These are representative sample records from Science.gov related to your search topic.
For comprehensive and current results, perform a real-time search at Science.gov.
1

A method for generating high resolution satellite image time series  

NASA Astrophysics Data System (ADS)

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and 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 then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation, environment and etc. applications.

Guo, Tao

2014-10-01

2

Satellite image time series simulation for environmental monitoring  

NASA Astrophysics Data System (ADS)

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.

Guo, Tao

2014-11-01

3

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

E-print Network

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

4

A Nonlinear Harmonic Model for Fitting Satellite Image Time Series: Analysis and Prediction of Land Cover Dynamics  

Microsoft Academic Search

Numerous efforts have been made to develop models to fit multispectral reflectance and vegetation index (VI) time series from satellite images for diverse land cover classes. The common objective of these models is to derive a set of measurable parameters that are able to characterize and to reproduce the land cover dynamics of natural- and human-induced ecosystems. Good-fitting models should

Hugo Carrao; P. Gonalves; Mário Caetano

2010-01-01

5

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

NASA Astrophysics Data System (ADS)

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.

Zhou, Zengguang; Tang, Ping; Zhang, Zheng

2014-11-01

6

Research of Haiti earthquake disaster by using time series ALOS satellite image  

Microsoft Academic Search

An earthquake with a presumed magnitude of 7.0 occurred on January 12, 2010 in Port au Prince\\/Haiti. The disaster area has been extracted using an overlay technique of multi spectral data from the ALOS satellite and the maximum likelihood classification technique to compare images before the earthquake strike and after it struck. In addition, the situation of reconstruction was interpreted

Hideki Hashiba; Toshiro Sugimura

2011-01-01

7

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

PubMed

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

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

2014-08-01

8

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

NASA Astrophysics Data System (ADS)

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

Khanal, Shiva; Duursma, Remko; Boer, Matthias

2014-05-01

9

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

Microsoft Academic Search

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)

Dirk Tiede; Stefan Lang

2009-01-01

10

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

NASA Technical Reports Server (NTRS)

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

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

2012-01-01

11

Landslide activity peak of late 1980's in Central Yamal, Russia, observed from satellite image time series  

NASA Astrophysics Data System (ADS)

A large set of cryogenic landslides occurred in Bovanenkovo region in Central Yamal peninsula, Arctic Russia in late 1980's. Database of satellite images was collected to follow landslide activity 1969-2011. Imagery used were CORONA, Landsat MSS/TM/ETM7, SPOT, Terra ASTER VNIR and Quickbird-2 images from years 1969, 1988, 1993, 1998, 2001, 2004 and 2011. Field data was collected from several years and sites. Earliest data was collected in 1993. More recent data was collected in 2004 and 2005. Main field data was collected in 2011 from Mordy-Jaha landslide field. CORONA image from 1969 is used as a starting date of analysis. Landsat TM image dated from 1988 just before the main landslide event in 1989. This image was compared to SPOT (1993,1998), Landsat ETM+ (1999, 2001), Landsat TM (2011) and Terra ASTER VNIR (2001) images to detect occurred landslides. Quickbird-2 (2004) (QB) images were used to help the interpretation of the SPOT and Landsat images and to detect small scale landslides (< 1 ha). All identified landslides were saved into a GIS database as points and the boundaries of the landslides were digitized. From SPOT, Landsat, ASTER and Quickbird-2 images bare soil were classified both with unsupervised and supervised methods. Characteristic spectral reflectance of landslides was estimated and images were reclassified. Change detection using NDVI verified well larger scale landslides, but was not generally reliable enough alone to estimate the occurrance and areas of the landslides. Errors caused by nearby Bovanenkovo gas fields anthropogenic disturbances like roads, quarriers and other infrastructure around the gas field were masked out with buffers. In data analysis we used ERDAS Imagine 2011 and ArcGIS 10. Final estimation of landslide occurrence was made with combined visual interpretations, change detection (NDVI), image classifications. Totally in the study area there were about 600 landslides.

Kumpula, Timo; Mikhaylova, Tatiana; Ukraintseva, Natalia; Forbes, Bruce

2013-04-01

12

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

NASA Astrophysics Data System (ADS)

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.

Tiede, Dirk; Lang, Stefan

2010-11-01

13

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

NASA Astrophysics Data System (ADS)

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.

Tiede, Dirk; Lang, Stefan

2009-09-01

14

A radar image time series  

NASA Technical Reports Server (NTRS)

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

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

1981-01-01

15

Crop growth dynamics modeling using time-series satellite imagery  

NASA Astrophysics Data System (ADS)

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.

Zhao, Yu

2014-11-01

16

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)

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.

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

2010-08-01

17

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

NASA Astrophysics Data System (ADS)

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

Tappin, D.

2012-04-01

18

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

PubMed

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

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

2013-05-01

19

Satellite imagery time series for the detection of looting activities at archaeological sites  

NASA Astrophysics Data System (ADS)

Clandestine excavations is one of the biggest man-made risks which affect the archaeological heritage, especially in some countries of Southern America, Asia and Middle East. To contrast and limit this phenomenon a systematic monitoring is required. The protection of archaeological heritage from clandestine excavations is generally based on a direct surveillance, but it is time consuming and expensive for remote archaeological sites and non practicable in several countries due to military or political restrictions. In such conditions, Very high resolution (VHR) satellite imagery offer a suitable chance thanks to their global coverage and frequent revisitation times. This paper is focused on the results we obtained from ongoing research focused on the use of VHR satellite images for the identification and monitoring of looting. A time series of satellite images (QuickBird-2 and World-View-1) has been exploited to analyze and monitor archaeological looting in the Nasca Ceremonial Centre of Cahuachi (Peru) dating back between the 4th centurt B.C. and the 4th century A.D. The Cahuachi study case herein presented put in evidence the limits of VHR satellite imagery in detecting features linked to looting activity. This suggested to experience local spatial autocorrelation statistics which allowed us to improve the reliability of satellite in mapping looted area.

Coluzzi, Rosa; Lasaponara, Rosa; Masini, Nicola

2010-05-01

20

A Critical Review of the Time Series of Total Solar Irradiance Satellite Observations  

Microsoft Academic Search

Continuous time series of total solar irradiance (TSI) observations have been constructed from the set of contiguous, redundant, overlapping total solar irradiance (TSI) measurements made by satellite experiments during the past 28 years. One, the ACRIM composite time series [Willson &Mordvinov, 2003], detects a significant upward trend in TSI of 0.04 percent per decade during solar cycles 21-23. Another, the

R. C. Willson

2006-01-01

21

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

NASA Astrophysics Data System (ADS)

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.

Bunker, Brian

22

De-noising of microwave satellite soil moisture time series  

NASA Astrophysics Data System (ADS)

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.

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

2013-04-01

23

Evaluating a Satellite-derived Time Series of Inundation Dynamics  

NASA Astrophysics Data System (ADS)

A new data set of inundation dynamics derived from a suite of satellites (Prigent et al.; Papa et al.) provides the first global, multi-year observations of monthly inundation extent. Initial global and regional evaluation of the data set using data on wetland/vegetation distributions from traditional and remote-sensing sources, GCPC rainfall, and altimeter-derived river heights indicates reasonable spatial distributions and seasonality. We extend the evaluation of this new data set - using independent multi-date, high-resolution satellite observations of inundated ecosystems and freeze-thaw dynamics, as well as climate data - focusing on a variety of boreal and tropical ecosystems representative of global wetlands. The goal is to investigate the strengths of the new data set, and develop strategies for improving weaknesses where identified.

Matthews, E.; Papa, F.; Prigent, C.; McDonald, K.

2006-12-01

24

Wavelet analysis of the Earth center of mass time series determined by satellite techniques  

NASA Astrophysics Data System (ADS)

The Earth center of mass (geocenter) time series with the sampling interval of one week are determined from Satellite Laser Ranging (SLR), Global Navigation Satellite System (GNSS) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) observations. The 3D geocenter time series were projected onto XY, YZ and ZX planes of the International Terrestrial Reference Frame (ITRF), thus, three complex-valued time series can be analyzed for each observation technique. The signal to noise ratio in these geocenter time series is very small and detectable oscillations are rather broadband, however, the annual oscillation can be noticed in each one of them. The wavelet transform technique with the Morlet wavelet function was applied to compute the mean and spectra-temporal polarization functions from the prograde (positive periods) and retrograde (negative periods) spectra of the examined complex-valued time series. The sign of the polarization function determines turning direction in the elliptical motion. If this function is positive or negative for oscillation with a chosen period, then this oscillation is prograde or retrograde, respectively. To estimate the significance level of polarization functions, corresponding to time series data length, the Monte Carlo experiment was performed using complex-valued white noise data. In order to detect similarity between elliptic oscillations in two different time series the spectra-temporal wavelet semblance function was computed. This function reveals that in the XY equatorial plane there is phase agreement between retrograde annual oscillation for SLR and GNSS techniques, and between prograde annual oscillation for DORIS and two other techniques. To construct a model of geocenter motion from GNSS and SLR center of mass time series the wavelet based semblance filtering method was applied. Common oscillations in the analyzed time series are dominated by the annual oscillation with amplitude less than 5 mm.

Kosek, Wies?aw; Wn?k, Agnieszka; Zbylut-Górska, Maria; Popi?ski, Waldemar

2014-10-01

25

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

NASA Astrophysics Data System (ADS)

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

Stewart, Chris

2014-05-01

26

Comparison of decadal global water vapor changes derived from independent satellite time series  

NASA Astrophysics Data System (ADS)

We analyze trends in total column water vapor (TCWV) retrieved from independent satellite observations and retrieval schemes. GOME-SCIAMACHY (Global Ozone Monitoring Experiment-SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) measurements are carried out in the visible part of the solar spectrum and present a partly cloud-corrected climatology that is available over land and ocean. The HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) product, provided by EUMETSAT's Satellite Application Facility on Climate Monitoring is based on passive microwave observations from the Special Sensor Microwave/Imager. It also includes the TCWV from cloudy pixels but is only available over oceans. The common observation time period is between 1996 and 2005. Due to the relatively short length of the period, the strong interannual variability with strong contributions from El Niño and La Niña events and the strong anomaly at the start of the common period, caused by the 1997/1998 El Niño, the observed trends should not be interpreted as long-term climate trends. After subtraction of average seasonality from monthly gridded data, a linear model and a level shift model have been fitted to the HOAPS and GOME-SCIAMACHY data, respectively. Autocorrelation and cross correlation of fit residuals are accounted for in assessing uncertainties in trends. The trends observed in both time series agree within uncertainty margins. This agreement holds true for spatial patterns, magnitudes, and global averages. The consistency increases confidence in the reliability of the trends because the methods, spectral range, and observation technique as well as the satellites and their orbits are completely independent of each other. The similarity of the trends in both data sets is an indication of sufficient stability in the observations for the time period of ? 10 years.

Mieruch, S.; Schröder, M.; Noël, S.; Schulz, J.

2014-11-01

27

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

NASA Technical Reports Server (NTRS)

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.

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

2013-01-01

28

D City Transformations by Time Series of Aerial Images  

NASA Astrophysics Data System (ADS)

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.

Adami, A.

2015-02-01

29

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

PubMed

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

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

2015-01-01

30

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

PubMed Central

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

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

2015-01-01

31

Time series analysis of satellite derived surface temperature for Lake Garda  

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

32

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

NASA Astrophysics Data System (ADS)

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

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

2004-12-01

33

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

NASA Astrophysics Data System (ADS)

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

Zoran, Maria; Savastru, Roxana; Savastru, Dan

2014-06-01

34

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

35

Satellite detection of multi-decadal time series of cyanobacteria accumulations in the Baltic Sea  

NASA Astrophysics Data System (ADS)

Cyanobacteria, primarily of the species Nodularia spumigena, form extensive surface accumulations in the Baltic Sea in July and August, ranging from diffuse flakes to dense surface scum. We describe the compilation of a 35 year (1979-2013) long time series of cyanobacteria surface accumulations in the Baltic Sea using multiple satellite sensors. This appears to be one of the longest satellite-based time series in biological oceanography. The satellite algorithm is based on increased remote sensing reflectance of the water in the red band, a measure of turbidity. Validation of the satellite algorithm using horizontal transects from a ship of opportunity showed the strongest relationship with phycocyanin fluorescence (an indicator of cyanobacteria), followed by turbidity and then by chlorophyll a fluorescence. The areal fraction with cyanobacteria accumulations (FCA) and the total accumulated area affected (TA) were used to characterize the intensity and extent of the accumulations. FCA was calculated as the ratio of the number of detected accumulations to the number of cloud free sea-surface views per pixel during the season (July-August). TA was calculated by adding the area of pixels where accumulations were detected at least once during the season. FCA and TA were correlated (R2 = 0.55) and both showed large interannual and decadal-scale variations. The average FCA was significantly higher for the 2nd half of the time series (13.8%, 1997-2013) than for the first half (8.6%, 1979-1996). However, that does not seem to represent a long-term trend but decadal-scale oscillations. Cyanobacteria accumulations were common in the 1970s and early 1980s (FCA between 11-17%), but rare (FCA below 4%) from 1985 to 1990; they increased again from 1991 and particularly from 1999, reaching maxima in FCA (~ 25%) and TA (~ 210 000 km2) in 2005 and 2008. After 2008 FCA declined to more moderate levels (6-17%). The timing of the accumulations has become earlier in the season, at a~mean rate of 0.6 days per year, resulting in approximately 20 days advancement during the study period. The interannual variations in FCA are positively correlated with the concentration of chlorophyll a in July-August sampled at the depth of ~ 5 m by a ship of opportunity program, but interannual variations in FCA are more pronounced.

Kahru, M.; Elmgren, R.

2014-02-01

36

Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea  

NASA Astrophysics Data System (ADS)

Cyanobacteria, primarily of the species Nodularia spumigena, form extensive surface accumulations in the Baltic Sea in July and August, ranging from diffuse flakes to dense surface scums. The area of these accumulations can reach ~ 200 000 km2. We describe the compilation of a 35-year-long time series (1979-2013) of cyanobacteria surface accumulations in the Baltic Sea using multiple satellite sensors. This appears to be one of the longest satellite-based time series in biological oceanography. The satellite algorithm is based on remote sensing reflectance of the water in the red band, a measure of turbidity. Validation of the satellite algorithm using horizontal transects from a ship of opportunity showed the strongest relationship with phycocyanin fluorescence (an indicator of cyanobacteria), followed by turbidity and then by chlorophyll a fluorescence. The areal fraction with cyanobacteria accumulations (FCA) and the total accumulated area affected (TA) were used to characterize the intensity and extent of the accumulations. The fraction with cyanobacteria accumulations was calculated as the ratio of the number of detected accumulations to the number of cloud-free sea-surface views per pixel during the season (July-August). The total accumulated area affected was calculated by adding the area of pixels where accumulations were detected at least once during the season. The fraction with cyanobacteria accumulations and TA were correlated (R2 = 0.55) and both showed large interannual and decadal-scale variations. The average FCA was significantly higher for the second half of the time series (13.8%, 1997-2013) than for the first half (8.6%, 1979-1996). However, that does not seem to represent a long-term trend but decadal-scale oscillations. Cyanobacteria accumulations were common in the 1970s and early 1980s (FCA between 11-17%), but rare (FCA below 4%) during 1985-1990; they increased again starting in 1991 and particularly in 1999, reaching maxima in FCA (~ 25%) and TA (~ 210 000 km2) in 2005 and 2008. After 2008, FCA declined to more moderate levels (6-17%). The timing of the accumulations has become earlier in the season, at a mean rate of 0.6 days per year, resulting in approximately 20 days advancement during the study period. The interannual variations in FCA are positively correlated with the concentration of chlorophyll a during July-August sampled at the depth of ~ 5 m by a ship of opportunity, but interannual variations in FCA are more pronounced as the coefficient of variation is over 5 times higher.

Kahru, M.; Elmgren, R.

2014-07-01

37

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)

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.

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

2001-01-01

38

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

NASA Astrophysics Data System (ADS)

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.

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

2014-10-01

39

Satellite Images  

NSDL National Science Digital Library

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

40

Trend analysis of time-series phenology derived from satellite data  

USGS Publications Warehouse

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

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

2005-01-01

41

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

NASA Astrophysics Data System (ADS)

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.

Lasaponara, R.; Masini, N.

2012-04-01

42

A snow extent time series assimilation using MODIS images and temperature data, case study Koohrang, Iran  

NASA Astrophysics Data System (ADS)

A unique advantage of satellite data is the possibility for delineation of snow line and calculation of snow cover area. Recent availability of remote sensing data offers promise for better performance of hydrological models, which contain a snow component. The near-daily coverage of Moderate Resolution Imaging Spectrometer (MODIS) data and its moderate resolution provide a powerful capability for time series analysis of snow cover area. However, because of several reasons like cloud cover, technical problems, etc., images are not available or usable. This paper suggests a regional solution to fill the gap of missing data for purpose of snow cover assessment. In this study 27 images of MODIS from NASA have been used to calculate basin scale snow cover area by applying NDSI technique. Also a temperature dataset was collected from the Koohrang station, which was measured by the Iranian meteorological organization for the period 2004-2008. The elevation of the Koohrang station is 2285 m above sea level and geographically it is located at latitude 32 26' and longitude 50 07'. The study considered snow cover derived from satellite imagery as dependent variable and temperature as independent variable. To find a relationship between snow extent and temperature we used the CURVEEXPERT 1.4 package. This program uses the Levenberg-Marquardt algorithm to solve nonlinear regressions by combination of steepest-descent method and a Taylor series technique. Our methodology is applied each time when snow extent is not available and it estimates snow extend based on the remaining data. A wide range of built in models were tested for this purpose but finally a Logistic, Exponential, Richards, Gompertz, Linear Fit and Exponential model were adopted because of high correlation relationship and low variance.

Abdollahi, K.; Batelaan, O.

2012-04-01

43

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

44

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

NASA Astrophysics Data System (ADS)

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.

Suepa, Tanita

45

Time series prediction of short term scintillations of Ku band satellite links in Sri Lanka  

Microsoft Academic Search

Scintillation effect is a dominant atmospheric impairment among the various contributing factors for low fade margin communication systems operating on high frequency bands such as Ku. Empirical analysis of scintillations can be done using signal strength measurements which can be treated as a time series prediction problem in forecasting the short-term scintillation effect. This paper discusses the time series based

Uthpala Premarathne; K. Samarasinghe

2010-01-01

46

Mapping crop key phenological stages in the North China Plain using NOAA time series images  

Microsoft Academic Search

Six key phenological stages were defined based on NOAA\\/AVHRR NDVI time series data collected in the Huang-Huai-Hai (HHH) Plain of China from 1990 through 2000. In a winter wheat-summer maize rotation, the recovering, heading and maturity stages of winter wheat and the emergence, tasseling and maturity stages of summer maize were recorded using 6km resolution 10-day composite NDVI. The satellite-derived

Jingfeng Xin; Zhenrong Yu; Louise van Leeuwen; Paul M Driessen

2002-01-01

47

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

NASA Astrophysics Data System (ADS)

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.

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

48

RESEARCH ARTICLE Time series analysis of infrared satellite data for detecting  

E-print Network

necessary for the time series analysis of the hybrid algorithm. The improved performance of the new, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf. These instruments provide data over potentially dangerous, high-temperature phenomena, such as volcanic eruptions

Wright, Robert

49

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

Microsoft Academic Search

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

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

2006-01-01

50

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

Microsoft Academic Search

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

P. Steigenberger; R. Schmid; M. Rothacher

2004-01-01

51

Mapping crop key phenological stages in the North China Plain using NOAA time series images  

NASA Astrophysics Data System (ADS)

Six key phenological stages were defined based on NOAA/AVHRR NDVI time series data collected in the Huang-Huai-Hai (HHH) Plain of China from 1990 through 2000. In a winter wheat-summer maize rotation, the recovering, heading and maturity stages of winter wheat and the emergence, tasseling and maturity stages of summer maize were recorded using 6 km resolution 10-day composite NDVI. The satellite-derived data proved to be consistent with the 'green wave' moving through the HHH Plain in spring. The recovering stage of winter wheat recorded by satellite was closely correlated to the temperatures measured in February whereas summer maize yields (at zone level) were correlated well with the satellite-derived length of the crop cycle. Comparison with synchronous phenological observations on the ground confirmed the coherence of satellite-derived phenology data. It is expected that satellite data with greater spatial and temporal resolutions and improved smoothing methods will increase the precision of the estimated data still further.

Xin, Jingfeng; Yu, Zhenrong; van Leeuwen, Louise; Driessen, Paul M.

2002-11-01

52

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

PubMed Central

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

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

2013-01-01

53

IMAGE Satellite Scaling  

NSDL National Science Digital Library

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

2012-08-03

54

Satellite Imaging Corporation: IKONOS Satellite Images  

NSDL National Science Digital Library

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

Satellite Imaging Corporation

55

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

NASA Astrophysics Data System (ADS)

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

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

2003-12-01

56

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

NASA Astrophysics Data System (ADS)

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.

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

2012-12-01

57

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

NASA Astrophysics Data System (ADS)

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

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

2001-12-01

58

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

NASA Astrophysics Data System (ADS)

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.

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

2015-03-01

59

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

NASA Technical Reports Server (NTRS)

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

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

2004-01-01

60

Detecting damage to coastal forests caused by the Tohoku earthquake in Japan using time-series remote sensing images  

NASA Astrophysics Data System (ADS)

The Tohoku earthquake of 2011 caused extensive damage to the coastal pine forest that protects inland areas from sea breezes. The tsunami uprooted, broke, and tilted the pine trees. In addition, subsequently, the leaves of coastal pine forest turned red and fell down after summer in 2011 in large areas. To detect damage to the coastal forest caused by the Tohoku earthquake, we analyzed time-series airborne orthophotos and high-resolution satellite image. After the earthquake, many coastal forests were washed away and there is no sign of coastal forest stands in the orthophotos. We compared orthophotos taken before and just after the earthquake by the Japan Geographical Survey Institute. We mapped the damaged forest in Aomori, Iwate, and Miyagi prefectures and classified the damage into three classes: extensive, moderate, and slight damage. We also obtained and high-resolution satellite image (DigitalGlobe, WorldView-2) observed after the summer in 2011. We surveyed the forest damage using field plots. We measured the damage of 50 - 60 trees in a circular plot. The tree damage was classified on a 0 to 10 point scale: a sound tree had 0 damage, while a tree with a completely damaged crown was scored 10. The most crown leaves of a tree scored 7-9 turned red and fell off. The average plots damage were calculated and a linear regression analysis was performed to compare the data for 21 field plots and satellite data. The coefficient of determination was large and we mapped the forest damage using satellite image.

Kodani, Eiji; Nakamura, Katsunori; Sakamoto, Tomoki; Kimura, Koki

2012-10-01

61

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

NASA Technical Reports Server (NTRS)

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

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

1994-01-01

62

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

NASA Astrophysics Data System (ADS)

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

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

2010-05-01

63

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 ...

64

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

NASA Astrophysics Data System (ADS)

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images—are anticipated.

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

2012-01-01

65

SVM spatio-temporal vegetation classification using HR satellite images  

Microsoft Academic Search

This paper proposes a new HR spatio-temporal vegetation classification approach based on SVM. A multi-band SVM approach is first applied on satellite images time series then a graph based SVM algorithm is used for temporal analysis.

S. Réjichi; F. Chaâbane

2011-01-01

66

SVM spatio-temporal vegetation classification using HR satellite images  

NASA Astrophysics Data System (ADS)

This paper proposes a new HR spatio-temporal vegetation classification approach based on SVM. A multi-band SVM approach is first applied on satellite images time series then a graph based SVM algorithm is used for temporal analysis.

Réjichi, S.; Chaâbane, F.

2011-11-01

67

IMAGE Satellite Scale Model  

NSDL National Science Digital Library

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

2012-08-03

68

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

NASA Astrophysics Data System (ADS)

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

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

2010-08-01

69

Satellite-Derived Soil Moisture Data at NSIDC: Enabling Hydrologic Applications and Research through a Multi-Mission Time Series  

NASA Astrophysics Data System (ADS)

Terrestrial soil moisture plays a critical role in understanding the Earth's water and energy cycles, improving weather and climate forecasting, and developing disaster prediction and monitoring services. Through satellite remote sensing, we have the unique ability to detect global soil moisture at increasingly higher spatial scales. NASA and JAXA satellite missions have provided more than a decade of remotely-sensed soil moisture observations. This record began with AMSR-E in 2002 and will carry into the future with the 2014 launch of NASA's Soil Moisture Active Passive (SMAP) instrument. Access to current and future NASA satellite-derived soil moisture data, including AMSR-E, AMSR2, Aquarius, and SMAP, is provided by the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). NSIDC also distributes field campaign data sets collected for validation of remotely-sensed measurements. In this presentation, we highlight and compare the soil moisture data that are and will be available at NSIDC, describing their temporal and spatial coverage, frequencies, resolutions, and data formats. Data Centers play an integral role in enabling multi-mission data discovery and usage through the value-adding services they provide. Through this presentation, we hope to also gain further insight into what tools, services, data formats, and/or metadata will enable the soil moisture community to more seamlessly and effectively utilize this valuable time series in their research and applications.

Leon, A.; Leslie, S. R.; Khalsa, S.

2013-12-01

70

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

NASA Astrophysics Data System (ADS)

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

Müller, Benjamin; Bernhardt, Matthias; Schulz, Karsten

2014-05-01

71

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

NASA Astrophysics Data System (ADS)

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.

Müller, B.; Bernhardt, M.; Schulz, K.

2014-12-01

72

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

NASA Astrophysics Data System (ADS)

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.

Müller, B.; Bernhardt, M.; Schulz, K.

2014-06-01

73

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

NASA Astrophysics Data System (ADS)

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

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

2010-08-01

74

Time-series monitoring result of land surface temperature variation at Mt. Baekdu using Landsat images  

NASA Astrophysics Data System (ADS)

The objectives of this study are to precisely observe time-series land surface temperature (LST) variations at Mt. Baekdu using total of 23 Landsat TM and ETM+ thermal infrared (TIR) images spanning the 26 years from 1987 to 2012. For this study, we focused on LST of vegetation area, because vegetation area has high surface emissivity. At the same time, we used land surface temperature difference (LSTD) algorithm, which measures the LST difference between reference and target area to minimize the atmospheric effect and the difficulty of surface emissivity determination. The results show that most of the LSTD variations are distributed from -1 °C to 1 °C. However, the north of Mt. Baekdu has some anomaly in June 2004, it represented about 3 °C.

Park, Sung-Hwan; Jung, Hyung-Sup; Shin, Han-Sup

2014-11-01

75

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

76

Patient specific dynamic geometric models from sequential volumetric time series image data.  

PubMed

Generating patient specific dynamic models is complicated by the complexity of the motion intrinsic and extrinsic to the anatomic structures being modeled. Using a physics-based sequentially deforming algorithm, an anatomically accurate dynamic four-dimensional model can be created from a sequence of 3-D volumetric time series data sets. While such algorithms may accurately track the cyclic non-linear motion of the heart, they generally fail to accurately track extrinsic structural and non-cyclic motion. To accurately model these motions, we have modified a physics-based deformation algorithm to use a meta-surface defining the temporal and spatial maxima of the anatomic structure as the base reference surface. A mass-spring physics-based deformable model, which can expand or shrink with the local intrinsic motion, is applied to the metasurface, deforming this base reference surface to the volumetric data at each time point. As the meta-surface encompasses the temporal maxima of the structure, any extrinsic motion is inherently encoded into the base reference surface and allows the computation of the time point surfaces to be performed in parallel. The resultant 4-D model can be interactively transformed and viewed from different angles, showing the spatial and temporal motion of the anatomic structure. Using texture maps and per-vertex coloring, additional data such as physiological and/or biomechanical variables (e.g., mapping electrical activation sequences onto contracting myocardial surfaces) can be associated with the dynamic model, producing a 5-D model. For acquisition systems that may capture only limited time series data (e.g., only images at end-diastole/end-systole or inhalation/exhalation), this algorithm can provide useful interpolated surfaces between the time points. Such models help minimize the number of time points required to usefully depict the motion of anatomic structures for quantitative assessment of regional dynamics. PMID:15544239

Cameron, B M; Robb, R A

2004-01-01

77

ASTER's Satellite Image Gallery  

NSDL National Science Digital Library

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

NASA Jet Propulsion Laboratory

78

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

NASA Astrophysics Data System (ADS)

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.

Ku, Taeyun; Lee, Jungsul; Choi, Chulhee

2010-02-01

79

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

PubMed

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

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

2014-06-01

80

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

NASA Astrophysics Data System (ADS)

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.

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

2014-10-01

81

Application of Time Series Landsat Images to Examining Land-use/Land-cover Dynamic Change  

PubMed Central

A hierarchical-based classification method was designed to develop time series land-use/land-cover datasets from Landsat images between 1977 and 2008 in Lucas do Rio Verde, Mato Grosso, Brazil. A post-classification comparison approach was used to examine land-use/land-cover change trajectories, which emphasis is on the conversions from vegetation or agropasture to impervious surface area, from vegetation to agropasture, and from agropasture to regenerating vegetation. Results of this research indicated that increase in impervious surface area mainly resulted from the loss of cerrado in the initial decade of the study period and from loss of agricultural lands in the last two decades. Increase in agropasture was mainly at the expense of losing cerrado in the first two decades and relatively evenly from the loss of primary forest and cerrado in the last decade. When impervious surface area was less than approximately 40 km2 before 1999, impervious surface area was negatively related to cerrado and forest, and positively related to agropasture areas, but after impervious surface area reached 40 km2 in 1999, no obvious relationship exists between them. PMID:25328256

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio; Li, Guiying

2013-01-01

82

Application of Time Series Landsat Images to Examining Land-use/Land-cover Dynamic Change.  

PubMed

A hierarchical-based classification method was designed to develop time series land-use/land-cover datasets from Landsat images between 1977 and 2008 in Lucas do Rio Verde, Mato Grosso, Brazil. A post-classification comparison approach was used to examine land-use/land-cover change trajectories, which emphasis is on the conversions from vegetation or agropasture to impervious surface area, from vegetation to agropasture, and from agropasture to regenerating vegetation. Results of this research indicated that increase in impervious surface area mainly resulted from the loss of cerrado in the initial decade of the study period and from loss of agricultural lands in the last two decades. Increase in agropasture was mainly at the expense of losing cerrado in the first two decades and relatively evenly from the loss of primary forest and cerrado in the last decade. When impervious surface area was less than approximately 40 km(2) before 1999, impervious surface area was negatively related to cerrado and forest, and positively related to agropasture areas, but after impervious surface area reached 40 km(2) in 1999, no obvious relationship exists between them. PMID:25328256

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio; Li, Guiying

2012-07-01

83

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

Microsoft Academic Search

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

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

1993-01-01

84

The impact of the day of observation of image composites on adequate time series generation  

NASA Astrophysics Data System (ADS)

Many remote sensing products that are useful for time series analysis and seasonal monitoring studies are offered in form of composites. A composite combines a number of observations of a defined period and selects or computes one value. This results in observations sampled at varying time intervals that rules out a high number of time series analysis techniques. This study investigates the impact of either using the actual day of observation to generate a time series from composites or assuming the starting or middle day of the compositing period. For this study 16-day MODIS VI composites of 1km spatial resolution from Terra and Aqua were employed. A 1100x500km region in central Mexico served as study site. Statistical measures including temporal cross-correlation and the root mean square error were used for time series analysis. A temporal shift of approximately seven days with a high variability is introduced when using the starting day of the compositing period. The middle day mitigates the mean error close to zero but still shows a high error variability. Only time series that take into account the day of observation and estimate from that samples at equidistant intervals can be used for a correct estimation of temporal characteristics.

Colditz, Rene R.; Ressl, Rainer A.

2013-10-01

85

Hailstreak Occurrence and Persistence Observed With AVHRR NDVI Image Time Series  

NASA Astrophysics Data System (ADS)

Hail is a major cause of crop loss and property damage in the United States. Hailstreaks are columns of hail that have swept the ground. The abrupt devegetation of the land surface by hailstreaks can have significant biogeophysical consequences. Changes in the surface energy balance and local wind fields can give rise to 'inland sea-breeze' phenomenon that may serve to trigger convection. We investigated the relationship between hail occurrences and the appearance and persistence of hailstreaks in composited image time series. Due to abrupt changes in vegetation density, hailstreaks can be identified in Normalized Difference Vegetation Index (NDVI) imagery. To enhance detection of hailstreaks, ?NDVI images were generated from a standard set of biweekly maximum AVHRR NDVI composites for the conterminous US produced by the USGS EROS Data Center. These data have a nominal spatial resolution of 1 km. Overlaying the digitized point locations of the National Weather Service reports of hail onto the ?NDVI imagery, hailstreaks were identified as dark areas coincident with or proximate to hail reports. From 1990-1999, 112 events of significant hailstreaks were observed. Hailstreaks appear mostly in the Great Plains states of Nebraska, Kansas, and the Dakotas, with significant clusters in Minnesota, Iowa, and Texas. The hailstreaks ranged in length from 9 to 367 km (median=66 km; mean=82 km) and in area from 21 to 8443 sq km (median=408 sq km; mean=707 sq km). A total of 79,227 sq km of vegetation were impacted by hailstreaks during the 1990s; however, this estimate is a lower bound due to the compositing process that selects for maximum NDVI. The seasonality of hailstreaks peaked in summer (69%), with 58% appearing in June or July. More hailstreaks appeared in the spring (26%) than in autumn (5%). Observed hailstreak persistence ranged from 9 to 95 d (median=34 d; mean=37 d; mode=28 d). Hailstreak persistence was a complex function of seasonal timing of the event, vegetation type and phenology, and event severity.

Henebry, G. M.; Ratcliffe, I. C.

2002-12-01

86

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

Microsoft Academic Search

Annual, inter-annual and long-term trends in time series derived from remote sensing can be used to distinguish between natural land cover variability and land cover change. However, the utility of using NDVI-derived phenology to detect change is often limited by poor quality data resulting from atmospheric and other effects. Here, we present a curve fitting methodology useful for time series

Bethany A. Bradley; Robert W. Jacob; John F. Hermance; John F. Mustard

2007-01-01

87

A multivariate approach for processing magnetization effects in triggered event-related functional magnetic resonance imaging time series  

Microsoft Academic Search

Triggered event-related functional magnetic resonance imaging requires sparse intervals of temporally resolved functional data acquisitions, whose initiation corresponds to the occurrence of an event, typically an epileptic spike in the electroencephalographic trace. However, conventional fMRI time series are greatly affected by non-steady-state magnetization effects, which obscure initial blood oxygen level-dependent (BOLD) signals. Here, conventional echo-planar imaging and a post-processing solution

Fabrizio Esposito; Francesco Di Salle; Franciszek Hennel; Ornella Santopaolo; Marcus Herdener; Klaus Scheffler; Rainer Goebel; Erich Seifritz

2006-01-01

88

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

89

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

NASA Astrophysics Data System (ADS)

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

Neugebauer, N.; Vuolo, F.

2012-04-01

90

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

91

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

Microsoft Academic Search

Summary In this study, trends of annual and seasonal surface air temperature time series were examined for 20 stations in Greece for the period 1955–2001, and satellite data for the period 1980–2001. Two statistical tests based on the least square method and one based on the Mann-Kendall test, which is also capable of detecting the starting year of possible climatic

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

2004-01-01

92

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

NASA Technical Reports Server (NTRS)

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

Jasinski, Michael F.; Borak, Jordan S.

2008-01-01

93

Volcano Watch Satellite Images  

NSDL National Science Digital Library

The University of Wisconsin's Space Science and Engineering Center displays these satellite images of the world's ten most active volcanoes. Users can view images of the Colima Volcano in Central Mexico or Mount Etna in Sicily, Italy. The latest images are updated every half-hour. Also, a Java animation feature splices together the last four images to show a simulation over a two-hour period.

94

What are Satellite Images?  

NSDL National Science Digital Library

This is an activity about image analysis. Learners will create a map of the room and discuss the perspectives shown in their drawings and how this relates to satelite images. Participants brainstorm a list of features that might be recognizable in satellite photos, search the Earth Images for these features, and place the images in categories depicting these features. This is activity 2 of 9 in Mars and Earth: Science Learning Activities for After School.

2012-12-28

95

The Time Series Toolbox  

NASA Astrophysics Data System (ADS)

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

Boži?, Bojan; Havlik, Denis

2010-05-01

96

Satellite Images: GOES  

NSDL National Science Digital Library

Updated every one to three hours, this Environment Canada page offers the latest Geosynchronous Operational Environmental Satellite (GOES) images (in MPEG format) from GOES-8 and GOES-9, covering Eastern Canada, Western Canada, Eastern North America, Western North America, Pacific North America, and North and South America. Images show global climate/weather patterns in the form of color graphics (full-size or scaled) and small or large animations. A particularly useful feature of the site is the FAQ section; users will find help with image interpretation by browsing the many questions and answers or by following links to other satellite information sites.

97

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

USGS Publications Warehouse

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

Eldenshink, J.

2006-01-01

98

Radiometric quality and performance of TIMESAT for smoothing moderate resolution imaging spectroradiometer enhanced vegetation index time series from western Bahia State, Brazil  

NASA Astrophysics Data System (ADS)

The launch of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua platforms in 1999 and 2002, respectively, with temporal resolutions of 1 to 2 days opened the possibility of using a longtime series of satellite images to map land use and land cover classes from different regions of the Earth, to study vegetation phenology, and to monitor regional and global climate change, among other applications. The main objectives of this study were twofold: to analyze the radiometric quality of the time series of enhanced vegetation index (EVI) products derived from the Terra MODIS sensor in western Bahia State, Brazil, and to identify the most appropriate filter to smooth MODIS EVI time series of the study area among those available in the public domain, the TIMESAT algorithm. The 2000 to 2011 time period was considered (a total of 276 scenes). The radiometric quality was analyzed based on the pixel reliability data set available in the MOD13Q1 product. The performances of the three smoothing filters available within TIMESAT (double logistic, Savitzky-Golay, and asymmetric Gaussian) were analyzed using the Graybill's F test and Willmott statistics. Five percent of the MODIS pixels from the study area were cloud-affected, almost all of which were from the rainy season. The double logistic filter presented the best performance.

Borges, Elane F.; Sano, Edson E.; Medrado, Euzébio

2014-01-01

99

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

NASA Astrophysics Data System (ADS)

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

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

2014-10-01

100

Grassland habitat mapping by intra-annual time series analysis - Comparison of RapidEye and TerraSAR-X satellite data  

NASA Astrophysics Data System (ADS)

Remote sensing concepts are needed to monitor open landscape habitats for environmental change and biodiversity loss. However, existing operational approaches are limited to the monitoring of European dry heaths only. They need to be extended to further habitats. Thus far, reported studies lack the exploitation of intra-annual time series of high spatial resolution data to take advantage of the vegetations' phenological differences. In this study, we investigated the usefulness of such data to classify grassland habitats in a nature reserve area in northeastern Germany. Intra-annual time series of 21 observations were used, acquired by a multi-spectral (RapidEye) and a synthetic aperture radar (TerraSAR-X) satellite system, to differentiate seven grassland classes using a Support Vector Machine classifier. The classification accuracy was evaluated and compared with respect to the sensor type - multi-spectral or radar - and the number of acquisitions needed. Our results showed that very dense time series allowed for very high accuracy classifications (>90%) of small scale vegetation types. The classification for TerraSAR-X obtained similar accuracy as compared to RapidEye although distinctly more acquisitions were needed. This study introduces a new approach to enable the monitoring of small-scale grassland habitats and gives an estimate of the amount of data required for operational surveys.

Schuster, Christian; Schmidt, Tobias; Conrad, Christopher; Kleinschmit, Birgit; Förster, Michael

2015-02-01

101

Satellite camera image navigation  

NASA Technical Reports Server (NTRS)

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).

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

1987-01-01

102

Interpreting Satellite Images  

NSDL National Science Digital Library

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

2012-08-03

103

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

NASA Astrophysics Data System (ADS)

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.

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

2014-10-01

104

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

Microsoft Academic Search

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

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

2005-01-01

105

TIME SERIES Syllabus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii  

E-print Network

TIME SERIES Contents Syllabus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv 1 Models for time series 1 1.1 Time series data . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.3 Analysing the effects of smoothing . . . . . . . . . . . . . . . . . . . . 12 4 Estimation

Weber, Richard

106

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

Microsoft Academic Search

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

Cynthia C. Piotrowski; John P. Dugan

2002-01-01

107

Satellite Hyperspectral Imaging Simulation  

NASA Technical Reports Server (NTRS)

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

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

1999-01-01

108

Satellite Hyperspectral Imaging Simulation  

NASA Technical Reports Server (NTRS)

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

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

1999-01-01

109

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

110

Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils.  

PubMed

In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time series as a function of image SNR (SNR(0)). This model has been used to study physiological noise in fMRI, to optimize fMRI acquisition parameters, and to estimate maximum attainable tSNR for a given set of MR image acquisition and processing parameters. In its current form, this noise model requires the accurate estimation of image SNR. For multi-channel receiver coils, this is not straightforward because it requires export and reconstruction of large amounts of k-space raw data and detailed, custom-made image reconstruction methods. Here we present a simple extension to the model that allows characterization of the temporal noise properties of EPI time series acquired with multi-channel receiver coils, and reconstructed with standard root-sum-of-squares combination, without the need for raw data or custom-made image reconstruction. The proposed extended model includes an additional parameter ? which reflects the impact of noise correlations between receiver channels on the data and scales an apparent image SNR (SNR'(0)) measured directly from root-sum-of-squares reconstructed magnitude images so that ??=?SNR'(0)/SNR(0) (under the condition of SNR(0)>50 and number of channels ?32). Using Monte Carlo simulations we show that the extended model parameters can be estimated with high accuracy. The estimation of the parameter ? was validated using an independent measure of the actual SNR(0) for non-accelerated phantom data acquired at 3T with a 32-channel receiver coil. We also demonstrate that compared to the original model the extended model results in an improved fit to human task-free non-accelerated fMRI data acquired at 7T with a 24-channel receiver coil. In particular, the extended model improves the prediction of low to medium tSNR values and so can play an important role in the optimization of high-resolution fMRI experiments at lower SNR levels. PMID:23284874

Hutton, Chloe; Balteau, Evelyne; Lutti, Antoine; Josephs, Oliver; Weiskopf, Nikolaus

2012-01-01

111

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

PubMed Central

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

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

2014-01-01

112

Remote Sensing Time Series Product Tool  

NASA Technical Reports Server (NTRS)

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

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

2006-01-01

113

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

NASA Astrophysics Data System (ADS)

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

Coluzzi, C.; Didonna, I.

2009-04-01

114

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

NASA Astrophysics Data System (ADS)

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.

Didonna, I.; Coluzzi, R.

2009-04-01

115

Exploring Time Series Plots  

NSDL National Science Digital Library

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

2012-08-03

116

Earth Exploration Toolbook Chapter: Annotating Change in Satellite Images  

NSDL National Science Digital Library

This chapter walks users through a technique for documenting change in before-and-after sets of satellite images. The technique can be used for any set of time-series images that are spatially registered to show the exact same area at the same scale. In the chapter, users examine three Landsat images of the Pearl River delta in southeastern China. In these images, users observe changes in land use, then identify and outline areas of new land that were created by dredging sediments from the river bottom. The final product is an annotated image that highlights new land and indicates when it was created.

117

Volume transport of the Antarctic Circumpolar Current: Production and validation of a 20 year long time series obtained from in situ and satellite observations  

NASA Astrophysics Data System (ADS)

A 20 year long volume transport time series of the Antarctic Circumpolar Current across the Drake Passage is estimated from the combination of information from in situ current meter data (2006-2009) and satellite altimetry data (1992-2012). A new method for transport estimates had to be designed. It accounts for the dependence of the vertical velocity structure on surface velocity and latitude. Yet unpublished velocity profile time series from Acoustic Doppler Current Profilers are used to provide accurate vertical structure estimates in the upper 350 m. The mean cross-track surface geostrophic velocities are estimated using an iterative error/correction scheme to the mean velocities deduced from two recent mean dynamic topographies. The internal consistency and the robustness of the method are carefully assessed. Comparisons with independent data demonstrate the accuracy of the method. The full-depth volume transport has a mean of 141 Sv (standard error of the mean 2.7 Sv), a standard deviation (std) of 13 Sv, and a range of 110 Sv. Yearly means vary from 133.6 Sv in 2011 to 150 Sv in 1993 and standard deviations from 8.8 Sv in 2009 to 17.9 Sv in 1995. The canonical ISOS values (mean 133.8 Sv, std 11.2 Sv) obtained from a year-long record in 1979 are very similar to those found here for year 2011 (133.6 Sv and 12 Sv). Full-depth transports and transports over 3000 m barely differ as in that particular region of Drake Passage the deep recirculations in two semiclosed basins have a close to zero net transport.

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

2014-08-01

118

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

NASA Astrophysics Data System (ADS)

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

Nitze, Ingmar; Barrett, Brian; Cawkwell, Fiona

2015-02-01

119

Time Series Data Library  

NSDL National Science Digital Library

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

Hyndman, Robert

120

Predicting chaotic time series  

Microsoft Academic Search

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

J. Doyne Farmer; John J. Sidorowich

1987-01-01

121

Mining Time Series Data  

NASA Astrophysics Data System (ADS)

Much of the world's supply of data is in the form of time series. In the last decade, there has been an explosion of interest in mining time series data. A number of new algorithms have been introduced to classify, cluster, segment, index, discover rules, and detect anomalies/novelties in time series. While these many different techniques used to solve these problems use a multitude of different techniques, they all have one common factor; they require some high level representation of the data, rather than the original raw data. These high level representations are necessary as a feature extraction step, or simply to make the storage, transmission, and computation of massive dataset feasible. A multitude of representations have been proposed in the literature, including spectral transforms, wavelets transforms, piecewise polynomials, eigenfunctions, and symbolic mappings. This chapter gives a high-level survey of time series Data Mining tasks, with an emphasis on time series representations.

Ratanamahatana, Chotirat Ann; Lin, Jessica; Gunopulos, Dimitrios; Keogh, Eamonn; Vlachos, Michail; Das, Gautam

122

Quantification of fluorescent spots in time series of 3D confocal microscopy images of endoplasmic reticulum exit sites based on the HMAX transform  

NASA Astrophysics Data System (ADS)

We present an approach for the quantification of fluorescent spots in time series of 3-D confocal microscopy images of endoplasmic reticulum exit sites of dividing cells. Fluorescent spots are detected based on extracted image regions of highest response using the HMAX transform and prior convolution of the 3-D images with a Gaussian kernel. The sensitivity of the involved parameters was studied and a quantitative evaluation using both 3-D synthetic and 3-D real data was performed. The approach was successfully applied to more than one thousand 3-D confocal microscopy images.

Matula, Petr; Verissimo, Fatima; Wörz, Stefan; Eils, Roland; Pepperkok, Rainer; Rohr, Karl

2010-03-01

123

Satellite Imaging Corporation  

NSDL National Science Digital Library

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

Monique Romeijn

124

Aerial Photographs and Satellite Images  

USGS Publications Warehouse

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

U.S. Geological Survey

1997-01-01

125

Periodic Time Series Models  

Microsoft Academic Search

This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting. Providing an up-to-date survey of the recent developments in periodic time series, the book presents a large number of empirical results. The first part of the book deals with model selection, diagnostic checking and

Philip Hans Franses; Richard Paap

2004-01-01

126

Satellite Image Mosaic Engine  

NASA Technical Reports Server (NTRS)

A computer program automatically builds large, full-resolution mosaics of multispectral images of Earth landmasses from images acquired by Landsat 7, complete with matching of colors and blending between adjacent scenes. While the code has been used extensively for Landsat, it could also be used for other data sources. A single mosaic of as many as 8,000 scenes, represented by more than 5 terabytes of data and the largest set produced in this work, demonstrated what the code could do to provide global coverage. The program first statistically analyzes input images to determine areas of coverage and data-value distributions. It then transforms the input images from their original universal transverse Mercator coordinates to other geographical coordinates, with scaling. It applies a first-order polynomial brightness correction to each band in each scene. It uses a data-mask image for selecting data and blending of input scenes. Under control by a user, the program can be made to operate on small parts of the output image space, with check-point and restart capabilities. The program runs on SGI IRIX computers. It is capable of parallel processing using shared-memory code, large memories, and tens of central processing units. It can retrieve input data and store output data at locations remote from the processors on which it is executed.

Plesea, Lucian

2006-01-01

127

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

NASA Astrophysics Data System (ADS)

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

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

2011-01-01

128

Random time series in astronomy.  

PubMed

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

Vaughan, Simon

2013-02-13

129

Random time series in Astronomy  

E-print Network

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 persistent aperiodic variations (`noise') from powerful systems like 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 multivariate time series.

Vaughan, Simon

2013-01-01

130

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

NASA Astrophysics Data System (ADS)

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.

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

2015-02-01

131

Pattern Recognition in Time Series  

NASA Astrophysics Data System (ADS)

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.

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

2012-03-01

132

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool  

NASA Technical Reports Server (NTRS)

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

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

2008-01-01

133

IMAGE Satellite 1/4-scale Model  

NSDL National Science Digital Library

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

2012-08-03

134

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

NASA Astrophysics Data System (ADS)

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

Bradley, Eliza Swan

135

Denoising Deterministic Time Series  

E-print Network

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

Steven P. Lalley; Andrew B. Nobel

2006-04-21

136

Maximum entropy bootstrap of climate time series  

NASA Astrophysics Data System (ADS)

The identification and estimation of trends is a fundamental task in the analysis of climate time series. Even more important than estimating a trend is assessing its significance, which is far from a trivial task, due to the serial dependence in the time series, which invalidates most methods based on independent data. The issue is particularly crucial in the case of short records, as often happens in palaeoclimate time series. Bootstrap is an appealing non-parametric alternative for assessing the significance of estimated linear trends. However, bootstrapping is also more delicate in the case of time series than in the case of independent data, since the temporal structure of the series should be preserved in the bootstrap samples. Furthermore, bootstrap procedures often assume stationarity, an assumption which is not verified by most climate time series. Maximum entropy bootstrap (Vinod, 2006) allows to preserve the basic temporal structure of the original time series in the bootstrap replicates without assuming stationary behavior. In this work maximum entropy bootstrap is applied to assess the significance of trends estimated from short (~17 years) records of satellite measurements of the height of the sea surface.

Barbosa, Susana

2010-05-01

137

User-guided automated segmentation of time-series ultrasound images for measuring vasoreactivity of the brachial artery induced by flow mediation  

NASA Astrophysics Data System (ADS)

Endothelial dysfunction in response to vasoactive stimuli is closely associated with diseases such as atherosclerosis, hypertension and congestive heart failure. The current method of using ultrasound to image the brachial artery along the longitudinal axis is insensitive for measuring the small vasodilatation that occurs in response to flow mediation. The goal of this study is to overcome this limitation by using cross-sectional imaging of the brachial artery in conjunction with the User-Guided Automated Boundary Detection (UGABD) algorithm for extracting arterial boundaries. High-resolution ultrasound imaging was performed on rigid plastic tubing, on elastic rubber tubing phantoms with steady and pulsatile flow, and on the brachial artery of a healthy volunteer undergoing reactive hyperemia. The area of cross section of time-series images was analyzed by UGABD by propagating the boundary from one frame to the next. The UGABD results were compared by linear correlation with those obtained by manual tracing. UGABD measured the cross-sectional area of the phantom tubing to within 5% of the true area. The algorithm correctly detected pulsatile vasomotion in phantoms and in the brachial artery. A comparison of area measurements made using UGABD with those made by manual tracings yielded a correlation of 0.9 and 0.8 for phantoms and arteries, respectively. The peak vasodilatation due to reactive hyperemia was two orders of magnitude greater in pixel count than that measured by longitudinal imaging. Cross-sectional imaging is more sensitive than longitudinal imaging for measuring flow-mediated dilatation of brachial artery, and thus may be more suitable for evaluating endothelial dysfunction.

Sehgal, Chandra M.; Kao, Yen H.; Cary, Ted W.; Arger, Peter H.; Mohler, Emile R.

2005-04-01

138

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

Microsoft Academic Search

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

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

2009-01-01

139

Time Series Hilary Term 2002  

E-print Network

, examples, objectives, informal analysis, overview of tech- niques for time series analysis 2. Stationary, nonlinear models, chaos Time series analysis is a very complex topic, far beyond what could be covered reading 1. P.J. Brockwell and R.A. Davis (1996). Introduction to Time Series and Forecasting. Springer. 2

140

Developing an automated global validation site time series system for VIIRS  

NASA Astrophysics Data System (ADS)

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.

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

2014-10-01

141

Observing Changes of Surface Solar Irradiance in Oregon: A Comparison of Satellite and Ground-Based Long-Term Time-Series  

NASA Astrophysics Data System (ADS)

Significant increases over time are found in direct normal irradiance (DNI) in Oregon using both ground and satellite-derived measurements of DNI. Linear regression of all locations in both data sets shows strong positive trends of .4% to .6% per year. Ground measurements are analyzed from 1980 (and at one site from 1978) until 2004. These 25 years of ground measurements come from three climatically diverse sites in the state of Oregon using an Eppley Normal Incidence Pyrheliometer (NIP). The NIP is a good candidate for long term trend analysis as its responsivity remains consistent over time. The sensitivity of the Eppley Precision Spectral Pyranometer (PSP) which measures total radiation, on the other hand, degrades over time, approximately .5% to 2% per year. This uniquely long data set is compared to DNI calculated from the International Satellite Cloud Climatology Project (ISCCP). The ISCCP D series applied here has 280 km x 280 km boxes, each of which includes one of the ground based sites, giving cloud and atmospheric input data from 1983 until 2001. Radiative transfer calculations are done using the two-stream method from the library for radiative transfer (libRadtran). The three hourly satellite observations allow comparison of different time integration periods. Besides annual average comparisons, monthly averages are examined to look for seasonal variation and confirm that the observations show a regional trend. Ground measurements of DNI for this length of time are rare, making this study a unique opportunity to test the capability to calculate direct normal irradiance based on ISCCP results. The agreement of the ISCCP derived irradiances to the measurements is very good: the trends differ between .08 and .3 W/m{2 depending on the site. From 1998 through 2002 satellite data were used to produce a solar radiation database on a 0.1i° grid. Comparisons between the modeled beam irradiance for the coordinates of the ground based station will be compared to the average for the area of the ISCCP grid to check how representative each ground site is of the ISCCP box. The successful verification of ISCCP for this application at three independent sites in this region allows us to use this approach to also analyze similar changes over other regions. Comparing these two methods of obtaining direct irradiance also provides valuable information about the sources of seasonal and inter-annual changes in cloud cover and other atmospheric constituents.

Riihimaki, L. D.; Vignola, F. E.; Lohmann, S.; Meyer, R.

2005-12-01

142

Development of an IUE Time Series Browser  

NASA Technical Reports Server (NTRS)

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

Massa, Derck

2005-01-01

143

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

E-print Network

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? We investigate the evolution of starspots on the rapidly-rotating ($P_{\\rm rot}$ $\\approx$ 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. We obtained continuous high-resolution and phase-resolved spectroscopy with the 1.2-m robotic STELLA telescope on Tenerife over six years. For each observing season, we obtained between 5 to 7 independent Doppler images, one per stellar rotation, making up a total of 36 maps. To quantify starspot area decay and growth, we match the ob...

Künstler, A; Strassmeier, K G

2015-01-01

144

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

NASA Technical Reports Server (NTRS)

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).

Mcpherron, R. L.

1976-01-01

145

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

PubMed

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

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

2014-04-30

146

Satellite image analysis using neural networks  

NASA Technical Reports Server (NTRS)

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

Sheldon, Roger A.

1990-01-01

147

Time-series of biomass burning products from ground-based FTIR measurements at Reunion Island (21°S, 55°E) and comparisons with the CTM IMAGES  

NASA Astrophysics Data System (ADS)

Reunion Island (21°S, 55°E) is part of the Network for the Detection of Atmospheric Composition Change (NDACC), a network dedicated to performing high-quality long-term ground-based observations of atmospheric trace gases at globally distributed sites. Up to now, only a few NDACC stations are located in the Southern Hemisphere, and particularly very few at tropical and subtropical latitudes. Furthermore, Reunion Island is situated in the Indian Ocean, at 2000 km from southeast Africa and at only 700 km from Madagascar. It is therefore a good location to study the transport of biomass burning products from these regions to Reunion Island. Ground-based Fourier transform infrared (FTIR) solar absorption observations are sensitive to a large number of biomass burning products. At present, we have a record of such FTIR observations at Reunion Island from three measurement campaigns, namely in October 2002, from August to October 2004, and from May to October 2007, and from continuous observations that started in May 2009. The measurements in 2007 and 2009-2010 allow the observation of seasonal variability. In this work, we present retrieved time-series of several biomass burning products such as C2H2, C2H6 and HCN. These ground-based data are compared to the CTM IMAGES. The Lagrangian particle dispersion model FLEXPART is used to explain the day-to-day variability of these species by the transport pathways.

Vigouroux, Corinne; de Mazière, Martine; Dils, Bart; Müller, Jean-François; Senten, Cindy; Stavrakou, Trissevgeni; Vanhaelewyn, Gauthier; Fally, Sophie; Duflot, Valentin; Baray, Jean-Luc

2010-05-01

148

Image Stacking Techniques for GEO Satellites  

NASA Astrophysics Data System (ADS)

The detection of GEO satellites at faint magnitudes requires careful image processing. Image stacking techniques - registration followed by the combination of image sets - are frequently employed to reduce the impact of photon/electronic noise, image sensor artefacts and gamma ray strikes. They allow improved photometric results and enhanced sensitivity to be obtained. We present a comparative study of six possible approaches to the technique in a GEO satellite detection context. The authors examine data from a contemporaneous GEO satellite photometry monitoring activity undertaken during March 2014 by the British Geological Surveys Satellite Geodesy Facility in the UK, SpaceInsight Ltd in Cyprus and the Defence Science and Technology Laboratory from the South Atlantic. Other results from the 3 site collection activity are also discussed.

Privett, G.; Appleby, G.; Sherwood, R.

2014-09-01

149

Interferometric imaging of geostationary satellites  

NASA Astrophysics Data System (ADS)

Even the longest geosatellite, at 40 m, subtends only 0.2 arcsec (1 microradian). Determining structure and orientation with 10 cm resolution requires a 90 m telescope at visual wavelengths, or an interferometer. We de- scribe the application of optical interferometry to observations of complex extended targets such as geosatellites, and discuss some of its challenges. We brie y describe our Navy Optical Interferometer (NOI) group's eorts toward interferometric observations of geosatellites, including the rst interferometric detection of a geosatellite. The NOI observes in 16 spectral channels (550{850 nm) using up to six 12-cm apertures, with baselines (separa- tions between apertures) of 16 to 79 m. We detected the geosatellite DirecTV-9S during glint seasons in March 2008 and March 2009, using a single 16 m baseline (resolution 1:6 m). Fringes on a longer baseline were too weak because the large-scale structure was over-resolved. The fringe strengths are consistent with a combination of two size scales, 1:3 m and & 3:5 m. Our near term NOI work is directed toward observing geosatellites with three or more 10 to 15 m baselines, using closure phase measurements to remove atmospheric turbulence eects and coherent data averaging to increase the SNR. Beyond the two- to three-year time frame, we plan to install larger apertures (1.4 and 1.8 m), allowing observations outside glint season, and to develop baseline bootstrap- ping, building long baselines from chains of short baselines, to avoid over-resolution while increasing maximum resolution. Our ultimate goal is to develop the design parameters for dedicated satellite imaging interferometry.

Armstrong, J. T.; Baines, E. K.; Hindsley, R. B.; Schmitt, H. R.; Restaino, S. R.; Jorgensen, A. M.; Mozurkewich, D.

2012-06-01

150

Conditional Heteroscedastic Time Series Models  

Microsoft Academic Search

Under the traditional linear time series or regression setting, the conditional variance of one-step-ahead prediction is time invariant. Experience in conjunction with data analysis, however, suggests that the variability of a process might well depend on the available information. This reality has motivated extensive research to relax the constant variance assumption imposed by the traditional linear time series model, and

Ruey S. Tsay

1987-01-01

151

Modelling Nonlinear Economic Time Series  

Microsoft Academic Search

This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader

Timo Terasvirta; Dag Tjostheim; Clive W. J. Granger

152

Time Series Techniques for Economists  

Microsoft Academic Search

The application of time series techniques in economics has become increasingly important, both for forecasting purposes and in the empirical analysis of time series in general. In this book, Terence Mills not only brings together recent research at the frontiers of the subject, but also analyses the areas of most importance to applied economics. It is an up-to-date text which

Terence C. Mills

1990-01-01

153

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

NASA Astrophysics Data System (ADS)

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

Vijay, Saurabh; Braun, Matthias

2014-05-01

154

Time Series of the Biscuit Fire  

NSDL National Science Digital Library

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

Cindy Starr

2003-08-04

155

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...

156

Animation of Archived Composite Infrared Satellite Images  

NSDL National Science Digital Library

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

157

Regression quantiles for time series  

E-print Network

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

Cai, Zongwu

2002-02-01

158

Clustering of financial time series  

NASA Astrophysics Data System (ADS)

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

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

2013-05-01

159

Rule Discovery from Time Series  

Microsoft Academic Search

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

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

1998-01-01

160

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

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.

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

2013-01-01

161

Information extraction from high resolution satellite images  

NASA Astrophysics Data System (ADS)

Information extracted from high resolution satellite images, such as roads, buildings, water and vegetation, has a wide range of applications in disaster assessment and environmental monitoring. At present, object oriented supervised learning is usually used in the objects identification from the high spatial resolution satellite images. In classical ways, we have to label some regions of interests from every image to be classified at first, which is labor intensive. In this paper, we build a feature base for information extraction in order to reduce the labeling efforts. The features stored are regulated and labeled. The labeled samples for a new coming image can be selected from the feature base. And the experiments are taken on GF-1 and ZY-3 images. The results show the feasibility of the feature base for image interpretation.

Yang, Haiping; Luo, Jiancheng; Shen, Zhanfeng; Xia, Liegang

2014-11-01

162

Reducing uncertainty on satellite image classification through spatiotemporal reasoning  

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

163

Aerial photographs and satellite images  

USGS Publications Warehouse

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

U.S. Geological Survey

1995-01-01

164

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

ERIC Educational Resources Information Center

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

Nous, Albert P.

165

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...

166

A Multivariate Approach to Estimate Complexity of FMRI Time Series  

E-print Network

A Multivariate Approach to Estimate Complexity of FMRI Time Series Henry Sch¨utze1,2 , Thomas magnetic resonance imaging, fMRI) produce large amounts of data. To adequately describe the underlying (MPSE), a multivariate entropy ap- proach that estimates spatio-temporal complexity of fMRI time series

167

Analysis of Music Time Series  

Microsoft Academic Search

The aim behind the modelling of this paper is the automatic transcription of music time series. Thus, the aim is somewhat\\u000a the contrary of the usual playing of notes: starting from the audio signal the corresponding musical notes should be generated.

Claus Weihs; Uwe Ligges; Katrin Sommer

168

CHEMICAL TIME-SERIES SAMPLING  

EPA Science Inventory

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

169

Finding semantics in time series  

Microsoft Academic Search

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

Peng Wang; Haixun Wang; Wei Wang

2011-01-01

170

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

171

Analysis of Multipsectral Time Series for supporting Forest Management Plans  

NASA Astrophysics Data System (ADS)

Adequate forest management requires specific plans based on updated and detailed mapping. Multispectral satellite time series have been largely applied to forest monitoring and studies at different scales tanks to their capability of providing synoptic information on some basic parameters descriptive of vegetation distribution and status. As a low expensive tool for supporting forest management plans in operative context, we tested the use of Landsat-TM/ETM time series (1987-2006) in the high Agri Valley (Southern Italy) for planning field surveys as well as for the integration of existing cartography. As preliminary activity to make all scenes radiometrically consistent the no-change regression normalization was applied to the time series; then all the data concerning available forest maps, municipal boundaries, water basins, rivers, and roads were overlapped in a GIS environment. From the 2006 image we elaborated the NDVI map and analyzed the distribution for each land cover class. To separate the physiological variability and identify the anomalous areas, a threshold on the distributions was applied. To label the non homogenous areas, a multitemporal analysis was performed by separating heterogeneity due to cover changes from that linked to basilar unit mapping and classification labelling aggregations. Then a map of priority areas was produced to support the field survey plan. To analyze the territorial evolution, the historical land cover maps were elaborated by adopting a hybrid classification approach based on a preliminary segmentation, the identification of training areas, and a subsequent maximum likelihood categorization. Such an analysis was fundamental for the general assessment of the territorial dynamics and in particular for the evaluation of the efficacy of past intervention activities.

Simoniello, T.; Carone, M. T.; Costantini, G.; Frattegiani, M.; Lanfredi, M.; Macchiato, M.

2010-05-01

172

Analysis of Galileo Style Geostationary Satellite Imaging: Image Reconstruction  

NASA Astrophysics Data System (ADS)

Earlier this year DARPA announced the Galileo project, with the conceptual idea of using optical interferometry to combine the light from two or more telescopes, with at least one of them being movable, to image geostationary satellites. This project aims at obtaining a NIIRS 8 image of a geosat with a resolution of 10cm. The design of this experiment creates challenging issues for the reconstruction of a satellite image. Among these issues are the lack of information about the absolute phase of the baselines, the difficulty to observe with short baselines, color differences between different satellite parts, and the time needed to obtain enough pointings to appropriately sample the UV-plane. We use simulations developed by our group to evaluate the effects of these issues on the reconstructed image quality, and the time required to reach the NIIRS 8 goal.

Schmitt, H.; Armstrong, J. T.; Baines, E. K.; Hindsley, R. B.; Jorgensen, A. M.; Mozurkewich, D.; Restaino, S. R.; van Belle, G.; Wilson, T. L.

2012-09-01

173

Antarctica: measuring glacier velocity from satellite images  

SciTech Connect

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

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

1986-11-28

174

Antarctica: Measuring glacier velocity from satellite images  

USGS Publications Warehouse

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

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

1986-01-01

175

A neuromorphic approach to satellite image understanding  

NASA Astrophysics Data System (ADS)

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

Partsinevelos, Panagiotis; Perakakis, Manolis

2014-05-01

176

AMOS Observations of NASA's IMAGE Satellite  

NASA Astrophysics Data System (ADS)

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

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

177

Entropy of electromyography time series  

NASA Astrophysics Data System (ADS)

A nonlinear analysis based on Renyi entropy is applied to electromyography (EMG) time series from back muscles. The time dependence of the entropy of the EMG signal exhibits a crossover from a subdiffusive regime at short times to a plateau at longer times. We argue that this behavior characterizes complex biological systems. The plateau value of the entropy can be used to differentiate between healthy and low back pain individuals.

Kaufman, Miron; Zurcher, Ulrich; Sung, Paul S.

2007-12-01

178

A review on time series data mining  

Microsoft Academic Search

Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically. The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously. Moreover time series data, which is characterized by its numerical and

Tak-chung Fu

2011-01-01

179

Analysing nonlinear time series with central subspace  

Microsoft Academic Search

Traditionally, time series analysis involves building an appropriate model and using either parametric or nonparametric methods to make inference about the model parameters. Motivated by recent developments for dimension reduction in time series, an empirical application of sufficient dimension reduction (SDR) to nonlinear time series modelling is shown in this article. Here, we use time series central subspace as a

Jin-Hong Park

2011-01-01

180

Analysing nonlinear time series with central subspace  

Microsoft Academic Search

Traditionally, time series analysis involves building an appropriate model and using either parametric or nonparametric methods to make inference about the model parameters. Motivated by recent developments for dimension reduction in time series, an empirical application of sufficient dimension reduction (SDR) to nonlinear time series modelling is shown in this article. Here, we use time series central subspace as a

Jin-Hong Park

2012-01-01

181

Learning graphical models for stationary time series  

Microsoft Academic Search

Probabilistic graphical models can be extended to time series by considering probabilistic dependencies between entire time series. For stationary Gaussian time series, the graphical model semantics can be expressed naturally in the frequency domain, leading to interesting families of structured time series models that are complementary to families defined in the time domain. In this paper, we present an algorithm

Francis R. Bach; Michael I. Jordan

2004-01-01

182

Multiple Indicator Stationary Time Series Models.  

ERIC Educational Resources Information Center

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

Sivo, Stephen A.

2001-01-01

183

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

184

Algorithm for Compressing Time-Series Data  

NASA Technical Reports Server (NTRS)

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

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

2012-01-01

185

Comparisons of zooplankton time series  

NASA Astrophysics Data System (ADS)

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

Mackas, David L.; Beaugrand, Gregory

2010-02-01

186

Stochastic Time-Series Spectroscopy  

E-print Network

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

Scoville, John

2015-01-01

187

United States Forest Disturbance Trends Observed Using Landsat Time Series  

NASA Technical Reports Server (NTRS)

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

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

2013-01-01

188

Impact of Sensor Degradation on the MODIS NDVI Time Series  

NASA Technical Reports Server (NTRS)

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

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

2012-01-01

189

Impact of Sensor Degradation on the MODIS NDVI Time Series  

NASA Technical Reports Server (NTRS)

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

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

2011-01-01

190

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

191

Star sightings by satellite for image navigation  

NASA Technical Reports Server (NTRS)

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.

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

1988-01-01

192

Automatic identification of oil spills on satellite images  

Microsoft Academic Search

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

Iphigenia Keramitsoglou; Constantinos Cartalis; Chris T. Kiranoudis

2006-01-01

193

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

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

194

DATA MINING OF MULTIPLE NONSTATIONARY TIME SERIES  

Microsoft Academic Search

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

RICHARD J. POVINELLI; XIN FENG

1999-01-01

195

Analyzing multimodal time series as dynamical systems  

Microsoft Academic Search

We propose a novel approach to discovering latent structures from multimodal time series. We view a time series as observed data from an underlying dynamical system. In this way, analyzing multimodal time series can be viewed as finding latent structures from dynamical systems. In light this, our approach is based on the concept of generating partition which is the theoretically

Shohei Hidaka; Chen Yu

2010-01-01

196

Time Series Analysis James D. Hamilton  

E-print Network

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

Landweber, Laura

197

Segmenting Time Series for Weather Forecasting  

E-print Network

Segmenting Time Series for Weather Forecasting Somayajulu G. Sripada, Ehud Reiter, Jim Hunter on 20-01-01 Table 1. shows an example time series from the domain of meteorology. It shows the wind,ereiter,jhunter,jyu}@csd.abdn.ac.uk Abstract We are investigating techniques for producing textual summaries of time series data. Deep

Sripada, Yaji

198

Neighborhood counting for financial time series forecasting  

Microsoft Academic Search

Time series data abound and analysis of such data is challenging and potentially rewarding. One example is financial time series analysis. Most of the intelligent data analysis methods can be applied in principle, but evolutionary computing is becoming increasingly popular and powerful. In this paper we focus on one task of financial time series analysis - stock price forecasting based

Zhiwei Lin; Yu Huang; Hui Wang; Sally I. McClean

2009-01-01

199

Intelligent techniques for forecasting multiple time series  

E-print Network

Intelligent techniques for forecasting multiple time series in real-world systems Neal Wagner to forecast multiple time series in a non-static environment. The value of the model lies in its ability to accurately capture and forecast a very large and constantly changing portfolio of time series efficiently

Michalewicz, Zbigniew

200

Wavelet analysis of geocenter time series and its geophysical excitation  

NASA Astrophysics Data System (ADS)

Geocenter time series with the weekly sampling interval are available now from Satellite Laser Ranging (SLR), Global Navigation Satellite System (GNSS) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) observations. The wavelet semblance filtering was applied to compute a common signal in these time series. To compute such common signal the Earth center of mass data were transformed into time - frequency domain using the discrete wavelet transform based on the Shannon wavelet function. Next the wavelet transform coefficients were used to compute the semblance function. Assuming a fixed semblance function threshold, zero values were assigned to wavelet transform coefficients of both time series, for which the semblance was below this threshold. The common signal in the considered time series was then computed using the inverse discrete wavelet transform of the thresholded coefficients.This common signal can be detected from the SLR and GNSS data only and it consists of the retrograde annual oscillation in the equatorial plane with amplitude of about few millimetres. To find a geophysical interpretation of this common signal the mass component of the atmospheric, ocean and land hydrology excitation functions were taken into account. The time frequency semblance function with application of the Morlet wavelet between these geophysical fluid excitation functions and such common signal were computed to explain geocenter motion fluctuations. This function shows time and frequency dependent correlation coefficient between this geocenter motion and fluid excitation.

Kosek, W.; Wnek, A.; Zbylut, M.; Popinski, W.

2013-12-01

201

Des satellites nous renvoient notre image Politique scientifique fdrale  

E-print Network

Des satellites nous renvoient notre image Politique scientifique fédérale Martine STÉLANDRE Faites, HEPL Sualem, ISELL, Hemes et ULg 2 Les satellites observent la Terre En l'an 2002, plus de 5.000 satellites flottaient autour de la Terre, dont environ 600 sont en fonctionnement. Ce sont des outils

Liège, Université de

202

Studies of soundings and imagings measurements from geostationary satellites  

NASA Technical Reports Server (NTRS)

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.

Suomi, V. E.

1973-01-01

203

Multivariate Time Series Similarity Searching  

PubMed Central

Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665

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

2014-01-01

204

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

NASA Astrophysics Data System (ADS)

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

Zhu, Xiaolin; Liu, Desheng

2014-10-01

205

A review of subsequence time series clustering.  

PubMed

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

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

2014-01-01

206

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

207

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

E-print Network

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

Lonardi, Stefano

208

A New Method For Robust High-Precision Time-Series Photometry From Well-Sampled Images: Application to Archival MMT/Megacam Observations of the Open Cluster M37  

E-print Network

We introduce new methods for robust high-precision photometry from well-sampled images of a non-crowded field with a strongly varying point-spread function. For this work, we used archival imaging data of the open cluster M37 taken by MMT 6.5m telescope. We find that the archival light curves from the original image subtraction procedure exhibit many unusual outliers, and more than 20% of data get rejected by the simple filtering algorithm adopted by early analysis. In order to achieve better photometric precisions and also to utilize all available data, the entire imaging database was re-analyzed with our time-series photometry technique (Multi-aperture Indexing Photometry) and a set of sophisticated calibration procedures. The merit of this approach is as follows: we find an optimal aperture for each star with a maximum signal-to-noise ratio, and also treat peculiar situations where photometry returns misleading information with more optimal photometric index. We also adopt photometric de-trending based on ...

Chang, S -W; Hartman, J D

2015-01-01

209

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

E-print Network

Spatial Cone Tree: An Index Structure for Correlation-based Similarity Queries on Spatial Time [14]. Finding highly correlated time series from spatial time series datasets collected by satellites correlation-based query processing[1, 7] on spatial time series data, the focus of this work, is crucial

Huang, Yan

210

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

NASA Astrophysics Data System (ADS)

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

Scott, Douglas; Petropoulos, George

2014-05-01

211

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...

212

Sea state variability observed by high resolution satellite radar images  

NASA Astrophysics Data System (ADS)

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

Pleskachevsky, A.; Lehner, S.

2012-04-01

213

Searching through composite time series Kaustav Das  

E-print Network

. In most cases the data is multivari- ate in nature, and the effect of an anomaly can potentiallySearching through composite time series Kaustav Das Carnegie Mellon University 5000 Forbes Avenue categorical and real valued time series data. Many classical statistical methods deal with univariate data

Gordon, Geoffrey J.

214

ARMA Model identification of hydrologic time series  

Microsoft Academic Search

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

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

1982-01-01

215

Linear Relations in Time Series Models. I.  

ERIC Educational Resources Information Center

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

Villegas, C.

1976-01-01

216

Generation of artificial helioseismic time-series  

NASA Technical Reports Server (NTRS)

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.

Schou, J.; Brown, T. M.

1993-01-01

217

Important Extrema of Time Series Eugene Fink  

E-print Network

Important Extrema of Time Series Eugene Fink Computer Science, Carnegie Mellon University Pittsburgh, Pennsylvania 15213 e.fink@cs.cmu.edu, www.cs.cmu.edu/eugene Harith Suman Gandhi 11336 Cypress lossy compression of a time series based on the assignment of importance levels to its minima and maxima

Fink, Eugene

218

Important Extrema of Time Series Eugene Fink  

E-print Network

Important Extrema of Time Series Eugene Fink Computer Science, Carnegie Mellon University Pittsburgh, Pennsylvania 15213 e.fink@cs.cmu.edu, www.cs.cmu.edu/#eugene Harith Suman Gandhi 11336 Cypress lossy compression of a time series based on the assignment of importance levels to its minima and maxima

Fink, Eugene

219

Time-Series Analysis of Counseling Research.  

ERIC Educational Resources Information Center

Explains and demonstrates time-series statistical analyses with a case example. Argues graphs and nonparometrical statistical analyses are not valid methods for evaluating behavior change due to counseling. Suggests the use of time-series statistical analyses enables counselors to employ a reliable method of measuring change in counseling…

Sharpley, Chris.

1981-01-01

220

Forecasting Enrollments with Fuzzy Time Series.  

ERIC Educational Resources Information Center

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…

Song, Qiang; Chissom, Brad S.

221

Volatility of linear and nonlinear time series.  

PubMed

Previous studies indicated that nonlinear properties of Gaussian distributed time series with long-range correlations, u(i), can be detected and quantified by studying the correlations in the magnitude series |u(i)|, the "volatility." However, the origin for this empirical observation still remains unclear and the exact relation between the correlations in u(i) and the correlations in |u(i)| is still unknown. Here we develop analytical relations between the scaling exponent of linear series u(i) and its magnitude series |u(i)|. Moreover, we find that nonlinear time series exhibit stronger (or the same) correlations in the magnitude time series compared with linear time series with the same two-point correlations. Based on these results we propose a simple model that generates multifractal time series by explicitly inserting long range correlations in the magnitude series; the nonlinear multifractal time series is generated by multiplying a long-range correlated time series (that represents the magnitude series) with uncorrelated time series [that represents the sign series sgn (u(i))]. We apply our techniques on daily deep ocean temperature records from the equatorial Pacific, the region of the El-Ninõ phenomenon, and find: (i) long-range correlations from several days to several years with 1/f power spectrum, (ii) significant nonlinear behavior as expressed by long-range correlations of the volatility series, and (iii) broad multifractal spectrum. PMID:16090007

Kalisky, Tomer; Ashkenazy, Yosef; Havlin, Shlomo

2005-07-01

222

Nonlinear dynamical models from time series  

E-print Network

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

Jose-Maria Fullana

2014-07-30

223

Time series change detection: Algorithms for land cover change  

NASA Astrophysics Data System (ADS)

The climate and earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, climate and ecosystem related observations from remote sensors on satellites, as well as outputs of climate or earth system models from large-scale computational platforms, provide terabytes of temporal, spatial and spatio-temporal data. These massive and information-rich datasets offer huge potential for advancing the science of land cover change, climate change and anthropogenic impacts. One important area where remote sensing data can play a key role is in the study of land cover change. Specifically, the conversion of natural land cover into humandominated cover types continues to be a change of global proportions with many unknown environmental consequences. In addition, being able to assess the carbon risk of changes in forest cover is of critical importance for both economic and scientific reasons. In fact, changes in forests account for as much as 20% of the greenhouse gas emissions in the atmosphere, an amount second only to fossil fuel emissions. Thus, there is a need in the earth science domain to systematically study land cover change in order to understand its impact on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Land cover conversions include tree harvests in forested regions, urbanization, and agricultural intensification in former woodland and natural grassland areas. These types of conversions also have significant public policy implications due to issues such as water supply management and atmospheric CO2 output. In spite of the importance of this problem and the considerable advances made over the last few years in high-resolution satellite data, data mining, and online mapping tools and services, end users still lack practical tools to help them manage and transform this data into actionable knowledge of changes in forest ecosystems that can be used for decision making and policy planning purposes. In particular, previous change detection studies have primarily relied on examining differences between two or more satellite images acquired on different dates. Thus, a technological solution that detects global land cover change using high temporal resolution time series data will represent a paradigm-shift in the field of land cover change studies. To realize these ambitious goals, a number of computational challenges in spatio-temporal data mining need to be addressed. Specifically, analysis and discovery approaches need to be cognizant of climate and ecosystem data characteristics such as seasonality, non-stationarity/inter-region variability, multi-scale nature, spatio-temporal autocorrelation, high-dimensionality and massive data size. This dissertation, a step in that direction, translates earth science challenges to computer science problems, and provides computational solutions to address these problems. In particular, three key technical capabilities are developed: (1) Algorithms for time series change detection that are effective and can scale up to handle the large size of earth science data; (2) Change detection algorithms that can handle large numbers of missing and noisy values present in satellite data sets; and (3) Spatio-temporal analysis techniques to identify the scale and scope of disturbance events.

Boriah, Shyam

224

Statistical criteria for characterizing irradiance time series.  

SciTech Connect

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

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

2010-10-01

225

Studies of soundings and imaging measurements from geostationary satellites  

NASA Technical Reports Server (NTRS)

Soundings and imaging measurements obtained from geostationary satellites for the period 1 Nov. 1972 to 31 Jan. 1973 are reported. The subjects discussed are: (1) investigation of meteorological data processing techniques, (2) sun glitter, (3) cloud growth rate, and (4) comparative studies in satellite stability.

Suomi, V. E.

1973-01-01

226

Time series of a CME blasting out from the Sun  

E-print Network

#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

Christian, Eric

227

Time Series of the Biscuit Fire with Smoke  

NSDL National Science Digital Library

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

Cindy Starr

2003-08-04

228

Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation  

Microsoft Academic Search

In this letter, we are interested in the annotation of large satellite images, using semantic concepts defined by the user. This annotation task combines a step of supervised classification of patches of the large image and the integration of the spatial information between these patches. Given a training set of images for each concept, learning is based on the latent

Marie Lienou; Henri Maitre; Mihai Datcu

2010-01-01

229

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

NASA Astrophysics Data System (ADS)

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

Huang, F.

2010-12-01

230

A New Method For Robust High-Precision Time-Series Photometry From Well-Sampled Images: Application to Archival MMT/Megacam Observations of the Open Cluster M37  

NASA Astrophysics Data System (ADS)

We introduce new methods for robust high-precision photometry from well-sampled images of a non-crowded field with a strongly varying point-spread function. For this work, we used archival imaging data of the open cluster M37 taken by MMT 6.5 m telescope. We find that the archival light curves from the original image subtraction procedure exhibit many unusual outliers, and more than 20% of data get rejected by the simple filtering algorithm adopted by early analysis. In order to achieve better photometric precision and also to utilize all available data, the entire imaging database was re-analyzed with our time-series photometry technique (Multi-aperture Indexing Photometry) and a set of sophisticated calibration procedures. The merit of this approach is as follows: we find an optimal aperture for each star with a maximum signal-to-noise ratio and also treat peculiar situations where photometry returns misleading information with a more optimal photometric index. We also adopt photometric de-trending based on a hierarchical clustering method, which is a very useful tool in removing systematics from light curves. Our method removes systematic variations that are shared by light curves of nearby stars, while true variabilities are preserved. Consequently, our method utilizes nearly 100% of available data and reduces the rms scatter several times smaller than archival light curves for brighter stars. This new data set gives a rare opportunity to explore different types of variability of short (?minutes) and long (?1 month) time scales in open cluster stars.

Chang, S.-W.; Byun, Y.-I.; Hartman, J. D.

2015-04-01

231

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

Microsoft Academic Search

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

Craig A. Lee; Carl Kesselman; Stephen Schwab

1996-01-01

232

Multiscale Entropy Analysis of Traffic Time Series  

NASA Astrophysics Data System (ADS)

There has been considerable interest in quantifying the complexity of different time series, such as physiologic time series, traffic time series. However, these traditional approaches fail to account for the multiple time scales inherent in time series, which have yielded contradictory findings when applied to real-world datasets. Then multi-scale entropy analysis (MSE) is introduced to solve this problem which has been widely used for physiologic time series. In this paper, we first apply the MSE method to different correlated series and obtain an interesting relationship between complexity and Hurst exponent. A modified MSE method called multiscale permutation entropy analysis (MSPE) is then introduced, which replaces the sample entropy (SampEn) with permutation entropy (PE) when measuring entropy for coarse-grained series. We employ the traditional MSE method and MSPE method to investigate complexities of different traffic series, and obtain that the complexity of weekend traffic time series differs from that of the workday time series, which helps to classify the series when making predictions.

Wang, Jing; Shang, Pengjian; Zhao, Xiaojun; Xia, Jianan

2013-02-01

233

Homogenising time series: beliefs, dogmas and facts  

NASA Astrophysics Data System (ADS)

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

Domonkos, P.

2011-06-01

234

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

235

Satellite images to aircraft in flight. [GEOS image transmission feasibility analysis  

NASA Technical Reports Server (NTRS)

A study has been initiated to evaluate the feasibility of transmitting selected GOES images to aircraft in flight. Pertinent observations that could be made from satellite images on board aircraft include jet stream activity, cloud/wind motion, cloud temperatures, tropical storm activity, and location of severe weather. The basic features of the Satellite Aircraft Flight Environment System (SAFES) are described. This system uses East GOES and West GOES satellite images, which are interpreted, enhanced, and then retransmitted to designated aircraft.

Camp, D.; Luers, J. K.; Kadlec, P. W.

1977-01-01

236

Evaluation of Landsat Archive for Global and Time Series Validation of AVHRR-MODIS Vegetation Index Data Records  

NASA Astrophysics Data System (ADS)

Spectral vegetation indices (VIs) derived from satellite images are useful in land surface phenology studies. These time series portray seasonal and annual changes to vegetation globally. Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are two satellite sensors which provide daily global VIs. AVHRR and MODIS time series could be used together to assess longer-term changes in global phenological and climate change studies. Inherent differences of sensor characteristics potentially lead to inconsistencies between these two sensor time series, which needs to be evaluated. Landsat provides the longest data record; i.e., Landsat-5 Thematic Mapper had been acquiring data since 1984. The Landsat dataset could provide an opportunity to evaluate multi-sensor continuity of these global datasets. The objective of this study was to evaluate the availability of low cloud coverage or cloud-free Landsat scenes at the United States Geological Survey (USGS) for validating data consistency and continuity of AVHRR-MODIS global vegetation index datasets over an 18-year time period, from 1984 to 2002. The USGS Landsat Global Archive file was obtained from the USGS Landsat website, which provided a graphic view of the geographic and temporal acquisition of Landsat data from 1972 to 2011. International Geosphere-Biosphere Programme (IGBP) land cover data were extracted from the 2002 MODIS land cover product (MYC12C1) to stratify available scenes for land cover types. Archived scene metadata were used to find Landsat scenes with less than 25% cloud cover. The most comprehensive data were available over the conterminous United States, the southern part of Canada, and the northern part of Mexico, where full 18-year time series of Landsat-5 TM image stack (10 - 20 image per year) could be generated at many PATH/ROW locations. Outside of North America, no single region had sufficient data archived for all years of the 1984 to 2002 time period to allow for a continuous time-series to be generated. However, occasional regional coverage of Landsat data were observed for South America, Europe, Southeast Asia, Austraia, and Africa, which could be used to assess global consistency of AVHRR and MODIS datasets, with the exception of a few years with insufficient data collection. We conclude that the USGS EROS Landsat-5 TM archive can provide a useful temporal and spatial coverage to validate data consistency and continuity of AVHRR-MODIS global vegetation index time series.

Connor, W.; Miura, T.

2012-12-01

237

Temporal registration of multispectral digital satellite images using their edge images  

NASA Technical Reports Server (NTRS)

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

Nack, M. L.

1975-01-01

238

Advanced spectral methods for climatic time series  

USGS Publications Warehouse

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

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

2002-01-01

239

Entropic Analysis of Electromyography Time Series  

NASA Astrophysics Data System (ADS)

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

Kaufman, Miron; Sung, Paul

2005-03-01

240

Nonlinear Analysis of Surface EMG Time Series  

NASA Astrophysics Data System (ADS)

Applications of nonlinear analysis of surface electromyography time series of patients with and without low back pain are presented. Limitations of the standard methods based on the power spectrum are discussed.

Zurcher, Ulrich; Kaufman, Miron; Sung, Paul

2004-04-01

241

Mining Causal Relationships in Multidimensional Time Series  

Microsoft Academic Search

Time series are ubiquitous in all domains of human endeavor. They are generated, stored, and manipulated during any kind of\\u000a activity. The goal of this chapter is to introduce a novel approach to mine multidimensional time-series data for causal relationships.\\u000a The main feature of the proposed system is supporting discovery of causal relations based on automatically discovered recurring\\u000a patterns in

Yasser F. O. Mohammad; Toyoaki Nishida

2010-01-01

242

Seminar on Computational Engineering: Time Series Analysis  

Microsoft Academic Search

This report reviews some basic ideas of time series analysis. The handling ofthe subjects is practical, and examples are used to simplify things to the reader.First we introduce a very important linear time series model ARMA. In chapter 3we take a quick look at state-space reconstruction. Chapter 4 handles the essentialart of characterization. Finally, we present some simple ideas about

Teemu Leppänen

2001-01-01

243

Time series prediction evolving Voronoi regions  

Microsoft Academic Search

Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only\\u000a information of the previous data. The main problem is the fact that most of the time series that represent real phenomena\\u000a include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able

Cristóbal Luque; José María Valls; Pedro Isasi

2011-01-01

244

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

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

245

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

246

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

PubMed

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

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

2003-03-01

247

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

NASA Technical Reports Server (NTRS)

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

1982-01-01

248

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

PubMed Central

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines. PMID:23554344

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

2013-01-01

249

The electromagnetic simulation of radar imaging of complicated satellites  

NASA Astrophysics Data System (ADS)

Research on electromagnetic scattering from electrical large space target, random rough surface, and the composite model of space target and rough surface, has been more and more important in recent years. This paper presents studies of geometrical modeling, simulation of rough surface of satellites and analyzing of radar satellite image from scattering phenomenology. The Gaussian random fluctuation is adopted in the electromagnetic compute to simulate diffuse reflectance caused by rough surface of satellite. The efficient and accurate simulation of complicated satellites is realizable. Wide-band electromagnetic scattering characteristics which are obtained by this method could be used to analyze the information of structure and shape of satellites more accurately. It is important for imagery interpretation of space targets.

Chen, Wenjing; Dong, Chunzhu; Huo, Chaoying; Ren, Hongmei

2014-11-01

250

On-Orbit Calibration of a Multi-Spectral Satellite Satellite Sensor Using a High Altitude Airborne Imaging Spectrometer  

NASA Technical Reports Server (NTRS)

Earth-looking satellites must be calibrated in order to quantitatively measure and monitor components of land, water and atmosphere of the Earth system. The inevitable change in performance due to the stress of satellite launch requires that the calibration of a satellite sensor be established and validated on-orbit. A new approach to on-orbit satellite sensor calibration has been developed using the flight of a high altitude calibrated airborne imaging spectrometer below a multi-spectral satellite sensor.

Green, R. O.; Shimada, M.

1996-01-01

251

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

252

Wavelet Analysis of Satellite Images for Coastal Watch  

NASA Technical Reports Server (NTRS)

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.

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

1997-01-01

253

Geospatial Visualization of Global Satellite Images with Vis-EROS  

SciTech Connect

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.

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

2011-04-13

254

Fusion of Multispectral and Panchromatic Satellite Images Using the Curvelet Transform  

Microsoft Academic Search

A useful technique in various applications of remote sensing involves the fusion of different types of satellite images, namely multispectral (MS) satellite images with a high spectral and low spatial resolution and panchromatic (Pan) satellite image with a low spectral and high spatial resolution. Recent studies show that wavelet-based image fusion provides high-quality spectral content in fused images. However, the

Myungjin Choi; Rae Young Kim; Myeong-Ryong Nam; Hong Oh Kim

2005-01-01

255

Observe animated satellite images of water vapor  

NSDL National Science Digital Library

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

GOES

256

Improved satellite image compression and reconstruction via genetic algorithms  

NASA Astrophysics Data System (ADS)

A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.

Babb, Brendan; Moore, Frank; Peterson, Michael; Lamont, Gary

2008-10-01

257

Homogenising time series: Beliefs, dogmas and facts  

NASA Astrophysics Data System (ADS)

For obtaining reliable information about climate change and climate variability the use of high quality data series is essentially important, and one basic tool of quality improvements is the statistical homogenisation of observed time series. In the recent decades large number of homogenisation methods has been developed, but the real effects of their application on time series are still not known entirely. The ongoing COST HOME project (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. The author believes that several old theoretical rules have to be re-evaluated. Some examples of the hot questions, a) Statistically detected change-points can be accepted only with the confirmation of metadata information? b) Do semi-hierarchic algorithms for detecting multiple change-points in time series function effectively in practise? c) Is it good to limit the spatial comparison of candidate series with up to five other series in the neighbourhood? Empirical results - those from the COST benchmark, and other experiments too - show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities seem like part of the climatic variability, thus the pure application of classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality, than in raw time series. The developers and users of homogenisation methods have to bear in mind that the eventual purpose of homogenisation is not to find change-points, but to have the observed time series with statistical properties those characterise well the climate change and climate variability.

Domonkos, P.

2010-09-01

258

Detecting chaos in irregularly sampled time series.  

PubMed

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

Kulp, C W

2013-09-01

259

Stochastic versions of chaotic time series: Generalized logistic and Hénon time series models  

NASA Astrophysics Data System (ADS)

Deterministic time series models, particularly those which exhibit chaotic behavior, have received considerable attention recently as an alternative to stochastic time series models. In many cases there exists a well-defined stochastic time series model from which the deterministic time series model is obtained in the limit as the degree of stochasticity goes to zero. The stochastic version may then be used as a means for fitting the deterministic model. In this paper we introduce a stochastic generalized (parametric) logistic time series model. Using simulated data, we show that model fitting methods developed for the stochastic logistic time series also work in the deterministic case. We then use these methods to fit the logistic model to the weevil data of Utida. We introduce as well a stochastic quadratic time series model for which the deterministic Hénon time series model is a limiting special case. Finally, we show how the stochastic version of a nonlinear deterministic time series model provides a mechanism for studying the stability of the deterministic model to small perturbations.

Gerr, Neil L.; Allen, Jeffery C.

1993-10-01

260

Learning time series for intelligent monitoring  

NASA Technical Reports Server (NTRS)

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

Manganaris, Stefanos; Fisher, Doug

1994-01-01

261

Time series of the northeast Pacific  

NASA Astrophysics Data System (ADS)

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

Peña, M. Angelica; Bograd, Steven J.

2007-10-01

262

ARMA Model Identification of Hydrologic Time Series  

NASA Astrophysics Data System (ADS)

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

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

1982-08-01

263

Predicting road accidents: Structural time series approach  

NASA Astrophysics Data System (ADS)

In this paper, the model for occurrence of road accidents in Malaysia between the years of 1970 to 2010 was developed and throughout this model the number of road accidents have been predicted by using the structural time series approach. The models are developed by using stepwise method and the residual of each step has been analyzed. The accuracy of the model is analyzed by using the mean absolute percentage error (MAPE) and the best model is chosen based on the smallest Akaike information criterion (AIC) value. A structural time series approach found that local linear trend model is the best model to represent the road accidents. This model allows level and slope component to be varied over time. In addition, this approach also provides useful information on improving the conventional time series method.

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-07-01

264

Nonlinear Dynamics Analysis of Traffic Time Series  

NASA Astrophysics Data System (ADS)

Traffic flow shows various complex behaviors. Numerous empirical data and numerical simulation results demonstrate the existence of distinct dynamics states. In order to obtain insight into the nature of the dynamics, we apply the nonlinear time series analysis approach to study the characteristic behavior of traffic flow at low and intermediate density values. A procedure called "embedding scheme" has been used to reconstruct the representation of the time series which is obtained from the deterministic NaSch traffic model. In the reconstructed phase space, we investigate the regular attractor and the chaotic attractor of traffic time series. Our results indicate that nonlinear technique can be successfully used for understanding of dynamics feature of traffic flow.

Li, Keping; Gao, Ziyou

265

Vehicle Detection and Classification from High Resolution Satellite Images  

NASA Astrophysics Data System (ADS)

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

Abraham, L.; Sasikumar, M.

2014-11-01

266

Clustering streamflow time series for regional classification  

NASA Astrophysics Data System (ADS)

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

Corduas, Marcella

2011-09-01

267

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

268

Mulstiscale Stochastic Generator of Multivariate Met-Ocean Time Series  

NASA Astrophysics Data System (ADS)

The design of maritime structures requires information on sea state conditions that influence its behavior during its life cycle. In the last decades, there has been a increasing development of sea databases (buoys, reanalysis, satellite) that allow an accurate description of the marine climate and its interaction with a given structure in terms of functionality and stability. However, these databases have a limited timelength, and its appliance entails an associated uncertainty. To avoid this limitation, engineers try to sample synthetically generated time series, statistically consistent, which allow the simulation of longer time periods. The present work proposes a hybrid methodology to deal with this issue. It is based in the combination of clustering algorithms (k-means) and an autoregressive logistic regression model (logit). Since the marine climate is directly related to the atmospheric conditions at a synoptic scale, the proposed methodology takes both systems into account; generating simultaneously circulation patterns (weather types) time series and the sea state time series related. The generation of these time series can be summarized in three steps: (1) By applying the clustering technique k-means the atmospheric conditions are classified into a representative number of synoptical patterns (2) Taking into account different covariates involved (such as seasonality, interannual variability, trends or autoregressive term) the autoregressive logistic model is adjusted (3) Once the model is able to simulate weather types time series the last step is to generate multivariate hourly metocean parameters related to these weather types. This is done by an autoregressive model (ARMA) for each variable, including cross-correlation between them. To show the goodness of the proposed method the following data has been used: Sea Level Pressure (SLP) databases from NCEP-NCAR and Global Ocean Wave (GOW) reanalysis from IH Cantabria. The synthetical met-ocean hourly time series obtained are statistically consistent (also in terms of extremes and persistence) and keep the temporal dependence structure of the initial stochastic process. This method constitutes a very useful tool in the designing phase of maritime structures or in other branches of coastal engineering.

Guanche, Yanira; Mínguez, Roberto; Méndez, Fernando J.

2013-04-01

269

Stratospheric ozone time series analysis using dynamical linear models  

NASA Astrophysics Data System (ADS)

We describe a hierarchical statistical state space model for ozone profile time series. The time series are from satellite measurements by the SAGE II and GOMOS instruments spanning years 1984-2012. The original data sets are combined and gridded monthly using 10 degree latitude bands, and covering 20-60 km with 1 km vertical spacing. Model components include level, trend, seasonal effect with solar activity, and quasi biennial oscillations as proxy variables. A typical feature of an atmospheric time series is that they are not stationary but exhibit both slowly varying and abrupt changes in the distributional properties. These are caused by external forcing such as changes in the solar activity or volcanic eruptions. Further, the data sampling is often nonuniform, there are data gaps, and the uncertainty of the observations can vary. When observations are combined from various sources there will be instrument and retrieval method related biases. The differences in sampling lead also to uncertainties. Standard classical ARIMA type of statistical time series methods are mostly useless for atmospheric data. A more general approach makes use of dynamical linear models and Kalman filter type of sequential algorithms. These state space models assume a linear relationship between the unknown state of the system and the observations and for the process evolution of the hidden states. They are still flexible enough to model both smooth trends and sudden changes. The above mentioned methodological challenges are discussed, together with analysis of change points in trends related to recovery of stratospheric ozone. This work is part of the ESA SPIN and ozone CCI projects.

Laine, Marko; Kyrölä, Erkki

2013-04-01

270

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks  

E-print Network

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth Accepted 18 September 2010 Keywords: MODIS NDVI time series data Gypsy moth Regional forest defoliation and image thresholding of maximum value normalized difference vegetation index (NDVI) datasets computed

Hargrove, William W.

271

Analysis of Polyphonic Musical Time Series  

Microsoft Academic Search

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

Katrin Sommer; Claus Weihs

2010-01-01

272

Fast correlation analysis on time series datasets  

Microsoft Academic Search

There has been increasing interest for efficient techniques for fast correlation analysis of time series data in different application domains. We present three algorithms for (1) bivariate correlation queries, (2) multivariate correlation queries, and (3) correlation queries based on a new correlation measure we introduce using dynamic time warping. To support these algorithms, we use a variant of the Compact

Philon Nguyen; Nematollaah Shiri

2008-01-01

273

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...

274

Integrated method for chaotic time series analysis  

DOEpatents

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

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

1998-09-29

275

Resampling and Subsampling for Financial Time Series  

E-print Network

and concentrate on bootstrap-based statistical inference for the conditional variance 2 t of the log. Statistical inference for such financial time series has received considerable interest in the last decades of the price change at time t is determined by the sign of t, while the order of magnitude of this change

Politis, Dimitris N.

276

Integrated method for chaotic time series analysis  

DOEpatents

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. 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.

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

1998-01-01

277

Nonlinear time series analysis of electrocardiograms  

Microsoft Academic Search

In recent years there has been an increasing number of papers in the literature, applying the methods and techniques of Nonlinear Dynamics to the time series of electrical activity in normal electrocardiograms (ECGs) of various human subjects. Most of these studies are based primarily on correlation dimension estimates, and conclude that the dynamics of the ECG signal is deterministic and

A. Bezerianos; T. Bountis; G. Papaioannou; P. Polydoropoulos

1995-01-01

278

Nonlinear Time Series Analysis via Neural Networks  

NASA Astrophysics Data System (ADS)

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.

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

279

Modeling Time Series with Calendar Variation  

Microsoft Academic Search

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

W. R. Bell; S. C. Hillmer

1983-01-01

280

Confidence interval estimation using standardized time series  

Microsoft Academic Search

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

Lee W. Schruben

1983-01-01

281

Modeling noisy time series: Physiological tremor  

E-print Network

Modeling noisy time series: Physiological tremor J. Timmer Fakult¨at f¨ur Physik, Hermann on the estimated parameters of models fitted to the data. For data of physiological tremor, i.e. a small amplitude this for data of physiological human hand tremor. These data contain up to 50 % observational noise. Inspired

Timmer, Jens

282

Nonlinear time-series analysis revisited  

E-print Network

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.

Elizabeth Bradley; Holger Kantz

2015-03-25

283

Time Series Prediction Competition: The CATS Benchmark  

E-print Network

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

Verleysen, Michel

284

Time-series models in marketing  

Microsoft Academic Search

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

Marnik G. Dekimpe; Dominique M. Hanssens

2000-01-01

285

Time series analysis of barometric pressure data  

Microsoft Academic Search

Time series of atmospheric pressure data, collected over a period of several years, were analysed to provide undergraduate students with educational examples of application of simple statistical methods of analysis. In addition to basic methods for the analysis of periodicities, a comparison of two forecast models, one based on autoregression algorithms, and the other making use of an artificial neural

Paola La Rocca; Daniele Riggi; Francesco Riggi

2010-01-01

286

Detecting Outbreaks by Time Series Analysis  

Microsoft Academic Search

Exceptional events in a time series are observations which can be regarded as qualitatively significant anomalies. The detection of such events is an interesting problem in several domains, in particular for the generation of alarms in clinical microbiology. We propose an approach to the detection of exceptional events based on model selection. For each mathematical form of a model, we

Gianfranco Cellarosi; Stefano Lodi; Claudio Sartori

2002-01-01

287

On Clustering fMRI Time Series  

Microsoft Academic Search

Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indicate whether sets of voxels are activated in a similar way or in different ways. Typically,

Cyril Goutte; Peter Toft; Egill Rostrup; Finn Å. Nielsen; Lars Kai Hansen

1999-01-01

288

SLEX Analysis of Multivariate Nonstationary Time Series  

Microsoft Academic Search

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

Hernando Ombao; Rainer von Sachs; Wensheng Guo

2005-01-01

289

Time Series Regression with a Unit Root  

Microsoft Academic Search

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

P. C. B. Phillips

1987-01-01

290

Specification Tests for Nonlinear Time Series Models  

Microsoft Academic Search

This paper proposes a new parametric model adequacy test for possibly nonlinear and nonstationary time series models (possibly with covariates) such as generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional duration (ACD). We consider the correct specification of parametric conditional distributions, not only some partic- ular conditional characteristics such as moments or symmetry. Knowing the true distribution is important in

Igor L. Kheifets

2010-01-01

291

Spatiotemporal topological kriging of runoff time series  

Microsoft Academic Search

This paper proposes a geostatistical method for estimating runoff time series in ungauged catchments. The method conceptualizes catchments as space-time filters and exploits the space-time correlations of runoff along the stream network topology. We hence term the method topological kriging or top kriging. It accounts for hydrodynamic and geomorphologic dispersion as well as routing and estimates runoff as a weighted

Jon Olav Skøien; Günter Blöschl

2007-01-01

292

SO2 EMISSIONS AND TIME SERIES MODELS  

EPA Science Inventory

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

293

Simultaneous Fusion and Denoising of Panchromatic and Multispectral Satellite Images  

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

294

Offset detection in GPS coordinate time series  

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

295

Monitoring Forest Regrowth Using a Multi-Platform Time Series  

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

296

EarthShots: Satellite Images of Environmental Change  

NSDL National Science Digital Library

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

2001-01-12

297

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

NASA Astrophysics Data System (ADS)

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

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

2010-12-01

298

Numbers to Pictures: How Satellite Images are Created  

NSDL National Science Digital Library

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

299

Knowledge-Based Road Network Extraction on SPOT Satellite Images  

Microsoft Academic Search

Automated delineation of linear cultural structures can help to improve the classification of remotely-sensed images. This topic also provides an excellent testbed for knowledge-based computer vision research. In this paper, a road network extraction system, useful on SPOT satellite images, is described. By applying semantic model-fitting operators, an initial spatial segmentation is obtained. To analyse the resulting primal road network,

Johan Van Cleynenbreugel; Freddy Fierens; Paul Suetens; André Oosterlinck

1988-01-01

300

[Satellite Image of New Mexico Fires May 2000  

NSDL National Science Digital Library

In the aftermath of the New Mexico fires, several resources have been posted online. The first resource, from the National Oceanic and Atmospheric Administration (NOAA), is a color satellite image (.jpg format) of fires in New Mexico from May 17, including the Cerro Grande fire.

2000-01-01

301

Galileo's First Images of Jupiter and the Galilean Satellites  

Microsoft Academic Search

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

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

1996-01-01

302

Satellite Image Analysis for Disaster and Crisis-Management Support  

Microsoft Academic Search

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

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

2007-01-01

303

Detection of Urban Zones in Satellite Images Using Visual Words  

E-print Network

an efficient study and planning of urban growth. In addition, this information may help government agencies, it is essential to have efficient tools for automatic detection and segmentation of urban areas. Because1 Detection of Urban Zones in Satellite Images Using Visual Words Lior Weizman and Jacob Goldberger

Goldberger, Jacob

304

Evaluating fusion techniques for multi-sensor satellite image data  

SciTech Connect

Satellite image data fusion is a topic of interest in many areas including environmental monitoring, emergency response, and defense. Typically any single satellite sensor cannot provide all of the benefits offered by a combination of different sensors (e.g., high-spatial but low spectral resolution vs. low-spatial but high spectral, optical vs. SAR). Given the respective strengths and weaknesses of the different types of image data, it is beneficial to fuse many types of image data to extract as much information as possible from the data. Our work focuses on the fusion of multi-sensor image data into a unified representation that incorporates the potential strengths of a sensor in order to minimize classification error. Of particular interest is the fusion of optical and synthetic aperture radar (SAR) images into a single, multispectral image of the best possible spatial resolution. We explore various methods to optimally fuse these images and evaluate the quality of the image fusion by using K-means clustering to categorize regions in the fused images and comparing the accuracies of the resulting categorization maps.

Martin, Benjamin W [ORNL] [ORNL; Vatsavai, Raju [ORNL] [ORNL

2013-01-01

305

Detecting Forest Disturbance in the Pacific Northwest From MODIS Time Series Using Temporal Segmentation  

NASA Astrophysics Data System (ADS)

Changes to the land surface of the Earth are occurring at unprecedented rates with significant implications for surface energy balance and regional to global scale cycles of carbon and water. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua and Terra satellite platforms provide over 11 years of consistent, synoptic observations of the biosphere. New methods have recently emerged to analyze time series of remote sensing images, thereby providing ecologically important information about disturbance and succession over large regions. In particular, the Landtrendr algorithm was developed to characterize long-term trends, including punctual and gradual disturbance events and subsequent vegetation regrowth, in dense time series of Landsat imagery. While this approach has shown to be useful and robust in a wide range of ecosystems, its application is limited to areas with sufficient Landsat archive depth and relatively cloud-free periods. Additionally, the approach requires significant effort in atmospheric correction and normalization steps, increasing the cost for large-area application. Here we present an adaptation of the Landtrendr algorithm to an 11-year time series of MODIS Normalized BRDF-Adjusted Reflectance (NBAR) data to detect forest disturbance in the Northwest Forest Plan (NWFP) area of Washington, Oregon, and California. The NWFP area represents a dynamic zone of forest management with an active disturbance regime that includes insect defoliation, wildfires, and logging. This work aims to explore how the size and severity of disturbance events influence detection and characterization of such events using MODIS data. We sampled disturbance events across gradients of size and severity that occurred during the MODIS era (2000-present) using a high-quality database of forest disturbance information derived from Landsat. One-third of these disturbance records were used to calibrate the model using MODIS NBAR time series, and the remaining two-thirds were kept aside for assessment of model performance. The results demonstrate how the Landtrendr approach can be expanded for use at continental and global scales with data of moderate spatial resolution such as MODIS.

Sulla-Menashe, D. J.; Yang, Z.; Braaten, J.; Krankina, O. N.; Kennedy, R. E.; Friedl, M. A.

2011-12-01

306

a New Approach for Optical and SAR Satellite Image Registration  

NASA Astrophysics Data System (ADS)

Over the last years several research studies have shown the high geometric accuracy of high resolution radar satellites like TerraSARX. Due to this fact, the impact of high resolution SAR images for image registration has increased. An aim of high accuracy image registration is the improvement of the absolute geometric accuracy of optical images by using SAR images as references. High accuracy image registration is required for different remote sensing applications and is an on-going research topic. The registration of images acquired by different sensor types, like optical and SAR images, is a challenging task. In our work, a novel approach is proposed, which is a combination of the classical feature-based and intensity-based registration approaches. In the first step of the method, spatial features, here roundabouts, are detected in the optical image. In the second step, the detected features are used to generate SAR like roundabout templates. In the third step, the templates are matched with the corresponding parts of the SAR image by using an intensitybased matching process. The proposed method is tested for a pair of TerraSAR-X and QuickBird images and a pair of TerraSAR-X and WorldView-2 images of a suburban area. The results show that the proposed method offers an alternative approach compared to the common optical and SAR images registration methods and it can be used for the geometric accuracy improvement of optical images.

Merkle, N.; Müller, R.; Schwind, P.; Palubinskas, G.; Reinartz, P.

2015-03-01

307

Univariate time series forecasting algorithm validation  

NASA Astrophysics Data System (ADS)

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.

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

2014-12-01

308

Delay Differential Analysis of Time Series  

PubMed Central

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

Lainscsek, Claudia; Sejnowski, Terrence J.

2015-01-01

309

Time series analysis of barometric pressure data  

NASA Astrophysics Data System (ADS)

Time series of atmospheric pressure data, collected over a period of several years, were analysed to provide undergraduate students with educational examples of application of simple statistical methods of analysis. In addition to basic methods for the analysis of periodicities, a comparison of two forecast models, one based on autoregression algorithms, and the other making use of an artificial neural network, was made. Results show that the application of artificial neural networks may give slightly better results compared to traditional methods.

La Rocca, Paola; Riggi, Daniele; Riggi, Francesco

2010-05-01

310

Fractal interpolation of rain rate time series  

Microsoft Academic Search

Meteorological radar databases exist providing rain rate maps over areas with a sampling period of 2–15 min. Such two-dimensional, rain rate map time series would have wide application in the simulation of rain scatter and attenuation of millimeter-wave radio networks, if the sampling period were considerably shorter, i.e., of the order of 10 s or less. However, scanning a large

Kevin S. Paulson

2004-01-01

311

Unsupervised Feature Learning for High-Resolution Satellite Image Classification  

SciTech Connect

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

Cheriyadat, Anil M [ORNL

2013-01-01

312

High-Resolution Imaging of Asteroids/Satellites  

NASA Astrophysics Data System (ADS)

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.

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

313

Analysis of Polyphonic Musical Time Series  

NASA Astrophysics Data System (ADS)

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.

Sommer, Katrin; Weihs, Claus

314

Sliced Inverse Regression for Time Series Analysis  

NASA Astrophysics Data System (ADS)

In this thesis, general nonlinear models for time series data are considered. A basic form is x _{t} = f(beta_sp{1} {T}X_{t-1},beta_sp {2}{T}X_{t-1},... , beta_sp{k}{T}X_ {t-1},varepsilon_{t}), where x_{t} is an observed time series data, X_{t } is the first d time lag vector, (x _{t},x_{t-1},... ,x _{t-d-1}), f is an unknown function, beta_{i}'s are unknown vectors, varepsilon_{t }'s are independent distributed. Special cases include AR and TAR models. We investigate the feasibility applying SIR/PHD (Li 1990, 1991) (the sliced inverse regression and principal Hessian methods) in estimating beta _{i}'s. PCA (Principal component analysis) is brought in to check one critical condition for SIR/PHD. Through simulation and a study on 3 well -known data sets of Canadian lynx, U.S. unemployment rate and sunspot numbers, we demonstrate how SIR/PHD can effectively retrieve the interesting low-dimension structures for time series data.

Chen, Li-Sue

1995-11-01

315

Time-series animation techniques for visualizing urban growth  

USGS Publications Warehouse

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.

Acevedo, W.; Masuoka, P.

1997-01-01

316

A Multiscale Approach to InSAR Time Series Analysis  

NASA Astrophysics Data System (ADS)

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.

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

2009-12-01

317

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

E-print Network

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

Plaza, Antonio J.

318

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

NASA Technical Reports Server (NTRS)

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.

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

2013-01-01

319

GRAPHICAL MODELLING OF MULTIVARIATE TIME SERIES WITH LATENT VARIABLES  

E-print Network

in most analyses involving time series data the presence of latent variables that affect the measuredGRAPHICAL MODELLING OF MULTIVARIATE TIME SERIES WITH LATENT VARIABLES Michael Eichler Department Abstract. In time series analysis, inference about cause-effect relationships among multiple times series

Eichler, Michael

320

LAG SPACE ESTIMATION IN TIME SERIES MODELLING Cyril Goutte  

E-print Network

LAG SPACE ESTIMATION IN TIME SERIES MODELLING Cyril Goutte Department for Mathematical Modelling, for time series modelling. This is an im­ portant aspect of time series modelling, as it conditions'enon map, and on a real data set. 1. INTRODUCTION Let us assume that a time series is obtained from

Mosegaard, Klaus

321

TIME SERIES PREDICTABILITY Minglei Duan, B.S.  

E-print Network

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

Povinelli, Richard J.

322

SVM Kernels for Time Series Analysis Stefan Ruping  

E-print Network

problems of time series analysis, the forecasting of time series, can be very easily stated as a pureSVM Kernels for Time Series Analysis Stefan R¨uping CS Department, AI Unit, University of Dortmund, 44221 Dortmund, Germany, E-Mail stefan.rueping@uni- dortmund.de Abstract. Time series analysis

Morik, Katharina

323

Structure-Based Statistical Features and Multivariate Time Series Clustering  

Microsoft Academic Search

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

Xiaozhe Wang; Anthony Wirth; Liang Wang

2007-01-01

324

Multivariate Time Series Analysis in Corporate Decision-Making Application  

Microsoft Academic Search

In order to solve nonlinear, non-stationary and complex problem with the time series in practical production and life, a multiple regression model for time series analysis is used in this paper. By introducing the principle of multiple regression, the multivariate time series analysis model not only overcome random factors of the time series, but also consider the many factors affecting

Yatao Li; Fen Ying

2011-01-01

325

Methods for the Estimation of Missing Values in Time Series  

Microsoft Academic Search

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

David S. Fung

2006-01-01

326

Flood Identification from Satellite Images Using Artificial Neural Networks  

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

327

Graph - Based High Resolution Satellite Image Segmentation for Object Recognition  

NASA Astrophysics Data System (ADS)

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.

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

2014-11-01

328

Optimized satellite image compression and reconstruction via evolution strategies  

NASA Astrophysics Data System (ADS)

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.

Babb, Brendan; Moore, Frank; Peterson, Michael

2009-05-01

329

Spacecraft design project: High temperature superconducting infrared imaging satellite  

NASA Astrophysics Data System (ADS)

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.

1991-12-01

330

Spacecraft design project: High temperature superconducting infrared imaging satellite  

NASA Technical Reports Server (NTRS)

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.

1991-01-01

331

A Multiscale Approach to InSAR Time Series Analysis  

NASA Astrophysics Data System (ADS)

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.

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

2010-12-01

332

Random dynamical models from time series  

NASA Astrophysics Data System (ADS)

In this work we formulate a consistent Bayesian approach to modeling stochastic (random) dynamical systems by time series and implement it by means of artificial neural networks. The feasibility of this approach for both creating models adequately reproducing the observed stationary regime of system evolution, and predicting changes in qualitative behavior of a weakly nonautonomous stochastic system, is demonstrated on model examples. In particular, a successful prognosis of stochastic system behavior as compared to the observed one is illustrated on model examples, including discrete maps disturbed by non-Gaussian and nonuniform noise and a flow system with Langevin force.

Molkov, Y. I.; Loskutov, E. M.; Mukhin, D. N.; Feigin, A. M.

2012-03-01

333

Modeling noisy time series: Physiological tremor  

E-print Network

Empirical time series often contain observational noise. We investigate the effect of this noise on the estimated parameters of models fitted to the data. For data of physiological tremor, i.e. a small amplitude oscillation of the outstretched hand of healthy subjects, we compare the results for a linear model that explicitly includes additional observational noise to one that ignores this noise. We discuss problems and possible solutions for nonlinear deterministic as well as nonlinear stochastic processes. Especially we discuss the state space model applicable for modeling noisy stochastic systems and Bock's algorithm capable for modeling noisy deterministic systems.

J. Timmer

1998-05-08

334

Bermuda Atlantic Time-series Study  

NSDL National Science Digital Library

The Bermuda Atlantic Time-series study (BATS) was initiated to collect oceanographic data over significantly long time periods. At this website, researchers will find BATS and hydrostation data dealing with hydrographic, chemical, and biological parameters throughout the water column for sites in the Sargasso Sea. Visitors can learn about the first BATS station, Hydrostation S, which was initiated in 1954 by Dr. Henry M. Stommel and has been visited biweekly almost continuously ever since. The website also features numerous questions that the group has proposed dealing with the ocean's physical, geochemical, and biological realms.

335

Forecasting chaotic time series with genetic algorithms  

NASA Astrophysics Data System (ADS)

This paper proposes the use of genetic algorithms-search procedures, modeled on the Darwinian theories of natural selection and survival of the fittest-to find equations that describe the behavior of a time series. The method permits global forecasts of such series. Very little data are sufficient to utilize the method and, as a byproduct, these algorithms sometimes indicate the functional form of the dynamic that underlies the data. The algorithms are tested with clean as well as with noisy chaotic data, and with the sunspot series.

Szpiro, George G.

1997-03-01

336

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

E-print Network

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

Lonardi, Stefano

337

An Active Testing Model for Tracking Roads in Satellite Images  

Microsoft Academic Search

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

Donald Geman; Bruno Jedynak

1996-01-01

338

Simulations of Non-resolved, Infrared Imaging of Satellites  

Microsoft Academic Search

Simulations of near-infrared, non-resolved imaging of earth-orbiting satellites during nighttime and daytime were created to consider the feasibility of such observations. By using the atmospheric radiative transfer code MODTRAN (MODerate resolution atmospheric TRANsmission), we incorporate site-specific mean weather conditions for several possible locations. In general, the dominant effect to be modeled is the sky radiance, which has a strong dependence

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

2009-01-01

339

Mapping Vineyard Areas Using WORLDVIEW-2 Satellite Images  

NASA Astrophysics Data System (ADS)

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.

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

2011-12-01

340

Analysis of Decadal Vegetation Dynamics Using Multi-Scale Satellite Images  

NASA Astrophysics Data System (ADS)

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

Chiang, Y.; Chen, K.

2013-12-01

341

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

PubMed

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

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

2014-12-01

342

The Registration of UAV Down-Looking Aerial Images to Satellite Images with Image Entropy and Edges  

Microsoft Academic Search

\\u000a In this paper, we propose a novel and efficient image registration algorithm between high resolution satellite images and\\u000a UAV down-looking aerial images. The algorithm is achieved by a composite deformable template matching. To overcome the limitations\\u000a of environment changes and different sensors, and to remain image information, we fuse the image edge and entropy features\\u000a as image representation. According to

Baojie Fan; Yingkui Du; Linlin Zhu; Yandong Tang

2010-01-01

343

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

344

GOES-R ABI: Next Generation Satellite Imaging  

NSDL National Science Digital Library

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

2014-09-14

345

The geostationary operational environmental satellite /GOES/ imaging communication system  

NASA Technical Reports Server (NTRS)

The SMS/GOES Satellite obtains day and night weather information from synchronous geostationary orbit by means of (1) earth imaging, (2) collection of environmental data from ground based sensors, platforms, and (3) monitoring of the space environment. SMS-1 and SMS-2 have been in orbit for 17 months and 8 months, respectively, and are presently taking full earth disk images in the visible and infrared every 30 minutes. SMS-1 is positioned to cover the eastern portion of the U.S. while SMS-2 is positioned to cover the western portion. This paper provides a general overview of the imaging communication portions of the SMS/GOES, related to the image data encoding and transmission as well as the method of the data time multiplexing and the manner in which the scan line to line synchronization is achieved.

Baker, W. L.; Savides, J.

1975-01-01

346

The Mount Wilson Ca ii K Plage Index Time Series  

NASA Astrophysics Data System (ADS)

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.

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

2010-06-01

347

Robust multi-scale image matching for deriving ice surface velocity field from sequential satellite images  

Microsoft Academic Search

The cross-correlation-based image matching method has been widely used to derive ice surface motion information from sequential satellite images through tracking spatial displacements of surface features over time. However, this conventional method is not adequate for handling areas with a high velocity variation, in which case a large search window has to be specified in order to find the correct

Hongxing Liu; Lei Wang; Sheng-Jung Tang; Kenneth C. Jezek

2012-01-01

348

Robust multi-scale image matching for deriving ice surface velocity field from sequential satellite images  

Microsoft Academic Search

The cross-correlation-based image matching method has been widely used to derive ice surface motion information from sequential satellite images through tracking spatial displacements of surface features over time. However, this conventional method is not adequate for handling areas with a high velocity variation, in which case a large search window has to be specified in order to find the correct

Hongxing Liu; Lei Wang; Sheng-Jung Tang; Kenneth C. Jezek

2011-01-01

349

WFPC2 Images of the Icy Galilean Satellites  

NASA Astrophysics Data System (ADS)

We have observed Europa, Ganymede and Callisto with the Wide Field Planetary Camera 2 (WFPC2) aboard the Hubble Space Telescope (HST). Images were obtained in six filters ranging from the ultraviolet to the near-infrared (effective wavelengths extend from lambda = 286 nm to lambda = 954 nm). The ultraviolet filters sample wavelengths not observable by either Voyager or Galileo imaging experiments. Observations made with the Planetary Camera have a scale of approximately 150 km per pixel. Leading and trailing hemispheres of each satellite were observed. HST images are compared with Voyager image mosaics projected onto a sphere with Minnaert limb-darkening and convolution to the same resolution as the HST images. Nearly all observed albedo features correspond to features in Voyager images, with a few notable exceptions. Small differences (on the order of 10%) are likely due to errors in the photometric calibration of Voyager mosaics and/or errors in the assumed photometric function for particular regions. The greatest differences between Voyager and HST images occur on Ganymede where HST images show a higher than expected albedo at visible and near-infrared wavelengths over a region extending from about 45deg S to the South pole at 275deg W longitude. Europa at lambda =675 and 954 nm (wavelengths not observed by Voyager) appears nearly featureless, in contrast to its appearance at shorter wavelengths. We compare spectrophotometry of these and other selected regions to search for evidence of compositional effects that may account for these observed differences.

Gilmore, D. M.; Noll, K. S.; Sartoretti, P.; McGrath, M.; Buratti, B.; Domingue, D.

1996-09-01

350

Ice Sheet Change Detection by Satellite Image Differencing  

NASA Technical Reports Server (NTRS)

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

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

2010-01-01

351

Using entropy to cut complex time series  

NASA Astrophysics Data System (ADS)

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

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

2013-03-01

352

Fractal fluctuations in cardiac time series  

NASA Technical Reports Server (NTRS)

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

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

1999-01-01

353

Normalizing the causality between time series  

E-print Network

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 ...

Liang, X San

2015-01-01

354

OPTIMAL TIME-SERIES SELECTION OF QUASARS  

SciTech Connect

We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly et al., we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics-one describing the fit confidence and the other describing the probability of a false alarm-which can be tuned, a priori, to achieve high quasar detection fractions (99% completeness with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as {approx}<3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5 < z < 3, where selection by color is extremely inefficient. This represents 1875 new quasars in a 290 deg{sup 2} field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discuss the utility of the method at high redshift and in the regime of noisy and sparse data. Our time-series selection complements well-independent selection based on quasar colors and has strong potential for identifying high-redshift quasars for Baryon Acoustic Oscillations and other cosmology studies in the LSST era.

Butler, Nathaniel R.; Bloom, Joshua S. [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States)

2011-03-15

355

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

Atmospheric Science Data Center

MISR Browse Images: Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) ... visual overview of the region observed during the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaign. ...

2013-03-22

356

Eyjafjallajökull Magma Monitoring From Time Series Data of TerraSAR-X  

NASA Astrophysics Data System (ADS)

The 2010 eruption of Eyjafjallajökull volcano and the resulting ash cloud highlights the need for research on Icelandic volcanoes. While most of the interest was sparked by the closure of air space over much of Europe, the potentially life-threatening consequences for the people living in the area directly beneath the volcano alone are incentive enough to better understand volcanic processes. Katla volcano is directly adjacent to Eyjafjallajökull volcano, and historically has been more active and produced larger eruptions. The consequences of an eruption at Katla could therefore be much more severe than those witnessed this spring at Eyjafjallajökull. Timely prediction of an impending eruption would greatly reduce the severity of these consequences, which is one of the ultimate goals of volcanic research. After a period of quiescence since a sill intrusion in 1999-2000, a subtle deformation signal was again detected at Eyjafjallajökull, beginning in the summer of 2009, at a continuous GPS station on the southern flank. We immediately began tasking the TerraSAR-X satellite to acquire SAR images every 11 days, giving a time series of SAR images prior to the eruption with unprecedented temporal sampling (although interrupted by snow during the winter). Here we present the results of InSAR time series analysis of this data set. After correcting for DEM errors and reduction of atmospheric signal we find a number of signals that we tentatively interpret as a combination of magma movement, elastic response to snow melting and landsliding.. The mean velocities from June 2009 to February 2010 show a subsidence pattern in the southeastern part of the volcano flanks and uplift in the southwest. However, such a different deformation signal between two areas so close could also imply atmospheric, topographic or phase unwrapping errors. To assess the contribution to the deformation signal from these possible error sources, we examined time series of displacements during this period for various areas. The results show a largely linear behavior between nearby areas from 18th June 2009 to 04 February 2010, followed by an excursion in the deformation signal during 17th October 2009. Significantly, the signal is smooth in time, implying that it is not due to atmospheric contamination. The deformation seems consistent with the continuous GPS station THEY, and can indeed indicate magma migration. However, further work is required to reliably separate out the deformation signals that are not related to volcanic processes.

Martins, J. C.; Spaans, K.; Hooper, A. J.; Sigmundsson, F.; Feigl, K.

2010-12-01

357

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

E-print Network

.e., parametrized) time series metric to cover both behav- ior and values proximities. The metric's parameters may are then applied on the fitted basis coefficients (e.g., Garcia-Escudero & Gordaliza (2005), Serban & Wasserman (2005)). A sec- ond class of works proposes new heuristics starting generally with the time hal-00741944

Paris-Sud XI, Université de

358

Satellite Images of Israel and the Middle East  

NSDL National Science Digital Library

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

359

Spatial Data Exploring by Satellite Image Distributed Processing  

NASA Astrophysics Data System (ADS)

Our society needs and environmental predictions encourage the applications development, oriented on supervising and analyzing different Earth Science related phenomena. Satellite images could be explored for discovering information concerning land cover, hydrology, air quality, and water and soil pollution. Spatial and environment related data could be acquired by imagery classification consisting of data mining throughout the multispectral bands. The process takes in account a large set of variables such as satellite image types (e.g. MODIS, Landsat), particular geographic area, soil composition, vegetation cover, and generally the context (e.g. clouds, snow, and season). All these specific and variable conditions require flexible tools and applications to support an optimal search for the appropriate solutions, and high power computation resources. The research concerns with experiments on solutions of using the flexible and visual descriptions of the satellite image processing over distributed infrastructures (e.g. Grid, Cloud, and GPU clusters). This presentation highlights the Grid based implementation of the GreenLand application. The GreenLand application development is based on simple, but powerful, notions of mathematical operators and workflows that are used in distributed and parallel executions over the Grid infrastructure. Currently it is used in three major case studies concerning with Istanbul geographical area, Rioni River in Georgia, and Black Sea catchment region. The GreenLand application offers a friendly user interface for viewing and editing workflows and operators. The description involves the basic operators provided by GRASS [1] library as well as many other image related operators supported by the ESIP platform [2]. The processing workflows are represented as directed graphs giving the user a fast and easy way to describe complex parallel algorithms, without having any prior knowledge of any programming language or application commands. Also this Web application does not require any kind of install for what the house-hold user is concerned. It is a remote application which may be accessed over the Internet. Currently the GreenLand application is available through the BSC-OS Portal provided by the enviroGRIDS FP7 project [3]. This presentation aims to highlight the challenges and issues of flexible description of the Grid based processing of satellite images, interoperability with other software platforms available in the portal, as well as the particular requirements of the Black Sea related use cases.

Mihon, V. D.; Colceriu, V.; Bektas, F.; Allenbach, K.; Gvilava, M.; Gorgan, D.

2012-04-01

360

Wavlet analysis of covariance with application to atmospheric time series  

E-print Network

basis, where each wavelet cross-correlation series is associated with a speci c physical time scale and correlation #12;4 between two time seriWavlet analysis of covariance with application to atmospheric time series Brandon Whichter Peter

Washington at Seattle, University of

361

V-uniform ergodicity of threshold autoregressive nonlinear time series  

E-print Network

We investigate conditions for the ergodicity of threshold autoregressive time series by embedding the time series in a general state Markov chain and apply a FosterLyapunov drift condition to demonstrate ergodicity of the Markov chain. We...

Boucher, Thomas Richard

2004-09-30

362

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

E-print Network

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

363

Learning to transform time series with a few examples  

E-print Network

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 ...

Rahimi, Ali, 1976-

2006-01-01

364

Path planning on satellite images for unmanned surface vehicles  

NASA Astrophysics Data System (ADS)

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.

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

2015-03-01

365

Multiscale Representations for Fast Pattern Matching in Stream Time Series  

Microsoft Academic Search

Similarity-based time-series retrieval has been a subject of long-term study due to its wide usage in many applications, such as financial data analysis, weather data forecasting, and multimedia data retrieval. Its original task was to find those time series similar to a pattern (query) time-series data, where both the pattern and data time series are static. Recently, with an increasing

Xiang Lian; Lei Chen; Jeffrey Xu Yu; Jinsong Han; Jian Ma

2009-01-01

366

A Quasi-Global Precipitation Time Series for Drought Monitoring  

E-print Network

," SERVIR award #NNH12AU22I for "A Long Time-Series Indicator of Agricultural Drought for the Greater HornA Quasi-Global Precipitation Time Series for Drought Monitoring Data Series 832 U.S. Department in the Early Warning Explorer. #12;A Quasi-Global Precipitation Time Series for Drought Monitoring By Chris C

Torgersen, Christian

367

Wavelet analysis of covariance with application to atmospheric time series  

E-print Network

1 Wavelet analysis of covariance with application to atmospheric time series Brandon Whitcher; 2 Abstract. Multi­scale analysis of univariate time series has appeared in the literature at an ever increasing rate. Here we introduce the multi­scale analysis of covariance between two time series using

Percival, Don

368

Multifractal measures of time series: curvature surfaces of f(?) curves  

NASA Astrophysics Data System (ADS)

The following sections are included: * Introduction * Reading time series * Four bin multifractals * Binomial multifractals * Finding the f(?) curvature from time series * Building time series with prescribed memory * Transition matrix variations under moving bin boundaries * Interpreting the curvature of the f(?) curves * Curvature surfaces of the data * Interpreting the curvature plots * Conclusion * Appendix * Bibliography

Martino, William; Frame, Michael

2015-03-01

369

Time Series Forecasting using Distribution Enhanced Linear Regression  

E-print Network

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

Bailey, James

370

Time Series Retrieval Using Multiple Reduced Muhammad Marwan Muhammad Fuad  

E-print Network

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

Boyer, Edmond

371

Time series analysis in R D G Rossiter  

E-print Network

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

Rossiter, D G "David"

372

Nonparametric multistep-ahead prediction in time series analysis  

Microsoft Academic Search

We consider the problem of multistep-ahead prediction in time series analysis by using nonparametric smoothing techniques. Forecasting is always one of the main objectives in time series analysis. Research has shown that non-linear time series models have certain advantages in multistep-ahead forecasting. Traditionally, nonparametric \\

Rong Chen; Lijian Yang; Christian Hafner

2004-01-01

373

Math 8444 Time Series Methods Spring 2014 Instructor: Jesse Frey  

E-print Network

and by appointment Textbook: Time Series Analysis and Its Applications: With R Examples, by Shumway and Stoffer processes, Forecasting, Diagnostic techniques, Seasonal time series models, Classical decomposition analysis using the methods from the course. Your data set should consist of one or more related time series

Frey, Jesse C.

374

Time Series Forecasting using Sparse Grids Jochen Garcke1  

E-print Network

historical exchange data of several currencies. Keywords: time series analysis, forecasting, machine learningTime Series Forecasting using Sparse Grids Jochen Garcke1 , Thomas Gerstner2 and Michael Griebel1 1@math.uni-frankfurt.de Abstract We present a machine learning approach for the forecasting of time series using the sparse grid

Sminchisescu, Cristian

375

Subsequence Matching on Structured Time Series Data Northeastern University  

E-print Network

. INTRODUCTION Modeling and analysis of time series stream data is a rich and rapidly growing research field, or tracking corporate business metrics. Analysis of time series stream data is widely used for manySubsequence Matching on Structured Time Series Data Huanmei Wu Northeastern University maggiewu

Kaeli, David R.

376

Bayesian Inference on Latent Structure in Time Series  

Microsoft Academic Search

SUMMARY A range of developments in Bayesian time series modelling in recent years has focussed on issues of identifying latent structure in time series. This has led to new uses and interpretations of existing theory for latent process decompositions of dynamic models, and to new models for univariate and multi- variate time series. This article draws together concepts and modelling

Omar Aguilar; Gabriel Huerta; Raquel Prado; Mike West

1999-01-01

377

Math 8444 Time Series Methods Spring 2012 Instructor: Jesse Frey  

E-print Network

and by appointment Textbook: Time Series Analysis and Its Applications: With R Examples, by Shumway and Stoffer related time series, and your analysis should include model fitting, model checking, and forecasting. More, Autoregressive processes, ARMA processes, Forecasting, Diagnostic techniques, Seasonal time series models

Frey, Jesse C.

378

FINDING OR NOT FINDING RULES IN TIME SERIES  

E-print Network

& Shimada, 2002). While traditional time series analysis focuses on modeling and forecasting, data mining 37 38 39 40 FINDING OR NOT FINDING RULES IN TIME SERIES Jessica Lin and Eamonn Keogh ABSTRACT Given the recent explosion of interest in streaming data and online algorithms, clustering of time series

Lin, Jessica

379

Meteorological Time Series Modeling Using an Adaptive Gene Expression Programming  

E-print Network

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

Fernandez, Thomas

380

A forecasting procedure for nonlinear autoregressive time series models  

Microsoft Academic Search

Forecasting for nonlinear time series is an important topic in time series analysis. Existing numerical algorithms for multi-step-ahead forecasting ignore accuracy checking, alternative Monte Carlo methods are also computationally very demanding and their accuracy is difficult to control too. In this paper a numerical forecasting procedure for nonlinear autoregressive time series models is proposed. The forecasting procedure can be used

Yuzhi Cai

2005-01-01

381

Characteristic-Based Clustering for Time Series Data  

Microsoft Academic Search

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

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

2006-01-01

382

Time series analysis of a Web search engine transaction log  

Microsoft Academic Search

In this paper, we use time series analysis to evaluate predictive scenarios using search engine transactional logs. Our goal is to develop models for the analysis of searchers' behaviors over time and investigate if time series analysis is a valid method for predicting relationships between searcher actions. Time series analysis is a method often used to understand the underlying characteristics

Ying Zhang; Bernard J. Jansen; Amanda Spink

2009-01-01

383

Large shocks in U.S. macroeconomic time series: 18601988  

E-print Network

for time series analysis, therefore it is important to detect them, estimate their effects and undertakeLarge shocks in U.S. macroeconomic time series: 1860­1988 Olivier DARN� and Amélie CHARLES Abstract.S. macroeconomic time series on the period 1860­1988, using outlier methodology. We show that most of these shocks

Boyer, Edmond

384

Short-term load forecasting with chaos time series analysis  

Microsoft Academic Search

This paper presents a new approach to short-term load forecasting in power systems. The proposed method makes use of chaos time series analysis that is based on deterministic chaos to capture characteristics of complicated load behaviour. Deterministic chaos allows us to reconstruct a time series and determine the number of input variables. This paper describes chaos time series analysis of

Hiroyuki Mori; Shouichi Urano

1996-01-01

385

Improved FMRI Time-series Registration Using Probability Density Priors  

E-print Network

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

Fessler, Jeffrey A.

386

Volatility of linear and nonlinear time series Tomer Kalisky,1  

E-print Network

with the same two-point correlations. Based on these results we propose a simple model that generates multifractal time series is generated by multiplying a long-range correlated time series that representsVolatility of linear and nonlinear time series Tomer Kalisky,1 Yosef Ashkenazy,2 and Shlomo Havlin1

Ashkenazy, Yossi "Yosef"

387

Financial time series: A physics perspective  

NASA Astrophysics Data System (ADS)

Physicists in the last few years have started applying concepts and methods of statistical physics to understand economic phenomena. The word ``econophysics'' is sometimes used to refer to this work. One reason for this interest is the fact that Economic systems such as financial markets are examples of complex interacting systems for which a huge amount of data exist and it is possible that economic problems viewed from a different perspective might yield new results. This article reviews the results of a few recent phenomenological studies focused on understanding the distinctive statistical properties of financial time series. We discuss three recent results-(i) The probability distribution of stock price fluctuations: Stock price fluctuations occur in all magnitudes, in analogy to earthquakes-from tiny fluctuations to very drastic events, such as market crashes, eg., the crash of October 19th 1987, sometimes referred to as ``Black Monday''. The distribution of price fluctuations decays with a power-law tail well outside the Lévy stable regime and describes fluctuations that differ by as much as 8 orders of magnitude. In addition, this distribution preserves its functional form for fluctuations on time scales that differ by 3 orders of magnitude, from 1 min up to approximately 10 days. (ii) Correlations in financial time series: While price fluctuations themselves have rapidly decaying correlations, the magnitude of fluctuations measured by either the absolute value or the square of the price fluctuations has correlations that decay as a power-law and persist for several months. (iii) Correlations among different companies: The third result bears on the application of random matrix theory to understand the correlations among price fluctuations of any two different stocks. From a study of the eigenvalue statistics of the cross-correlation matrix constructed from price fluctuations of the leading 1000 stocks, we find that the largest 5-10% of the eigenvalues and the corresponding eigenvectors show systematic deviations from the predictions for a random matrix, whereas the rest of the eigenvalues conform to random matrix behavior-suggesting that these 5-10% of the eigenvalues contain system-specific information about correlated behavior. .

Gopikrishnan, Parameswaran; Plerou, Vasiliki; Amaral, Luis A. N.; Rosenow, Bernd; Stanley, H. Eugene

2000-06-01

388

An ozone time series, 1979-2008, based on SBUV(/2) and occultation data  

NASA Astrophysics Data System (ADS)

A high-quality zonal, monthly-mean ozone time series spanning 1979-2008, based on SBUV (Solar Backscatter UltraViolet) and SBUV2 data, has been constructed. Corrections to the individual SBUV(/2) instruments, determined by examining their differences with coincident measurements from the SAGE (Satellite Aerosol and Gas Experiment) I, SAGE II, and ACE (Atmospheric Chemistry Experiment) solar occultation instruments, have been applied. This dataset combines the high spatial sampling of SBUV with the established accuracy of occultation measurements. A preliminary analysis of this time series will be presented.

McLinden, C. A.; Fioletov, V.; McElroy, C. T.

2008-12-01

389

Preliminary Experiments on Image Processing for Satellite Orbital Maintenance  

Microsoft Academic Search

Rescuing a satellite once it has been launched is very difficult. Because we can only obtain information about a satellite by telemetry from the satellite itself, we cannot obtain any more information once a satellite has failed. Furthermore, space debris reentering the atmosphere is becoming a significant problem because the number of satellites is increasing. Therefore, we are currently studying

Shinichi Kimura; Makoto Takeuchi; Yasufumi Nagai; Heihachiro Kamimura; Satomi Kawamoto; Fuyuhito Terui; Hiroshi Yamamoto; Shin-ichiro Nishida; Shinichi Nakasuka; Shinichi Ukawa; Hidekazu Hashimoto; Nobuhiro Takahashi; Keisuke Yoshihara

390

Exploratory joint and separate tracking of geographically related time series  

NASA Astrophysics Data System (ADS)

Target tracking techniques have usually been applied to physical systems via radar, sonar or imaging modalities. But the same techniques - filtering, association, classification, track management - can be applied to nontraditional data such as one might find in other fields such as economics, business and national defense. In this paper we explore a particular data set. The measurements are time series collected at various sites; but other than that little is known about it. We shall refer to as the data as representing the Megawatt hour (MWH) output of various power plants located in Afghanistan. We pose such questions as: 1. Which power plants seem to have a common model? 2. Do any power plants change their models with time? 3. Can power plant behavior be predicted, and if so, how far to the future? 4. Are some of the power plants stochastically linked? That is, do we observed a lack of power demand at one power plant as implying a surfeit of demand elsewhere? The observations seem well modeled as hidden Markov. This HMM modeling is compared to other approaches; and tests are continued to other (albeit self-generated) data sets with similar characteristics. Keywords: Time-series analysis, hidden Markov models, statistical similarity, clustering weighted

Balasingam, Balakumar; Willett, Peter; Levchuk, Georgiy; Freeman, Jared

2012-05-01

391

Hydroxyl time series and recirculation in turbulent nonpremixed swirling flames  

SciTech Connect

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

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

2006-10-15

392

A New SBUV Ozone Profile Time Series  

NASA Technical Reports Server (NTRS)

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

McPeters, Richard

2011-01-01

393

Extraction of stochastic dynamics from time series  

NASA Astrophysics Data System (ADS)

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

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

2012-07-01

394

Evolutionary factor analysis of replicated time series.  

PubMed

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

Motta, Giovanni; Ombao, Hernando

2012-09-01

395

Images from the GOES 8 satellite were used along with auxiliary information such as snow cover to produce an  

E-print Network

ABSTRACT Images from the GOES 8 satellite were used along with auxiliary information such as snow through 2002[1]. Both global and beam irradiance values were derived from the satellite images and diffuse from the satellite images. This article presents new and independent tests of this satellite database

Oregon, University of

396

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

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

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

2013-12-01

397

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

398

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

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

399

Concepts for on-board satellite image registration, volume 1  

NASA Technical Reports Server (NTRS)

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.

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

1980-01-01

400

Advanced DTM Generation from Very High Resolution Satellite Stereo Images  

NASA Astrophysics Data System (ADS)

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.

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

2015-03-01

401

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

E-print Network

and Forecasting. · Durbin and Koopman (2001). Time Series Analysis by State- Space Models. · Embrechts Time Series 1. Introduction 2. Examples 3. Linear processes 3.1 Preliminaries 3.2 Wold Decomposition 3PhySto Workshop 9/04 Part II: Time Series Models in Finance 1. Classification of white noise 2. Examples 3

402

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

PubMed

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. PMID:25122353

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

2014-07-01

403

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

NASA Astrophysics Data System (ADS)

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.

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

2014-07-01

404

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

NASA Technical Reports Server (NTRS)

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

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

2011-01-01

405

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

406

Spectrophotometric Time Series of ? Carinae's Great Eruption  

NASA Astrophysics Data System (ADS)

? Carinae (? Car) is one of the most massive binaries in the Milky Way teDH97, and its expanding circumstellar nebula has been studied in detail teSmith06. It was seen as the second brightest star in the sky during its 1800s ``Great Eruption'' (GE), but only visual estimates of its brightness were recorded teSF11. In 2011 we discovered several light echoes (LEs) which appear to be from the 1838-1858 ? Car eruptions teRest12_eta. Subsequent spectroscopic follow-up revealed that its outburst spectral type was most similar to those of G-type supergiants, rather than reported LBV outburst spectral types of F-type (or earlier) teRest12_eta. These differences between the GE and the extragalactic transients presumed to be its analogues raise questions about traditional scenarios for the outburst and warrant continued monitoring of its echoes. We propose to obtain a spectrophotometric time series of the GE from different directions, allowing the original eruption of ? Car to be studied as a function of time as well as latitude. A detailed spectroscopic study of the LEs of ? Car would help us understand (episodic) mass- loss in the most massive evolved stars and draw a connection to the most energetic core-collapse supernovae that are being discovered in synoptic surveys. The recent observation (September 2012), of the SN impostor SN 2009ip transitioning to a real SN explosion teSmith12 highlights the importance of studying ? Car's GE in great detail.

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

2013-08-01

407

Orthorectified image mosaic of Antarctica from 1963 Argon satellite photography: image processing and glaciological applications  

Microsoft Academic Search

Using the state?of?the?art digital imaging technology, extended block adjustment, orthorectification and mosaicking, individual Declassified Intelligence Satellite Argon photographic images are precisely assembled into a map quality mosaic of coastal Antarctica. The geometric accuracy of the mosaic is estimated to be approximately equivalent to the original resolution of the Argon photography, which is about 140 m. We compare the Argon mosaic with

K. Kim; K. C. Jezek; H. Liu

2007-01-01

408

Imaging Geostationary Satellites with a Common-Mount Interferometer: Image Quality and Fringe Tracking  

NASA Astrophysics Data System (ADS)

Imaging geostationary satellites is difficult because they are both too large and too small. They are too small to resolve with existing ground-based single telescopes, and they are too large (and too faint) to resolve with existing ground-based interferometers. Earth-rotation synthesis also does not work with geostationary satellites. We have designed a common-mount telescopes which we believe is the right instrument for geostationary satellite imaging, and described it previously in a number of publications (e.g. AMOS papers Mozurkewich et al. 2011, Jorgensen et al. 2011, Schmitt et al. 2011, and others). In this paper we will provide an overview of the instrument and explore its capabilities in more detail, using a typical geostationary satellite as an example. Specifically we will look at the fringe-tracking capability which is required for phase measurement and thus imaging. We will also look at the required integration time and its relationship to fringe-tracking capability and image quality.

Jorgensen, A.; Schmitt, H.; Mozurkewich, D.; Armstrong, J. T.; Hindsley, R. B.; Baines, E. K.

2012-09-01

409

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

E-print Network

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

Kovacic, Stanislav

410

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

NASA Astrophysics Data System (ADS)

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.

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

2014-02-01

411

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

NASA Astrophysics Data System (ADS)

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.

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

2012-10-01

412

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

E-print Network

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

Frank, Thomas D.

413

Time-series model of channel migration  

NASA Astrophysics Data System (ADS)

Channel migration is an important process for creating a mosaic of aquatic and riparian habitats in alluvial river corridors. Historical analysis and numerical modeling of channel migration have been limited by their conceptual basis that forcing by streamflow can be represented in terms of the net geomorphic effect over an interval of time without regard for intra-interval dynamics. A reach-scale model of channel migration was developed incorporating non-linear empirical functions of streamflow and vegetation growth to account for the area of new and abandoned active channel and lateral changes in its centerline on a daily basis. The time-series model addresses four significant issues in channel migration analysis: temporal gaps in the availability of channel form data; the legacy of high flows that persist in channel form, re-occupation of the valley bottom by the active channel and temporal scaling of floodplain turnover, and attribution of changes in channel processes to flood regulation. The model was applied to the middle Green River in western Washington for 1936 to 2002, which spans 26 years of unregulated high flows and 41 years of flood regulation. The channel centerline migrated laterally 104 m across the valley bottom and channel migration rather than widening or avulsion created most (74 to 100 percent) of the new active channel during this period. Despite a contraction in the simulated median active channel width from 90 m during 1936-1961 to 69 m during 1962-2002, channel migration continued to create new channel area (27 m2/m of valley length) after flood regulation began. The channel frequently re-occupied areas of the valley bottom but, nonetheless, progressively migrated over multiple decades. A power function of cumulative discharge since the day of initial channel location represented channel migration over the range of intervals between aerial photography (2 to 66 years) and, thus, serves as a way to scale channel migration over time. The model can be used prospectively as a tool for water managers to simulate the impacts of different dam operations on habitat creation in the middle Green River.

Konrad, C. P.

2009-12-01

414

Detecting inhomogeneities in pan evaporation time series  

NASA Astrophysics Data System (ADS)

There is increasingly growing demand for evaporation data for studies of surface water and energy fluxes, especially for studies which address the impacts of global warming. To serve this purpose, a homogeneous evaporation data are necessary. This paper describes the use of two tests for detecting and adjusting discontinuities in Class A pan evaporation time series for 28 stations across Australia, and illustrates the benefit of using corrected records in climate studies. The two tests being the bivariate test of Maronna and Yohai (1978), also known as the Potter method (WMO 2003), and the RHTest of Wang and Feng (2004). Overall, 58 per cent of the inhomogeneities detected by the bivariate test were also identified by the RHTest. The fact that the other 42 per cent of inhomogeneities were not consistent