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1

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

2

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

Microsoft Academic Search

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

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

2007-01-01

3

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

4

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

5

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

NASA Technical Reports Server (NTRS)

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

6

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

PubMed

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

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

2015-03-01

7

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

8

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

9

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

10

RESEARCH ARTICLE Time series analysis of infrared satellite data for detecting  

E-print Network

RESEARCH ARTICLE Time series analysis of infrared satellite data for detecting thermal anomalies necessary for the time series analysis of the hybrid algorithm. The improved performance of the new precursors to larger eruptions. Keywords MODIS . Time series analysis . MODVOLC . GOES . Kilauea volcano

Wright, Robert

11

EARSeL eProceedings x, issue/year 1 USE OF INTRA-ANNUAL SATELLITE IMAGERY TIME-SERIES FOR LAND  

E-print Network

EARSeL eProceedings x, issue/year 1 USE OF INTRA-ANNUAL SATELLITE IMAGERY TIME-SERIES FOR LAND Automatic image classification often fails at separating a large number of land cover classes present a study exploring the usefulness of intra-annual satellite images time-series for automatic land

Gonçalves, Paulo

12

On Fire regime modelling using satellite TM time series  

NASA Astrophysics Data System (ADS)

Wildfires can cause an environment deterioration modifying vegetation dynamics because they have the capacity of changing vegetation diversity and physiognomy. In semiarid regions, like the northwestern Patagonia, fire disturbance is also important because it could impact on the potential productivity of the ecosystem. There is reduction plant biomass and with that reducing the animal carrying capacity and/or the forest site quality with negative economics implications. Therefore knowledge of the fires regime in a region is of great importance to understand and predict the responses of vegetation and its possible effect on the regional economy. Studies of this type at a landscape level can be addressed using GIS tools. Satellite imagery allows detect burned areas and through a temporary analysis can be determined to fire regime and detecting changes at landscape scale. The study area of work is located on the east of the city of Bariloche including the San Ramon Ranch (22,000 ha) and its environs in the ecotone formed by the sub Antarctic forest and the patagonian steppe. We worked with multiespectral Landsat TM images and Landsat ETM + 30m spatial resolution obtained at different times. For the spatial analysis we used the software Erdas Imagine 9.0 and ArcView 3.3. A discrimination of vegetation types has made and was determined areas affected by fires in different years. We determined the level of change on vegetation induced by fire. In the future the use of high spatial resolution images combined with higher spectral resolution will allows distinguish burned areas with greater precision on study area. Also the use of digital terrain models derived from satellite imagery associated with climatic variables will allows model the relationship between them and the dynamics of vegetation.

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

2009-04-01

13

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

14

Surface deformation time series and source modeling for a volcanic complex system based on satellite wide swath and image mode interferometry: The Lazufre system, central Andes  

Microsoft Academic Search

The variable spatio-temporal scales of Earth's surface deformation in potentially hazardous volcanic areas pose a challenge for observation and assessment. Here we used Envisat data acquired in Wide Swath Mode (WSM) and Image Mode (IM) from ascending and descending geometry, respectively, to study time-dependent ground uplift at the Lazufre volcanic system in Chile and Argentina. A least-squares adjustment was performed

Jan Anderssohn; Mahdi Motagh; Thomas R. Walter; Matthias Rosenau; Hermann Kaufmann; Onno Oncken

2009-01-01

15

International Journal of Geoinformatics, Vol. 1, No. 1, March 2005 Time Series Processing of MODIS Satellite  

E-print Network

on the processing of time series of MODIS NDVI/EVI and LST satellite data in a Geographical Information System (GIS). The required data preparations for the integration of MODIS data in GIS is described with focus with an outlier detector to eliminate originally undetected cloud pixels. Further analysis of time series

Neteler, Geogr. Markus - Fondazione Edmund Mach

16

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

17

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

18

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

Microsoft Academic Search

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

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

2011-01-01

19

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

20

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

21

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

22

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

23

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

24

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

25

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

26

Advanced tools for astronomical time series and image analysis  

NASA Astrophysics Data System (ADS)

The algorithms described here, which I have developed for applications in X-ray and ?-ray astronomy, will hopefully be of use in other ways, perhaps aiding in the exploration of modern astronomy's data cornucopia. The goal is to describe principled approaches to some ubiquitous problems, such as detection and characterization of periodic and aperiodic signals, estimation of time delays between multiple time series, and source detection in noisy images with noisy backgrounds. The latter problem is related to detection of clusters in data spaces of various dimensions. A goal of this work is to achieve a unifying view of several related topics: signal detection and characterization, cluster identification, classification, density estimation, and multivariate regression. In addition to being useful for analysis of data from space-based and ground-based missions, these algorithms may be a basis for a future automatic science discovery facility, and in turn provide analysis tools for the Virtual Observatory. This chapter has ties to those by Larry Bretthorst, Tom Loredo, Alanna Connors, Fionn Murtagh, Jim Berger, David van Dyk, Vicent Martinez & Enn Saar.

Scargle, Jeffrey D.

27

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

NASA Astrophysics Data System (ADS)

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

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

2014-06-01

28

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

NASA Astrophysics Data System (ADS)

The characterization of vegetation reaction to disturbance is of primary importance since changes in the status or types of vegetation play an active role in ecological processes (such as productivity level, creation of altered patches, modification in vegetation structure and shifts in vegetation cover composition), as well as in land surface processes (such as surface energy, water balance, carbon cycle). The assessment of disturbance impacts on ecological resources requires investigations performed at different temporal and spatial scales, from local up to regional level. In such a context, satellite technologies can be profitably used for investigating the dynamics of vegetation after disturbance at different temporal and spatial scales; although, dynamical processes induced by disturbance are very difficult to study since they affect the complex soil-surface-atmosphere system, due to the existence of feedback mechanisms involving human activity, ecological patterns and different subsystems of climate. In this study, a time series of normalized difference vegetation index (NDVI) data derived from SPOT-VEGETATION was used to examine the recovery characteristics of drought and fire affected vegetation in some test areas of the Mediterranean ecosystems of Southern Italy. The vegetation indices operate by contrasting intense chlorophyll pigment absorption in the red against the high reflectance of leaf mesophyll in the near infrared. The simplest form of vegetation index is simply a ratio between two digital values from these two spectral bands. The most widely used index is the well-known normalized difference vegetation index NDVI = [NIR-R]/ [NIR+R]. The normalization of the NDVI reduces the effects of variations caused by atmospheric contaminations. High values of the vegetation index identify pixels covered by substantial proportions of healthy vegetation. NDVI is indicative of plant photosynthetic activity and has been found to be related to the green leaf area index and the fraction of photosynthetically active radiation absorbed by vegetation. Therefore variations in NDVI values become indicative of variations in vegetation composition and dynamics. In this study, we analyze the temporal series from 1998 to 2005 of NDVI satellite SPOT VEGETATION data acquired for a shrubland test site In order to eliminate the phenological fluctuations, for each decadal composition of each pixel, we focused on the departure NDVId = [NDVI - ]/?, where is the decadal mean and ? is the decadal standard deviation. The decadal mean and the standard deviation were calculated for each decade, e.g. 1st decade of January, by averaging over all years in the record. We analyzed both: 1) Post-disturbance NDVI spatial patterns on each image date were compared to the pre-disturbace pattern to determine the extent to which the pre-disturbance pattern was re-established, and the rate of this recovery. 2) time variation of NDVI from 1998 to 2005 of two pixels for the disturbance affected and disturbance unaffected areas. Results show the ability of NDVI time series to capture the different impacts/effects of different disturbances (drought and fire in the current case) and the capability of VEGETATION-NDVI data set to monitoring vegetation status from local up to a global scale.

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

2009-04-01

29

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

30

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

31

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

32

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

33

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

34

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.

35

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

USGS Publications Warehouse

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

U.S. Geological Survey

2008-01-01

36

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

37

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

NASA Astrophysics Data System (ADS)

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

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

2014-03-01

38

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

NASA Astrophysics Data System (ADS)

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

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

2014-08-01

39

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

40

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

41

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

E-print Network

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

Radeloff, Volker C.

42

Tools for Generating Useful Time-series Data from PhenoCam Images  

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

43

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

44

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

45

A Motion Correction Framework for Time Series Sequences in Microscopy Images  

PubMed Central

With the advent of in vivo laser scanning fluorescence microscopy techniques, time-series and three-dimensional volumes of living tissue and vessels at micron scales can be acquired to firmly analyze vessel architecture and blood flow. Analysis of a large number of image stacks to extract architecture and track blood flow manually is cumbersome and prone to observer bias. Thus, an automated framework to accomplish these analytical tasks is imperative. The first initiative toward such a framework is to compensate for motion artifacts manifest in these microscopy images. Motion artifacts in in vivo microscopy images are caused by respiratory motion, heart beats, and other motions from the specimen. Consequently, the amount of motion present in these images can be large and hinders further analysis of these images. In this article, an algorithmic framework for the correction of time-series images is presented. The automated algorithm is comprised of a rigid and a nonrigid registration step based on shape contexts. The framework performs considerably well on time-series image sequences of the islets of Langerhans and provides for the pivotal step of motion correction in the further automatic analysis of microscopy images. PMID:23410911

Kumar, Ankur N.; Short, Kurt W.; Piston, David W.

2014-01-01

46

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

NASA Astrophysics Data System (ADS)

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

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

2013-09-01

47

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

NASA Astrophysics Data System (ADS)

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

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

2013-07-01

48

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

49

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

50

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

51

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

52

Surface differential rotation of IL Hya from time-series Doppler images  

NASA Astrophysics Data System (ADS)

We present a time-series Doppler imaging study of the K-subgiant component in the RS CVn-type binary system IL Hya (P orb=12.905 d). From re-processing the unique long-term spectroscopic dataset of 70 days taken in 1996/97, we perform a thorough cross-correlation analysis to derive surface differential rotation. As a result we get solar-type differential rotation with a shear value ? of 0.05, in agreement with preliminary suggestions from previous attempts. A possible surface pattern of meridional circulation is also detected.

K?vári, Zsolt; Kriskovics, Levente; Oláh, Katalin; Vida, Krisztián; Bartus, János; Strassmeier, Klaus G.; Weber, Michael

2014-08-01

53

Global distributions, time series and error characterization of atmospheric ammonia (NH3) from IASI satellite observations  

NASA Astrophysics Data System (ADS)

Ammonia (NH3) emissions in the atmosphere have increased substantially over the past decades, largely because of intensive livestock production and use of fertilizers. As a short-lived species, NH3 is highly variable in the atmosphere and its concentration is generally small, except near local sources. While ground-based measurements are possible, they are challenging and sparse. Advanced infrared sounders in orbit have recently demonstrated their capability to measure NH3, offering a new tool to refine global and regional budgets. In this paper we describe an improved retrieval scheme of NH3 total columns from the measurements of the Infrared Atmospheric Sounding Interferometer (IASI). It exploits the hyperspectral character of this instrument by using an extended spectral range (800-1200 cm-1) where NH3 is optically active. This scheme consists of the calculation of a dimensionless spectral index from the IASI level1C radiances, which is subsequently converted to a total NH3 column using look-up tables built from forward radiative transfer model simulations. We show how to retrieve the NH3 total columns from IASI quasi-globally and twice daily above both land and sea without large computational resources and with an improved detection limit. The retrieval also includes error characterization of the retrieved columns. Five years of IASI measurements (1 November 2007 to 31 October 2012) have been processed to acquire the first global and multiple-year data set of NH3 total columns, which are evaluated and compared to similar products from other retrieval methods. Spatial distributions from the five years data set are provided and analyzed at global and regional scales. In particular, we show the ability of this method to identify smaller emission sources than those previously reported, as well as transport patterns over the ocean. The five-year time series is further examined in terms of seasonality and interannual variability (in particular as a function of fire activity) separately for the Northern and Southern Hemispheres.

Van Damme, M.; Clarisse, L.; Heald, C. L.; Hurtmans, D.; Ngadi, Y.; Clerbaux, C.; Dolman, A. J.; Erisman, J. W.; Coheur, P. F.

2014-03-01

54

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

55

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

56

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

NASA Astrophysics Data System (ADS)

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

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

2009-05-01

57

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

58

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.

59

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

60

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

PubMed

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

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

2013-01-01

61

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

62

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

63

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

64

ASTER's Satellite Image Gallery  

NSDL National Science Digital Library

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

Laboratory, Nasa J.

65

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

NASA Astrophysics Data System (ADS)

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

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

2014-11-01

66

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

67

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

NASA Technical Reports Server (NTRS)

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

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

1993-01-01

68

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

69

IMAGE TIME SERIES FOR NEAR REAL TIME AIRBORNE MONITORING OF DISASTER SITUATIONS AND TRAFFIC APPLICATIONS  

E-print Network

response, middleware Near real time monitoring of natural disasters, mass events, and large traffic disasters with airborne optical sensors is a focus of research and development at the German Aerospace Center (DLR). For this purpose, a new airborne camera system was developed named 3K camera system (3K = “3Kopf-Kamera”). Image data are processed onboard on five onboard processing units using data from a real time GPS/IMU system. Processed data are sent to ground via two types of data links, a commercial microwave operating in the S-band. This system is called ARGOS. The data received by the ground antenna are further processed and distributed directly to the ground forces and to different institutions like the ZKI (Center for Satellite Based Crisis Information) and a traffic portal of DLR. The time span between acquisition and reception at the end user is about 5 minutes. Main focus in the current development is the traffic processor, which extracts traffic information like traffic density and vehicle velocity from 3K image sequences. The information from a road database is used for the road and vehicle detection in the georeferenced images. Another application is the thematic mapping of natural disasters like floods, land slides, and damages after earth quakes. The requirements of civil security and rescue forces for a near real time airborne monitoring system were integrated in the first prototype system, which should be ready in the middle of 2009. National cooperation partners are the Federal Office of Civil Protection and

P. Reinartz; F. Kurz; D. Rosenbaum; J. Leitloff; G. Palubinskas

70

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

71

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

72

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

NASA Astrophysics Data System (ADS)

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

Didan, K.; Barreto-Munoz, A.

2009-12-01

73

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.

74

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

75

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.

76

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

NASA Astrophysics Data System (ADS)

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

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

2014-10-01

77

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

78

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

79

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

80

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

E-print Network

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

Dobigeon, Nicolas

81

MOBY Normalized Water-Leaving Radiance Time-series Uncertainty Reduction for Improved Multiplatform Satellite Sensor Vicarious Calibration  

Microsoft Academic Search

The Marine Optical Buoy (MOBY), a radiometric buoy stationed in the waters off Lanai, Hawaii, is the primary ocean observatory for vicarious calibration of satellite ocean color sensors. Since late 1996, MOBY has been the primary basis for the on-orbit vicarious calibrations of the USA Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Japanese Ocean Color and Temperature Sensor (OCTS) and Global

S. Flora; S. Brown; D. Clark; M. Feinholz; T. Houlihan; C. Johnson; K. Kinkade; Y. Kim; L. Koval; M. Murphy; O. Ondrusek; D. Peters; E. Stengel; K. Voss; M. Yarbrough

2007-01-01

82

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

83

Interpreting Satellite Images  

NSDL National Science Digital Library

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

84

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

85

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

86

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

87

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

NASA Astrophysics Data System (ADS)

A 20 year long transport time series of the Antarctic Circumpolar Current (ACC) has been produced from in situ and altimetric satellite observations across the Drake Passage (see Part I : mean 140 Sv, std 10 Sv ). Variations of the total, baroclinic and barotropic volume transports are analysed and their relation to the atmospheric forcing discussed. Sea level anomalies are used to understand the spatial patterns associated with transport variations. The spectral content of the volume transport time series present different significant periods: a five year trend, annual and semi annual periods that disappears between 2000-2002 and high frequencies. Variations in the volume transports are principally caused by variations in the Yaghan Basin, and more precisely by variations in the Subantarctic Front (SAF) and the Polar Front (PF). The total transport ranges between 100 Sv and 180 Sv, with several extreme cases (larger than 165 Sv and smaller than 110 Sv). The altimetric maps and velocity profiles corresponding to these extreme transport values are analysed to examine the typical patterns associated with these situations. Eastward rossby wave propagation is identified and associated with the semi annual period. The 5 year trend is caused by an indirect response of the baroclinic component of the transport to atmospheric variations via the Southern Annular Mode (SAM), with a lag of around 1,5 years. This 5-year trend disappears soon after 2000. The barotropic transport does not show this-5 year trend.

Koenig, Zoé; Provost, Christine; Ferrari, Ramiro; Sennéchael, Nathalie; Park, Young-Hyang

2014-05-01

88

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

89

IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 27, NO. 1, JANUARY 2008 87 Classification of fMRI Time Series in a  

E-print Network

IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 27, NO. 1, JANUARY 2008 87 Classification of fMRI Time imaging (fMRI) data. We project the fMRI time series on a low-dimensional subspace spanned by wavelet to the dataset and it also creates mean- ingful clusters allowing the separation of the activated regions from

Meyer, Francois

90

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)

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

91

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

92

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

NASA Astrophysics Data System (ADS)

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

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

2010-12-01

93

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

2009-08-13

94

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

95

Contrail Detection in Satellite Images  

NASA Astrophysics Data System (ADS)

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

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

96

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

97

Infrared Satellite Images  

NSDL National Science Digital Library

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

2008-03-28

98

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

99

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

100

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

101

Reading Time Series Plots  

NSDL National Science Digital Library

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

Olds, Shelley

102

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

103

MODIS Vegetation Indices time series improvement considering real acquisition dates  

NASA Astrophysics Data System (ADS)

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

Testa, S.; Borgogno Mondino, E.

2013-12-01

104

NASA SSTI CLARK 3-meter imaging satellite  

NASA Technical Reports Server (NTRS)

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

Sebestyen, George; Hayduk, Robert J.

1996-01-01

105

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

106

Modelling bursty time series  

NASA Astrophysics Data System (ADS)

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

Vajna, Szabolcs; Tóth, Bálint; Kertész, János

2013-10-01

107

Uncertainties assessment and satellite validation over 2 years time series of multispectral and hyperspectral measurements in coastal waters at Long Island Sound Coastal Observatory  

NASA Astrophysics Data System (ADS)

Optical remote sensing of coastal waters from space is a basic requirement for monitoring global water quality and assessing anthropogenic impacts. However, this task remains highly challenging due to the optical complexity of the atmosphere-water system in coastal areas. In order to support present and future multi- and hyper-spectral calibration/validation activities for the Ocean Color Radiometry (OCR) satellites, as well as the development of new measurements and retrieval techniques for coastal waters, City College of New York along with the Naval Research Laboratory (Stennis) has established a scientifically comprehensive observation platform, the Long Island Sound Coastal Observatory (LISCO). As an integral part of the NASA AERONET - Ocean Color Network, LISCO is equipped with a multispectral SeaPRISM system. In addition, LISCO expands its observational capabilities through hyperspectral measurements with a HyperSAS system. The related multi- and hyperspectral data processing and data quality analysis are described. The three main OCR satellites, MERIS, MODIS and SeaWiFS, have been evaluated against the LISCO dataset of quality-checked measurements of SeaPRISM and HyperSAS. Adjacency effects impacting satellite data have been analyzed and found negligible. The remote sensing reflectances retrieved from satellite and in situ data are also compared. These comparisons show satisfactory correlations (R2 > 0.91 at 547nm) and consistencies (median value of the absolute percentage difference ~ 7.4%). It is also found that merging of the SeaPRISM and HyperSAS data at LISCO site significantly improve the overall data quality which makes this dataset highly suitable for satellite data validation purposes or for potential vicarious calibration activities.

Ahmed, S. A.; Harmel, T.; Gilerson, A.; Tonizzo, A.; Hlaing, S.; Weidemann, A.; Arnone, R. A.

2011-11-01

108

Satellite image compression based on fractal theory  

NASA Astrophysics Data System (ADS)

Fractal image coding algorithm is a promising technique to improve the storage efficiency for satellite applications, and it can obtain good reconstructed quality with very high compression ratio in contrast with other image compression methods. In this paper, a fractal coding algorithm for satellite image compression is proposed. The framework of this algorithm is simple, so that it can be designed for hardware and applied on satellite conveniently. In addition, some improved methods are also introduced in this paper.

Zhou, Yiming; Liu, Pinxiong; Huang, Yumin

2009-12-01

109

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.

110

Causality between time series  

E-print Network

Given two time series, can one tell, in a rigorous and quantitative way, the cause and effect between them? Based on a recently rigorized physical notion namely information flow, we arrive at a concise formula and give this challenging question, which is of wide concern in different disciplines, a positive answer. Here causality is measured by the time rate of change of information flowing from one series, say, X2, to another, X1. The measure is asymmetric between the two parties and, particularly, if the process underlying X1 does not depend on X2, then the resulting causality from X2 to X1 vanishes. The formula is tight in form, involving only the commonly used statistics, sample covariances. It has been validated with touchstone series purportedly generated with one-way causality. It has also been applied to the investigation of real world problems; an example presented here is the cause-effect relation between two climate modes, El Ni\\~no and Indian Ocean Dipole, which have been linked to the hazards in f...

Liang, X San

2014-01-01

111

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

112

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

113

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

NASA Astrophysics Data System (ADS)

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

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

2009-04-01

114

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

NASA Astrophysics Data System (ADS)

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

Akhoondzadeh, M.

2013-01-01

115

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

116

Providing web-based tools for time series access and analysis  

NASA Astrophysics Data System (ADS)

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

Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane

2014-05-01

117

Time Series of the Biscuit Fire  

NSDL National Science Digital Library

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

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

2003-08-04

118

Langevin equations from time series  

NASA Astrophysics Data System (ADS)

We discuss the link between the approach to obtain the drift and diffusion of one-dimensional Langevin equations from time series, and Pope and Ching’s relationship for stationary signals. The two approaches are based on different interpretations of conditional averages of the time derivatives of the time series at given levels. The analysis provides a useful indication for the correct application of Pope and Ching’s relationship to obtain stochastic differential equations from time series and shows its validity, in a generalized sense, for nondifferentiable processes originating from Langevin equations.

Racca, E.; Porporato, A.

2005-02-01

119

FROG: Time-series analysis  

NASA Astrophysics Data System (ADS)

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

Allan, Alasdair

2014-06-01

120

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

121

Automatic registration of satellite images  

Microsoft Academic Search

Image registration is one of the basic image processing operations in remote sensing. With the increase in the number of images collected every day from different sensors, automated registration of multi-sensor\\/multi-spectral images has become an important issue. A wide range of registration techniques has been developed for many different types of applications and data. Given the diversity of the data,

LEILA M. G. FONSECA; MAX H. M. COSTA

1997-01-01

122

Offshore wind resource assessment through satellite images  

E-print Network

1 Slide no. 4 Offshore wind resource assessment through satellite images Charlotte Bay Hasager images for offshore wind ressource assessment in lieu of in-situ mast observations #12;4 Slide no Hasager, Dellwik, Nielsen and Furevik, 2004, Validation of ERS-2 SAR offshore wind-speed maps in the North

123

Using Satellite Images to Understand Earth's Atmosphere  

NSDL National Science Digital Library

In this Earth Exploration Toolbook chapter, students select, explore, and analyze satellite imagery. They do so in the context of a case study of the origins of atmospheric carbon monoxide and aerosols, tiny solid airborne particles such as smoke from forest fires and dust from desert wind storms. They use the software tool ImageJ to animate a year of monthly images of aerosol data and then compare the animation to one created for monthly images of carbon monoxide data. Students select, explore and analyze satellite imagery using NASA Earth Observatory (NEO) satellite data and NEO Image Composite Explorer (ICE) tool to investigate seasonal and geographic patterns and variations in concentration of CO and aerosols in the atmosphere.

Todd Ensign

124

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

125

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

126

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

127

Meteorology 5323 Time Series Analysis  

E-print Network

Meteorology 5323 Time Series Analysis Fall Semester 2012 MWF 11:00 ­ 11:50 am Room 5600 NWC Course of data sets. TSA is primarily an introductory course in the application of statistics to the analysis analysis, for example, that found in radar and atmospheric turbulence measurements, analysis, and theory

Droegemeier, Kelvin K.

128

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...

129

The qualitative analyses of cloud cover on optical satellite image  

Microsoft Academic Search

The remote sensing technology has become the important information source in environment investigation, Moreover, optical satellite images are the most important information source. Although the optical satellite images may provides high resolution, multi-spectral images and better vision images than active satellite, the disadvantage is affected by the atmospheric condition easily. In general, the cloud cover is the most common noise,

Chih-Heng Liu; Mei-Ling Yeh; Tine-Yin Chou; Lung-Shih Yang

2008-01-01

130

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

131

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...

132

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.

133

4-D Display Of Satellite Cloud Images  

NASA Astrophysics Data System (ADS)

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

Hibbard, William L.

1988-01-01

134

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

135

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

136

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

137

Albedo Pattern Recognition and Time-Series Analyses in Malaysia  

NASA Astrophysics Data System (ADS)

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

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

2012-07-01

138

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

139

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

140

Observe animated satellite images of water vapor  

NSDL National Science Digital Library

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

Education, Terc. C.; Littell, Mcdougal

2003-01-01

141

GNSS Network Time Series Analysis  

NASA Astrophysics Data System (ADS)

Time series of GNSS station results of both the EUPOS®-RIGA and LATPOS networks has been developed at the Institute of Geodesy and Geoinformation (University of Latvia) using Bernese v.5.0 software. The base stations were selected among the EPN and IGS stations in surroundings of Latvia. In various day solutions the base station selection has been miscellaneous. Most frequently 5 - 8 base stations were selected from a set of stations {BOR1, JOEN, JOZE, MDVJ, METS, POLV, PULK, RIGA, TORA, VAAS, VISO, VLNS}. The rejection of "bad base stations" was performed by Bernese software depending on the quality of proper station data in proper day. This caused a reason of miscellaneous base station selection in various days. The results of time series are analysed. The question aroused on the nature of some outlying situations. The seasonal effect of the behaviour of the network has been identified when distance and elevation changes between stations has been analysed. The dependence from various influences has been recognised.

Balodis, J.; Janpaule, I.; Haritonova, D.; Normand, M.; Silabriedis, G.; Zarinjsh, A.; Zvirgzds, J.

2012-04-01

142

Imaging of the outer planets and satellites.  

NASA Technical Reports Server (NTRS)

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

Murray, B. C.

1973-01-01

143

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

144

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

145

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

146

Time Series Simulation with Quasi Monte Carlo Methods  

Microsoft Academic Search

This paper compares quasi Monte Carlo methods, in particularso-called (t, m, s)-nets, with classical Monte Carlo approaches forsimulating econometric time-series models. Quasi Monte Carlomethods have found successful application in many fields, such asphysics, image processing, and the evaluation of financederivatives. However, they are rarely used in econometrics. Here,we apply both traditional and quasi Monte Carlo simulation methodsto time-series models that

Jenny X. Li; Peter Winker

2003-01-01

147

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

148

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

149

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

150

Uneven cloud and fog removing for satellite remote sensing image  

Microsoft Academic Search

Haze is an important influence factor of visible light RS data's obtaining and using. Based on dark channel prior and haze image model, this paper studies the dehaze technology from a single satellite RS image. Aim at the characteristic of uneven cloud in satellite RS image and the problem of the unreasonable estimate for airlight in dehaze method, this paper

Liya Zhou; Zhiyuan Qin

2011-01-01

151

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

152

A Review of Subsequence Time Series Clustering  

PubMed Central

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

Teh, Ying Wah

2014-01-01

153

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

154

Managing an Archive of Weather Satellite Images  

NASA Astrophysics Data System (ADS)

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

Seaman, Rob

1993-01-01

155

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

156

Des satellites nous renvoient notre image Politique scientifique fdrale  

E-print Network

Des satellites nous renvoient notre image Politique scientifique fédérale Martine ST�LANDRE 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

157

Optical Auroral Imaging Conjugate to the FAST Satellite  

Microsoft Academic Search

Most satellite data are essentially point observations made along the satellite orbital track. Although reasonable assumptions can be made in many cases, fundamentally, one cannot differentiate between spatial and temporal structures. To overcome this shortcoming we recorded video images of the aurora conjugate to the FAST satellite from an instrumented jet aircraft. A total of 29 flights were made during

L. M. Peticolas; H. C. Stenbaek-Nielsen

2003-01-01

158

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

159

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

E-print Network

Title: IKONOS Satellite Image of North York, Ontario Data Creator / Copyright Owner: Geo: -79.3930 degrees #12;Index Map: N/A Keywords (Place): Toronto, Ontario; North York, Ontario; York University, Ontario Keywords (Subject): Satellite Imagery, Aerial Images Restrictions: Data is licensed

160

Measuring earthquakes from optical satellite images.  

PubMed

Système pour l'Observation de la Terre images are used to map ground displacements induced by earthquakes. Deformations (offsets) induced by stereoscopic effect and roll, pitch, and yaw of satellite and detector artifacts are estimated and compensated. Images are then resampled in a cartographic projection with a low-bias interpolator. A subpixel correlator in the Fourier domain provides two-dimensional offset maps with independent measurements approximately every 160 m. Biases on offsets are compensated from calibration. High-frequency noise (0.125 m(-1)) is approximately 0.01 pixels. Low-frequency noise (lower than 0.001 m(-1)) exceeds 0.2 pixels and is partially compensated from modeling. Applied to the Landers earthquake, measurements show the fault with an accuracy of a few tens of meters and yields displacement on the fault with an accuracy of better than 20 cm. Comparison with a model derived from geodetic data shows that offsets bring new insights into the faulting process. PMID:18349918

Van Puymbroeck, N; Michel, R; Binet, R; Avouac, J P; Taboury, J

2000-07-10

161

Combination methods of tropospheric time series  

NASA Astrophysics Data System (ADS)

In this article we present two methods for combination of different Global Navigation Satellite Systems (GNSS) Zenith Total Delay (ZTD) time-series for the same GNSS site, but from different producers or different processing setups. One method has been setup at ASI/CGS, the other at KNMI. Using Near Real-Time (NRT) ZTD data covering 1 year from the E-GVAP project, the performance of the two methods is inter-compared and validation is made against a combined ZTD solution from EUREF, based on post-processed ZTDs. Further, validation of the ASI combined solutions is made against independent ZTDs derived from radiosonde, Numerical Weather Prediction (NWP) model and Very Long Baseline Interferometry (VLBI) ZTD.It is found that the two combined solutions perform quite similar, with a bias from -0.17 mm to 1.52 mm and a standard deviation from 1.60 mm to 3.82 mm. Compared with respect to EUREF post-processed solutions, the NRT combined solutions shows a small but positive bias which could be due to a different way of dealing with phase ambiguities in the data reduction process. Further, it is found that the ASI combined solution compares better to both radiosonde, NWP model and VLBI ZTDs than the individual time-series upon which it is based.It is concluded that the combined NRT solutions appear a promising tool for rapid control of the NRT ZTDs produced today by a number of Analysis Centres (ACs) across Europe for use in meteorology. It is known that the NRT processing is prone to certain types of errors rarely seen in post-processing. These errors can lead to a large number of ZTDs from a given AC having correlated errors, which can do serious damage if the data are used in Numerical Weather Prediction, even if it is a rare occurrence. Identification and blocking of such data is therefore a goal in the NRT GNSS data processing and validation.

Pacione, R.; Pace, B.; Vedel, H.; de Haan, S.; Lanotte, R.; Vespe, F.

2011-01-01

162

Volatility of linear and nonlinear time series  

NASA Astrophysics Data System (ADS)

Previous studies indicated that nonlinear properties of Gaussian distributed time series with long-range correlations, ui , can be detected and quantified by studying the correlations in the magnitude series ?ui? , the “volatility.” However, the origin for this empirical observation still remains unclear and the exact relation between the correlations in ui and the correlations in ?ui? is still unknown. Here we develop analytical relations between the scaling exponent of linear series ui and its magnitude series ?ui? . 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(ui) ]. 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.

Kalisky, Tomer; Ashkenazy, Yosef; Havlin, Shlomo

2005-07-01

163

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

164

Efficient Time Series Matching by Wavelets  

Microsoft Academic Search

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

Kin-pong Chan; Ada Wai-chee Fu

1999-01-01

165

From time series to superstatistics Christian Beck  

E-print Network

From time series to superstatistics Christian Beck School of Mathematical Sciences, Queen Mary of a statistics", in short, a "superstatistics". We describe how to proceed from a given experimental time series. We discuss how to extract the two relevant well separated superstatistical time scales and T

Texas at Austin. University of

166

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

167

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

168

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

169

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

NASA Astrophysics Data System (ADS)

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

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

2010-12-01

170

Time Series of the Biscuit Fire with Smoke  

NSDL National Science Digital Library

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

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

2003-08-04

171

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

172

Wavelet Analysis of Variance for Time Series with Missing Values  

E-print Network

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

Washington at Seattle, University of

173

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

Microsoft Academic Search

Recently, terrestrial biogeochemists and soil scientists have called for new approaches to study human impacts on soil biogeochemical properties at landscape-wide, 100-1000 km2 spatial scales (Trumbore and Czimczik 2008). Here, we use satellite remote sensing to map land cover across a 16,000 km2 region in the Konya Basin, south-central Turkey, in support of research into agricultural and pastoral land use

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

2010-01-01

174

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

175

Bayesian Time Series: Models and Computations for the Analysis of Time Series in the Physical Sciences  

Microsoft Academic Search

. This articles discusses developments in Bayesian time series modellingand analysis relevant in studies of time series in the physical and engineeringsciences. With illustrations and references, we discuss: Bayesian inferenceand computation in various state-space models, with examples in analysingquasi-periodic series; isolation and modelling of various components of error intime series; decompositions of time series into significant latent subseries; nonlineartime series

Mike West

1995-01-01

176

Using Image Tour to Explore Multiangle, Multispectral Satellite Image  

NASA Technical Reports Server (NTRS)

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

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

2006-01-01

177

Generalized satellite image processing: eight years of ocean colour data for any region on earth  

NASA Astrophysics Data System (ADS)

During the past decade, the world's oceans have been systematically observed by orbiting spectroradiometers such as MODIS and MERIS. These sensors have generated a huge amount of data with unprecedented temporal and spatial coverage. The data is freely available, but not always accessible for marine researchers with no image processing experience. In order to provide historical and current oceanographic parameters for the jellyfish forecasting in the JELLYFOR project, a tool for the generalized processing and archiving of satellite data was created (GRIMAS). Using this generalized software, the large amount of remote sensing data can be accessed, and parameters such as chlorophyll a concentration (CHL), sea surface temperature (SST) and total suspended matter concentration (TSM) can be extracted and gridded for any region on earth. Time-series and climatologies can be easily extracted from this data archive. The products generated can be based on the standard products, as supplied by space agencies, or can be new or regionally calibrated products. All available MODIS and MERIS L2 images from an eight year period (2003-2010) were processed in order to create a gridded dataset of CHL, SST (MODIS only) and of TSM for the three JELLYFOR regions. For two of the regions, data for an extended region was also processed. Multi-year composites (climatologies) of satellite data and time-series can provide a wealth of information for different projects in any region. Climatologies from the two sensors are in good agreement, while significant differences can occur on a scene per scene basis. Total suspended matter concentrations match favourably with in situ data derived from sensors on autonomous buoys. MODIS sea surface temperature corresponds closely to temperature continuously measured underway on research vessels.

Vanhellemont, Quinten; Ruddick, Kevin

2011-11-01

178

Segmentation of Infrared Satellite Images V. Lakshmanan1,2  

E-print Network

Segmentation of Infrared Satellite Images V. Lakshmanan1,2 , R. Rabin1,3 , V. DeBrunner2 1 National Watershed Segmentation Think of the image as a raised relief map and let water seep through from the bottom Watershed · Watershed segmentation and contouring work very well one image at a time but are not consistent

Lakshmanan, Valliappa

179

Spectral analysis of multiple time series  

NASA Technical Reports Server (NTRS)

Application of spectral analysis for mathematically determining relationship of random vibrations in structures and concurrent events in electric circuits, physiology, economics, and seismograms is discussed. Computer program for performing spectral analysis of multiple time series is described.

Dubman, M. R.

1972-01-01

180

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

181

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

182

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

183

Fractal interpolation of rain rate time series  

NASA Astrophysics Data System (ADS)

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 radar at this rate is physically infeasible. This paper investigates a stochastic numerical method to interpolate point rain rate time series to shorter sampling periods while conserving the expected first- and second-order statistics. The proposed method should generally be applicable to the temporal interpolation of radar-derived rain rate maps. The method is based on the experimentally measured simple-scaling properties of log rain rate time series. It is tested against 9 gauge years of rapid response drop-counting rain gauge data, with a 10 s integration time, collected in the southern UK. The data are subsampled to yield time series with a 10 s rain rate measurement every 320, 640, and 1280 s. The subsampled time series are then interpolated back to a 10 s sample interval, and the first- and second-order statistics are compared with the original time series.

Paulson, Kevin S.

2004-11-01

184

Correlated Wishart ensembles and chaotic time series  

NASA Astrophysics Data System (ADS)

We study the correlated Wishart ensembles in the context of time series analysis. We are interested in the statistics of eigenlevels, viz. variables associated with independent eigenmodes in the system. The motivation of this work is to study the effect of time series correlations on the Wishart ensembles. In this connection, we derive the level density and the two-point function for the correlated Wishart ensembles by using the binary correlation method. Using our analytic results we analyze spectra of autocovariance matrices derived from single variable stationary time series. We consider the stochastic time series of Gaussian variables with exponentially decaying correlations and time series of chaotic maps, viz. the Arnold map, the Standard map and the stadium billiard map. In both cases, correlated time series are encountered and analyzed under the framework of random matrix theory. It is shown that the eigenlevel statistics for the chaotic maps follow closely those of correlated Wishart ensembles. It is indicated that the presence of collective modes in the spectra of autocovariance matrices is related to the integrability of the system.

Vinayak; Pandey, Akhilesh

2010-03-01

185

Correlated Wishart ensembles and chaotic time series.  

PubMed

We study the correlated Wishart ensembles in the context of time series analysis. We are interested in the statistics of eigenlevels, viz. variables associated with independent eigenmodes in the system. The motivation of this work is to study the effect of time series correlations on the Wishart ensembles. In this connection, we derive the level density and the two-point function for the correlated Wishart ensembles by using the binary correlation method. Using our analytic results we analyze spectra of autocovariance matrices derived from single variable stationary time series. We consider the stochastic time series of Gaussian variables with exponentially decaying correlations and time series of chaotic maps, viz. the Arnold map, the Standard map and the stadium billiard map. In both cases, correlated time series are encountered and analyzed under the framework of random matrix theory. It is shown that the eigenlevel statistics for the chaotic maps follow closely those of correlated Wishart ensembles. It is indicated that the presence of collective modes in the spectra of autocovariance matrices is related to the integrability of the system. PMID:20370309

Vinayak; Pandey, Akhilesh

2010-03-01

186

Apodization and Smoothing Alter Voxel Time Series Correlations A. S. Nencka1  

E-print Network

Ã?64 matrix image time series was created in Matlab with the center element and one other element containing on the mean correlation between voxels in an ROI. A 64Ã?64 image time series was created in Matlab Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States Introduction

Rowe, Daniel B.

187

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

188

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

189

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

190

The effects of invisible watermarking on satellite image classification  

Microsoft Academic Search

Remotely sensed satellite images are an important source of geographical data commonly used as input for various types of classification algorithms. For example, these algorithms are commonly used to classify earth land cover, analyze crop conditions, assess mineral and petroleum deposits, and quantify urban growth. Many vendors of digital images are using or are considering the use of invisible watermarking

Gregory L. Heileman; Yunlong Yang

2003-01-01

191

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

192

Saliency and Gist Features for Target Detection in Satellite Images  

Microsoft Academic Search

Reliably detecting objects in broad-area overhead or satellite images has become an increasingly pressing need, as the capabilities for image acquisition are growing rapidly. The problem is particularly difficult in the presence of large in- traclass variability, e.g., finding \\

Zhicheng Li; Laurent Itti

2011-01-01

193

Ice sheet change detection by satellite image differencing  

Microsoft Academic Search

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

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

2010-01-01

194

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

195

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

196

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

197

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

198

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

199

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

200

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

201

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

202

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

NASA Astrophysics Data System (ADS)

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

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

2012-10-01

203

Antenna Automation For NOAA Satellite Images Reception  

NASA Astrophysics Data System (ADS)

In this paper, we present a novel, precise and efficient software tool (LAAR-TRACK) for Low Earth Orbit (LEO) Satellites orbit determination. It's based on using orbital elements, which are given by the NORAD (North American Aerospace Defence) by taking into considerations orbital perturbations due to the atmospheric drag, the influence of the moon and the sun and the geopotential field. The LAAR-TRACK gives the azimuth and the elevation that must have the antenna for pointing in real time the LEO satellites. This software is loaded on a computer directly connected, via the parallel port, to the tracking interface that we have developed, and which will be detailed in this paper. By this way the antenna can be automatically directed for receiving NOAA (National Oceanic and Atmospheric Administration) HRPT (High Resolution Picture Transmission) pictures.

Rahal, W. L.; Benabadji, N.; Belbachir, A. H.

2008-06-01

204

A survey of volcanic deformation on Java using ALOS PALSAR interferometric time series  

NASA Astrophysics Data System (ADS)

Of the hundreds of volcanic centers throughout the Indonesian archipelago, few are adequately monitored for pre-eruptive activity due to socioeconomic and logistical barriers, with the result that volcanic hazards in the region are not well quantified. The advent of satellite-borne L-band synthetic aperture radar provides an opportunity for detection and measurement of volcanic deformation over broad regions in heavily vegetated tropical island arcs. We use data from the PALSAR instrument on the Japanese ALOS satellite to conduct a comprehensive survey of volcanic deformation on the Indonesian island of Java, over a time period of two years (2007-2008). To obtain the most complete, temporally continuous record of ground deformation, we use a temporally overlapping set of short-time-interval radar image pairs to produce a deformation time series. Consistent with previous results from other regions, our survey suggests that volcanoes experiencing small eruptions are typically fed by magma bodies too small and/or too shallow or deep to produce a recognizable InSAR signal. However, we identified a deformation event at Lamongan volcano which is likely linked to a magmatic intrusion at several kilometers' depth, and a second one at Slamet volcano at a shallower depth that may have been related to a subsequent eruption. This initial test of a broad application of L-band data allowed us to better define the satellite imaging criteria required for successful observation, as well as developing a useful methodology for monitoring deformation over a wide region.

Philibosian, Belle; Simons, Mark

2011-11-01

205

Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases  

Microsoft Academic Search

The increasing interest in time series data mining in the last decade has resulted in the introduction of a variety of similarity measures, representations and algorithms. Surprisingly, this massive research effort has had little impact on real world applications. Real world practitioners who work with time series on a daily basis rarely take advantage of the wealth of tools that

Nitin Kumar; Venkata Nishanth Lolla; Eamonn J. Keogh; Stefano Lonardi

2005-01-01

206

Modeling Multiple Time Series for Anomaly Detection  

Microsoft Academic Search

Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online man- ner for real-life monitoring tasks. We introduce three algo- rithms that work in a constructed feature space and evaluate them with a real data set from the NASA shuttle program. Our offline and

Philip K. Chan; Matthew V. Mahoney

2005-01-01

207

Set membership prediction of nonlinear time series  

Microsoft Academic Search

In this paper, a prediction method for nonlinear time series based on a set membership (SM) approach is proposed. The method does not require the choice of the functional form of the model used for prediction, but assumes a bound on the rate of variation of the regression function defining the model. At the contrary, most of the existing prediction

Mario Milanese; Carlo Novara

2005-01-01

208

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

209

Mining Approximate Motifs in Time Series  

Microsoft Academic Search

The problem of discovering previously unknown frequent patterns in time series, also called motifs, has been recently introduced. A motif is a subseries pattern that appears a signican t number of times. Results demonstrate that motifs may provide valuable insights about the data and have a wide range of applications in data mining tasks. The main motivation for this study

Pedro Gabriel Ferreira; Paulo J. Azevedo; Cândida G. Silva; Rui M. M. Brito

2006-01-01

210

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

211

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), ...

212

Modeling Time Series Data for Supervised Learning  

ERIC Educational Resources Information Center

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

Baydogan, Mustafa Gokce

2012-01-01

213

Unsupervised Outlier Detection in Time Series Data  

Microsoft Academic Search

Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar

Zakia Ferdousi; Akira Maeda

2006-01-01

214

Unsupervised Fraud Detection in Time Series data  

Microsoft Academic Search

Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar

Zakia Ferdousi; Akira Maeda

2006-01-01

215

Automated time series forecasting for biosurveillance  

Microsoft Academic Search

SUMMARY For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of

Howard S. Burkom; Sean Patrick Murphy; Galit Shmueli

2007-01-01

216

An Online Algorithm for Segmenting Time Series  

Microsoft Academic Search

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

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

2001-01-01

217

Overview of the Radiant Time Series Method  

E-print Network

are published in ASHRAE Handbook of Fundamentals #12;RTSM Algorithm Conduction Gains Split all heat gains COOLING LOAD Solar Gains Internal Gains #12;RTSM Solution Technique Takes Advantage of Steady Periodic Nature of the Cooling Load Calculation Based on: Radiant Time Series: Steady Periodic Zone Response

218

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

219

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

220

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

221

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

222

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

223

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

224

Intrinsic superstatistical components of financial time series  

NASA Astrophysics Data System (ADS)

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

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

2014-12-01

225

Forbidden patterns in financial time series.  

PubMed

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

Zanin, Massimiliano

2008-03-01

226

Forbidden patterns in financial time series  

NASA Astrophysics Data System (ADS)

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

Zanin, Massimiliano

2008-03-01

227

Detecting anomalous phase synchronization from time series  

SciTech Connect

Modeling approaches are presented for detecting an anomalous route to phase synchronization from time series of two interacting nonlinear oscillators. The anomalous transition is characterized by an enlargement of the mean frequency difference between the oscillators with an initial increase in the coupling strength. Although such a structure is common in a large class of coupled nonisochronous oscillators, prediction of the anomalous transition is nontrivial for experimental systems, whose dynamical properties are unknown. Two approaches are examined; one is a phase equational modeling of coupled limit cycle oscillators and the other is a nonlinear predictive modeling of coupled chaotic oscillators. Application to prototypical models such as two interacting predator-prey systems in both limit cycle and chaotic regimes demonstrates the capability of detecting the anomalous structure from only a few sets of time series. Experimental data from two coupled Chua circuits shows its applicability to real experimental system.

Tokuda, Isao T. [School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292 (Japan); Kumar Dana, Syamal [Instrument Division, India Institute of Chemical Biology, Jadavpur, Kolkata 700032 (India); Kurths, Juergen [Department of Physics, Humboldt University Berlin, 12489 Berlin, Germany and Potsdam Institute for Climate Impact Research, 14473 Potsdam, Gernany (Germany)

2008-06-15

228

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

229

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

230

Turbulencelike Behavior of Seismic Time Series  

SciTech Connect

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

Manshour, P.; Saberi, S. [Department of Physics, Sharif University of Technology, Tehran 11155-9161 (Iran, Islamic Republic of); Sahimi, Muhammad [Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-1211 (United States); Peinke, J. [Institute of Physics, Carl von Ossietzky University, D-26111 Oldenburg (Germany); Pacheco, Amalio F. [Department of Theoretical Physics, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza (Spain); Rahimi Tabar, M. Reza [Department of Physics, Sharif University of Technology, Tehran 11155-9161 (Iran, Islamic Republic of); Institute of Physics, Carl von Ossietzky University, D-26111 Oldenburg (Germany); CNRS UMR 6202, Observatoire de la Cote d'Azur, BP 4229, 06304 Nice Cedex 4 (France)

2009-01-09

231

Wind Velocity Fluctuations Time Series Analysis  

Microsoft Academic Search

The temporal structure of wind was investigated by means of temporal correlations of 10-min wind time series measured over a period of one year (2004). The Hurst exponent (H), one of a number of methods to identify the existence of long-range correlations in experimental data, has been applied to quantify self-similarity scaling and correlations in the mesoscale temporal range. The

A M TARQUIS; M. C. MORATÓ; M. T. CASTELLANOS; J. L. DE MIGUEL

2006-01-01

232

Measuring nonlinear behavior in time series data  

NASA Astrophysics Data System (ADS)

Stationary Test is an important test in detect the time series behavior since financial and economic data series always have missing data, structural change as well as jumps or breaks in the data set. Moreover, stationary test is able to transform the nonlinear time series variable to become stationary by taking difference-stationary process or trend-stationary process. Two different types of hypothesis testing of stationary tests that are Augmented Dickey-Fuller (ADF) test and Kwiatkowski-Philips-Schmidt-Shin (KPSS) test are examine in this paper to describe the properties of the time series variables in financial model. Besides, Least Square method is used in Augmented Dickey-Fuller test to detect the changes of the series and Lagrange multiplier is used in Kwiatkowski-Philips-Schmidt-Shin test to examine the properties of oil price, gold price and Malaysia stock market. Moreover, Quandt-Andrews, Bai-Perron and Chow tests are also use to detect the existence of break in the data series. The monthly index data are ranging from December 1989 until May 2012. Result is shown that these three series exhibit nonlinear properties but are able to transform to stationary series after taking first difference process.

Wai, Phoong Seuk; Ismail, Mohd Tahir

2014-12-01

233

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

234

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

235

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.

2012-11-15

236

Aerosol Properties from Multi-angle Satellite Imaging  

Microsoft Academic Search

The MISR instrument, flying aboard the NASA Earth Observing System's Terra satellite, is pioneering the use of multi-angle imaging to monitor aerosols globally, from space. MISR obtains nine along-track images at view angles ranging from +70o through nadir to -70o, in each of four wavelengths, near-simultaneously. The instrument systematically covers air-mass-factors between one and three, and in mid-latitudes, samples scattering

R. A. Kahn; J. V. Martonchik; D. J. Diner; O. Kalashnikova

2004-01-01

237

What do Satellite Images Tell Us About Mars?  

NSDL National Science Digital Library

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

238

[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

239

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

240

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

241

Interpretation of a compositional time series  

NASA Astrophysics Data System (ADS)

Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA. In this data set, the proportion of annual precipitation falling in winter, spring, summer and autumn is considered a 4-component time series. Three invertible log-ratios are defined for calculations, balancing rainfall in autumn vs. winter, in summer vs. spring, and in autumn-winter vs. spring-summer. Results suggest a 2-year correlation range, and certain oscillatory behaviour in the last balance, which does not occur in the other two.

Tolosana-Delgado, R.; van den Boogaart, K. G.

2012-04-01

242

An Operational Geodatabase Service for Disseminating Raster Time Series Data  

NASA Astrophysics Data System (ADS)

The volume of raster time series data available for earth science applications is rapidly expanding with improvements in spatial and temporal resolution of earth imaging from remote sensing missions. Current dissemination systems are typically designed for mission efficiency rather than supporting the various needs of diverse user communities. This promotes the building of multiple archives of the same dataset by end users who acquire the skills needed to establish and maintain their own data streams. Such processing often becomes a barrier to the adoption of new datasets. This presentation describes the development of an operational geodatabase service for the dissemination of raster time series. The service combines innovative geocoding schemes with traditional database and geospatial capabilities to facilitate direct access to raster time series. It includes functionality such as search and retrieval, data segmentation, trend analysis and direct integration into third-party applications using predefined data schemas. The service allows end users to interact with data using simple web-based tools without the need for complex data processing skills. A live implementation of the service is demonstrated using sample global environmental datasets.

Asante, K. O.

2009-12-01

243

Feature detection in satellite images using neural network technology  

NASA Technical Reports Server (NTRS)

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

Augusteijn, Marijke F.; Dimalanta, Arturo S.

1992-01-01

244

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

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

2013-03-22

245

Mapping Cropping Practices Using MODIS Time Series: Harnessing the Data Explosion  

Microsoft Academic Search

The MODIS (Moderate Resolution Imaging Spectroradiometer) 250m EVI dataset provides a valuable ongoing means of characterising\\u000a and monitoring changes in land use and resource condition. However the multiple factors that influence a time series of greenness\\u000a data make the data difficult to analyse and interpret. Without prior knowledge, underlying models for time series in a given\\u000a remote sensing image are

Peter Tan; Leo Lymburner; Medhavy Thankappan; Adam Lewis

246

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

247

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

248

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.

2007-09-21

249

The Features of the Geodetic Reference of Satellite Images  

NASA Astrophysics Data System (ADS)

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

Gojamanov, M. H.

2012-07-01

250

Spectrally Consistent Satellite Image Fusion with Improved Image Priors  

E-print Network

]. Image fusion can be done at several levels: Pixel level, feature level, object level and decision level, depending on the intended use of the fused image. This paper is only concerned with pixel level fusion and when the terms "image fusion" or "fusion" are used, pixel level fusion is intended. In the current

251

Modeling satellite imaging sensors over optically complex bodies of water  

NASA Astrophysics Data System (ADS)

Although several currently operating remote sensing satellites can take effective data from case-1 waters, which are dominated by phytoplankton, few instruments have the appropriate spatial and radiometric resolution for taking effective data from Case 2 waters, which contain significant levels of chlorophyll, suspended material, and color-dissolved organic matter. The Operational Land Imager, which was launched on February 11th 2013, should have sufficient spatial and radiometric resolution to take useful data from Case 2 waters as well as the continental Earth. The purpose of this study was to compare the constituent retrieval accuracy of the Operational Land Imager over these waters to that of existing sensors. The models used to evaluate the sensors were based on signal-to-noise ratios calculated from image data, spectral response functions, and bit depths of each satellite. The sensor models were used to sample radiance spectra from different Hydrolight simulations, which were calculated based on user-specified levels of the Case 2 constituents. Then, the concentrations were retrieved for each satellite based on the sensor models, and the error was found with respect to the known levels for each spectral curve. Thus, we present an approximation of how effective the Operational Land Imager will be for monitoring Case 2 waters, compared to existing sensors.

Nevins, Robert; Gerace, Aaron

2013-05-01

252

Radar Interferometry Time Series Analysis and Tools  

NASA Astrophysics Data System (ADS)

We consider the use of several multi-interferogram analysis techniques for identifying transient ground motions. Our approaches range from specialized InSAR processing for persistent scatterer and small baseline subset methods to the post-processing of geocoded displacement maps using a linear inversion-singular value decomposition solution procedure. To better understand these approaches, we have simulated sets of interferograms spanning several deformation phenomena, including localized subsidence bowls with constant velocity and seasonal deformation fluctuations. We will present results and insights from the application of these time series analysis techniques to several land subsidence study sites with varying deformation and environmental conditions, e.g., arid Phoenix and coastal Houston-Galveston metropolitan areas and rural Texas sink holes. We consistently find that the time invested in implementing, applying and comparing multiple InSAR time series approaches for a given study site is rewarded with a deeper understanding of the techniques and deformation phenomena. To this end, and with support from NSF, we are preparing a first-version of an InSAR post-processing toolkit to be released to the InSAR science community. These studies form a baseline of results to compare against the higher spatial and temporal sampling anticipated from TerraSAR-X as well as the trade-off between spatial coverage and resolution when relying on ScanSAR interferometry.

Buckley, S. M.

2006-12-01

253

Automatic Crowd Analysis from Very High Resolution Satellite Images  

NASA Astrophysics Data System (ADS)

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

Sirmacek, B.; Reinartz, P.

2011-04-01

254

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

255

a Hierarchical Image Matching Method for Stereo Satellite Imagery  

NASA Astrophysics Data System (ADS)

Image matching is an essential and difficult task in digital photogrammetry and computer vision. This paper presents a triangulationbased hierarchical image matching algorithm for stereo satellite imagery. It uses a coarse-to-fine hierarchical strategy and combines feature points and grid points to provide a dense, precise and reliable matching result. First, some seed points are extracted at the top level of image pyramid using the SIFT algorithm with RANSAC approach to remove mismatches and enhance robustness. These points are used to construct an initial triangulation. Then, feature point and grid point matching are conducted based on the triangle constraint. In the process of hierarchical image matching, the parallaxes from upper levels are transferred to levels beneath with triangle constraint and epipolar geometrical constraint. At last, outliers are detected and removed based on local smooth constraint of parallax. Also, bidirectional image matching method is adopted to verify the matching results and increase the number of matched points. Experiments with ALOS images show that the proposed method has the capacity to generate reliable and dense matching results for surface reconstruction from stereo satellite imagery.

Yan, F.; Wang, W.; Liu, S.; Chen, W.

2013-07-01

256

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

257

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

258

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

259

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

260

Satellite images for recognition of landscape and landuse changes  

Microsoft Academic Search

Satellite images from 1985, 1988 and 1996 are used for a visual interpretation of the coastal area around João Pessoa, situated in the northeast Brazilian state of Para??ba. The interpretation resulted in the recognition of several changes and geographic relationships: changes in the course of the river Para??ba over a distance of several kilometres, coastline changes resulting from the S–N

K. A. Ulbricht; W. D. Heckendorff

1998-01-01

261

Time Series Analysis of SOLSTICE Measurements  

NASA Astrophysics Data System (ADS)

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

Wen, G.; Cahalan, R. F.

2003-12-01

262

A satellite imager for atmospheric X-rays  

NASA Astrophysics Data System (ADS)

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

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

1985-02-01

263

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

264

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

NASA Astrophysics Data System (ADS)

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

Hong, S.; Wdowinski, S.

2013-05-01

265

Modelling high-frequency economic time series  

NASA Astrophysics Data System (ADS)

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

Tang, Lei-Han; Huang, Zhi-Feng

2000-12-01

266

Tremor classification and tremor time series analysis  

NASA Astrophysics Data System (ADS)

The separation between physiologic tremor (PT) in normal subjects and the pathological tremors of essential tremor (ET) or Parkinson's disease (PD) was investigated on the basis of monoaxial accelerometric recordings of 35 s hand tremor epochs. Frequency and amplitude were insufficient to separate between these conditions, except for the trivial distinction between normal and pathologic tremors that is already defined on the basis of amplitude. We found that waveform analysis revealed highly significant differences between normal and pathologic tremors, and, more importantly, among different forms of pathologic tremors. We found in our group of 25 patients with PT and 15 with ET a reasonable distinction with the third momentum and the time reversal invariance. A nearly complete distinction between these two conditions on the basis of the asymmetric decay of the autocorrelation function. We conclude that time series analysis can probably be developed into a powerful tool for the objective analysis of tremors.

Deuschl, Günther; Lauk, Michael; Timmer, Jens

1995-03-01

267

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

268

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

269

Modeling of aggregated hydrologic time series  

NASA Astrophysics Data System (ADS)

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

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

1986-10-01

270

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

271

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

Microsoft Academic Search

Although there have been many studies conducted on the use of satellite remote sensing for water quality monitoring and assessment\\u000a in inland water bodies, relatively few studies have considered the problem of atmospheric intervention of the satellite signal.\\u000a The problem is especially significant when using time series multi-spectral satellite data to monitor water quality surveillance\\u000a in inland waters such as

Diofantos G. Hadjimitsis; Chris Clayton

2009-01-01

272

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

273

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

274

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

275

Incremental stock time series data delivery and visualization  

Microsoft Academic Search

SB-Tree is a binary tree data structure proposed to represent time series according to the importance of data points. Its use in stock data management is distinguished by preserving the critical data points' attribute values, retrieving time series data according to the importance of data points and facilitating multi-resolution time series retrieval. As new stock data are available continuously, an

Tak-chung Fu; Fu-lai Chung; Pui-ying Tang; Robert Wing Pong Luk; Chak-man Ng

2005-01-01

276

Evolutionary Time Series Segmentation for Stock Data Mining  

Microsoft Academic Search

Stock data in the form of multiple time series are difficult to process, analyze and mine. However, when they can be transformed into meaningful symbols like technical patterns, it becomes easier. Most recent work on time series queries concentrates only on how to identify a given pattern from a time series. Researchers do not consider the problem of identifying a

Korris Fu-lai Chung; Tak-chung Fu; Robert W. P. Luk; Vincent T. Y. Ng

2002-01-01

277

Extracting Biochemical Reaction Kinetics from Time Series Data  

E-print Network

Extracting Biochemical Reaction Kinetics from Time Series Data Edmund J. Crampin1 , Patrick E. Mc consider the problem of inferring kinetic mechanisms for biochemical reactions from time series data. Using systems. In this paper we discuss an approach to inferring reaction kinetics from time series data using

McSharry, Patrick E.

278

Extracting Biochemical Reaction Kinetics from Time Series Data  

E-print Network

Extracting Biochemical Reaction Kinetics from Time Series Data Edmund J. Crampin 1? , Patrick E. Mc consider the problem of inferring kinetic mechanisms for biochemical reactions from time series data. Using systems. In this paper we discuss an approach to inferring reaction kinetics from time series data using

McSharry, Patrick E.

279

Local Support Vector Regression for Financial Time Series Prediction  

Microsoft Academic Search

We consider the regression problem for financial time series. Typically, financial time series are non-stationary and volatile in nature. Because of its good generalization power and the tractability of the problem, the Support Vector Regression (SVR) has been extensively applied in financial time series prediction. The standard SVR adopts thep-norm (p =1 or 2) to model the functional complexity of

Kaizhu Huang; Haiqin Yang; Irwin King; Michael R. Lyu

2006-01-01

280

A multivariate time series clustering approach for crime trends prediction  

Microsoft Academic Search

In recent past, there is an increased interest in time series clustering research, particularly for finding useful similar trends in multivariate time series in various applied areas such as environmental research, finance, and crime. Clustering multivariate time series has potential for analyzing large volume of crime data at different time points as law enforcement agencies are interested in finding crime

B. Chandra; Manish Gupta

2008-01-01

281

Simulations of Non-resolved, Infrared Imaging of Satellites  

NASA Astrophysics Data System (ADS)

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

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

282

Building detection based on saliency for high resolution satellite images  

NASA Astrophysics Data System (ADS)

For building detection from single very high spatial resolution (VHR) satellite images, we take advantage of visual saliency and Bayesian model to rapidly locate roof-top areas. We firstly generate saliency map of an image by a salient contrast filter using low-level feature. This filter distinguishes salient pixels if a pixel is visually different from its surroundings in color or texture. Secondly, a Bayesian model is proposed to generate all closed rectangular contours as mid-level content in the image. We suggest the area enclosed by contour corresponds to high saliency values. Finally, the roof-top areas are extracted by fusing different level information mentioned above. Experimental results demonstrate the effectiveness of our algorithm.

Yang, Ping; Jiang, Zhiguo; Feng, Hao; Ma, Yibing

2013-10-01

283

An observed 20-year time series of Agulhas leakage  

NASA Astrophysics Data System (ADS)

We provide a time series of Agulhas leakage anomalies over the last 20-years from satellite altimetry. Until now, measuring the interannual variability of Indo-Atlantic exchange has been the major barrier in the investigation of the dynamics and large scale impact of Agulhas leakage. We compute the difference of transport between the Agulhas Current and Agulhas Return Current, which allows us to deduce Agulhas leakage. The main difficulty is to separate the Agulhas Return Current from the southern limb of the subtropical "supergyre" south of Africa. For this purpose, an algorithm that uses absolute dynamic topography data is developed. The algorithm is applied to a state-of-the-art ocean model. The comparison with a Lagrangian method to measure the leakage allows us to validate the new method. An important result is that it is possible to measure Agulhas leakage in this model using the velocity field along a section that crosses both the Agulhas Current and the Agulhas Return Current. In the model a good correlation is found between measuring leakage using the full depth velocities and using only the surface geostrophic velocities. This allows us to extend the method to along-track absolute dynamic topography from satellites. It is shown that the accuracy of the mean dynamic topography does not allow us to determine the mean leakage but that leakage anomalies can be accurately computed.

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

2014-07-01

284

Correcting and combining time series forecasters.  

PubMed

Combined forecasters have been in the vanguard of stochastic time series modeling. In this way it has been usual to suppose that each single model generates a residual or prediction error like a white noise. However, mostly because of disturbances not captured by each model, it is yet possible that such supposition is violated. The present paper introduces a two-step method for correcting and combining forecasting models. Firstly, the stochastic process underlying the bias of each predictive model is built according to a recursive ARIMA algorithm in order to achieve a white noise behavior. At each iteration of the algorithm the best ARIMA adjustment is determined according to a given information criterion (e.g. Akaike). Then, in the light of the corrected predictions, it is considered a maximum likelihood combined estimator. Applications involving single ARIMA and artificial neural networks models for Dow Jones Industrial Average Index, S&P500 Index, Google Stock Value, and Nasdaq Index series illustrate the usefulness of the proposed framework. PMID:24239986

Firmino, Paulo Renato A; de Mattos Neto, Paulo S G; Ferreira, Tiago A E

2014-02-01

285

Spectrophotometric Time Series of ? Carinae's Great Eruption  

NASA Astrophysics Data System (ADS)

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

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

2014-02-01

286

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

287

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

288

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

NASA Astrophysics Data System (ADS)

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

Vermeesch, P.

2012-04-01

289

Detection of cavity migration risks using radar interferometric time series  

NASA Astrophysics Data System (ADS)

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

Chang, L.; Hanssen, R. F.

2012-12-01

290

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

291

Intercomparison of six Mediterranean zooplankton time series  

NASA Astrophysics Data System (ADS)

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

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

2012-05-01

292

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

293

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

294

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

295

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

NASA Technical Reports Server (NTRS)

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

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

1986-01-01

296

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

297

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

298

Digital surface model generation from CORONA satellite images  

NASA Astrophysics Data System (ADS)

Digital surface models (DSMs) are used for various analyses in environmental science, e.g. for erosion and water studies. Aerial photos and maps, which are necessary for the extraction of DSMs, often do not exist due to financial or political reasons. This situation can be also encountered in Morocco and, in particular, a test area of the international research project IMPETUS was used in this study. Therefore, stereo satellite images of CORONA have been used, as they allow DSM generation, have a ground resolution of 1.83 m, reasonable price (US$12-18 per filmstrip of 188×14 km) and large coverage (especially of Asia and eastern Europe). The software program ERDAS IMAGINE OrthoBASE Pro was used to generate DSMs automatically from CORONA satellite images with best vertical accuracy of about 10 m and planimetric accuracy of about 3 m. These DSMs could afterwards be used to generate orthoimages, e.g. for mapping change detection and generating thematic maps or land use classifications.

Altmaier, Angela; Kany, Christoph

299

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

NSDL National Science Digital Library

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

Whittaker, Tom; Ackerman, Steve

300

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

301

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

302

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

NASA Astrophysics Data System (ADS)

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

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

2009-10-01

303

Optical Auroral Imaging Conjugate to the FAST Satellite  

NASA Astrophysics Data System (ADS)

Most satellite data are essentially point observations made along the satellite orbital track. Although reasonable assumptions can be made in many cases, fundamentally, one cannot differentiate between spatial and temporal structures. To overcome this shortcoming we recorded video images of the aurora conjugate to the FAST satellite from an instrumented jet aircraft. A total of 29 flights were made during Moon-down periods in January and February of 1997 and 1998. In each flight the aircraft was flown along the FAST satellite path such that the aurora of interest would be in the magnetic zenith at the time of the pass. A variety of aurorae were encountered between 20 and 23 MLT. We present the main findings from our analysis of these conjunctions. The auroral luminosity measured by the cameras corresponds well with the precipitating energy flux measured by FAST. In the discrete aurora the luminosity is accounted for by energetic electrons, but in the diffuse (evening) aurora equatorward of the discrete arcs proton precipitation can contribute significantly. In relatively quiet multiple arcs, the observed orientation of the arcs agree well with the orientation of the field-aligned current sheets derived from the FAST magnetometer data implying that the currents represent the arc morphology. In dynamic aurora the geometry of the current sheets are more complicated and the relationship does not hold. Complicated electron distribution functions and field measurements are also observed as arcs change morphology and propagate westward. There is a current threshold (at 100 km altitude) of approximately 2.2 ? A/m2 in field-aligned current density for the formation of large spirals as for example often seen in the westward traveling surge. Black aurora is found to be associated with spatially restricted regions within a region of generally isotropic pitch angle distributions in which the strong pitch angle diffusion is suppressed at energies greater than 2 keV. If downgoing electric fields existed in these regions, they were too small for any particle or field measurements on FAST to confirm their existence. In a few cases of flickering aurora the data indicate that the modulating process extends below the altitude of the satellite. The FAST data, with its very high temporal resolution, combined with the auroral video provide a unique data set for the study of small scale temporal and spatial structures in the aurora that has yet to be fully explored.

Peticolas, L. M.; Stenbaek-Nielsen, H. C.

2003-12-01

304

Measuring persistence in time series of temperature anomalies  

NASA Astrophysics Data System (ADS)

Studies on persistence are important for the clarification of statistical properties of the analyzed time series and for understanding the dynamics of the systems which create these series. In climatology, the analysis of the autocorrelation function has been the main tool to investigate the persistence of a time series. In this paper, we propose to use a more sophisticated econometric instrument. Using this tool, we obtain an estimate of the persistence in global land and ocean and hemispheric temperature time series.

Triacca, Umberto; Pasini, Antonello; Attanasio, Alessandro

2014-11-01

305

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

NASA Astrophysics Data System (ADS)

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

Casu, Francesco; Manconi, Andrea

2013-04-01

306

Time-series photometry of the O4 I(n)fp star ? Puppis  

NASA Astrophysics Data System (ADS)

We report a time-series analysis of the O4 I(n)fp star ?Pup, based on optical photometry obtained with the SMEI (Solar Mass Ejection Imager) instrument on the Coriolis satellite, 2003-2006. A single astrophysical signal is found, with P = 1.780 938 ± 0.000 093 d and a mean semi-amplitude of 6.9 ± 0.3 mmag. There is no evidence for persistent coherent signals with semi-amplitudes in excess of ˜2 mmag on any of the time-scales previously reported in the literature. In particular, there is no evidence for a signature of the proposed rotation period, ˜5.1 d; ? Pup is therefore probably not an oblique magnetic rotator. The 1.8-d signal varies in amplitude by a factor ˜2 on time-scales of 10-100d (and probably by more on longer time-scales), and exhibits modest excursions in phase, but there is no evidence for systematic changes in period over the 1000-d span of our observations. Rotational modulation and stellar-wind variability appear to be unlikely candidates for the underlying mechanism; we suggest that the physical origin of the signal may be pulsation associated with low-? oscillatory convection modes.

Howarth, Ian D.; Stevens, Ian R.

2014-12-01

307

Application of GPS Technique to Calibration of Satellite Images  

NASA Astrophysics Data System (ADS)

This paper presents the results of accuracy of static GPS method used for calibration of satellite images to produce maps in the scale of 1:50000. The analysis of accuracy covers dual-frequency ionospheric-free solutions which were used in elaboration of GPS data. The vectors in length from 10 to 180 kilometers and short fifteen-minute sessions of GPS observations were used. The GPS measurements were performed in the summer 2002 in the part of Poland including about 700 control points. The GPS measurements were carried out with various GPS receivers such as: Ashtech MD-12, Ashtech Z-12, Ashtech FX, Ashtech Z-Surveyor. In our case the required accuracy of control points should be better than one meter. The results of performed analysis show that dual-frequency ionospheric-free solutions successfully fulfill the requirements.

Bakula, M.; Oszczak, S.

2003-04-01

308

Using a weighted zeroblock coder for satellite image compression  

NASA Astrophysics Data System (ADS)

In this paper, we propose an embedded satellite image compression method using Weighted ZeroBlock Coding (WZBC) and optimal sorting. In order to reduce average codeword length, Set Partition Embedded block (SPECK) and Embedded ZeroBlock Coder (EZBC) both encode significant block-sets with fixed-length bits, while WZBC assigns different-length bits to encode block-sets which contain different numbers of significant subblocks. In view of the context correlation among coefficients/blocks, WZBC employs a weight context to optimize the scanning order of the significance testing and the ratedistortion performance. Experimental results show that the proposed WZBC in binary coding mode provides excellent coding performance compared with those of SPECK and Set Partitioning In Hierarchical Trees (SPIHT) which use arithmetic coding, and can even closely approach that of JPEG2000. When arithmetic coding is extensively used, the proposed method has clear advantages.

Wu, Jiaji; Xing, Yan; Jeong, Jechang; Shi, Guangming; Jiao, Licheng

2010-07-01

309

Galileo's first images of Jupiter and the Galilean satellites  

USGS Publications Warehouse

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

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

1996-01-01

310

An adaptive technique to maximize lossless image data compression of satellite images  

NASA Technical Reports Server (NTRS)

Data compression will pay an increasingly important role in the storage and transmission of image data within NASA science programs as the Earth Observing System comes into operation. It is important that the science data be preserved at the fidelity the instrument and the satellite communication systems were designed to produce. Lossless compression must therefore be applied, at least, to archive the processed instrument data. In this paper, we present an analysis of the performance of lossless compression techniques and develop an adaptive approach which applied image remapping, feature-based image segmentation to determine regions of similar entropy and high-order arithmetic coding to obtain significant improvements over the use of conventional compression techniques alone. Image remapping is used to transform the original image into a lower entropy state. Several techniques were tested on satellite images including differential pulse code modulation, bi-linear interpolation, and block-based linear predictive coding. The results of these experiments are discussed and trade-offs between computation requirements and entropy reductions are used to identify the optimum approach for a variety of satellite images. Further entropy reduction can be achieved by segmenting the image based on local entropy properties then applying a coding technique which maximizes compression for the region. Experimental results are presented showing the effect of different coding techniques for regions of different entropy. A rule-base is developed through which the technique giving the best compression is selected. The paper concludes that maximum compression can be achieved cost effectively and at acceptable performance rates with a combination of techniques which are selected based on image contextual information.

Stewart, Robert J.; Lure, Y. M. Fleming; Liou, C. S. Joe

1994-01-01

311

Satellite image analysis for surveillance, vegetation and climate change  

SciTech Connect

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

Cai, D Michael [Los Alamos National Laboratory

2011-01-18

312

A very compact imaging spectrometer for the micro-satellite STSAT3  

Microsoft Academic Search

STSAT3, a ?150-kg micro-satellite, is the third experimental micro-satellite of the STSAT (Science Technology Satellite) series designated for the Long-Term Plan for Korea's Space Development by the Ministry of Education, Science and Technology of Korea. A Compact Imaging Spectrometer (COMIS) for use in the STSAT3 micro-satellite is currently under construction. It is scheduled to be launched into a low sun-synchronous

Jun Ho Lee; Kyung In Kang; Jong Ho Park

2011-01-01

313

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

E-print Network

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

314

Empirical Correction of Residual Error in the ICESat-1 Altimetry Time Series at Lake Vostok  

Microsoft Academic Search

Antarctica's subglacial Lake Vostok is a large (~15,690 km2), relatively flat feature that is located in a broader, stable ice sheet surface. These characteristics make it well suited for calibration of satellite altimetry time series data and, critically, for assessment of elevation accuracy. Lake Vostok's stability is documented by analysis of multi-year GPS data from a network of stations around

C. A. Shuman; D. J. Harding; J. P. Dimarzio; X. Sun; V. P. Suchdeo; A. Brenner

2009-01-01

315

VIIRS Nighttime Lights: Advances in Satellite Low-Light Imaging  

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

316

Trajectory Boundary Modeling of Time Series for Anomaly Detection  

Microsoft Academic Search

We a ddress the problem of online detection o f unanticipated modes of mechanical failure given a small set of time series under normal conditions, with the requirement that the anomaly detection model be manually verifiable a nd modifiable. We specify a set of time series features, which are linear combinations of the c urrent and p ast values, and

Matthew V. Mahoney; Philip K. Chan

317

Simulation of time series by distorted Gaussian processes  

NASA Technical Reports Server (NTRS)

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

Greenhall, C. A.

1977-01-01

318

Crude oil price forecasting using fuzzy time series  

Microsoft Academic Search

Predicting oil price movements is very important for investors. Fuzzy time series which combine people's subjective attitude and objective history values can help people to solve forecasting problems. It has been applied to many areas such as stock index, university enrollments, exchange rates and tourism forecasting. This paper brings fuzzy time series into short term crude oil price forecasting. We

Xiaoxiao Zhang; Qizong Wu; Jianfeng Zhang

2010-01-01

319

Improved singular spectrum analysis for time series with missing data  

NASA Astrophysics Data System (ADS)

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

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

2014-12-01

320

Discovering Ecosystem Models from Time-Series Data  

E-print Network

Discovering Ecosystem Models from Time-Series Data Dileep George, 1 Kazumi Saito, 2 Pat Langley, 1. Ecosystem models are used to interpret and predict the in- teractions of species and their environment. In this paper, we address the task of inducing ecosystem models from background knowledge and time- series data

Langley, Pat

321

Learning to transform time series with a few examples.  

PubMed

We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account. PMID:17699921

Rahimi, Ali; Recht, Ben; Darrell, Trevor

2007-10-01

322

Measuring Time Series Predictability Using Support Vector Regression  

Microsoft Academic Search

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a

João Ricardo Sato; Sergi Costafreda; Pedro Alberto Morettin; Michael John Brammer

2008-01-01

323

ON TIME SERIES ANALYSIS OF PUBLIC HEALTH AND BIOMEDICAL DATA  

Microsoft Academic Search

A time series is a sequence of observations made over time. Examples in public health include daily ozone concentrations, weekly admissions to an emergency department or annual expenditures on health care in the United States. Time series models are used to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in

Scott L. Zeger; Rafael Irizarry; Roger D. Peng

2006-01-01

324

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

Fernandez, Thomas

325

Forecasting, Structural Time Series Models and the Kalman Filter  

Microsoft Academic Search

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

Andrew C. Harvey

1989-01-01

326

Dynamic Modelling of Chaotic Time Series with Neural Networks  

E-print Network

that produced the time series [7]. Presently one still does not have a capable theory to guarantee behavior. In order to use this theory, one needs to address the choices of predictor implementation. Due that produced the time series [1], but recently nonlinear models have also been proposed to replace the linear

Slatton, Clint

327

Time Series of North Pacific Volcanic Eruptions  

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

328

Aerosol Properties from Multi-angle Satellite Imaging  

NASA Astrophysics Data System (ADS)

The MISR instrument, flying aboard the NASA Earth Observing System's Terra satellite, is pioneering the use of multi-angle imaging to monitor aerosols globally, from space. MISR obtains nine along-track images at view angles ranging from +70o through nadir to -70o, in each of four wavelengths, near-simultaneously. The instrument systematically covers air-mass-factors between one and three, and in mid-latitudes, samples scattering angles extending from about 60o to 160o. These data contain information about particle size distribution, shape, composition, and amount. Large air-mass-factors provide sensitivity to optical depth even for very thin hazes. Provided the aerosol optical depth is of order 0.15 or larger, size- and shape-discrimination makes it possible to distinguish non-spherical mineral dust and thin cirrus from spherical pollution particles over dark water, and to obtain information about the single scattering albedo as well as the size distribution of pollution components. If discrete features are visible in aerosol plumes, the height of the aerosol itself is obtained via stereo-matching. Such information is of value for identifying aerosol sources and sinks, for assessing the direct radiative impact of aerosols on global climate, and for aerosol transport model validation. Having constraints on aerosol micro-physical properties also improves the accuracy of optical depth retrievals. This work is performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Kahn, R. A.; Martonchik, J. V.; Diner, D. J.; Kalashnikova, O.

2004-05-01

329

Estimation of Vegetation Height Through Satellite Image Texture Analysis  

NASA Astrophysics Data System (ADS)

Vegetation height plays a crucial role in various ecological and environmental applications, such as biodiversity assessment and monitoring, landscape characterization, conservation planning and disaster management. Its estimation is traditionally based on in situ measurements or airborne Light Detection And Ranging (LiDAR) sensors. However, such methods are often proven insufficient in covering large area landscapes due to high demands in cost, labor and time. Considering a multispectral image from a passive satellite sensor as the only available source of information, we propose and evaluate new ways of discriminating vegetated habitat species according to their height, through calculation of texture analysis measures, based on local variance, entropy and local binary patterns. The methodology is applied in a Quickbird image of Le Cesine protected site, Italy. The proposed methods are proven particularly effective in discriminating low and mid phanerophytes from tall phanerophytes, having a height of less and more than 2 meters, respectively. The results indicate a promising alternative in vegetation height estimation when in situ or LiDAR data are not available or affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.

Petrou, Z. I.; Tarantino, C.; Adamo, M.; Blonda, P.; Petrou, M.

2012-07-01

330

Mg II core-to-wing index: Comparison of SBUV2 and SOLSTICE time series  

NASA Astrophysics Data System (ADS)

The Mg II core-to-wing index is a ratio of the Mg II chromospheric emission at 280 nm to the photospheric radiation in the line wings and is used as an indicator of solar activity. Since October 1991, the Solar-Stellar Irradiance Comparison Experiment (SOLSTICE) has made daily irradiance measurements in the range 119-420 nm from the Upper Atmosphere Research Satellite (UARS). A new Mg II index, based on the SOLSTICE observations at a spectral resolution of 0.24 nm, is presented and compared to previous measurements. Spectral irradiance measurements of the Mg II doublet at low spectral resolution (~1nm) have been made by the Solar Backscatter UltraViolet (SBUV) instrument on Nimbus 7 since November 1978 and subsequently by the SBUV2 instruments on NOAA 9 and NOAA 11 satellites. We compare the SOLSTICE data with the Mg II time series derived from SBUV2 data by the groups at the National Oceanic and Atmospheric Administration (NOAA) and at the Goddard Space Flight Center (GSFC). SOLSTICE data are convolved to the lower SBUV2 resolution, and the NOAA and GSFC algorithms are then applied to this data set. The SOLSTICE Mg II indices constructed in this manner simulate the SBUV2 indices and can be used to validate the SBUV2 time series and identify data problems. From our analysis, we conclude that the NOAA Mg II time series is the most consistent during the period 1978-1994. The new GSFC Mg II time series has comparable accuracy for the period starting in 1989. We also derive the linear transformation equations required to put the high- and low-resolution time series onto common scales.

de Toma, Giuliana; White, Oran R.; Knapp, Barry G.; Rottman, Gary J.; Woods, Thomas N.

1997-02-01

331

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

SciTech Connect

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

Marois, C

2007-01-04

332

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

E-print Network

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

Horiuchi, Timothy K.

333

An ISAR imaging algorithm for the space satellite based on empirical mode decomposition theory  

NASA Astrophysics Data System (ADS)

Currently, high resolution imaging of the space satellite is a popular topic in the field of radar technology. In contrast with regular targets, the satellite target often moves along with its trajectory and simultaneously its solar panel substrate changes the direction toward the sun to obtain energy. Aiming at the imaging problem, a signal separating and imaging approach based on the empirical mode decomposition (EMD) theory is proposed, and the approach can realize separating the signal of two parts in the satellite target, the main body and the solar panel substrate and imaging for the target. The simulation experimentation can demonstrate the validity of the proposed method.

Zhao, Tao; Dong, Chun-zhu

2014-11-01

334

Sensor-Generated Time Series Events: A Definition Language  

PubMed Central

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

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

2012-01-01

335

Simultaneous observations of equatorial plasma depletion by IMAGE and ROCSAT-1 satellites  

Microsoft Academic Search

(1) Simultaneous observations of the equatorial ionosphere by the ROCSAT-1 and IMAGE satellites were used to study zonal propagation characteristics of equatorial plasma bubbles. IMAGE far ultraviolet (FUV) nighttime images have indicated signatures of depression in the brightness of equatorial airglow arcs. Using the list of airglow brightness depression events observed by IMAGE, we surveyed ROCSAT-1 Ionospheric Plasma and Electrodynamics

Chin S. Lin; Thomas J. Immel; Huey-Ching Yeh; Stephen B. Mende; J. L. Burch

2005-01-01

336

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

E-print Network

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

337

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

E-print Network

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

338

Characterizing time series: when Granger causality triggers complex networks  

NASA Astrophysics Data System (ADS)

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

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

2012-08-01

339

Modeling multivariate covariance nonstationary time series and their dependency structure  

SciTech Connect

The parametric modeling of covariance nonstationary time series and the computation of their changing interdependency structure from the fitted model are treated. The nonstationary time series are modeled by a multivariate time varying autoregressive (AR) model. The time evolution of the AR parameters is expressed as linear combinations of discrete Legendre orthogonal polynomial functions of time. The model is fitted by a Householder transformation-AIC order determination, regression subset selection method. The computation of the instantaneous dependence, feedback and causality structure of the time series from the fitted model, is discussed. An example of the modeling and determination of instantaneous causality in a human implanted electrode seizure event EEG is shown.

Gersch, W.

1985-08-01

340

Biological visual attention guided automatic image segmentation with application in satellite imaging  

NASA Astrophysics Data System (ADS)

Taking inspiration from the significantly superior performance of humans to extract and interpret visual information, the exploitation of biological visual mechanisms can contribute to the improvement of the performance of computational image processing systems. Computational models of visual attention have already been shown to significantly improve the speed of scene understanding by attending only the regions of interest, while distributing the resources where they are required. However, there are only few attention-based computational systems that have been used in practical applications dealing with real data and up to now, none of the computational attention models was demonstrated to work under a wide range of image content, characteristics and scales such as those encountered in satellite imaging. This paper outlines some of the difficulties that the current generation of visual attention-inspired models encounter when dealing with satellite images. It then proposes a novel algorithm for automatic image segmentation and regions of interest search that combines elements of human visual attention with Legendre moments applied on the probability density function of color histograms. The experimental results demonstrate that the proposed approach obtains better results than one of the most evolved current computational attention model proposed in the literature.

Sina, M. I.; Cretu, A.-M.; Payeur, P.

2012-03-01

341

Satellite-borne high-resolution 3D active imaging lidar  

Microsoft Academic Search

Owing to the notable advantages over range, resolution and accuracy, satellite-borne high-resolution 3D imaging lidar has found widespread applications in aerospace reconnaissance, deep-space detection, earth observation, disaster evaluation, and so on. Based on the principle of 3D laser imaging, the typical satellite-borne high-resolution 3D active imaging lidar systems are reviewed and the development trend is analyzed. Some conclusions can be

Fangpei Zhang; Haizhong Xue; Zhongjie Liu; Yubing Zhang; Yuhua Xing; Guangyan Dong; Shengguo Wang; Xiafei Wu; Yingxiang Song

2011-01-01

342

Identification of environmental anomaly hot spots in West Africa from time series of NDVI and rainfall  

NASA Astrophysics Data System (ADS)

Studies of the impact of human activity on vegetation dynamics of the Sahelian belt of Africa have been recently re-invigorated by new scientific findings that highlighted the primary role of climate in the drought crises of the 1970s-1980s. Time series of satellite observations revealed a re-greening of the Sahelian belt that indicates no noteworthy human effect on vegetation dynamics at sub continental scale from the 1980s to late 1990s. However, several regional/local crises related to natural resources occurred in the last decades despite the re-greening thus underlying that more detailed studies are needed. In this study we used time-series (1998-2010) of SPOT-VGT NDVI and FEWS-RFE rainfall estimates to analyse vegetation - rainfall correlation and to map areas of local environmental anomalies where significant vegetation variations (increase/decrease) are not fully explained by seasonal changes of rainfall. Some of these anomalous zones (hot spots) were further analysed with higher resolution images Landsat TM/ETM+ to evaluate the reliability of the identified anomalous behaviour and to provide an interpretation of some example hot spots. The frequency distribution of the hot spots among the land cover classes of the GlobCover map shows that increase in vegetation greenness is mainly located in the more humid southern part and close to inland water bodies where it is likely to be related to the expansion/intensification of irrigated agricultural activities. On the contrary, a decrease in vegetation greenness occurs mainly in the northern part (12°-15°N) in correspondence with herbaceous vegetation covers where pastoral and cropping practices are often critical due to low and very unpredictable rainfall. The results of this study show that even if a general positive re-greening due to increased rainfall is evident for the entire Sahel, some local anomalous hot spots exist and can be explained by human factors such as population growth whose level reaches the ecosystem carrying capacity as well as population displacement leading to vegetation recovery.

Boschetti, Mirco; Nutini, Francesco; Brivio, Pietro Alessandro; Bartholomé, Etienne; Stroppiana, Daniela; Hoscilo, Agata

2013-04-01

343

Earth Exploration Toolbook Chapter: Measuring Distance and Area in Satellite Images  

NSDL National Science Digital Library

This chapter describes how to set a scale and measure distances and areas on satellite images. Using ImageJ, a freely available image analysis program that runs on most operating systems, users set the spatial calibration of an image, then select and measure distances and areas on it. The measurement results are reported in real-world units. The technique is most useful and accurate for nadir view (straight down) images. In this chapter, users examine satellite images of the Aral Sea, which has shrunk dramatically since 1960 because the rivers that flow into it have been tapped for irrigation. Users access satellite images of the region, then set a scale and measure the width of the sea each year. On another set of images, they highlight areas that represent water and measure them to see how these areas of the sea changed.

344

Improving multispectral satellite image compression using onboard subpixel registration  

NASA Astrophysics Data System (ADS)

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

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

2013-09-01

345

A controlled time-series trial of clinical reminders: using computerized firm systems to make quality improvement research a routine part of mainstream practice.  

PubMed Central

OBJECTIVE: To explore the feasibility of conducting unobtrusive interventional research in community practice settings by integrating firm-system techniques with time-series analysis of relational-repository data. STUDY SETTING: A satellite teaching clinic divided into two similar, but geographically separated, primary care group practices called firms. One firm was selected by chance to receive the study intervention. Forty-two providers and 2,655 patients participated. STUDY DESIGN: A nonrandomized controlled trial of computer-generated preventive reminders. Net effects were determined by quantitatively combining population-level data from parallel experimental and control interrupted time series extending over two-month baseline and intervention periods. DATA COLLECTION: Mean rates at which mammography, colorectal cancer screening, and cholesterol testing were performed on patients due to receive each maneuver at clinic visits were the trial's outcome measures. PRINCIPAL FINDINGS: Mammography performance increased on the experimental firm by 154 percent (0.24 versus 0.61, p = .03). No effect on fecal occult blood testing was observed. Cholesterol ordering decreased on both the experimental (0.18 versus 0.1 1, p = .02) and control firms (0.13 versus 0.07, p = .03) coincident with national guidelines retreating from recommending screening for young adults. A traditional uncontrolled interrupted time-series design would have incorrectly attributed the experimental-firm decrease to the introduction of reminders. The combined analysis properly indicated that no net prompting effect had occurred, as the difference between firms in cholesterol testing remained stochastically stable over time (0.05 versus 0.04, p = .75). A logistic-regression analysis applied to individual-level data produced equivalent findings. The trial incurred no supplementary data collection costs. CONCLUSIONS: The apparent validity and practicability of our reminder implementation study should encourage others to develop computerized firm systems capable of conducting controlled time-series trials. Images Fig. 1 PMID:10737451

Goldberg, H. I.; Neighbor, W. E.; Cheadle, A. D.; Ramsey, S. D.; Diehr, P.; Gore, E.

2000-01-01

346

Phase distribution and phase correlation of financial time series.  

PubMed

The scaling, phase distribution, and phase correlation of financial time series are investigated based on the Dow Jones Industry Average and NASDAQ 10-min intraday data for a period from 1 Aug. 1997 to 31 Dec. 2003. The returns of the two indices are shown to have nice scaling behaviors and belong to stable distributions according to the criterion of Lévy's alpha stable distribution condition. An approach catching characteristic features of financial time series based on the concept of instantaneous phase is further proposed to study the phase distribution and correlation. Analysis of the phase distribution concludes that return time series fall into a class which is different from other nonstationary time series. The correlation between returns of the two indices probed by the distribution of phase difference indicates that there was a remarkable change of trading activities after the event of the 9/11 attack, and this change persisted in later trading activities. PMID:16486227

Wu, Ming-Chya; Huang, Ming-Chang; Yu, Hai-Chin; Chiang, Thomas C

2006-01-01

347

Fractal and natural time analysis of geoelectrical time series  

NASA Astrophysics Data System (ADS)

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

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

2013-05-01

348

14.384 Time Series Analysis, Fall 2007  

E-print Network

The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain ...

Mikusheva, Anna, 1976-

349

Nonstationary time series prediction combined with slow feature analysis  

NASA Astrophysics Data System (ADS)

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. This paper presents a new technique of combining the driving force of a time series obtained using the Slow Feature Analysis (SFA) approach, then introducing the driving force into a predictive model to predict non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill.

Wang, G.; Chen, X.

2015-01-01

350

Symbolic Dynamic Analysis of Transient Time Series for Fault  

E-print Network

test-case generator. [DOI: 10.1115/1.4007699] Keywords: transient time-series analysis, symbolic dynamics, fault detection, aircraft gas turbine engines 1 Introduction Performance monitoring of aircraft

Ray, Asok

351

Mixed Membership Models for Time Series Emily B. Fox  

E-print Network

20 Mixed Membership Models for Time Series Emily B. Fox Department of Statistics, University Nonparametric Mixed Membership Models ............................... 420 Hierarchical Dirichlet Process Topic .......................................................... 425 20.2.1 Markov Switching Processes as a Mixed Membership Model ........................ 426 Hidden

Airoldi, Edoardo "Edo"

352

Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection  

PubMed Central

Heliotropic leaf movement or leaf ‘solar tracking’ occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (?NDVImo-mi) and between winter and summer (?NDVIW-S). In this paper, we showed that the ?NDVImo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ?NDVIW-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ?NDVImo-mi and ?NDVIW-S. For an 11-year time series without rainfall events, Landsat ?NDVIW-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ?NDVImo-mi and ?NDVIW-S have potential to detect early water stress of paraheliotropic vegetation. PMID:25188305

Chávez, Roberto O.; Clevers, Jan G. P. W.; Verbesselt, Jan; Naulin, Paulette I.; Herold, Martin

2014-01-01

353

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

PubMed

Heliotropic leaf movement or leaf 'solar tracking' occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (?NDVI mo-mi) and between winter and summer (?NDVI W-S). In this paper, we showed that the ?NDVI mo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ?NDVI W-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ?NDVI mo-mi and ?NDVI W-S. For an 11-year time series without rainfall events, Landsat ?NDVI W-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ?NDVI mo-mi and ?NDVI W-S have potential to detect early water stress of paraheliotropic vegetation. PMID:25188305

Chávez, Roberto O; Clevers, Jan G P W; Verbesselt, Jan; Naulin, Paulette I; Herold, Martin

2014-01-01

354

Workshop on Satellites for Solar Energy Resource Information -Washington, DC, April 10-11, 1996 POTENTIALS OF IMAGES FROM GEOSTATIONARY SATELLITE DATA FOR THE  

E-print Network

Workshop on Satellites for Solar Energy Resource Information - Washington, DC, April 10-11, 1996 POTENTIALS OF IMAGES FROM GEOSTATIONARY SATELLITE DATA FOR THE ASSESSMENT OF SOLAR ENERGY PARAMETERS Lucien by meteorological geostationary satellites are currently used to map global radiation. Several methods exist which

Paris-Sud XI, Université de

355

Forecasting Time Series by Means of Evolutionary Algorithms  

Microsoft Academic Search

\\u000a The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the\\u000a information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series.\\u000a The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides,

Cristóbal Luque Del Arco-calderón; Pedro Isasi Viñuela; Julio César Hernández Castro

2004-01-01

356

Linearity analysis on stationary segments of hydrologic time series  

Microsoft Academic Search

The rainfall-runoff process is widely perceived as being non-linear; however, the degree of non-linearity might not be significant in hydrologic time series. Evidence of non-linearity was reported in the past in the detrended time series of daily precipitation, but found not to be significant in annual series. The objective of this study is to detect non-linearity in monthly hydrologic time

Huey-Long Chen; A. Ramachandra Rao

2003-01-01

357

Segmented Regressions and Causality (with applications to macroeconomic time series)  

Microsoft Academic Search

The segmented regression approach described in Bianchi (1993a,b) is applied in this paper to pairs of time series, with the aim of establishing empirical relationships and directions of causality between variables. The approach is based on the asumption of regime shifts in univariate time series occurring infrequently over time. Cause-effect relationships are established on the basis of the timing of

Marco BIANCHI

1993-01-01

358

Dynamic Neural Networks for Nonstationary Hydrological Time Series Modeling  

Microsoft Academic Search

Evidence of nonstationary trends in hydrological time series, which result from natural and\\/or anthropogenic climatic variability\\u000a and change, has raised a number of questions as to the adequacy of conventional statistical methods for long-term (seasonal\\u000a to annual) hydrologic time series forecasting. Most conventional statistical methods that are used in hydrology will suffer\\u000a from severe limitations as they assume a stationary

P. Coulibaly; C. K. Baldwin

359

Structural Periodic Measures for Time-Series Data  

Microsoft Academic Search

This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based\\u000a on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity\\u000a detection and we introduce new periodic distance measures for time-series sequences. We combine these new measures with an\\u000a effective metric tree index structure for efficiently answering k-Nearest-Neighbor queries.

Michail Vlachos; Philip S. Yu; Vittorio Castelli; Christopher Meek

2006-01-01

360

Multiple Time Series Ising Model for Financial Market Simulations  

NASA Astrophysics Data System (ADS)

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated.

Takaishi, Tetsuya

2015-01-01

361

Quantifying memory in complex physiological time-series.  

PubMed

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

362

Automated classification of Persistent Scatterers Interferometry time series  

NASA Astrophysics Data System (ADS)

We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends, such as uncorrelated, linear, quadratic, bilinear and discontinuous, that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, or uplifts due to deep injections in reservoirs.

Berti, M.; Corsini, A.; Franceschini, S.; Iannacone, J. P.

2013-08-01

363

Automated classification of Persistent Scatterers Interferometry time-series  

NASA Astrophysics Data System (ADS)

We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends (such as uncorrelated, linear, quadratic, bilinear and discontinuous) that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, uplifts due to deep injections in reservoirs.

Berti, M.; Corsini, A.; Franceschini, S.; Iannacone, J. P.

2013-02-01

364

Quantifying Memory in Complex Physiological Time-Series  

PubMed Central

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

Shirazi, Amir H.; Raoufy, Mohammad R.; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R.; Amodio, Piero; Jafari, G. Reza; Montagnese, Sara; Mani, Ali R.

2013-01-01

365

Image Analysis as a Tool for Satellite-Earth Propagation Studies  

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

366

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

SciTech Connect

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

Kashgarian, M; Guilderson, T P

2001-02-23

367

Retrieval of water quality from China's first satellite-based Hyperspectral Imager (HJ-1A HSI) data  

Microsoft Academic Search

On September 6, 2008, a Micro-satellite Constellation for Monitoring and Forecasting Environment and Disaster was successfully launched in China. This Micro-satellite Constellation includes two small satellites, Satellite-A (abbreviated as HJ-1A) and Satellite-B (abbreviated as HJ-1B). HJ-1A is installed with a Hyperspectral Imager (abbreviated as HSI), which is China's first satellite-based hyperspectral remote sensor. The advantages of HJ-1A HSI is that

Qiao Wang; Junsheng Li; Qian Shen; Chuanqing Wu; Jianlin Yu

2010-01-01

368

Application prospects of survey satellite imaging for geodinamics observating  

Microsoft Academic Search

Block structures is problem of earth crust and which can display in satellite supervisions. The theoretical substantiation of blocks vertical movements and earth crust (dimension < 140x140km) to influence on topography of ocean surface is given. That anomalies investigated satellite altimetry methods. Blocks inclinations (dimension < 140x140km) to influence of vertical movements air (inside atmosphere), it is discussed. They are

I. L. Uchytel; V. N. Yaroshenko; I. I. Gladkykh; B. B. Kapochkin

2004-01-01

369

Satellite image processing and analyzing for marine oil spills  

Microsoft Academic Search

Oil spills are seriously affecting marine ecosystem and cause political and scientific concern. In order to implement an emergency in case of oil spills, it is necessary to monitor oil spill using remote sensing. Techniques for monitoring oil spills includes optical, microwave, and radar approaches using aircraft or satellites. However, Satellites have wider coverage and lower price. Recent years, with

Ying Li; Shuiming Yu; Long Ma; Yu Liu; Qijun Li

2008-01-01

370

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

NASA Astrophysics Data System (ADS)

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

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

2015-02-01

371

Investigating MAI's Precision: Single Interferogram and Time Series Filtering  

NASA Astrophysics Data System (ADS)

Multiple aperture InSAR (MAI) is a technique to obtain along-track displacements from InSAR phase data. Because InSAR measurements are insensitive to along-track displacements, it is only possible to retrieve them using none-interferometric approaches, either pixel-offset tracking or using data from different orbital configurations and assuming continuity/ displacement model. These approaches are limited by precision and data acquisition conflicts, respectively. MAI is promising in this respect as its precision is better than the former and its data is available whether additional acquisitions are there or not. Here we study the MAI noise and develop a filter to reduce it. We test the filtering with empirical noise and simulated signal data. Below we describe the filtered results single interferogram precision, and a Kalman filter approach for MAI time series. We use 14 interferograms taken over the larger Los Angeles/San Gabrial Mountains area in CA. The interferograms include a variety of decorrelation sources, both terrain-related (topographic variations, vegetation and agriculture), and imaging-related (spatial and temporal baselines of 200-500m and 1-12 months, respectively). Most of the pixels are in the low to average coherence range (below 0.7). The data were collected by ESA and made available by the WInSAR consortium. We assume the data contain “zero” along-track signal (less then the theoretical 4 cm for our coherence range), and use the images as 14 dependent realizations of the MAI noise. We find a wide distribution of phase values ? = 2-3 radians (wrapped). We superimpose a signal on our MAI noise interferograms using along-track displacement (-88 - 143 cm) calculated for the 1812 Wrightwood earthquake. To analyze single MAI interferograms, we design an iterative quantile-based filter and test it on the noise+signal MAI interferograms. The residuals reveal the following MAI noise characteristics: (1) a constant noise term, up to 90 cm (2) a displacement gradient term, up to 0.75cm/km (3) a coherence dependent root residuals sum of squares (RRSS), down to 5 cm at 0.8 coherence In the figure we present two measures of the MAI rmse. Prior to phase gradient correction the RRSS follows the circled line. With the correction, the RRSS follows the solid line. We next evaluate MAI's precision given a time series. We use a Kalman Filter to estimate the spatially and temporally correlated components of the MAI data. We reference the displacements to a given area in the interferograms, weight the data with coherence, and model the reminder terms of the spatially correlated noise as a quadratic phase gradient across the image. The results (not displayed) again vary with coherence. MAI single interferogram precision

Bechor Ben Dov, N.; Herring, T.

2010-12-01

372

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

USGS Publications Warehouse

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

Barnhart, William D.; Lohman, Rowena B.

2013-01-01

373

Simulation and graphical representation of the orbit and the imaging parameter of Earth observation satellites  

Microsoft Academic Search

Synthetic aperture radar (SAR) satellites are widely used for Earth observation applications as weather conditions and cloudiness do not affect them. However, in order to be usable, data provided by those satellites need to be processed. This processing requires determination of imaging parameters that are closely linked to the spacecraft position and velocity on its orbit. For example, the difference

Jean-François Vandenrijt

2005-01-01

374

Multiscale techniques for the detection of precipitation using thermal IR satellite images  

Microsoft Academic Search

It is thought that satellite thermal infrared (IR) images can aid to the detection of precipitation, an interesting possibility due to the existence of geostationary satellites with thermal IR sensors which would enable a good spatial and temporal tracking of rain and storms. In this letter, we explore the application of multiscale\\/multifractal techniques in the design of new methods for

Antonio Turiel; Jacopo Grazzini; Hussein Yahia

2005-01-01

375

Time series over the Brahmaputra River from CryoSat-2/SIRAL altimetry  

NASA Astrophysics Data System (ADS)

CryoSat-2 was launched in 2010 with the purpose of monitoring polar ice caps, but the satellite has also proven to be useful for studies at lower latitudes. CryoSat-2 carries a new type of instrument, the SIRAL altimeter, which implements SAR and SARIn mode in addition to the standard LRM. In these modes the along-track resolution is 300m, giving rise to new opportunities for inland water altimetry, which requires a high along-track resolution in order to accurately capture the return signals from the water bodies. Here, we have investigated the possibilities for monitoring river water levels with CryoSat-2 as a part of the EU FP7 LOTUS (Preparing Land and Ocean Take Up from Sentinel-3). The LOTUS project will develop new methodologies, data processing chains, and applications of the SAR mode data for the inland water levels in rivers and lakes. Time series analysis for CryoSat-2 altimetry is not straightforward due to the satellite's very long repeat period of 369 days. It is therefore necessary to take new methods into use. Using slope correction, i.e. taking advantage of the drifting orbit, we have derived time series from retracked heights in all three modes of the CryoSat-2 altimeter over the Brahmaputra River. From the time series we can estimate the amplitude and the seasonal signal of the flow in the river. Presented here is a comparison of the results between modes and with Envisat time series.

Villadsen, Heidi; Baltazar Andersen, Ole; Knudsen, Per; Nielsen, Karina; Stenseng, Lars

2014-05-01

376

Time Series Case Based Reasoning for Image Categorisation  

E-print Network

of Computer Science, University of Liverpool, Ashton Building, Ashton Street, Liverpool L69 3BX, UK. 2 Centre for Medical Statistics and Health Evaluation, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool L69 3GS, UK. 3 School of Health Sciences, University of Liverpool, Thompson Yates Building

Coenen, Frans

377

Image Categorisation Using Time Series Case Based Ashraf Elsayed1  

E-print Network

of Computer Science, University of Liverpool, Ashton Building, Ashton Street, Liverpool L69 3BX, UK. 2 Centre for Medical Statistics and Health Evaluation, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool L69 3GS, UK. 3 School of Health Sciences, University of Liverpool, Thompson Yates Building

Coenen, Frans

378

Spatiotemporal filtering of regional GNSS network's position time series with missing data using principle component analysis  

NASA Astrophysics Data System (ADS)

The existing spatiotemporal analysis methods suppose that the involved time series are complete and have the same data interval. However missing data inevitably occur in the position time series of Global Navigation Satellite Systems networks for many reasons. In this paper, we develop a modified principal component analysis to extract the Common Mode Error (CME) from the incomplete position time series. The principle of the proposed method is that a time series can be reproduced from its principle components. The method is equivalent to the method of Dong et al. (J Geophys Res 111:3405-3421, 2006) in case of no missing data in the time series and to the extended `stacking' approach under the assumption of a uniformly spatial response. The new method is first applied to extract the CME from the position time series of the Crustal Movement Observation Network of China (CMONOC) over the period of 1999-2009 where the missing data occur in all stations with the different gaps. The results show that the CMEs are significant in CMONOC. The size of the first principle components for the North, East and Up coordinates are as large as 40, 41 and 37 % of total principle components and their spatial responses are not uniform. The minimum amplitudes of the first eigenvectors are only 41, 15 and 29 % for the North, East and Up coordinate components, respectively. The extracted CMEs of our method are close to the data filling method, and the Root Mean Squared error (RMS) values computed from the differences of maximum CMEs between two methods are only 0.31, 0.52 and 1.55 mm for North, East and Up coordinates, respectively. The RMS of the position time series is greatly reduced after filtering out the CMEs. The accuracies of the reconstructed missing data using the two methods are also comparable. To further comprehensively test the efficiency of our method, the repeated experiments are then carried out by randomly deleting different percentages of data at some stations. The results show that the CMEs can be extracted with high accuracy at the non missing-data epochs. And at the missing-data epochs, the accuracy of extracted CMEs has a strong dependence on the number of stations with missing data.

Shen, Yunzhong; Li, Weiwei; Xu, Guochang; Li, Bofeng

2014-01-01

379

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

NASA Astrophysics Data System (ADS)

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

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

2014-10-01

380

Nonlinear time-series modeling of unconfined groundwater head  

NASA Astrophysics Data System (ADS)

paper presents a nonlinear transfer function noise model for time-series modeling of unconfined groundwater hydrographs. The motivation for its development was that existing groundwater time-series models were unable to simulate large recharge events and multiyear droughts. This was because existing methods do not partition rainfall to runoff and do not account for nonlinear soil water drainage. To account for these nonlinear processes, a vertically integrated soil moisture module was added to an existing transfer function noise model. The soil moisture module has a highly flexible structure that allowed 84 different forms to be built. Application of the time-series model requires numerical calibration of parameters for the transfer functions, noise model and, for the nonlinear models, the soil moisture module. This was undertaken using the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) global calibration scheme. However, reproducible calibration to the global optima was challenging and a number of modifications were required to the transfer function noise model. In trialing the 84 nonlinear models and 2 linear models, each was applied to eleven observation bores within a paired catchment study area in Great Western, Victoria, Australia. In comparison with existing groundwater hydrograph time-series models, the proposed nonlinear time-series model performed significantly better at all observation bores during calibration and evaluation periods. Both the linear and nonlinear models were also used to quantify the impact of revegetation within the paired catchment; however, results were inconclusive, which is likely due to time-series data for the state of the revegetation being unavailable. By analyzing the application of 84 nonlinear models to each bore, an optimal structure for the soil moisture module was identified. It is unlikely, however, that this model structure would be appropriate for all climates and geologies. To encourage further investigations, open-source code for the highly flexible groundwater time-series modeling framework is available and we invite others to develop new models.

Peterson, T. J.; Western, A. W.

2014-10-01

381

A method of using commercial virtual satellite image to check the pattern painting spot effect  

NASA Astrophysics Data System (ADS)

A method of using commercial virtual satellite image to check the pattern painting spot effect contrast with the satellite images before painting and after painting have been discussed. Using a housetop as the testing platform analyses and discusses the factors' influence such as resolution of satellite image, spot size and color of pattern painting spot and pattern painting camouflage method choosing to the plan implement. The pattern painting design and spot size used in the testing has been ensured, and housetop pattern painting has been painted. Finally, the small spot pattern painting camouflage effect of engineering using upon painting pattern size, color and texture have been checked, contrasting with the satellite image before painting and after painting.

Wang, Zheng-gang; Kang, Qing; Shen, Zhi-qiang; Cui, Chang-bin

2014-02-01

382

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

NASA Technical Reports Server (NTRS)

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

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

2012-01-01

383

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

NASA Astrophysics Data System (ADS)

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

Liang, Yu-Li

384

Deformation time series at Llaima volcano, southern Andes  

NASA Astrophysics Data System (ADS)

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

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

2010-05-01

385

High Resolution Soil Water from Regional Databases and Satellite Images  

NASA Technical Reports Server (NTRS)

This viewgraph presentation provides information on the ways in which plant growth can be inferred from satellite data and can then be used to infer soil water. There are several steps in this process, the first of which is the acquisition of data from satellite observations and relevant information databases such as the State Soil Geographic Database (STATSGO). Then probabilistic analysis and inversion with the Bayes' theorem reveals sources of uncertainty. The Markov chain Monte Carlo method is also used.

Morris, Robin D.; Smelyanskly, Vadim N.; Coughlin, Joseph; Dungan, Jennifer; Clancy, Daniel (Technical Monitor)

2002-01-01

386

Instantaneous dynamic change detection based on three-line-array stereoscopic images of TH-1 satellite  

NASA Astrophysics Data System (ADS)

TH-1 satellite loading three-line array stereoscopic camera, can scanning 3 times from different directions on the same region or target within the time for about 1 minute, conducive to regional monitoring or target instantaneous change monitoring. Based on the time difference of forward, nadir and backward images of the three-line-array camera of TH-1 Satellite, this paper gives a method to get regional dynamic change image by processing of geometric and physical consistency under the principle of photogrammetry, and to construct the model of change detection by the quantitative results of change detection under the improvement and optimization of noise filtering algorithm. The experimental results show that, by using the detection results of forward, nadir and backward images of the three-line -array camera of TH-1 Satellite, moving distance and velocity can be accurately calculated, and quantitative monitoring of topography changes can be achieved, which not only has temporal resolution, but also can't be achieved by other environmental monitoring satellites. It's significant for flood, fire, clouds, or motion detectors. TH-1 satellite is China's first generation of transmission photogrammetry satellite. With the more satellites networking operation, and higher spatial and temporal resolution, The TH satellites will play a greater role in the field of Earth observation. This article merely uses the principles of photogrammetry to consider photography deformation from different directions, and thorough study will aim at shadow and sun elevation angle, to fully realize the monitoring of changes in topography and moving targets.

Zheng, Tuanjie; Cheng, Jiasheng; Li, Heyuan

2014-05-01

387

Detecting and adjusting for artifacts in fMRI time series data.  

PubMed

We present a new method to detect and adjust for noise and artifacts in functional MRI time series data. We note that the assumption of stationary variance, which is central to the theoretical treatment of fMRI time series data, is often violated in practice. Sporadic events such as eye, mouth, or arm movements can increase noise in a spatially global pattern throughout an image, leading to a non-stationary noise process. We derive a restricted maximum likelihood (ReML) algorithm that estimates the variance of the noise for each image in the time series. These variance parameters are then used to obtain a weighted least squares estimate of the regression parameters of a linear model. We apply this approach to a typical fMRI experiment with a block design and show that the noise estimates strongly vary across different images and that our method detects and appropriately weights images that are affected by artifacts. Furthermore, we show that the noise process has a global spatial distribution and that the variance increase is multiplicative rather than additive. The new algorithm results in significantly increased sensitivity in the ability to detect regions of activation. The new method may be particularly useful for studies that involve special populations (e.g., children or elderly) where sporadic, artifact-generating events are more likely. PMID:15975828

Diedrichsen, Jörn; Shadmehr, Reza

2005-09-01

388

Data Rods: High Speed, Time-Series Analysis of Massive Cryospheric Data Sets Using Object-Oriented Database Methods  

NASA Astrophysics Data System (ADS)

Change over time, is the central driver of climate change detection. The goal is to diagnose the underlying causes, and make projections into the future. In an effort to optimize this process we have developed the Data Rod model, an object-oriented approach that provides the ability to query grid cell changes and their relationships to neighboring grid cells through time. The time series data is organized in time-centric structures called "data rods." A single data rod can be pictured as the multi-spectral data history at one grid cell: a vertical column of data through time. This resolves the long-standing problem of managing time-series data and opens new possibilities for temporal data analysis. This structure enables rapid time- centric analysis at any grid cell across multiple sensors and satellite platforms. Collections of data rods can be spatially and temporally filtered, statistically analyzed, and aggregated for use with pattern matching algorithms. Likewise, individual image pixels can be extracted to generate multi-spectral imagery at any spatial and temporal location. The Data Rods project has created a series of prototype databases to store and analyze massive datasets containing multi-modality remote sensing data. Using object-oriented technology, this method overcomes the operational limitations of traditional relational databases. To demonstrate the speed and efficiency of time-centric analysis using the Data Rods model, we have developed a sea ice detection algorithm. This application determines the concentration of sea ice in a small spatial region across a long temporal window. If performed using traditional analytical techniques, this task would typically require extensive data downloads and spatial filtering. Using Data Rods databases, the exact spatio-temporal data set is immediately available No extraneous data is downloaded, and all selected data querying occurs transparently on the server side. Moreover, fundamental statistical calculations such as running averages are easily implemented against the time-centric columns of data.

Liang, Y.; Gallaher, D. W.; Grant, G.; Lv, Q.

2011-12-01

389

The Compact High Resolution Imaging Spectrometer (CHRIS): the future of hyperspectral satellite sensors. Imagery of Oostende coastal and inland waters  

Microsoft Academic Search

The gap between airborne imaging spectroscopy and traditional multi spectral satellite sensors is decreasing thanks to a new generation of satellite sensors of which CHRIS mounted on the small and low-cost PROBA satellite is the prototype. Although image acquisition and analysis are still in a test phase, the high spatial and spectral resolution and pointability have proved their potential. Because

Barbara Van Mol; Kevin Ruddick

390

Rules extraction in short memory time series using genetic algorithms  

NASA Astrophysics Data System (ADS)

Data mining is performed using genetic algorithm on artificially generated time series data with short memory. The extraction of rules from a training set and the subsequent testing of these rules provide a basis for the predictions on the test set. The artificial time series are generated using the inverse whitening transformation, and the correlation function has an exponential form with given time constant indicative of short memory. A vector quantization technique is employed to classify the daily rate of return of this artificial time series into four categories. A simple genetic algorithm based on a fixed format of rules is introduced to do the forecasting. Comparing to the benchmark tests with random walk and random guess, genetic algorithms yield substantially better prediction rates, between 50% to 60%. This is an improvement compared with the 47% for random walk prediction and 25% for random guessing method.

Fong, L. Y.; Szeto, K. Y.

2001-04-01

391

Forecasting Smoothed Non-Stationary Time Series Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time variability of it. The final model is mainly dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small.

Norouzzadeh, P.; Rahmani, B.; Norouzzadeh, M. S.

392

Hybrid Machine Learning Model for Continuous Microarray Time Series  

NASA Astrophysics Data System (ADS)

A hybrid machine learning model of the principal component analysis and neural network is described for the continuous microarray gene expression time series. The methodology can model numerically the continuous gene expression time series. The proposed model can give us the extracted features from the gene expressions time series with higher prediction accuracies. It can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. In this chapter, we describe the background, the machine learning algorithms, and then the application of the hybrid machine learning in the microarray analysis. The machine learning model is compared with other popular continuous prediction methods. Based on the results of two public microarray datasets, the hybrid method outperforms the other continuous prediction methods.

Ao, Sio-Iong

393

Local prediction of turning points of oscillating time series  

E-print Network

For oscillating time series, the prediction is often focused on the turning points. In order to predict the turning point magnitudes and times it is proposed to form the state space reconstruction only from the turning points and modify the local (nearest neighbor) model accordingly. The model on turning points gives optimal prediction at a lower dimensional state space than the optimal local model applied directly on the oscillating time series and is thus computationally more efficient. Monte Carlo simulations on different oscillating nonlinear systems showed that it gives better predictions of turning points and this is confirmed also for the time series of annual sunspots and total stress in a plastic deformation experiment.

D. Kugiumtzis

2008-08-06

394

Principal Component Analysis of Type Ia Supernova Spectrophotometric Time Series  

NASA Astrophysics Data System (ADS)

The spectrophotometric time series of over one hundred Type Ia supernovae from the Nearby Supernova Factory (Aldering, et al. 2002) provide unique opportunities for improving the standardization of Type Ia supernova magnitudes. We present results found by performing a Principal Component Analysis (PCA) on the spectral time series. We use Expectation Maximization PCA (Bailey, 2012), which can handle noisy or missing data, appropriate for the uneven phase coverage of the time series. We analyze the relationship between the number of principal components used to model supernovae and the amount of dispersion found in supernovae’s corrected magnitudes. Additionally, we interpret the information contained in the individual principal components, such as their correspondence with already known Type Ia supernova characteristics like stretch and color.

Saunders, Clare; Aldering, G. S.; Bailey, S. J.; Birchall, D.; Childress, M.; Fakhouri, H.; Hayden, B.; Kim, A. G.; Nordin, J.; Nugent, P. E.; Perlmutter, S.; Rubin, D.; Runge, K.; Sofiatti, C.; Suzuki, N.; Thomas, R.; Weaver, B.; Pecontal, E.; Buton, C.; Copin, Y.; Chotard, N.; Gangler, E.; Pereira, R.; Smadja, G.; Cellier-Holzem, F.; Canto, A.; Antilogus, P.; Bongard, S.; Fleury, M.; Guy, J.; Pain, R.; Chen, J.; Tao, C.; Feindt, U.; Greskovic, P.; Kowalski, M.; Lombardo, S.; Rigault, M.; Baltay, C.; Rabinowitz, D. L.

2014-01-01

395

Time series fMRI measures detect changes in pontine raphé following acute tryptophan depletion  

PubMed Central

Serotonin is synthesized from its precursor, tryptophan, by brainstem raphé neurons and their synaptic terminals in limbic regions. The omission of tryptophan from an Acute Tryptophan Depletion (ATD) diet transiently diminishes serotonin synthesis, alters raphé activity, and mimics symptoms of depression. Raphé functional magnetic resonance imaging (fMRI) poses challenges using signal-averaging analyses. Time-series properties of fMRI blood oxygenation level dependent (BOLD) signals may hold promise, so we analyzed raphé signals for changes with the ATD diet. Eleven remitted (previously depressed) patients were awake with eyes-closed during seven-minute resting scans with 0.5 s?1 sampling. BOLD signal time-series data were frequency-filtered using wavelet transforms, yielding three octave-width frequency bands from 0.25 to 0.03 s?1 and an unbounded band below 0.03 s?1. Spectral power, reflecting signal information, increased in pontine raphé at high frequencies (0.25 to 0.125 s?1) during ATD (compared to control, balanced, diet, P<0.004) but was unchanged at other frequencies. Functional connectivity, the correlation between time-series data from pairs of regions, weakened between pontine raphé and anterior thalamus at low frequencies during ATD (P<0.05). This preliminarily supports using fMRI time-series features to assess pontine raphé function. Whether, and how, high frequency activity oscillations interfere with low frequency signaling requires further study. PMID:21236648

Salomon, Ronald M.; Cowan, Ronald L.; Rogers, Baxter P.; Dietrich, Mary S.; Bauernfeind, Amy Lynn; Kessler, Robert M.; Gore, John C.

2011-01-01

396

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

NASA Astrophysics Data System (ADS)

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

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

2014-12-01