Science.gov

Sample records for time-series satellite images

  1. A method for generating high resolution satellite image time series

    NASA Astrophysics Data System (ADS)

    Guo, Tao

    2014-10-01

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

  2. Satellite image time series simulation for environmental monitoring

    NASA Astrophysics Data System (ADS)

    Guo, Tao

    2014-11-01

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

  3. Discovering significant evolution patterns from satellite image time series.

    PubMed

    Petitjean, François; Masseglia, Florent; Gançarski, Pierre; Forestier, Germain

    2011-12-01

    Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors. PMID:22131300

  4. Spatio-Temporal Mining of PolSAR Satellite Image Time Series

    NASA Astrophysics Data System (ADS)

    Julea, A.; Meger, N.; Trouve, E.; Bolon, Ph.; Rigotti, C.; Fallourd, R.; Nicolas, J.-M.; Vasile, G.; Gay, M.; Harant, O.; Ferro-Famil, L.

    2010-12-01

    This paper presents an original data mining approach for describing Satellite Image Time Series (SITS) spatially and temporally. It relies on pixel-based evolution and sub-evolution extraction. These evolutions, namely the frequent grouped sequential patterns, are required to cover a minimum surface and to affect pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. In this paper, a specific application to fully polarimetric SAR image time series is presented. Preliminary experiments performed on a RADARSAT-2 SITS covering the Chamonix Mont-Blanc test-site are used to illustrate the proposed approach.

  5. Global near real-time disturbance monitoring using MODIS satellite image time series

    NASA Astrophysics Data System (ADS)

    Verbesselt, J.; Kalomenopoulos, M.; de Jong, R.; Zeileis, A.; Herold, M.

    2012-12-01

    Global disturbance monitoring in forested ecosystems is critical to retrieve information on carbon storage dynamics, biodiversity, and other socio-ecological processes. Satellite remote sensing provides a means for cost-effective monitoring at frequent time steps over large areas. However, for information about current change processes, it is required to analyse image time series in a fast and accurate manner and to detect abnormal change in near real time. An increasing number of change detection techniques have become available that are able to process historical satellite image time series data to detect changes in the past. However, methods that detect changes near real-time, i.e. analysing newly acquired data with respect to the historical series, are lacking. We propose a statistical technique for monitoring change in near-real time by comparing current data with a seasonal-trend model fitted onto the historical time series. As such, identification of consistent and abnormal change in near-real time becomes possible as soon as new image data is captured. The method is based on the "Break For Additive Seasonal Trend" (BFAST) concept (http://bfast.r-forge.r-project.org/). Disturbances are detected by analysing 16-daily MODIS combined vegetation and temperature indices. Validation is carried out by comparing the detected disturbances with available disturbance data sets (e.g. deforestation in Brazil and MODIS fire products). Preliminary results demonstrated that abrupt changes at the end of time series can be successfully detected while the method remains robust for strong seasonality and atmospheric noise. Cloud masking, however, was identified as a critical issue since periods of persistent cloudiness can be detected as abnormal change. The proposed method is an automatic and robust change detection approach that can be applied on different types of data (e.g. future sensors like the Sentinel constellation that provide higher spatial resolution at regular time steps). The methods for near real-time changes detection are publicly available within the BFAST package for R (http://bfast.r-forge.r-project.org/). Keywords: forest change monitoring, time series imagery, near real-time, change detection

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

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

    NASA Astrophysics Data System (ADS)

    Champion, N.

    2012-08-01

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

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

    NASA Astrophysics Data System (ADS)

    Julea, Andreea; Méger, Nicolas

    2013-10-01

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

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

    PubMed

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

    2014-08-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

  11. Results of analyses of human impact on environment by vegetation satellite images time series around large industry centers

    NASA Astrophysics Data System (ADS)

    Shevyrnogov, A.; Vysotskaya, G.; Sukhinin, A.; Shevyrnogov, E.

    In this paper efficiency of an application of vegetation index image time series for detection of long-standing vegetation dynamics is shown. Influence of large industry centers of Siberia on near-by vegetation is demonstrated. Analyses of data shows that industrial waste influence is more hardly in the Siberian North. These regions are characterized by critical conditions of vegetation existence. Under conditions of the Krasnoyarsk region south human impact is also important, but possibility of vegetation self-rehabilitation is higher. Nowadays unique economical situation exists in Russia with temporary abrupt fall of industrial production and its following increase. It has allowed to analyze a degree of human impact on environment during a relatively short time interval.

  12. Results of analysis of human impact on environment using the time series of vegetation satellite images around large industrial centers

    NASA Astrophysics Data System (ADS)

    Shevyrnogov, A.; Vysotskaya, G.; Sukhinin, A.; Frolikova, O.; Tchernetsky, M.

    The paper shows the efficiency of an application of the vegetation index image time series to determine long-term vegetation dynamics. The influence of large industrial centers of Siberia on the near-by vegetation is demonstrated. The analysis of the data shows that the influence of industrial waste is stronger in the Siberian North. These regions are characterized by critical conditions for vegetation existence. In the south of the Krasnoyarsk region, human impact is also important, but the possibility of vegetation self-rehabilitation is higher. The present-day economic situation in Russia is unique, with a temporary abrupt fall of industrial production and its following increase. Thus, we managed to analyze the degree of human impact on the environment within a relatively short-time interval.

  13. Assessing burn severity using satellite time series

    NASA Astrophysics Data System (ADS)

    Veraverbeke, Sander; Lhermitte, Stefaan; Verstraeten, Willem; Goossens, Rudi

    2010-05-01

    In this study a multi-temporal differenced Normalized Burn Ratio (dNBRMT) is presented to assess burn severity of the 2007 Peloponnese (Greece) wildfires. 8-day composites were created using the daily near infrared (NIR) and mid infrared (MIR) reflectance products of the Moderate Resolution Imaging Spectroradiometer (MODIS). Prior to the calculation of the dNBRMT a pixel-based control plot selection procedure was initiated for each burned pixel based on time series similarity of the pre-fire year 2006 to estimate the spatio-temporal NBR dynamics in the case that no fire event would have occurred. The dNBRMT is defined as the one-year post-fire integrated difference between the NBR values of the control and focal pixels. Results reveal the temporal dependency of the absolute values of bi-temporal dNBR maps as the mean temporal standard deviation of the one-year post-fire bi-temporal dNBR time series equaled 0.14 (standard deviation of 0.04). The dNBRMT's integration of temporal variability into one value potentially enhances the comparability of fires across space and time. In addition, the dNBRMT is robust to random noise thanks to the averaging effect. The dNBRMT, based on coarse resolution imagery with high temporal frequency, has the potential to become either a valuable complement to fine resolution Landsat dNBR mapping or an imperative option for assessing burn severity at a continental to global scale.

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

    NASA Astrophysics Data System (ADS)

    Tiede, Dirk; Lang, Stefan

    2010-11-01

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

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

    NASA Astrophysics Data System (ADS)

    Tiede, Dirk; Lang, Stefan

    2009-09-01

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

  16. Continuous evaluation of land cover restoration of tsunami struck plains in Japan by using several kinds of optical satellite image in time series

    NASA Astrophysics Data System (ADS)

    Hashiba, H.

    2015-09-01

    The Mw 9.0 earthquake that struck Japan in 2011 was followed by a large-scale tsunami in the Tohoku region. The damage in the coastal plane was extensively displayed through many satellite images. Furthermore, satellite imaging is requested for the ongoing evaluation of the restoration process. The reconstruction of the urban structure, farmlands, grassland, and coastal forest that collapsed under the large tsunami requires effective long-term monitoring. Moreover, the post-tsunami land cover dynamics can be effectively modeled using time-constrained satellite data to establish a prognosis method for the mitigation of future tsunami impact. However, the remote satellite capture of a long-term restoration process is compromised by accumulating spatial resolution effects and seasonal influences. Therefore, it is necessary to devise a method for data selection and dataset structure. In the present study, the restoration processes were investigated in four years following the disaster in a part of the Sendai plain, northeast Japan, from same-season satellite images acquired by different optical sensors. Coastal plains struck by the tsunami are evaluated through land-cover classification processing using the clustering method. The changes in land cover are analyzed from time-series optical images acquired by Landsat-5/TM, 7/ETM+, 8/OLI, EO-1/ALI, and ALOS-1/AVNIR-2. The study reveals several characteristics of the change in the inundation area and signs of artificial and natural restoration.

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Georgopoulou, Danai; Koutsias, Nikos

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Zhao, Yu

    2014-11-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-07-01

    We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

  1. On Fire regime modelling using satellite TM time series

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    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.

  2. Automated analysis of protein subcellular location in time series images

    PubMed Central

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

    2010-01-01

    Motivation: Image analysis, machine learning and statistical modeling have become well established for the automatic recognition and comparison of the subcellular locations of proteins in microscope images. By using a comprehensive set of features describing static images, major subcellular patterns can be distinguished with near perfect accuracy. We now extend this work to time series images, which contain both spatial and temporal information. The goal is to use temporal features to improve recognition of protein patterns that are not fully distinguishable by their static features alone. Results: We have adopted and designed five sets of features for capturing temporal behavior in 2D time series images, based on object tracking, temporal texture, normal flow, Fourier transforms and autoregression. Classification accuracy on an image collection for 12 fluorescently tagged proteins was increased when temporal features were used in addition to static features. Temporal texture, normal flow and Fourier transform features were most effective at increasing classification accuracy. We therefore extended these three feature sets to 3D time series images, but observed no significant improvement over results for 2D images. The methods for 2D and 3D temporal pattern analysis do not require segmentation of images into single cell regions, and are suitable for automated high-throughput microscopy applications. Availability: Images, source code and results will be available upon publication at http://murphylab.web.cmu.edu/software Contact: murphy@cmu.edu PMID:20484328

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

  4. Evaluating a Satellite-derived Time Series of Inundation Dynamics

    NASA Astrophysics Data System (ADS)

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

    2006-12-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Bunker, Brian

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  8. D City Transformations by Time Series of Aerial Images

    NASA Astrophysics Data System (ADS)

    Adami, A.

    2015-02-01

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

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

    NASA Astrophysics Data System (ADS)

    Stewart, Chris

    2014-05-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

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

  11. Vegetation cover quality assessment through MODIS time series satellite data in an urban region

    NASA Astrophysics Data System (ADS)

    Zoran, M. A.; Savastru, R. S.; Savastru, D. M.; Pavelescu, G. M.; Tautan, M. N.; Miclos, S. I.; Baschir, L. A.

    2013-08-01

    To preserve urban vegetation land cover quality and mitigate its degradation is an important task for urban planning and environmental management of Bucharest metropolitan area in Romania. Since vegetation land cover dynamics directly affect the urban landscape characteristics and air quality, remote sensing represents an effective tool for vegetation land cover quality assessment at regional scale. In particular, the use of satellite-based vegetation indices, like the NDVI (Normalized Difference Vegetation Index), can provide important information when evaluating Urban Vegetation Cover Quality (UVCQ) patterns in urban areas, which represents one of the most sensitive landscape components to urban environmental degradation. This paper proposes an approach for the regional-scale assessment of UVCQ by means of an NDVI-based (functional) indicator using freely available time series MODIS Terra/Aqua (Moderate Resolution Imaging Spectroradiometer) satellite data. As a case study, Bucharest metropolitan area landscape experiencing climate and anthropogenic changes, increasing human pressure and high vulnerability to degradation was chosen. As UVCQ indicator, the NDVI-based vegetation cover classification was produced by means of unsupervised multivariate statistical techniques and compared with spatio-temporal changes during 2002-2012 period, statistical indicators, and field data related to land cover management observed in the study area. Results demonstrate that the obtained remotely sensed vegetation land cover characterization can be effectively considered as a proxy of the UVCQ status of the examined area. Due to the large availability over time and low cost of satellite images, the proposed approach can be applied to wider urban/periurban regions, to monitor vegetation quality and indirectly control vegetation land degradation.

  12. Mapping forest fuels through vegetation phenology: the role of coarse-resolution satellite time-series.

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  14. Time series analysis of satellite derived surface temperature for Lake Garda

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  19. MODVOLC2: A Hybrid Time Series Analysis for Detecting Thermal Anomalies Applied to Thermal Infrared Satellite Data

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

    We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found that MODVOLC2 achieved good results on multiple sensors (MODIS and GOES), which provides confidence that MODVOLC2 can be run on future instruments regardless of their spatial and temporal resolutions. The improved performance of MODVOLC2 over MODVOLC makes possible the detection of lower temperature thermal anomalies that will be useful in improving our ability to document Earth’s volcanic eruptions as well as detect possible low temperature thermal precursors to larger eruptions.

  20. Spatiotemporal densification of river water level time series by multimission satellite altimetry

    NASA Astrophysics Data System (ADS)

    Tourian, M. J.; Tarpanelli, A.; Elmi, O.; Qin, T.; Brocca, L.; Moramarco, T.; Sneeuw, N.

    2016-02-01

    Limitations of satellite radar altimetry for operational hydrology include its spatial and temporal sampling as well as measurement problems caused by local topography and heterogeneity of the reflecting surface. In this study, we develop an approach that eliminates most of these limitations to produce an approximately 3 day temporal resolution water level time series from the original typically (sub)monthly data sets for the Po River in detail, and for Congo, Mississippi, and Danube Rivers. We follow a geodetic approach by which, after estimating and removing intersatellite biases, all virtual stations of several satellite altimeters are connected hydraulically and statistically to produce water level time series at any location along the river. We test different data-selection strategies and validate our method against the extensive available in situ data over the Po River, resulting in an average correlation of 0.7, Root-Mean-Square Error of 0.8 m, bias of -0.4 m, and Nash-Sutcliffe Efficiency coefficient of 0.5. We validate the transferability of our method by applying it to the Congo, Mississippi, and Danube Rivers, which have very different geomorphological and climatic conditions. The methodology yields correlations above 0.75 and Nash-Sutcliffe coefficients of 0.84 (Congo), 0.34 (Mississippi), and 0.35 (Danube).

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

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Kahru, M.; Elmgren, R.

    2014-02-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Kahru, M.; Elmgren, R.

    2014-07-01

    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.

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

    SciTech Connect

    Zhao, Naizhuo; Zhou, Yuyu; Samson, Eric L.

    2014-09-19

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

  5. Time series analysis of thermal variation on Italian volcanic active areas by using IR satellite data

    NASA Astrophysics Data System (ADS)

    Silvestri, M.; Buongiorno, M. F.; Pieri, D. C.

    2014-12-01

    To monitoring of active volcanoes the systematic acquisition of medium/high resolution thermal data and the subsequent analysis of time series may improve the capability to detect small surface temperature variation related to changes in volcanic activity level and contribute to the early warning systems. Examples on the processing of long time series based EO data of Mt Etna activity and Phlegraean Fields observation by using remote sensing techniques and at different spatial resolution data (ASTER - 90mt, AVHRR -1km, MODIS-1km, MSG SEVIRI-3km) are showed. The use of TIR sensors with high spatial resolution offers the possibility to obtain detailed information on the areas where there are significant changes, detecting variation in fumaroles fields and summit craters before eruptions. Thanks to ASTER thermal infrared (TIR, 5 bands) regions of the electromagnetic spectrum we have obtained the surface temperature map on the volcano area. For this study we have considered the ASTER's night observations that show well defined episodes of increasing thermal emission of crater thanks to a more uniform background temperature. Two different procedures are shown, both using the TIR high spatial resolution data: for Phlegraean Fields (active but quiescent volcano) the analysis of time series of surface temperature which may improve the capability to detect small surface temperature variation related to changes in volcanic activity level; for Mt. Etna (active volcano) a semi-automatic procedure which extract the summit area radiance values with the goal of detecting variation related to eruptive events. The advantage of direct download of EO data by means INGV antennas even though low spatial resolution offers the possibility of a systematic data processing having a daily updating of information for prompt response and hazard mitigation. At the same time the comparison of surface temperature retrievals at different scale is an important issue for future satellite sensors.

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

    USGS Publications Warehouse

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

    2005-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

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

    SciTech Connect

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

    2014-10-07

    Ambient fine particulate matter (PM2.5) is a leading environmental risk factor for premature mortality. We use aerosol optical depth (AOD) retrieved from two satellite instruments, MISR and SeaWiFS, to produce a unified 15-year global time series (1998-2012) of ground-level PM2.5 concentration at a resolution of 1 degrees x 1 degrees. The GEOS-Chem chemical transport model (CTM) is used to relate each individual AOD retrieval to ground-level PM2.5. Four broad areas showing significant, spatially coherent, annual trends are examined in detail: the Eastern U.S. (-0.39 +/- 0.10 mu g m(-3) yr(-1)), the Arabian Peninsula (0.81 +/- 0.21 mu g m(-3) yr(-1)), South Asia (0.93 +/- 0.22 mu g m(-3) yr(-1)) and East Asia (0.79 +/- 0.27 mu g m(-3) yr(-1)). Over the period of dense in situ observation (1999-2012), the linear tendency for the Eastern U.S. (-0.37 +/- 0.13 mu g m(-3) yr(-1)) agrees well with that from in situ measurements (-0.38 +/- 0.06 mu g m(-3) yr(-1)). A GEOS-Chem simulation reveals that secondary inorganic aerosols largely explain the observed PM2.5 trend over the Eastern U.S., South Asia, and East Asia, while mineral dust largely explains the observed trend over the Arabian Peninsula.

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

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

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

    NASA Astrophysics Data System (ADS)

    Lasaponara, R.; Masini, N.

    2012-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Suepa, Tanita

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

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

    NASA Technical Reports Server (NTRS)

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    ERIC Educational Resources Information Center

    Tataw, Oben Moses

    2013-01-01

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

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

    ERIC Educational Resources Information Center

    Tataw, Oben Moses

    2013-01-01

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

  17. Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

    PubMed

    Gordonov, Simon; Hwang, Mun Kyung; Wells, Alan; Gertler, Frank B; Lauffenburger, Douglas A; Bathe, Mark

    2016-01-18

    Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at . PMID:26658688

  18. A tool for NDVI time series extraction from wide-swath remotely sensed images

    NASA Astrophysics Data System (ADS)

    Li, Zhishan; Shi, Runhe; Zhou, Cong

    2015-09-01

    Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

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

    NASA Astrophysics Data System (ADS)

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

    2002-11-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

    Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, for some years, this technology has also been used to retrieve water levels from reservoirs, wetlands and in general any inland water body, although the radar altimetry technique has been especially applied to rivers and lakes. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series of rivers and lakes available through the web service "Database for Hydrological Time Series over Inland Waters" (DAHITI). The new method is based on an extended outlier rejection and a Kalman filter approach incorporating cross-calibrated multi-mission altimeter data from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, including their uncertainties. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparisons with in situ gauge data and results from external inland altimeter databases. The new approach yields rms differences with respect to in situ data between 4 and 36 cm for lakes and 8 and 114 cm for rivers. For most study cases, more accurate height information than from other available altimeter databases can be achieved.

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2004-01-01

    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%).

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

    NASA Technical Reports Server (NTRS)

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

    1994-01-01

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

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

    NASA Astrophysics Data System (ADS)

    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

    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.

  10. Urban green spatio- temporal changes assessment through time-series satellite data

    NASA Astrophysics Data System (ADS)

    Zoran, Maria A.; Savastru, Roxana S.; Savastru, Dan M.; Tautan, Marina N.; Baschir, Laurentiu V.

    2015-10-01

    Understanding spatio-temporal changes of urban environments is essential for regional and local planning and environmental management. With the rapid changes of Bucharest city in Romania during past decades, green spaces have been fragmented and dispersed causing impairment and dysfunction of these urban elements. The main goal of this study is to address these tasks in synergy with in-situ data and new analytical methods. Spatio- temporal monitoring of urban vegetation land cover changes is important for policy decisions, regulatory actions and subsequent land use activities. This study explored the use of time-series MODIS Terra/Aqua Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST) and evapotranspiration (ET) data to provide vegetation change detection information for metropolitan area of Bucharest. Training and validation are based on a reference dataset collected from IKONOS high resolution remote sensing data. The mean detection accuracy for period 2002- 2014 was assessed to be of 87%, with a reasonable balance between change commission errors (20.24%), change omission errors (25.65%), and Kappa coefficient of 0.72. Annual change detection rates across the urban/periurban areas over the study period (2002-2014) were estimated at 0.79% per annum in the range of 0.46% (2002) to 0.77% (2014).Vegetation dynamics in urban areas at seasonal and longer timescales reflect large-scale interactions between the terrestrial biosphere and the climate system. Extracted green space areas were further analyzed quantitatively in relation with air quality data and extreme climate events. The results have been analyzed in terms of environmental impacts and future climate trends.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    EPA Science Inventory

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Müller, Benjamin; Bernhardt, Matthias; Schulz, Karsten

    2014-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-08-01

    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 (0.50.5), the mean annual maximum of NIA agrees well with the static open water extent of the Global Lakes and Wetlands database (GLWD) (Lehner and Dll, 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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2008-07-01

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

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

    PubMed

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

    2015-12-20

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

  1. Efficient Gaussian Process-Based Modelling and Prediction of Image Time Series.

    PubMed

    Lorenzi, Marco; Ziegler, Gabriel; Alexander, Daniel C; Ourselin, Sebastien

    2015-01-01

    In this work we propose a novel Gaussian process-based spatio-temporal model of time series of images. By assuming separability of spatial and temporal processes we provide a very efficient and robust formulation for the marginal likelihood computation and the posterior prediction. The model adaptively accounts for local spatial correlations of the data, and the covariance structure is effectively parameterised by the Kronecker product of covariance matrices of very small size, each encoding only a single direction in space. We provide a simple and flexible framework for within- and between-subject modelling and prediction. In particular, we introduce the Hoffman-Ribak method for efficient inference on posterior processes and its uncertainty. The proposed framework is applied in the context of longitudinal modelling in Alzheimer's disease. We firstly demonstrate the advantage of our non-parametric method for modelling of within-subject structural changes. The results show that non-parametric methods demonstrably outperform conventional parametric methods. Then the framework is extended to optimize complex parametrized covariate kernels. Using Bayesian model comparison via marginal likelihood the framework enables to compare different hypotheses about individual change processes of images. PMID:26221708

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

    NASA Astrophysics Data System (ADS)

    Ku, Taeyun; Lee, Jungsul; Choi, Chulhee

    2010-02-01

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

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

    PubMed

    Chao Rodrguez, Y; el Anjoumi, A; Domnguez Gmez, J A; Rodrguez Prez, D; Rico, E

    2014-06-01

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

  4. An estimation model of population in China using time series DMSP night-time satellite imagery from 2002-2010

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaoyong; Zhang, Zhijie; Chang, Yuguang; Chen, Zhengchao

    2015-12-01

    Accurate data on the spatial distribution and potential growth estimation of human population are playing pivotal role in addressing and mitigating heavy lose caused by earthquake. Traditional demographic data is limited in its spatial resolution and is extremely hard to update. With the accessibility of massive DMSP/OLS night time imagery, it is possible to model population distribution at the county level across China. In order to compare and improve the continuity and consistency of time-series DMSP night-time satellite imagery obtained by different satellites in same year or different years by the same satellite from 2002-2010, normalized method was deployed for the inter-correction among imageries. And we referred to the reference F162007 Jixi city, whose social-economic has been relatively stable. Through binomial model, with average R2 0.90, then derived the correction factor of each year. The normalization obviously improved consistency comparing to previous data, which enhanced the correspondent accuracy of model. Then conducted the model of population density between average night-time light intensity in eight-economic districts. According to the two parameters variation law of consecutive years, established the prediction model of next following years with R2of slope and constant typically 0.85 to 0.95 in different regions. To validate the model, taking the year of 2005 as example, retrieved quantitatively population distribution in per square kilometer based on the model, then compared the results to the statistical data based on census, the difference of the result is acceptable. In summary, the estimation model facilitates the quick estimation and prediction in relieving the damage to people, which is significant in decision-making.

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  6. Time Series of Imaging Spectroscopy of Dust Radiative Forcing in Snow

    NASA Astrophysics Data System (ADS)

    Painter, T. H.; Skiles, M.; Bryant, A. C.; Seidel, F. C.

    2011-12-01

    The two most critical properties for understanding snowmelt totals and timing are the distribution of snow water equivalent (SWE) and the distribution of snow albedo, respectively. Despite their importance in controlling volume and timing of runoff, the snowpack is still poorly quantified in the US and not at all in most of the globe, leaving runoff models poorly constrained. While in the western US, we have relatively sparse measurements of SWE, mostly at lower and middle elevations and only a few per basin, albedo is even more drastically under sampled. With in situ measurements, we have begun to understand that changes in albedo from light absorbing impurities can have first order impacts on duration of snow cover and timing and magnitude of downstream runoff. To fully understand this control on runoff response however, we need time series of spatially explicit, quantitative retrievals of albedo and radiative forcing by dust and black carbon. Here we present an analysis of albedo and radiative forcing by mainly dust in snow in the Upper Colorado River Basin from data acquired by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) in the snowmelt season of 2011. AVIRIS measures reflected radiance continuously across the wavelength range 350 to 2500 nm at nominal 10 nm full-width half-maximum spectral response. The imaging spectrometer is unique in providing a direct measurement of the spectrally resolved radiative impact of dust and black carbon in snow. These acquisitions were anchored by ground-based directional reflectance measurements at time of acquisition (ideally isomorphic with the AVIRIS derived surface directional reflectances), in situ measurements of spectral irradiance at time of acquisition (to constrain the atmospheric compensation), and snow samples of dust loading.

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

    Previous analyses of satellite and ground-based measurements of hydrogen chloride (HCl) and chlorine monoxide (ClO) have suggested that total inorganic chlorine in the upper stratosphere is on the decline. We create HCl and ClO time series using satellite data sets with the intension of extending them to beyond November 2008 so that an update can be made on the long term evolution of these two species. We use the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) data for the HCl analysis, and the Odin Sub-Millimetre Radiometer (SMR) and the Aura Microwave Limb Sounder (Aura-MLS) measurements for the study of ClO. Altitudes between 35 and 45 km and three latitude bands between 60° S-60° N for HCl, and 20° S-20° N for ClO are studied. HCl shows values to be reducing from peak 1997 values by -4.4% in the tropics and between -6.4% to -6.7% per decade in the mid-latitudes. Trend values are significantly different from a zero trend at the 2 sigma level. ClO is decreasing in the tropics by -7.1% ± 7.8%/decade based on measurements made from December 2001. As both of these species contribute most to the chlorine budget at these altitudes then HCl and ClO should decrease at similar rates. The results found here confirm how effective the 1987 Montreal protocol objectives and its amendments have been in reducing the total amount of inorganic chlorine.

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

    NASA Technical Reports Server (NTRS)

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

    1993-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  11. Time-series MODIS satellite and in-situ data for spatio-temporal distribution of aerosol pollution assessment over Bucharest metropolitan area

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

    With the increasing industrialization and urbanization, especially in the metropolis regions, aerosol pollution has highly negative effects on environment. Urbanization is responsible of three major changes that may have impact on the urban atmosphere: replacement of the natural surfaces with buildings and impermeable pavements, heat of anthropogenic origin and air pollution. The importance of aerosols for radiative and atmospheric chemical processes is widely recognized. They can scatter and/or absorb solar radiation leading to changes of the radiation budget. Also, the so-called indirect effect of aerosols describes the cloud-aerosol interactions, which can modify the chemical and physical processes in the atmosphere. Their high spatial variability and short lifetime make spaceborne sensors especially well suited for their observation. Remote sensing is a key application in global-change science and urban climatology. Since the launch of the MODerate resolution Imaging Spectroradiometer (MODIS) there is detailed global aerosol information available, both over land and oceans The aerosol parameters can be measured directly in situ or derived from satellite remote sensing observations. All these methods are important and complementary. The objective of this work was to document the seasonal and inter-annual patterns of the aerosol pollution particulate matter in two size fractions (PM10 and PM2.5) loading and air quality index (AQI) over Bucharest metropolitan area in Romania based on in-situ and MODIS (Terra-Moderate Resolution Imaging Spectoradiometer) satellite time series data over 2010-2012 period. Accurate information of urban air pollution is required for environmental and health policy, but also to act as a basis for designing and stratifying future monitoring networks.

  12. The Time Series Toolbox

    NASA Astrophysics Data System (ADS)

    Božić, Bojan; Havlik, Denis

    2010-05-01

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

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

    NASA Technical Reports Server (NTRS)

    Jasinski, Michael F.; Borak, Jordan S.

    2008-01-01

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

  14. Impact of climate and anthropogenic changes on urban surface albedo assessed from time-series MODIS satellite data

    NASA Astrophysics Data System (ADS)

    Zoran, Maria A.; Dida, Adrian I.; Zoran, Liviu Florin V.

    2015-10-01

    Urbanization may be considered the most significant anthropogenic force that has brought about fundamental changes in urban land cover and landscape pattern around the globe, being one of the crucial issues of global change in the 21st century affecting urban ecosystem. In the physical climate system, land surface albedo determines the radiation balance of the surface and affects the surface temperature and boundary-layer structure of the atmosphere. Due to anthropogenic and natural factors, urban land covers changes result is the land surfaces albedo changes. The main aim of this paper is to investigate the albedo patterns dynamics due to the impact of atmospheric pollution and climate variations on land cover of Bucharest metropolitan area, Romania based on satellite remote sensing MODIS Terra/Aqua (Moderate Imaging Spectroradiometer) data over 2000-2014 time period. This study is based on MODIS derived biogeophysical parameters land surface BRDF/albedo products and in-situ monitoring ground data (as air temperature, aerosols distribution, relative humidity, etc.). For urban land cover changes over the same investigated period have been used also IKONOS satellite data. Due to deforestation in the periurban areas albedo changes appear to be the most significant biogeophysical effect in temperate forests. As the physical climate system is very sensitive to surface albedo, urban/periurban vegetation systems could significantly feedback to the projected climate change modeling scenarios through albedo changes.

  15. Monitoring irrigation volumes using high-resolution NDVI image time series: calibration and validation in the Kairouan plain (Tunisia)

    NASA Astrophysics Data System (ADS)

    Saadi, S.; Simonneaux, V.; Boulet, G.; Mougenot, B.; Lili Chabaane, Z.

    2015-10-01

    The increasing availability of high resolution high repetitively VIS-NIR remote sensing, like the forthcoming Sentinel-2 mission to be launched in 2015, offers unprecedented opportunity to improve agricultural monitoring. In this study, regional evapotranspiration and crop water consumption were estimated over an irrigated area located in the Kairouan plain (central Tunisia) using the FAO-56 dual crop coefficient water balance model combined with NDVI image time series providing estimates of the actual basal crop coefficient (Kcb) and vegetation fraction cover. Three time series of high-resolution SPOT5 images have been acquired for the 2008-2009, 2011-2012 and 2012-2013 hydrological years. We also benefited from a SPOT4 time series acquired in the frame of the SPOT4-Take5 experiment. The SPOT5 images were radiometrically corrected, first, using the SMAC6s Algorithm, and then improved using invariant objects located on the scene. The method was first calibrated using ground measurements of evapotranspiration achieved using eddy-correlation devices installed on irrigated wheat and barley plots. For other crops for which no calibration data was available, parameters were taken from bibliography. Then, the model was run to spatialize irrigation over the whole area and a validation was done using cumulated seasonal water volumes obtained from ground survey for three irrigated perimeters. In a subsequent step, evapotranspiration estimates were obtained using a large aperture scintillometer and were used for an additional validation of the model outputs.

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

    USGS Publications Warehouse

    Eldenshink, J.

    2006-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Schwatke, Christian; Dettmering, Denise; Boergens, Eva

    2015-04-01

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

  18. Identifying drought-induced correlations in the satellite time series of hot pixels recorded in the Brazilian Amazon by means of the detrended fluctuation analysis

    NASA Astrophysics Data System (ADS)

    Stosic, Tatijana; Telesca, Luciano; Lemos da Costa, Simara Lúcia; Stosic, Borko

    2016-02-01

    In this work we study the long-term correlations in the satellite daily number of hot pixels recorded in the Brazilian Amazon during the period 1999-2012. While the highest peak in daily hot pixel frequencies occurred in 2007, coincident with a severe drought, for other intense droughts such as that occurred in 2005 (one-in-a-hundred year event for its high severity) and 2010, the corresponding number of hot pixels recorded was compatible or lower than that reached during e.g. 2004, with no reported severe drought. On the other hand, we find that the most severe droughts coincide with the peaks of the Detrended Fluctuation Analysis (DFA) scaling exponent of the time series of the daily anomalies in hot pixels. This finding is striking because it highlights the effectiveness of the DFA in disclosing that long-term hot pixel anomaly correlations are clearly associated with the drought events, that were not identifiable by examining hot pixel frequencies of the original time series. The dynamics of the time series of daily anomalies in hot pixels is, therefore, influenced by drought events. The coincidence of the peaks of the scaling exponent with drought events suggests the increase of the persistence of the hot pixel time series during the driest periods.

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  20. 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)

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

    2014-01-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Yang, Tingting; Xu, Yongsheng

    2015-04-01

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

  6. Unsupervised classification of saturated areas using a time series of remotely sensed images

    NASA Astrophysics Data System (ADS)

    Dealwis, D. A.; Easton, Z. M.; Dahlke, H. E.; Philpot, W. D.; Steenhuis, T. S.

    2007-06-01

    The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or sighting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. Much of the non-point source pollution in these watersheds originates from these HAAs. Thus, a technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs, should a proper technique be developed. The objective of this study is to develop a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution LANDSAT 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas that were susceptible to saturation. We found that within a single landcover type, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas. This methodology appears promising for delineating saturated areas in the landscape.

  7. Unsupervised classification of saturated areas using a time series of remotely sensed images

    NASA Astrophysics Data System (ADS)

    de Alwis, D. A.; Easton, Z. M.; Dahlke, H. E.; Philpot, W. D.; Steenhuis, T. S.

    2007-09-01

    The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or siting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. A technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs. This study describes a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution Landsat 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas susceptible to saturation. We found that within a single land cover, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of the vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas (accuracies from 49 to 79%). This methodology shows that remote sensing can be used to capture temporal variations in vegetation phenology as well as spatial/temporal variation in surface water content, and appears promising for delineating saturated areas in the landscape.

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

    NASA Astrophysics Data System (ADS)

    Maeda, T.

    2012-04-01

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

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

    PubMed

    Wang, Jing; Huang, Jing-feng; Wang, Xiu-zhen; Jin, Meng-ting; Zhou, Zhen; Guo, Qiao-ying; Zhao, Zhe-wen; Huang, Wei-jiao; Zhang, Yao; Song, Xiao-dong

    2015-10-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    SciTech Connect

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

    2008-01-01

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

  12. 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)

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

    2014-05-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  15. 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)

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

    2014-05-01

    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.

  16. Remote Sensing Time Series Product Tool

    NASA Technical Reports Server (NTRS)

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

    2006-01-01

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

  17. Refining measurements of lateral channel movement from image time series by quantifying spatial variations in registration error

    NASA Astrophysics Data System (ADS)

    Lea, Devin M.; Legleiter, Carl J.

    2016-04-01

    Remotely sensed data provides information on river morphology useful for examining channel change at yearly-to-decadal time scales. Although previous studies have emphasized the need to distinguish true geomorphic change from errors associated with image registration, standard metrics for assessing and summarizing these errors, such as the root-mean-square error (RMSE) and 90th percentile of the distribution of ground control point (GCP) error, fail to incorporate the spatial structure of this uncertainty. In this study, we introduce a framework for evaluating whether observations of lateral channel migration along a meandering channel are statistically significant, given the spatial distribution of registration error. An iterative leave-one-out cross-validation approach was used to produce local error metrics for an image time series from Savery Creek, Wyoming, USA, and to evaluate various transformation equations, interpolation methods, and GCP placement strategies. Interpolated error surfaces then were used to create error ellipses representing spatially variable buffers of detectable change. Our results show that, for all five sequential image pairs we examined, spatially distributed estimates of registration error enabled detection of a greater number of statistically significant lateral migration vectors than the spatially uniform RMSE or 90th percentile of GCP error. Conversely, spatially distributed error metrics prevented changes from being mistaken as real in areas of greater registration error. Our results also support the findings of previous studies: second-order polynomial functions on average yield the lowest RMSE, and errors are reduced by placing GCPs on the floodplain rather than on hillslopes. This study highlights the importance of characterizing the spatial distribution of image registration errors in the analysis of channel change.

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

    NASA Astrophysics Data System (ADS)

    Nitze, Ingmar; Barrett, Brian; Cawkwell, Fiona

    2015-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Bindhu, V. M.; Narasimhan, B.

    2015-03-01

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

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

    PubMed

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

    2015-05-01

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

  2. Fire Monitoring - The use of medium resolution satellites (AVHRR, MODIS, TET) for long time series processing and the implementation in User Driven Applications and Services

    NASA Astrophysics Data System (ADS)

    Fuchs, E.-M.; Stein, E.; Strunz, G.; Strobl, C.; Frey, C.

    2015-04-01

    This paper introduces fire monitoring works of two different projects, namely TIMELINE (TIMe Series Processing of Medium Resolution Earth Observation Data assessing Long -Term Dynamics In our Natural Environment) and PHAROS (Project on a Multi-Hazard Open Platform for Satellite Based Downstream Services). It describes the evolution from algorithm development from in applied research to the implementation in user driven applications and systems. Concerning TIMELINE, the focus of the work lies on hot spot detection. A detailed description of the choice of a suitable algorithm (round robin approach) will be given. Moreover, strengths and weaknesses of the AVHRR sensor for hot spot detection, a literature review, the study areas and the selected approach will be highlighted. The evaluation showed that the contextual algorithm performed best, and will therefore be used for final implementation. Concerning the PHAROS project, the key aspect is on the use of satellite-based information to provide valuable support to all phases of disaster management. The project focuses on developing a pre-operational sustainable service platform that integrates space-based EO (Earth Observation), terrestrial sensors and communication and navigation assets to enhance the availability of services and products following a multi-hazard approach.

  3. Subsidence history of the city of Morelia, Mexico based on InSAR images processed as time series

    NASA Astrophysics Data System (ADS)

    Jaramillo, S. H.; Suárez, G.; López-Quiroz, P.

    2012-04-01

    The city of Morelia in central Mexico sits on lacustrine and fluvio-lacustrine deposits. Subsidence due to the extraction of water from the subsoil is evidenced by the presence of differential soil compaction, causing faulting and cracking of the ground and adjacent constructions. In order to study the subsidence history of the past nine years, twenty-eight ENVISAT Synthetic Aperture Radar (SAR) images acquired between May 2003 and September 2010 were processed using ROI_PAC. All scenes are descending orbit images. The resulting interferograms were filtered using an adaptive filter and, in order to increase coherence and signal-to-noise ratio, they were unwrapped using the "branch-cut" algorithm. A subset of the resulting interferograms was selected based on the following criteria. Only interferograms with spatial baseline of less than 400 m and a temporal baseline of less than 420 days were considered. The primary objective of our work was to determine the temporal evolution of the subsidence in different parts of the city. To this end, selected pixels are inverted in an independent manner from neighbouring pixels using a time series analysis. Preliminary results suggest that the central part of the basin, near the fault known as the "Central Camionera", the subsidence is almost constant with a value of 3 to 4 cm/yr until 2008. From this date on, the subsidence rates increase to values with an average of 7 to 8 cm/yr. This increase in the subsidence rate is clearly appreciated in the appearance of two clearly visible circular patterns from 2008 to 2010. Currently, an inversion is being conducted to obtain the overall subsidence history of the basin.

  4. Satellite camera image navigation

    NASA Technical Reports Server (NTRS)

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

    1987-01-01

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

  5. Classification of Satellite Derived Chlorophyll a Space-Time Series by Means of Quantile Regression: An Application to the Adriatic Sea

    NASA Astrophysics Data System (ADS)

    Girardi, P.; Pastres, R.; Gaetan, C.; Mangin, A.; Taji, M. A.

    2015-12-01

    In this paper, we present the results of a classification of Adriatic waters, based on spatial time series of remotely sensed Chlorophyll type-a. The study was carried out using a clustering procedure combining quantile smoothing and an agglomerative clustering algorithms. The smoothing function includes a seasonal term, thus allowing one to classify areas according to “similar” seasonal evolution, as well as according to “similar” trends. This methodology, which is here applied for the first time to Ocean Colour data, is more robust with respect to other classical methods, as it does not require any assumption on the probability distribution of the data. This approach was applied to the classification of an eleven year long time series, from January 2002 to December 2012, of monthly values of Chlorophyll type-a concentrations covering the whole Adriatic Sea. The data set was made available by ACRI (http://hermes.acri.fr) in the framework of the Glob-Colour Project (http://www.globcolour.info). Data were obtained by calibrating Ocean Colour data provided by different satellite missions, such as MERIS, SeaWiFS and MODIS. The results clearly show the presence of North-South and West-East gradient in the level of Chlorophyll, which is consistent with literature findings. This analysis could provide a sound basis for the identification of “water bodies” and of Chlorophyll type-a thresholds which define their Good Ecological Status, in terms of trophic level, as required by the implementation of the Marine Strategy Framework Directive. The forthcoming availability of Sentinel-3 OLCI data, in continuity of the previous missions, and with perspective of more than a 15-year monitoring system, offers a real opportunity of expansion of our study as a strong support to the implementation of both the EU Marine Strategy Framework Directive and the UNEP-MAP Ecosystem Approach in the Mediterranean.

  6. 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)

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

    2014-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  8. Satellite Hyperspectral Imaging Simulation

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

  9. 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)

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

    2010-12-01

    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.

  10. Image sets for satellite image processing systems

    NASA Astrophysics Data System (ADS)

    Peterson, Michael R.; Horner, Toby; Temple, Asael

    2011-06-01

    The development of novel image processing algorithms requires a diverse and relevant set of training images to ensure the general applicability of such algorithms for their required tasks. Images must be appropriately chosen for the algorithm's intended applications. Image processing algorithms often employ the discrete wavelet transform (DWT) algorithm to provide efficient compression and near-perfect reconstruction of image data. Defense applications often require the transmission of images and video across noisy or low-bandwidth channels. Unfortunately, the DWT algorithm's performance deteriorates in the presence of noise. Evolutionary algorithms are often able to train image filters that outperform DWT filters in noisy environments. Here, we present and evaluate two image sets suitable for the training of such filters for satellite and unmanned aerial vehicle imagery applications. We demonstrate the use of the first image set as a training platform for evolutionary algorithms that optimize discrete wavelet transform (DWT)-based image transform filters for satellite image compression. We evaluate the suitability of each image as a training image during optimization. Each image is ranked according to its suitability as a training image and its difficulty as a test image. The second image set provides a test-bed for holdout validation of trained image filters. These images are used to independently verify that trained filters will provide strong performance on unseen satellite images. Collectively, these image sets are suitable for the development of image processing algorithms for satellite and reconnaissance imagery applications.

  11. Predicting chaotic time series

    SciTech Connect

    Farmer, J.D.; Sidorowich, J.J.

    1987-08-24

    We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow.

  12. Time Series Database

    Energy Science and Technology Software Center (ESTSC)

    2007-11-02

    TSDB is a Python module for storing large volumes of time series data. TSDB stores data in binary files indexed by a timestamp. Aggregation functions (such as rate, sum, avg, etc.) can be performed on the data, but data is never discarded. TSDB is presently best suited for SNMP data but new data types are easily added.

  13. Thirty Years of Vegetation Change in the Coastal Santa Cruz Mountains of Northern California Detected Using Landsat Satellite Image Analysis

    NASA Technical Reports Server (NTRS)

    Potter, Christopher

    2015-01-01

    Results from Landsat satellite image times series analysis since 1983 of this study area showed gradual, statistically significant increases in the normalized difference vegetation index (NDVI) in more than 90% of the (predominantly second-growth) evergreen forest locations sampled.

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

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

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

  15. Random time series in astronomy.

    PubMed

    Vaughan, Simon

    2013-02-13

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

  16. Pattern Recognition in Time Series

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  18. Aerial Photographs and Satellite Images

    USGS Publications Warehouse

    U.S. Geological Survey

    1997-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Bradley, Eliza Swan

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

  20. Robust ordering of independent components in functional magnetic resonance imaging time series data using canonical correlation analysis

    NASA Astrophysics Data System (ADS)

    Youssef, Tamer; Youssef, Abou-Bakr M.; LaConte, Stephen M.; Hu, Xiaoping P.; Kadah, Yasser M.

    2003-05-01

    The application of independent components analysis (ICA) to functional magnetic resonance imaging data has been proven useful to decompose the signal in terms of its basic sources. The main advantage is that ICA requires no prior assumption about the neuronal activity or the noise structure, which are usually unknown in fMRI. This enables the detection of true activation components free of random and physiological noise. Hence, this technique is superior to other techniques such as subspace modeling or canonical correlation analysis, which have underlying assumptions about the signal components. Nevertheless, this technique suffers from a fundamental limitation of not providing a consistent ordering of the signal components as a result of the whitening step involved in ICA. This mandates human intervention to pick out the relevant activation components from the outcome of ICA, which poses a significant obstacle to the practicality of this technique. In this work, a simple yet robust technique is proposed for ranking the resultant independent components. This technique adds a second step to ICA based on canonical correlation analysis and the prior information about the activation paradigm. This enables the proposed technique to provide a consistent and reproducible ordering of independent components. The proposed technique was applied to real event-related functional magnetic resonance imaging data and the results confirm the practicality and robustness of the proposed method.

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  2. Evaluating the impact of abrupt changes in forest policy and management practices on landscape dynamics: analysis of a Landsat image time series in the Atlantic Northern Forest.

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  5. Satellite Image Mosaic Engine

    NASA Technical Reports Server (NTRS)

    Plesea, Lucian

    2006-01-01

    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.

  6. GPS Position Time Series @ JPL

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

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

  7. 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)

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

    2011-11-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Testa, S.; Borgogno Mondino, E.

    2013-12-01

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

  9. Developing consistent time series landsat data products

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Landsat series satellite has provided earth observation data record continuously since early 1970s. There are increasing demands on having a consistent time series of Landsat data products. In this presentation, I will summarize the work supported by the USGS Landsat Science Team project from 20...

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

    NASA Astrophysics Data System (ADS)

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

    2005-04-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2008-01-01

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

  12. Mapping crop based on phenological characteristics using time-series NDVI of operational land imager data in Tadla irrigated perimeter, Morocco

    NASA Astrophysics Data System (ADS)

    Ouzemou, Jamal-eddine; El Harti, Abderrazak; EL Moujahid, Ali; Bouch, Naima; El Ouazzani, Rabii; Lhissou, Rachid; Bachaoui, El Mostafa

    2015-10-01

    Morocco is a primarily arid to semi-arid country. These climatic conditions make irrigation an imperative and inevitable technique. Especially, agriculture has a paramount importance for the national economy. Retrieving of crops and their location as well as their spatial extent is useful information for agricultural planning and better management of irrigation water resource. Remote sensing technology was often used in management and agricultural research. Indeed, it's allows crops extraction and mapping based on phenological characteristics, as well as yield estimation. The study area of this work is the Tadla irrigated perimeter which is characterized by heterogeneous areas and extremely small size fields. Our principal objectives are: (1) the delimitation of the major crops for a good water management, (2) the insulation of sugar beet parcels for modeling its yields. To achieve the traced goals, we have used Landsat-8 OLI (Operational Land Imager) data pan-sharpened to 15 m. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classifications were applied to the Normalized Difference Vegetation Index (NDVI) time-series of 10 periods. Classifications were calculated for a site of more than 124000 ha. This site was divided into two parts: the first part for selecting, training datasets and the second one for validating the classification results. The SVM and SAM methods classified the principal crops with overall accuracies of 85.27% and 57.17% respectively, and kappa coefficient of 80% and 43% respectively. The study showed the potential of using time-series OLI NDVI data for mapping different crops in irrigated, heterogeneous and undersized parcels in arid and semi-arid environment.

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

    NASA Technical Reports Server (NTRS)

    Mcpherron, R. L.

    1976-01-01

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

  14. Development of an IUE Time Series Browser

    NASA Technical Reports Server (NTRS)

    Massa, Derck

    2005-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2013-01-01

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

  16. 13 years time series of stratospheric and mesospheric ozone profiles measured by the NDACC microwave radiometer SOMORA over Switzerland: comparison to radiosonde and MLS/AURA satellite ozone profiles.

    NASA Astrophysics Data System (ADS)

    Maillard Barras, Eliane; Haefele, Alexander; Ruffieux, Dominique; Kämpfer, Niklaus

    2014-05-01

    The microwave radiometer SOMORA measures ozone volume mixing ratio in the stratosphere and lower mesosphere since January 2000 with a time resolution of 30 min. The ozone vertical distribution is calculated from the measurement of the rotational emission line of ozone at 142.17 GHz. Ozone profiles are retrieved using ARTS/Qpack, a general environment for radiative transfer simulation and retrieval of ozone profiles based on the optimal estimation method (OEM) of Rodgers. SOMORA is an instrument of the NDACC.The measurement time series has been influenced by the upgrade from an acousto optical spectrometer (AOS) setup to a digital FFT spectrometer setup in 2010. The ozone profiles dataset measured by the AOS (2000-2010) and FFT spectrometer (since 2010) is then homogenized using one year parallel measurements by adding an altitude dependent offset to the AOS ozone profiles. The ozone profiles measured by AOS show a slightly better vertical resolution above 55 km than the ozone profiles measured by FFT due to the higher spectral resolution.The homogenized 13 years SOMORA time series has been validated against Payerne radiosonde (RS) ozone profiles, GROMOS microwave radiometer ozone profiles of Bern, another NDACC instrument, and MLS/AURA satellite simultaneous ozones profiles, and the results will be shown. For the whole period of respective common measurements, SOMORA ozone profiles are within 5% of Payerne RS, 15% of GROMOS and 10% of MLS ozone profiles.

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  18. Time series with tailored nonlinearities

    NASA Astrophysics Data System (ADS)

    Räth, C.; Laut, I.

    2015-10-01

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

  19. FROG: Time-series analysis

    NASA Astrophysics Data System (ADS)

    Allan, Alasdair

    2014-06-01

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

  20. Langevin equations from time series

    NASA Astrophysics Data System (ADS)

    Racca, E.; Porporato, A.

    2005-02-01

    We discuss the link between the approach to obtain the drift and diffusion of one-dimensional Langevin equations from time series, and Pope and Chings 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 Chings relationship to obtain stochastic differential equations from time series and shows its validity, in a generalized sense, for nondifferentiable processes originating from Langevin equations.

  1. Langevin equations from time series.

    PubMed

    Racca, E; Porporato, A

    2005-02-01

    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. PMID:15783455

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  3. A principal component network analysis of prefrontal-limbic functional magnetic resonance imaging time series in schizophrenia patients and healthy controls.

    PubMed

    Rădulescu, Anca R; Mujica-Parodi, Lilianne R

    2009-12-30

    We investigated neural regulation of emotional arousal. We hypothesized that the interactions between the components of the prefrontal-limbic system determine the global trajectories of the individual's brain activation, with the strengths and modulations of these interactions being potentially key components underlying the differences between healthy individuals and those with schizophrenia. Using affect-valent facial stimuli presented to 11 medicated schizophrenia patients and 65 healthy controls, we activated neural regions associated with the emotional arousal response during functional magnetic resonance imaging (fMRI). Performing first a random effects analysis of the fMRI data to identify activated regions, we obtained 352 data-point time series for six brain regions: bilateral amygdala, hippocampus and two prefrontal regions (Brodmann Areas 9 and 45). Since standard statistical methods are not designed to capture system features and evolution, we used principal component analyses on two types of pre-processed data: contrasts and group averages. We captured an important characteristic of the evolution of our six-dimensional brain network: all subject trajectories are almost embedded in a two-dimensional plane. Moreover, the direction of the largest principal component was a significant differentiator between the control and patient populations: the left and right amygdala coefficients were substantially higher in the case of patients, and the coefficients of Brodmann Area 9 were, to a lesser extent, higher in controls. These results are evidence that modulations between the regions of interest are the important determinant factors for the system's dynamical behavior. We place our results within the context of other principal component analyses used in neuroimaging, as well as of our existing theoretical model of prefrontal-limbic dysregulation. PMID:19880294

  4. Clustering of financial time series

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

    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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    NASA Astrophysics Data System (ADS)

    Vijay, Saurabh; Braun, Matthias

    2014-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Shih, Hsiao-chien

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

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

    NASA Astrophysics Data System (ADS)

    Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane

    2014-05-01

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

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

    USGS Publications Warehouse

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

    2013-01-01

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

  11. Satellite image classification using convolutional learning

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  13. Satellite image analysis using neural networks

    NASA Technical Reports Server (NTRS)

    Sheldon, Roger A.

    1990-01-01

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

  14. Entropy of electromyography time series

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

    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.

  15. Inductive time series modeling program

    SciTech Connect

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

    1985-10-01

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

  16. Modeling North Pacific Time Series

    NASA Astrophysics Data System (ADS)

    Overland, J. E.; Percival, D. B.; Mofjeld, H. O.

    2002-05-01

    We present a case study in modeling the North Pacific (NP) index, a time series of the wintertime Aleutian low sea level pressure from 1900 to 1999. We consider three statistical models, namely, a Gaussian stationary autoregressive process, a Gaussian fractionally difference (FD) or ``long-memory" process, and a ``signal plus noise" process consisting of a square wave oscillation with a pentadecadal period embedded in Gaussian white noise. Each model depends upon three parameters, so all three models are equally simple. The shortness of the time series makes it unrealistic to formally prefer one model over the other: we estimate it would take a 300 year record to differentiate between the models. Although the models fit equally well, they have quite different implications for the long-term behavior of the NP index, e.g. generation of regimes of characteristic lengths. Additional information and physical arguments may add support for a particular model. The FD - ``long memory" process suggests multiple physical contributions with different damping constants many North Pacific biological time series which are influenced by atmospheric and oceanic processes, show regime-like ecosystem reorganizations.

  17. Image Stacking Techniques for GEO Satellites

    NASA Astrophysics Data System (ADS)

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

    2014-09-01

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

  18. 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)

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

    2013-04-01

    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.

  19. Introduction to Time Series Analysis

    NASA Technical Reports Server (NTRS)

    Hardin, J. C.

    1986-01-01

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

  20. Data compression for satellite images

    NASA Technical Reports Server (NTRS)

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

    1976-01-01

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

  1. ARMA Modeling of Time Series.

    PubMed

    Cadzow, J A

    1982-02-01

    A method for efficiently generating a rational model of a wide-sense stationary time series is presented. In this method the autoregressive parameters associated with an ARMA model consisting of q zeros and p poles are optimally chosen with the selection being based on a finite set of time series observations. This selection is made so that a set of Yule-Walker equation approximations are ``best'' satisfied. The resultant autoregressive parameter estimates have the desired statistical feature of being unbiased and consistent. This estimation method has been found to provide a modeling performance which typically equals or exceeds that of contemporary alternatives. Moreover, this method is amenable to a computationally efficient adaptive solution procedure. The autoregressive parameters characterizing the resultant ARMA model estimate can serve the role of decision variables in pattern classification schemes. For example, these parameters can be utilized in determining whether or not a member(s) of a given signal class is contained within a noise corrupted measurement signal. This approach has been found to be particularly effective in Doppler radar and array processing applications in which one is looking for the presence of spectral lines (i.e., sinusoids) in the measurement signal. PMID:21869015

  2. Analysis of time series from stochastic processes

    PubMed

    Gradisek; Siegert; Friedrich; Grabec

    2000-09-01

    Analysis of time series from stochastic processes governed by a Langevin equation is discussed. Several applications for the analysis are proposed based on estimates of drift and diffusion coefficients of the Fokker-Planck equation. The coefficients are estimated directly from a time series. The applications are illustrated by examples employing various synthetic time series and experimental time series from metal cutting. PMID:11088809

  3. Algorithm for Compressing Time-Series Data

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  5. Aerial photographs and satellite images

    USGS Publications Warehouse

    U.S. Geological Survey

    1995-01-01

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

  6. Traffic Flow Estimation from Single Satellite Images

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  7. Reducing uncertainty on satellite image classification through spatiotemporal reasoning

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

    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.

  8. Feature extraction for change analysis in SAR time series

    NASA Astrophysics Data System (ADS)

    Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan

    2015-10-01

    In remote sensing, the change detection topic represents a broad field of research. If time series data is available, change detection can be used for monitoring applications. These applications require regular image acquisitions at identical time of day along a defined period. Focusing on remote sensing sensors, radar is especially well-capable for applications requiring regularity, since it is independent from most weather and atmospheric influences. Furthermore, regarding the image acquisitions, the time of day plays no role due to the independence from daylight. Since 2007, the German SAR (Synthetic Aperture Radar) satellite TerraSAR-X (TSX) permits the acquisition of high resolution radar images capable for the analysis of dense built-up areas. In a former study, we presented the change analysis of the Stuttgart (Germany) airport. The aim of this study is the categorization of detected changes in the time series. This categorization is motivated by the fact that it is a poor statement only to describe where and when a specific area has changed. At least as important is the statement about what has caused the change. The focus is set on the analysis of so-called high activity areas (HAA) representing areas changing at least four times along the investigated period. As first step for categorizing these HAAs, the matching HAA changes (blobs) have to be identified. Afterwards, operating in this object-based blob level, several features are extracted which comprise shape-based, radiometric, statistic, morphological values and one context feature basing on a segmentation of the HAAs. This segmentation builds on the morphological differential attribute profiles (DAPs). Seven context classes are established: Urban, infrastructure, rural stable, rural unstable, natural, water and unclassified. A specific HA blob is assigned to one of these classes analyzing the CovAmCoh time series signature of the surrounding segments. In combination, also surrounding GIS information is included to verify the CovAmCoh based context assignment. In this paper, the focus is set on the features extracted for a later change categorization procedure.

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

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

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

  10. Salton Sea Satellite Image Showing Fault Slip

    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...

  11. Adaptive median filtering for preprocessing of time series measurements

    NASA Technical Reports Server (NTRS)

    Paunonen, Matti

    1993-01-01

    A median (L1-norm) filtering program using polynomials was developed. This program was used in automatic recycling data screening. Additionally, a special adaptive program to work with asymmetric distributions was developed. Examples of adaptive median filtering of satellite laser range observations and TV satellite time measurements are given. The program proved to be versatile and time saving in data screening of time series measurements.

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

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

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

    ERIC Educational Resources Information Center

    Nous, Albert P.

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  16. Antarctica: Measuring glacier velocity from satellite images

    USGS Publications Warehouse

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

    1986-01-01

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

  17. A neuromorphic approach to satellite image understanding

    NASA Astrophysics Data System (ADS)

    Partsinevelos, Panagiotis; Perakakis, Manolis

    2014-05-01

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

  18. Antarctica: measuring glacier velocity from satellite images

    SciTech Connect

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

    1986-11-28

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

  19. Automated Construction of Coverage Catalogues of Aster Satellite Image for Urban Areas of the World

    NASA Astrophysics Data System (ADS)

    Miyazaki, H.; Iwao, K.; Shibasaki, R.

    2012-07-01

    We developed an algorithm to determine a combination of satellite images according to observation extent and image quality. The algorithm was for testing necessity for completing coverage of the search extent. The tests excluded unnecessary images with low quality and preserve necessary images with good quality. The search conditions of the satellite images could be extended, indicating the catalogue could be constructed with specified periods required for time series analysis. We applied the method to a database of metadata of ASTER satellite images archived in GEO Grid of National Institute of Advanced Industrial Science and Technology (AIST), Japan. As indexes of populated places with geographical coordinates, we used a database of 3372 populated place of more than 0.1 million populations retrieved from GRUMP Settlement Points, a global gazetteer of cities, which has geographical names of populated places associated with geographical coordinates and population data. From the coordinates of populated places, 3372 extents were generated with radiuses of 30 km, a half of swath of ASTER satellite images. By merging extents overlapping each other, they were assembled into 2214 extents. As a result, we acquired combinations of good quality for 1244 extents, those of low quality for 96 extents, incomplete combinations for 611 extents. Further improvements would be expected by introducing pixel-based cloud assessment and pixel value correction over seasonal variations.

  20. A review of subsequence time series clustering.

    PubMed

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

    2014-01-01

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

  1. A Review of Subsequence Time Series Clustering

    PubMed Central

    Teh, Ying Wah

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Scott, Douglas; Petropoulos, George

    2014-05-01

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

  3. Thin cloud removal from single satellite images.

    PubMed

    Liu, Jun; Wang, Xing; Chen, Min; Liu, Shuguang; Zhou, Xiran; Shao, Zhenfeng; Liu, Ping

    2014-01-13

    A novel method for removing thin clouds from single satellite image is presented based on a cloud physical model. Given the unevenness of clouds, the cloud background is first estimated in the frequency domain and an adjustment function is used to suppress the areas with greater gray values and enhance the dark objects. An image, mainly influenced by transmission, is obtained by subtracting the cloud background from the original cloudy image. The final image with proper color and contrast is obtained by decreasing the effect of transmission using the proposed max-min radiation correction approach and an adaptive brightness factor. The results indicate that the proposed method can more effectively remove thin clouds, improve contrast, restore color information, and retain detailed information compared with the commonly used image enhancement and haze removal methods. PMID:24515022

  4. Integration of Multisensor Remote Sensing Data for the Retrieval of Consistent Times Series of High-Resolution NDVI Images for Crop Monitoring in Landscapes Dominated By Small-Scale Farming Agricultural

    NASA Astrophysics Data System (ADS)

    Sedano, F.; Kempeneers, P.

    2014-12-01

    There is a need for timely and accurate information of food supply and early warnings of production shortfalls. Crop growth models commonly rely on information on vegetation dynamics from low and moderate spatial resolution remote sensing imagery. While the short revisit period of these sensors captures the temporal dynamics of crops, they are not able to monitor small-scale farming areas where environmental factors, crop type and management practices often vary at subpixel level. Although better suited to retrieve fine spatial structure, time series of higher resolution imagery (circa 30 m) are often incomplete due to larger revisit periods and persistent cloud coverage. However, as the Landsat archive expands and more fine resolution Earth observation sensors become available, the possibilities of multisensor integration to monitor crop dynamics with higher level of spatial detail are expanding. We have integrated remote sensing imagery from two moderate resolution sensors (MODIS and PROBA-V) and three medium resolution platforms (Landsat 7- 8; and DMC) to improve the characterization of vegetation dynamics in agricultural landscapes dominated by small-scale farms. We applied a data assimilation method to produce complete temporal sequences of synthetic medium-resolution NDVI images. The method implements a Kalman filter recursive algorithm that incorporates models, observations and their respective uncertainties to generate medium-resolution images at time steps for which only moderate-resolution imagery is available. The results for the study sites show that the time series of synthetic NDVI images captured seasonal vegetation dynamics and maintained the spatial structure of the landscape at higher spatial resolution. A more detailed characterization of spatiotemporal dynamics of vegetation in agricultural systems has the potential to improve the estimates of crop growth models and allow a more precise monitoring and forecasting of crop productivity.

  5. Estimating seasonal evapotranspiration from temporal satellite images

    USGS Publications Warehouse

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

    2012-01-01

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

  6. Nonparametric causal inference for bivariate time series

    NASA Astrophysics Data System (ADS)

    McCracken, James M.; Weigel, Robert S.

    2016-02-01

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

  7. Association mining of dependency between time series

    NASA Astrophysics Data System (ADS)

    Hafez, Alaaeldin

    2001-03-01

    Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  9. Volatility of linear and nonlinear time series.

    PubMed

    Kalisky, Tomer; Ashkenazy, Yosef; Havlin, Shlomo

    2005-07-01

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

  10. Generation of artificial helioseismic time-series

    NASA Technical Reports Server (NTRS)

    Schou, J.; Brown, T. M.

    1993-01-01

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

  11. Surrogate Test for Pseudoperiodic Time Series Data

    NASA Astrophysics Data System (ADS)

    Small, Michael; Yu, Dejin; Harrison, Robert G.

    2001-10-01

    For time series exhibiting strong periodicities, standard (linear) surrogate methods are not useful. We describe a new algorithm that can test against the null hypothesis of a periodic orbit with uncorrelated noise. We demonstrate the application of this method to artificial data and experimental time series, including human electrocardiogram recordings during sinus rhythm and ventricular tachycardia.

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

    NASA Astrophysics Data System (ADS)

    Zhu, Xiaolin; Liu, Desheng

    2014-10-01

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

  13. Absolute image registration for geosynchronous satellites

    NASA Technical Reports Server (NTRS)

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

    1980-01-01

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

  14. Landslide monitoring using airphotos time series and GIS

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  15. Structural generative descriptions for time series classification.

    PubMed

    García-Treviño, Edgar S; Barria, Javier A

    2014-10-01

    In this paper, we formulate a novel time series representation framework that captures the inherent data dependency of time series and that can be easily incorporated into existing statistical classification algorithms. The impact of the proposed data representation stage in the solution to the generic underlying problem of time series classification is investigated. The proposed framework, which we call structural generative descriptions moves the structural time series representation to the probability domain, and hence is able to combine statistical and structural pattern recognition paradigms in a novel fashion. Two algorithm instantiations based on the proposed framework are developed. The algorithms are tested and compared using different publicly available real-world benchmark data. Results reported in this paper show the potential of the proposed representation framework, which in the experiments investigated, performs better or comparable to state-of-the-art time series description techniques. PMID:24860046

  16. Statistical criteria for characterizing irradiance time series.

    SciTech Connect

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

    2010-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  18. Star sightings by satellite for image navigation

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

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

  19. Network structure of multivariate time series.

    PubMed

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-01-01

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

  20. Network structure of multivariate time series

    NASA Astrophysics Data System (ADS)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-01

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

  1. Network structure of multivariate time series

    PubMed Central

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  3. Double regions growing algorithm for automated satellite image mosaicking

    NASA Astrophysics Data System (ADS)

    Tan, Yihua; Chen, Chen; Tian, Jinwen

    2011-12-01

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

  4. Time series change detection: Algorithms for land cover change

    NASA Astrophysics Data System (ADS)

    Boriah, Shyam

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

  5. Complex network approach to fractional time series

    NASA Astrophysics Data System (ADS)

    Manshour, Pouya

    2015-10-01

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

  6. Detecting nonlinear structure in time series

    SciTech Connect

    Theiler, J.

    1991-01-01

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

  7. Advanced spectral methods for climatic time series

    USGS Publications Warehouse

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

    2002-01-01

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

  8. Spectral analysis of multiple time series

    NASA Technical Reports Server (NTRS)

    Dubman, M. R.

    1972-01-01

    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.

  9. Complex network approach to fractional time series

    SciTech Connect

    Manshour, Pouya

    2015-10-15

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

  10. Nonlinear Analysis of Surface EMG Time Series

    NASA Astrophysics Data System (ADS)

    Zurcher, Ulrich; Kaufman, Miron; Sung, Paul

    2004-04-01

    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.

  11. Entropic Analysis of Electromyography Time Series

    NASA Astrophysics Data System (ADS)

    Kaufman, Miron; Sung, Paul

    2005-03-01

    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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  13. Long GPS coordinate time series: Multipath and geometry effects

    NASA Astrophysics Data System (ADS)

    King, Matt A.; Watson, Christopher S.

    2010-04-01

    Within analyses of Global Positioning System (GPS) observations, unmodeled subdaily signals propagate into long-period signals via a number of different mechanisms. In this paper, we investigate the effects of time-variable satellite geometry and the propagation of a time-constant unmodeled multipath signal. Multipath reflectors at H = 0.1 m, 0.2 m, and 1.5 m below the antenna are modeled, and their effects on GPS coordinate time series are examined. Simulated time series at 20 global IGS sites for 2000.0-2008.0 were derived using the satellite geometry as defined by daily broadcast orbits. We observe the introduction of time-variable biases in the time series of up to several millimeters. The frequency and magnitude of the signal is dependent on site location and multipath source. When adopting realistic GPS observation geometries obtained from real data (e.g., including the influence of local obstructions and hardware specific tracking), we observe generally larger levels of coordinate variation. In these cases, we observe spurious signals across the frequency domain, including very high frequency abrupt changes (offsets) in addition to secular trends. Velocity biases of more than 0.5 mm/yr are evident at some sites. The propagated signal has noise characteristics that fall between flicker and random walk and shows spectral peaks at harmonics of the draconitic year for a GPS satellite (˜351 days). When a perfectly repeating synthetic constellation is used, the simulations show near-negligible time correlated noise highlighting that subtle variations in the GPS constellation can propagate multipath signals differently over time, producing significant temporal variations in time series.

  14. Deriving 250m LAI time series by non-linear temporal regression

    NASA Astrophysics Data System (ADS)

    Colditz, R.; Llamas, R.

    2012-04-01

    The leaf area index (LAI) forms part of the 13 terrestrial essential climate variables and is key ingest to many vegetation productivity, hydrology and biogeochemistry models. LAI is modeled from satellite images including MODIS and SPOT VGT data in a consistent and operational manner that allows for time series generation. With approximately 1km spatial resolution these products are useful for global analysis but lack the necessary spatial detail for national and regional studies. This study aims at improving the spatial resolution of existing LAI data by relating their temporal course to time series of vegetation indices (VI). MODIS data are particular appropriate providing the LAI at 1km and VI at 1km and 250m spatial resolution. The study area (1100x500km) in central Mexico represents all major biomes and vegetation types of the country and a land surface heterogeneity beyond 1km cell size. Previous to model building LAI and VI time series are generated using appropriate tools (TiSeG) for filtering low-quality data and interpolation of data gaps. Plots show a good temporal correspondence between LAI and VI time series, and temporal cross-correlation indicates high coefficients with no or minor lag. For each pixel linear and non-linear regression models are built at the 1km resolution using the temporal domain and applied to 250m VI data. In addition, multiple regression models with spectral information will be tested. The resulting 250m LAI time series introduces the spatial detail of the 250m VI to the LAI and retains the high temporal consistency. To evaluate the modeled results 250m LAI time series are coarsened to 1km spatial resolution using an empirically-derived aggregation model that takes into account the MODIS-specific spatial point spread function. Error maps of the correlation coefficient and RMSE indicate regions of higher errors that correspond to specific biomes used in MODIS LAI retrieval. For instance, while the vast majority of the study site depicts mean correlation coefficients of 0.85 and an RMSE of 0.3 the mountainous regions that correspond to the evergreen broadleaf forest biome show higher errors (r=0.6, RMSE 0.7) that are also related remaining artifacts in the time series.

  15. Managing an archive of weather satellite images

    NASA Technical Reports Server (NTRS)

    Seaman, R. L.

    1992-01-01

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

  16. Road Extraction from High Resolution Satellite Images

    NASA Astrophysics Data System (ADS)

    Özkaya, M.

    2012-07-01

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

  17. Studies of soundings and imagings measurements from geostationary satellites

    NASA Technical Reports Server (NTRS)

    Suomi, V. E.

    1973-01-01

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

  18. Coupling between time series: A network view

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  19. Detecting chaos in irregularly sampled time series.

    PubMed

    Kulp, C W

    2013-09-01

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

  20. Visibility Graph Based Time Series Analysis.

    PubMed

    Stephen, Mutua; Gu, Changgui; Yang, Huijie

    2015-01-01

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

  1. Visibility Graph Based Time Series Analysis

    PubMed Central

    Stephen, Mutua; Gu, Changgui; Yang, Huijie

    2015-01-01

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

  2. Image stretching on a curved surface to improve satellite gridding

    NASA Technical Reports Server (NTRS)

    Ormsby, J. P.

    1975-01-01

    A method for substantially reducing gridding errors due to satellite roll, pitch and yaw is given. A gimbal-mounted curved screen, scaled to 1:7,500,000, is used to stretch the satellite image whereby visible landmarks coincide with a projected map outline. The resulting rms position errors averaged 10.7 km as compared with 25.6 and 34.9 km for two samples of satellite imagery upon which image stretching was not performed.

  3. Learning time series for intelligent monitoring

    NASA Technical Reports Server (NTRS)

    Manganaris, Stefanos; Fisher, Doug

    1994-01-01

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

  4. Hinging hyperplanes for time-series segmentation.

    PubMed

    Xiaolin Huang; Matijas, Marin; Suykens, Johan A K

    2013-08-01

    Division of a time series into segments is a common technique for time-series processing, and is known as segmentation. Segmentation is traditionally done by linear interpolation in order to guarantee the continuity of the reconstructed time series. The interpolation-based segmentation methods may perform poorly for data with a level of noise because interpolation is noise sensitive. To handle the problem, this paper establishes an explicit expression for segmentation from a compact representation for piecewise linear functions using hinging hyperplanes. This expression enables the use of regression to obtain a continuous reconstructed signal and, as a consequence, application of advanced techniques in segmentation. In this paper, a least squares support vector machine with lasso using a hinging feature map is given and analyzed, based on which a segmentation algorithm and its online version are established. Numerical experiments conducted on synthetic and real-world datasets demonstrate the advantages of our methods compared to existing segmentation algorithms. PMID:24808567

  5. Time series of the northeast Pacific

    NASA Astrophysics Data System (ADS)

    Pea, M. Angelica; Bograd, Steven J.

    2007-10-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

  7. Detecting smoothness in noisy time series

    SciTech Connect

    Cawley, R.; Hsu, G.; Salvino, L.W.

    1996-06-01

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

  8. Building Chaotic Model From Incomplete Time Series

    NASA Astrophysics Data System (ADS)

    Siek, Michael; Solomatine, Dimitri

    2010-05-01

    This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual and missing value estimates; and (2) the accuracy of the built chaotic model predictions. The model results indicate that the proposed methods are able to build a chaotic model from incomplete time series and to provide reliable and accurate predictions.

  9. Monitoring forest dynamics with multi-scale and time series imagery.

    PubMed

    Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

    2016-05-01

    To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable. PMID:27056478

  10. Detecting long-term vegetation change in an arid rangeland ecosystem: Investigating effects of spatial image support within satellite time-series

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Arid rangelands within the southwestern United States have been severely degraded over the past century due to intensive land-use practices (e.g., livestock overgrazing, recreation) and the increasing effects of drought and climate change. Consequently, there is a critical need to develop monitoring...

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

    NASA Astrophysics Data System (ADS)

    Paul, F.

    2015-04-01

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

  12. Three Analysis Examples for Time Series Data

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  13. Nonlinear Time Series Analysis via Neural Networks

    NASA Astrophysics Data System (ADS)

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

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

  14. Nonlinear time-series analysis revisited.

    PubMed

    Bradley, Elizabeth; Kantz, Holger

    2015-09-01

    In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-series analysis: the analysis of observed data-typically univariate-via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-series analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-series analysis can be helpful in understanding, characterizing, and predicting dynamical systems. PMID:26428563

  15. Nonlinear time-series analysis revisited

    NASA Astrophysics Data System (ADS)

    Bradley, Elizabeth; Kantz, Holger

    2015-09-01

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

  16. Modeling Time Series Data for Supervised Learning

    ERIC Educational Resources Information Center

    Baydogan, Mustafa Gokce

    2012-01-01

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

  17. Modeling Time Series Data for Supervised Learning

    ERIC Educational Resources Information Center

    Baydogan, Mustafa Gokce

    2012-01-01

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

  18. Integrated method for chaotic time series analysis

    DOEpatents

    Hively, Lee M.; Ng, Esmond G.

    1998-01-01

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

  19. Integrated method for chaotic time series analysis

    DOEpatents

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

    1998-09-29

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

  20. Layered Ensemble Architecture for Time Series Forecasting.

    PubMed

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  2. Sea state variability observed by high resolution satellite radar images

    NASA Astrophysics Data System (ADS)

    Pleskachevsky, A.; Lehner, S.

    2012-04-01

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

  3. Lake Chapala change detection using time series

    NASA Astrophysics Data System (ADS)

    López-Caloca, Alejandra; Tapia-Silva, Felipe-Omar; Escalante-Ramírez, Boris

    2008-10-01

    The Lake Chapala is the largest natural lake in Mexico. It presents a hydrological imbalance problem caused by diminishing intakes from the Lerma River, pollution from said volumes, native vegetation and solid waste. This article presents a study that allows us to determine with high precision the extent of the affectation in both extension and volume reduction of the Lake Chapala in the period going from 1990 to 2007. Through satellite images this above-mentioned period was monitored. Image segmentation was achieved through a Markov Random Field model, extending the application towards edge detection. This allows adequately defining the lake's limits as well as determining new zones within the lake, both changes pertaining the Lake Chapala. Detected changes are related to a hydrological balance study based on measuring variables such as storage volumes, evapotranspiration and water balance. Results show that the changes in the Lake Chapala establish frail conditions which pose a future risk situation. Rehabilitation of the lake requires a hydrologic balance in its banks and aquifers.

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  5. Using Image Tour to Explore Multiangle, Multispectral Satellite Image

    NASA Technical Reports Server (NTRS)

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

    2006-01-01

    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.

  6. Stratospheric ozone time series analysis using dynamical linear models

    NASA Astrophysics Data System (ADS)

    Laine, Marko; Kyrölä, Erkki

    2013-04-01

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

  7. Correlation dimension of underground muon time series

    NASA Astrophysics Data System (ADS)

    Bergamasco, L.; Serio, M.; Osborne, A. R.

    1992-11-01

    This paper presents results obtained for the correlation dimension of cosmic ray muon time series detected at a depth underground of 570 hg/sq cm over a period of eight years. The corresponding galactic primaries have rigidities of the order of a few teravolts and are therefore at least partially under the influence of solar modulation effects. The principal indication is that the physical mechanisms which shape the muon time series in the frequency range below 2 x 10 exp -5 Hz are entirely stochastic in behavior and have a monofractal structure. The correlation dimension is finite and shows an inverse dependence on the level of solar activity, oscillating between 4.5 +/- 0.4 at solar maximum and 6.2 +/- 0.6 at solar minimum.

  8. Intrinsic superstatistical components of financial time series

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  9. Modelling population change from time series data

    USGS Publications Warehouse

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

    1992-01-01

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

  10. Multivariate Voronoi Outlier Detection for Time Series

    PubMed Central

    Zwilling, Chris E.

    2015-01-01

    Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data. PMID:25984575

  11. Univariate time series forecasting algorithm validation

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

  13. Delay differential analysis of time series.

    PubMed

    Lainscsek, Claudia; Sejnowski, Terrence J

    2015-03-01

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

  14. Turbulencelike Behavior of Seismic Time Series

    SciTech Connect

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

    2009-01-09

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

  15. Applying time series analysis to performance logs

    NASA Astrophysics Data System (ADS)

    Kubacki, Marcin; Sosnowski, Janusz

    2015-09-01

    Contemporary computer systems provide mechanisms for monitoring various performance parameters (e.g. processor or memory usage, disc or network transfers), which are collected and stored in performance logs. An important issue is to derive characteristic features describing normal and abnormal behavior of the systems. For this purpose we use various schemes of analyzing time series. They have been adapted to the specificity of performance logs and verified using data collected from real systems. The presented approach is useful in evaluating system dependability.

  16. Delay Differential Analysis of Time Series

    PubMed Central

    Lainscsek, Claudia; Sejnowski, Terrence J.

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  18. Analysis of Polyphonic Musical Time Series

    NASA Astrophysics Data System (ADS)

    Sommer, Katrin; Weihs, Claus

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

  19. Sliced Inverse Regression for Time Series Analysis

    NASA Astrophysics Data System (ADS)

    Chen, Li-Sue

    1995-11-01

    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.

  20. Measuring nonlinear behavior in time series data

    NASA Astrophysics Data System (ADS)

    Wai, Phoong Seuk; Ismail, Mohd Tahir

    2014-12-01

    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.

  1. Time series regression studies in environmental epidemiology.

    PubMed

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

    2013-08-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

  3. Interpretation of a compositional time series

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Vanhellemont, Quinten; Ruddick, Kevin

    2011-11-01

    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.

  6. Use of Time Series Analyses for Cloud Types Extraction and Variability in the Context of Global Climate Change

    NASA Astrophysics Data System (ADS)

    Dim, J. R.; Murakami, H.; Hori, M.; Nakajima, T. Y.

    2009-12-01

    In the context of global climate change, the contribution of various types of clouds to the Earth radiation budget is an ongoing debate. In this study, cloud physical properties time series are used to introduce a new cloud classification approach based on the thermal brightness image segmentation techniques, and understand the variability of cloud types and their interconnectivity in view of future satellite observations of the climate. This approach, termed the “thermal brightness gradient and orientation technique” relies on pixels’ geometric relations as inferred by the cloud brightness temperature gradient and orientation, and the cloud top pressure. The observation data used for this study are global time series cloud properties data derived from the National Oceanic and Atmospheric Administration-Advanced Very-High-Resolution Radiometer (NOAA-AVHRR) satellite sensors. For data consistency and reliability, contemporaneous and retroactive satellite inter-calibrations are respectively performed, and prior to the analyses, between the NOAA-AVHRR and the Terra/Moderate Resolution Imaging Spectrometer (Terra/MODIS), and between the NOAA-AVHRR sensors series. Results of the new classification method introduced in this study are compared with those obtained by the ISCCP cloud classification algorithm which relies on the cloud optical depth (COD) and the cloud top pressure for cloud types’ identification. Statistics of the two Classification schemes show a matching rate between 70 to 85%. The best agreements are obtained at mid and low latitudes while most of the major differences are found at polar areas.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  8. InSAR time series analysis of crustal deformation in southern California from 1992-2010

    NASA Astrophysics Data System (ADS)

    Liu, Z.; Lundgren, P.

    2010-12-01

    Since early the 1990’s, Interferometric Satellite Aperture Radar (InSAR) data has had some success imaging surface deformation of plate boundary deformation zones. The ~18 years of extensive data collection over southern California now make it possible to generate a long time interval InSAR-based line-of-sight (LOS) velocity map to examine the resolution of both steady-state and transient deformation processes. We perform InSAR time series analysis on an extensive catalog of ERS-1/2 and Envisat data from 1992 up to the present in southern California by applying a variant of the Small Baseline Subset (SBAS) time series analysis approach. Despite the limitation imposed by atmospheric phase delay, the large number of data acquisitions and long duration of data sampling allow us to effectively suppress the atmospheric noise through spatiotemporal smoothing in the time series analysis. We integrate an updated version of a California GPS velocity solution with InSAR to constrain the long wavelength deformation signals while estimating and removing the effect of orbital error. A large number of interferograms (> 800) over 5 tracks in southern California have been processed and analyzed. We examine the time dependency of resulting deformation patterns. Preliminary results from the ~18 year time series already reveal some interesting features. For example, the InSAR LOS displacements show significant transient variations in greater spatial resolution following the 1999 Mw7.1 Hector Mine earthquake. The 7-year post-seismic rate map demonstrates a broad transient deformation pattern and much localized deformation near the fault surface trace, reflecting a combined effect from afterslip, poroelastic, and viscoelastic relaxation at different spatiotemporal scales. We observe a variation of deformation rate across the Blackwater-Little lake fault system in the Eastern California Shear Zone, suggesting a possible transient variation over this part of the plate boundary. The InSAR-derived deformation map and time series also provide great spatiotemporal details of deformation signals caused by different sources, enabling the separation of tectonic and non-tectonic signals when combined with continuous GPS data from dense networks such as the Earthscope PBO.

  9. Time Series Photometry of KZ Lacertae

    NASA Astrophysics Data System (ADS)

    Joner, Michael D.

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  11. A Multiscale Approach to InSAR Time Series Analysis

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

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

  12. Time-series animation techniques for visualizing urban growth

    USGS Publications Warehouse

    Acevedo, W.; Masuoka, P.

    1997-01-01

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

  13. Homogenization of precipitation time series with ACMANT

    NASA Astrophysics Data System (ADS)

    Domonkos, Peter

    2015-10-01

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

  14. Hurst exponents for short time series

    NASA Astrophysics Data System (ADS)

    Qi, Jingchao; Yang, Huijie

    2011-12-01

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

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

    NASA Technical Reports Server (NTRS)

    Nack, M. L.

    1975-01-01

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

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

    USGS Publications Warehouse

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

    2005-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  18. Singular spectrum analysis for time series with missing data

    USGS Publications Warehouse

    Schoellhamer, D.H.

    2001-01-01

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

  19. Time series analyses of global change data.

    PubMed

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

    1994-01-01

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

  20. Time series analysis of temporal networks

    NASA Astrophysics Data System (ADS)

    Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

    2016-01-01

    A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

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

    PubMed

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

    2003-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    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.

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

    NASA Technical Reports Server (NTRS)

    1982-01-01

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

  4. The electromagnetic simulation of radar imaging of complicated satellites

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

    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.

  5. Fractal fluctuations in cardiac time series

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

  6. Tremor classification and tremor time series analysis

    NASA Astrophysics Data System (ADS)

    Deuschl, Günther; Lauk, Michael; Timmer, Jens

    1995-03-01

    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.

  7. Multifractal analysis of polyalanines time series

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  8. Normalizing the causality between time series.

    PubMed

    Liang, X San

    2015-08-01

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

  9. Using entropy to cut complex time series

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  10. Normalizing the causality between time series

    NASA Astrophysics Data System (ADS)

    Liang, X. San

    2015-08-01

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

  11. OPTIMAL TIME-SERIES SELECTION OF QUASARS

    SciTech Connect

    Butler, Nathaniel R.; Bloom, Joshua S.

    2011-03-15

    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.

  12. Optimal Time-series Selection of Quasars

    NASA Astrophysics Data System (ADS)

    Butler, Nathaniel R.; Bloom, Joshua S.

    2011-03-01

    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 lsim3% (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 deg2 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.

  13. A Multiscale Approach to InSAR Time Series Analysis

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  14. Hybrid perturbation methods based on statistical time series models

    NASA Astrophysics Data System (ADS)

    San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario

    2016-04-01

    In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.

  15. Wavelet Analysis of Satellite Images for Coastal Watch

    NASA Technical Reports Server (NTRS)

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

    1997-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Williams, Peter E.; Pesnell, W. Dean

    2013-01-01

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

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

    SciTech Connect

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

    2011-04-13

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

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

    NASA Astrophysics Data System (ADS)

    Kato, A.; Miura, T.

    2014-12-01

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

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

    NASA Technical Reports Server (NTRS)

    Green, R. O.; Shimada, M.

    1996-01-01

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

  20. Detection of Satellite Attitude Jitter Based on Image Processing

    NASA Astrophysics Data System (ADS)

    Liu, S.; Tong, X.; Ye, Z.; Tang, X.; Xu, Y.; Li, L.; Wang, F.; Xie, H.; Xie, J.; Li, T.

    2014-12-01

    High-resolution satellite imageries (HRSIs) always suffer from mechanical vibration during scan, resulting in attitude jitter and non-ignorable errors in geo-positioning and mapping. Therefore, it is critical to detect and estimate the attitude jitter for further possible compensation to explore the full geometric potential of HRSI. We bring up with a solution to detect the attitude jitter effect based on image processing using images recorded by a sensor system with parallax observation. Three methods of attitude jitter detection are investigated. The first one is based on analysis of the co-registration errors between images with very small parallax observation (e.g. different bands of multispectral image). The second one is based on stereo images using sensor imaging models to investigate the geometric inconsistance in image space. The third one is based on analysis of the co-registration errors of two DOM products from the images. Phase correlation, geometric constraint cross correlation and least squares matching are used in our methods correspondingly for high accuracy image matching, and the RANSAC algorithm is adopted to remove mismatched points and outliers. Finally, the image disparities from each method are used to investigate the effect and characteristic of satellite attitude jitter. We applied our methods on different satellites to investigate their attitude jitter characteristics. Results of experiment with multispectral images obtained by the ASTER camera equipped on Terra satellite showed that there exist more than one frequency with amplitude up to 0.3 pixel. Experimental results with panchromatic image strips captured by LROC revealed that there exist at least two attitude jitter frequencies with amplitude up to 0.6 pixel. Three methods were all used to investigate the attitude jitter of Chinese ZY-3 satellite and the results from different methods showed good consistency, and a distinct periodic attitude fluctuation with frequency around 0.65Hz always exist, and the amplitude decreased from the early 1 pixel down to within 0.5 pixel, revealing that ZY-3 platform becomes steady with good potential of high positioning accuracy.Key words: satellite attitude jitter, high resolution satellite imagery, ASTER, LROC, ZY-3

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

    NASA Astrophysics Data System (ADS)

    Paul, F.

    2015-11-01

    Although animated images are very popular on the internet, they have so far found only limited use for glaciological applications. With long time series of satellite images becoming increasingly available and glaciers being well recognized for their rapid changes and variable flow dynamics, animated sequences of multiple satellite images reveal glacier dynamics in a time-lapse mode, making the otherwise slow changes of glacier movement visible and understandable to the wider public. For this study, animated image sequences were created for four regions in the central Karakoram mountain range over a 25-year time period (1990-2015) from freely available image quick-looks of orthorectified Landsat scenes. The animations play automatically in a web browser and reveal highly complex patterns of glacier flow and surge dynamics that are difficult to obtain by other methods. In contrast to other regions, surging glaciers in the Karakoram are often small (10 km2 or less), steep, debris-free, and advance for several years to decades at relatively low annual rates (about 100 m a-1). These characteristics overlap with those of non-surge-type glaciers, making a clear identification difficult. However, as in other regions, the surging glaciers in the central Karakoram also show sudden increases of flow velocity and mass waves travelling down glacier. The surges of individual glaciers are generally out of phase, indicating a limited climatic control on their dynamics. On the other hand, nearly all other glaciers in the region are either stable or slightly advancing, indicating balanced or even positive mass budgets over the past few decades.

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

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

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

  3. Vehicle Detection and Classification from High Resolution Satellite Images

    NASA Astrophysics Data System (ADS)

    Abraham, L.; Sasikumar, M.

    2014-11-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  5. Financial time series: A physics perspective

    NASA Astrophysics Data System (ADS)

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

    2000-06-01

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

  6. Jovian satellite positions from Hubble Space Telescope images

    NASA Astrophysics Data System (ADS)

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

    2004-02-01

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

  7. Urban area extraction from a satellite image

    NASA Astrophysics Data System (ADS)

    Marthon, Philippe; Caron, Vincent; Cubero-Castan, Eliane

    1995-11-01

    In a SPOT image, urban areas generally appear as agglomerates of numerous little uniform regions. So, they have a typical feature which is a high edge density. In a single sweeping of the image, each edge pixel is tested: if all the surfaces of neighboring regions are less than a predetermined threshold, the current edge pixel is removed. At the end of sweeping, all the internal edges of urban regions are removed but the external boundary or silhouette is kept. This method has been successfully tested on SPOT XS3 images of the region of Bourges, France.

  8. Annual dynamics of impervious surface in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Weng, Qihao

    2016-03-01

    Information on impervious surface distribution and dynamics is useful for understanding urbanization and its impacts on hydrological cycle, water management, surface energy balances, urban heat island, and biodiversity. Numerous methods have been developed and successfully applied to estimate impervious surfaces. Previous methods of impervious surface estimation mainly focused on the spectral differences between impervious surfaces and other land covers. Moreover, the accuracy of estimation from single or multi-temporal images was often limited by the mixed pixel problem in coarse- or medium-resolution imagery or by the intra-class spectral variability problem in high resolution imagery. Time series satellite imagery provides potential to resolve the above problems as well as the spectral confusion with similar surface characteristics due to phenological change, inter-annual climatic variability, and long-term changes of vegetation. Since Landsat time series has a long record with an effective spatial resolution, this study aimed at estimating and mapping impervious surfaces by analyzing temporal spectral differences between impervious and pervious surfaces that were extracted from dense time series Landsat imagery. Specifically, this study developed an efficient method to extract annual impervious surfaces from time series Landsat data and applied it to the Pearl River Delta, southern China, from 1988 to 2013. The annual classification accuracy yielded from 71% to 91% for all classes, while the mapping accuracy of impervious surfaces ranged from 80.5% to 94.5%. Furthermore, it is found that the use of more than 50% of Scan Line Corrector (SLC)-off images after 2003 did not substantially reduced annual classification accuracy, which ranged from 78% to 91%. It is also worthy to note that more than 80% of classification accuracies were achieved in both 2002 and 2010 despite of more than 40% of cloud cover detected in these two years. These results suggested that the proposed method was effective and efficient in mapping impervious surfaces and detecting impervious surface changes by using temporal spectral differences from dense time series Landsat imagery. The value of full sampling was revealed for enhancing temporal resolution and identifying temporal differences between impervious and pervious surfaces in time series analysis.

  9. Rapid technique to cross-calibrate satellite imager visible channels

    NASA Astrophysics Data System (ADS)

    Nguyen, Louis; Doelling, David R.; Minnis, Patrick; Ayers, J. K.

    2004-10-01

    Rapid and accurate calibrations of satellite imager sensors are critical for remote sensing of surface, cloud and radiative properties. A post-launch technique has been developed to routinely cross calibrate and normalize the imager visible (VIS) channel on-board operational geostationary (GEO) and low-Earth-orbit (LEO) satellites. As a reference calibration source, this simple approach uses the self-calibrating sensor from the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Scanner (VIRS) to calibrate other GEO and LEO satellites. The VIRS sensors have been found to be a stable and reliable reference source. This technique uses VIRS to calibrate the eighth Geostationary Operational Environmental Satellite (GOES-8) VIS sensor using collocated data with similar viewing zenith, solar zenith, and relative azimuth angles. GOES-8 is then used as a transfer medium to cross calibrate other GEO and LEO satellites. Post-launch VIS (~0.65 μm) calibration coefficients for GOES-8, -9, -10, -12, Meteosat-7, -8, and NOAA-14 AVHRR satellites are presented. GOES-8 had a non-linear degradation rate of 11% the first year of operational service and 4% in last year before it was decommissioned. GOES-9 degraded linearly at 7.9% per year during 1995-1998. GOES-10 degraded 12% the first year and 1.6% less each year after that. GOES-12 degraded 6% per year. The VIRS visible channel calibration is in good agreement with the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board the Terra and Aqua satellites supporting its use as a reference.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-07-01

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

  13. An Imaging System for Satellite Hypervelocity Impact Debris Characterization

    NASA Technical Reports Server (NTRS)

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

    2015-01-01

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

  14. Evaluating fusion techniques for multi-sensor satellite image data

    SciTech Connect

    Martin, Benjamin W; Vatsavai, Raju

    2013-01-01

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

  15. Multifractal modelling of rainfall time series

    NASA Astrophysics Data System (ADS)

    Volpi, E.; Lombardo, F.; Napolitano, F.

    2009-09-01

    The literature produced in the last thirty years about the high space-time variability of rainfall deals with the development of stochastic models capable of representing the non-linearity and intermittence of rainfall to downscale information on rainfall fields to spatial and temporal scales useful for hydrological models, i.e. transferring to finer scales the information on rainfall observed or forecasted at large scales. Traditionally, these models are based upon point processes (e.g. Waymire and Gupta, 1981, Rodriguez-Iturbe et al., 1986). However, this approach may involve a large number of parameters in modelling the process, leading to several problems in parameter estimation. Another approach to this problem is based on the empirical detection of some regularities in hydrological observations, such as the scale-invariance properties of rainfall (e.g. Lovejoy and Schertzer, 1985). These models assume a power law dependence of all statistical moments on the scale of aggregation. Scaling properties can provide simple relationships to link the statistical distribution of the rainfall process at different spatial and temporal scales, in the ranges of which the power-low assumption can be verified (Marani, 2003). This work focuses on the analysis of the scaling properties of rainfall time series from a high density rain gauge network covering the Rome's urban area. The network consists of 24 sites, and the gauge record at each site has 10-minute time resolution and about 16-year length (1992-2007). The aim of the study is the identification of temporal scaling regimes, their ranges of validity, and the evaluation of the corresponding scaling properties. REFERENCES Lovejoy S. and Schertzer D. Generalized scale invariance in the atmosphere and fractal models of rain. Water Resour. Res., 21(8), 1233-1250, 1985. Marani, M. (2003) On the correlation structure of continuous and discrete point rainfall, Water Resour. Res., 39(5), 1128, doi:10.1029/2002WR001456. Rodriguez-Iturbe I., Cox D. and Eagleson P.S. Spatial modelling of total storm rainfall. Proc. R. Soc. London, A403, 27-50, 1986. Waymire E. and Gupta V.K. The mathematical structure of rainfall representations. Water Resour. Res., 17 (5), 1261-1294, 1981.

  16. Time-series model of channel migration

    NASA Astrophysics Data System (ADS)

    Konrad, C. P.

    2009-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Chang, L.; Hanssen, R. F.

    2012-12-01

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

  18. a New Approach for Optical and SAR Satellite Image Registration

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  19. Exploratory joint and separate tracking of geographically related time series

    NASA Astrophysics Data System (ADS)

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

    2012-05-01

    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

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

    SciTech Connect

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

    2006-10-15

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

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

    NASA Astrophysics Data System (ADS)

    Hong, S.; Wdowinski, S.

    2013-05-01

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

  2. Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling.

    PubMed

    Soni, Kirti; Parmar, Kulwinder Singh; Kapoor, Sangeeta; Kumar, Nishant

    2016-05-15

    A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627±0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend. PMID:26925737

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  5. High-Resolution Imaging of Asteroids/Satellites

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

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

    SciTech Connect

    Cheriyadat, Anil M

    2013-01-01

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

  7. Using Progressive Resolution to Visualize large Satellite Image dataset

    NASA Astrophysics Data System (ADS)

    ho, yuan; ramanmurthy, mohan

    2014-05-01

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

  8. Feature detection in satellite images using neural network technology

    NASA Technical Reports Server (NTRS)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

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

  9. Mapping giant salvinia with satellite imagery and image analysis.

    PubMed

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

    2008-04-01

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

  10. Laboratory Imaging of Satellites and Orbital Appearance Estimation

    NASA Astrophysics Data System (ADS)

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

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

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

    Atmospheric Science Data Center

    2013-03-22

    ... 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 ...

  12. Identification of geostationary satellites using polarization data from unresolved images

    NASA Astrophysics Data System (ADS)

    Speicher, Andy

    In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. Since resolved images of geosynchronous satellites are not technically feasible with current technology, another method of distinguishing space objects was explored that exploits the polarization signature from unresolved images. The objective of this study was to collect and analyze visible-spectrum polarization data from unresolved images of geosynchronous satellites taken over various solar phase angles. Different collection geometries were used to evaluate the polarization contribution of solar arrays, thermal control materials, antennas, and the satellite bus as the solar phase angle changed. Since materials on space objects age due to the space environment, it was postulated that their polarization signature may change enough to allow discrimination of identical satellites launched at different times. The instrumentation used in this experiment was a United States Air Force Academy (USAFA) Department of Physics system that consists of a 20-inch Ritchey-Chretien telescope and a dual focal plane optical train fed with a polarizing beam splitter. A rigorous calibration of the system was performed that included corrections for pixel bias, dark current, and response. Additionally, the two channel polarimeter was calibrated by experimentally determining the Mueller matrix for the system and relating image intensity at the two cameras to Stokes parameters S0 and S1. After the system calibration, polarization data was collected during three nights on eight geosynchronous satellites built by various manufacturers and launched several years apart. Three pairs of the eight satellites were identical buses to determine if identical buses could be correctly differentiated. When Stokes parameters were plotted against time and solar phase angle, the data indicates that there were distinguishing features in S0 (total intensity) and S1 (linear polarization) that may lead to positive identification or classification of each satellite.

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

    NASA Astrophysics Data System (ADS)

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

    2009-10-01

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

  14. High temperature superconducting infrared imaging satellite

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Vermeesch, P.

    2012-04-01

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

  16. Mapping cropland-use intensity across Europe using MODIS NDVI time series

    NASA Astrophysics Data System (ADS)

    Estel, Stephan; Kuemmerle, Tobias; Levers, Christian; Baumann, Matthias; Hostert, Patrick

    2016-02-01

    Global agricultural production will likely need to increase in the future due to population growth, changing diets, and the rising importance of bioenergy. Intensifying already existing cropland is often considered more sustainable than converting more natural areas. Unfortunately, our understanding of cropping patterns and intensity is weak, especially at broad geographic scales. We characterized and mapped cropping systems in Europe, a region containing diverse cropping systems, using four indicators: (a) cropping frequency (number of cropped years), (b) multi-cropping (number of harvests per year), (c) fallow cycles, and (d) crop duration ratio (actual time under crops) based on the MODIS Normalized Difference Vegetation Index (NDVI) time series from 2000 to 2012. Second, we used these cropping indicators and self-organizing maps to identify typical cropping systems. The resulting six clusters correspond well with other indicators of agricultural intensity (e.g., nitrogen input, yields) and reveal substantial differences in cropping intensity across Europe. Cropping intensity was highest in Germany, Poland, and the eastern European Black Earth regions, characterized by high cropping frequency, multi-cropping and a high crop duration ratio. Contrarily, we found lowest cropping intensity in eastern Europe outside the Black Earth region, characterized by longer fallow cycles. Our approach highlights how satellite image time series can help to characterize spatial patterns in cropping intensity—information that is rarely surveyed on the ground and commonly not included in agricultural statistics: our clustering approach also shows a way forward to reduce complexity when measuring multiple indicators. The four cropping indicators we used could become part of continental-scale agricultural monitoring in order to identify target regions for sustainable intensification, where trade-offs between intensification and the environmental should be explored.

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

    NASA Astrophysics Data System (ADS)

    Merline, William

    2012-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  20. Optimized satellite image compression and reconstruction via evolution strategies

    NASA Astrophysics Data System (ADS)

    Babb, Brendan; Moore, Frank; Peterson, Michael

    2009-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

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

    NASA Technical Reports Server (NTRS)

    1991-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    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).

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  5. Decadal Time Series of UV Irradiances at two NDSC Sites

    NASA Astrophysics Data System (ADS)

    McKenzie, R. L.; Johnston, P. V.; Kotkamp, M.; O'Neill, M.; Hofmann, D. J.

    2005-05-01

    The Network for the Detection of Stratospheric Change (NDSC) comprises a small number of well-instrumented unpolluted measurement sites, selected to represent large geographical areas. Its aim is to better understand the causes and effects of long term changes in atmospheric composition. In order to monitor long term ozone change and its effects, UV spectrometers were installed at the mid-latitude southern hemisphere NDSC site (Lauder New Zealand), and the tropical NDSC site (Mauna Loa Observatory, Hawaii). At NIWA's Lauder site, measurements began in December 1989; while at NOAA's Mauna Loa Observatory, measurements began in June 1995. Since deployment, data have been obtained with a high success rate. The instrumentation and data-processing are similar at both sites, and comply with the exacting standards required by the NDSC. Here we present time series of data products from these spectrometers (e.g., erythemally-weighted UV irradiance) to compare and contrast the results from each site and to illustrate the causes for variabilities, and their influences on validation of radiative transfer models and satellite data products.

  6. Automatic analysis of stereoscopic image pairs from GOES satellites

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

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

  7. Mapping Vineyard Areas Using WORLDVIEW-2 Satellite Images

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  8. Numerical simulations of imaging satellites with optical interferometry

    NASA Astrophysics Data System (ADS)

    Ding, Yuanyuan; Wang, Chaoyan; Chen, Zhendong

    2015-08-01

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

  9. Synergy use of satellite images for Vrancea seismic area analysis

    NASA Astrophysics Data System (ADS)

    Zoran, Maria A.; Ninomiya, Yoshiki; Zoran, Liviu Florin V.

    2004-10-01

    The seismic hazard of Romania is relatively high, mainly due to the subcrustal earthquakes located at the sharp bend of the Southeast Carpathians, in Vrancea region, one of the most seismically active area in Europe. It is crossed by a series of principal and secondary faults. Vrancea area is assumed to be a conjunction of 4 tectonic blocks which lie on the edge of the Eurasian plate. Several GPS monitoring data revealed the motion of the blocks both in horizontal direction (relative motion of 5- 6 millimeters/year), as well as in vertical direction(of a few millimeters/ year).All data information available on the study area have been integrated in a unique database of geologic maps, thematic maps from cartography, land use maps provided by satellite images acquired in different spectral wavelengths by Landsat MSS, TM and ETM, SAR ERS and ASTER during a long term period (1975-2002). Satellite data are excellent for recognizing the continuity and regional relationships of faults . Synergy use of satellite data and image analysis techniques is essential for neotectonic applications, improving greatly the interpretability of the images and subsequent more accurate terrain features and lineament analysis of geologic structures in active seismic areas.

  10. Efficient Algorithms for Segmentation of Item-Set Time Series

    NASA Astrophysics Data System (ADS)

    Chundi, Parvathi; Rosenkrantz, Daniel J.

    We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

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

    NASA Astrophysics Data System (ADS)

    Frey, Corinne; Kuenzer, Claudia

    2013-04-01

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

  12. Resolution-independent characteristic scale dedicated to satellite images.

    PubMed

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

    2007-10-01

    We study the problem of finding the characteristic scale of a given satellite image. This feature is defined so that it does not depend on the spatial resolution of the image. This is a different problem than achieving scale invariance, as often studied in the literature. Our approach is based on the use of a linear scale space and the total variation (TV). The critical scale is defined as the one at which the normalized TV reaches its maximum. It is shown experimentally, both on synthetic and real data, that the computed characteristic scale is resolution independent. PMID:17926932

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  14. Apparatus for statistical time-series analysis of electrical signals

    NASA Technical Reports Server (NTRS)

    Stewart, C. H. (Inventor)

    1973-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Wildey, R. L.

    1985-01-01

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

  16. Scene context dependency of pattern constancy of time series imagery

    NASA Astrophysics Data System (ADS)

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

    2008-04-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2008-01-01

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

  18. State estimation and absolute image registration for geosynchronous satellites

    NASA Technical Reports Server (NTRS)

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

    1980-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Chiang, Y.; Chen, K.

    2013-12-01

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

  20. Seasonal signals in the reprocessed GPS coordinate time series

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  2. Satellites

    SciTech Connect

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

    1986-01-01

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

  3. How to analyse irregularly sampled geophysical time series?

    NASA Astrophysics Data System (ADS)

    Eroglu, Deniz; Ozken, Ibrahim; Stemler, Thomas; Marwan, Norbert; Wyrwoll, Karl-Heinz; Kurths, Juergen

    2015-04-01

    One of the challenges of time series analysis is to detect dynamical changes in the dynamics of the underlying system.There are numerous methods that can be used to detect such regime changes in regular sampled times series. Here we present a new approach, that can be applied, when the time series is irregular sampled. Such data sets occur frequently in real world applications as in paleo climate proxy records. The basic idea follows Victor and Purpura [1] and considers segments of the time series. For each segment we compute the cost of transforming the segment into the following one. If the time series is from one dynamical regime the cost of transformation should be similar for each segment of the data. Dramatic changes in the cost time series indicate a change in the underlying dynamics. Any kind of analysis can be applicable to the cost time series since it is a regularly sampled time series. While recurrence plots are not the best choice for irregular sampled data with some measurement noise component, we show that a recurrence plot analysis based on the cost time series can successfully identify the changes in the dynamics of the system. We tested this method using synthetically created time series and will use these results to highlight the performance of our method. Furthermore we present our analysis of a suite of calcite and aragonite stalagmites located in the eastern Kimberley region of tropical Western Australia. This oxygen isotopic data is a proxy for the monsoon activity over the last 8,000 years. In this time series our method picks up several so far undetected changes from wet to dry in the monsoon system and therefore enables us to get a better understanding of the monsoon dynamics in the North-East of Australia over the last couple of thousand years. [1] J. D. Victor and K. P. Purpura, Network: Computation in Neural Systems 8, 127 (1997)

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

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

  5. DEM time series of an agricultural watershed

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  6. Ice Sheet Change Detection by Satellite Image Differencing

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  7. Real-time processing of multispectral satellite remote sensing images

    NASA Astrophysics Data System (ADS)

    Piazza, Enrico

    1999-10-01

    This work mainly deal with the processing of Remote Sensed Images gathered from a radiometer flown aboard an environmental satellite. In this work some NOAA/AVHRR images have been used with focus on the southern Europe and the Mediterranean Basin. The images are navigated by resolving the satellite motion and the senors's scanning laws. And calibrated in order to obtain true radiance or temperature values. The first and second band of the AVHRR Radiometer are useful to distinguish between land and sea pixels. While the two thermal infrared channels (4 and 5) proved to be useful to find out the cloud affected pixels and to estimate the value of Sea Surface Temperature on the cloud free pixels. The presented method has been tested on an Intel Pentium 200 MHz based computer. It took 91 sec to perform the following steps: (1) Classify each pixel in one of the following categories: land, sea, cloud, cloud edge, coast line, outside the swath. (2) Estimate on sea, cloudy-free, pixels the Sea Surface Temperature by the means of a low noise split window algorithm on a multispectral image. This performance allows this estimation to be done in real time.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  9. A Computer Evolution in Teaching Undergraduate Time Series

    ERIC Educational Resources Information Center

    Hodgess, Erin M.

    2004-01-01

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

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

    ERIC Educational Resources Information Center

    Price, Larry R.

    2012-01-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    NASA Astrophysics Data System (ADS)

    Howarth, Ian D.; Stevens, Ian R.

    2014-12-01

    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.

  13. Analysis of Crop Phenology Using Time-Series MODIS Data and Climate Data

    NASA Astrophysics Data System (ADS)

    Ren, J.; Campbell, J. B.; Thomas, R. Q.; Shao, Y.

    2014-12-01

    Understanding crop phenology is fundamental to agricultural production, management, planning and decision-making. In the continental United States, key phenological stages are strongly influenced by meteorological and climatological conditions. This study is conducted in the Midwestern United States to estimate phonological information for corn and soybean. A time series of the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites from 2001 to 2013 was analyzed with the TIMESAT program to automatically retrieve key phenological stages. The temperature data from CRUNCEP was analyzed with R based on the crop model to calculate potential planting date and harvest date by AgroIBIS crop phenology algorithm. With these two methods, start of season (planting date), end of season (harvesting date), and length of growing season from 2001 to 2013 were determined and compared. The results showed a good relationship between estimates derived from satellites and estimates calculated by the crop model formula. Crop progress reports from USDA NASS were used to validate our estimates. We will present the relationship between our estimates and validation data. We will select some specific sites to investigate finer scale local changes of crop phenology during the last decade.

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

    NASA Astrophysics Data System (ADS)

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

    2004-09-01

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

  15. Functional and stochastic models estimation for GNSS coordinates time series

    NASA Astrophysics Data System (ADS)

    Galera Monico, J. F.; Silva, H. A.; Marques, H. A.

    2014-12-01

    GNSS has been largely used in Geodesy and correlated areas for positioning. The position and velocity of terrestrial stations have been estimated using GNSS data based on daily solutions. So, currently it is possible to analyse the GNSS coordinates time series aiming to improve the functional and stochastic models what can help to understand geodynamic phenomena. Several sources of errors are mathematically modelled or estimated in the GNSS data processing to obtain precise coordinates what in general is carried out by using scientific software. However, due to impossibility to model all errors some kind of noises can remain contaminating the coordinate time series, especially those related with seasonal effects. The noise affecting GNSS coordinate time series can be composed by white and coloured noises what can be characterized from Variance Component Estimation technique through Least Square Method. The methodology to characterize noise in GNSS coordinates time series will be presented in this paper so that the estimated variance can be used to reconstruct stochastic and functional models of the times series providing a more realistic and reliable modeling of time series. Experiments were carried out by using GNSS time series for few Brazilian stations considering almost ten years of daily solutions. The noises components were characterized as white, flicker and random walk noise and applied to estimate the times series functional model considering semiannual and annual effects. The results show that the adoption of an adequate stochastic model considering the noises variances of time series can produce more realistic and reliable functional model for GNSS coordinate time series. Such results may be applied in the context of the realization of the Brazilian Geodetic System.

  16. Extracting white noise statistics in GPS coordinate time series

    NASA Astrophysics Data System (ADS)

    Montillet, J.-P.; Tregoning, P.; McClusky, S.; Yu, K.

    2012-04-01

    The noise in GPS coordinate time series is known to follow a power-law noise model with different components (white noise, flicker noise, random-walk). This work proposes an algorithm to estimate the white noise statistics, through the decomposition of the GPS coordinate time series into a sequence of sub-time series using the Empirical Mode Decomposition algorithm. The proposed algorithm estimates the Hurst parameter for each sub time series, then selects the sub time series related to the white noise based on the Hurst parameter threshold. The algorithm is applied to simulated GPS time series and real data. Both simulated GPS coordinate time series and real data are employed to test this new method, results are compared to the standard (CATS software) Maximum Likelihood (ML) estimator approach. For a comparison with the Maximum Likelihood approach (CATS software), the number of epochs for the selected GPS time series is varied between 3 and 8 years. The results are promising when compared to CATS, but suffer from a larger standard deviation. The results demonstrate that this proposed algorithm has very low computational complexity and can be more than one hundred times faster than the CATS ML method, at the cost of a moderate increase of the uncertainty (~ 5%) of the white noise amplitude. Reliable white noise statistics are useful for a range of applications including improving the filtering of GPS time series, checking the validity of estimated coseismic offsets and estimating unbiased uncertainties of site velocities. The low complexity and computational efficiency of the algorithm can greatly speed up the processing of geodetic time series.

  17. Spatial Data Exploring by Satellite Image Distributed Processing

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

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

  19. Path planning on satellite images for unmanned surface vehicles

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

  2. Time Series Analysis of Satellie-Measured Vegetation Phenology and Aerosol Optical Thickness over the Korean Peninsula

    NASA Astrophysics Data System (ADS)

    Park, S.

    2015-04-01

    The spatiotemporal influences of climatic factors and atmospheric aerosol on vegetative phenological cycles of the Korean Peninsula was analysed based on four major forest types. High temporal-resolution satellite data can overcome limitations of ground-based phenological studies with reasonable spatial resolution. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) (MOD13Q1 and MYD13Q1) and aerosol (MOD04_D3) data were downloaded from the USGS Earth Observation and Science (EROS) Data Center and NASA Goddard Space Flight Center. Harmonic analysis was used to describe and compare the periodic phenomena of the vegetative phenology and atmospheric aerosol optical thickness (AOT). The method transforms complex timeseries to a sum of various sinusoidal functions, or harmonics. Each harmonic curve, or term (or Fourier series), from time-series data us defined by a unique amplitude and a phase, indicating the half of the height and the peak time of a curve. Therefore, the mean, phase, and amplitude of harmonic terms of the data provided the temporal relationships between AOT and VI time series. The phenological characteristics of evergreen forest, deciduous forest, and grassland were similar to each other, but the inter-annual VI amplitude of mixed forest was differentiated from the other forest types. Overall, forests with high VI amplitude reached their maximum greenness earlier, and the phase of VI, or the peak time of greenness, was significantly influenced by air temperature. AOT time-series showed strong seasonal and inter-annual variations. Generally, aerosol concentrations were peaked during late spring and early summer. However, inter-annual AOT variations did not have significant relationships with those of VI. Weak relationships between inter-annual AOT and VI variations indicate that the impacts of aerosols on vegetation growth may be limited for the temporal scale investigated in the region.

  3. A multiscale approach to InSAR time series analysis

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  5. InSAR time series monitoring at Istanbul city shows faulting, landslides and soil compaction

    NASA Astrophysics Data System (ADS)

    Wiencke, K.; Walter, T. R.; Manzo, M.; Manconi, A.; Solaro, G.; Lanari, R.

    2012-04-01

    Satellite remote sensing data is providing important information for understanding and monitoring geohazards, yet availability of such data often has remained difficult. In an attempt to ease access to earth science and especially satellite radar data in near real time, the geohazard scientific community initiated the Geohazard Supersites. As a contribution to the Group on Earth Observations (GEO), this concept is supported by the European Space Agency (ESA). Here we describe the use of the Geohazard Supersites platform to understand and monitor deformation activity in the vicinity of Istanbul city. Istanbul, with more than 10 million inhabitants, is one of the largest multiethnic cities in the world. Among several problems, this megacity is menaced by the hazard of earthquakes. In the 20th century, a progressive westward migration of earthquake events has ruptured more than 700 km of the North Anatolian Fault. The presence of a seismic gap implies that the next major event could occur nearby the city centre itself. We have used the Small BAseline Subset (SBAS) approach, to study the evolving deformation history of the last two decades at unprecedented spatial and temporal detail. Images provided by ESA and acquired by the ERS1, ERS2 and ENVISAT satellites between 1992 and now have been analyzed. Our results show several ongoing deformation phenomena, in particular, the co-seismic displacement caused by the North Anatolian Fault seismic events, and the extended subsidence pattern in urban areas underlain by young sediments. As seen from the InSAR time series data, subsidence rates changed after 1999. The rate of change may relate to time-dependent rheologic responses that will be investigated in further detail in a separate paper. Singular value decomposition of these data significantly augments the interpretability of the observed deformations. We find that deformation regions are overlapping in time and space, though at different signatures, trends and periodicities. Since natural hazards, involving earthquakes, landslides and flooding, have to be explored in a dynamically evolving context, the spatial and temporal resolution of satellite geodesy provides additional information and offers a new, invaluable monitoring capability for complex scenarios as those demonstrated here for Istanbul.

  6. Nonlinear parametric model for Granger causality of time series

    NASA Astrophysics Data System (ADS)

    Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2006-06-01

    The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

  7. Sensor-Generated Time Series Events: A Definition Language

    PubMed Central

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

    2012-01-01

    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.

  8. Clustering Financial Time Series by Network Community Analysis

    NASA Astrophysics Data System (ADS)

    Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

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

  9. Satellite Gravity Gradients Complementing Seismology for Imaging of the Lithosphere

    NASA Astrophysics Data System (ADS)

    Ebbing, J.

    2014-12-01

    Satellite gravity gradients as for example derived from the recent GOCE satellite mission can be used to improve modeling of the Earth's lithosphere and thereby contribute to a better understanding of the Earth's dynamic processes. In general, gravity gradient data are sensitive to shallower structures than the gravity field itself and provide information about the variations in both the horizontal and vertical plane. Validation of satellite data in different orbit heights show that the gradients in different heights have a significantly different sensitivity, which can be exploited to construct the most reasonable lithospheric setting. To explore the benefit by using gravity gradients in addition to conventional gravity data, a case example from the well-explored and understood North-East Atlantic Margin will be shown. Here, a 3D model of the lithosphere preexisted that incorporates a wealth of geophysical data sets, e.g. seismics, magnetics and borehole information. The model is initially optimized for near-surface gravity data. However, in the NE Atlantic the gravity field is affected by a regional trend, which is reflected as well in the geoid, and associated to sub-lithospheric density domains. Using this model for sensitivity analysis shows that the satellite gravity gradients are little affected by the sub-lithospheric field, but are especially sensitive to the density contrasts from the lower crust to 100 km depth. Another important observation is that modeling of the gravity gradients requires depth-dependent crustal densities and temperature dependent upper mantle densities. A too simplified use of average densities leads to clear misfit to the observed data. Satellite gravity gradients are as well sensitive to compositional changes in the upper mantle, but for to decipher the thermal structure and composition integration with information from seismic tomography is needed. In summary, the sensitivity analysis in the NE Atlantic region shows, that satellite gravity gradients are a valuable addition to image the lithosphere in 3D and consequently to distinguish the lithospheric and sub-lithospheric components in the gravity field.

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

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-08-01

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

  12. Estimation of connectivity measures in gappy time series

    NASA Astrophysics Data System (ADS)

    Papadopoulos, G.; Kugiumtzis, D.

    2015-10-01

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

  13. Factorizing Markov Models for Categorical Time Series Prediction

    NASA Astrophysics Data System (ADS)

    Freudenthaler, Christoph; Rendle, Steffen; Schmidt-Thieme, Lars

    2011-09-01

    During the last decade, recommender systems became a popular class of models for many commercial websites. One of the best state-of-the-art methods for recommender systems are Matrix and Tensor Factorization models. Besides, Markov Chain models are common for representing sequential data problems (e.g. categorical time series data). The item recommendation problem of recommender systems in fact is a categorical time series problem where each user represents an individual categorical time series. In this paper we combine factorization models with Markov Chain models. To increase efficiency of parameter estimation we introduce our generalized Factorized Markov Chain model.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1980-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

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

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

    NASA Astrophysics Data System (ADS)

    Millard, Lindsay Demoore

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

    The traditional model for space-based earth observations involves long mission times, high cost, and long development time. Because of the significant time and monetary investment required, riskier instrument development missions or those with very specific scientific goals are unlikely to successfully obtain funding. However, a niche for earth observations exploiting new technologies in focused, short lifetime missions is opening with the growth of the small satellite market and launch opportunities for these satellites. These low-cost, short-lived missions provide an experimental platform for testing new sensor technologies that may transition to larger, more long-lived platforms. The low costs and short lifetimes also increase acceptable risk to sensors, enabling large decreases in cost using commercial off the shelf (COTS) parts and allowing early-career scientists and engineers to gain experience with these projects. We are building a low-cost long-wave infrared spectral sensor, funded by the NASA Experimental Project to Stimulate Competitive Research program (EPSCOR), to demonstrate the ways in which a university's scientific and instrument development programs can fit into this niche. The sensor is a low-mass, power efficient thermal hyperspectral imager with electronics contained in a pressure vessel to enable the use of COTS electronics, and will be compatible with small satellite platforms. The sensor, called Thermal Hyperspectral Imager (THI), is based on a Sagnac interferometer and uses an uncooled 320x256 microbolometer array. The sensor will collect calibrated radiance data at long-wave infrared (LWIR, 8-14 microns) wavelengths in 230-meter pixels with 20 wavenumber spectral resolution from a 400-km orbit.

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

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

  20. Nonstationary time series prediction combined with slow feature analysis

    NASA Astrophysics Data System (ADS)

    Wang, G.; Chen, X.

    2015-01-01

    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.

  1. Fractal and natural time analysis of geoelectrical time series

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  2. Financial Time-series Analysis: a Brief Overview

    NASA Astrophysics Data System (ADS)

    Chakraborti, A.; Patriarca, M.; Santhanam, M. S.

    Prices of commodities or assets produce what is called time-series. Different kinds of financial time-series have been recorded and studied for decades. Nowadays, all transactions on a financial market are recorded, leading to a huge amount of data available, either for free in the Internet or commercially. Financial time-series analysis is of great interest to practitioners as well as to theoreticians, for making inferences and predictions. Furthermore, the stochastic uncertainties inherent in financial time-series and the theory needed to deal with them make the subject especially interesting not only to economists, but also to statisticians and physicists [1]. While it would be a formidable task to make an exhaustive review on the topic, with this review we try to give a flavor of some of its aspects.

  3. Time series modeling of system self-assessment of survival

    SciTech Connect

    Lu, H.; Kolarik, W.J.

    1999-06-01

    Self-assessment of survival for a system, subsystem or component is implemented by assessing conditional performance reliability in real-time, which includes modeling and analysis of physical performance data. This paper proposes a time series analysis approach to system self-assessment (prediction) of survival. In the approach, physical performance data are modeled in a time series. The performance forecast is based on the model developed and is converted to the reliability of system survival. In contrast to a standard regression model, a time series model, using on-line data, is suitable for the real-time performance prediction. This paper illustrates an example of time series modeling and survival assessment, regarding an excessive tool edge wear failure mode for a twist drill operation.

  4. Nonstationary time series prediction combined with slow feature analysis

    NASA Astrophysics Data System (ADS)

    Wang, G.; Chen, X.

    2015-07-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  6. Estimation of Parameters from Discrete Random Nonstationary Time Series

    NASA Astrophysics Data System (ADS)

    Takayasu, H.; Nakamura, T.

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

  7. Multiple Time Series Ising Model for Financial Market Simulations

    NASA Astrophysics Data System (ADS)

    Takaishi, Tetsuya

    2015-01-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Song, Huihui

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

  9. The extraction of multiple cropping index of China based on NDVI time-series

    NASA Astrophysics Data System (ADS)

    Huang, Haitao; Gao, Zhiqiang

    2011-09-01

    Multiple cropping index reflects the intensity of arable land been used by a certain planting system. The bond between multiple cropping index and NDVI time-series is the crop cycle rule, which determines the crop process of seeding, jointing, tasseling, ripeness and harvesting and so on. The cycle rule can be retrieved by NDVI time-series for that peaks and valleys on the time-series curve correspond to different periods of crop growth. In this paper, we aim to extract the multiple cropping index of China from NDVI time-series. Because of cloud contamination, some NDVI values are depressed. MVC (Maximum Value Composite) synthesis is used to SPOT-VGT data to remove the noise, but this method doesn't work sufficiently. In order to accurately extract the multiple cropping index, the algorithm HANTS (Harmonic Analysis of Time Series) is employed to remove the cloud contamination. The reconstructed NDVI time-series can explicitly characterize the biophysical process of planting, seedling, elongating, heading, harvesting of crops. Based on the reconstructed curve, we calculate the multiple cropping index of arable land by extracting the number of peaks of the curve for that one peak represents one season crop. This paper presents a method to extracting the multiple cropping index from remote sensing image and then the multiple cropping index of China is extracted from VEGETATION decadal composites NDVI time series of year 2000 and 2009. From the processed data, we can get the spatial distribution of tillage system of China, and then further discussion about cropping index change between the 10 years is conducted.

  10. Time Series Analysis of Insar Data: Methods and Trends

    NASA Technical Reports Server (NTRS)

    Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

    2015-01-01

    Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

  11. Scale-dependent intrinsic entropies of complex time series.

    PubMed

    Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

    2016-04-13

    Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. PMID:26953181

  12. Multitaper Analysis Applied to a 3-month Time Series

    NASA Astrophysics Data System (ADS)

    Komm, R. W.; Anderson, E.; Hill, F.; Howe, R.; Fodor, I.; Stark, P.

    We show the benefit of multitapering by applying this technique to a 3-month helioseismic time series, then deriving p-mode parameters using the GONG peakfitting algorithm. A multitaper spectrum is an average over uncorrelated spectra derived from the same time series by applying a set of orthogonal tapers. Thus, a multitaper spectrum has less variance or noise than a single taper spectrum and has better leakage properties than a periodogram. We use generalized sine tapers, which are orthogonal tapers taking the gap structure of the time series into account. We applied this technique with great success to a variety of time series from SOHO-SOI/MDI and GONG. The benefit of multitapering is that more modes can be fitted than in a periodogram due to the reduced noise. The improvement depends on ell and other details of the time series and is typically between 20% and 60% for low to medium ell values for GONG as well as MDI data. For example, for the 3-month GONG time series covering months 12--14, the number of good fits increases by 10% on average for all modes from ell = 0--150, using 5 generalized sine tapers. The largest improvement is at ell <= 70 where at low frequencies one extra ridge can be fitted in the multitaper spectrum.

  13. Robust extrema features for time-series data analysis.

    PubMed

    Vemulapalli, Pramod K; Monga, Vishal; Brennan, Sean N

    2013-06-01

    The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm. PMID:23599059

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

    SciTech Connect

    Kashgarian, M; Guilderson, T P

    2001-02-23

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

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

    EPA Science Inventory

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

  16. Analyzing time varying trends in stratospheric ozone time series using state space approach

    NASA Astrophysics Data System (ADS)

    Laine, M.; Latva-Pukkila, N.; Kyrl, E.

    2013-08-01

    We describe a hierarchical statistical state space model for ozone profile time series. The time series are from satellite measurements by the SAGE II and GOMOS instruments spanning years 1984-2011. The original data sets are combined and gridded monthly using 10 latitude bands, and covering 25-55 km with 1 km vertical spacing. In the analysis, mean densities are studied separately for 25-35 km, 35-45 km, and 45-55 km layers, also. Model components include level, trend and seasonal effect with solar activity and Quasi-Biennial Oscillations as proxy variables. We will show how the chosen statistical model is well suited for trend analysis of atmospheric time series that are not stationary but can exhibit both slowly varying and abrupt changes in the distributional properties. The dynamic linear model state space approach provides well defined statistical model for assessing the long term background changes in the ozone time series. The modelling assumptions can be evaluated and the method provides realistic uncertainty estimates for the model based statements on the quantities of interest. We discuss the methodological challenges and practical implementation. The modelling result agree with the hypothesized trend change point for stratospheric ozone at around the year 1997 for mid latitude regions. This is a companion article to Kyrl et al. (2013).