the two cases.

  9. 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.

  10. Analysis of Polyphonic Musical Time Series

    NASA Astrophysics Data System (ADS)

    Sommer, Katrin; Weihs, Claus

    A general model for pitch tracking of polyphonic musical time series will be introduced. Based on a model of Davy and Godsill (Bayesian harmonic models for musical pitch estimation and analysis, Technical Report 431, Cambridge University Engineering Department, 2002) Davy and Godsill (2002) the different pitches of the musical sound are estimated with MCMC methods simultaneously. Additionally a preprocessing step is designed to improve the estimation of the fundamental frequencies (A comparative study on polyphonic musical time series using MCMC methods. In C. Preisach et al., editors, Data Analysis, Machine Learning, and Applications, Springer, Berlin, 2008). The preprocessing step compares real audio data with an alphabet constructed from the McGill Master Samples (Opolko and Wapnick, McGill University Master Samples [Compact disc], McGill University, Montreal, 1987) and consists of tones of different instruments. The tones with minimal Itakura-Saito distortion (Gray et al., Transactions on Acoustics, Speech, and Signal Processing ASSP-28(4):367-376, 1980) are chosen as first estimates and as starting points for the MCMC algorithms. Furthermore the implementation of the alphabet is an approach for the recognition of the instruments generating the musical time series. Results are presented for mixed monophonic data from McGill and for self recorded polyphonic audio data.

  11. Detection of Urban Zones in Satellite Images Using Visual Words

    E-print Network

    Goldberger, Jacob

    1 Detection of Urban Zones in Satellite Images Using Visual Words Lior Weizman and Jacob Goldberger classification. An important usage of remotely sensed data is extracting urban regions to update GIS databases methods for urban extraction that exist today are sensitive to atmospheric and radiometric parameters

  12. Geography 169: Satellite Remote Sensing and Imaging Geographic Information Systems

    E-print Network

    Geography 169: Satellite Remote Sensing and Imaging Geographic Information Systems MW 9:00 to 11 sensing is performed from orbital or sub-orbital platforms using instruments which measure electromagnetic radiation reflected or emitted from the terrain. Remote sensing is a technique that can be used in a wide

  13. Geography 169: Satellite Remote Sensing and Imaging Geographic Information Systems

    E-print Network

    or sub-orbital platforms using instruments which measure electromagnetic radiation reflected or emittedGeography 169: Satellite Remote Sensing and Imaging Geographic Information Systems MW 10:00 to 11 upper-division undergraduates with a comprehensive overview of remote sensing systems and applications

  14. Geography 169: Satellite Remote Sensing and Imaging Geographic Information Systems

    E-print Network

    or sub-orbital platforms using instruments which measure electromagnetic radiation reflected or emittedGeography 169: Satellite Remote Sensing and Imaging Geographic Information Systems MW 12:00 to 1 upper-division undergraduates with a comprehensive overview of remote sensing systems and applications

  15. A Multiscale Approach to InSAR Time Series Analysis

    NASA Astrophysics Data System (ADS)

    Simons, M.; Hetland, E. A.; Muse, P.; Lin, Y.; Dicaprio, C. J.

    2009-12-01

    We describe progress in the development of MInTS (Multiscale analysis of InSAR Time Series), an approach to constructed self-consistent time-dependent deformation observations from repeated satellite-based InSAR images of a given region. MInTS relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. In essence, MInTS allows one to considers all data at the same time as opposed to one pixel at a time, thereby taking advantage of both spatial and temporal characteristics of the deformation field. This approach also permits a consistent treatment of all data independent of the presence of localized holes due to unwrapping issues in any given interferogram. Specifically, the presence of holes is accounted for through a weighting scheme that accounts for the extent of actual data versus the area of holes associated with any given wavelet. In terms of the temporal representation, we have the flexibility to explicitly parametrize known processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). Our approach also allows for the temporal parametrization to include a set of general functions in order to account for unexpected processes. We allow for various forms of model regularization using a cross-validation approach to select penalty parameters. We also experiment with the use of sparsity inducing regularization as a way to select from a large dictionary of time functions. The multiscale analysis allows us to consider various contributions (e.g., orbit errors) that may affect specific scales but not others. The methods described here are all embarrassingly parallel and suitable for implementation on a cluster computer. We demonstrate the use of MInTS using a large suite of ERS-1/2 and Envisat interferograms for Long Valley Caldera, and validate our results by comparing with ground-based observations.

  16. An Imaging System for Satellite Hypervelocity Impact Debris Characterization

    NASA Technical Reports Server (NTRS)

    Moraguez, Matthew; Patankar, Kunal; Fitz-Coy, Norman; Liou, J.-C.; Cowardin, Heather

    2015-01-01

    This paper discusses the design of an automated imaging system for size characterization of debris produced by the DebriSat hypervelocity impact test. The goal of the DebriSat project is to update satellite breakup models. A representative LEO satellite, DebriSat, was constructed and subjected to a hypervelocity impact test. The impact produced an estimated 85,000 debris fragments. The size distribution of these fragments is required to update the current satellite breakup models. An automated imaging system was developed for the size characterization of the debris fragments. The system uses images taken from various azimuth and elevation angles around the object to produce a 3D representation of the fragment via a space carving algorithm. The system consists of N point-and-shoot cameras attached to a rigid support structure that defines the elevation angle for each camera. The debris fragment is placed on a turntable that is incrementally rotated to desired azimuth angles. The number of images acquired can be varied based on the desired resolution. Appropriate background and lighting is used for ease of object detection. The system calibration and image acquisition process are automated to result in push-button operations. However, for quality assurance reasons, the system is semi-autonomous by design to ensure operator involvement. This paper describes the imaging system setup, calibration procedure, repeatability analysis, and the results of the debris characterization.

  17. 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.

  18. 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.

  19. Feature detection in satellite images using neural network technology

    NASA Technical Reports Server (NTRS)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

    A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

  20. High-Resolution Imaging of Asteroids/Satellites

    NASA Astrophysics Data System (ADS)

    Merline, William J.; Tamblyn, Peter M.; Carry, Benoit; Drummond, Jack; Conrad, Al; Howell, Steve B.; Christou, Julian; Chapman, Clark R.; Dumas, Christophe

    2013-08-01

    We propose to make high-resolution observations of asteroids using two separate observational paths. We request LGS AO on Keck, as part of our ongoing program to measure size, 3D shape, and pole position, and to search for satellites. Second, we wish to make use of the new capability for speckle imaging on Gemini-N. We have demonstrated that AO imaging allows determination of the pole/dimensions in 1 or 2 nights, rather than the years of observations with lightcurve inversion techniques that only yield poles and axial ratios, not true dimensions. Detection of new satellites allows an accurate mass determination. Accurately determining the volume from the often-irregular shape allows us to derive densities to greater precision in cases where the mass is known. Satellites also provide a real-life lab for testing collisional models. We have demonstrated the tremendous fidelity of our shape/sizes of asteroids, and have pioneered asteroid satellite detection. The new DSSI instrument provides a potentially game-changing opportunity by pushing diffraction-limited imaging into the visible region, where the resolution will be roughly twice what we can get at Keck in the NIR. We will apply both techniques to determination of sizes of asteroids and search for binaries, particularly among understudied populations such as the NEOs and Trojans.

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

    SciTech Connect

    Cheriyadat, Anil M

    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.

  2. 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

  3. 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.

  4. A Multiscale Approach to InSAR Time Series Analysis

    NASA Astrophysics Data System (ADS)

    Hetland, E. A.; Muse, P.; Simons, M.; Lin, N.; Dicaprio, C. J.

    2010-12-01

    We present a technique to constrain time-dependent deformation from repeated satellite-based InSAR observations of a given region. This approach, which we call MInTS (Multiscale InSAR Time Series analysis), relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. As opposed to single pixel InSAR time series techniques, MInTS takes advantage of both spatial and temporal characteristics of the deformation field. We use a weighting scheme which accounts for the presence of localized holes due to decorrelation or unwrapping errors in any given interferogram. We represent time-dependent deformation using a dictionary of general basis functions, capable of detecting both steady and transient processes. The estimation is regularized using a model resolution based smoothing so as to be able to capture rapid deformation where there are temporally dense radar acquisitions and to avoid oscillations during time periods devoid of acquisitions. MInTS also has the flexibility to explicitly parametrize known time-dependent processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). We use cross validation to choose the regularization penalty parameter in the inversion of for the time-dependent deformation field. We demonstrate MInTS using a set of 63 ERS-1/2 and 29 Envisat interferograms for Long Valley Caldera.

  5. Time Series Photometry of KZ Lacertae

    NASA Astrophysics Data System (ADS)

    Joner, Michael D.

    2016-01-01

    We present BVRI time series photometry of the high amplitude delta Scuti star KZ Lacertae secured using the 0.9-meter telescope located at the Brigham Young University West Mountain Observatory. In addition to the multicolor light curves that are presented, the V data from the last six years of observations are used to plot an O-C diagram in order to determine the ephemeris and evaluate evidence for period change. We wish to thank the Brigham Young University College of Physical and Mathematical Sciences as well as the Department of Physics and Astronomy for their continued support of the research activities at the West Mountain Observatory.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Hong, S.; Wdowinski, S.

    2013-05-01

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

  8. Development of a rule-based algorithm for rice cultivation mapping using Landsat 8 time series

    NASA Astrophysics Data System (ADS)

    Karydas, Christos G.; Toukiloglou, Pericles; Minakou, Chara; Gitas, Ioannis Z.

    2015-06-01

    In the framework of ERMES project (FP7 66983), an algorithm for mapping rice cultivation extents using mediumhigh resolution satellite data was developed. ERMES (An Earth obseRvation Model based RicE information Service) aims to develop a prototype of downstream service for rice yield modelling based on a combination of Earth Observation and in situ data. The algorithm was designed as a set of rules applied on a time series of Landsat 8 images, acquired throughout the rice cultivation season of 2014 from the plain of Thessaloniki, Greece. The rules rely on the use of spectral indices, such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Seasonal Wetness Index (NSWI), extracted from the Landsat 8 dataset. The algorithm is subdivided into two phases: a) a hard classification phase, resulting in a binary map (rice/no-rice), where pixels are judged according to their performance in all the images of the time series, while index thresholds were defined after a trial and error approach; b) a soft classification phase, resulting in a fuzzy map, by assigning scores to the pixels which passed (as `rice') the first phase. Finally, a user-defined threshold of the fuzzy score will discriminate rice from no-rice pixels in the output map. The algorithm was tested in a subset of Thessaloniki plain against a set of selected field data. The results indicated an overall accuracy of the algorithm higher than 97%. The algorithm was also applied in a study are in Spain (Valencia) and a preliminary test indicated a similar performance, i.e. about 98%. Currently, the algorithm is being modified, so as to map rice extents early in the cultivation season (by the end of June), with a view to contribute more substantially to the rice yield prediction service of ERMES. Both algorithm modes (late and early) are planned to be tested in extra Mediterranean study areas, in Greece, Italy, and Spain.

  9. Time series analysis of temporal networks

    E-print Network

    Sikdar, Sandipan; Mukherjee, Animesh

    2015-01-01

    An important feature of all real-world networks is that the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. We mainly focus on the temporal network of human face- to-face contacts and observe that it represents a stochastic process with memory that can be modeled as ARIMA. We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the fr...

  10. Hurst exponents for short time series

    NASA Astrophysics Data System (ADS)

    Qi, Jingchao; Yang, Huijie

    2011-12-01

    A concept called balanced estimator of diffusion entropy is proposed to detect quantitatively scalings in short time series. The effectiveness is verified by detecting successfully scaling properties for a large number of artificial fractional Brownian motions. Calculations show that this method can give reliable scalings for short time series with length ˜102. It is also used to detect scalings in the Shanghai Stock Index, five stock catalogs, and a total of 134 stocks collected from the Shanghai Stock Exchange Market. The scaling exponent for each catalog is significantly larger compared with that for the stocks included in the catalog. Selecting a window with size 650, the evolution of scaling for the Shanghai Stock Index is obtained by the window's sliding along the series. Global patterns in the evolutionary process are captured from the smoothed evolutionary curve. By comparing the patterns with the important event list in the history of the considered stock market, the evolution of scaling is matched with the stock index series. We can find that the important events fit very well with global transitions of the scaling behaviors.

  11. Homogenization of precipitation time series with ACMANT

    NASA Astrophysics Data System (ADS)

    Domonkos, Peter

    2015-10-01

    New method for the time series homogenization of observed precipitation (PP) totals is presented; this method is a unit of the ACMANT software package. ACMANT is a relative homogenization method; minimum four time series with adequate spatial correlations are necessary for its use. The detection of inhomogeneities (IHs) is performed with fitting optimal step function, while the calculation of adjustment terms is based on the minimization of the residual variance in homogenized datasets. Together with the presentation of PP homogenization with ACMANT, some peculiarities of PP homogenization as, for instance, the frequency and seasonal variation of IHs in observed PP data and their relation to the performance of homogenization methods are discussed. In climatic regions of snowy winters, ACMANT distinguishes two seasons, namely, rainy season and snowy season, and the seasonal IHs are searched with bivariate detection. ACMANT is a fully automatic method, is freely downloadable from internet and treats either daily or monthly input. Series of observed data in the input dataset may cover different periods, and the occurrence of data gaps is allowed. False zero values instead of missing data code or physical outliers should be corrected before running ACMANT. Efficiency tests indicate that ACMANT belongs to the best performing methods, although further comparative tests of automatic homogenization methods are needed to confirm or reject this finding.

  12. High-Resolution Imaging of Asteroids/Satellites with AO

    NASA Astrophysics Data System (ADS)

    Merline, William

    2012-02-01

    We propose to make high-resolution observations of asteroids using AO, to measure size, shape, and pole position (spin vectors), and/or to search for satellites. We have demonstrated that AO imaging allows determination of the pole/dimensions in 1 or 2 nights on a single target, rather than the years of observations with lightcurve inversion techniques that only yield poles and axial ratios, not true dimensions. Our new technique (KOALA) combines AO imaging with lightcurve and occultation data for optimum size/shape determinations. We request that LGS be available for faint targets, but using NGS AO, we will measure several large and intermediate asteroids that are favorably placed in spring/summer of 2012 for size/shape/pole. Accurately determining the volume from the often-irregular shape allows us to derive densities to much greater precision in cases where the mass is known, e.g., from the presence of a satellite. We will search several d! ozen asteroids for the presence of satellites, particularly in under-studied populations, particularly NEOs (we have recently achieved the first-ever optical image of an NEO binary [Merline et al. 2008b, IAUC 8977]). Satellites provide a real-life lab for testing collisional models. We will search for satellites around special objects at the request of lightcurve observers, and we will make a search for debris in the vicinity of Pluto, in support of the New Horizons mission. Our shape/size work requires observations over most of a full rotation period (typically several hours).

  13. Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites.

    PubMed

    Yan, Wai Yeung; Mahendrarajah, Prathees; Shaker, Ahmed; Faisal, Kamil; Luong, Robin; Al-Ahmad, Mohamed

    2014-12-01

    This studypresents a remote sensing application of using time series Landsat satellite images for monitoring the Trail Road and Nepean municipal solid waste (MSW) disposal sites in Ottawa, Ontario, Canada. Currently, the Trail Road landfill is in operation; however, during the 1960s and 1980s, the city relied heavily on the Nepean landfill. More than 400 Landsat satellite images were acquired from the US Geological Survey (USGS) data archive between 1984 and 2011. Atmospheric correction was conducted on the Landsat images in order to derive the landfill sites' land surface temperature (LST). The findings unveil that the average LST of the landfill was always higher than the immediate surrounding vegetation and air temperature by 4 to 10 °C and 5 to 11.5 °C, respectively. During the summer, higher differences of LST between the landfill and its immediate surrounding vegetation were apparent, while minima were mostly found in fall. Furthermore, there was no significant temperature difference between the Nepean landfill (closed) and the Trail Road landfill (active) from 1984 to 2007. Nevertheless, the LST of the Trail Road landfill was much higher than the Nepean by 15 to 20 °C after 2007. This is mainly due to the construction and dumping activities (which were found to be active within the past few years) associated with the expansion of the Trail Road landfill. The study demonstrates that the use of the Landsat data archive can provide additional and viable information for the aid of MSW disposal site monitoring. PMID:25150051

  14. 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.

  15. 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.

  16. 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.

  17. The Mount Wilson Ca ii K Plage Index Time Series

    NASA Astrophysics Data System (ADS)

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

    2010-06-01

    It is well established that both total and spectral solar irradiance are modulated by variable magnetic activity on the solar surface. However, there is still disagreement about the contribution of individual solar features for changes in the solar output, in particular over decadal time scales. Ionized Ca ii K line spectroheliograms are one of the major resources for these long-term trend studies, mainly because such measurements have been available now for more than 100 years. In this paper we introduce a new Ca ii K plage and active network index time series derived from the digitization of almost 40 000 photographic solar images that were obtained at the 60-foot solar tower, between 1915 and 1985, as a part of the monitoring program of the Mount Wilson Observatory. We describe here the procedure we applied to calibrate the images and the properties of our new defined index, which is strongly correlated to the average fractional area of the visible solar disk occupied by plages and active network. We show that the long-term variation of this index is in an excellent agreement with the 11-year solar-cycle trend determined from the annual international sunspot numbers series. Our time series agrees also very well with similar indicators derived from a different reduction of the same data base and other Ca ii K spectroheliograms long-term synoptic programs, such as those at Kodaikanal Observatory (India), and at the National Solar Observatory at Sacramento Peak (USA). Finally, we show that using appropriate proxies it is possible to extend this time series up to date, making this data set one of the longest Ca ii K index series currently available.

  18. 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.

  19. 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.

  20. 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.

  1. Automatic analysis of stereoscopic image pairs from GOES satellites

    NASA Technical Reports Server (NTRS)

    Hasler, A. F.; Strong, J.; Morris, R.; Pierce, H.

    1988-01-01

    An algorithm for automatic analysis of stereoscopic images is applied to stereo pairs of severe local storms and hurricanes observed by two GOES satellites. The automatically derived height fields and manual analyses are compared. It is found that the automatic analysis produces a more detailed structure in less time than manual analysis, although the two methods have similar quality. In areas where the features are very small, however, it is suggested that manual analysis is superior.

  2. Mapping Vineyard Areas Using WORLDVIEW-2 Satellite Images

    NASA Astrophysics Data System (ADS)

    Sertel, E.; Ozelkan, E.; Yay, I.; Seker, D. Z.; Ormeci, C.

    2011-12-01

    The observation of Earth surface from the space has lead to new research possibilities in many fields like agriculture, hydrology, geology, geodesy etc. Different satellite image data have been used for agricultural monitoring for different scales namely local, regional and global. It is important to monitor agricultural field in local scale to determine the crop yield, diseases, and to provide Farmer Registries. Worldview-2 is a new satellite system that could be used for agricultural applications especially in local scale. It is the first high resolution 8-band multispectral commercial satellite launched in October 2009. The satellite has an altitude of 770 kilometers and its spatial resolution for panchromatic mode and multispectral mode are 46 cm and 1.85 meter, respectively. In addition to red (630 - 690 nm), blue (450 - 510 nm), Green (510 - 580 nm) and Near Infrared (770 - 895 nm) bands, Worldview-2 has four new spectral bands lying on beginning of blue (400 - 450 nm), yellow (585 - 625 nm), red edge (705 - 745 nm) and Near Infrared (860 - 1040 nm) regions of the electromagnetic spectrum. Since Worldview-2 data are comparatively new, there have not been many studies in the literature about the usage of these new data for different applications. In this research, Worldview-2 data were used to delineate the vineyard areas and identify different grape types in Sarkoy, Turkey. Phenological observations of grape fields have been conducted for the last three years over a huge test area owned by the Government Viniculture Institute. Based on the phenological observations, it was found that July and August period is the best data acquisition time for satellite data since leaf area index is really higher. In August 2011, Worldview-2 data of the region were acquired and spectral measurements were collected in the field for different grape types using a spectroradiometer. Satellite image data and spectral measurements were correlated and satellite image data were classified to determine the location, extent and type of vineyards within the study region. A Digital Elevation Model generated from 1/25.000 scaled topographic maps was used to create slope and aspect map of the research area. These maps and vineyard parcels obtained from remote sensing techniques were integrated into a Geographic Information System. Spatial analyses were conducted in GIS to evaluate the appropriateness of vineyard areas for grape growth. Possible suitable vineyard sites for new plantation were selected through spatial queries to provide useful information to governmental authorities and farmers.

  3. Numerical simulations of imaging satellites with optical interferometry

    NASA Astrophysics Data System (ADS)

    Ding, Yuanyuan; Wang, Chaoyan; Chen, Zhendong

    2015-08-01

    Optical interferometry imaging system, which is composed of multiple sub-apertures, is a type of sensor that can break through the aperture limit and realize the high resolution imaging. This technique can be utilized to precisely measure the shapes, sizes and position of astronomical objects and satellites, it also can realize to space exploration and space debris, satellite monitoring and survey. Fizeau-Type optical aperture synthesis telescope has the advantage of short baselines, common mount and multiple sub-apertures, so it is feasible for instantaneous direct imaging through focal plane combination.Since 2002, the researchers of Shanghai Astronomical Observatory have developed the study of optical interferometry technique. For array configurations, there are two optimal array configurations proposed instead of the symmetrical circular distribution: the asymmetrical circular distribution and the Y-type distribution. On this basis, two kinds of structure were proposed based on Fizeau interferometric telescope. One is Y-type independent sub-aperture telescope, the other one is segmented mirrors telescope with common secondary mirror.In this paper, we will give the description of interferometric telescope and image acquisition. Then we will mainly concerned the simulations of image restoration based on Y-type telescope and segmented mirrors telescope. The Richardson-Lucy (RL) method, Winner method and the Ordered Subsets Expectation Maximization (OS-EM) method are studied in this paper. We will analyze the influence of different stop rules too. At the last of the paper, we will present the reconstruction results of images of some satellites.

  4. Satellite retrieval of convective cloud base temperature based on the NPP/VIIRS Imager

    E-print Network

    Daniel, Rosenfeld

    Satellite retrieval of convective cloud base temperature based on the NPP/VIIRS Imager Yannian Zhu1 the Suomi National Polar-Orbiting Partnership (NPP) satellite provided a quantum jump in the satellite) and validate it over the Atmospheric System Research Southern Great Plains site for the satellite early

  5. Normalizing the causality between time series

    NASA Astrophysics Data System (ADS)

    Liang, X. San

    2015-08-01

    Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a giant for the mainframe computer market.

  6. Normalizing the causality between time series.

    PubMed

    Liang, X San

    2015-08-01

    Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a giant for the mainframe computer market. PMID:26382363

  7. Normalizing the causality between time series

    E-print Network

    Liang, X San

    2015-01-01

    Recently, a rigorous yet concise formula has been derived to evaluate the information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing three types of fundamental mechanisms that govern the marginal entropy change of the flow recipient. A normalized or relative flow measures its importance relative to other mechanisms. In analyzing realistic series, both absolute and relative information flows need to be taken into account, since the normalizers for a pair of reverse flows belong to two different entropy balances; it is quite normal that two identical flows may differ a lot in relative importance in their respective balances. We have reproduced these results with several autoregressive models. We have also shown applications to a climate change problem and a financial analysis problem. For the former, reconfirmed is the role of the Indian Ocean Dipole as ...

  8. 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.

  9. 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.

  10. Uncertain Time-Series Similarity: Return to the Basics

    E-print Network

    Palpanas, Themis

    Uncertain Time-Series Similarity: Return to the Basics Michele Dallachiesa, Besmira Nushi efficiency, prod- uct quality and safety, hydrologic and geologic observing systems, pollution management proposed specifically for modeling and processing uncertain time series, an important model for temporal

  11. Learning to transform time series with a few examples

    E-print Network

    Rahimi, Ali, 1976-

    2006-01-01

    I describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. I apply this algorithm to tracking, where one transforms a time ...

  12. 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 precipitation data to precision agricultural data. In this paper, we will focus on the exploration of satellite images using the linked ArcView XGobi XploRe software environment. INTRODUCTION Multispectral satellite

  13. 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

  14. 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.

  15. On Modeling and Forecasting Time Series of Smooth Curves

    E-print Network

    Shen, Haipeng

    On Modeling and Forecasting Time Series of Smooth Curves Haipeng Shen October 16, 2008 Abstract We consider modeling a time series of smooth curves and develop methods for forecasting such curves through a smooth factor model, time series modeling and forecasting of the factor scores, and dynamic

  16. Wavelet-Based Surrogates for Testing Time Series

    E-print Network

    Percival, Don

    Wavelet-Based Surrogates for Testing Time Series Don Percival Applied Physics Lab, University. Davison, EPFL (Lausanne, Switzerland) #12;Overview of Talk · background on surrogate data): method of surrogate data (useful for identifying nonlinear time series) ­ let X be time series

  17. WaveletBased Surrogates for Testing Time Series

    E-print Network

    Percival, Don

    Wavelet­Based Surrogates for Testing Time Series Don Percival Applied Physics Lab, University. Davison, EPFL (Lausanne, Switzerland) #12; Overview of Talk . background on surrogate data): method of surrogate data (useful for identifying nonlinear time series) -- let X be time series

  18. Ice Sheet Change Detection by Satellite Image Differencing

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  19. 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.

  20. Satellites

    SciTech Connect

    Burns, J.A.; Matthews, M.S.

    1986-01-01

    The present work is based on a conference: Natural Satellites, Colloquium 77 of the IAU, held at Cornell University from July 5 to 9, 1983. Attention is given to the background and origins of satellites, protosatellite swarms, the tectonics of icy satellites, the physical characteristics of satellite surfaces, and the interactions of planetary magnetospheres with icy satellite surfaces. Other topics include the surface composition of natural satellites, the cratering of planetary satellites, the moon, Io, and Europa. Consideration is also given to Ganymede and Callisto, the satellites of Saturn, small satellites, satellites of Uranus and Neptune, and the Pluto-Charon system.

  1. Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China.

    PubMed

    Liu, Liangyun; Tang, Huan; Caccetta, Peter; Lehmann, Eric A; Hu, Yong; Wu, Xiaoliang

    2013-12-01

    The Three-North Shelter Forest Program is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land use and land cover change, but it is still challenging to accurately quantify the change in forest extent from time-series satellite images. In this paper, 30 Landsat MSS/TM/ETM+ epochs from 1974 to 2012 were collected, and the high-quality ground surface reflectance (GSR) time-series images were processed by integrating the 6S atmosphere transfer model and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the time-series Landsat GSR images based on the integrated forest z-score (IFZ) model by Huang et al. (2009a), which was improved by multi-phenological IFZ models and the smoothing processing of IFZ data for afforestation mapping. The mapping result showed a large increase in the extent of forest, from 380,394 ha (14.8% of total district area) in 1974 to 1,128,380 ha (43.9%) in 2010. Finally, the land cover and forest change map was validated with an overall accuracy of 89.1% and a kappa coefficient of 0.858. The forest change time was also successfully retrieved, with 22.2% and 86.5% of the change pixels attributed to the correct epoch and within three epochs, respectively. The results confirmed a great achievement of the ecological revegetation projects in Yulin district over the last 40 years and also illustrated the potential of the time-series of Landsat images for detecting forest changes and estimating tree age for the artificial forest in a semi-arid zone strongly influenced by human activities. PMID:23813096

  2. Hydroxyl time series and recirculation in turbulent nonpremixed swirling flames

    SciTech Connect

    Guttenfelder, Walter A.; Laurendeau, Normand M.; Ji, Jun; King, Galen B.; Gore, Jay P.; Renfro, Michael W.

    2006-10-15

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

  3. First satellite imaging of auroral pulsations by the Fast Auroral Imager on e-POP

    NASA Astrophysics Data System (ADS)

    Lui, A. T. Y.; Cogger, L. L.; Howarth, A.; Yau, A. W.

    2015-09-01

    We report the first satellite imaging of auroral pulsations by the Fast Auroral Imager (FAI) on board the Enhanced Polar Outflow Probe (e-POP) satellite. The near-infrared camera of FAI is capable of providing up to two auroral images per second, ideal for investigation of pulsating auroras. The auroral pulsations were observed within the auroral bulge formed during a substorm interval on 19 February 2014. This first satellite view of these pulsations from FAI reveals that (1) several pulsating auroral channels (PACs) occur within the auroral bulge, (2) periods of the intensity pulsations span over one decade within the auroral bulge, and (3) there is no apparent trend of longer pulsation periods associated with higher latitudes for these PACs. Although PACs resemble in some respect stable pulsating auroras reported previously, they have several important differences in characteristics.

  4. Multi sensor satellite imagers for commercial remote sensing

    NASA Astrophysics Data System (ADS)

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

    2005-10-01

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

  5. Noise reduction by recycling dynamically coupled time series.

    PubMed

    Mera, M Eugenia; Morán, Manuel

    2011-12-01

    We say that several scalar time series are dynamically coupled if they record the values of measurements of the state variables of the same smooth dynamical system. We show that much of the information lost due to measurement noise in a target time series can be recovered with a noise reduction algorithm by crossing the time series with another time series with which it is dynamically coupled. The method is particularly useful for reduction of measurement noise in short length time series with high uncertainties. PMID:22225347

  6. 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

  7. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  9. 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.

  10. 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.

  11. Transmission of linear regression patterns between time series: From relationship in time series to complex networks

    NASA Astrophysics Data System (ADS)

    Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

    2014-07-01

    The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

  12. ERK'2007, Portoroz, B:191-194 191 Motion correction of contrast-enhanced MRI time series of kidney

    E-print Network

    Kovacic, Stanislav

    ERK'2007, Portoroz, B:191-194 191 Motion correction of contrast-enhanced MRI time series of kidney In this paper we focus on motion correction of contrast enhanced kidney MRI time series, which is an important: motion correction, image registration, contrast-enhanced MRI. 1 Introduction The kidneys maintain normal

  13. Extracting the Geometry of Buildings from Satellite Images Extracting the Geometry

    E-print Network

    Extracting the Geometry of Buildings from Satellite Images 1 Extracting the Geometry of Buildings case. From the technology side in image processing, the study of building extraction from aerial from Satellite Images Using Fuzzy Multiple Layer Perceptrons Abstract This paper presents Computer

  14. Teaching while selecting images for satellite-based forest mapping Froduald Kabanza and Kami Rousseau

    E-print Network

    Kabanza, Froduald

    Teaching while selecting images for satellite-based forest mapping Froduald Kabanza and Kami.kabanza, kami.rousseau}@usherbrooke.ca Abstract Satellite images are increasingly being used to monitor environmental temporal changes. The general approach is to compare old images to recent ones acquired from

  15. Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imagery

    NASA Astrophysics Data System (ADS)

    Sun, Chao; Liu, Yongxue; Zhao, Saishuai; Zhou, Minxi; Yang, Yuhao; Li, Feixue

    2016-03-01

    Salt marshes are seen as the most dynamic and valuable ecosystems in coastal zones, and in these areas, it is crucial to obtain accurate remote sensing information on the spatial distributions of species over time. However, discriminating various types of salt marsh is rather difficult because of their strong spectral similarities. Previous salt marsh mapping studies have focused mainly on high spatial and spectral (i.e., hyperspectral) resolution images combined with auxiliary information; however, the results are often limited to small regions. With a high temporal and moderate spatial resolution, the Chinese HuanJing-1 (HJ-1) satellite optical imagery can be used not only to monitor phenological changes of salt marsh vegetation over short-time intervals, but also to obtain coverage of large areas. Here, we apply HJ-1 satellite imagery to the middle coast of Jiangsu in east China to monitor changes in saltmarsh vegetation cover. First, we constructed a monthly NDVI time-series to classify various types of salt marsh and then we tested the possibility of using compressed time-series continuously, to broaden the applicability of this particular approach. Our principal findings are as follows: (1) the overall accuracy of salt marsh mapping based on the monthly NDVI time-series was 90.3%, which was ?16.0% higher than the single-phase classification strategy; (2) a compressed time-series, including NDVI from six key months (April, June-September, and November), demonstrated very little reduction (2.3%) in overall accuracy but led to obvious improvements in unstable regions; and (3) a simple rule for Spartina alterniflora identification was established using a scene solely from November, which may provide an effective way for regularly monitoring its distribution.

  16. Developing Geostationary Satellite Imaging at the Navy Precision Optical Interferometer

    NASA Astrophysics Data System (ADS)

    van Belle, G.; von Braun, K.; Armstrong, J. T.; Baines, E. K.; Schmitt, H. R.; Jorgensen, A. M.; Elias, N.; Mozurkewich, D.; Oppenheimer, R.; Restaino, S.

    The Navy Precision Optical Interferometer (NPOI) is a six-beam long-baseline optical interferometer, located in Flagstaff, Arizona; the facility is operated by a partnership between Lowell Observatory, the US Naval Observatory, and the Naval Research Laboratory. NPOI operates every night of the year (except holidays) in the visible with baselines between 8 and 100 meters (up to 432m is available), conducting programs of astronomical research and technology development for the partners. NPOI is the only such facility as yet to directly observe geostationary satellites, enabling milliarcsecond resolution of these objects. To enhance this capability towards true imaging of geosats, a program of facility upgrades will be outlined. These upgrades include AO-assisted large apertures feeding each beam line, new visible and near-infrared instrumentation on the back end, and infrastructure supporting baseline-wavelength bootstrapping which takes advantage of the spectral and morphological features of geosats. The large apertures will enable year-round observations of objects brighter than 10th magnitude in the near-IR. At its core, the system is enabled by a approach that tracks the low-resolution (and thus, high signal-to-noise), bright near-IR fringes between aperture pairs, allowing multi-aperture phasing for high-resolution visible light imaging. A complementary program of visible speckle and aperture masked imaging at Lowell's 4.3-m Discovery Channel Telescope, for constraining the low-spatial frequency imaging information, will also be outlined, including results from a pilot imaging study.

  17. 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.

  18. 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.

  19. Portable EDITOR (PEDITOR): A portable image processing system. [satellite images

    NASA Technical Reports Server (NTRS)

    Angelici, G.; Slye, R.; Ozga, M.; Ritter, P.

    1986-01-01

    The PEDITOR image processing system was created to be readily transferable from one type of computer system to another. While nearly identical in function and operation to its predecessor, EDITOR, PEDITOR employs additional techniques which greatly enhance its portability. These cover system structure and processing. In order to confirm the portability of the software system, two different types of computer systems running greatly differing operating systems were used as target machines. A DEC-20 computer running the TOPS-20 operating system and using a Pascal Compiler was utilized for initial code development. The remaining programmers used a Motorola Corporation 68000-based Forward Technology FT-3000 supermicrocomputer running the UNIX-based XENIX operating system and using the Silicon Valley Software Pascal compiler and the XENIX C compiler for their initial code development.

  20. A low cost thermal infrared hyperspectral imager for small satellites

    NASA Astrophysics Data System (ADS)

    Crites, S. T.; Lucey, P. G.; Wright, R.; Garbeil, H.; Horton, K. A.; Wood, M.

    2012-06-01

    The growth of the small satellite market and launch opportunities for these satellites is creating a new niche for earth observations that contrasts with the long mission durations, high costs, and long development times associated with traditional space-based earth observations. Low-cost, short-lived missions made possible by this new approach provide an experimental platform for testing new sensor technologies that may transition to larger, more long-lived platforms. The low costs and short lifetimes also increase acceptable risk to sensors, enabling large decreases in cost using commercial off-the-shelf (COTS) parts and allowing early-career scientists and engineers to gain experience with these projects. We are building a low-cost long-wave infrared spectral sensor, funded by the NASA Experimental Project to Stimulate Competitive Research program (EPSCoR), to demonstrate ways in which a university's scientific and instrument development programs can fit into this niche. The sensor is a low-mass, power-efficient thermal hyperspectral imager with electronics contained in a pressure vessel to enable use of COTS electronics and will be compatible with small satellite platforms. The sensor, called Thermal Hyperspectral Imager (THI), is based on a Sagnac interferometer and uses an uncooled 320x256 microbolometer array. The sensor will collect calibrated radiance data at long-wave infrared (LWIR, 8-14 microns) wavelengths in 230 meter pixels with 20 wavenumber spectral resolution from a 400 km orbit. We are currently in the laboratory and airborne testing stage in order to demonstrate the spectro-radiometric quality of data that the instrument provides.

  1. Airport runway detection in satellite images by Adaboost learning

    NASA Astrophysics Data System (ADS)

    Zongur, Ugur; Halici, Ugur; Aytekin, Orsan; Ulusoy, Ilkay

    2009-09-01

    Advances in hardware and pattern recognition techniques, along with the widespread utilization of remote sensing satellites, have urged the development of automatic target detection systems in satellite images. Automatic detection of airports is particularly essential, due to the strategic importance of these targets. In this paper, a runway detection method using a segmentation process based on textural properties is proposed for the detection of airport runways, which is the most distinguishing element of an airport. Several local textural features are extracted including not only low level features such as mean, standard deviation of image intensity and gradient, but also Zernike Moments, Circular-Mellin Features, Haralick Features, as well as features involving Gabor Filters, Wavelets and Fourier Power Spectrum Analysis. Since the subset of the mentioned features, which have a role in the discrimination of airport runways from other structures and landforms, cannot be predicted trivially, Adaboost learning algorithm is employed for both classification and determining the feature subset, due to its feature selector nature. By means of the features chosen in this way, a coarse representation of possible runway locations is obtained. Promising experimental results are achieved and given.

  2. 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.

  3. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2009-10-01

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

  5. 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.

  6. 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

  7. Nonlinear multivariate and time series analysis by neural network methods

    NASA Astrophysics Data System (ADS)

    Hsieh, William W.

    2004-03-01

    Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

  8. 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.

  9. 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.

  10. Assessing Fuel Moisture With Satellite Imaging Radar for Improved Fire Danger Prediction in Boreal Alaska

    NASA Astrophysics Data System (ADS)

    Brown, J.; Bourgeau-Chavez, L. L.; Riordan, K.; Garwood, G.; Slawski, J.; Alden, S.; Cella, B.; Murphy, K.; Kwart, M.

    2005-12-01

    Wildfire is a common occurrence in boreal regions and Alaskan natural resource management agencies devote considerable resources to fire management and suppression. Currently these agencies rely on the Canadian Forest Fire Danger Rating System's Fire Weather Index (FWI) for the assessment of the potential for wildfire. FWI is based solely on point source weather data collected daily in a sparse network across the state of Alaska. There are problems with the current FWI system, particularly in the determination of the spring start up values and problems mid-summer within permafrost regions. Melting permafrost causes increased moisture not accounted for in the weather-based system. The drought code (DC), which is an estimate of moisture in the deep compact duff layers, is the most affected by the default start up values because it has a 52 day lag period. Research has been conducted to improve the prediction of wildfire potential in Alaska using satellite c-band (5.3 cm wavelength) imaging radar. Imaging radar is sensitive to the moisture content of the features being imaged including vegetation and soils. We have been investigating the relationship between in situ soil moisture, c-band backscatter and fire danger codes for several years at a variety of burned and unburned sites in interior Alaska. Focus has been on recently burned (0-7 years) boreal forests because they allow moisture in the ground layer to be measured directly from a satellite sensor without interference of the forest canopy, and because they are a common feature across the Alaskan landscape. Studies of unburned forests adjacent to burned forests have revealed similarities in the temporal patterns of in situ moisture monitored throughout a fire season. Our research has resulted in the development of algorithms to predict DC from c-band backscatter. This will improve current weather-based estimates by providing a means for calibration of the DC throughout the season, and add additional point-sources of fuel moisture estimation. While the FWI codes provide good indicators of general fuel moisture, they do not depict the spatially varying patterns of fuel moisture across a landscape. Knowing the spatial variation in fuel moisture is important when land managers determine fire danger, prescribe a burn, or predict fire behavior. Techniques have been developed to map spatially varying soil moisture across a burned landscape using a combination of Landsat and c-band imaging radar. Further analysis of radar data acquired in unburned boreal forests is also underway. Time series analysis is proving to be instrumental in deriving spatial soil moisture information across an entire landscape from radar satellite imagery, since each location is compared to itself through time rather than to other surrounding locations. This allows the time-variant feature of soil moisture to be revealed while minimizing the time-invariant features that confound radar backscatter such as biomass and surface roughness.

  11. Images of war: using satellite images for human rights monitoring in Turkish Kurdistan.

    PubMed

    de Vos, Hugo; Jongerden, Joost; van Etten, Jacob

    2008-09-01

    In areas of war and armed conflict it is difficult to get trustworthy and coherent information. Civil society and human rights groups often face problems of dealing with fragmented witness reports, disinformation of war propaganda, and difficult direct access to these areas. Turkish Kurdistan was used as a case study of armed conflict to evaluate the potential use of satellite images for verification of witness reports collected by human rights groups. The Turkish army was reported to be burning forests, fields and villages as a strategy in the conflict against guerrilla uprising. This paper concludes that satellite images are useful to validate witness reports of forest fires. Even though the use of this technology for human rights groups will depend on some feasibility factors such as prices, access and expertise, the images proved to be key for analysis of spatial aspects of conflict and valuable for reconstructing a more trustworthy picture. PMID:18958914

  12. Crater Relaxation and Stereo Imaging of Icy Satellites

    NASA Astrophysics Data System (ADS)

    Phillips, C. B.; Hammond, N. P.; Nimmo, F.; robuchon, G.; Beyer, R. A.; Roberts, J. H.

    2011-12-01

    Over billions of years, crater depths relax at a rate that is dependent on the internal properties of the target body. Thus, measuring the depth of craters can give insight into the thermal history and subsurface structure of terrestrial bodies and icy satellites (Dombard and McKinnon, 2006). The extensive surface imaging coverage provided by Cassini (and more modest coverage from Galileo), in combination with the development of new automated stereo imaging programs, now allows for detailed measurements of crater depths on the moons of Saturn and Jupiter, and thus more accurate estimates of crater relaxation. We utilize these resources to create digital elevation models (DEMs) of large craters (D>70km) on icy satellites, beginning with Rhea and Dione. We extract crater profiles from our DEMs to determine current crater depths. An estimate of initial crater depth requires extrapolations from a crater assumed to be unrelaxed, either scaled up in size if on the same body, or scaled by gravity if on another satellite; initial and current crater depths are combined to yield a measured relaxation percentage for different crater diameter size bins. Our topographic measurements are compared with the results of a coupled thermal evolution-viscoelastic relaxation code, allowing us to investigate the thermal history of each satellite. Our model predicts the expected degree of crater relaxation for craters of different sizes and ages based on assumptions about the initial thermal state of the satellite and its subsurface structure. So far, in the case of Rhea, our numerical simulations under-predict the amount of crater relaxation we observe, suggesting that Rhea is warmer than we initially modeled; in fact, it appears that internal temperatures must approach the melting point of ice in order to achieve the amount of relaxation we observe. Our numerical model has been benchmarked against standard analytical solutions (Robuchon et al., 2011), and thus we believe that the code itself is not in error and that Rhea experienced more heating early in its history than previously thought. We also find that for 100 km diameter craters on Rhea, other factors in addition to viscous relaxation are important in determining their final depth. We have completed our measurements for all large craters on Rhea that are captured, to date, in Cassini ISS stereo pairs, and are currently working to produce topographic profiles of all available large craters on Dione. We will present results from our Dione crater profiles and numerical modeling of Dione's thermal history, and will compare our results for degree of crater relaxation and subsurface thermal profile with those previously determined for Rhea. Since Rhea and Dione have similar compositions and surface gravities, craters of equivalent diameters on their surfaces likely had similar initial depths. Thus, our comparisons of final crater depths on these two satellites will help us understand the details of any similarities or differences in their relaxation histories. Dombard, A. J., and McKinnon W. B. (2006), JGR, 111 E01001; Robuchon, G., et al. (2011), Icarus 214, 82-90

  13. Correlation and Coherence Analysis of Paired Time-Series.

    NASA Astrophysics Data System (ADS)

    Crockett, R.

    2012-04-01

    Changes in radon and other soil-gas concentrations, and other parameters, before and after earthquakes have been widely reported. However, in the majority of such radon cases, changes in magnitude in single time-series have been reported, often large changes recorded using integrating detectors, and the majority of radon time-series analysis is reported for single time-series. With a single time-series, recorded at a single location, there is no measure of the spatial extent of any anomaly and, to a great extent, only anomalies in magnitude can be investigated. With two (or more) time-series from different locations, it is possible to investigate the spatial extent of anomalies and also investigate anomalies in time, i.e. frequency and phase components, as well as anomalies in magnitude. Techniques for investigating paired time-series for simultaneous similar anomalous features, developed and adapted from techniques more familiar in the field of signal analysis, will be presented. A paired radon time-series dataset is used to illuminate these techniques. This is not a restriction to radon time-series: it is simply that the investigation at the University of Northampton has been conducted on radon datasets. The particular time-series are characterised by weak, intermittent, out-of-phase 24-hour cycles. The correlation analysis (Crockett et al., 2006) reveals two anomalous short periods where the time-series correlate, these periods temporally corresponding to UK earthquakes. The coherence analysis (Crockett, 2012) reveals anomalous short periods where the time-series cohere at 24-hour and 12-hour cycles: two of these periods confirm the periods revealed by the correlation analysis but there is a third period which also temporally corresponds to a UK earthquake.

  14. 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.

  15. Bayesian network application to satellite image classification for stormwater management

    NASA Astrophysics Data System (ADS)

    Park, Mi-Hyun

    2004-12-01

    Proper management of stormwater runoff is required to protect receiving water quality since most wastewater sources have been treated to secondary standards or beyond. Urban stormwater runoff has become the primary source of many pollutants, which is caused by runoff from highly developed, impervious land use, and managing stormwater has become the primary objective of new regulatory efforts. However, monitoring and modeling is inherently difficult, and empirical methods to estimate stormwater pollution have been developed using land use data. Conventional land use data collecting methods from ground surveys are expensive and time consuming, and may not be available. This study demonstrated alternative approaches to use satellite image classification using Bayesian networks. The Bayesian network structure shows the most influential input variables for classification. The network also reveals the relationships among variables, which is useful when dealing with missing information. First, urban land use in the given area was classified and converted to corresponding stormwater pollutant loading maps based on the existing relationships between land use and the pollutant loads. Secondly, the pollutant loads for each water quality parameter were estimated directly from satellite imagery. The resulting thematic maps spatially estimated stormwater pollutant loadings, which identified areas generating high stormwater pollutant emissions. The results show that stormwater pollutants are highly correlated to impervious areas because of their high runoff coefficients, even when they had low event mean concentrations. These results are useful in developing best management strategies for stormwater pollution and in establishing total maximum daily loads in the watershed.

  16. 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...

  17. Multiscale/Multitemporal Urban pattern morphology monitoring in southern Italy by using Landsat TM time series

    NASA Astrophysics Data System (ADS)

    Coluzzi, R.; Didonna, I.

    2009-04-01

    The size distribution and the dynamic expansion of urban areas is a key issue for the management of city growth and mitigation of negative impacts on environment and ecosystems. Even if urban growth is perceived as necessary for a sustainable economy, uncontrolled or sprawling urban growth can cause various problems such as loss of open space, landscape alteration, environmental pollution, traffic congestion, infrastructure pressure, and other social and economical issues. To face these drawbacks, a continuous monitoring of the urban growth evolution in terms of type and extent of changes over time is essential for supporting planners and decision makers in future urban planning. The analysis of the city size distribution deals with different disciplines such as geography, economy, demography, ecology, physics, statistics because the evolution of a city is a dynamic process involving a number of different factors. The main issue of great importance in modelling urban growth includes spatial and temporal dynamics, scale dynamics, man-induced land use change. The understanding and the monitoring of urban expansion processes are a challenging issue concerning the availability of both (i) time-series data set and (ii) updated information relating to current urban spatial structure and city edges in order to define and locate the evolution trends. In such a context, an effective contribution can be offered by satellite remote sensing technologies, which are able to provide both historical data archive and up-to-date imagery. Satellite technologies represent a cost-effective mean for obtaining useful data that can be easily and systematically updated for the whole globe. The use of satellite imagery along with spatial analysis techniques can be used for the monitoring and planning purposes as these enable the reporting of ongoing trends of urban growth at a detailed level. This paper analyses the spatial characterization of urban expansion by using multidate Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) satellite imagery. The investigation was focused on four small towns in southern Italy, for which the border was extracted from NASA Landsat images acquired in 1976 (MSS), in 1991 (TM) and 1999 (ETM). The border was analyzed using the box counting method, which is a well-know technique to estimate the spatial fractal dimension, that quantifies the shape irregularity of an object. The obtained results show that the fractal dimension of the border of the investigated towns is a good indicator of the dynamics of the regular/irregular urban expansion.

  18. Galileo's first images of Jupiter and the Galilean satellites

    USGS Publications Warehouse

    Belton, M.J.S.; Head, J. W., III; Ingersoll, A.P.; Greeley, R.; McEwen, A.S.; Klaasen, K.P.; Senske, D.; Pappalardo, R.; Collins, G.; Vasavada, A.R.; Sullivan, R.; Simonelli, D.; Geissler, P.; Carr, M.H.; Davies, M.E.; Veverka, J.; Gierasch, P.J.; Banfield, D.; Bell, M.; Chapman, C.R.; Anger, C.; Greenberg, R.; Neukum, G.; Pilcher, C.B.; Beebe, R.F.; Burns, J.A.; Fanale, F.; Ip, W.; Johnson, T.V.; Morrison, D.; Moore, J.; Orton, G.S.; Thomas, P.; West, R.A.

    1996-01-01

    The first images of Jupiter, Io, Europa, and Ganymede from the Galileo spacecraft reveal new information about Jupiter's Great Red Spot (GRS) and the surfaces of the Galilean satellites. Features similar to clusters of thunderstorms were found in the GRS. Nearby wave structures suggest that the GRS may be a shallow atmospheric feature. Changes in surface color and plume distribution indicate differences in resurfacing processes near hot spots on lo. Patchy emissions were seen while Io was in eclipse by Jupiter. The outer margins of prominent linear markings (triple bands) on Europa are diffuse, suggesting that material has been vented from fractures. Numerous small circular craters indicate localized areas of relatively old surface. Pervasive brittle deformation of an ice layer appears to have formed grooves on Ganymede. Dark terrain unexpectedly shows distinctive albedo variations to the limit of resolution.

  19. Terrestrial Myriametric Radio Burst Observed by IMAGE and Geotail Satellites

    NASA Technical Reports Server (NTRS)

    Fung, Shing F.; Hashimoto, KoZo; Kojima, Hirotsugu; Boardson, Scott A.; Garcia, Leonard N.; Matsumoto, Hiroshi; Green, James L.; Reinisch, Bodo W.

    2013-01-01

    We report the simultaneous detection of a terrestrial myriametric radio burst (TMRB) by IMAGE and Geotail on 19 August 2001. The TMRB was confined in time (0830-1006 UT) and frequency (12-50kHz). Comparisons with all known nonthermal myriametric radiation components reveal that the TMRB might be a distinct radiation with a source that is unrelated to the previously known radiation. Considerations of beaming from spin-modulation analysis and observing satellite and source locations suggest that the TMRB may have a fan beamlike radiation pattern emitted by a discrete, dayside source located along the poleward edge of magnetospheric cusp field lines. TMRB responsiveness to IMF Bz and By orientations suggests that a possible source of the TMRB could be due to dayside magnetic reconnection instigated by northward interplanetary field condition.

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

    NASA Technical Reports Server (NTRS)

    Wildey, R. L.

    1985-01-01

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

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

    SciTech Connect

    Cai, D Michael

    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.

  2. Estimation of longterm basin scale evapotranspiration from streamflow time series

    E-print Network

    Jackson, Robert B.

    Estimation of longterm basin scale evapotranspiration from streamflow time series Sari Palmroth,1 longterm annual evapotranspiration (ETQ) at the watershed scale by combining continuous daily streamflow (Q), Estimation of longterm basin scale evapotranspiration from streamflow time series, Water Resour. Res., 46, W

  3. 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…

  4. 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 2005, at the Medgidia meteorological station, situated in the South ­ East of Romania, on the Black Sea coast. The complexity of the problem of modeling such meteorological time series derives from

  5. Comparison of official IVS nutation time series from VLBI analysis

    NASA Astrophysics Data System (ADS)

    Gattano, C.; Lambert, S.; Bizouard, C.

    2015-12-01

    We carried out comparisons between the official IVS nutation time series using VLBI data. We studied differences between those time series and differences between derived products such as amplitude and phase of nutation components, including free core nutation, and noise color.

  6. 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

  7. Spectral Procedures Enhance the Analysis of Three Agricultural Time Series

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Many agricultural and environmental variables are influenced by cyclic processes that occur naturally. Consequently their time series often have cyclic behavior. This study developed times series models for three different phenomenon: (1) a 60 year-long state average crop yield record, (2) a four ...

  8. 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 a discussion on data access and #12;le format aspects of photometry. Introduction Presently there is time series photometry data available in public databases, but the access to these varies from one collection

  9. Symbolic Time-Series Analysis of Engine Combustion Measurements

    E-print Network

    Tennessee, University of

    980624 Symbolic Time-Series Analysis of Engine Combustion Measurements C.E.A. Finney University Engineers, Inc. ABSTRACT We present techniques of symbolic time-series analysis which are useful in spark-ignition engines under lean fueling exhibit pat- terns that can be explained as the result

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

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

    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 develops an improved SSA (ISSA) for processing the incomplete time series and the modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach is 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. Both the mean absolute error and mean root mean squared error of the reconstructed time series by ISSA are also 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 standard deviation (SD) derived by ISSA is 12.27 mg L-1, smaller than the 13.48 mg L-1 derived by SSAM.

  11. Efficient Mining of Partial Periodic Patterns in Time Series Database

    E-print Network

    Dong, Guozhu

    Efficient Mining of Partial Periodic Patterns in Time Series Database In ICDE 99 Jiawei Han \\Lambda peri­ odic patterns in time­series databases, is an interesting data mining problem. Previous studies several algorithms for efficient mining of par­ tial periodic patterns, by exploring some interesting

  12. Multifractal Analysis of Aging and Complexity in Heartbeat Time Series

    NASA Astrophysics Data System (ADS)

    Muñoz D., Alejandro; Almanza V., Victor H.; del Río C., José L.

    2004-09-01

    Recently multifractal analysis has been used intensively in the analysis of physiological time series. In this work we apply the multifractal analysis to the study of heartbeat time series from healthy young subjects and other series obtained from old healthy subjects. We show that this multifractal formalism could be a useful tool to discriminate these two kinds of series. We used the algorithm proposed by Chhabra and Jensen that provides a highly accurate, practical and efficient method for the direct computation of the singularity spectrum. Aging causes loss of multifractality in the heartbeat time series, it means that heartbeat time series of elderly persons are less complex than the time series of young persons. This analysis reveals a new level of complexity characterized by the wide range of necessary exponents to characterize the dynamics of young people.

  13. A Time-Series Processor for Sonar Mapping and Novelty T. Horiuchi1,2,3

    E-print Network

    Horiuchi, Timothy K.

    A Time-Series Processor for Sonar Mapping and Novelty Detection T. Horiuchi1,2,3 and R. Etienne-series processor chip, intended for sonar mapping and novelty detection applications, has been designed, fabricated and tested. The chip, when coupled with a sonar bearing and range estimation unit, receives an image

  14. Adaptive Optics for Satellite Imaging and Space Debris Ranging

    NASA Astrophysics Data System (ADS)

    Bennet, F.; D'Orgeville, C.; Price, I.; Rigaut, F.; Ritchie, I.; Smith, C.

    Earth's space environment is becoming crowded and at risk of a Kessler syndrome, and will require careful management for the future. Modern low noise high speed detectors allow for wavefront sensing and adaptive optics (AO) in extreme circumstances such as imaging small orbiting bodies in Low Earth Orbit (LEO). The Research School of Astronomy and Astrophysics (RSAA) at the Australian National University have been developing AO systems for telescopes between 1 and 2.5m diameter to image and range orbiting satellites and space debris. Strehl ratios in excess of 30% can be achieved for targets in LEO with an AO loop running at 2kHz, allowing the resolution of small features (<30cm) and the capability to determine object shape and spin characteristics. The AO system developed at RSAA consists of a high speed EMCCD Shack-Hartmann wavefront sensor, a deformable mirror (DM), and realtime computer (RTC), and an imaging camera. The system works best as a laser guide star system but will also function as a natural guide star AO system, with the target itself being the guide star. In both circumstances tip-tilt is provided by the target on the imaging camera. The fast tip-tilt modes are not corrected optically, and are instead removed by taking images at a moderate speed (>30Hz) and using a shift and add algorithm. This algorithm can also incorporate lucky imaging to further improve the final image quality. A similar AO system for space debris ranging is also in development in collaboration with Electro Optic Systems (EOS) and the Space Environment Management Cooperative Research Centre (SERC), at the Mount Stromlo Observatory in Canberra, Australia. The system is designed for an AO corrected upward propagated 1064nm pulsed laser beam, from which time of flight information is used to precisely range the target. A 1.8m telescope is used for both propagation and collection of laser light. A laser guide star, Shack-Hartmann wavefront sensor, and DM are used for high order correction, and tip-tilt correction provided by reflected sunlight from the target. The system is expected to achieve a Strehl ratio of 30% at 1064nm, and enable ranging to targets in excess of 2000 km. The system is currently installed and is undergoing commissioning as a natural guide star AO system, before the system is upgraded for laser guide star AO and debris ranging. This ranging system is aimed at demonstrating the capabilities of AO corrected laser ranging, and will be used as a platform to further develop space environment management techniques and strategies. SERC will continue this development and focus in particular on the development of a high power (>2kW) laser which can modify the orbit of debris using photon pressure. The AO systems we are developing aim to show how ground based systems can be used to manage the space environment. AO imaging systems can be used for satellite surveillance, while laser ranging can be used to determine precise orbital data used in the critical conjunction analysis required to maintain a safe space environment.

  15. 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.

  16. VIIRS Nighttime Lights: Advances in Satellite Low-Light Imaging

    NASA Astrophysics Data System (ADS)

    Hsu, F.; Baugh, K.; Elvidge, C.; Zhizhin, M. N.

    2013-12-01

    The Soumi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) represents a major advance in low-light imaging over previous data sources. Building on 18 years of experience compositing nighttime data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), NOAA's NGDC Earth Observation Group created the first global VIIRS nighttime lights composite product by adapting their algorithms to process these new data. Compositing nighttime data involves combining only high quality data components over a period of time to improve sensitivity and coverage. Flag image were compiled to describe image quality. The initial flag categories included: daytime, twilight, stray light, non-zero lunar illuminance, noisy edge of scan data, clouds, and no data. High quality data included in the nighttime lights composite is defined as not having any of these attributes present. After the initial adaptation of heritage OLS algorithms, the authors sought to improve the sharpness of lights in the composite by adding additional flag categories. These include a refined VIIRS cloud mask, a mask based on cloud optical thickness, and a ranking of sharpness of light.. The VIIRS cloud mask, which is a JPSS retained intermediate product cloud mask (IICMO) is refined to reject the misclassification of hot sources like gas flares as cloud. Another JPSS retained intermediate product, cloud optical thickness, also provides valuable information about the clarity of atmosphere. The authors also implemented a sharp light detector to further characterize the quality of light for each pixel. Results of compositing multiple months in 2013 using these new flag categories are presented to demonstrate the improvements in nighttime lights composite quality.

  17. 1Columbia 5/06 Structural Break Detection in Time Series ModelsStructural Break Detection in Time Series Models

    E-print Network

    tY ,as,0/with nnmm nn } ~ { ,ntY #12;10Columbia 5/06 Examples (cont) 2. Segmented GARCH model1Columbia 5/06 Structural Break Detection in Time Series ModelsStructural Break Detection in Time Series Models Richard A. Davis Thomas Lee Gabriel Rodriguez-Yam Colorado State University (http

  18. 1Davis 4/06 Structural Break Detection in Time Series ModelsStructural Break Detection in Time Series Models

    E-print Network

    tY ,as,0/with nnmm nn } ~ { ,ntY #12;10Davis 4/06 Examples (cont) 2. Segmented GARCH model: where 01Davis 4/06 Structural Break Detection in Time Series ModelsStructural Break Detection in Time Series Models Richard A. Davis Thomas Lee Gabriel Rodriguez-Yam Colorado State University (http

  19. Mobile Visualization and Analysis Tools for Spatial Time-Series Data

    NASA Astrophysics Data System (ADS)

    Eberle, J.; Hüttich, C.; Schmullius, C.

    2013-12-01

    The Siberian Earth System Science Cluster (SIB-ESS-C) provides access and analysis services for spatial time-series data build on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and climate data from meteorological stations. Until now a webportal for data access, visualization and analysis with standard-compliant web services was developed for SIB-ESS-C. As a further enhancement a mobile app was developed to provide an easy access to these time-series data for field campaigns. The app sends the current position from the GPS receiver and a specific dataset (like land surface temperature or vegetation indices) - selected by the user - to our SIB-ESS-C web service and gets the requested time-series data for the identified pixel back in real-time. The data is then being plotted directly in the app. Furthermore the user has possibilities to analyze the time-series data for breaking points and other phenological values. These processings are executed on demand of the user on our SIB-ESS-C web server and results are transfered to the app. Any processing can also be done at the SIB-ESS-C webportal. The aim of this work is to make spatial time-series data and analysis functions available for end users without the need of data processing. In this presentation the author gives an overview on this new mobile app, the functionalities, the technical infrastructure as well as technological issues (how the app was developed, our made experiences).

  20. Using Satellite Images for Wireless Network Planing in Baku City

    NASA Astrophysics Data System (ADS)

    Gojamanov, M.; Ismayilov, J.

    2013-04-01

    It is a well known fact that the Information-Telecommunication and Space research technologies are the fields getting much more benefits from the achievements of the scientific and technical progress. In many cases, these areas supporting each other have improved the conditions for their further development. For instance, the intensive development in the field of the mobile communication has caused the rapid progress of the Space research technologies and vice versa.Today it is impossible to solve one of the most important tasks of the mobile communication as Radio Frecance planning without the 2D and 3D digital maps. The compiling of such maps is much more efficient by means of the space images. Because the quality of the space images has been improved and developed, especially at the both spectral and spatial resolution points. It has been possible to to use 8 Band images with the spatial resolution of 50 sm. At present, in relation to the function 3G of mobile communications one of the main issues facing mobile operator companies is a high-precision 3D digital maps. It should be noted that the number of mobile phone users in the Republic of Azerbaijan went forward other Community of Independent States Countries. Of course, using of aerial images for 3D mapping would be optimal. However, depending on a number of technical and administrative problems aerial photography cannot be used. Therefore, the experience of many countries shows that it will be more effective to use the space images with the higher resolution for these issues. Concerning the fact that the mobile communication within the city of Baku has included 3G function there were ordered stereo images wih the spatial resolution of 50 cm for the 150 sq.km territory occupying the central part of the city in order to compile 3D digital maps. The images collected from the WorldView-2 satellite are 4-Band Bundle(Pan+MS1) stereo images. Such kind of imagery enable to automatically classificate some required clutter classes.Meanwhile, there were created 12 GPS points in the territory and there have been held some appropriate observations in these points for the geodesic reference of the space images in the territory. Moreover, it would like to mention that there have been constructed 37 permanently acting GPS stations in the territory of Azerbaijan at present. It significantly facilitates the process of the geodesic reference of the space images in order to accomplish such kind of mentioned projects. The processing of the collected space images was accomplished by means of Erdas LPS 10 program. In the first stage there was created the main component of the 3D maps- Digital Elevevation Model. In this model the following clutter classes are presented: Open; Open areas in urban; Airport, Sea, Inland water; Forest; Parks in urban; Semi Open Area; Open Wet Area; Urban/Urban Mean; Dense urban, Villages, Industrial/Commercial, Residential/Suburban; Dense residential/Suburban; Block of BUILDINGS; Dense Urban High; Buildings, Urban Mixed, Mixed dense urban

  1. A multiscale approach to InSAR time series analysis

    NASA Astrophysics Data System (ADS)

    Simons, M.; Hetland, E. A.; Muse, P.; Lin, Y. N.; Dicaprio, C.; Rickerby, A.

    2008-12-01

    We describe a new technique to constrain time-dependent deformation from repeated satellite-based InSAR observations of a given region. This approach, which we call MInTS (Multiscale analysis of InSAR Time Series), relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. This approach also permits a consistent treatment of all data independent of the presence of localized holes in any given interferogram. In essence, MInTS allows one to considers all data at the same time (as opposed to one pixel at a time), thereby taking advantage of both spatial and temporal characteristics of the deformation field. In terms of the temporal representation, we have the flexibility to explicitly parametrize known processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). Our approach also allows for the temporal parametrization to includes a set of general functions (e.g., splines) in order to account for unexpected processes. We allow for various forms of model regularization using a cross-validation approach to select penalty parameters. The multiscale analysis allows us to consider various contributions (e.g., orbit errors) that may affect specific scales but not others. The methods described here are all embarrassingly parallel and suitable for implementation on a cluster computer. We demonstrate the use of MInTS using a large suite of ERS-1/2 and Envisat interferograms for Long Valley Caldera, and validate our results by comparing with ground-based observations.

  2. 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.

  3. Horizontal coseismic deformation of the 1999 Chi-Chi earthquake measured from SPOT satellite images: Implications for the seismic

    E-print Network

    Avouac, Jean-Philippe

    Horizontal coseismic deformation of the 1999 Chi-Chi earthquake measured from SPOT satellite images displacement field by correlating optical satellite images acquired before and after the earthquake. These data deformation of the 1999 Chi-Chi earthquake measured from SPOT satellite images: Implications for the seismic

  4. Detecting leaf pulvinar movements on NDVI time series of desert trees: a new approach for water stress detection.

    PubMed

    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 (?NDVI mo-mi) and between winter and summer (?NDVI W-S). In this paper, we showed that the ?NDVI mo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ?NDVI W-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 ?NDVI mo-mi and ?NDVI W-S. For an 11-year time series without rainfall events, Landsat ?NDVI W-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ?NDVI mo-mi and ?NDVI W-S have potential to detect early water stress of paraheliotropic vegetation. PMID:25188305

  5. Characterizing time series: when Granger causality triggers complex networks

    NASA Astrophysics Data System (ADS)

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

    2012-08-01

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

  6. Estimation of connectivity measures in gappy time series

    NASA Astrophysics Data System (ADS)

    Papadopoulos, G.; Kugiumtzis, D.

    2015-10-01

    A new method is proposed to compute connectivity measures on multivariate time series with gaps. Rather than removing or filling the gaps, the rows of the joint data matrix containing empty entries are removed and the calculations are done on the remainder matrix. The method, called measure adapted gap removal (MAGR), can be applied to any connectivity measure that uses a joint data matrix, such as cross correlation, cross mutual information and transfer entropy. MAGR is favorably compared using these three measures to a number of known gap-filling techniques, as well as the gap closure. The superiority of MAGR is illustrated on time series from synthetic systems and financial time series.

  7. 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.

  8. 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.

  9. 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.

  10. Applications of Time Series in Finance and Macroeconomics 

    E-print Network

    Ibarra Ramirez, Raul

    2011-08-08

    This dissertation contains three applications of time series in finance and macroeconomics. The first essay compares the cumulative returns for stocks and bonds at investment horizons from one to ten years by using a test for spatial dominance...

  11. Testing time series reversibility using complex network methods

    E-print Network

    Donges, Jonathan F; Kurths, Jürgen

    2012-01-01

    The absence of time-reversal symmetry is a fundamental property of many nonlinear time series. Here, we propose a set of novel statistical tests for time series reversibility based on standard and horizontal visibility graphs. Specifically, we statistically compare the distributions of time-directed variants of the common graph-theoretical measures degree and local clustering coefficient. Unlike other tests for reversibility, our approach does not require constructing surrogate data and can be applied to relatively short time series. We demonstrate its performance for realisations of paradigmatic model systems with known time-reversal properties as well as pickling up signatures of nonlinearity in some well-studied real-world neuro-physiological time series.

  12. 14.384 Time Series Analysis, Fall 2008

    E-print Network

    Schrimpf, Paul

    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 ...

  13. 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 ...

  14. Space-time series forecasting by artificial neural networks

    NASA Astrophysics Data System (ADS)

    Cheng, Tao; Wang, Jiaqiu; Li, Xia

    2008-12-01

    Spatio-Temporal Autoregressive Integrated Moving Average (STAIRMA) model family is a very useful tool in modeling space-time series data. It assumes that space-time series data is correlated linearly in space and time. However, in reality most space-time series contains nonlinear space-time autocorrelation structure, which can't be modeled by STARIMA. Artificial neural networks (ANN) have shown great flexibility in modeling and forecasting nonlinear dynamic process. In the paper, we developed an architecture approach to model space-time series data using artificial neural network (ANN). The model is tested with forest fire prediction in Canada. The experimental result demonstrates that STANN achieves much better prediction accuracy than STARIMA model.

  15. Fractal and natural time analysis of geoelectrical time series

    NASA Astrophysics Data System (ADS)

    Ramirez Rojas, A.; Moreno-Torres, L. R.; Cervantes, F.

    2013-05-01

    In this work we show the analysis of geoelectric time series linked with two earthquakes of M=6.6 and M=7.4. That time series were monitored at the South Pacific Mexican coast, which is the most important active seismic subduction zone in México. The geolectric time series were analyzed by using two complementary methods: a fractal analysis, by means of the detrended fluctuation analysis (DFA) in the conventional time, and the power spectrum defined in natural time domain (NTD). In conventional time we found long-range correlations prior to the EQ-occurrences and simultaneously in NTD, the behavior of the power spectrum suggest the possible existence of seismo electric signals (SES) similar with the previously reported in equivalent time series monitored in Greece prior to earthquakes of relevant magnitude.

  16. Nonstationary time series prediction combined with slow feature analysis

    NASA Astrophysics Data System (ADS)

    Wang, G.; Chen, X.

    2015-07-01

    Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

  17. Distinguishing between Chaotic and Stochastic Systems in Financial Time Series

    NASA Astrophysics Data System (ADS)

    Menna, Massimiliano; Rotundo, Giulia; Tirozzi, Brunello

    In last years several mathematical methods were successfully used for financial time series modeling. The main problem is to check whether irregularities of data are generated by a stochastic process or they are due to some deterministic chaos and to the presence of low-dimensional strange attractor. We focus on a test based on the correlation dimension. In particular we examine the time series of the daily closure prices of the Italian car industry ``FIAT'' shares.

  18. Estimation of Parameters from Discrete Random Nonstationary Time Series

    NASA Astrophysics Data System (ADS)

    Takayasu, H.; Nakamura, T.

    For the analysis of nonstationary stochastic time series we introduce a formulation to estimate the underlying time-dependent parameters. This method is designed for random events with small numbers that are out of the applicability range of the normal distribution. The method is demonstrated for numerical data generated by a known system, and applied to time series of traffic accidents, batting average of a baseball player and sales volume of home electronics.

  19. Some results of analysis of source position time series

    E-print Network

    Malkin, Zinovy

    2015-01-01

    Source position time series produced by International VLBI Service for Geodesy and astrometry (IVS) Analysis Centers were analyzed. These series was computed using different software and analysis strategy. Comparison of this series showed that they have considerably different scatter and systematic behavior. Based on the inspection of all the series, new sources were identified as sources with irregular (non-random) position variations. Two statistics used to estimate the noise level in the time series, namely RMS and ADEV were compared.

  20. Image Analysis as a Tool for Satellite-Earth Propagation Studies

    NASA Technical Reports Server (NTRS)

    Akturan, Riza; Lin, Hsin-Piao; Vogel, Wolfhard J.

    1996-01-01

    We present a progress report on a useful new method to assess propagation problems for outdoor mobile Earth-satellite paths. The method, Photogrammetric Satellite Service Prediction (PSSP) is based on the determination of Land Mobile Satellite Systems (LMSS) service attributes at the locations of static or mobile LMSS service users by evaluating fisheye images of their environment. This paper gives an overview of the new method and its products.

  1. 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.

  2. High resolution Doppler imager on the Upper Atmosphere Research Satellite

    SciTech Connect

    Skinner, W.R.; Hays, P.B.; Grassl, H.J.; Gell, D.A.; Burrage, M.D.; Marshall, A.R.; Ortland, D.A.

    1994-12-31

    The High Resolution Doppler Imager (HRDI) on the Upper Atmosphere Research Satellite has been providing measurements of the wind field in the stratosphere, mesosphere and lower thermosphere since November 1991. Examination of various calibration data indicates the instrument has remained remarkably stable since launch. The instrument has a thermal drift of about 30 m/s/{degree}C (slightly dependent on wavelength) and a long-term temporal drift that has amounted to about 80 m/s since launch. These effects are removed in the data processing leaving an uncertainty in the instrument stability of {minus}2 nVs. The temperature control of the instrument has improved significantly since launch as a new method was implemented. The initial temperature control held the instrument temperature at about {+-}1{degree}C. The improved method, which holds constant the temperature of the optical bench instead of the radiator, keeps the instrument temperature at about 0.2{degree}C. The calibrations indicate very little change in the sensitivity of the instrument. The detector response has shown no degradation and the optics have not changed their transmittance.

  3. a Novel Approach for Edge Detection of Low Contrast Satellite Images

    NASA Astrophysics Data System (ADS)

    Singh, K. K.; Bajpai, M. K.; Pandey, R. K.

    2015-03-01

    Discrimination between texture edges and geometrical edges is very difficult in low contrast images. Satellite images are low contrast images. It is important to extract the edges that are not clearly visible in case of Satellite images. The present work encompasses a new edge detection algorithm using newly constructed differentiator. Chebyshev polynomial based fractional order differentiator has been used for filtering operation on an image. High pass and Low pass filters are designed with the concept of Quadrature Mirror Filter (QMF). Pre-processing has been performed by using this filter. Sobel edge detection method has been applied on this pre-processed image. The algorithm has been tested with two different satellite images.

  4. Characterizing and estimating noise in InSAR and InSAR time series with MODIS

    USGS Publications Warehouse

    Barnhart, William D.; Lohman, Rowena B.

    2013-01-01

    InSAR time series analysis is increasingly used to image subcentimeter displacement rates of the ground surface. The precision of InSAR observations is often affected by several noise sources, including spatially correlated noise from the turbulent atmosphere. Under ideal scenarios, InSAR time series techniques can substantially mitigate these effects; however, in practice the temporal distribution of InSAR acquisitions over much of the world exhibit seasonal biases, long temporal gaps, and insufficient acquisitions to confidently obtain the precisions desired for tectonic research. Here, we introduce a technique for constraining the magnitude of errors expected from atmospheric phase delays on the ground displacement rates inferred from an InSAR time series using independent observations of precipitable water vapor from MODIS. We implement a Monte Carlo error estimation technique based on multiple (100+) MODIS-based time series that sample date ranges close to the acquisitions times of the available SAR imagery. This stochastic approach allows evaluation of the significance of signals present in the final time series product, in particular their correlation with topography and seasonality. We find that topographically correlated noise in individual interferograms is not spatially stationary, even over short-spatial scales (<10 km). Overall, MODIS-inferred displacements and velocities exhibit errors of similar magnitude to the variability within an InSAR time series. We examine the MODIS-based confidence bounds in regions with a range of inferred displacement rates, and find we are capable of resolving velocities as low as 1.5 mm/yr with uncertainties increasing to ?6 mm/yr in regions with higher topographic relief.

  5. Precise attitude rate estimation using star images obtained by mission telescope for satellite missions

    NASA Astrophysics Data System (ADS)

    Inamori, Takaya; Hosonuma, Takayuki; Ikari, Satoshi; Saisutjarit, Phongsatorn; Sako, Nobutada; Nakasuka, Shinichi

    2015-02-01

    Recently, small satellites have been employed in various satellite missions such as astronomical observation and remote sensing. During these missions, the attitudes of small satellites should be stabilized to a higher accuracy to obtain accurate science data and images. To achieve precise attitude stabilization, these small satellites should estimate their attitude rate under the strict constraints of mass, space, and cost. This research presents a new method for small satellites to precisely estimate angular rate using star blurred images by employing a mission telescope to achieve precise attitude stabilization. In this method, the angular velocity is estimated by assessing the quality of a star image, based on how blurred it appears to be. Because the proposed method utilizes existing mission devices, a satellite does not require additional precise rate sensors, which makes it easier to achieve precise stabilization given the strict constraints possessed by small satellites. The research studied the relationship between estimation accuracy and parameters used to achieve an attitude rate estimation, which has a precision greater than 1 × 10-6 rad/s. The method can be applied to all attitude sensors, which use optics systems such as sun sensors and star trackers (STTs). Finally, the method is applied to the nano astrometry satellite Nano-JASMINE, and we investigate the problems that are expected to arise with real small satellites by performing numerical simulations.

  6. Homogenization of Antarctic Radiosonde Temperature Time Series using ERA-Interim Innovation Statistics

    NASA Astrophysics Data System (ADS)

    Radanovics, Sabine

    2010-05-01

    RAOBCORE (Radiosonde Observation Correction using Reanalysis) is a method to homogenize radiosonde temperature time series and was developed at the Institute for Meteorology and Geophysics at the University of Vienna by Leopold Haimberger. RAOBCORE uses differences between the observations and the background forecast from ERA-40 reanalyses (so-called innovations) to find and correct artificial breaks. This worked insufficiently in the antarctic, because there is a too strong Brewer-Dobson circulation in ERA-40 during the last years, which led to vertically inconsistent temperature trends in the polar regions, especially in the antarctic. There is a very strong annual cycle in the ERA-40 innovations for antarctic radiosonde stations and the inhomogeneities in the background forecast caused by changes in the available satellite data are larger than in the global mean. Meanwhile the new ERA-Interim reanalysis is available and it is much more homogenous in the antarctic region and therefore a better reference for homogenizing radiosonde data since 1989. The annual cycle is much smaller in ERA-Interim than in ERA-40 innovations and the vertical structure of the temperature trends is more realistic. In the new RAOBCORE version breaks in antarctic radiosonde temperature time series are only corrected if at least five surrounding antarctic stations are available as a reference for calculating the break size. Breaks prior to 1989 are practically not correctable without neighbour stations, because ERA-40 innovations are still used until 1989 and the background forecast has large inhomogeneities due to changes in satellite data. The effect of the improvement in the correction calculation for antarctic stations and using ERA-Interim innovations on homogenization of the antarctic radiosonde temperature time series was investigated. With the new corrections the time series correspond much better to the antarctic mean and the spatial homogeneity of radiosonde temperature trends is improved. The break size calculation method has more influence for breaks prior to 1989 than in the ERA-Interim period.

  7. Reconstructing Ocean Circulation using Coral (triangle)14C Time Series

    SciTech Connect

    Kashgarian, M; Guilderson, T P

    2001-02-23

    We utilize monthly {sup 14}C data derived from coral archives in conjunction with ocean circulation models to address two questions: (1) how does the shallow circulation of the tropical Pacific vary on seasonal to decadal time scales and (2) which dynamic processes determine the mean vertical structure of the equatorial Pacific thermocline. Our results directly impact the understanding of global climate events such as the El Nino-Southern Oscillation (ENSO). To study changes in ocean circulation and water mass distribution involved in the genesis and evolution of ENSO and decadal climate variability, it is necessary to have records of climate variables several decades in length. Continuous instrumental records are limited because technology for continuous monitoring of ocean currents (e.g. satellites and moored arrays) has only recently been available, and ships of opportunity archives such as COADS contain large spatial and temporal biases. In addition, temperature and salinity in surface waters are not conservative and thus can not be independently relied upon to trace water masses, reducing the utility of historical observations. Radiocarbon in sea water is a quasi-conservative water mass tracer and is incorporated into coral skeletal material, thus coral {sup 14}C records can be used to reconstruct changes in shallow circulation that would be difficult to characterize using instrumental data. High resolution {Delta}{sup 14}C timeseries such as ours, provide a powerful constraint on the rate of surface ocean mixing and hold great promise to augment one time oceanographic surveys. {Delta}{sup 14}C timeseries such as these, not only provide fundamental information about the shallow circulation of the Pacific, but can also be directly used as a benchmark for the next generation of high resolution ocean models used in prognosticating climate. The measurement of {Delta}{sup 14}C in biological archives such as tree rings and coral growth bands is a direct record of the invasion of fossil fuel CO{sub 2} and bomb {sup 14}C into the atmosphere and surface oceans. Therefore the {Delta}{sup 14}C data that are produced in this study can be used to validate the ocean uptake of fossil fuel CO2 in coupled ocean-atmosphere models. This study takes advantage of the quasi-conservative nature of {sup 14}C as a water mass tracer by using {Delta}{sup 14}C time series in corals to identify changes in the shallow circulation of the Pacific. Although the data itself provides fundamental information on surface water mass movement the true strength is a combined approach which is greater than the individual parts; the data helps uncover deficiencies in ocean circulation models and the model results place long {Delta}{sup 14}C time series in a dynamic framework which helps to identify those locations where additional observations are most needed.

  8. Prioritization criteria of objective index for disaster management by satellite image processing

    NASA Astrophysics Data System (ADS)

    Poursaber, Mohammad R.; Ariki, Yasuo; Safi, Mohammad

    2014-10-01

    The outputs obtained from satellite image processing generally presents various information based on the interpretation technique, selected objects for object based processing, precision of processing, the number and time of images used for this process. This issue should be managed well during a disaster management process based on satellite images. Very high resolution (VHR) optical satellite data are potential sources to provide detailed information on damage and geological changes for a large area in a short time. In this paper, we studied tsunami triggered area, which was caused on 11 March 2011 by Tohoku earthquake, using VHR data from GeoEye-1satellite images. A set of pre and post-earthquake images were used to perform visual change analysis through comparison of these data. These images include the data of the same area before the disaster in normal condition and after the disaster which caused changes and also some modification imposed to that area. Upon occurrence of a disaster, the images are used to estimate the extent of the damage. Then based on disaster management criteria and the needs for recovery and reconstruction, the priorities for object based classification indexes are defined. In post-disaster management, they are used for reconstruction and sustainable development activities. Finally a classified characteristic definition has been proposed which can be used as sample indexes prioritization criteria for disaster management based on satellite image processing. This prioritization criteria are based on an object based processing technique and can be further developed for other image processing methods.

  9. Research on Complicated Imaging Condition of GEO Optical High Resolution Earth Observing Satellite

    NASA Astrophysics Data System (ADS)

    Guo, Linghua

    2012-07-01

    The requirement for high time and space resolution of optical remote sensing satellite in disaster, land resources, environment, marine monitoring and meteorology observation, etc is getting urgent and strict. For that reason, a remote sensing satellite system solely located in MEO or LEO cannot operate continuous observation and Surveillance. GEO optical high resolution earth observing satellite in the other hand can keep the mesoscale and microscale target under continuous surveillance by controlling line of sight(LOS), and can provide imaging observation of an extensive region in a short time. The advantages of GEO satellite such as real-time observation of the mesoscale and microscale target, rapid response of key events, have been recognized by lots of countries and become a new trend of remote sensing satellite. As many advantages as the GEO remote sensing satellite has, its imaging condition is more complicated. Many new characteristics of imaging observation and imaging quality need to be discussed. We analyze each factor in the remote sensing link, using theoretical analysis and modeling simulation to get coefficient of each factor to represent its effect on imaging system. Such research achievements can provide reference for satellite mission analysis and system design.

  10. A 40 Year Time Series of SBUV Observations: the Version 8.6 Processing

    NASA Technical Reports Server (NTRS)

    McPeters, Richard; Bhartia, P. K.; Flynn, L.

    2012-01-01

    Under a NASA program to produce long term data records from instruments on multiple satellites (MEaSUREs), data from a series of eight SBUV and SBUV 12 instruments have been reprocessed to create a 40 year long ozone time series. Data from the Nimbus 4 BUV, Nimbus 7 SBUV, and SBUV/2 instruments on NOAA 9, 11, 14, 16, 17, and 18 were used covering the period 1970 to 1972 and 1979 to the present. In past analyses an ozone time series was created from these instruments by adjusting ozone itself, instrument by instrument, for consistency during overlap periods. In the version 8.6 processing adjustments were made to the radiance calibration of each instrument to maintain a consistent calibration over the entire time series. Data for all eight instruments were then reprocessed using the adjusted radiances. Reprocessing is necessary to produce an accurate latitude dependence. Other improvements incorporated in version 8.6 included the use of the ozone cross sections of Brion, Daumont, and Malicet, and the use of a cloud height climatology derived from Aura OMI measurements. The new cross sections have a more accurate temperature dependence than the cross sections previously used. The OMI-based cloud heights account for the penetration of UV into the upper layers of clouds. The consistency of the version 8.6 time series was evaluated by intra-instrument comparisons during overlap periods, comparisons with ground-based instruments, and comparisons with measurements made by instruments on other satellites such as SAGE II and UARS MLS. These comparisons show that for the instruments on NOAA 16, 17 and 18, the instrument calibrations were remarkably stable and consistent from instrument to instrument. The data record from the Nimbus 7 SBUV was also very stable, and SAGE and ground-based comparisons show that the' calibration was consistent with measurements made years laterby the NOAA 16 instrument. The calibrations of the SBUV/2 instruments on NOAA 9, 11, and 14 were more of a problem. The rapidly drifting orbits of these satellites resulted in relative time and altitude dependent differences that are significant. Despite these problems, total column ozone appears to be consistent to better than 1% over the entire time series, while the ozone vertical distribution is consistent to approximately 5%.

  11. Time-Series analysis of MODIS NDVI data along with ancillary data for Land use/Land cover mapping of Uttarakhand

    NASA Astrophysics Data System (ADS)

    Patakamuri, S. K.; Agrawal, S.; Krishnaveni, M.

    2014-12-01

    Land use and land cover plays an important role in biogeochemical cycles, global climate and seasonal changes. Mapping land use and land cover at various spatial and temporal scales is thus required. Reliable and up to date land use/land cover data is of prime importance for Uttarakhand, which houses twelve national parks and wildlife sanctuaries and also has a vast potential in tourism sector. The research is aimed at mapping the land use/land cover for Uttarakhand state of India using Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The study also incorporated smoothening of time-series plots using filtering techniques, which helped in identifying phenological characteristics of various land cover types. Multi temporal Normalized Difference Vegetation Index (NDVI) data for the year 2010 was used for mapping the Land use/land cover at 250m coarse resolution. A total of 23 images covering a single year were layer stacked and 150 clusters were generated using unsupervised classification (ISODATA) on the yearly composite. To identify different types of land cover classes, the temporal pattern (or) phenological information observed from the MODIS (MOD13Q1) NDVI, elevation data from Shuttle Radar Topography Mission (SRTM), MODIS water mask (MOD44W), Nighttime Lights Time Series data from Defense Meteorological Satellite Program (DMSP) and Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) data were used. Final map product is generated by adopting hybrid classification approach, which resulted in detailed and accurate land use and land cover map.

  12. LONG-TERM MONITORING OF SEAGRASSES USING A WV-2 SATELLITE IMAGE, HISTORICAL AERIAL PHOTOGRAPHY AND FIELD DATA

    E-print Network

    Gilbes, Fernando

    LONG-TERM MONITORING OF SEAGRASSES USING A WV-2 SATELLITE IMAGE, HISTORICAL AERIAL PHOTOGRAPHY in seagrass ecosystem. Although remote sensing techniques with multispectral imagery have been recently used in-situ data with a satellite image and historical aerial photography. A current satellite imagery

  13. Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos

    NASA Astrophysics Data System (ADS)

    Liang, Yu-Li

    Multimedia data is increasingly important in scientific discovery and people's daily lives. Content of massive multimedia is often diverse and noisy, and motion between frames is sometimes crucial in analyzing those data. Among all, still images and videos are commonly used formats. Images are compact in size but do not contain motion information. Videos record motion but are sometimes too big to be analyzed. Sequential images, which are a set of continuous images with low frame rate, stand out because they are smaller than videos and still maintain motion information. This thesis investigates features in different types of noisy sequential images, and the proposed solutions that intelligently combined multiple features to successfully retrieve visual information from on-line videos and cloudy satellite images. The first task is detecting supraglacial lakes above ice sheet in sequential satellite images. The dynamics of supraglacial lakes on the Greenland ice sheet deeply affect glacier movement, which is directly related to sea level rise and global environment change. Detecting lakes above ice is suffering from diverse image qualities and unexpected clouds. A new method is proposed to efficiently extract prominent lake candidates with irregular shapes, heterogeneous backgrounds, and in cloudy images. The proposed system fully automatize the procedure that track lakes with high accuracy. We further cooperated with geoscientists to examine the tracked lakes and found new scientific findings. The second one is detecting obscene content in on-line video chat services, such as Chatroulette, that randomly match pairs of users in video chat sessions. A big problem encountered in such systems is the presence of flashers and obscene content. Because of various obscene content and unstable qualities of videos capture by home web-camera, detecting misbehaving users is a highly challenging task. We propose SafeVchat, which is the first solution that achieves satisfactory detection rate by using facial features and skin color model. To harness all the features in the scene, we further developed another system using multiple types of local descriptors along with Bag-of-Visual Word framework. In addition, an investigation of new contour feature in detecting obscene content is presented.

  14. Deformation time series at Llaima volcano, southern Andes

    NASA Astrophysics Data System (ADS)

    Bathke, Hannes; Walter, Thomas; Motagh, Mahdi; Shirzaei, Manoochehr

    2010-05-01

    Llaima volcano, with an edifice height of 3125 m and a volume of about 400 km³, is one of the largest and most active volcanoes in South America. Its eruptive history suggest a potential for very large and hazardous eruptions including pyroclastic flows, air falls and material remobilization in the form of lahars affecting regions even at the lower apron and beyond, posing a significant risk to civilizations, infrastructure and traffic ways. Llaima volcano is near constantly active; since the 17th century strombolian eruptions occurred at a mean frequency of one eruptive phase every five years. Although this strong activity and socioeconomic importance the source of magma, possible magma reservoirs and deformations prior to or associated with eruptions are hitherto unknown. One of the problems for establishing a monitoring system is that Llaima is difficult to access and located in vegetated and topographically rough terrain. To better understand the volcano physics, we created an InSAR time series based on the PS technique using 18 Envisat images from Dezember 2002 to November 2008. Using the StaMPS software we obtained 24,000 stable pixels in the vicinity of the volcano, that allow to investigate a spatiotemporal displacement field. Associated with the recent eruptions, we observed non-linear subsidence at the vicinity of the volcano base. We assessed the validiy of the deformation signal, using statistical tests and discussed the possible influence of athmospheric and topographic errors. To investigate the cause of the observed spatiotemporal deformation we employed an inverse source modelling approach, and simulated the dislocation source as an analytical pressurized spherical model. The inverted source can reproduce the observed deformation and allows to constrain the location of the magma reservoir under Llaima. Moreover we observed a signal might be associated to a slow landslide at the eastern flank of the volcano between December 2007 and Januar 2008. In this presentation we will give the detail of data processing, modelling and interpretation.

  15. Stationary determinism in Observed Time Series: the earth's surface temperature

    E-print Network

    Rafael M. Gutierrez

    1999-08-06

    In this work we address the feasibility of estimating and isolating the stationary and deterministic content of observational time series, {\\bf Ots}, which in general have very limited characteristics. In particular, we study the valuable earth's surface mean temperature time series, {\\bf Tts}, by applying several treatments intended to isolate the stationary and deterministic content. We give particular attention to the sensitivity of results on the different parameters involved. The effects of such treatments were assessed by means of several methods designed to estimate the stationarity of time series. In order to strengthen the significance of the results obtained we have created a comparative framework with seven test time series of well-know origin and characteristics with a similar small number of data points. We have obtained a greater understanding of the potential and limitations of the different methods when applied to real world time series. The study of the stationarity and deterministic content of the {\\bf Tts} gives useful information about the particular complexity of global climatic evolution and the general important problem of the isolation of a real system from its surroundings by measuring and treating the obtained observations without any other additional information about the system.

  16. Wavelet packet time series analysis of aluminum electrolytic cells

    NASA Astrophysics Data System (ADS)

    Johnson, Arthur, III; Li, Ching-Chung

    2001-03-01

    For decades the process of aluminum electrolysis has facilitated the production of aluminum. The process occurs within aluminum electrolytic cells, where alumina (Al2O3) is dissolved in liquid cryolite (Na3AlF6). The dissolved alumina is reduced by the carbon anode and forms carbon dioxide. Complexes containing aluminum ions migrate to the cathode surface (bath-metal interface) where aluminum metal is produced. The monitoring of the electrolysis process is done through the use of the cell resistance. Using resistance set point values that are indirectly related to the desired alumina concentration in the bath (cryolite), the computed resistance can indicate if the cell is operating within acceptable production conditions. The resistance time series is a nonstationary random process. We have applied the principal component method to shortsegments of each time series to identify key components. However the principal components are data dependent. In order to study the time series' localized structure we use a wavelet packet based approach to analyze this nonstationary process. We use Daubechies 3 orthonormal wavelet and scaling function as our basis functions and model each short segment of the resistance time series as a locally stationary wavelet process. The use of wavelet packets increases the separability of the innovations into individual packets. Hence each wavelet packet time series represents a single subprocess. The analysis of individual subprocesses yields information for making inference of how the process evolves during unstable operating conditions.

  17. Multitask Gaussian processes for multivariate physiological time-series analysis.

    PubMed

    Dürichen, Robert; Pimentel, Marco A F; Clifton, Lei; Schweikard, Achim; Clifton, David A

    2015-01-01

    Gaussian process (GP) models are a flexible means of performing nonparametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariate time-series tools. Here, we propose a method using multitask GPs (MTGPs) which can model multiple correlated multivariate physiological time series simultaneously. The flexible MTGP framework can learn the correlation between multiple signals even though they might be sampled at different frequencies and have training sets available for different intervals. Furthermore, prior knowledge of any relationship between the time series such as delays and temporal behavior can be easily integrated. A novel normalization is proposed to allow interpretation of the various hyperparameters used in the MTGP. We investigate MTGPs for physiological monitoring with synthetic data sets and two real-world problems from the field of patient monitoring and radiotherapy. The results are compared with standard Gaussian processes and other existing methods in the respective biomedical application areas. In both cases, we show that our framework learned the correlation between physiological time series efficiently, outperforming the existing state of the art. PMID:25167541

  18. Characterizing Complex Time Series from the Scaling of Prediction Error.

    NASA Astrophysics Data System (ADS)

    Hinrichs, Brant Eric

    This thesis concerns characterizing complex time series from the scaling of prediction error. We use the global modeling technique of radial basis function approximation to build models from a state-space reconstruction of a time series that otherwise appears complicated or random (i.e. aperiodic, irregular). Prediction error as a function of prediction horizon is obtained from the model using the direct method. The relationship between the underlying dynamics of the time series and the logarithmic scaling of prediction error as a function of prediction horizon is investigated. We use this relationship to characterize the dynamics of both a model chaotic system and physical data from the optic tectum of an attentive pigeon exhibiting the important phenomena of nonstationary neuronal oscillations in response to visual stimuli.

  19. Kernel Least Mean Kurtosis Based Online Chaotic Time Series Prediction

    NASA Astrophysics Data System (ADS)

    Qu, Hua; Ma, Wen-Tao; Zhao, Ji-Hong; Chen, Ba-Dong

    2013-11-01

    Based on the kernel methods and the nonlinear feature of chaotic time series, we develop a new algorithm called kernel least mean kurtosis (KLMK) by applying the kernel trick to the least mean kurtosis (LMK) algorithm, which maps the input data to a high dimensional feature space. The KLMK algorithm can overcome the shortcomings of the original LMK for nonlinear time series prediction, and it is easy to implement a sample by sample adaptation procedure. Theoretical analysis suggests that the KLMK algorithm may converge in a mean square sense in nonlinear chaotic time series prediction under certain conditions. Simulation results show that the performance of KLMK is better than those of LMK and the kernel least mean square (KLMS) algorithm.

  20. On fractal analysis of cardiac interbeat time series

    NASA Astrophysics Data System (ADS)

    Guzmán-Vargas, L.; Calleja-Quevedo, E.; Angulo-Brown, F.

    2003-09-01

    In recent years the complexity of a cardiac beat-to-beat time series has been taken as an auxiliary tool to identify the health status of human hearts. Several methods has been employed to characterize the time series complexity. In this work we calculate the fractal dimension of interbeat time series arising from three groups: 10 young healthy persons, 8 elderly healthy persons and 10 patients with congestive heart failures. Our numerical results reflect evident differences in the dynamic behavior corresponding to each group. We discuss these results within the context of the neuroautonomic control of heart rate dynamics. We also propose a numerical simulation which reproduce aging effects of heart rate behavior.

  1. First time-series optical photometry from Antarctica

    E-print Network

    K. G. Strassmeier; R. Briguglio; T. Granzer; G. Tosti; I. DiVarano; I. Savanov; M. Bagaglia; S. Castellini; A. Mancini; G. Nucciarelli; O. Straniero; E. Distefano; S. Messina; G. Cutispoto

    2008-07-18

    Beating the Earth's day-night cycle is mandatory for long and continuous time-series photometry and had been achieved with either large ground-based networks of observatories at different geographic longitudes or when conducted from space. A third possibility is offered by a polar location with astronomically-qualified site characteristics. Aims. In this paper, we present the first scientific stellar time-series optical photometry from Dome C in Antarctica and analyze approximately 13,000 CCD frames taken in July 2007. We conclude that high-precision CCD photometry with exceptional time coverage and cadence can be obtained at Dome C in Antarctica and be successfully used for time-series astrophysics.

  2. Detection of "noisy" chaos in a time series

    NASA Technical Reports Server (NTRS)

    Chon, K. H.; Kanters, J. K.; Cohen, R. J.; Holstein-Rathlou, N. H.

    1997-01-01

    Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". The output from most biological systems is probably the result of both the internal dynamics of the systems, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

  3. 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...

  4. Correlation analysis for long time series by robustly estimated autoregressive stochastic processes

    NASA Astrophysics Data System (ADS)

    Schuh, Wolf-Dieter; Brockmann, Jan-Martin; Kargoll, Boris

    2015-04-01

    Modern sensors and satellite missions deliver huge data sets and long time series of observations. These data sets have to be handled with care because of changing correlations, conspicuous data and possible outliers. Tailored concepts for data selection and robust techniques to estimate the correlation characteristics allow for a better/optimal exploitation of the information of these measurements. In this presentation we give an overview of standard techniques for estimating correlations occurring in long time series in the time domain as well as in the frequency domain. We discuss the pros and cons especially with the focus on the intensified occurrence of conspicuous data and outliers. We present a concept to classify the measurements and isolate conspicuous data. We propose to describe the varying correlation behavior of the measurement series by an autoregressive stochastic process and give some hints how to construct adaptive filters to decorrelate the measurement series and to handle the huge covariance matrices. As study object we use time series from gravity gradient data collected during the GOCE low orbit operation campaign (LOOC). Due to the low orbit these data from 13-Jun-2014 to 21-Oct-2014 have more or less the same potential to recover the Earth gravity field with the same accuracy than all the data from the rest of the entire mission. Therefore these data are extraordinarily valuable but hard to handle, because of conspicuous data due to maneuvers during the orbit lowering phases, overall increase in drag, saturation of ion thrusters and other (currently) unexplained effects.

  5. The New GFZ RL05 GRACE Gravity Field Model Time Series

    NASA Astrophysics Data System (ADS)

    Dahle, Ch.; Flechtner, F.; Gruber, Ch.; König, D.; König, R.; Michalak, G.; Neumayer, K. H.

    2012-04-01

    As the GRACE Science Data System (SDS) plans to publish several years (2005 - 2010) of an improved new release (RL05) of GRACE gravity field products by the 10th anniversary of the launch of the GRACE satellites (March 17th, 2012), the German Research Center for Geosciences (GFZ) as part of the SDS has reprocessed the GRACE mission data over the above-mentioned period. The reprocessing of this new GFZ RL05 time series is based on updated Level-1B instrument data, improved background models (e.g. a new model release to de-aliase atmospheric and oceanic short-term mass variations or use of static and time variable gravity information from EIGEN-6C) and modified processing standards (e.g. for accelerometer data parameterization or GPS data processing). Two first test years of RL05 solutions already indicated both notable noise reduction and signal improvement compared to its precursor RL04. Based on a longer time-series, we will present the final RL05 validation. Also, since in the later mission phase (starting end of 2010) the GRACE accelerometers have to be turned off approx. every 5 months due to on-board battery problems, an alternative processing method using models instead of accelerometer data is examined in order to provide an uninterrupted time series.

  6. Global coseismic deformations, GNSS time series analysis, and earthquake scaling laws

    NASA Astrophysics Data System (ADS)

    Métivier, Laurent; Collilieux, Xavier; Lercier, Daphné; Altamimi, Zuheir; Beauducel, François

    2014-12-01

    We investigate how two decades of coseismic deformations affect time series of GPS station coordinates (Global Navigation Satellite System) and what constraints geodetic observations give on earthquake scaling laws. We developed a simple but rapid model for coseismic deformations, assuming different earthquake scaling relations, that we systematically applied on earthquakes with magnitude larger than 4. We found that coseismic displacements accumulated during the last two decades can be larger than 10 m locally and that the cumulative displacement is not only due to large earthquakes but also to the accumulation of many small motions induced by smaller earthquakes. Then, investigating a global network of GPS stations, we demonstrate that a systematic global modeling of coseismic deformations helps greatly to detect discontinuities in GPS coordinate time series, which are still today one of the major sources of error in terrestrial reference frame construction (e.g., the International Terrestrial Reference Frame). We show that numerous discontinuities induced by earthquakes are too small to be visually detected because of seasonal variations and GPS noise that disturb their identification. However, not taking these discontinuities into account has a large impact on the station velocity estimation, considering today's precision requirements. Finally, six groups of earthquake scaling laws were tested. Comparisons with our GPS time series analysis on dedicated earthquakes give insights on the consistency of these scaling laws with geodetic observations and Okada coseismic approach.

  7. 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.

  8. 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.

  9. Scale dependence of the directional relationships between coupled time series

    NASA Astrophysics Data System (ADS)

    Shirazi, Amir Hossein; Aghamohammadi, Cina; Anvari, Mehrnaz; Bahraminasab, Alireza; Rahimi Tabar, M. Reza; Peinke, Joachim; Sahimi, Muhammad; Marsili, Matteo

    2013-02-01

    Using the cross-correlation of the wavelet transformation, we propose a general method of studying the scale dependence of the direction of coupling for coupled time series. The method is first demonstrated by applying it to coupled van der Pol forced oscillators and coupled nonlinear stochastic equations. We then apply the method to the analysis of the log-return time series of the stock values of the IBM and General Electric (GE) companies. Our analysis indicates that, on average, IBM stocks react earlier to possible common sector price movements than those of GE.

  10. Application of nonlinear time series models to driven systems

    SciTech Connect

    Hunter, N.F. Jr.

    1990-01-01

    In our laboratory we have been engaged in an effort to model nonlinear systems using time series methods. Our objectives have been, first, to understand how the time series response of a nonlinear system unfolds as a function of the underlying state variables, second, to model the evolution of the state variables, and finally, to predict nonlinear system responses. We hope to address the relationship between model parameters and system parameters in the near future. Control of nonlinear systems based on experimentally derived parameters is also a planned topic of future research. 28 refs., 15 figs., 2 tabs.

  11. An introduction to chaotic and random time series analysis

    NASA Technical Reports Server (NTRS)

    Scargle, Jeffrey D.

    1989-01-01

    The origin of chaotic behavior and the relation of chaos to randomness are explained. Two mathematical results are described: (1) a representation theorem guarantees the existence of a specific time-domain model for chaos and addresses the relation between chaotic, random, and strictly deterministic processes; (2) a theorem assures that information on the behavior of a physical system in its complete state space can be extracted from time-series data on a single observable. Focus is placed on an important connection between the dynamical state space and an observable time series. These two results lead to a practical deconvolution technique combining standard random process modeling methods with new embedded techniques.

  12. Easily Adaptable Complexity Measure for Finite Time Series

    E-print Network

    Da-Guan Ke; Qin-Ye Tong

    2008-11-25

    We present a complexity measure for any finite time series. This measure has invariance under any monotonic transformation of the time series, has a degree of robustness against noise, and has the adaptability of satisfying almost all the widely accepted but conflicting criteria for complexity measurements. Surprisingly, the measure is developed from Kolmogorov complexity, which is traditionally believed to represent only randomness and to satisfy one criterion to the exclusion of the others. For familiar iterative systems, our treatment may imply a heuristic approach to transforming symbolic dynamics into permutation dynamics and vice versa.

  13. Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

    NASA Astrophysics Data System (ADS)

    Jia, Kun; Liang, Shunlin; Zhang, Lei; Wei, Xiangqin; Yao, Yunjun; Xie, Xianhong

    2014-12-01

    Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.

  14. A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

    PubMed

    Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

    2015-01-01

    Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity. PMID:26557064

  15. A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series

    PubMed Central

    Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M.; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

    2015-01-01

    Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity. PMID:26557064

  16. Towards understanding temporal and spatial dynamics of seagrass landscapes using time-series remote sensing

    NASA Astrophysics Data System (ADS)

    Lyons, Mitchell B.; Roelfsema, Chris M.; Phinn, Stuart R.

    2013-03-01

    The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (?200 km2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.

  17. A Spatio-temporal disaggregation method to derive time series of Normalized Difference Vegetation Index and Land Surface Temperature at fine spatial resolution

    NASA Astrophysics Data System (ADS)

    Bindhu, V. M.; Narasimhan, B.

    2014-12-01

    Estimation of evapotranspiration (ET) from remote sensing based energy balance models have evolved as a promising tool in the field of water resources management. Performance of energy balance models and reliability of ET estimates is decided by the availability of remote sensing data at high spatial and temporal resolutions. However huge tradeoff in the spatial and temporal resolution of satellite images act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. Hence a need exists to derive finer resolution data from the available coarse resolution imagery, which could be applied to deliver ET estimates at scales to the range of individual fields. The current study employed a spatio-temporal disaggregation method to derive fine spatial resolution (60 m) images of NDVI by integrating the information in terms of crop phenology derived from time series of MODIS NDVI composites with fine resolution NDVI derived from a single AWiFS data acquired during the season. The disaggregated images of NDVI at fine resolution were used to disaggregate MODIS LST data at 960 m resolution to the scale of Landsat LST data at 60 m resolution. The robustness of the algorithm was verified by comparison of the disaggregated NDVI and LST with concurrent NDVI and LST images derived from Landsat ETM+. The results showed that disaggregated NDVI and LST images compared well with the concurrent NDVI and LST derived from ETM+ at fine resolution with a high Nash Sutcliffe Efficiency and low Root Mean Square Error. The proposed disaggregation method proves promising in generating time series of ET at fine resolution for effective water management.

  18. Radarsat Satellite Images: A New Geography Tool for Upper Elementary Classrooms.

    ERIC Educational Resources Information Center

    Kirman, Joseph M.

    1999-01-01

    Describes the Canadian Radarsat Satellite and remote sensing in order to demonstrate that teachers can incorporate this technology into the classroom. Maintains that third, fourth, fifth, and sixth grade students can understand and interpret remote sensing images and Landsat images. Provides a list of teaching resources other than the expensive…

  19. Remote Sensing: Analyzing Satellite Images to Create Higher Order Thinking Skills.

    ERIC Educational Resources Information Center

    Marks, Steven K.; And Others

    1996-01-01

    Presents a unit that uses remote-sensing images from satellites and other spacecraft to provide new perspectives of the earth and generate greater global awareness. Relates the levels of Bloom's hierarchy to different aspects of the remote sensing unit to confirm that the concepts and principles of remote sensing and related images belong in…

  20. Object Recognition on Satellite Images with Biologically-Inspired Computational Approaches

    E-print Network

    Payeur, Pierre

    on irrelevant parts of the scene. On the other hand, classical image processing and computer vision techniquesObject Recognition on Satellite Images with Biologically-Inspired Computational Approaches M, a novel technique for top-down attention is devised which is based on the energy of bottom- up feature

  1. MODIS Land Surface Temperature time series reconstruction with Open Source GIS: A new quality of temperature based ecological indicators in complex terrain (Invited)

    NASA Astrophysics Data System (ADS)

    Neteler, M.

    2009-12-01

    In complex terrain like the Central European Alps, meteorological stations and ground surveys are usually sparsely and/or irregularly distributed and often favor agricultural areas. The application of traditional geospatial interpolation methods in complex terrain remains challenging and difficult to optimize. An alternative data source is remote sensing: high temporal resolution satellite data are continuously gaining interest since these data are intrinsically spatialized: continuous field of observations is obtained with this tool instead of point data. The increasing data availability suggests using these time series as surrogate to certain measures from meteorological stations, especially for temperature and related derivatives. The Terra and Aqua satellites with the Moderate Resolution Imaging Spectroradiometer (MODIS) provide four Earth coverages per day at various resolutions. We analyzed 8 years (2000 to 2008) of daily land surface temperature (LST) data from MODIS in an area located in the Southern European Alps. A method was developed to reconstruct incomplete maps (cloud coverage, invalid pixels) based on image statistics and on a model that includes additional GIS layers. The original LST map resolution of 1000m could be improved to 200m in this process which renders the resulting LST maps applicable at regional scales. We propose the use of these reconstructed daily LST time series as surrogate to meteorological observations especially in the area of epidemiological modeling where data are typically aggregated to decadal indicators. From these daily LST map series, derivable indicators include: 1) temperatures minima, means and maxima for annual/monthly/decadal periods; 2) unusual hot summers;3) the calculation of growing degree days, and 4) spring temperature increase or autumnal temperature decrease. Since more than 8 years of MODIS LST data are available today, even preliminary gradients can be extracted to assess multi-annual temperature trends. Multi-annual LST time series were extracted at pixel positions of meteo-stations to be compared with air temperature time series. A Wilcoxon rank sum test performed on the two daily mean temperature curves (Arco, Italy, meteo-station and LST) confirmed that these two datasets are not statistically different (W = 63775.5, p-value = 0.6232). Likewise other comparisons were performed. The MODIS LST processing chain was implemented with Free/Open Source GIS tools from the OSGeo (www.osgeo.org) software stack, which permit to process maps as parallel batch jobs on a high performance computing (HPC) facility. In the course of the project a convenient batch processing has been implemented into the main GRASS GIS distribution. The application of reconstructed daily LST time series is presented in a case study on the expansion of the tiger mosquito (Aedes albopictus) in northern Italy. This mosquito acts as a vector of viral zoonoses and is rapidly expanding from the tropics into temperate climatic zones. Time series of reconstructed daily LST maps improve the understanding of vector distribution and, subsequently, disease patterns, as changing environmental conditions, can be monitored continuously. Preliminary results are shown for the LST indicators based prediction of the tiger mosquito distribution in parts of Northern Italy.

  2. Detection of Artificial Satellites in Images Acquired in Track Rate Mode

    NASA Astrophysics Data System (ADS)

    Levesque, M.

    2011-09-01

    For surveillance of space needs, satellites must be re-observed periodically to measure their position and update their orbital parameters. This represents an incredible volume of data for which an automatic processing capability is desired. Previous developments [1,2,3] produced automatic detection algorithms for images acquired in Step Stare mode (SSM) with sidereal tracking. However, it was proven that the track rate mode (TRM) [6] is more sensitive. Hence, the algorithmic framework was redesigned and applied to this mode. When an imaging sensor tracks a satellite (or a satellite cluster), the stars appear as streaks while the satellites are point-like objects. A series of algorithms was developed for the detection of satellites and star streaks. The centroids of the star streaks are first detected. They are necessary for the astrometric calibration of the image. Thereafter, the satellites are detected using two sets of logical conditions; they are detected with the maximum of sensitivity against the dark sky background, and with the contrast criteria if they are overlapping star streaks. This algorithm framework automatically extracts all required information from the image and adapts the processing parameters and strategy consequently, so no a priori knowledge is require for their execution, which is a requirement for automatic processing capacity.

  3. [The possibility of using high resolution satellite images for detection of marine mammals].

    PubMed

    Platonov, N G; Mordvintsev, I N; Rozhnov, V V

    2013-01-01

    The possibility of using modern systems of remote sensing in the optical range from high spatial resolution satellites for detection of marine mammals and traces of their activity is investigated. An image obtained by the GeoEye satellite within the FEAC project was used for the analysis. The image covers Herald Island and adjacent waters, which are a part of the Wrangel Island Reserve, during the seasonal thaw (June 2009). It is shown that marine mammals (polar bears, walruses, and whales) can be identified on such images. The absence of synchronous ground truth observations reduces the reliability of the results. PMID:23789427

  4. The image-processing system for the Earth Resources Technology Satellite.

    NASA Technical Reports Server (NTRS)

    Johnson, R. H.

    1972-01-01

    Description of the image-processing system for the Earth Resources Technology Satellite (ERTS), which will process video-tape recordings received from the satellite into imagery and digitized video data suitable for earth-resource study and analysis. The system is made up of three subsystems. The bulk processor produces 70-mm images and digitized records, corrected for systematic geometric and radiometric errors as well as for sensor-calibration and other errors. The precision processor modifies the bulk images to significantly increase geometric precision. The special processor transfers either bulk or precision data to computer-compatible tape.

  5. Visualizing Variable-Length Time Series Motifs Jessica Lin1

    E-print Network

    Lin, Jessica

    of a time series motif in an insect behavior dataset [23]. This motif consists of four very similar subsequences of length 109. Figure 1. A motif of length 109 from an insect dataset. The top plot shows]; however, they either do so via post-processing, scale poorly, or quantize the whole data rather than

  6. Costationarity of locally stationary time series Alessandro Cardinali

    E-print Network

    Nason, Guy

    Costationarity of locally stationary time series Alessandro Cardinali University of Bristol, U stationary means second-order stationary. Address for correspondence: Alessandro Cardinali, CTEU, Bristol Heart Institute, Bristol Royal Infirmary, Marlborough Street, Bristol, BS2 8HW. Email: A.Cardinali

  7. Cross-Sectional Time Series Designs: A General Transformation Approach.

    ERIC Educational Resources Information Center

    Velicer, Wayne F.; McDonald, Roderick P.

    1991-01-01

    The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)

  8. Evaluation of Scaling Invariance Embedded in Short Time Series

    PubMed Central

    Pan, Xue; Hou, Lei; Stephen, Mutua; Yang, Huijie; Zhu, Chenping

    2014-01-01

    Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length . Calculations with specified Hurst exponent values of show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias () and sharp confidential interval (standard deviation ). Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records. PMID:25549356

  9. Financial Time Series Segmentation Based On Turning Points

    E-print Network

    Si, Yain Whar "Lawrence"

    they contain more information than other points. TPs represent the trend of the stock change and they Faculty of Science and Technology University of Macau {ma96589, fstasp, fstzgg}@umac.mo Abstract--Segments extracted from financial time series are widely used in trend analysis as well as in predicting future

  10. Learning States and Rules for Detecting Anomalies in Time Series

    E-print Network

    Chan, Philip K.

    these states, we introduce a segmentation algorithm called Gecko that can determine a reasonable number automaton. Our empirical results, on data obtained from the NASA shuttle program, indicate that the Gecko anomalies. To identify states, we introduce Gecko, which is able to segment time series data and determine

  11. IDENTIFICATION OF REGIME SHIFTS IN TIME SERIES USING NEIGHBORHOOD STATISTICS

    EPA Science Inventory

    The identification of alternative dynamic regimes in ecological systems requires several lines of evidence. Previous work on time series analysis of dynamic regimes includes mainly model-fitting methods. We introduce two methods that do not use models. These approaches use state-...

  12. Resampling Methodology in Spatial Prediction and Repeated Measures Time Series 

    E-print Network

    Rister, Krista Dianne

    2012-02-14

    . . . . . . . . . . . . . . . . . 3 B. Replicated Time Series Data . . . . . . . . . . . . . . . . . 4 1. Problem Background . . . . . . . . . . . . . . . . . . 4 2. Basics of the Moving Block Bootstrap . . . . . . . . . 5 3. Basics of Subsampling... . . . . . . . . . . . . . . 15 4. Bootstrap Prediction Intervals . . . . . . . . . . . . . 17 E. Theoretical Results . . . . . . . . . . . . . . . . . . . . . . 18 1. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . 18 2. Derivation of the Taylor Series Based Bias...

  13. Analyses of transient chaotic time series Mukeshwar Dhamala,1

    E-print Network

    Lai, Ying-Cheng

    the scaling law for the probability of finding periodic orbits. The scaling law implies that unstable periodic periodic orbits, from transient chaotic time series. Theoretical arguments and numerical experiments show periodic orbits of low periods can be extracted even when noise is present. In addition, we test

  14. On the Blind Classification of Time Series Alessandro Bissacco

    E-print Network

    Soatto, Stefano

    want to recognize a person from her gait regardless of speed, or detect a ball bouncing regardless in the space of dynamical models that takes into account their dynamics, including transients, output maps be considered when comparing different time series. Pre- vious work on kernel distances between dynamical models

  15. Analysis of Complex Intervention Effects in Time-Series Experiments.

    ERIC Educational Resources Information Center

    Bower, Cathleen

    An iterative least squares procedure for analyzing the effect of various kinds of intervention in time-series data is described. There are numerous applications of this design in economics, education, and psychology, although until recently, no appropriate analysis techniques had been developed to deal with the model adequately. This paper…

  16. [Impact of time series correction on forest CO2 flux].

    PubMed

    Wu, Jiabing; Guan, Dexin; Zhao, Xiaosong; Han, Shijie; Jin, Changjie

    2004-10-01

    Detrending correction and sonic anemometer tilt correction were made to modify the raw time series measured from eddy covariance system in broad-leaved Korean pine forest of Changbai Mountains during the growing season of 2003, and the impact of different correction methods on CO2 flux was analyzed quantificationally. The results showed that the forest CO2 flux during growing season was overestimated when calculated from raw time series. The ratio of correction to origin flux (Fc(raw)) was 1.6% and 1.8% for linear and nonlinear detrend, respectively, which suggested that there was little difference between these two detrending methods. It was 3.7% and 4.7% for the planar fit coordinate transforming (PF) correction and the streamline coordinate system transforming (ST) correction, respectively, suggesting that there was a clear difference between these two sonic anemometer tilt correction methods. When detrended time series used, it was 5.5% and 4.6% for ST correction and PF correction, respectively. It was recommended that raw time series should be corrected synthetically with linear detrend method and PF method. PMID:15624817

  17. Trajectory Boundary Modeling of Time Series for Anomaly Detection

    E-print Network

    Chan, Philip K.

    on the Space Shuttle Marrotta fuel control valve data set. Keywords Time series anomaly detection, Machine valves used in the space shuttle. Because not all failure modes can be anticipated, this is an ideal task through d-dimensional feature space. The idea is that a test series should follow a similar trajectory

  18. Fast and Flexible Multivariate Time Series Subsequence Search Kanishka Bhaduri

    E-print Network

    Oza, Nikunj C.

    such situations using a subset of the fields in the time series database where the event "Landing Gear Retracted all situations in the database that correspond to a "go-around" situation in which a landing has been aborted and the aircraft has been directed to circle back for another landing. One can find

  19. Gene clustering methods for time series microarray data Laney Kuenzel

    E-print Network

    1 Introduction The development of advanced microarray technology over the past two decades conGene clustering methods for time series microarray data Laney Kuenzel Biochemistry 218 June 6, 2010- stitutes a revolution in genomics. To- day, microarrays can measure expression levels for thousands

  20. Approximate Embedding-Based Subsequence Matching of Time Series

    E-print Network

    Kollios, George

    order of magnitude compared to brute-force search, with very small losses (accuracy Applications]: Data Mining; H.2.4 [Systems]: Multi- media Databases General Terms Algorithms 1. INTRODUCTION Time series data naturally appear in a wide variety of domains, including scientific measurements

  1. Model Identification in Time-Series Analysis: Some Empirical Results.

    ERIC Educational Resources Information Center

    Padia, William L.

    Model identification of time-series data is essential to valid statistical tests of intervention effects. Model identification is, at best, inexact in the social and behavioral sciences where one is often confronted with small numbers of observations. These problems are discussed, and the results of independent identifications of 130 social and…

  2. Workshop on Modern Nonparametric Methods for Time Series, Reliability & Optimization 2012

    E-print Network

    of the workshop aims at nonparametric methods for regression and time series, reliability analysis24 Workshop on Modern Nonparametric Methods for Time Series, Reliability & Optimization 2012 Leuventrainstation Workshop2012 Leuventrainstation Workshop on Modern Nonparametric Methods for Time Series

  3. Classification of time series patterns from complex dynamic systems

    SciTech Connect

    Schryver, J.C.; Rao, N.

    1998-07-01

    An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately, the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.

  4. A multidisciplinary database for geophysical time series management

    NASA Astrophysics Data System (ADS)

    Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.

    2013-12-01

    The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

  5. Complexity analysis of the turbulent environmental fluid flow time series

    E-print Network

    Dragutin T. Mihailovic; Emilija Nikolic-Djoric; Nusret Dreskovic; Gordan Mimic

    2013-09-25

    We have used the Kolmogorov complexities, sample and permutation entropies to quantify the randomness degree in river flow time series of two mountain rivers in Bosnia and Herzegovina, representing the turbulent environmental fluid, for the period 1926-1990. In particular, we have examined the monthly river flow time series from two rivers (Miljacka and Bosnia) in mountain part of their flow and then calculated the Kolmogorov Complexity (KL) based on the Lempel-Ziv Algorithm (LZA) (Lower - KLL and Upper - KLU), Sample Entropy (SE) and Permutation Entropy (PE) values for each time series. The results indicate that the KLL, KLU, SE and PE values in two rivers are close to each other regardless of the amplitude differences in their monthly flow rates. We have illustrated the changes in mountain river flow complexity by experiments using (i) the data set for the Bosnia River and (ii) anticipated human activities and projected climate changes. We have explored the sensitivity of considered measures in dependence on the length of time series. In addition, we have divided the period 1926-1990 into three sub-intervals: (a) 1926-1945, (b)1946-1965 and (c)1966-1990, and calculated the KLL, KLU, SE and PE values for the various time series in these sub-intervals. It is found that during the period 1946-1965, there is a decrease in their complexities, and corresponding changes in the SE and PE, in comparison to the period 1926-1990. This complexity loss may be primarily attributed to (i) human interventions, after Second World War, on these rivers because of their use for water consumption and (ii) climate change in recent time.

  6. Complexity analysis of the turbulent environmental fluid flow time series

    NASA Astrophysics Data System (ADS)

    Mihailovi?, D. T.; Nikoli?-?ori?, E.; Dreškovi?, N.; Mimi?, G.

    2014-02-01

    We have used the Kolmogorov complexities, sample and permutation entropies to quantify the randomness degree in river flow time series of two mountain rivers in Bosnia and Herzegovina, representing the turbulent environmental fluid, for the period 1926-1990. In particular, we have examined the monthly river flow time series from two rivers (the Miljacka and the Bosnia) in the mountain part of their flow and then calculated the Kolmogorov complexity (KL) based on the Lempel-Ziv Algorithm (LZA) (lower-KLL and upper-KLU), sample entropy (SE) and permutation entropy (PE) values for each time series. The results indicate that the KLL, KLU, SE and PE values in two rivers are close to each other regardless of the amplitude differences in their monthly flow rates. We have illustrated the changes in mountain river flow complexity by experiments using (i) the data set for the Bosnia River and (ii) anticipated human activities and projected climate changes. We have explored the sensitivity of considered measures in dependence on the length of time series. In addition, we have divided the period 1926-1990 into three subintervals: (a) 1926-1945, (b) 1946-1965, (c) 1966-1990, and calculated the KLL, KLU, SE, PE values for the various time series in these subintervals. It is found that during the period 1946-1965, there is a decrease in their complexities, and corresponding changes in the SE and PE, in comparison to the period 1926-1990. This complexity loss may be primarily attributed to (i) human interventions, after the Second World War, on these two rivers because of their use for water consumption and (ii) climate change in recent times.

  7. SPOT4 (Take 5) Time Series Over 45 Sites To Prepare Sentinel-2 Applications And Methods

    NASA Astrophysics Data System (ADS)

    Hagolle, O.; Huc, M.; Dedieu, G.; Sylvander, S.; Houpert, L.; Leroy, M.; Clesse, D.; Daniaud, F.; Arino, O.; Koetz, B.; Paganini, M.; Seifert, E. M.; Pinnock, N.; Hoersh, B.; Bartholom, E.; Achard, F.; Mayaux, P.; Masek, J.; Claverie, M.; Vermote, E.; Fernandes, R.

    2013-12-01

    This paper presents the SPOT4 (Take 5) experiment, aimed at providing time series of optical images simulating the repetitivity, resolution and large swath of Sentinel-2 images, in order to help users set up and test their applications and methods, before the mission is launched. In 2016, when Sentinel-2 constellation is complete, and for at least seven years, users will have access to high resolution time series of images acquired every 5 days, anywhere among the Earth land surfaces. This new dataset will drastically change and enhance the way land surfaces are monitored using remote sensing. Sentinel-2 frequent revisit will assure that a given surface will be observed at least once a month, except in the most cloudy periods and regions. Such a repetitivity will enable to develop operational applications that rely on regular updates of surface reflectances. New methods and algorithms will have to be developed, in order to handle time series covering very large areas. The methods will need to be robust to the data gaps due to clouds and given the number of images to handle, the methods will have to be automatic. At the Sentinel-2 preparatory symposium in 2012, the user community voiced a high interest to develop such new methods and applications well in advance before the launch of Sentinel-2, enabling a timely start of operational applications as soon as the data becomes available. The SPOT4 (Take 5) experiment is providing the users with time series of observations close to those of the Sentinel-2 mission in terms of temporal revisit and spatial resolution. When CNES offered to use SPOT4 for technical experiments, at the end of its commercial life, CESBIO pro- posed to change SPOT4 orbit, in order to place it on a 5 days repeat cycle orbit. CNES started this experiment on the 31st of January 2013, and it lasted until June the 19th, 2013.Time series of SPOT4 images have been acquired every 5th day, over 45 sites scattered in nearly all continents, and covering very diverse applications (land cover and land use, agriculture, phenology, hydrology, snow monitoring, coasts monitoring, habitats characterization and biodiversity...).

  8. Merging climate and multi-sensor time-series data in real-time drought monitoring across the U.S.A.

    USGS Publications Warehouse

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

    2011-01-01

    Droughts occur repeatedly in the United States resulting in billions of dollars of damage. Monitoring and reporting on drought conditions is a necessary function of government agencies at multiple levels. A team of Federal and university partners developed a drought decision- support tool with higher spatial resolution relative to traditional climate-based drought maps. The Vegetation Drought Response Index (VegDRI) indicates general canopy vegetation condition assimilation of climate, satellite, and biophysical data via geospatial modeling. In VegDRI, complementary drought-related data are merged to provide a comprehensive, detailed representation of drought stress on vegetation. Time-series data from daily polar-orbiting earth observing systems [Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS)] providing global measurements of land surface conditions are ingested into VegDRI. Inter-sensor compatibility is required to extend multi-sensor data records; thus, translations were developed using overlapping observations to create consistent, long-term data time series

  9. Optimization design method of satellite imaging chain related with optical axis jitter

    NASA Astrophysics Data System (ADS)

    Sun, Xiaofeng; Wang, Humei; Wang, Shitao

    2014-11-01

    As the improvement of imaging resolution of earth observation satellite, the optical axis disturbance (referred as LOS jitter) introduced by satellite moving components, such as reaction wheel, CMG, cryocooler etc., become one of the important factors that limits the imaging quality. So far as we know, there are several methods to control the frequency and amplitude of LOS jitter, such as satellite attitude control system (ACS), vibration isolator, image stabilization system etc. Each method has its own application range: ACS can only response to low frequency disturbance to about one tenth Hz, but it can deal with large amplitude disturbance; vibration isolator usually attenuates LOS jitter amplitude in high frequency, but may magnify jitter in low frequency; image stabilization can stabilize the LOS jitter in low-mid frequency, but limited to small amplitude. So it is necessary to use several methods together to insure the imaging quality. Here comes the question, how to design and allocate the system specification reasonably to satisfy the requirement of imaging and to make it possible for these methods to realize. This paper presents a new optimization method based on the frequency domain for the satellite imaging chain related with optical axis jitter. First describe the performance of each link of the imaging chain in the frequency domain, then through the calculation of image MTF using LOS jitter PSD, build up the relation between the imaging quality and the frequency performance of mixed links, then combine the frequency performance and the spectral decomposition method, the relation between each link and system imaging quality can be built. Then Based on this method, the requirement of imaging quality related to each link can be allocate and optimize quantitatively, which is essential for the design of imaging chain related with optical axis jitter.

  10. Nighttime lights time series of tsunami damage, recovery, and economic metrics in Sumatra, Indonesia.

    PubMed

    Gillespie, Thomas W; Frankenberg, Elizabeth; Chum, Kai Fung; Thomas, Duncan

    2014-01-01

    On 26 December 2004, a magnitude 9.2 earthquake off the west coast of the northern Sumatra, Indonesia resulted in 160,000 Indonesians killed. We examine the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light imagery brightness values for 307 communities in the Study of the Tsunami Aftermath and Recovery (STAR), a household survey in Sumatra from 2004 to 2008. We examined night light time series between the annual brightness and extent of damage, economic metrics collected from STAR households and aggregated to the community level. There were significant changes in brightness values from 2004 to 2008 with a significant drop in brightness values in 2005 due to the tsunami and pre-tsunami nighttime light values returning in 2006 for all damage zones. There were significant relationships between the nighttime imagery brightness and per capita expenditures, and spending on energy and on food. Results suggest that Defense Meteorological Satellite Program nighttime light imagery can be used to capture the impacts and recovery from the tsunami and other natural disasters and estimate time series economic metrics at the community level in developing countries. PMID:25419471

  11. Automated urban change detection using scanned cartographic and satellite image data

    USGS Publications Warehouse

    Spooner, Jeffrey D.

    1991-01-01

    The objective of this study was to develop a digital procedure to measure the amount of urban change that has occurred in an area since the publication of its corresponding 1:24,000-scale topographic map. Traditional change detection techniques are dependent upon the visual comparison of high-altitude aerial photographs or, more recently, satellite image data to a corresponding map. Analytical change detection techniques typically involve the digital comparison of satellite images to one another. As a result of this investigation, a new technique has been developed that analytically compares the most recently published map to a corresponding digital satellite image. Scanned cartographic and satellite image data are combined in a single file with a structural component derived from the satellite image. This investigation determined that with this combination of data the spectral characteristics of urban change are predictable. A supervised classification was used to detect and delimit urban change. Although it was not intended to identify the specific nature of any change, this procedure does provide a means of differentiating between areas that have or have not experienced urbanization to determine appropriate map revision strategies.

  12. Thermokarst Lake Gyre Flow Speed and Direction Derivation Using Image Matching from Sequential Satellite Images

    NASA Astrophysics Data System (ADS)

    Zhan, S.; Wang, S.; Beck, R. A.; Liu, H.; Hinkel, K. M.

    2014-12-01

    Thermokarst lakes on the Arctic Coastal Plain of northern Alaska are closely coupled with the regional climate through energy, water and carbon budgets. These lakes exhibit striking elongated shapes perpendicular to the prevailing wind direction. This has led to the hypothesis that the expansion of lakes is caused by thermomechanical processes induced by wind-driven water circulation. The predominant bimodal wind regime in the region (easterly and westerly wind) redistributes lake sediment towards the west and east shores to form protective littoral shelves while the north and south shores are preferentially eroded. Previous research on wind-driven circulation in thermokarst lakes was mainly based on in situ studies which can only collect sparse measurements and is time-consuming. Examination of satellite imagery clearly reveals the wide-spread presence of gyres in thermokarst lakes. It allows the study of gyres and other circulation patterns at both lake and regional scales. This study examines the movement (speed, direction) of a 10-km-wide gyre using a Landsat-7 and an ASTER scene taken about 40 minutes apart. These two images are matched using a robust image matching technique based on cross-correlation. Flow speed and direction for the gyre are extracted from the images and are compared with the in situ measurements collected during previous field work. This study provides insight into the evolution of thermokarst lakes and their interaction with the local climate by quantifying gyre circulation rates over entire lakes.

  13. Mapping Giant Salvinia with Satellite Imagery and Image Analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands)...

  14. Applications of satellite image processing to the analysis of Amazonian cultural ecology

    NASA Technical Reports Server (NTRS)

    Behrens, Clifford A.

    1991-01-01

    This paper examines the application of satellite image processing towards identifying and comparing resource exploitation among indigenous Amazonian peoples. The use of statistical and heuristic procedures for developing land cover/land use classifications from Thematic Mapper satellite imagery will be discussed along with actual results from studies of relatively small (100 - 200 people) settlements. Preliminary research indicates that analysis of satellite imagery holds great potential for measuring agricultural intensification, comparing rates of tropical deforestation, and detecting changes in resource utilization patterns over time.

  15. Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

    NASA Technical Reports Server (NTRS)

    Michaels, Anthony F.; Knap, Anthony H.

    1992-01-01

    Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

  16. Cryogenic infrared imaging beryllium telescope for Infrared Astronomical Satellite (IRAS)

    NASA Technical Reports Server (NTRS)

    Devereux, W. P.

    1983-01-01

    The IRAS mission is the result of an international project involving the cooperation of the U.S., the United Kingdom, and the Netherlands. The Infrared Astronmical Satellite was placed into orbit on January 25, 1983. Its main function is to provide a survey of the entire sky as viewed in four octaves of infrared radiation in the wavelenth region from 8 to 120 microns. The cylindrical structure of the satellite contains a large dewar vessel with 70 liters of superfluid helium. The helium has the function to maintain the contents of the vessel at 2.5 K for the duration of the mission. The IRAS optics is a Ritchey-Chretien telescope of 24 inches aperture. Because of the operational requirements of the mission, it had been specified that all optical components should be beryllium. Attention is given to the cold performance test conducted with IRAS, plans for future infrared telescopes, and reflectance limits.

  17. Satellite Images of Mali Villages - Geographic Information Systems & Science

    Cancer.gov

    Workers from the Laboratory of Malaria and Vector Research at the National Institute of Allergy and Infectious Disease (NIAID) visited the villages of Kemena and Sougoula in Mali, Africa, as part of an effort to control leishmaniasis (a parasitic disease spread by the bite of infected sand flies). They presented the village chiefs with maps of their villages created from satellite imagery with assistance from NCI GIS staff.

  18. Analyzing single-molecule time series via nonparametric Bayesian inference.

    PubMed

    Hines, Keegan E; Bankston, John R; Aldrich, Richard W

    2015-02-01

    The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. PMID:25650922

  19. Ensemble vs. time averages in financial time series analysis

    NASA Astrophysics Data System (ADS)

    Seemann, Lars; Hua, Jia-Chen; McCauley, Joseph L.; Gunaratne, Gemunu H.

    2012-12-01

    Empirical analysis of financial time series suggests that the underlying stochastic dynamics are not only non-stationary, but also exhibit non-stationary increments. However, financial time series are commonly analyzed using the sliding interval technique that assumes stationary increments. We propose an alternative approach that is based on an ensemble over trading days. To determine the effects of time averaging techniques on analysis outcomes, we create an intraday activity model that exhibits periodic variable diffusion dynamics and we assess the model data using both ensemble and time averaging techniques. We find that ensemble averaging techniques detect the underlying dynamics correctly, whereas sliding intervals approaches fail. As many traded assets exhibit characteristic intraday volatility patterns, our work implies that ensemble averages approaches will yield new insight into the study of financial markets’ dynamics.

  20. Fast Nonparametric Clustering of Structured Time-Series.

    PubMed

    Hensman, James; Rattray, Magnus; Lawrence, Neil D

    2015-02-01

    In this publication, we combine two Bayesian nonparametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a variation on the GP prior which enables us to model structured time-series data, i.e., data containing groups where we wish to model inter- and intra-group variability. Our innovation in the DP model is an implementation of a new fast collapsed variational inference procedure which enables us to optimize our variational approximation significantly faster than standard VB approaches. In a biological time series application we show how our model better captures salient features of the data, leading to better consistency with existing biological classifications, while the associated inference algorithm provides a significant speed-up over EM-based variational inference. PMID:26353249

  1. Assestment of correlations and crossover scale in electroseismic time series

    NASA Astrophysics Data System (ADS)

    Guzman-Vargas, L.; Ramírez-Rojas, A.; Angulo-Brown, F.

    2009-04-01

    Evaluating complex fluctuations in electroseismic time series is an important task not only for earthquake prediction but also for understanding complex processes related to earthquake preparation. Previous studies have reported alterations, as the emergence of correlated dynamics in geoelectric potentials prior to an important earthquake (EQ). In this work, we apply the detrended fluctuation analysis and introduce a statistical procedure to characterize the presence of crossovers in scaling exponents, to analyze the fluctuations of geoelectric time series monitored in two sites located in Mexico. We find a complex behavior characterized by the presence of a crossover in the correlation exponents in the vicinity of a M=7.4 EQ occurred on Sept. 14, 1995. Finally, we apply the t-student test to evaluate the level of significance between short and large scaling exponents.

  2. Metagenomics meets time series analysis: unraveling microbial community dynamics.

    PubMed

    Faust, Karoline; Lahti, Leo; Gonze, Didier; de Vos, Willem M; Raes, Jeroen

    2015-06-01

    The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic patterns, help to build predictive models or, on the contrary, quantify irregularities that make community behavior unpredictable. Microbial communities can change abruptly in response to small perturbations, linked to changing conditions or the presence of multiple stable states. With sufficient samples or time points, such alternative states can be detected. In addition, temporal variation of microbial interactions can be captured with time-varying networks. Here, we apply these techniques on multiple longitudinal datasets to illustrate their potential for microbiome research. PMID:26005845

  3. Examination of time series through randomly broken windows

    NASA Technical Reports Server (NTRS)

    Sturrock, P. A.; Shoub, E. C.

    1981-01-01

    In order to determine the Fourier transform of a quasi-periodic time series (linear problem), or the power spectrum of a stationary random time series (quadratic problem), data should be recorded without interruption over a long time interval. The effect of regular interruption such as the day/night cycle is well known. The effect of irregular interruption of data collection (the "breaking" of the window function) with the simplifying assumption that there is a uniform probability p that each interval of length tau, of the total interval of length T = N sub tau, yields no data, is investigated. For the linear case it is found that the noise-to-signal ratio will have a (one-sigma) value less than epsilon if N exceeds p(-1)(1-p)epsilon(-2). For the quadratic case, the same requirement is met by the less restrictive requirement that N exceed p(-1)(1-p)epsilon(-1).

  4. Time series, correlation matrices and random matrix models

    SciTech Connect

    Vinayak; Seligman, Thomas H.

    2014-01-08

    In this set of five lectures the authors have presented techniques to analyze open classical and quantum systems using correlation matrices. For diverse reasons we shall see that random matrices play an important role to describe a null hypothesis or a minimum information hypothesis for the description of a quantum system or subsystem. In the former case various forms of correlation matrices of time series associated with the classical observables of some system. The fact that such series are necessarily finite, inevitably introduces noise and this finite time influence lead to a random or stochastic component in these time series. By consequence random correlation matrices have a random component, and corresponding ensembles are used. In the latter we use random matrices to describe high temperature environment or uncontrolled perturbations, ensembles of differing chaotic systems etc. The common theme of the lectures is thus the importance of random matrix theory in a wide range of fields in and around physics.

  5. Time series analysis of ozone data in Isfahan

    NASA Astrophysics Data System (ADS)

    Omidvari, M.; Hassanzadeh, S.; Hosseinibalam, F.

    2008-07-01

    Time series analysis used to investigate the stratospheric ozone formation and decomposition processes. Different time series methods are applied to detect the reason for extreme high ozone concentrations for each season. Data was convert into seasonal component and frequency domain, the latter has been evaluated by using the Fast Fourier Transform (FFT), spectral analysis. The power density spectrum estimated from the ozone data showed peaks at cycle duration of 22, 20, 36, 186, 365 and 40 days. According to seasonal component analysis most fluctuation was in 1999 and 2000, but the least fluctuation was in 2003. The best correlation between ozone and sun radiation was found in 2000. Other variables which are not available cause to this fluctuation in the 1999 and 2001. The trend of ozone is increasing in 1999 and is decreasing in other years.

  6. Multi-dimensional sparse time series: feature extraction

    E-print Network

    Franciosi, Marco

    2008-01-01

    We show an analysis of multi-dimensional time series via entropy and statistical linguistic techniques. We define three markers encoding the behavior of the series, after it has been translated into a multi-dimensional symbolic sequence. The leading component and the trend of the series with respect to a mobile window analysis result from the entropy analysis and label the dynamical evolution of the series. The diversification formalizes the differentiation in the use of recurrent patterns, from a Zipf law point of view. These markers are the starting point of further analysis such as classification or clustering of large database of multi-dimensional time series, prediction of future behavior and attribution of new data. We also present an application to economic data. We deal with measurements of money investments of some business companies in advertising market for different media sources.

  7. Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

    NASA Astrophysics Data System (ADS)

    Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.

    2015-06-01

    This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.

  8. A noise model for InSAR time series

    NASA Astrophysics Data System (ADS)

    Agram, P. S.; Simons, M.

    2015-04-01

    Interferometric synthetic aperture radar (InSAR) time series methods estimate the spatiotemporal evolution of surface deformation by incorporating information from multiple SAR interferograms. While various models have been developed to describe the interferometric phase and correlation statistics in individual interferograms, efforts to model the generalized covariance matrix that is directly applicable to joint analysis of networks of interferograms have been limited in scope. In this work, we build on existing decorrelation and atmospheric phase screen models and develop a covariance model for interferometric phase noise over space and time. We present arguments to show that the exploitation of the full 3-D covariance structure within conventional time series inversion techniques is computationally challenging. However, the presented covariance model can aid in designing new inversion techniques that can at least mitigate the impact of spatial correlated nature of InSAR observations.

  9. The multiscale analysis between stock market time series

    NASA Astrophysics Data System (ADS)

    Shi, Wenbin; Shang, Pengjian

    2015-11-01

    This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.

  10. A method for detecting changes in long time series

    SciTech Connect

    Downing, D.J.; Lawkins, W.F.; Morris, M.D.; Ostrouchov, G.

    1995-09-01

    Modern scientific activities, both physical and computational, can result in time series of many thousands or even millions of data values. Here the authors describe a statistically motivated algorithm for quick screening of very long time series data for the presence of potentially interesting but arbitrary changes. The basic data model is a stationary Gaussian stochastic process, and the approach to detecting a change is the comparison of two predictions of the series at a time point or contiguous collection of time points. One prediction is a ``forecast``, i.e. based on data from earlier times, while the other a ``backcast``, i.e. based on data from later times. The statistic is the absolute value of the log-likelihood ratio for these two predictions, evaluated at the observed data. A conservative procedure is suggested for specifying critical values for the statistic under the null hypothesis of ``no change``.

  11. Deviations from uniform power law scaling in nonstationary time series

    NASA Technical Reports Server (NTRS)

    Viswanathan, G. M.; Peng, C. K.; Stanley, H. E.; Goldberger, A. L.

    1997-01-01

    A classic problem in physics is the analysis of highly nonstationary time series that typically exhibit long-range correlations. Here we test the hypothesis that the scaling properties of the dynamics of healthy physiological systems are more stable than those of pathological systems by studying beat-to-beat fluctuations in the human heart rate. We develop techniques based on the Fano factor and Allan factor functions, as well as on detrended fluctuation analysis, for quantifying deviations from uniform power-law scaling in nonstationary time series. By analyzing extremely long data sets of up to N = 10(5) beats for 11 healthy subjects, we find that the fluctuations in the heart rate scale approximately uniformly over several temporal orders of magnitude. By contrast, we find that in data sets of comparable length for 14 subjects with heart disease, the fluctuations grow erratically, indicating a loss of scaling stability.

  12. Segmentation of biological multivariate time-series data

    NASA Astrophysics Data System (ADS)

    Omranian, Nooshin; Mueller-Roeber, Bernd; Nikoloski, Zoran

    2015-03-01

    Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets from the yeast Saccharomyces cerevisiae and the diatom Thalassiosira pseudonana.

  13. Learning time series evolution by unsupervised extraction of correlations

    SciTech Connect

    Deco, G.; Schuermann, B. )

    1995-03-01

    As a consequence, we are able to model chaotic and nonchaotic time series. Furthermore, one critical point in modeling time series is the determination of the dimension of the embedding vector used, i.e., the number of components of the past that are needed to predict the future. With this method we can detect the embedding dimension by extracting the influence of the past on the future, i.e., the correlation of remote past and future. Optimal embedding dimensions are obtained for the Henon map and the Mackey-Glass series. When noisy data corrupted by colored noise are used, a model is still possible. The noise will then be decorrelated by the network. In the case of modeling a chemical reaction, the most natural architecture that conserves the volume is a symplectic network which describes a system that conserves the entropy and therefore the transmitted information.

  14. Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

    NASA Technical Reports Server (NTRS)

    Spruce, Joseph P.; Ryan, Robert E.; Smoot, James; Kuper, Phillip; Prados, Donald; Russell, Jeffrey; Ross, Kenton; Gasser, Gerald; Sader, Steven; McKellip, Rodney

    2007-01-01

    This report details one of three experiments performed during FY 2007 for the NASA RPC (Rapid Prototyping Capability) at Stennis Space Center. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria dispar). The intent of the RPC experiment was to assess the degree to which VIIRS data can provide forest disturbance monitoring information as an input to a forest threat EWS (Early Warning System) as compared to the level of information that can be obtained from MODIS data. The USDA Forest Service (USFS) plans to use MODIS products for generating broad-scaled, regional monitoring products as input to an EWS for forest health threat assessment. NASA SSC is helping the USFS to evaluate and integrate currently available satellite remote sensing technologies and data products for the EWS, including the use of MODIS products for regional monitoring of forest disturbance. Gypsy moth defoliation of the mid-Appalachian highland region was selected as a case study. Gypsy moth is one of eight major forest insect threats listed in the Healthy Forest Restoration Act (HFRA) of 2003; the gypsy moth threatens eastern U.S. hardwood forests, which are also a concern highlighted in the HFRA of 2003. This region was selected for the project because extensive gypsy moth defoliation occurred there over multiple years during the MODIS operational period. This RPC experiment is relevant to several nationally important mapping applications, including agricultural efficiency, coastal management, ecological forecasting, disaster management, and carbon management. In this experiment, MODIS data and VIIRS data simulated from MODIS were assessed for their ability to contribute broad, regional geospatial information on gypsy moth defoliation. Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data were used to assess the quality of gypsy moth defoliation mapping products derived from MODIS data and from simulated VIIRS data. The project focused on use of data from MODIS Terra as opposed to MODIS Aqua mainly because only MODIS Terra data was collected during 2000 and 2001-years with comparatively high amounts of gypsy moth defoliation within the study area. The project assessed the quality of VIIRS data simulation products. Hyperion data was employed to assess the quality of MODIS-based VIIRS simulation datasets using image correlation analysis techniques. The ART (Application Research Toolbox) software was used for data simulation. Correlation analysis between MODIS-simulated VIIRS data and Hyperion-simulated VIIRS data for red, NIR (near-infrared), and NDVI (Normalized Difference Vegetation Index) image data products collectively indicate that useful, effective VIIRS simulations can be produced using Hyperion and MODIS data sources. The r(exp 2) for red, NIR, and NDVI products were 0.56, 0.63, and 0.62, respectively, indicating a moderately high correlation between the 2 data sources. Temporal decorrelation from different data acquisition times and image misregistration may have lowered correlation results. The RPC experiment also generated MODIS-based time series data products using the TSPT (Time Series Product Tool) software. Time series of simulated VIIRS NDVI products were produced at approximately 400-meter resolution GSD (Ground Sampling Distance) at nadir for comparison to MODIS NDVI products at either 250- or 500-meter GSD. The project also computed MODIS (MOD02) NDMI (Normalized Difference Moisture Index) products at 500-meter GSD for comparison to NDVI-based products. For each year during 2000-2006, MODIS and VIIRS (simulated from MOD02) time series were computed during the peak gypsy moth defoliation time frame in the study area (approximately June 10 through July 27). Gypsy moth defoliation mapping products from simated VIIRS and MOD02 time series were produced using multiple methods, including image classifica

  15. FTSPlot: fast time series visualization for large datasets.

    PubMed

    Riss, Michael

    2014-01-01

    The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of O(n x log(N)); the visualization itself can be done with a complexity of O(1) and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with < 20 ms ms. The current 64-bit implementation theoretically supports datasets with up to 2(64) bytes, on the x86_64 architecture currently up to 2(48) bytes are supported, and benchmarks have been conducted with 2(40) bytes/1 TiB or 1.3 x 10(11) double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments. PMID:24732865

  16. The complexity of carbon flux time series in Europe

    NASA Astrophysics Data System (ADS)

    Lange, Holger; Sippel, Sebastian

    2014-05-01

    Observed geophysical time series usually exhibit pronounced variability, part of which is process-related and deterministic ("signal"), another part is due to random fluctuations ("noise"). To discern these two sources for fluctuations is notoriously difficult using conventional analysis methods, unless sophisticated model assumptions are made. Here, we present an almost parameter-free innovative approach with the potential to draw a distinction between deterministic processes and structured noise, based on ordinal pattern statistics. The method determines one measure for the information content of time series (Shannon entropy) and two complexity measures, one based on global properties of the order pattern distribution (Jensen-Shannon complexity) and one based on local (derivative) properties (Fisher information or complexity). Each time series gets classified via its location in an entropy-complexity plane; using this representation, the method draws a qualitative distinction between different types of natural processes. As a case study, we investigate Gross Primary Productivity (GPP) and respiration which are key variables in terrestrial ecosystems quantifying carbon allocation and biomass growth of vegetation. Changes in GPP and ecosystem respiration can be induced by land use change, environmental disasters or extreme events, and changing climate. Numerous attempts to quantify these variables on larger spatial scales exist. Here, we investigate gridded time series at monthly resolution for the European continent either based on upscaled measurements ("observations") or modelled with two different process-based terrestrial ecosystem models ("simulations"). The complexity analysis is either visualized as maps of Europe showing "hotspots" of complexity for GPP and respiration, or used to provide a detailed observations-simulations and model-model comparison. Values found for information and complexity will be compared to known artificial reference processes, either stochastic (long-range correlated noise) or deterministic ones (chaotic maps). The spatial patterns emerging can be used for a classification of European ecosystems according to their complexity; finally, this classification may be compared to existing landscape classifications based on other properties of the terrestrial biota or on climate.

  17. An online novel adaptive filter for denoising time series measurements.

    PubMed

    Willis, Andrew J

    2006-04-01

    A nonstationary form of the Wiener filter based on a principal components analysis is described for filtering time series data possibly derived from noisy instrumentation. The theory of the filter is developed, implementation details are presented and two examples are given. The filter operates online, approximating the maximum a posteriori optimal Bayes reconstruction of a signal with arbitrarily distributed and non stationary statistics. PMID:16649562

  18. Rényi’s information transfer between financial time series

    NASA Astrophysics Data System (ADS)

    Jizba, Petr; Kleinert, Hagen; Shefaat, Mohammad

    2012-05-01

    In this paper, we quantify the statistical coherence between financial time series by means of the Rényi entropy. With the help of Campbell’s coding theorem, we show that the Rényi entropy selectively emphasizes only certain sectors of the underlying empirical distribution while strongly suppressing others. This accentuation is controlled with Rényi’s parameter q. To tackle the issue of the information flow between time series, we formulate the concept of Rényi’s transfer entropy as a measure of information that is transferred only between certain parts of underlying distributions. This is particularly pertinent in financial time series, where the knowledge of marginal events such as spikes or sudden jumps is of a crucial importance. We apply the Rényian information flow to stock market time series from 11 world stock indices as sampled at a daily rate in the time period 02.01.1990-31.12.2009. Corresponding heat maps and net information flows are represented graphically. A detailed discussion of the transfer entropy between the DAX and S&P500 indices based on minute tick data gathered in the period 02.04.2008-11.09.2009 is also provided. Our analysis shows that the bivariate information flow between world markets is strongly asymmetric with a distinct information surplus flowing from the Asia-Pacific region to both European and US markets. An important yet less dramatic excess of information also flows from Europe to the US. This is particularly clearly seen from a careful analysis of Rényi information flow between the DAX and S&P500 indices.

  19. A data-fitting procedure for chaotic time series

    SciTech Connect

    McDonough, J.M.; Mukerji, S.; Chung, S.

    1998-10-01

    In this paper the authors introduce data characterizations for fitting chaotic data to linear combinations of one-dimensional maps (say, of the unit interval) for use in subgrid-scale turbulence models. They test the efficacy of these characterizations on data generated by a chaotically-forced Burgers` equation and demonstrate very satisfactory results in terms of modeled time series, power spectra and delay maps.

  20. Time series regression model for infectious disease and weather.

    PubMed

    Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

    2015-10-01

    Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. PMID:26188633

  1. Distance metric-based forest cover change detection using MODIS time series

    NASA Astrophysics Data System (ADS)

    Huang, Xiaoman; Friedl, Mark A.

    2014-06-01

    More than 12 years of global observations are now available from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). As this time series grows, the MODIS archive provides new opportunities for identification and characterization of land cover at regional to global spatial scales and interannual to decadal temporal scales. In particular, the high temporal frequency of MODIS provides a rich basis for monitoring land cover dynamics. At the same time, the relatively coarse spatial resolution of MODIS (250-500 m) presents significant challenges for land cover change studies. In this paper, we present a distance metric-based change detection method for identifying changed pixels at annual time steps using 500 m MODIS time series data. The approach we describe uses distance metrics to measure (1) the similarity between a pixel's annual time series to annual time series for pixels of the same land cover class and (2) the similarity between annual time series from different years at the same pixel. Pre-processing, including gap-filling, smoothing and temporal subsetting of MODIS 500 m Nadir BRDF-adjusted Reflectance (NBAR) time series is essential to the success of our method. We evaluated our approach using three case studies. We first explored the ability of our method to detect change in temperate and boreal forest training sites in North America and Eurasia. We applied our method to map regional forest change in the Pacific Northwest region of the United States, and in tropical forests of the Xingu River Basin in Mato Grosso, Brazil. Results from these case studies show that the method successfully identified pixels affected by logging and fire disturbance in temperate and boreal forest sites. Change detection results in the Pacific Northwest compared well with a Landsat-based disturbance map, yielding a producer's accuracy of 85%. Assessment of change detection results for the Xingu River Basin demonstrated that detection accuracy improves as the fraction of deforestation within a MODIS pixel increases, but that relatively small changes in forest cover were still detectable from MODIS. Annually, over 80% of pixels with >20% deforested area were correctly identified and the timing of change showed good agreement with reference data. Errors of commission were largely associated with pixels located at the edges of disturbance events and inadequate characterization of land cover changes unrelated to deforestation in the reference data. Although our case studies focused on forests, this method is not specific to detection of forest cover change and has the potential to be applied to other types of land cover change including urban and agricultural expansion and intensification.

  2. Dynamical Analysis and Visualization of Tornadoes Time Series

    PubMed Central

    2015-01-01

    In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns. PMID:25790281

  3. Relationship between cross-sectional and time series studies

    SciTech Connect

    Evans, J.S.; Kinney, P.L.; Koehler, J.L.; Cooper, D.W.

    1984-05-01

    Two classes of observational studies have provided quantitative estimates of the influence of air pollution on mortality. These are cross-sectional and time series studies. Cross-sectional studies examine geographical variations in community mortality rates and air pollution levels and typically involve fitting the parameters of proportional exposure-mortality functions by least squares regression analysis. Control for health-related covariates of pollution, such as socioeconomic status and smoking, is accomplished by inclusion of additional variables in the regression equation. Typically the averaging time for these analyses is one year; and therefore, many assume that these studies reflect chronic effects. Time series studies examine temporal variatons in death rates and air pollution levels in a single community. Using days as the unit of observation and averaging over the community, the parameters of the models are estimated by least squares techniques. Control for covariates is accomplished by reexpressing variables as deviations from trend lines and by including weather variables in the regression equation. Studies using these techniques reflect only the acute effects of air pollution. The similarity in the disease-specific findings of time series studies and cross-sectional studies which were analyzed is discussed. It is possible that the cross-sectional studies which have been conducted to date have reflected the impacts of acute rather than chronic effects of air pollution.

  4. Time series analysis for psychological research: examining and forecasting change

    PubMed Central

    Jebb, Andrew T.; Tay, Louis; Wang, Wei; Huang, Qiming

    2015-01-01

    Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials. PMID:26106341

  5. Graphical LASSO based Model Selection for Time Series

    NASA Astrophysics Data System (ADS)

    Jung, Alexander; Hannak, Gabor; Goertz, Norbert

    2015-10-01

    We propose a novel graphical model selection (GMS) scheme for high-dimensional stationary time series or discrete time process. The method is based on a natural generalization of the graphical LASSO (gLASSO), introduced originally for GMS based on i.i.d. samples, and estimates the conditional independence graph (CIG) of a time series from a finite length observation. The gLASSO for time series is defined as the solution of an l1-regularized maximum (approximate) likelihood problem. We solve this optimization problem using the alternating direction method of multipliers (ADMM). Our approach is nonparametric as we do not assume a finite dimensional (e.g., an autoregressive) parametric model for the observed process. Instead, we require the process to be sufficiently smooth in the spectral domain. For Gaussian processes, we characterize the performance of our method theoretically by deriving an upper bound on the probability that our algorithm fails to correctly identify the CIG. Numerical experiments demonstrate the ability of our method to recover the correct CIG from a limited amount of samples.

  6. Deducing acidification rates based on short-term time series

    PubMed Central

    Lui, Hon-Kit; Arthur Chen, Chen-Tung

    2015-01-01

    We show that, statistically, the simple linear regression (SLR)-determined rate of temporal change in seawater pH (?pH), the so-called acidification rate, can be expressed as a linear combination of a constant (the estimated rate of temporal change in pH) and SLR-determined rates of temporal changes in other variables (deviation largely due to various sampling distributions), despite complications due to different observation durations and temporal sampling distributions. Observations show that five time series data sets worldwide, with observation times from 9 to 23 years, have yielded ?pH values that vary from 1.61?×?10?3 to ?2.5?×?10?3?pH unit yr?1. After correcting for the deviation, these data now all yield an acidification rate similar to what is expected under the air-sea CO2 equilibrium (?1.6?×?10?3?~??1.8?×?10?3?pH unit yr?1). Although long-term time series stations may have evenly distributed datasets, shorter time series may suffer large errors which are correctable by this method. PMID:26143749

  7. Data visualization in interactive maps and time series

    NASA Astrophysics Data System (ADS)

    Maigne, Vanessa; Evano, Pascal; Brockmann, Patrick; Peylin, Philippe; Ciais, Philippe

    2014-05-01

    State-of-the-art data visualization has nothing to do with plots and maps we used few years ago. Many opensource tools are now available to provide access to scientific data and implement accessible, interactive, and flexible web applications. Here we will present a web site opened November 2013 to create custom global and regional maps and time series from research models and datasets. For maps, we explore and get access to data sources from a THREDDS Data Server (TDS) with the OGC WMS protocol (using the ncWMS implementation) then create interactive maps with the OpenLayers javascript library and extra information layers from a GeoServer. Maps become dynamic, zoomable, synchroneaously connected to each other, and exportable to Google Earth. For time series, we extract data from a TDS with the Netcdf Subset Service (NCSS) then display interactive graphs with a custom library based on the Data Driven Documents javascript library (D3.js). This time series application provides dynamic functionalities such as interpolation, interactive zoom on different axes, display of point values, and export to different formats. These tools were implemented for the Global Carbon Atlas (http://www.globalcarbonatlas.org): a web portal to explore, visualize, and interpret global and regional carbon fluxes from various model simulations arising from both human activities and natural processes, a work led by the Global Carbon Project.

  8. Dynamical analysis and visualization of tornadoes time series.

    PubMed

    Lopes, António M; Tenreiro Machado, J A

    2015-01-01

    In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns. PMID:25790281

  9. Financial time series analysis based on information categorization method

    NASA Astrophysics Data System (ADS)

    Tian, Qiang; Shang, Pengjian; Feng, Guochen

    2014-12-01

    The paper mainly applies the information categorization method to analyze the financial time series. The method is used to examine the similarity of different sequences by calculating the distances between them. We apply this method to quantify the similarity of different stock markets. And we report the results of similarity in US and Chinese stock markets in periods 1991-1998 (before the Asian currency crisis), 1999-2006 (after the Asian currency crisis and before the global financial crisis), and 2007-2013 (during and after global financial crisis) by using this method. The results show the difference of similarity between different stock markets in different time periods and the similarity of the two stock markets become larger after these two crises. Also we acquire the results of similarity of 10 stock indices in three areas; it means the method can distinguish different areas' markets from the phylogenetic trees. The results show that we can get satisfactory information from financial markets by this method. The information categorization method can not only be used in physiologic time series, but also in financial time series.

  10. Spectral signature generalization and expansion can improve the accuracy of satellite image classification.

    PubMed

    Laborte, Alice G; Maunahan, Aileen A; Hijmans, Robert J

    2010-01-01

    Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification. PMID:20463895

  11. Cloud pattern prediction from geostationary meteorological satellite images for solar energy forecasting

    NASA Astrophysics Data System (ADS)

    Cros, S.; Sébastien, N.; Liandrat, O.; Schmutz, N.

    2014-10-01

    Surface solar radiation forecasting permits to predict photovoltaic plant production for a massive and safe integration of solar energy into the electric network. For short-term forecasts (intra-day), methods using images from meteorological geostationary satellites are more suitable than numerical weather prediction models. Forecast schemes consist in assessing cloud motion vectors and in extrapolating cloud patterns from a given satellite image in order to predict cloud cover state above a PV plant. Atmospheric motion vectors retrieval techniques have been studied for several decades in order to improve weather forecasts. However, solar energy forecasting requires the extraction of cloud motion vectors on a finer spatial- and time-resolution than those provided for weather forecast applications. Even if motion vector retrieval is a wide research field in image processing related topics, only block-matching techniques are operationally used for solar energy forecasts via satellite images. In this paper, we propose two motion vectors extraction methods originating from video compression techniques (correlation phase and optical flow methods). We implemented them on a 6-day dataset of Meteosat-10 satellite diurnal images. We proceeded to cloud pattern extrapolation and compared predicted cloud maps against actual ones at different time horizons from 15 minutes to 4 hours ahead. Forecast scores were compared to the state-of-the-art (block matching) method. Correlation phase methods do not outperform block-matching but their computation time is about 25 times shorter. Optical flow based method outperforms all the methods with a satisfactory time computing.

  12. Measurement and interpretation of subtle deformation signals at Unimak Island from 2003 to 2010 using weather model-assisted time series InSAR

    NASA Astrophysics Data System (ADS)

    Gong, W.; Meyer, F. J.; Lee, C.-W.; Lu, Z.; Freymueller, J.

    2015-02-01

    A 7 year time series of satellite radar images over Unimak Island, Alaska—site of Westdahl Volcano, Fisher Caldera, and Shishaldin Volcano—was processed using a model-free Persistent Scatterer Interferometry technique assisted by numerical weather prediction model. The deformation-only signals were optimally extracted from atmosphere-contaminated phase records. The reconstructed deformation time series maps are compared with campaign and continuous Global Positioning System (GPS) measurements as well as Small Baseline Subset interferometric synthetic aperture radar (InSAR) results for quality assessment and geophysical interpretation. We observed subtle surface inflation at Westdahl Volcano that can be fit by a Mogi source located at approximately 3.6 km north of Westdahl peak and at depth of about 6.9 km that is consistent with the GPS-estimated depth for the 1998 to 2001 time period. The magma chamber volume change decays during the period of 2003 to 2010. The deformation field over Fisher Caldera is steadily subsiding over time. Its best fit analytical model is a sill source that is about 7.9 km in length, 0.54 km in width, and located at about 5.5 km below sea level underneath the center of Fisher Caldera with strike angle of N52°E. Very little deformation was detected near Shishaldin peak; however, a region approximately 15 km east of Shishaldin, as well as an area at the Tugamak range at about 30 km northwest of Shishaldin, shows evidence for movement toward the satellite, with a temporal signature correlated with the 2004 Shishaldin eruption. The cause of these movements is unknown.

  13. A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

    ERIC Educational Resources Information Center

    Ngan, Chun-Kit

    2013-01-01

    Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

  14. Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

    NASA Astrophysics Data System (ADS)

    Hardtke, Leonardo A.; Blanco, Paula D.; Valle, Héctor F. del; Metternicht, Graciela I.; Sione, Walter F.

    2015-06-01

    Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l'Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km2 in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Additionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1 km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R2 = 0.01-0.28, while our algorithm performed showed a stronger correlation coefficient (R2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

  15. Satellite retrieval of convective cloud base temperature based on the NPP/VIIRS Imager

    NASA Astrophysics Data System (ADS)

    Zhu, Yannian; Rosenfeld, Daniel; Yu, Xing; Liu, Guihua; Dai, Jin; Xu, Xiaohong

    2014-02-01

    The advent of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (NPP) satellite provided a quantum jump in the satellite capabilities of retrieving cloud properties, because it nearly tripled the resolution in the thermal channels (375 m). This allowed us to develop a methodology for retrieving convective cloud base temperature (Tb) and validate it over the Atmospheric System Research Southern Great Plains site for the satellite early afternoon overpass time. The standard error of the Tb retrieval was only 1.1°C. The knowledge of Tb allows the calculation of cloud base height and the depth of the boundary layer, as well as the boundary layer water vapor mixing ratio with an accuracy of about 10%. The feasibility of retrieving cloud base temperature and height is an essential component that is required for retrieving cloud condensation nuclei (CCN) from satellites by using convective clouds as natural CCN chambers.

  16. Monitoring landslide deformation with Pleiades very-high resolution satellite images at decimeter accuracy

    NASA Astrophysics Data System (ADS)

    Stumpf, Andre; Malet, Jean-Philippe; Allemand, Pascal; Ulrich, Patrice

    2014-05-01

    Recent advances in image-matching techniques and VHR satellite imaging theoretically offer the possibility to measure Earth surface displacements with decimetric precision. However, this possibility has yet not been explored and requirements of ground control and external topographic datasets are generally considered as important bottlenecks that hinder the application of optical image correlation for displacement measurements on a regular base. This study combines approaches for spaceborne stereo-photogrammetry, orthorectification and sub-pixel image correlation to analyze a series of Pleiades satellite images and measure the horizontal surface displacement of three large landslides (La Valette, Poche, Super-Sauze) located in the Barcelonnette basin (Southern French Alps). The influence of the number of ground-control points on the accuracy of the image orientation, the extracted surface models and displacement rates is quantified through comparisons with airborne laser scans and global navigation satellite measurements at permanent stations. The comparison shows a maximum error of 0.13 m which is one order of magnitude more accurate than what has been previously reported with spaceborne optical images. A series of 4 stereo-pairs is analyzed to capture the seasonal displacement rates over a period of 1.5 years providing valuable insights into the diverse and dynamic deformation patterns of the three observed landslides. The obtained results indicate that the approach can be applied without significant loss in accuracy when no ground control points are available and, therefore, greatly facilitate regular measurements for a broad range of applications.

  17. The study of enhanced earth observations on a satellite image chain

    NASA Astrophysics Data System (ADS)

    Yong, Sang-Soon; Choi, Myungjin; Ra, Sung-Woong

    2011-10-01

    The Multi-Spectral Camera (MSC) on the KOrea Multi-Propose SATellite (KOMPSAT)-2 was developed and launched as a main payload to provide a One(1) m panchromatic image and four(4) band four(4) m multi-spectral images at an altitude of 685 km covering a swath width of 15 km. These images, archived around the world, are a useful resource for space applications in agriculture, cartography, geology, forestry, regional planning, surveillance, and national security. The image quality of KOMPSAT-2 depends upon its image chain, which is comprised of an on-board system in the satellite and a processing system at the ground station. Therefore, in this study we determine the factors that have a major impact on the image quality through an investigation of the entire image chain. Consequently, two methods, involving a compression algorithm and a deconvolution technique, were determined as having a significant influence on the KOMPSAT-2 image quality. The compression algorithm of KOMPSAT-2 is rate-controlled JPEG-like algorithm that controls the mismatch between the input and output data rate. The ability to control the input/output data rate may be useful during the operation of the satellite but can also lower the overall image quality. The deconvolution technique may increase the sharpness of images, but it can also amplify the image noise level. Therefore, we propose methods of wavelet-based compression and denoising as an alternative to currently existing algorithms. Satisfactory results were obtained through experimentation with these two algorithms, and they are expected to be successfully implemented into the future KOMPSAT series to yield high-quality images for enhanced earth observation.

  18. Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac

    E-print Network

    Klinger, Yann

    Measurement of ground displacement from optical satellite image correlation using the free open Received in revised form 4 March 2014 Accepted 7 March 2014 Available online xxxx Keywords: Satellite image of the most efficient techniques to determine horizontal ground displacements due to earthquakes, landslides

  19. Measuring earthquakes from optical satellite images Nade` ge Van Puymbroeck, Re mi Michel, Renaud Binet, Jean-Philippe Avouac, and

    E-print Network

    Avouac, Jean-Philippe

    Measuring earthquakes from optical satellite images Nade` ge Van Puymbroeck, Re´ mi Michel, Renaud to map ground displacements induced by earthquakes. Deformations offsets induced by stereoscopic effect and roll, pitch, and yaw of satellite and detector artifacts are estimated and compensated. Images

  20. Relative Radiometric Normalization and Atmospheric Correction of a SPOT 5 Time Series

    PubMed Central

    Hajj, Mahmoud El; Bégué, Agnès; Lafrance, Bruno; Hagolle, Olivier; Dedieu, Gérard; Rumeau, Matthieu

    2008-01-01

    Multi-temporal images acquired at high spatial and temporal resolution are an important tool for detecting change and analyzing trends, especially in agricultural applications. However, to insure a reliable use of this kind of data, a rigorous radiometric normalization step is required. Normalization can be addressed by performing an atmospheric correction of each image in the time series. The main problem is the difficulty of obtaining an atmospheric characterization at a given acquisition date. In this paper, we investigate whether relative radiometric normalization can substitute for atmospheric correction. We develop an automatic method for relative radiometric normalization based on calculating linear regressions between unnormalized and reference images. Regressions are obtained using the reflectances of automatically selected invariant targets. We compare this method with an atmospheric correction method that uses the 6