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Sample records for time-series satellite images

  1. Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

    NASA Astrophysics Data System (ADS)

    Zhou, Z.-G.; Tang, P.; Zhou, M.

    2016-06-01

    Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., z-value or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.

  2. Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

    NASA Astrophysics Data System (ADS)

    Champion, Nicolas

    2016-06-01

    Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pl

  3. Building Change Detection in Very High Resolution Satellite Stereo Image Time Series

    NASA Astrophysics Data System (ADS)

    Tian, J.; Qin, R.; Cerra, D.; Reinartz, P.

    2016-06-01

    There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.

  4. 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.

  5. Feasibility of anomaly occurrence in aerosols time series obtained from MODIS satellite images during hazardous earthquakes

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, Mehdi; Jahani Chehrebargh, Fatemeh

    2016-09-01

    Earthquake is one of the most devastating natural disasters that its prediction has not materialized comprehensive. Remote sensing data can be used to access information which is closely related to an earthquake. The unusual variations of lithosphere, atmosphere and ionosphere parameters before the main earthquakes are considered as earthquake precursors. To date the different precursors have been proposed. This paper examines one of the parameters which can be derived from satellite imagery. The mentioned parameter is Aerosol Optical Depth (AOD) that this article reviews its relationship with earthquake. Aerosol parameter can be achieved through various methods such as AERONET ground stations or using satellite images via algorithms such as the DDV (Dark Dense Vegetation), Deep Blue Algorithm and SYNTAM (SYNergy of Terra and Aqua Modis). In this paper, by analyzing AOD's time series (derived from MODIS sensor on the TERRA platform) for 16 major earthquakes, seismic anomalies were observed before and after earthquakes. Before large earthquakes, rate of AOD increases due to the pre-seismic changes before the strong earthquake, which produces gaseous molecules and therefore AOD increases. Also because of aftershocks after the earthquake there is a significant change in AOD due to gaseous molecules and dust. These behaviors suggest that there is a close relationship between earthquakes and the unusual AOD variations. Therefore the unusual AOD variations around the time of earthquakes can be introduced as an earthquake precursor.

  6. 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.

  7. 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

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

    NASA Astrophysics Data System (ADS)

    Khanal, Shiva; Duursma, Remko; Boer, Matthias

    2014-05-01

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

  9. 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

  10. 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.

  11. 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.

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

    PubMed

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

    2015-03-01

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

  13. Satellite time series analysis using Empirical Mode Decomposition

    NASA Astrophysics Data System (ADS)

    Pannimpullath, R. Renosh; Doolaeghe, Diane; Loisel, Hubert; Vantrepotte, Vincent; Schmitt, Francois G.

    2016-04-01

    Geophysical fields possess large fluctuations over many spatial and temporal scales. Satellite successive images provide interesting sampling of this spatio-temporal multiscale variability. Here we propose to consider such variability by performing satellite time series analysis, pixel by pixel, using Empirical Mode Decomposition (EMD). EMD is a time series analysis technique able to decompose an original time series into a sum of modes, each one having a different mean frequency. It can be used to smooth signals, to extract trends. It is built in a data-adaptative way, and is able to extract information from nonlinear signals. Here we use MERIS Suspended Particulate Matter (SPM) data, on a weekly basis, during 10 years. There are 458 successive time steps. We have selected 5 different regions of coastal waters for the present study. They are Vietnam coastal waters, Brahmaputra region, St. Lawrence, English Channel and McKenzie. These regions have high SPM concentrations due to large scale river run off. Trend and Hurst exponents are derived for each pixel in each region. The energy also extracted using Hilberts Spectral Analysis (HSA) along with EMD method. Normalised energy computed for each mode for each region with the total energy. The total energy computed using all the modes are extracted using EMD method.

  14. Assessing differences in phenology patterns between burned and non burned areas using MODIS and Landsat time series satellite images. A case study in Peloponnisos (Greece) and Sardinia (Italy)

    NASA Astrophysics Data System (ADS)

    Koutsias, Nikos; Bajocco, Sofia

    2016-04-01

    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 satellite images can be used to characterize vegetation phenology and thus can be helpful for assessing, for example, phenology patterns between burned and non-burned areas. The aim of this study is to define phenological patterns for the fire ignition points in two Mediterranean study areas located in Italy (Sardinia) and Greece (Peloponnisos) and compare them with control points created after random sampling techniques restricted to certain buffer zones. Remotely sensed data from MODIS (2000-2015) and LANDSAT (1984-2015) satellites were acquired and processed to extract the temporal profiles of the spectral signal of fire ignition points and of control points. Apart of the use of the original spectral data, we used vegetation indices commonly found in vegetation studies as well as in burned area mapping studies. Different metrics linked to key phenological events have been derived and used to assess vegetation phenology in the fire-affected areas.

  15. 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

    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.

  16. 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

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

    NASA Astrophysics Data System (ADS)

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

    2010-08-01

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

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

    NASA Astrophysics Data System (ADS)

    Tappin, D.

    2012-04-01

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

  19. Estimating Reliability of Disturbances in Satellite Time Series Data Based on Statistical Analysis

    NASA Astrophysics Data System (ADS)

    Zhou, Z.-G.; Tang, P.; Zhou, M.

    2016-06-01

    Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with "Change/ No change" by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

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

    PubMed

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

    2013-05-01

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

  1. 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

  2. Scaling features of texts, images and time series

    NASA Astrophysics Data System (ADS)

    Pavlov, Alexey N.; Ebeling, Werner; Molgedey, Lutz; Ziganshin, Amir R.; Anishchenko, Vadim S.

    2001-11-01

    In the given paper, we consider the scaling features of long letter sequences like human writings, discretized images and discretized financial data. Using several approaches we show that the symbolic strings and time series being analyzed have a complex multiscale structure and demonstrate different scalings for large and small fluctuations. We discuss complex phenomena in the scaling behavior of partition functions in the case of high frequency DAX-future data.

  3. 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.

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

    NASA Astrophysics Data System (ADS)

    Coluzzi, Rosa; Lasaponara, Rosa; Masini, Nicola

    2010-05-01

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

  5. 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

  6. Vegetation classification in eastern China using time series NDVI images

    NASA Astrophysics Data System (ADS)

    Han, Guifeng; Xu, Jianhua

    2007-11-01

    The SPOT/VGT NDVI (S10) time series data of eastern China (1998-2005) are smoothed with two methods, the moving average and the Savitzky-Golay filter, after they are downloaded from the official website of VITO. Then the monthly maximal NDVI images (total 93 images) are extracted from 279 NDVI (S10) images and the Principal Component Analysis (PCA) is applied on the 93 images. There are 3 components that each explains more than 1% of the variance, in which the principal components 1, 2 and 3 explain respectively 93.25%, 2.77% and 1.21% of the variance in the original 93 maximum NDVI images. The principal component 1 is interpreted as the "climate" component, and principal components 2 and 3 are interpreted as the "growth season" and "non-growth season" components respectively. Principal components 1, 2 and 3 are composed to a 3-band color image which is classified into 7 classes (including 18 subclasses) by ISODATA. The overall accuracy of classification in five samples is 83.6%, and the kappa index is 0.82. Finally, the unique intra-annual NDVI curve of each vegetation class is displayed.

  7. 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

  8. Muscle segmentation in time series images of Drosophila metamorphosis.

    PubMed

    Yadav, Kuleesha; Lin, Feng; Wasser, Martin

    2015-01-01

    In order to study genes associated with muscular disorders, we characterize the phenotypic changes in Drosophila muscle cells during metamorphosis caused by genetic perturbations. We collect in vivo images of muscle fibers during remodeling of larval to adult muscles. In this paper, we focus on the new image processing pipeline designed to quantify the changes in shape and size of muscles. We propose a new two-step approach to muscle segmentation in time series images. First, we implement a watershed algorithm to divide the image into edge-preserving regions, and then, we classify these regions into muscle and non-muscle classes on the basis of shape and intensity. The advantage of our method is two-fold: First, better results are obtained because classification of regions is constrained by the shape of muscle cell from previous time point; and secondly, minimal user intervention results in faster processing time. The segmentation results are used to compare the changes in cell size between controls and reduction of the autophagy related gene Atg 9 during Drosophila metamorphosis.

  9. 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.

  10. Advanced tools for astronomical time series and image analysis

    NASA Astrophysics Data System (ADS)

    Scargle, Jeffrey D.

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

  11. 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.

  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

  15. Ground Deformation Monitoring in Qingdao Coastal Areas by Time-Series Terrasar-X Images

    NASA Astrophysics Data System (ADS)

    Hou, A. Y.; Qiao, X.; Li, D.

    2016-06-01

    As a new generation of high resolution and short revisit period of radar satellite, TerraSAR-X is not only able to meet the requirements of monitoring large scale surface subsidence, but also make it possible to monitor the small deformation of the short period. This articles takes the coastal areas of the west coast of Qingdao as the research object. With Small baselines subsets interferometry synthetic aperture radar (SBASI), this paper obtained the period the average annual rate of change from the time series analysis of TerraSAR-X data from April 2015 to October 2014.In order to enrich the historical deformation data of the study area, it analyse the time series of ALOS images from December 2010 to October 2008 with the same method. Finally,it analyse and demonstrate the experimental results.

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

    NASA Astrophysics Data System (ADS)

    Ratana, Piyachat

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

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

    NASA Astrophysics Data System (ADS)

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

    2004-12-01

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

  18. 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

  19. 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.

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

    NASA Astrophysics Data System (ADS)

    Boergens, Eva; Dettmering, Denise; Schwatke, Christian

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    area index and the fraction of photosynthetically active radiation absorbed by vegetation. Therefore variations in NDVI values become indicative of variations in vegetation composition and dynamics. In this study, we analyze the temporal series from 1998 to 2005 of NDVI satellite SPOT VEGETATION data acquired for a shrubland test site In order to eliminate the phenological fluctuations, for each decadal composition of each pixel, we focused on the departure NDVId = [NDVI - ]/, where is the decadal mean and  is the decadal standard deviation. The decadal mean and the standard deviation were calculated for each decade, e.g. 1st decade of January, by averaging over all years in the record. We analyzed both: 1) Post-disturbance NDVI spatial patterns on each image date were compared to the pre-disturbace pattern to determine the extent to which the pre-disturbance pattern was re-established, and the rate of this recovery. 2) time variation of NDVI from 1998 to 2005 of two pixels for the disturbance affected and disturbance unaffected areas. Results show the ability of NDVI time series to capture the different impacts/effects of different disturbances (drought and fire in the current case) and the capability of VEGETATION-NDVI data set to monitoring vegetation status from local up to a global scale.

  2. Cloud Removal from SENTINEL-2 Image Time Series Through Sparse Reconstruction from Random Samples

    NASA Astrophysics Data System (ADS)

    Cerra, D.; Bieniarz, J.; Müller, R.; Reinartz, P.

    2016-06-01

    In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satellite image time series based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.

  3. 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

  4. 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).

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

    NASA Astrophysics Data System (ADS)

    Willson, R. C.

    2006-12-01

    Continuous time series of total solar irradiance (TSI) observations have been constructed from the set of contiguous, redundant, overlapping total solar irradiance (TSI) measurements made by satellite experiments during the past 28 years. One, the ACRIM composite time series [Willson &Mordvinov, 2003], detects a significant upward trend in TSI of 0.04 percent per decade during solar cycles 21-23. Another, the PMOD composite [Frohlich &Lean, 1998], detects no significant trend using different combinations of TSI data sets, computational philosophy and assumptions. The potential significance of the ACRIM upward trend as a climate forcing makes it important to explore the trend difference to determine which of the two composite TSI time series best represents the measurement database. Two types of experiments have provided TSI data: self-calibrating, precision TSI monitors and Earth radiation budget (ERB) experiments. TSI monitors provide much higher accuracy and precision and are capable of self- calibrating the degradation of their sensors. The ERB experiments are designed to provide less accurate and precise TSI `boundary value' results for ERB modeling and cannot self-calibrate sensor degradation. While the optimum composite TSI time series utilizes TSI monitor results to the maximum extent possible, a two year gap in the TSI monitoring record between the ACRIM1 and ACRIM2 experiments (1989 - 1991) would have prevented compilation of a continuous record over the 28 years of satellite observations were it not for the availability of ERB results during the gap. The relationship between ACRIM1 and ACRIM2 results across the ACRIM gap can be derived using the overlapping ERB data sets: the Nimbus7/ERB and/or the ERBS/ERBE. These two choices are embodied in the construction of ACRIM and PMOD composites, respectively. The philosophy of the ACRIM composite is to use the unaltered results published by the experiment science teams and the Nimbus7/ERB ACRIM gap ratio. The

  6. 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.

  7. 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.

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

    USGS Publications Warehouse

    ,

    2008-01-01

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

  9. 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.

  10. 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

    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

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

    NASA Astrophysics Data System (ADS)

    Abdollahi, K.; Batelaan, O.

    2012-04-01

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

  12. 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

  13. 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.

  14. Identifying and monitoring urban heat island in Bucharest using satellite time series and low cost meteorological sensors

    NASA Astrophysics Data System (ADS)

    Sandric, Ionut; Onose, Diana; Vanau, Gabriel; Ioja, Cristian

    2016-04-01

    The present study is focusing on the identification of urban heat island in Bucharest using both remote sensing products and low cost temperature sensors. The urban heat island in Bucharest was analyzed through a network of sensors located in 56 points (47 inside the administrative boundary of the city, 9 outside) 2009-2011. The network lost progressively its initial density, but was reformed during a new phase, 2013-2015. Time series satellite images from MODIS were intersected with the sensors for both phases. Statistical analysis were conducted to identify the temporal and spatial pattern of extreme temperatures in Bucharest. Several environmental factors like albedou, presence and absence of vegetation were used to fit a regression model between MODIS satellite products sensors in order to upscale the temperatures values recorded by MODIS For Bucharest, an important role for air temperature values in urban environments proved to have the local environmental conditions that leads to differences in air temperature at Bucharest city scale between 3-5 °C (both in the summer and in the winter). The UHI maps shows a good correlation with the presence of green areas. Differences in air temperature between higher tree density areas and isolated trees can reach much higher values, averages over 24 h periods still are in the 3-5 °C range The results have been obtained within the project UCLIMESA (Urban Heat Island Monitoring under Present and Future Climate), ongoing between 2013 and 2015 in the framework of the Programme for Research-DevelopmentInnovation for Space Technology and Advanced Research (STAR), administrated by the Romanian Space Agency Keywords: time series, urban heat island

  15. 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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  17. 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

  18. 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

  19. 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…

  20. Validation of Vegetation Index Time Series from Suomi NPP Visible Infrared Imaging Radiometer Suite Using Tower Radiation Flux Measurements

    NASA Astrophysics Data System (ADS)

    Miura, T.; Kato, A.; Wang, J.; Vargas, M.; Lindquist, M.

    2015-12-01

    Satellite vegetation index (VI) time series data serve as an important means to monitor and characterize seasonal changes of terrestrial vegetation and their interannual variability. It is, therefore, critical to ensure quality of such VI products and one method of validating VI product quality is cross-comparison with in situ flux tower measurements. In this study, we evaluated the quality of VI time series derived from Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) spacecraft by cross-comparison with in situ radiation flux measurements at select flux tower sites over North America and Europe. VIIRS is a new polar-orbiting satellite sensor series, slated to replace National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer in the afternoon overpass and to continue the highly-calibrated data streams initiated with Moderate Resolution Imaging Spectrometer of National Aeronautics and Space Administration's Earth Observing System. The selected sites covered a wide range of biomes, including croplands, grasslands, evergreen needle forest, woody savanna, and open shrublands. The two VIIRS indices of the Top-of-Atmosphere (TOA) Normalized Difference Vegetation Index (NDVI) and the atmospherically-corrected, Top-of-Canopy (TOC) Enhanced Vegetation Index (EVI) (daily, 375 m spatial resolution) were compared against the TOC NDVI and a two-band version of EVI (EVI2) calculated from tower radiation flux measurements, respectively. VIIRS and Tower VI time series showed comparable seasonal profiles across biomes with statistically significant correlations (> 0.60; p-value < 0.01). "Start-of-season (SOS)" phenological metric values extracted from VIIRS and Tower VI time series were also highly compatible (R2 > 0.95), with mean differences of 2.3 days and 5.0 days for the NDVI and the EVI, respectively. These results indicate that VIIRS VI time series can capture seasonal evolution of

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  2. On the use of satellite VEGETATION time series for monitoring post fire vegetation recovery

    NASA Astrophysics Data System (ADS)

    de Santis, F.; Didonna, I.

    2009-04-01

    Fire is one of the most critical factors of disturbance in worldwide ecosystems. The effects of fires on soil, plants, landscape and ecosystems depend on many factors, among them fire frequency, fire severity and plant resistance. The characterization of vegetation post-fire behaviour is a fundamental issue to model and evaluate the fire resilience, which the ability of vegetation to recover after fire. Recent changes in fire regime, due to abandonment of local land use practice and climate change, can induce significant variations in vegetation fire resilience. In the Mediterranean-type communities, post fire vegetation trends have been analysed in a wide range of habitats, although pre- and post-fire investigation has been widely performed at stand level. But, factors controlling regeneration at the landscape scale are less well known. In this study, a time series of normalized difference vegetation index (NDVI) data derived from SPOT-VEGETATION was used to examine the recovery characteristics of fire affected vegetation in some test areas of the Mediterranean ecosystems of Southern Italy. The vegetation indices operate by contrasting intense chlorophyll pigment absorption in the red against the high reflectance of leaf mesophyll in the near infrared. SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) data from 1998 to 2005 were analyzed in order to evaluate the resilient effects in a some significant test sites of southern Italy. In particular, we considered: (i) one stable area site, one site affected by one fire during the investigated time window, (iii) one site affected by two consecutive fires during the investigated time window. In order to eliminate the phenological fluctuations, for each decadal composition of each pixel, we focused on the departure NDVId = [NDVI - ]/, where is the decadal mean and  is the decadal standard deviation. The decadal mean and the standard deviation were calculated for each decade, e.g. 1st

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

  4. 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.

  5. 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.

  6. DAHITI - An Innovative Approach for Estimating Water Level Time Series over Inland Water using Multi-Mission Satellite Altimetry

    NASA Astrophysics Data System (ADS)

    Schwatke, Christian; Dettmering, Denise

    2016-04-01

    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 lakes, reservoirs, rivers, wetlands and in general any inland water body. In this contribution, a new approach for the estimation of inland water level time series is presented. The method is the basis for the computation of time series of rivers and lakes available through the web service 'Database for Hydrological Time Series over Inland Water' (DAHITI). It 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 new approach yields RMS differences with respect to in situ data between 4 cm and 36 cm for lakes and 8 cm and 114 cm for rivers, respectively. Within this presentation, the new approach will be introduced and examples for water level time series for a variety of lakes and rivers will be shown 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.

  7. 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

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  9. 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.

  10. 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%).

  11. 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.

  12. 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.

  13. 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

  14. 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 ...

  15. 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.

  16. Nonlinear denoising of functional magnetic resonance imaging time series with wavelets

    NASA Astrophysics Data System (ADS)

    Stausberg, Sven; Lehnertz, Klaus

    2009-04-01

    In functional magnetic resonance imaging (fMRI) the blood oxygenation level dependent (BOLD) effect is used to identify and delineate neuronal activity. The sensitivity of a fMRI-based detection of neuronal activation, however, strongly depends on the relative levels of signal and noise in the time series data, and a large number of different artifact and noise sources interfere with the weak signal changes of the BOLD response. Thus, noise reduction is important to allow an accurate estimation of single activation-related BOLD signals across brain regions. Techniques employed so far include filtering in the time or frequency domain which, however, does not take into account possible nonlinearities of the BOLD response. We here evaluate a previously proposed method for nonlinear denoising of short and transient signals, which combines the wavelet transform with techniques from nonlinear time series analysis. We adopt the method to the problem at hand and show that successful noise reduction and, more importantly, preservation of the shape of individual BOLD signals can be achieved even in the presence of in-band noise.

  17. Corn response to climate stress detected with satellite-based NDVI time series

    DOE PAGESBeta

    Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

    2016-03-23

    Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

  18. 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

  19. 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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    The Japanese continuous GPS network (GEONET) with ~1450 stations provide a unique opportunity to study ongoing subduction zone dynamics, and crustal deformation at various spatiotemporal scales. Recently we completed a reanalysis of GPS position time series for the entire GEONET from 1996 to 2012 using JPL GIPSY/OASIS-II based GPS Network Processor [Owen et al., 2006] and raw data provided by Geospatial Information Authority of Japan (GSI) and Caltech. We use the JPL precise GPS orbits reestimated from the present through 1996 [Desai et al., 2011], troposphere global mapping function, and single receiver phase ambiguity resolution strategy [Bertiger et al., 2010] in the analysis. The resultant GPS time series solution shows improved repeatability and consistency over the ~16 yrs span, in comparison with 1996-2006 GPS position estimates used in our previous analysis [Liu et al., 2010a,b]. We apply a time-series analysis framework to estimate bias, offsets caused by instrument changes, earthquakes and other unknown sources, linear trends, seasonal variations, post-seismic deformation and other transient signals. The principal component analysis method is used to estimate the common mode error across the network [Dong et al. 2006]. We construct an interplate fault geometry from a composite plate boundary model [Wang et al. 2004] and apply a Kalman filter based network inversion method to image the spatiotemporal slip variation of slip transient events on the plate interface. The highly precise GPS time series enables the detectability of much smaller transient signals and start to reveal previously unobserved features of slow slip events. For example, the application to 2009-2011 Bungo Channel slow slip event shows it has a complex slip history with the major event initiating in late 2009 beneath the northeast corner of the region and migrating southwestward and updip. At ~2010.75 there is activation of a smaller slip subevent to the east of main slip region

  1. 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.

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

    NASA Astrophysics Data System (ADS)

    Neeti, N.; Eastman, R.

    2012-12-01

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

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

    PubMed

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

    2014-06-01

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

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

    PubMed

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

    2014-06-01

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

  6. 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.

  7. Using Landsat 8 Image Time Series for Crop Mapping in a Region of Cerrado, Brazil

    NASA Astrophysics Data System (ADS)

    Bendini, H.; Sanches, I. D.; Körting, T. S.; Fonseca, L. M. G.; Luiz, A. J. B.; Formaggio, A. R.

    2016-06-01

    The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

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

    PubMed

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

    2012-07-01

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

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

    PubMed

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

    2008-02-01

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

  10. Hailstreak Occurrence and Persistence Observed With AVHRR NDVI Image Time Series

    NASA Astrophysics Data System (ADS)

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

    2002-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Neugebauer, N.; Vuolo, F.

    2012-04-01

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

  12. 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

    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.

  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. 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.

  15. Estimation of the stand ages of tropical secondary forests after shifting cultivation based on the combination of WorldView-2 and time-series Landsat images

    NASA Astrophysics Data System (ADS)

    Fujiki, Shogoro; Okada, Kei-ichi; Nishio, Shogo; Kitayama, Kanehiro

    2016-09-01

    We developed a new method to estimate stand ages of secondary vegetation in the Bornean montane zone, where local people conduct traditional shifting cultivation and protected areas are surrounded by patches of recovering secondary vegetation of various ages. Identifying stand ages at the landscape level is critical to improve conservation policies. We combined a high-resolution satellite image (WorldView-2) with time-series Landsat images. We extracted stand ages (the time elapsed since the most recent slash and burn) from a change-detection analysis with Landsat time-series images and superimposed the derived stand ages on the segments classified by object-based image analysis using WorldView-2. We regarded stand ages as a response variable, and object-based metrics as independent variables, to develop regression models that explain stand ages. Subsequently, we classified the vegetation of the target area into six age units and one rubber plantation unit (1-3 yr, 3-5 yr, 5-7 yr, 7-30 yr, 30-50 yr, >50 yr and 'rubber plantation') using regression models and linear discriminant analyses. Validation demonstrated an accuracy of 84.3%. Our approach is particularly effective in classifying highly dynamic pioneer vegetation younger than 7 years into 2-yr intervals, suggesting that rapid changes in vegetation canopies can be detected with high accuracy. The combination of a spectral time-series analysis and object-based metrics based on high-resolution imagery enabled the classification of dynamic vegetation under intensive shifting cultivation and yielded an informative land cover map based on stand ages.

  16. Speckle imaging of satellites

    SciTech Connect

    Fitch, J.P.; Lawrence, T.W.; Goodman, D.M.; Johansson, E.M.

    1991-12-01

    We performed a series of experiments using the Air Force Optical Station`s 1.6 m telescope and a bare CCD detector to capture speckle images of various satellites. The speckle images were processed with bispectral techniques for recovering image Fourier phase as well as projection onto convex sets for recovering image Fourier magnitude from the projected autocorrelation. Results of imaging point stars and binaries are shown as a baseline assessment of our technique. We have reconstructed high quality images of numerous satellites and will show reconstructions of a very familiar satellite: the Hubble Space Telescope. To our knowledge, this is the first demonstration of the use of bare CCDs for speckle imaging of relatively bright objects such as artificial satellites. 8 refs.

  17. Speckle imaging of satellites

    SciTech Connect

    Fitch, J.P.; Lawrence, T.W.; Goodman, D.M.; Johansson, E.M.

    1991-12-01

    We performed a series of experiments using the Air Force Optical Station's 1.6 m telescope and a bare CCD detector to capture speckle images of various satellites. The speckle images were processed with bispectral techniques for recovering image Fourier phase as well as projection onto convex sets for recovering image Fourier magnitude from the projected autocorrelation. Results of imaging point stars and binaries are shown as a baseline assessment of our technique. We have reconstructed high quality images of numerous satellites and will show reconstructions of a very familiar satellite: the Hubble Space Telescope. To our knowledge, this is the first demonstration of the use of bare CCDs for speckle imaging of relatively bright objects such as artificial satellites. 8 refs.

  18. 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.

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

    NASA Astrophysics Data System (ADS)

    Provost, Christine; Garcia, Omar; GarçOn, VéRonique

    1992-11-01

    We study the dominant periodic variations of sea surface temperature (SST) in the Brazil-Malvinas Confluence region from a satellite-derived data set compiled by Olson et al. (1988). This data set is composed of 202 sea surface temperature images with a 4 × 4 km resolution and extends over 3 years (from July 1984 to July 1987). Each image is a 5-day composite. The dominant signal, as already observed by Podesta et al. (1991), has a 1-year period. We first fit a single-frequency sinusoidal model of the annual cycle in order to estimate mean temperature, amplitude, and phase at 159 points uniformly distributed over the region. The residuals are generally small (less than 2°C). The largest departures from this cycle are located either in the Brazil-Malvinas frontal region or in the southeastern part of the region. Other periods in SST variations are identified by means of periodograms of the 159 residual time series in which the annual cycle has been substracted. The periodograms show that a semiannual frequency signal is present at almost every location. The ratio of the semiannual amplitude to the annual amplitude increases southward from 0% at 30°S to reach up to 45% at 50°S. In the south the semiannual signal creates an asymmetry, and the resulting (total) annual cycle has a cold period (winter) longer than the warm one (summer). In the frontal region the annual and semiannual signals have an important interannual variation. This semiannual frequency is associated with the semiannual wave present in the atmospheric forcing of the southern hemisphere. Differential heating over the mid-latitude oceans and the high-latitude ice-covered Antarctic Continent has been suggested as the cause of this semiannual wave (Van Loon, 1967).

  20. 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

  1. Quantifying Forest and Coastal Disturbance from Industrial Mining Using Satellite Time Series Analysis Under Very Cloudy Conditions

    NASA Astrophysics Data System (ADS)

    Alonzo, M.; Van Den Hoek, J.; Ahmed, N.

    2015-12-01

    The open-pit Grasberg mine, located in the highlands of Western Papua, Indonesia, and operated by PT Freeport Indonesia (PT-FI), is among the world's largest in terms of copper and gold production. Over the last 27 years, PT-FI has used the Ajkwa River to transport an estimated 1.3 billion tons of tailings from the mine into the so-called Ajkwa Deposition Area (ADA). The ADA is the product of aggradation and lateral expansion of the Ajkwa River into the surrounding lowland rainforest and mangroves, which include species important to the livelihoods of indigenous Papuans. Mine tailings that do not settle in the ADA disperse into the Arafura Sea where they increase levels of suspended particulate matter (SPM) and associated concentrations of dissolved copper. Despite the mine's large-scale operations, ecological impact of mine tailings deposition on the forest and estuarial ecosystems have received minimal formal study. While ground-based inquiries are nearly impossible due to access restrictions, assessment via satellite remote sensing is promising but hindered by extreme cloud cover. In this study, we characterize ridgeline-to-coast environmental impacts along the Ajkwa River, from the Grasberg mine to the Arafura Sea between 1987 and 2014. We use "all available" Landsat TM and ETM+ images collected over this time period to both track pixel-level vegetation disturbance and monitor changes in coastal SPM levels. Existing temporal segmentation algorithms are unable to assess both acute and protracted trajectories of vegetation change due to pervasive cloud cover. In response, we employ robust, piecewise linear regression on noisy vegetation index (NDVI) data in a manner that is relatively insensitive to atmospheric contamination. Using this disturbance detection technique we constructed land cover histories for every pixel, based on 199 image dates, to differentiate processes of vegetation decline, disturbance, and regrowth. Using annual reports from PT-FI, we show

  2. Grouped frequent sequential patterns derived from terrestrial image time series to monitor landslide behaviour - Application to the dynamics of the Sanières/Roche Plombée rockslide.

    NASA Astrophysics Data System (ADS)

    Péricault, Youen; Pothier, Catherine; Méger, Nicolas; Trouvé, Emmanuel; Vernier, Flavien; Rigotti, Christophe; Malet, Jean-Philippe

    2016-04-01

    Image time series acquired with remote sensing methods based on optical terrestrial photogrammetry have great potential for understanding and monitoring the Earth surface dynamics at local scale, and are particularly interesting for landslide monitoring. Image correlation techniques can be applied to calculate the displacement fields, in either the image geometry or the terrain geometry if orthorectification procedures are applied. The resulting products are times series of displacement vectors for each epoch in which knowledge extraction techniques can be applied to discover relevant movement patterns in space and time. We used an unsupervised method (Grouped Frequent Sequential patterns / GFS-patterns) based on the mining of the displacement field. The method was originally developed for the analysis of time series of satellite images. It involves the extraction of trends / sub-trends affecting each pixel covering at least a minimum surface area and sufficiently connected to each other. The results of the mining are presented in spatio-temporal location maps (STL-map) of each GFS-pattern. In these maps, the spatial information is given by the pixel locations and the time information is displayed using a color ramp. The method is tested on a time series of 36 optical terrestrial images of the Sanières/Roche Plombée rockslide (South East French Alps) from 28 of July to 1 September 2014. From this series 35 2D displacement fields were calculated for epochs of three days, and the time series of vector magnitude and direction were analysed with GFS-patterns / STL-map. The method allowed identifying several patterns corresponding to different kinematical behaviour of the rockslide (long-term creep at the top of the slope, surficial movement of the debris at the base of the slope). The unsupervised knowledge extraction method GFS-pattern / STL-map, originally developed to analyse time series of satellite images showed in this study real possibilities of use for

  3. 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.

  4. 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.

  5. 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

    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.

  6. 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.

  7. Time-series Doppler imaging of the red giant HD 208472. Active longitudes and differential rotation

    NASA Astrophysics Data System (ADS)

    Özdarcan, O.; Carroll, T. A.; Künstler, A.; Strassmeier, K. G.; Evren, S.; Weber, M.; Granzer, T.

    2016-10-01

    Context. HD 208472 is among the most active RS CVn binaries with cool starspots. Decade-long photometry has shown that the spots seem to change their longitudinal appearance with a period of about six years, coherent with brightness variations. Aims: Our aim is to spatially resolve the stellar surface of HD 208472 and relate the photometric results to the true longitudinal and latitudinal spot appearance. Furthermore, we investigate the surface differential rotation pattern of the star. Methods: We employed three years of high-resolution spectroscopic data with a high signal-to-noise ratio (S/N) from the STELLA robotic observatory and determined new and more precise stellar physical parameters. Precalculated synthetic spectra were fit to each of these spectra, and we provide new spot-corrected orbital elements. A sample of 34 absorption lines per spectrum was used to calculate mean line profiles with a S/N of several hundred. A total of 13 temperature Doppler images were reconstructed from these line profiles with the inversion code iMap. Differential rotation was investigated by cross-correlating successive Doppler images in each observing season. Results: Spots on HD 208472 are distributed preferably at high latitudes and less frequently around mid-to-low latitudes. No polar-cap like structure is seen at any epoch. We observed a flip-flop event between 2009 and 2010, manifested as a flip of the spot activity from phase 0.0 to phase 0.5, while the overall brightness of the star continued to increase and reached an all-time maximum in 2014. Cross-correlation of successive Doppler images suggests a solar-like differential rotation that is ≈15 times weaker than that of the Sun. Based on data obtained with the STELLA robotic telescope in Tenerife, an AIP facility jointly operated by AIP and IAC, and the Potsdam Automatic Photoelectric Telescopes (APT) in Arizona, jointly operated by AIP and Fairborn Observatory.Radial velocity measurements are only available at the

  8. Capturing coupled riparian and coastal disturbance from industrial mining using cloud-resilient satellite time series analysis

    NASA Astrophysics Data System (ADS)

    Alonzo, Michael; van den Hoek, Jamon; Ahmed, Nabil

    2016-10-01

    The socio-ecological impacts of large scale resource extraction are frequently underreported in underdeveloped regions. The open-pit Grasberg mine in Papua, Indonesia, is one of the world’s largest copper and gold extraction operations. Grasberg mine tailings are discharged into the lowland Ajkwa River deposition area (ADA) leading to forest inundation and degradation of water bodies critical to indigenous peoples. The extent of the changes and temporal linkages with mining activities are difficult to establish given restricted access to the region and persistent cloud cover. Here, we introduce remote sensing methods to “peer through” atmospheric contamination using a dense Landsat time series to simultaneously quantify forest loss and increases in estuarial suspended particulate matter (SPM) concentration. We identified 138 km2 of forest loss between 1987 and 2014, an area >42 times larger than the mine itself. Between 1987 and 1998, the rate of disturbance was highly correlated (Pearson’s r = 0.96) with mining activity. Following mine expansion and levee construction along the ADA in the mid-1990s, we recorded significantly (p < 0.05) higher SPM in the Ajkwa Estuary compared to neighboring estuaries. This research provides a means to quantify multiple modes of ecological damage from mine waste disposal or other disturbance events.

  9. Capturing coupled riparian and coastal disturbance from industrial mining using cloud-resilient satellite time series analysis

    PubMed Central

    Alonzo, Michael; Van Den Hoek, Jamon; Ahmed, Nabil

    2016-01-01

    The socio-ecological impacts of large scale resource extraction are frequently underreported in underdeveloped regions. The open-pit Grasberg mine in Papua, Indonesia, is one of the world’s largest copper and gold extraction operations. Grasberg mine tailings are discharged into the lowland Ajkwa River deposition area (ADA) leading to forest inundation and degradation of water bodies critical to indigenous peoples. The extent of the changes and temporal linkages with mining activities are difficult to establish given restricted access to the region and persistent cloud cover. Here, we introduce remote sensing methods to “peer through” atmospheric contamination using a dense Landsat time series to simultaneously quantify forest loss and increases in estuarial suspended particulate matter (SPM) concentration. We identified 138 km2 of forest loss between 1987 and 2014, an area >42 times larger than the mine itself. Between 1987 and 1998, the rate of disturbance was highly correlated (Pearson’s r = 0.96) with mining activity. Following mine expansion and levee construction along the ADA in the mid-1990s, we recorded significantly (p < 0.05) higher SPM in the Ajkwa Estuary compared to neighboring estuaries. This research provides a means to quantify multiple modes of ecological damage from mine waste disposal or other disturbance events. PMID:27725748

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. 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.

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

    NASA Astrophysics Data System (ADS)

    Coluzzi, C.; Didonna, I.

    2009-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Didonna, I.; Coluzzi, R.

    2009-04-01

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

  18. Assessing ecosystem function of a Piñon-Juniper woodland using a time series of high resolution satellite imagery and eddy covariance measurements

    NASA Astrophysics Data System (ADS)

    Krofcheck, D. J.; Eitel, J.; Vierling, L. A.; Schulthess, U.; Litvak, M. E.

    2011-12-01

    Combining recent advancements in satellite remote sensing with current eddy covariance measurement networks is a powerful way to improve our understanding of ecosystem processes. Remote sensing of semi-arid ecosystems requires temporal coverage sufficient to capture discrete responses in productivity as a result of stochastic patterns of precipitation, and adequate spatial resolution to monitor the patchwork of ecosystem heterogeneity. Eddy-covariance towers continuously measure ecosystem-atmosphere carbon and water exchange. However, even with ancillary data regarding phenologic patterns of the region, tower measurements are unable to inform us about differential response from the collection of plant functional types present within the measured tower footprint. We therefore tested the integration of eddy covariance data with a time series of high resolution (5 meter) RapidEye satellite images collected from late 2009 through mid 2011 over a 49 x 49 km area of piñon-juniper (PJ) woodland south of Mountainair, NM that includes two eddy covariance towers. One tower is in intact PJ woodland and the second tower is in a 200 m x 200 m section of PJ woodland in which all piñon >7 cm dbh (~1600 trees) were girdled to simulate the widespread piñon mortality that occurred throughout the SW in 2002. Due to the high spatial and temporal variability in soil moisture and sparse canopy cover at these sites (maximum LAI is ~ 2.1 and 1.8 in the control and girdled sites, respectively), we used site-specific lab based soil moisture reflectance curves to correct for moisture driven variability in soil reflectance. We used three vegetation indices to compare the phenological patterns of specific plant functional types at both tower sites: the traditional vegetation indices NDVI and MSAVI2, as well as a red-edge (690-730 nm) index NDRE which has demonstrated ability to remotely sense plant stress. We combine these remotely-sensed phenological patterns with the flux tower

  19. 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

  20. Modeling urban expansion in Yangon, Myanmar using Landsat time-series and stereo GeoEye Images

    NASA Astrophysics Data System (ADS)

    Sritarapipat, Tanakorn; Takeuchi, Wataru

    2016-06-01

    This research proposed a methodology to model the urban expansion based dynamic statistical model using Landsat and GeoEye Images. Landsat Time-Series from 1978 to 2010 have been applied to extract land covers from the past to the present. Stereo GeoEye Images have been employed to obtain the height of the building. The class translation was obtained by observing land cover from the past to the present. The height of the building can be used to detect the center of the urban area (mainly commercial area). It was assumed that the class translation and the distance of multi-centers of the urban area also the distance of the roads affect the urban growth. The urban expansion model based on the dynamic statistical model was defined to refer to three factors; (1) the class translation, (2) the distance of the multicenters of the urban areas, and (3) the distance from the roads. Estimation and prediction of urban expansion by using our model were formulated and expressed in this research. The experimental area was set up in Yangon, Myanmar. Since it is the major of country's economic with more than five million population and the urban areas have rapidly increased. The experimental results indicated that our model of urban expansion estimated urban growth in both estimation and prediction steps in efficiency.

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

    NASA Astrophysics Data System (ADS)

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

    2006-12-01

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

  2. 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

  3. 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

  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. Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging.

    PubMed

    Locascio, J J; Jennings, P J; Moore, C I; Corkin, S

    1997-01-01

    Although functional magnetic resonance imaging (fMRI) methods yield rich temporal and spatial data for even a single subject, universally accepted data analysis techniques have not been developed that use all the potential information from fMRI of the brain. Specifically, temporal correlations and confounds are a problem in assessing change within pixels. Spatial correlations across pixels are a problem in determining regions of activation and in correcting for multiple significance tests. We propose methods that address these issues in the analysis of task-related changes in mean signal intensity for individual subjects. Our approach to temporally based problems within pixels is to employ a model based on autoregressive-moving average (ARMA or "Box-Jenkins") time series methods, which we call CARMA (Contrasts and ARMA). To adjust for performing multiple significance tests across pixels, taking into account between-pixel correlations, we propose adjustment of P values with "resampling methods." Our objective is to produce two- or three-dimensional brain maps that provide, at each pixel in the map, an estimated P value with absolute meaning. That is, each P value approximates the probability of having obtained by chance the observed signal effect at that pixel, given that the null hypothesis is true. Simulated and real data examples are provided. PMID:20408214

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Trend analysis of air temperature time series in Greece and their relationship with circulation using surface and satellite data: recent trends and an update to 2013

    NASA Astrophysics Data System (ADS)

    Feidas, H.

    2016-07-01

    In this paper, the surface and lower tropospheric temperature trends in Greece and their relationship to the atmospheric circulation for the period 1955-2013 were examined, updating the study of Feidas et al. (Theor Appl Climatol 79:185-208, 2004) for data observed during the 12-year period 2002-2013. The trend analysis is based on a combination of three statistical tests. The trends are now examined for all the seasonal time series, new atmospheric circulation indices were added in the analysis, and maps with the spatial distribution of correlation between air temperature and atmospheric circulation were constructed and analysed. The series updated to 2013 for 18 stations reveal a clearer positive trend than that found for the period 1955-2001 on both the annual and the seasonal timescales. The warming signal detected only in summer in the study of Feidas et al. (Theor Appl Climatol 79:185-208, 2004) has now intensified and spread in other seasons. This warming appears to be mainly caused by the very high temperatures in the last decade (after 2004) of the record. At the national scale, there is now a match between surface temperature trends in Greece and Northern Hemisphere (NH) but only for summer, spring and annual time series, which are the only time series presenting a statistically significant warming trend in Greece. Satellite-induced lower tropospheric temperatures now show a statistically significant tropospheric temperature warming trend for the period 1979-2013, for both areas (Greece and NH). Lower tropospheric and surface air temperatures for the same period (1979-2013) show a very good agreement, with differences only in winter and summer for Greece. The influence of atmospheric circulation on the temperature variability in Greece was also examined using two more circulation indices: the Eastern Mediterranean Pattern Index (EMPI) and the North-Sea Caspian Pattern Index (NCPI). EMPI and especially NCPI explain better now the temperature variance in

  13. Time Series Explorer

    NASA Astrophysics Data System (ADS)

    Loredo, Thomas

    The key, central objectives of the proposed Time Series Explorer project are to develop an organized collection of software tools for analysis of time series data in current and future NASA astrophysics data archives, and to make the tools available in two ways: as a library (the Time Series Toolbox) that individual science users can use to write their own data analysis pipelines, and as an application (the Time Series Automaton) providing an accessible, data-ready interface to many Toolbox algorithms, facilitating rapid exploration and automatic processing of time series databases. A number of time series analysis methods will be implemented, including techniques that range from standard ones to state-of-the-art developments by the proposers and others. Most of the algorithms will be able to handle time series data subject to real-world problems such as data gaps, sampling that is otherwise irregular, asynchronous sampling (in multi-wavelength settings), and data with non-Gaussian measurement errors. The proposed research responds to the ADAP element supporting the development of tools for mining the vast reservoir of information residing in NASA databases. The tools that will be provided to the community of astronomers studying variability of astronomical objects (from nearby stars and extrasolar planets, through galactic and extragalactic sources) will revolutionize the quality of timing analyses that can be carried out, and greatly enhance the scientific throughput of all NASA astrophysics missions past, present, and future. The Automaton will let scientists explore time series - individual records or large data bases -- with the most informative and useful analysis methods available, without having to develop the tools themselves or understand the computational details. Both elements, the Toolbox and the Automaton, will enable deep but efficient exploratory time series data analysis, which is why we have named the project the Time Series Explorer. Science

  14. 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

  15. Time Series Database

    SciTech Connect

    Dugan, Jon M.

    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.

  16. 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.

  17. Disaggregating times series data

    SciTech Connect

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

    1997-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-01-01

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

  19. 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.

  20. 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

  1. Shadow imaging of geosynchronous satellites

    NASA Astrophysics Data System (ADS)

    Douglas, Dennis Michael

    Geosynchronous (GEO) satellites are essential for modern communication networks. If communication to a GEO satellite is lost and a malfunction occurs upon orbit insertion such as a solar panel not deploying there is no direct way to observe it from Earth. Due to the GEO orbit distance of ~36,000 km from Earth's surface, the Rayleigh criteria dictates that a 14 m telescope is required to conventionally image a satellite with spatial resolution down to 1 m using visible light. Furthermore, a telescope larger than 30 m is required under ideal conditions to obtain spatial resolution down to 0.4 m. This dissertation evaluates a method for obtaining high spatial resolution images of GEO satellites from an Earth based system by measuring the irradiance distribution on the ground resulting from the occultation of the satellite passing in front of a star. The representative size of a GEO satellite combined with the orbital distance results in the ground shadow being consistent with a Fresnel diffraction pattern when observed at visible wavelengths. A measurement of the ground shadow irradiance is used as an amplitude constraint in a Gerchberg-Saxton phase retrieval algorithm that produces a reconstruction of the satellite's 2D transmission function which is analogous to a reverse contrast image of the satellite. The advantage of shadow imaging is that a terrestrial based redundant set of linearly distributed inexpensive small telescopes, each coupled to high speed detectors, is a more effective resolved imaging system for GEO satellites than a very large telescope under ideal conditions. Modeling and simulation efforts indicate sub-meter spatial resolution can be readily achieved using collection apertures of less than 1 meter in diameter. A mathematical basis is established for the treatment of the physical phenomena involved in the shadow imaging process. This includes the source star brightness and angular extent, and the diffraction of starlight from the satellite

  2. Aerial Photographs and Satellite Images

    USGS Publications Warehouse

    ,

    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.

  3. 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

  4. 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.

  5. Longitudinal Analysis of Image Time Series with Diffeomorphic Deformations: A Computational Framework Based on Stationary Velocity Fields

    PubMed Central

    Hadj-Hamou, Mehdi; Lorenzi, Marco; Ayache, Nicholas; Pennec, Xavier

    2016-01-01

    We propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced. We investigate the group-wise differences between the patients with Alzheimer's disease and the healthy control group, and show that the proposed pipeline increases the sensitivity with no decrease in the specificity of the statistical study done on the longitudinal deformations. PMID:27375408

  6. Longitudinal Analysis of Image Time Series with Diffeomorphic Deformations: A Computational Framework Based on Stationary Velocity Fields.

    PubMed

    Hadj-Hamou, Mehdi; Lorenzi, Marco; Ayache, Nicholas; Pennec, Xavier

    2016-01-01

    We propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced. We investigate the group-wise differences between the patients with Alzheimer's disease and the healthy control group, and show that the proposed pipeline increases the sensitivity with no decrease in the specificity of the statistical study done on the longitudinal deformations. PMID:27375408

  7. Detection method for small and dim targets from a time series of images observed by a space-based optical detection system

    NASA Astrophysics Data System (ADS)

    Pan, HaiBin; Song, GuangHua; Xie, LiJun; Zhao, Yao

    2014-05-01

    To revisit cataloged space targets, a space-based optical detection system normally observes space targets continuously in a target tracking mode. In the time series of images produced by continuous observation, there are not only the target but also complicated background clutter (a mass of stars) and noises. The existing method only can detect the target with an signal-to-noise ratio (SNR) greater than 6 from these images. This paper presents a detection method for the target with an SNR less than 6. The proposed method consists of an SNR enhancement algorithm and an adaptive background and noise suppression algorithm. Simulation and analytical results show the proposed method detects the target submerged in noise and background clutter when SNR is equal to 3 and the detection probability and the false alarm probability both reach very high performance. This proposed method can help solve the problem of revisiting some weak cataloged space targets.

  8. 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.

  9. 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.

  10. WR Time Series Photometry

    NASA Astrophysics Data System (ADS)

    Pablo, H.; Moffat, A. F. J.

    We take a comprehensive look at Wolf Rayet photometric variability using the MOST satellite. This sample, consisting of 6 WR stars and 6 WC stars defies all typical photometric analysis. We do, however, confirm the presence of unusual periodic signals resembling sawtooth waves which are present in 11 out of 12 stars in this sample.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

  13. 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

  14. 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.

  15. 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

  16. 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

  17. 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...

  18. 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.

  19. Collecting and Animating Online Satellite Images.

    ERIC Educational Resources Information Center

    Irons, Ralph

    1995-01-01

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

  20. 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

  1. Ordinal analysis of time series

    NASA Astrophysics Data System (ADS)

    Keller, K.; Sinn, M.

    2005-10-01

    In order to develop fast and robust methods for extracting qualitative information from non-linear time series, Bandt and Pompe have proposed to consider time series from the pure ordinal viewpoint. On the basis of counting ordinal patterns, which describe the up-and-down in a time series, they have introduced the concept of permutation entropy for quantifying the complexity of a system behind a time series. The permutation entropy only provides one detail of the ordinal structure of a time series. Here we present a method for extracting the whole ordinal information.

  2. 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.

  3. NASA SSTI CLARK 3-meter imaging satellite

    NASA Technical Reports Server (NTRS)

    Sebestyen, George; Hayduk, Robert J.

    1996-01-01

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

  4. 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).

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

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

  6. Permutations and time series analysis.

    PubMed

    Cánovas, Jose S; Guillamón, Antonio

    2009-12-01

    The main aim of this paper is to show how the use of permutations can be useful in the study of time series analysis. In particular, we introduce a test for checking the independence of a time series which is based on the number of admissible permutations on it. The main improvement in our tests is that we are able to give a theoretical distribution for independent time series.

  7. 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.

  8. Geologic information from satellite images

    NASA Technical Reports Server (NTRS)

    Lee, K.; Knepper, D. H.; Sawatzky, D. L.

    1974-01-01

    Extracting geologic information from ERTS and Skylab/EREP images is best done by a geologist trained in photo-interpretation. The information is at a regional scale, and three basic types are available: rock and soil, geologic structures, and landforms. Discrimination between alluvium and sedimentary or crystalline bedrock, and between units in thick sedimentary sequences is best, primarily because of topographic expression and vegetation differences. Discrimination between crystalline rock types is poor. Folds and fractures are the best displayed geologic features. They are recognizable by topographic expression, drainage patterns, and rock or vegetation tonal patterns. Landforms are easily discriminated by their familiar shapes and patterns. Several examples demonstrate the applicability of satellite images to tectonic analysis and petroleum and mineral exploration.

  9. Terrestrial laser scanning point clouds time series for the monitoring of slope movements: displacement measurement using image correlation and 3D feature tracking

    NASA Astrophysics Data System (ADS)

    Bornemann, Pierrick; Jean-Philippe, Malet; André, Stumpf; Anne, Puissant; Julien, Travelletti

    2016-04-01

    Dense multi-temporal point clouds acquired with terrestrial laser scanning (TLS) have proved useful for the study of structure and kinematics of slope movements. Most of the existing deformation analysis methods rely on the use of interpolated data. Approaches that use multiscale image correlation provide a precise and robust estimation of the observed movements; however, for non-rigid motion patterns, these methods tend to underestimate all the components of the movement. Further, for rugged surface topography, interpolated data introduce a bias and a loss of information in some local places where the point cloud information is not sufficiently dense. Those limits can be overcome by using deformation analysis exploiting directly the original 3D point clouds assuming some hypotheses on the deformation (e.g. the classic ICP algorithm requires an initial guess by the user of the expected displacement patterns). The objective of this work is therefore to propose a deformation analysis method applied to a series of 20 3D point clouds covering the period October 2007 - October 2015 at the Super-Sauze landslide (South East French Alps). The dense point clouds have been acquired with a terrestrial long-range Optech ILRIS-3D laser scanning device from the same base station. The time series are analyzed using two approaches: 1) a method of correlation of gradient images, and 2) a method of feature tracking in the raw 3D point clouds. The estimated surface displacements are then compared with GNSS surveys on reference targets. Preliminary results tend to show that the image correlation method provides a good estimation of the displacement fields at first order, but shows limitations such as the inability to track some deformation patterns, and the use of a perspective projection that does not maintain original angles and distances in the correlated images. Results obtained with 3D point clouds comparison algorithms (C2C, ICP, M3C2) bring additional information on the

  10. 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

  11. Satellite image maps of Pakistan

    USGS Publications Warehouse

    ,

    1997-01-01

    Georeferenced Landsat satellite image maps of Pakistan are now being made available for purchase from the U.S. Geological Survey (USGS). The first maps to be released are a series of Multi-Spectral Scanner (MSS) color image maps compiled from Landsat scenes taken before 1979. The Pakistan image maps were originally developed by USGS as an aid for geologic and general terrain mapping in support of the Coal Resource Exploration and Development Program in Pakistan (COALREAP). COALREAP, a cooperative program between the USGS, the United States Agency for International Development, and the Geological Survey of Pakistan, was in effect from 1985 through 1994. The Pakistan MSS image maps (bands 1, 2, and 4) are available as a full-country mosaic of 72 Landsat scenes at a scale of 1:2,000,000, and in 7 regional sheets covering various portions of the entire country at a scale of 1:500,000. The scenes used to compile the maps were selected from imagery available at the Eros Data Center (EDC), Sioux Falls, S. Dak. Where possible, preference was given to cloud-free and snow-free scenes that displayed similar stages of seasonal vegetation development. The data for the MSS scenes were resampled from the original 80-meter resolution to 50-meter picture elements (pixels) and digitally transformed to a geometrically corrected Lambert conformal conic projection. The cubic convolution algorithm was used during rotation and resampling. The 50-meter pixel size allows for such data to be imaged at a scale of 1:250,000 without degradation; for cost and convenience considerations, however, the maps were printed at 1:500,000 scale. The seven regional sheets have been named according to the main province or area covered. The 50-meter data were averaged to 150-meter pixels to generate the country image on a single sheet at 1:2,000,000 scale

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

    PubMed

    Wu, Di; Sun, Da-Wen

    2013-07-15

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

  13. 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.

  14. 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.

  15. 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...

  16. 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.

  17. 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

  18. Time-Series Analysis: A Cautionary Tale

    NASA Technical Reports Server (NTRS)

    Damadeo, Robert

    2015-01-01

    Time-series analysis has often been a useful tool in atmospheric science for deriving long-term trends in various atmospherically important parameters (e.g., temperature or the concentration of trace gas species). In particular, time-series analysis has been repeatedly applied to satellite datasets in order to derive the long-term trends in stratospheric ozone, which is a critical atmospheric constituent. However, many of the potential pitfalls relating to the non-uniform sampling of the datasets were often ignored and the results presented by the scientific community have been unknowingly biased. A newly developed and more robust application of this technique is applied to the Stratospheric Aerosol and Gas Experiment (SAGE) II version 7.0 ozone dataset and the previous biases and newly derived trends are presented.

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

    NASA Astrophysics Data System (ADS)

    Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane

    2014-05-01

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

  20. 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

  1. 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

  2. Interferometric imaging of geostationary satellites

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

  3. Cloudsat Satellite Images of Amanda

    NASA Video Gallery

    NASA's CloudSat satellite flew over Hurricane Amanda on May 25, at 5 p.m. EDT and saw a deep area of moderate to heavy-moderate precipitation below the freezing level (where precipitation changes f...

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Database for Hydrological Time Series of Inland Waters (DAHITI)

    NASA Astrophysics Data System (ADS)

    Schwatke, Christian; Dettmering, Denise

    2016-04-01

    Satellite altimetry was designed for ocean applications. However, since some years, satellite altimetry is also used over inland water to estimate water level time series of lakes, rivers and wetlands. The resulting water level time series can help to understand the water cycle of system earth and makes altimetry to a very useful instrument for hydrological applications. In this poster, we introduce the "Database for Hydrological Time Series of Inland Waters" (DAHITI). Currently, the database contains about 350 water level time series of lakes, reservoirs, rivers, and wetlands which are freely available after a short registration process via http://dahiti.dgfi.tum.de. In this poster, we introduce the product of DAHITI and the functionality of the DAHITI web service. Furthermore, selected examples of inland water targets are presented in detail. DAHITI provides time series of water level heights of inland water bodies and their formal errors . These time series are available within the period of 1992-2015 and have varying temporal resolutions depending on the data coverage of the investigated water body. The accuracies of the water level time series depend mainly on the extent of the investigated water body and the quality of the altimeter measurements. Hereby, an external validation with in-situ data reveals RMS differences between 5 cm and 40 cm for lakes and 10 cm and 140 cm for rivers, respectively.

  9. Albedo Pattern Recognition and Time-Series Analyses in Malaysia

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

  10. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

    Combining climate dynamics and land cover at a relative coarse resolution allows a very interesting approach to global studies, because in many cases these studies are based on a quite high temporal resolution, but they may be limited in large areas like the Mediterranean. However, the current availability of long time series of Landsat imagery and spatially detailed surface climate models allow thinking on global databases improving the results of mapping in areas with a complex history of landscape dynamics, characterized by fragmentation, or areas where relief creates intricate climate patterns that can be hardly monitored or modeled at coarse spatial resolutions. DinaCliVe (supported by the Spanish Government and ERDF, and by the Catalan Government, under grants CGL2012-33927 and SGR2009-1511) is the name of the project that aims analyzing land cover and land use dynamics as well as vegetation stress, with a particular emphasis on droughts, and the role that climate variation may have had in such phenomena. To meet this objective is proposed to design a massive database from long time series of Landsat land cover products (grouped in quinquennia) and monthly climate records (in situ climate data) for the Iberian Peninsula (582,000 km2). The whole area encompasses 47 Landsat WRS2 scenes (Landsat 4 to 8 missions, from path 197 to 202 and from rows 30 to 34), and 52 Landsat WRS1 scenes (for the previous Landsat missions, 212 to 221 and 30 to 34). Therefore, a mean of 49.5 Landsat scenes, 8 quinquennia per scene and a about 6 dates per quinquennium , from 1975 to present, produces around 2376 sets resulting in 30 m x 30 m spatial resolution maps. Each set is composed by highly coherent geometric and radiometric multispectral and multitemporal (to account for phenology) imagery as well as vegetation and wetness indexes, and several derived topographic information (about 10 Tbyte of data). Furthermore, on the basis on a previous work: the Digital Climatic Atlas of

  12. 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").

  13. Northern Everglades, Florida, satellite image map

    USGS Publications Warehouse

    Thomas, Jean-Claude; Jones, John W.

    2002-01-01

    These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

  14. South Florida Everglades: satellite image map

    USGS Publications Warehouse

    Jones, John W.; Thomas, Jean-Claude; Desmond, G.B.

    2001-01-01

    These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

  15. Multivariate Time Series Similarity Searching

    PubMed Central

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

    2014-01-01

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

  16. Multivariate time series similarity searching.

    PubMed

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

    2014-01-01

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

  17. 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…

  18. 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.

  19. 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

  20. 4-D display of satellite cloud images

    NASA Technical Reports Server (NTRS)

    Hibbard, William L.

    1987-01-01

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

  1. Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series

    NASA Astrophysics Data System (ADS)

    Lu, Meng; Pebesma, Edzer; Sanchez, Alber; Verbesselt, Jan

    2016-07-01

    Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis.

  2. 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.

  3. 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.

  4. Direct imaging photometry with the MOST satellite

    NASA Astrophysics Data System (ADS)

    Rowe, J. F.; Matthews, J. M.; Kuschnig, R.; Guenther, D. B.; Moffat, A. F. J.; Rucinski, S. M. R.; Sasselov, D.; Walker, G. A. H.; Weiss, W. W.

    Canada's first space telescope, MOST (Microvariablity and Oscillations of Stars) was successfully launched on June 30, 2003 with a primary mission to perform ultra-high-precision photometry to detect acoustic oscillations in solar-like stars. MOST has the ability to observe single fields for uninterrupted periods of up to two months and targets can be observed either through Fabry lens imaging or Direct imaging. This report reviews the Direct imaging capabilities of the MOST satellite and the extraction of accurate stellar photometry. MOST is a Canadian Space Agency mission, operated jointly by Dynacon, Inc., and the Universities of Toronto and British Columbia, with assistance from the University of Vienna.

  5. 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.

  6. 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

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

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

  9. Imaging artificial satellites: An observational challenge

    NASA Astrophysics Data System (ADS)

    Smith, D. A.; Hill, D. C.

    2016-10-01

    According to the Union of Concerned Scientists, as of the beginning of 2016 there are 1381 active satellites orbiting the Earth, and the United States' Space Surveillance Network tracks about 8000 manmade orbiting objects of baseball-size and larger. NASA estimates debris larger than 1 cm to number more than half a million. The largest ones can be seen by eye—unresolved dots of light that move across the sky in minutes. For most astrophotographers, satellites are annoying streaks that can ruin hours of work. However, capturing a resolved image of an artificial satellite can pose an interesting challenge for a student, and such a project can provide connections between objects in the sky and commercial and political activities here on Earth.

  10. 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.

  11. 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.

  12. 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

  13. Kolmogorov space in time series data

    NASA Astrophysics Data System (ADS)

    Kanjamapornkul, Kabin; Pinčák, Richard

    2016-10-01

    We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for time series data. Our concept is realized as the algorithm of empirical mode decomposition and intrinsic time scale decomposition and it is subsequently used for preliminary analysis on the real time series data.

  14. 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.

  15. A review of subsequence time series clustering.

    PubMed

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

    2014-01-01

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

  16. 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.

  17. 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

  18. 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.

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

    NASA Astrophysics Data System (ADS)

    Long, Di; Singh, Vijay P.

    2010-11-01

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

  20. Fusion of Hyperspectral Hyperion and Multispectral Landsat Time Series Imagery to Improve Results and Capabilities

    NASA Astrophysics Data System (ADS)

    Franks, S.; Neigh, C. S. R.; Campell, P. K.; Sun, G.; Zhang, Q.; Middleton, E.

    2015-12-01

    Since the opening of the USGS archive to no cost Landsat data distribution, time series analysis has grown immensely. With this new era of possibilities, people are able to do science in ways that were never able to be done. The aim of this project is to explore how EO-1 Hyperion data can add value to an already valuable resource. We used a region of interest that had Landsat time series data and coincident Hyperion data to determine how Landsat classifications can be improved by using hyperspectral data with much greater spectral resolution. We hope to find innovative ways to fuse the data sources and come up with new and improved ways to study our changing Earth. With the HyspIRI (Hyperspectral Infrared Imager) satellite being launched shortly, this provides an opportunity to evaluate potential benefits that it may provide when in conjunction with other technologies and missions.

  1. Embedded Implementation of VHR Satellite Image Segmentation.

    PubMed

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-05-27

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.

  2. Embedded Implementation of VHR Satellite Image Segmentation.

    PubMed

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-01-01

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage. PMID:27240370

  3. Embedded Implementation of VHR Satellite Image Segmentation

    PubMed Central

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-01-01

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage. PMID:27240370

  4. Nonparametric causal inference for bivariate time series.

    PubMed

    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.

  5. 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.

  6. 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.

  7. 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.

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

    NASA Astrophysics Data System (ADS)

    Tarnopolski, M.

    2015-06-01

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

  9. 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.

  10. Forecasting Enrollments with Fuzzy Time Series.

    ERIC Educational Resources Information Center

    Song, Qiang; Chissom, Brad S.

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

  11. Salient Segmentation of Medical Time Series Signals

    PubMed Central

    Woodbridge, Jonathan; Lan, Mars; Sarrafzadeh, Majid; Bui, Alex

    2016-01-01

    Searching and mining medical time series databases is extremely challenging due to large, high entropy, and multidimensional datasets. Traditional time series databases are populated using segments extracted by a sliding window. The resulting database index contains an abundance of redundant time series segments with little to no alignment. This paper presents the idea of “salient segmentation”. Salient segmentation is a probabilistic segmentation technique for populating medical time series databases. Segments with the lowest probabilities are considered salient and are inserted into the index. The resulting index has little redundancy and is composed of aligned segments. This approach reduces index sizes by more than 98% over conventional sliding window techniques. Furthermore, salient segmentation can reduce redundancy in motif discovery algorithms by more than 85%, yielding a more succinct representation of a time series signal.

  12. 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.

  13. Automatic Approach to Vhr Satellite Image Classification

    NASA Astrophysics Data System (ADS)

    Kupidura, P.; Osińska-Skotak, K.; Pluto-Kossakowska, J.

    2016-06-01

    In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture), which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the preliminary step

  14. Network structure of multivariate time series.

    PubMed

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-21

    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.

  15. 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.

  16. 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

  17. Homogenising time series: beliefs, dogmas and facts

    NASA Astrophysics Data System (ADS)

    Domonkos, P.

    2011-06-01

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

  18. Fundamental Constraints on Imaging Geosynchronous Satellites

    NASA Astrophysics Data System (ADS)

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

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

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

    NASA Astrophysics Data System (ADS)

    Boriah, Shyam

    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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Wide swath and high resolution optical imaging satellite of Japan

    NASA Astrophysics Data System (ADS)

    Katayama, Haruyoshi; Kato, Eri; Imai, Hiroko; Sagisaka, Masakazu

    2016-05-01

    The "Advanced optical satellite" (tentative name) is a follow-on mission from ALOS. Mission objectives of the advanced optical satellite is to build upon the existing advanced techniques for global land observation using optical sensors, as well as to promote data utilization for social needs. Wide swath and high resolution optical imager onboard the advanced optical satellite will extend the capabilities of earlier ALOS missions. The optical imager will be able to collect high-resolution (< 1 m) and wide-swath (70 km) images with high geo-location accuracy. This paper introduces a conceptual design of the advanced optical satellite.

  6. Earthshots: Satellite images of environmental change – Chernobyl, Ukraine

    USGS Publications Warehouse

    ,

    2013-01-01

    The Landsat 5 image from April 29, 1986, was the first satellite image of the accident. The data from Landsat were used to help confirm that an explosion had happened at Chernobyl and that the plant had been shut down.

  7. 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.

  8. Homogenising time series: Beliefs, dogmas and facts

    NASA Astrophysics Data System (ADS)

    Domonkos, P.

    2010-09-01

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

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

    PubMed

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

    2013-06-01

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

  10. Detecting chaos in irregularly sampled time series

    NASA Astrophysics Data System (ADS)

    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.

  11. 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

  12. 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.

  13. 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

  14. 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.

  15. Learning time series for intelligent monitoring

    NASA Technical Reports Server (NTRS)

    Manganaris, Stefanos; Fisher, Doug

    1994-01-01

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

  16. Climate Time Series Analysis and Forecasting

    NASA Astrophysics Data System (ADS)

    Young, P. C.; Fildes, R.

    2009-04-01

    This paper will discuss various aspects of climate time series data analysis, modelling and forecasting being carried out at Lancaster. This will include state-dependent parameter, nonlinear, stochastic modelling of globally averaged atmospheric carbon dioxide; the computation of emission strategies based on modern control theory; and extrapolative time series benchmark forecasts of annual average temperature, both global and local. The key to the forecasting evaluation will be the iterative estimation of forecast error based on rolling origin comparisons, as recommended in the forecasting research literature. The presentation will conclude with with a comparison of the time series forecasts with forecasts produced from global circulation models and a discussion of the implications for climate modelling research.

  17. 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...

  18. 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.

  19. 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.

  20. 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

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  2. 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.

  3. A time-series analysis of flood disaster around Lena river using Landsat TM/ETM+

    NASA Astrophysics Data System (ADS)

    Sakai, Toru; Hatta, Shigemi; Okumura, Makoto; Takeuchi, Wataru; Hiyama, Tetsuya; Inoue, Gen

    2010-05-01

    Landsat satellite has provided a continuous record of earth observation since 1972, gradually improving sensors (i.e. MSS, TM and ETM+). Already processed archives of Landsat image are now available free of charge from the internet. The Landsat image of 30 m spatial resolution with multiple spectral bands between 450 and 2350 nm is appropriate for detailed mapping of natural resource at wide geographical areas. However, one of the biggest concerns in the use of Landsat image is the uncertainty in the timing of acquisitions. Although detection of land cover change usually requires acquisitions before and after the change, the Landsat image is often unavailable because of the long-term intervals (16 days) and variation in atmosphere. Nearly cloud-free image is acquired at least once per year (total of 22 or 23 scenes per year). Therefore, it may be difficult to acquire appropriate images for monitoring natural disturbances caused at short-term intervals (e.g., flood, forest fire and hurricanes). Our objectives are: (1) to examine whether a time-series of Landsat image is available for monitoring a flood disaster, and (2) to evaluate the impact and timing of the flood disaster around Lena river in Siberia. A set of Landsat TM/ETM+ satellite images was used to enable acquisition of cloud-free image, although Landsat ETM+ images include failure of the Scan Line Corrector (SLC) from May 2003. The overlap area of a time series of 20 Landsat TM/ETM+ images (path 120-122, row 17) from April 2007 to August 2007 was clipped (approximately 33 km × 90 km), and the other area was excluded from the analyses. Image classification was performed on each image separately using an unsupervised ISODATA method, and each Landsat TM/ETM+ image was classified into three land cover types: (1) ice, (2) water, and (3) land. From three land cover types, the area of Lena river was estimated. The area of Lena river dramatically changed after spring breakup. The middle part of Lena river around

  4. Aerosol Climate Time Series Evaluation In ESA Aerosol_cci

    NASA Astrophysics Data System (ADS)

    Popp, T.; de Leeuw, G.; Pinnock, S.

    2015-12-01

    Within the ESA Climate Change Initiative (CCI) Aerosol_cci (2010 - 2017) conducts intensive work to improve algorithms for the retrieval of aerosol information from European sensors. By the end of 2015 full mission time series of 2 GCOS-required aerosol parameters are completely validated and released: Aerosol Optical Depth (AOD) from dual view ATSR-2 / AATSR radiometers (3 algorithms, 1995 - 2012), and stratospheric extinction profiles from star occultation GOMOS spectrometer (2002 - 2012). Additionally, a 35-year multi-sensor time series of the qualitative Absorbing Aerosol Index (AAI) together with sensitivity information and an AAI model simulator is available. Complementary aerosol properties requested by GCOS are in a "round robin" phase, where various algorithms are inter-compared: fine mode AOD, mineral dust AOD (from the thermal IASI spectrometer), absorption information and aerosol layer height. As a quasi-reference for validation in few selected regions with sparse ground-based observations the multi-pixel GRASP algorithm for the POLDER instrument is used. Validation of first dataset versions (vs. AERONET, MAN) and inter-comparison to other satellite datasets (MODIS, MISR, SeaWIFS) proved the high quality of the available datasets comparable to other satellite retrievals and revealed needs for algorithm improvement (for example for higher AOD values) which were taken into account for a reprocessing. The datasets contain pixel level uncertainty estimates which are also validated. The paper will summarize and discuss the results of major reprocessing and validation conducted in 2015. The focus will be on the ATSR, GOMOS and IASI datasets. Pixel level uncertainties validation will be summarized and discussed including unknown components and their potential usefulness and limitations. Opportunities for time series extension with successor instruments of the Sentinel family will be described and the complementarity of the different satellite aerosol products

  5. Modelling of nonlinear filtering Poisson time series

    NASA Astrophysics Data System (ADS)

    Bochkarev, Vladimir V.; Belashova, Inna A.

    2016-08-01

    In this article, algorithms of non-linear filtering of Poisson time series are tested using statistical modelling. The objective is to find a representation of a time series as a wavelet series with a small number of non-linear coefficients, which allows distinguishing statistically significant details. There are well-known efficient algorithms of non-linear wavelet filtering for the case when the values of a time series have a normal distribution. However, if the distribution is not normal, good results can be expected using the maximum likelihood estimations. The filtration is studied according to the criterion of maximum likelihood by the example of Poisson time series. For direct optimisation of the likelihood function, different stochastic (genetic algorithms, annealing method) and deterministic optimization algorithms are used. Testing of the algorithm using both simulated series and empirical data (series of rare words frequencies according to the Google Books Ngram data were used) showed that filtering based on the criterion of maximum likelihood has a great advantage over well-known algorithms for the case of Poisson series. Also, the most perspective methods of optimisation were selected for this problem.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. Wavelet analysis of radon time series

    NASA Astrophysics Data System (ADS)

    Barbosa, Susana; Pereira, Alcides; Neves, Luis

    2013-04-01

    Radon is a radioactive noble gas with a half-life of 3.8 days ubiquitous in both natural and indoor environments. Being produced in uranium-bearing materials by decay from radium, radon can be easily and accurately measured by nuclear methods, making it an ideal proxy for time-varying geophysical processes. Radon time series exhibit a complex temporal structure and large variability on multiple scales. Wavelets are therefore particularly suitable for the analysis on a scale-by-scale basis of time series of radon concentrations. In this study continuous and discrete wavelet analysis is applied to describe the variability structure of hourly radon time series acquired both indoors and on a granite site in central Portugal. A multi-resolution decomposition is performed for extraction of sub-series associated to specific scales. The high-frequency components are modeled in terms of stationary autoregressive / moving average (ARMA) processes. The amplitude and phase of the periodic components are estimated and tidal features of the signals are assessed. Residual radon concentrations (after removal of periodic components) are further examined and the wavelet spectrum is used for estimation of the corresponding Hurst exponent. The results for the several radon time series considered in the present study are very heterogeneous in terms of both high-frequency and long-term temporal structure indicating that radon concentrations are very site-specific and heavily influenced by local factors.

  11. 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…

  12. 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.

  13. Haar Wavelet Analysis of Climatic Time Series

    NASA Astrophysics Data System (ADS)

    Zhang, Zhihua; Moore, John; Grinsted, Aslak

    2014-05-01

    In order to extract the intrinsic information of climatic time series from background red noise, we will first give an analytic formula on the distribution of Haar wavelet power spectra of red noise in a rigorous statistical framework. The relation between scale aand Fourier period T for the Morlet wavelet is a= 0.97T . However, for Haar wavelet, the corresponding formula is a= 0.37T . Since for any time series of time step δt and total length Nδt, the range of scales is from the smallest resolvable scale 2δt to the largest scale Nδt in wavelet-based time series analysis, by using the Haar wavelet analysis, one can extract more low frequency intrinsic information. Finally, we use our method to analyze Arctic Oscillation which is a key aspect of climate variability in the Northern Hemisphere, and discover a great change in fundamental properties of the AO,-commonly called a regime shift or tripping point. Our partial results have been published as follows: [1] Z. Zhang, J.C. Moore and A. Grinsted, Haar wavelet analysis of climatic time series, Int. J. Wavelets, Multiresol. & Inf. Process., in press, 2013 [2] Z. Zhang, J.C. Moore, Comment on "Significance tests for the wavelet power and the wavelet power spectrum", Ann. Geophys., 30:12, 2012

  14. Directionality volatility in electroencephalogram time series

    NASA Astrophysics Data System (ADS)

    Mansor, Mahayaudin M.; Green, David A.; Metcalfe, Andrew V.

    2016-06-01

    We compare time series of electroencephalograms (EEGs) from healthy volunteers with EEGs from subjects diagnosed with epilepsy. The EEG time series from the healthy group are recorded during awake state with their eyes open and eyes closed, and the records from subjects with epilepsy are taken from three different recording regions of pre-surgical diagnosis: hippocampal, epileptogenic and seizure zone. The comparisons for these 5 categories are in terms of deviations from linear time series models with constant variance Gaussian white noise error inputs. One feature investigated is directionality, and how this can be modelled by either non-linear threshold autoregressive models or non-Gaussian errors. A second feature is volatility, which is modelled by Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes. Other features include the proportion of variability accounted for by time series models, and the skewness and the kurtosis of the residuals. The results suggest these comparisons may have diagnostic potential for epilepsy and provide early warning of seizures.

  15. Satellite image collection modeling for large area hazard emergency response

    NASA Astrophysics Data System (ADS)

    Liu, Shufan; Hodgson, Michael E.

    2016-08-01

    Timely collection of critical hazard information is the key to intelligent and effective hazard emergency response decisions. Satellite remote sensing imagery provides an effective way to collect critical information. Natural hazards, however, often have large impact areas - larger than a single satellite scene. Additionally, the hazard impact area may be discontinuous, particularly in flooding or tornado hazard events. In this paper, a spatial optimization model is proposed to solve the large area satellite image acquisition planning problem in the context of hazard emergency response. In the model, a large hazard impact area is represented as multiple polygons and image collection priorities for different portion of impact area are addressed. The optimization problem is solved with an exact algorithm. Application results demonstrate that the proposed method can address the satellite image acquisition planning problem. A spatial decision support system supporting the optimization model was developed. Several examples of image acquisition problems are used to demonstrate the complexity of the problem and derive optimized solutions.

  16. Mulstiscale Stochastic Generator of Multivariate Met-Ocean Time Series

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

  17. 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

  18. 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.

  19. Homogeneous global mean temperature time series

    SciTech Connect

    Peterson, T.C.; Easterling, D.R.; Vose, R.S.; Eischeid, J.K.

    1993-11-01

    A multi-agency effort has been underway to create a homogeneous global baseline data set suitable for studying climate change. The joint release of the Global Historical Climatology Network (GHCN, Vose et al, 1992) version I in 1992 by the National Climatic Data Center/NOAA and the Carbon Dioxide Information Analysis Center/DOE gave the climate research community the largest monthly land surface global climate data set available to date with over 6,000 temperature stations, 39% of which have more than 50 years of data and 10% have more than 100 years of data (see Figure 1). Fifteen different global or regional data sets were merged to create GHCN version 1. Ten of these source data sets have temperature data but only two have been tested and adjusted for inhomogeneities in the station time series. The majority of the station temperature time series in GHCN have not been systematically examined for discontinuities.

  20. Forbidden patterns in financial time series

    NASA Astrophysics Data System (ADS)

    Zanin, Massimiliano

    2008-03-01

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

  1. 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.

  2. Multifractal Analysis of Sunspot Number Time Series

    NASA Astrophysics Data System (ADS)

    Kasde, Satish Kumar; Gwal, Ashok Kumar; Sondhiya, Deepak Kumar

    2016-07-01

    Multifractal analysis based approaches have been recently developed as an alternative framework to study the complex dynamical fluctuations in sunspot numbers data including solar cycles 20 to 23 and ascending phase of current solar cycle 24.To reveal the multifractal nature, the time series data of monthly sunspot number are analyzed by singularity spectrum and multi resolution wavelet analysis. Generally, the multifractility in sunspot number generate turbulence with the typical characteristics of the anomalous process governing the magnetosphere and interior of Sun. our analysis shows that singularities spectrum of sunspot data shows well Gaussian shape spectrum, which clearly establishes the fact that monthly sunspot number has multifractal character. The multifractal analysis is able to provide a local and adaptive description of the cyclic components of sunspot number time series, which are non-stationary and result of nonlinear processes. Keywords: Sunspot Numbers, Magnetic field, Multifractal analysis and wavelet Transform Techniques.

  3. Visibility graphlet approach to chaotic time series.

    PubMed

    Mutua, Stephen; Gu, Changgui; Yang, Huijie

    2016-05-01

    Many novel methods have been proposed for mapping time series into complex networks. Although some dynamical behaviors can be effectively captured by existing approaches, the preservation and tracking of the temporal behaviors of a chaotic system remains an open problem. In this work, we extended the visibility graphlet approach to investigate both discrete and continuous chaotic time series. We applied visibility graphlets to capture the reconstructed local states, so that each is treated as a node and tracked downstream to create a temporal chain link. Our empirical findings show that the approach accurately captures the dynamical properties of chaotic systems. Networks constructed from periodic dynamic phases all converge to regular networks and to unique network structures for each model in the chaotic zones. Furthermore, our results show that the characterization of chaotic and non-chaotic zones in the Lorenz system corresponds to the maximal Lyapunov exponent, thus providing a simple and straightforward way to analyze chaotic systems.

  4. Detecting anomalous phase synchronization from time series

    SciTech Connect

    Tokuda, Isao T.; Kumar Dana, Syamal; Kurths, Juergen

    2008-06-15

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

  5. 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.

  6. Nonlinear Time Series Analysis of Sunspot Data

    NASA Astrophysics Data System (ADS)

    Suyal, Vinita; Prasad, Awadhesh; Singh, Harinder P.

    2009-12-01

    This article deals with the analysis of sunspot number time series using the Hurst exponent. We use the rescaled range ( R/ S) analysis to estimate the Hurst exponent for 259-year and 11 360-year sunspot data. The results show a varying degree of persistence over shorter and longer time scales corresponding to distinct values of the Hurst exponent. We explain the presence of these multiple Hurst exponents by their resemblance to the deterministic chaotic attractors having multiple centers of rotation.

  7. Data mining in medical time series.

    PubMed

    Mikut, Ralf; Reischl, Markus; Burmeister, Ole; Loose, Tobias

    2006-12-01

    This article proposes a modular, computer-based methodology to describe and compare medical problems using data mining methods. The methodology focuses on a mathematical formulation of typical classification problems, systematic extraction of interpretable features from time series, and an evaluation adapted to problem-specific preferences and limitations (computational power, interpretability, etc.). The approach is applied to instrumented gait analysis and to the individual design of myoelectric controllers for hand prostheses.

  8. Aggregated Indexing of Biomedical Time Series Data

    PubMed Central

    Woodbridge, Jonathan; Mortazavi, Bobak; Sarrafzadeh, Majid; Bui, Alex A.T.

    2016-01-01

    Remote and wearable medical sensing has the potential to create very large and high dimensional datasets. Medical time series databases must be able to efficiently store, index, and mine these datasets to enable medical professionals to effectively analyze data collected from their patients. Conventional high dimensional indexing methods are a two stage process. First, a superset of the true matches is efficiently extracted from the database. Second, supersets are pruned by comparing each of their objects to the query object and rejecting any objects falling outside a predetermined radius. This pruning stage heavily dominates the computational complexity of most conventional search algorithms. Therefore, indexing algorithms can be significantly improved by reducing the amount of pruning. This paper presents an online algorithm to aggregate biomedical times series data to significantly reduce the search space (index size) without compromising the quality of search results. This algorithm is built on the observation that biomedical time series signals are composed of cyclical and often similar patterns. This algorithm takes in a stream of segments and groups them to highly concentrated collections. Locality Sensitive Hashing (LSH) is used to reduce the overall complexity of the algorithm, allowing it to run online. The output of this aggregation is used to populate an index. The proposed algorithm yields logarithmic growth of the index (with respect to the total number of objects) while keeping sensitivity and specificity simultaneously above 98%. Both memory and runtime complexities of time series search are improved when using aggregated indexes. In addition, data mining tasks, such as clustering, exhibit runtimes that are orders of magnitudes faster when run on aggregated indexes.

  9. Mixed Spectrum Analysis on fMRI Time-Series.

    PubMed

    Kumar, Arun; Lin, Feng; Rajapakse, Jagath C

    2016-06-01

    Temporal autocorrelation present in functional magnetic resonance image (fMRI) data poses challenges to its analysis. The existing approaches handling autocorrelation in fMRI time-series often presume a specific model of autocorrelation such as an auto-regressive model. The main limitation here is that the correlation structure of voxels is generally unknown and varies in different brain regions because of different levels of neurogenic noises and pulsatile effects. Enforcing a universal model on all brain regions leads to bias and loss of efficiency in the analysis. In this paper, we propose the mixed spectrum analysis of the voxel time-series to separate the discrete component corresponding to input stimuli and the continuous component carrying temporal autocorrelation. A mixed spectral analysis technique based on M-spectral estimator is proposed, which effectively removes autocorrelation effects from voxel time-series and identify significant peaks of the spectrum. As the proposed method does not assume any prior model for the autocorrelation effect in voxel time-series, varying correlation structure among the brain regions does not affect its performance. We have modified the standard M-spectral method for an application on a spatial set of time-series by incorporating the contextual information related to the continuous spectrum of neighborhood voxels, thus reducing considerably the computation cost. Likelihood of the activation is predicted by comparing the amplitude of discrete component at stimulus frequency of voxels across the brain by using normal distribution and modeling spatial correlations among the likelihood with a conditional random field. We also demonstrate the application of the proposed method in detecting other desired frequencies.

  10. Cluster analysis of respiratory time series.

    PubMed

    Adams, J M; Attinger, E O; Attinger, F M

    1978-03-01

    We have investigated the respiratory control system with the hypothesis that, although many variables such as minute ventilation (VI), tidal volume (VT), breathing period (TT), inspiratory duration (TI), and expiratory duration (TE) may be observed, the controller functions more simply by manipulating only 2 or 3 of these. Thus, if tidal volume is the only independent variable, TI being determined by the "off-switch" threshold, these variables should have very similar time courses. Anesthetized dogs were subjected to CO2 breathing and carotid sinus perfusion to stimulate both chemoreceptors. The time series of the variables VI, VT, TT, TE, and TI as well as PACO2 were determined on a breath by breath basis. Derived characteristics of these time series were compared using Cluster Analysis and the latent dimensionality of respiratory control determined by Factor Analysis. The characteristics of the time series clustered into 4 groups: magnitude (of the response), speed, variability and relative change. One respiratory factor accounted for 86% of the variance for the variability characteristics, 2 factors for magnitude (91%) and relative change (85%) and 3 factors for speed (89%). The respiratory variables were analysed for each of the 4 groups of characteristics with the following results: VT and TI clustered together only for the magnitude and relative change characteristics where as TT and TE clustered closely for all four characteristics. One latent factor was associated with the [TT-TE] group and the other usually with PACO2.

  11. 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.

  12. 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.

  13. Pseudotime estimation: deconfounding single cell time series

    PubMed Central

    Reid, John E.; Wernisch, Lorenz

    2016-01-01

    Motivation: Repeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions on cell progression. Results: We present a principled probabilistic model with a Bayesian inference scheme to analyse such data. We demonstrate our method’s utility on public microarray, nCounter and RNA-seq datasets from three organisms. Our method almost perfectly recovers withheld capture times in an Arabidopsis dataset, it accurately estimates cell cycle peak times in a human prostate cancer cell line and it correctly identifies two precocious cells in a study of paracrine signalling in mouse dendritic cells. Furthermore, our method compares favourably with Monocle, a state-of-the-art technique. We also show using held-out data that uncertainty in the temporal dimension is a common confounder and should be accounted for in analyses of repeated cross-sectional time series. Availability and Implementation: Our method is available on CRAN in the DeLorean package. Contact: john.reid@mrc-bsu.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27318198

  14. 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.

  15. 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

  16. Geostationary satellite imaging spectrometry for GEOSS: importance and expected benefits

    NASA Astrophysics Data System (ADS)

    Smith, W., Sr.; Mango, S.

    2008-12-01

    Satellite infrared hyperspectral instruments provide atmospheric soundings with high spatial resolution. Already implemented aboard polar orbiting satellites, these instruments have provided data that are proving to improve greatly global Numerical Weather Prediction (NWP). When implemented aboard geostationary satellites as imaging spectrometers, even greater impacts on global NWP are expected from their capability to observe vertically resolved cloud and water vapor tracer winds. Possibly most important, geostationary imaging spectrometry will enable much improved mesoscale severe weather prediction because of the ability to observe atmospheric dynamics through nearcontinuous observation of the three dimensional water vapor and temperature distribution of the atmosphere. Furthermore, hyperspectral measurements of greenhouse and pollutant gas fluxes from geostationary orbit are expected to be an important ingredient for understanding climate change and producing timely air quality forecasts. In this paper, the Global Earth Observation System of Systems (GEOSS), recent improvements in the satellite observing system, and the importance and expected benefits of geostationary satellite imaging spectrometry for the GEOSS are discussed. Demonstration of a few of the expected measurement capabilities of these systems is provided from experimental aircraft and satellite measurements. Finally, the status of the development of the geostationary satellite imaging spectrometer is provided.

  17. 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

  18. Mapping afforestation and its carbon stock using time-series Landsat stacks

    NASA Astrophysics Data System (ADS)

    Liu, L.; Wu, Y.

    2015-12-01

    The Three Norths Shelter Forest Programme (TNSFP) is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land cover and cover changes, but it is still challenging to accurately quantify the plantation and its carbon stock from time-series satellite images. In this paper, the Yulin district, Shaanxi province, representing a typical afforestation area in the TNSFP region, was selected as the study area, and there were twenty-nine Landsat MSS/TM/ETM+ epochs were collected from 1974 to 2012 to reconstruct the forest changes and carbon stock in last 40 years. 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 based on the integrated forest z-score (IFZ) model, 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, with a RMSE value of 4.32 years and a determination coefficient (R²) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R² values from 0.50 to 0.727. 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 since 1974, the forest AGB density increased from 15.72 t

  19. Bottom Topographic Changes of Poyang Lake During Past Decade Using Multi-temporal Satellite Images

    NASA Astrophysics Data System (ADS)

    Zhang, S.

    2015-12-01

    Poyang Lake, as a well-known international wetland in the Ramsar Convention List, is the largest freshwater lake in China. It plays crucial ecological role in flood storage and biological diversity. Poyang Lake is facing increasingly serious water crises, including seasonal dry-up, decreased wetland area, and water resource shortage, all of which are closely related to progressive bottom topographic changes over recent years. Time-series of bottom topography would contribute to our understanding of the lake's evolution during the past several decades. However, commonly used methods for mapping bottom topography fail to frequently update quality bathymetric data for Poyang Lake restricted by weather and accessibility. These deficiencies have limited our ability to characterize the bottom topographic changes and understanding lake erosion or deposition trend. To fill the gap, we construct a decadal bottom topography of Poyang Lake with a total of 146 time series medium resolution satellite images based on the Waterline Method. It was found that Poyang Lake has eroded with a rate of -14.4 cm/ yr from 2000 to 2010. The erosion trend was attributed to the impacts of human activities, especially the operation of the Three Gorge Dams, sand excavation, and the implementation of water conservancy project. A decadal quantitative understanding bottom topography of Poyang Lake might provide a foundation to model the lake evolutionary processes and assist both researchers and local policymakers in ecological management, wetland protection and lake navigation safety.

  20. 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

  1. 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.

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

    PubMed

    Hadjimitsis, Diofantos G; Clayton, Chris

    2009-12-01

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

  3. 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

  4. Modeling stylized facts for financial time series

    NASA Astrophysics Data System (ADS)

    Krivoruchenko, M. I.; Alessio, E.; Frappietro, V.; Streckert, L. J.

    2004-12-01

    Multivariate probability density functions of returns are constructed in order to model the empirical behavior of returns in a financial time series. They describe the well-established deviations from the Gaussian random walk, such as an approximate scaling and heavy tails of the return distributions, long-ranged volatility-volatility correlations (volatility clustering) and return-volatility correlations (leverage effect). The model is tested successfully to fit joint distributions of the 100+ years of daily price returns of the Dow Jones 30 Industrial Average.

  5. Time Series Photometry of KZ Lacertae

    NASA Astrophysics Data System (ADS)

    Joner, Michael D.

    2016-01-01

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

  6. Forecasting spore concentrations: A time series approach

    NASA Astrophysics Data System (ADS)

    Stephen, Elaine; Raffery, Adrian E.; Dowding, Paul

    1990-06-01

    Fungal basidiospores and Cladosporium spores are the two most numerous spore types in the air of Dublin and its surroundings. They are known to have allergenic components, and the aim of the study described here is to develop a predictive model for these spores. A very simple model, which combines an estimated diurnal rhythm with a simple, one-parameter time series model, provided golld short-term forecasts. The one-step prediction error variance was reduced by 88% for Cladosporium spores and by 98% for basidiospores.

  7. Synchronization Analysis of Nonstationary Bivariate Time Series

    NASA Astrophysics Data System (ADS)

    Kurths, J.

    First the concept of synchronization in coupled complex systems is presented and it is shown that synchronization phenomena are abundant in science, nature, engineer- ing etc. We use this concept to treat the inverse problem and to reveal interactions between oscillating systems from observational data. First it is discussed how time varying phases and frequencies can be estimated from time series and second tech- niques for detection and quantification of hidden synchronization is presented. We demonstrate that this technique is effective for the analysis of systems' interrelation from noisy nonstationary bivariate data and provides other insights than traditional cross correlation and spectral analysis. For this, model examples and geophysical data are discussed.

  8. 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

  9. 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.

  10. 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

  11. 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

  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. 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.

  14. Partial spectral analysis of hydrological time series

    NASA Astrophysics Data System (ADS)

    Jukić, D.; Denić-Jukić, V.

    2011-03-01

    SummaryHydrological time series comprise the influences of numerous processes involved in the transfer of water in hydrological cycle. It implies that an ambiguity with respect to the processes encoded in spectral and cross-spectral density functions exists. Previous studies have not paid attention adequately to this issue. Spectral and cross-spectral density functions represent the Fourier transforms of auto-covariance and cross-covariance functions. Using this basic property, the ambiguity is resolved by applying a novel approach based on the spectral representation of partial correlation. Mathematical background for partial spectral density, partial amplitude and partial phase functions is presented. The proposed functions yield the estimates of spectral density, amplitude and phase that are not affected by a controlling process. If an input-output relation is the subject of interest, antecedent and subsequent influences of the controlling process can be distinguished considering the input event as a referent point. The method is used for analyses of the relations between the rainfall, air temperature and relative humidity, as well as the influences of air temperature and relative humidity on the discharge from karst spring. Time series are collected in the catchment of the Jadro Spring located in the Dinaric karst area of Croatia.

  15. 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.

  16. Renewal, modulation, and superstatistics in times series.

    PubMed

    Allegrini, Paolo; Barbi, Francesco; Grigolini, Paolo; Paradisi, Paolo

    2006-04-01

    We consider two different approaches, to which we refer to as renewal and modulation, to generate time series with a nonexponential distribution of waiting times. We show that different time series with the same waiting time distribution are not necessarily statistically equivalent, and might generate different physical properties. Renewal generates aging and anomalous scaling, while modulation yields no significant aging and either ordinary or anomalous diffusion, according to the dynamic prescription adopted. We show, in fact, that the physical realization of modulation generates two classes of events. The events of the first class are determined by the persistent use of the same exponential time scale for an extended lapse of time, and consequently are numerous; the events of the second class are identified with the abrupt changes from one to another exponential prescription, and consequently are rare. The events of the second class, although rare, determine the scaling of the diffusion process, and for this reason we term them as crucial events. According to the prescription adopted to produce modulation, the distribution density of the time distances between two consecutive crucial events might have, or not, a diverging second moment. In the former case the resulting diffusion process, although going through a transition regime very extended in time, will eventually become anomalous. In conclusion, modulation rather than ruling out the action of renewal events, produces crucial events hidden by clouds of exponential events, thereby setting the challenge for their identification.

  17. Radar Interferometry Time Series Analysis and Tools

    NASA Astrophysics Data System (ADS)

    Buckley, S. M.

    2006-12-01

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

  18. Hurst exponents for short time series.

    PubMed

    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 ~10(2). 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.

  19. 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.

  20. 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.

  1. 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.

  2. Improved satellite image compression and reconstruction via genetic algorithms

    NASA Astrophysics Data System (ADS)

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

    2008-10-01

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

  3. Aerosol Climate Time Series in ESA Aerosol_cci

    NASA Astrophysics Data System (ADS)

    Popp, Thomas; de Leeuw, Gerrit; Pinnock, Simon

    2016-04-01

    Within the ESA Climate Change Initiative (CCI) Aerosol_cci (2010 - 2017) conducts intensive work to improve algorithms for the retrieval of aerosol information from European sensors. Meanwhile, full mission time series of 2 GCOS-required aerosol parameters are completely validated and released: Aerosol Optical Depth (AOD) from dual view ATSR-2 / AATSR radiometers (3 algorithms, 1995 - 2012), and stratospheric extinction profiles from star occultation GOMOS spectrometer (2002 - 2012). Additionally, a 35-year multi-sensor time series of the qualitative Absorbing Aerosol Index (AAI) together with sensitivity information and an AAI model simulator is available. Complementary aerosol properties requested by GCOS are in a "round robin" phase, where various algorithms are inter-compared: fine mode AOD, mineral dust AOD (from the thermal IASI spectrometer, but also from ATSR instruments and the POLDER sensor), absorption information and aerosol layer height. As a quasi-reference for validation in few selected regions with sparse ground-based observations the multi-pixel GRASP algorithm for the POLDER instrument is used. Validation of first dataset versions (vs. AERONET, MAN) and inter-comparison to other satellite datasets (MODIS, MISR, SeaWIFS) proved the high quality of the available datasets comparable to other satellite retrievals and revealed needs for algorithm improvement (for example for higher AOD values) which were taken into account for a reprocessing. The datasets contain pixel level uncertainty estimates which were also validated and improved in the reprocessing. For the three ATSR algorithms the use of an ensemble method was tested. The paper will summarize and discuss the status of dataset reprocessing and validation. The focus will be on the ATSR, GOMOS and IASI datasets. Pixel level uncertainties validation will be summarized and discussed including unknown components and their potential usefulness and limitations. Opportunities for time series extension

  4. Mapping Brazilian savanna vegetation gradients with Landsat time series

    NASA Astrophysics Data System (ADS)

    Schwieder, Marcel; Leitão, Pedro J.; da Cunha Bustamante, Mercedes Maria; Ferreira, Laerte Guimarães; Rabe, Andreas; Hostert, Patrick

    2016-10-01

    Global change has tremendous impacts on savanna systems around the world. Processes related to climate change or agricultural expansion threaten the ecosystem's state, function and the services it provides. A prominent example is the Brazilian Cerrado that has an extent of around 2 million km2 and features high biodiversity with many endemic species. It is characterized by landscape patterns from open grasslands to dense forests, defining a heterogeneous gradient in vegetation structure throughout the biome. While it is undisputed that the Cerrado provides a multitude of valuable ecosystem services, it is exposed to changes, e.g. through large scale land conversions or climatic changes. Monitoring of the Cerrado is thus urgently needed to assess the state of the system as well as to analyze and further understand ecosystem responses and adaptations to ongoing changes. Therefore we explored the potential of dense Landsat time series to derive phenological information for mapping vegetation gradients in the Cerrado. Frequent data gaps, e.g. due to cloud contamination, impose a serious challenge for such time series analyses. We synthetically filled data gaps based on Radial Basis Function convolution filters to derive continuous pixel-wise temporal profiles capable of representing Land Surface Phenology (LSP). Derived phenological parameters revealed differences in the seasonal cycle between the main Cerrado physiognomies and could thus be used to calibrate a Support Vector Classification model to map their spatial distribution. Our results show that it is possible to map the main spatial patterns of the observed physiognomies based on their phenological differences, whereat inaccuracies occurred especially between similar classes and data-scarce areas. The outcome emphasizes the need for remote sensing based time series analyses at fine scales. Mapping heterogeneous ecosystems such as savannas requires spatial detail, as well as the ability to derive important

  5. Time Series Analysis of SOLSTICE Measurements

    NASA Astrophysics Data System (ADS)

    Wen, G.; Cahalan, R. F.

    2003-12-01

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

  6. 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

  7. Use of MODIS satellite images for detailed lake morphometry: Application to basins with large water level fluctuations

    NASA Astrophysics Data System (ADS)

    Ovakoglou, George; Alexandridis, Thomas K.; Crisman, Thomas L.; Skoulikaris, Charalampos; Vergos, George S.

    2016-09-01

    Lake morphometry is essential for managing water resources and limnetic ecosystems. For reservoirs that receive high sediment loads, frequent morphometric mapping is necessary to define both the effective life of the reservoir and its water storage capacity for irrigation, power generation, flood control and domestic water supply. The current study presents a methodology for updating the digital depth model (DDM) of lakes and reservoirs with wide intra and interannual fluctuations of water levels using satellite remote sensing. A time series of Terra MODIS satellite images was used to map shorelines formed during the annual water level change cycle, and were validated with concurrent Landsat ETM+ satellite images. The shorelines were connected with in-situ observation of water levels and were treated as elevation contours to produce the DDM using spatial interpolation. The accuracy of the digitized shorelines is within the mapping accuracy of the satellite images, while the resulting DDM is validated using in-situ elevation measurements. Two versions of the DDM were produced to assess the influence of seasonal water fluctuation. Finally, the methodology was applied to Lake Kerkini (Greece) to produce an updated DDM, which was compared with the last available bathymetric survey (1991) and revealed changes in sediment distribution within the lake.

  8. Scaling laws from geomagnetic time series

    USGS Publications Warehouse

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

    1998-01-01

    The notion of extended self-similarity (ESS) is applied here for the X - component time series of geomagnetic field fluctuations. Plotting nth order structure functions against the fourth order structure function we show that low-frequency geomagnetic fluctuations up to the order n = 10 follow the same scaling laws as MHD fluctuations in solar wind, however, for higher frequencies (f > l/5[h]) a clear departure from the expected universality is observed for n > 6. ESS does not allow to make an unambiguous statement about the non triviality of scaling laws in "geomagnetic" turbulence. However, we suggest to use higher order moments as promising diagnostic tools for mapping the contributions of various remote magnetospheric sources to local observatory data. Copyright 1998 by the American Geophysical Union.

  9. Deconvolution of time series in the laboratory

    NASA Astrophysics Data System (ADS)

    John, Thomas; Pietschmann, Dirk; Becker, Volker; Wagner, Christian

    2016-10-01

    In this study, we present two practical applications of the deconvolution of time series in Fourier space. First, we reconstruct a filtered input signal of sound cards that has been heavily distorted by a built-in high-pass filter using a software approach. Using deconvolution, we can partially bypass the filter and extend the dynamic frequency range by two orders of magnitude. Second, we construct required input signals for a mechanical shaker in order to obtain arbitrary acceleration waveforms, referred to as feedforward control. For both situations, experimental and theoretical approaches are discussed to determine the system-dependent frequency response. Moreover, for the shaker, we propose a simple feedback loop as an extension to the feedforward control in order to handle nonlinearities of the system.

  10. 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

  11. 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.

  12. 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.

  13. Inferring phase equations from multivariate time series.

    PubMed

    Tokuda, Isao T; Jain, Swati; Kiss, István Z; Hudson, John L

    2007-08-10

    An approach is presented for extracting phase equations from multivariate time series data recorded from a network of weakly coupled limit cycle oscillators. Our aim is to estimate important properties of the phase equations including natural frequencies and interaction functions between the oscillators. Our approach requires the measurement of an experimental observable of the oscillators; in contrast with previous methods it does not require measurements in isolated single or two-oscillator setups. This noninvasive technique can be advantageous in biological systems, where extraction of few oscillators may be a difficult task. The method is most efficient when data are taken from the nonsynchronized regime. Applicability to experimental systems is demonstrated by using a network of electrochemical oscillators; the obtained phase model is utilized to predict the synchronization diagram of the system.

  14. 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.

  15. 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.

  16. Weighted statistical parameters for irregularly sampled time series

    NASA Astrophysics Data System (ADS)

    Rimoldini, Lorenzo

    2014-01-01

    Unevenly spaced time series are common in astronomy because of the day-night cycle, weather conditions, dependence on the source position in the sky, allocated telescope time and corrupt measurements, for example, or inherent to the scanning law of satellites like Hipparcos and the forthcoming Gaia. Irregular sampling often causes clumps of measurements and gaps with no data which can severely disrupt the values of estimators. This paper aims at improving the accuracy of common statistical parameters when linear interpolation (in time or phase) can be considered an acceptable approximation of a deterministic signal. A pragmatic solution is formulated in terms of a simple weighting scheme, adapting to the sampling density and noise level, applicable to large data volumes at minimal computational cost. Tests on time series from the Hipparcos periodic catalogue led to significant improvements in the overall accuracy and precision of the estimators with respect to the unweighted counterparts and those weighted by inverse-squared uncertainties. Automated classification procedures employing statistical parameters weighted by the suggested scheme confirmed the benefits of the improved input attributes. The classification of eclipsing binaries, Mira, RR Lyrae, Delta Cephei and Alpha2 Canum Venaticorum stars employing exclusively weighted descriptive statistics achieved an overall accuracy of 92 per cent, about 6 per cent higher than with unweighted estimators.

  17. 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.

  18. 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.

  19. The application of image processing to satellite navigation. [considering landmarks

    NASA Technical Reports Server (NTRS)

    Hord, R. M.

    1975-01-01

    Given the locations of several landmarks on a satellite acquired image and their true geographic coordinates, the position and orientation of the satellite can be determined. Two methods for automatically locating the image coordinates of specified landmarks are described. The first, a particularly fast sequential similarity detection algorithm for template matching was originally described by Nagel and Rosenfeld. The second method involves iteratively resampling the picture function in the vicinity of the anticipated landmark. A variety of other speedup methods is also described. An application to SMS imagery is envisioned.

  20. 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

  1. Development of Time-Series Human Settlement Mapping System Using Historical Landsat Archive

    NASA Astrophysics Data System (ADS)

    Miyazaki, H.; Nagai, M.; Shibasaki, R.

    2016-06-01

    Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

  2. 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.

  3. 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.

  4. 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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  6. Characterization of noisy symbolic time series

    NASA Astrophysics Data System (ADS)

    Kulp, Christopher W.; Smith, Suzanne

    2011-02-01

    The 0-1 test for chaos is a recently developed time series characterization algorithm that can determine whether a system is chaotic or nonchaotic. While the 0-1 test was designed for deterministic series, in real-world measurement situations, noise levels may not be known and the 0-1 test may have difficulty distinguishing between chaos and randomness. In this paper, we couple the 0-1 test for chaos with a test for determinism and apply these tests to noisy symbolic series generated from various model systems. We find that the pairing of the 0-1 test with a test for determinism improves the ability to correctly distinguish between chaos and randomness from a noisy series. Furthermore, we explore the modes of failure for the 0-1 test and the test for determinism so that we can better understand the effectiveness of the two tests to handle various levels of noise. We find that while the tests can handle low noise and high noise situations, moderate levels of noise can lead to inconclusive results from the two tests.

  7. Peat conditions mapping using MODIS time series

    NASA Astrophysics Data System (ADS)

    Poggio, Laura; Gimona, Alessandro; Bruneau, Patricia; Johnson, Sally; McBride, Andrew; Artz, Rebekka

    2016-04-01

    Large areas of Scotland are covered in peatlands, providing an important sink of carbon in their near natural state but act as a potential source of gaseous and dissolved carbon emission if not in good conditions. Data on the condition of most peatlands in Scotland are, however, scarce and largely confined to sites under nature protection designations, often biased towards sites in better condition. The best information available at present is derived from labour intensive field-based monitoring of relatively few designated sites (Common Standard Monitoring Dataset). In order to provide a national dataset of peat conditions, the available point information from the CSM data was modelled with morphological features and information derived from MODIS sensor. In particular we used time series of indices describing vegetation greenness (Enhanced Vegetation Index), water availability (Normalised Water Difference index), Land Surface Temperature and vegetation productivity (Gross Primary productivity). A scorpan-kriging approach was used, in particular using Generalised Additive Models for the description of the trend. The model provided the probability of a site to be in favourable conditions and the uncertainty of the predictions was taken into account. The internal validation (leave-one-out) provided a mis-classification error of around 0.25. The derived dataset was then used, among others, in the decision making process for the selection of sites for restoration.

  8. Time series models of symptoms in schizophrenia.

    PubMed

    Tschacher, Wolfgang; Kupper, Zeno

    2002-12-15

    The symptom courses of 84 schizophrenia patients (mean age: 24.4 years; mean previous admissions: 1.3; 64% males) of a community-based acute ward were examined to identify dynamic patterns of symptoms and to investigate the relation between these patterns and treatment outcome. The symptoms were monitored by systematic daily staff ratings using a scale composed of three factors: psychoticity, excitement, and withdrawal. Patients showed moderate to high symptomatic improvement documented by effect size measures. Each of the 84 symptom trajectories was analyzed by time series methods using vector autoregression (VAR) that models the day-to-day interrelations between symptom factors. Multiple and stepwise regression analyses were then performed on the basis of the VAR models. Two VAR parameters were found to be associated significantly with favorable outcome in this exploratory study: 'withdrawal preceding a reduction of psychoticity' as well as 'excitement preceding an increase of withdrawal'. The findings were interpreted as generating hypotheses about how patients cope with psychotic episodes.

  9. Correcting and combining time series forecasters.

    PubMed

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

    2014-02-01

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

  10. Satellite Image Atlas of Glaciers of the World

    USGS Publications Warehouse

    Williams, Richard S.; Ferrigno, Jane G.

    2005-01-01

    In 1978, the USGS began the preparation of the 11-chapter USGS Professional Paper 1386, 'Satellite Image Atlas of Glaciers of the World'. Between 1979 and 1981, optimum satellite images were distributed to a team of 70 scientists, representing 25 nations and 45 institutions, who agreed to author sections of the Professional Paper concerning either a geographic area (chapters B-K) or a glaciological topic (included in Chapter A). The scientists used Landsat 1, 2, and 3 multispectral scanner (MSS) images and Landsat 2 and 3 return beam vidicon (RBV) images to inventory the areal occurrence of glacier ice on our planet within the boundaries of the spacecrafts' coverage (between about 82? north and south latitudes). Some later contributors also used Landsat 4 and 5 MSS and Thematic Mapper, Landsat 7 Enhanced Thematic Mapper-Plus (ETM+), and other satellite images. In addition to analyzing images of a specific geographic area, each author was asked to summarize up-to-date information about the glaciers within each area and compare their present-day areal distribution with reliable historical information (from published maps, reports, and photographs) about their past extent. Because of the limitations of Landsat images for delineating or monitoring small glaciers in some geographic areas (the result of inadequate spatial resolution, lack of suitable seasonal coverage, or absence of coverage), some information on the areal distribution of small glaciers was derived from ancillary sources, including other satellite images. Completion of the atlas will provide an accurate regional inventory of the areal extent of glaciers on our planet during a relatively narrow time interval (1972-1981).

  11. Analytical models integrated with satellite images for optimized pest management

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The global field protection (GFP) was developed to protect and optimize pest management resources integrating satellite images for precise field demarcation with physical models of controlled release devices of pesticides to protect large fields. The GFP was implemented using a graphical user interf...

  12. Satellite images for land cover monitoring - Navigating through the maze

    USGS Publications Warehouse

    Künzer, Claudia; Fosnight, Gene

    2001-01-01

    The focus of this publication is satellite systems for land cover monitoring. On the reverse is a table that compares a selection of these systems, whose data are globally available in a form suitable for land cover analysis. We hope the information presented will help you assess the utility of remotely sensed image to meet your needs.

  13. 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

  14. 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.

  15. Glacier Volume Change Estimation Using Time Series of Improved Aster Dems

    NASA Astrophysics Data System (ADS)

    Girod, Luc; Nuth, Christopher; Kääb, Andreas

    2016-06-01

    Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be

  16. 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.

  17. Satellite images analysis for shadow detection and building height estimation

    NASA Astrophysics Data System (ADS)

    Liasis, Gregoris; Stavrou, Stavros

    2016-09-01

    Satellite images can provide valuable information about the presented urban landscape scenes to remote sensing and telecommunication applications. Obtaining information from satellite images is difficult since all the objects and their surroundings are presented with feature complexity. The shadows cast by buildings in urban scenes can be processed and used for estimating building heights. Thus, a robust and accurate building shadow detection process is important. Region-based active contour models can be used for satellite image segmentation. However, spectral heterogeneity that usually exists in satellite images and the feature similarity representing the shadow and several non-shadow regions makes building shadow detection challenging. In this work, a new automated method for delineating building shadows is proposed. Initially, spectral and spatial features of the satellite image are utilized for designing a custom filter to enhance shadows and reduce intensity heterogeneity. An effective iterative procedure using intensity differences is developed for tuning and subsequently selecting the most appropriate filter settings, able to highlight the building shadows. The response of the filter is then used for automatically estimating the radiometric property of the shadows. The customized filter and the radiometric feature are utilized to form an optimized active contour model where the contours are biased to delineate shadow regions. Post-processing morphological operations are also developed and applied for removing misleading artefacts. Finally, building heights are approximated using shadow length and the predefined or estimated solar elevation angle. Qualitative and quantitative measures are used for evaluating the performance of the proposed method for both shadow detection and building height estimation.

  18. 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

  19. Nonparametric, nonnegative deconvolution of large time series

    NASA Astrophysics Data System (ADS)

    Cirpka, O. A.

    2006-12-01

    There is a long tradition of characterizing hydrologic systems by linear models, in which the response of the system to a time-varying stimulus is computed by convolution of a system-specific transfer function with the input signal. Despite its limitations, the transfer-function concept has been shown valuable for many situations such as the precipitation/run-off relationships of catchments and solute transport in agricultural soils and aquifers. A practical difficulty lies in the identification of the transfer function. A common approach is to fit a parametric function, enforcing a particular shape of the transfer function, which may be in contradiction to the real behavior (e.g., multimodal transfer functions, long tails, etc.). In our nonparametric deconvolution, the transfer function is assumed an auto-correlated random time function, which is conditioned on the data by a Bayesian approach. Nonnegativity, which is a vital constraint for solute-transport applications, is enforced by the method of Lagrange multipliers. This makes the inverse problem nonlinear. In nonparametric deconvolution, identifying the auto-correlation parameters is crucial. Enforcing too much smoothness prohibits the identification of important features, whereas insufficient smoothing leads to physically meaningless transfer functions, mapping noise components in the two data series onto each other. We identify optimal smoothness parameters by the expectation-maximization method, which requires the repeated generation of many conditional realizations. The overall approach, however, is still significantly faster than Markov-Chain Monte-Carlo methods presented recently. We apply our approach to electric-conductivity time series measured in a river and monitoring wells in the adjacent aquifer. The data cover 1.5 years with a temporal resolution of 1h. The identified transfer functions have lengths of up to 60 days, making up 1440 parameters. We believe that nonparametric deconvolution is an

  20. 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.

  1. Vegetation Dynamics of NW Mexico using MODIS time series data

    NASA Astrophysics Data System (ADS)

    Valdes, M.; Bonifaz, R.; Pelaez, G.; Leyva Contreras, A.

    2010-12-01

    Northwestern Mexico is an area subjected to a combination of marine and continental climatic influences which produce a highly variable vegetation dynamics throughout time. Using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices data (NDVI and EVI) from 2001 to 2008, mean and standard deviation image values of the time series were calculated. Using this data, annual vegetation dynamics was characterized based on the different values for the different vegetation types. Annual mean values were compared and inter annual variations or anomalies were analyzed calculating departures of de mean. An anomaly was considered if the value was over or under two standard deviations. Using this procedure it was possible determine spatio-temporal patterns over the study area and relate them to climatic conditions.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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

  9. 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.

  10. 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).

  11. Quantifying intra and inter-annual variation of MODIS derived leaf area index time-series 2000-2008

    NASA Astrophysics Data System (ADS)

    Lanorte, A.; de Santis, F.; Lasaponara, R.

    2009-04-01

    The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard NASA's Terra and Aqua satellites. Terra MODIS and Aqua MODIS image the entire Earth's surface every one to two days and provide vital information for global-change research. MODIS derived leaf area index (LAI) is an important parameter for describing vegetation canopy structure in the terrestrial ecosystem on the global, continental, and regional scales. In this study we analyse intra and inter-annual variation of MODIS derived leaf area index time-series 2000-2008 data for Mediterranean ecosystems of Southern Italy. The objective is to explore seasonal trends in the phenology of southern Italy woodlands and shrublands and inter-annual long-term variations related to plant's photosynthesis process or growth status.

  12. Automatic Crowd Analysis from Very High Resolution Satellite Images

    NASA Astrophysics Data System (ADS)

    Sirmacek, B.; Reinartz, P.

    2011-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    Several authors have shown that the analysis of time series of remote sensing data is able to characterize the properties of different earth systems. They have used different techniques, ARIMA, Fourier Analysis and others, to estimate cyclical and trend variations from the information stored in the time series. Most systems in earth cycle due to seasonal changes in the flow of energy and/or matter, ecosystems cycle due to seasonal changes in evapotranspiration, day length and temperature. Time series analysis can characterized the period, amplitude and offset of the cycle. Trend analysis, on the other hand, can be used to detect permanent changes in certain systems. Changes in sea surface temperature or the artic ice pack due to climate change would belong to this category. Performing this analysis requires data with a good temporal resolution, usually in the range of days and a good temporal extent, usually in the multiyear timescale. As a result users have to process a big set of satellite images, sometimes in the order of thousands, to extract the information required. This processing consumes a great amount of time and resources due to the big amount of data and computer power involved. To facilitate the generation of long-term time series of remote sensing data the European Space Agency has developed a system able to extract all the information available for a small area from the MEdium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT in an easily accessible format. This service allows the user to specify an area of interest of rectangular or circular shape giving its geographical location and its size, specify the period of time that he is interested in and obtain several files with all the information available from the MERIS sensor for this location. For each MERIS product the information is given in two formats. One is highly portable following the XML standard. Output is also given in Google Earth and Excel formats allowing for a fast and easy

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

    NASA Astrophysics Data System (ADS)

    Vermeesch, P.

    2012-04-01

    Decadal trends in the aeolian dust record of the Sahara affect the global climate system and the nutrient budget of the Atlantic Ocean. One proposed cause of these trends are changes in the frequency and intensity of dust storms, which have hitherto been hard to quantify. Because sand flux scales with the cube of wind speed, dune migration rates can be used as a proxy for storminess. Relative changes in the storminess of the Sahara can thus be monitored by tracking the migration rates of individual sand dunes over time. The Bodélé Depression of northern Chad was selected as a target area for this method, because it is the most important point-source of aeolian dust on the planet and features the largest and fastest dunes on Earth. A collection of co-registered Landsat, SPOT, and ASTER scenes, combined with declassified American spy satellite images was used to construct a 45 year record of dune migration in the Bodélé Depression. One unexpected outcome of the study was the observation of binary dune interactions in the imagery sequence, which reveals that when two barchan dunes collide, a transfer of mass occurs so that one dune appears to travel through the other unscathed, like a solitary wave. This confirms a controversial numerical model prediction and settles a decade-old debate in aeolian geomorphology. The COSI-Corr change detection method was used to measure the dune migration rates from 1984 until 1987, 1990, 1996, 2000, 2003, 2005, 2007, 2008, 2009, and 2010. An algorithm was developed to automatically warp the resulting displacement fields back to a common point in time. Thus, individual image pixels of a dune field were tracked over time, allowing the extraction of a time series from the co-registered satellite images without further human intervention. The automated analysis was extended further back into the past by comparison of the 1984 image with declassified American spy satellite (Corona) images from 1965 and 1970. Due to the presence of

  15. Seasonal and interannual variabilities of coccolithophore blooms in the Bay of Biscay and the Celtic Sea observed from a 18-year time-series of non-algal Suspended Particulate Matter images

    NASA Astrophysics Data System (ADS)

    Perrot, Laurie; Gohin, Francis; Ruiz-Pino, Diana; Lampert, Luis

    2016-04-01

    Coccolithophores belong to the nano-phytoplankton size-class and produce CaCO3 scales called coccoliths which form the «shell» of the algae cell. Coccoliths are in the size range of a few μm and can also be detached from the cell in the water. This phytoplankton group has an ubiquitous distribution in all oceans but blooms only in some oceanic regions, like the North East Atlantic ocean and the South Western Atlantic (Patagonian Sea). At a global scale coccolithopore blooms are studied in regard of CaCO3 production and three potential feedback on climate change: albedo modification by the way of dimethylsulfide (DMS) production and atmospheric CO2 source by calcification and a CO2 pump by photosynthesis. As the oceans are more and more acidified by anthropogenic CO2 emissions, coccolithophores generally are expected to be negatively affected. However, recent studies have shown an increase in coccolithophore occurrence in the North Atlantic. A poleward expansion of the coccolithophore Emiliana Huxleyi has also been pointed out. By using a simplified fuzzy method applied to a 18-year time series of SeaWiFS (1998-2002) and MODIS (2003-2015) spectral reflectance, we assessed the seasonal and inter-annual variability of coccolithophore blooms in the vicinity of the shelf break in the Bay of Biscay and the Celtic Sea After identification of the coccolith pixels by applying the fuzzy method, the abundance of coccoliths is assessed from a database of non-algal Suspended Particulate Matter (SPM). Although a regular pattern in the phenology of the blooms is observed, starting south in April in Biscay and moving northwards until July in Ireland, there is a high seasonal and interannual variability in the extent of the blooms. Year 2014 shows very low concentrations of detached coccoliths (twice less than average) from space and anomalies point out the maximum level in 2001. Non-algal SPM, derived from a procedure defined for the continental shelf, appears to be well

  16. 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 ξ follow the γ statistics i.e., p(ξ)=γ-1(k) ξ^k exp(-ξ). This probability function has well-known properties and its Shannon entropy can be expressed in terms of the γ-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.

  17. 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.

  18. Targeting Villages for Rural Development Using Satellite Image Analysis.

    PubMed

    Varshney, Kush R; Chen, George H; Abelson, Brian; Nowocin, Kendall; Sakhrani, Vivek; Xu, Ling; Spatocco, Brian L

    2015-03-01

    Satellite imagery is a form of big data that can be harnessed for many social good applications, especially those focusing on rural areas. In this article, we describe the common problem of selecting sites for and planning rural development activities as informed by remote sensing and satellite image analysis. Effective planning in poor rural areas benefits from information that is not available and is difficult to obtain at any appreciable scale by any means other than algorithms for estimation and inference from remotely sensed images. We discuss two cases in depth: the targeting of unconditional cash transfers to extremely poor villages in sub-Saharan Africa and the siting and planning of solar-powered microgrids in remote villages in India. From these cases, we draw out some common lessons broadly applicable to informed rural development.

  19. 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.

  20. Targeting Villages for Rural Development Using Satellite Image Analysis.

    PubMed

    Varshney, Kush R; Chen, George H; Abelson, Brian; Nowocin, Kendall; Sakhrani, Vivek; Xu, Ling; Spatocco, Brian L

    2015-03-01

    Satellite imagery is a form of big data that can be harnessed for many social good applications, especially those focusing on rural areas. In this article, we describe the common problem of selecting sites for and planning rural development activities as informed by remote sensing and satellite image analysis. Effective planning in poor rural areas benefits from information that is not available and is difficult to obtain at any appreciable scale by any means other than algorithms for estimation and inference from remotely sensed images. We discuss two cases in depth: the targeting of unconditional cash transfers to extremely poor villages in sub-Saharan Africa and the siting and planning of solar-powered microgrids in remote villages in India. From these cases, we draw out some common lessons broadly applicable to informed rural development. PMID:27442844

  1. 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

  2. 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.

  3. Outlier detection using some methods of mathematical statistic in meteorological time-series

    NASA Astrophysics Data System (ADS)

    Elias, Michal; Dousa, Jan

    2016-06-01

    In many applications of Global Navigate Satellite Systems the meteorological time-series play a very important role, especially when representing source of input data for other calculations such as corrections for very precise positioning. We are interested in those corrections which are related to the troposphere delay modelling. Time-series might contain some non-homogeneities, depending on the type of the data source. In this paper the outlier detection is discussed. For investigation we used method based on the autoregressive model and the results of its application were compared with the regression model.

  4. Urban Area Monitoring using MODIS Time Series Data

    NASA Astrophysics Data System (ADS)

    Devadiga, S.; Sarkar, S.; Mauoka, E.

    2015-12-01

    Growing urban sprawl and its impact on global climate due to urban heat island effects has been an active area of research over the recent years. This is especially significant in light of rapid urbanization that is happening in some of the first developing nations across the globe. But so far study of urban area growth has been largely restricted to local and regional scales, using high to medium resolution satellite observations, taken at distinct time periods. In this presentation we propose a new approach to detect and monitor urban area expansion using long time series of MODIS data. This work characterizes data points using a vector of several annual metrics computed from the MODIS 8-day and 16-day composite L3 data products, at 250M resolution and over several years and then uses a vector angle mapping classifier to detect and segment the urban area. The classifier is trained using a set of training points obtained from a reference vector point and polygon pre-filtered using the MODIS VI product. This work gains additional significance, given that, despite unprecedented urban growth since 2000, the area covered by the urban class in the MODIS Global Land Cover (MCD12Q1, MCDLCHKM and MCDLC1KM) product hasn't changed since the launch of Terra and Aqua. The proposed approach was applied to delineate the urban area around several cities in Asia known to have maximum growth in the last 15 years. Results were verified using high resolution Landsat data.

  5. Prospects of application of survey satellite image for meteorology

    NASA Astrophysics Data System (ADS)

    Kapochkina, A. B.; Kapochkin, B. B.; Kucherenko, N. V.

    The maximal interest is represented with the information from geostationary satellites. These satellites repeat shootings the chosen territories, allowing to study dynamics of images. Most interesting shootings in IR a range. Studying of survey image is applied to studying linear elements of clouds (LEC). It is established, that "LEC " arise only above breaks of an earth's crust. In research results of the complex analysis of the satellite data, hydrometeorological supervision, seismicity, supervision over deformations of a surface of the Earth are used. It is established that before formation "LEC " in a ground layer arise anomalies of temperature and humidity. The situation above Europe 16 May, 2001 is considered. "LEC " in Europe block carry of air weights from the west to the east. Synoptic conditions above the Great Britain July, 7-10, 2000 is considered. Moving "LEC" trace distribution of deformation waves to an earth's crust. Satellite shootings Europe before earthquake in Greece 14.08.2003 are considered. These days ground supervision were conducted and the data of the geostationary satellite were analyzed. During moving "LEC " occur failures (destruction houses & of gas mains), earthquake. The situation above Iberian peninsula 12-16.11.2001 is considered. "LEC" arose before flooding in Europe. The situation before flooding in Germany June, 6-8, 2002 and flooding on the river Kuban June, 16-23, 2002 is considered. In case of occurrence of tectonic compression of an earth's crust there are "LEC ", tracer intensive movements of air upwards and downwards above negative and positive anomalies of the form of a terrestrial surface, accordingly. Such meteorological situations are dangerous to flights of aircraft, the fast gravitational anomalies influencing into orbits of movement of satellites trace. The situation above equatorial Atlantic 26.03.2003 years is considered. At tectonic compression of continental scale overcast covers the whole continents for more

  6. Satellite observations and instrumentation for imaging energetic neutral atoms

    NASA Astrophysics Data System (ADS)

    Voss, Henry D.; Mobilia, Joseph; Collin, Henry L.; Imhof, William L.

    1992-06-01

    Direct measurements of energetic neutral atoms (ENA) and ions have been obtained with the cooled solid state detectors on the low altitude (220 km) three-axis stabilized S81-1/SEEP satellite and on the spinning 400 km X 5.5 Re CRRES satellite. During magnetic storms ENA and ion precipitation (E > 10 keV) is evident over the equatorial region from the LE spectrometer on the SEEP payload (ONR 804). The spinning motion of the CRRES satellite allows for simple mapping of the magnetosphere using the IMS-HI (ONR 307-8-3) neutral spectrometer. This instrument covers the energy range from 20 to 1000 keV and uses a 7 kG magnetic field to screen out protons less than about 50 MeV. ENA and the resulting low- altitude ion belt have been observed with the IMS-HI instrument. Recently, an advanced spectrometer (SEPS) has been developed to image electrons, ions, and neutrals on the despun platform of the POLAR satellite (approximately 1.8 X 9 Re) for launch in the mid-90's as part of the NASA ISTP/GGS program. For this instrument a 256 element solid state pixel array has been developed that interfaces to 256 amplifier strings using a custom 16 channel microcircuit chip. In addition, this instrument features a motor controlled iris wheel and anticoincidence electronics.

  7. 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.

  8. 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.

  9. 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.

  10. 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

    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.

  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

  12. 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.

  13. Reconstruction of time-delay systems from chaotic time series.

    PubMed

    Bezruchko, B P; Karavaev, A S; Ponomarenko, V I; Prokhorov, M D

    2001-11-01

    We propose a method that allows one to estimate the parameters of model scalar time-delay differential equations from time series. The method is based on a statistical analysis of time intervals between extrema in the time series. We verify our method by using it for the reconstruction of time-delay differential equations from their chaotic solutions and for modeling experimental systems with delay-induced dynamics from their chaotic time series.

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

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

    PubMed

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

    2014-12-01

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

  16. 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.

  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. Time series photometry of faint cataclysmic variables with a CCD

    NASA Astrophysics Data System (ADS)

    Abbott, Timothy Mark Cameron

    1992-08-01

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

  19. 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

  20. 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

  1. 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

  2. Detection of Barchan Dunes in High Resolution Satellite Images

    NASA Astrophysics Data System (ADS)

    Azzaoui, M. A.; Adnani, M.; El Belrhiti, H.; Chaouki, I. E.; Masmoudi, C.

    2016-06-01

    Barchan dunes are the fastest moving sand dunes in the desert. We developed a process to detect barchans dunes on High resolution satellite images. It consisted of three steps, we first enhanced the image using histogram equalization and noise reduction filters. Then, the second step proceeds to eliminate the parts of the image having a texture different from that of the barchans dunes. Using supervised learning, we tested a coarse to fine textural analysis based on Kolomogorov Smirnov test and Youden's J-statistic on co-occurrence matrix. As an output we obtained a mask that we used in the next step to reduce the search area. In the third step we used a gliding window on the mask and check SURF features with SVM to get barchans dunes candidates. Detected barchans dunes were considered as the fusion of overlapping candidates. The results of this approach were very satisfying in processing time and precision.

  3. Image processing in the BRITE nano-satellite mission

    NASA Astrophysics Data System (ADS)

    Popowicz, Adam

    2016-07-01

    The BRITE nano-satellite mission is an international Austrian-Canadian-Polish project of six small space tele- scopes measuring photometric variability of the brightest stars in the sky. Due to the limited space onboard and the weight constraints, the CCD detectors are poorly shielded and suffer from proton impact. Shortly after the launch, various CCD defects emerged, producing various sources of impulsive noise in the images. In this paper, the methods of BRITE data-processing are described and their efficiency evaluated. The proposed algorithm, developed by the BRITE photometric team, consists of three main parts: (1) image classification, (2) image processing with aperture photometry and (3) tunable optimization of parameters. The presented pipeline allows one to achieve milli-magnitude precision in photometry. Some first scientific results of the mission have just been published.

  4. 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

  5. Multi sensor satellite imagers for commercial remote sensing

    NASA Astrophysics Data System (ADS)

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

    2005-10-01

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

  6. 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)

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

    Atmospheric Science Data Center

    2016-06-13

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

  8. Application of small baseline subsets D-InSAR technique to estimate time series land deformation of Jinan area, China

    NASA Astrophysics Data System (ADS)

    Liu, Xiangtong; Cao, Qiuxiang; Xiong, Zhuguo; Yin, Haitao; Xiao, Genru

    2016-04-01

    Jinan, located in the South of the North China Plain, is an area where underground water has been exploited excessively. However, land deformation surveys only focus on the small district obtained by GPS and Leveling. Here, we use interferometric synthetic aperture radar (InSAR) time-series of ASAR data to resolve land subsidence in the entire Jinan region. In our research, we get 20 interferograms with a temporal threshold of 700 days and spatial-baseline threshold of 300 m from 14 ASAR satellite images on a descending orbit, and then get the surface displacement using Small Baseline InSAR (SBAS D-InSAR) retrained with a periodic model. Meanwhile, the accuracy of our work is proved by the results of GPS measurements. Finally, several settlement funnels are observed with extreme values of -20 cm, and their generation is related to massive groundwater extraction.

  9. Application of small baseline subsets D-InSAR technique to estimate time series land deformation of Jinan area, China

    NASA Astrophysics Data System (ADS)

    Liu, Xiangtong; Cao, Qiuxiang; Xiong, Zhuguo; Yin, Haitao; Xiao, Genru

    2016-04-01

    Jinan, located in the South of the North China Plain, is an area where underground water has been exploited excessively. However, land deformation surveys only focus on the small district obtained by GPS and Leveling. Here, we use interferometric synthetic aperture radar (InSAR) time-series of ASAR data to resolve land subsidence in the entire Jinan region. In our research, we get 20 interferograms with a temporal threshold of 700 days and spatial-baseline threshold of 300 m from 14 ASAR satellite images on a descending orbit, and then get the surface displacement using Small Baseline InSAR (SBAS D-InSAR) retrained with a periodic model. Meanwhile, the accuracy of our work is proved by the results of GPS measurements. Finally, several settlement funnels are observed with extreme values of -20 cm, and their generation is related to massive groundwater extraction.

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

    PubMed

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

    2013-12-01

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

  11. 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…

  12. Measurements of spatial population synchrony: influence of time series transformations.

    PubMed

    Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

    2015-09-01

    Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.

  13. Using Time-Series Regression to Predict Academic Library Circulations.

    ERIC Educational Resources Information Center

    Brooks, Terrence A.

    1984-01-01

    Four methods were used to forecast monthly circulation totals in 15 midwestern academic libraries: dummy time-series regression, lagged time-series regression, simple average (straight-line forecasting), monthly average (naive forecasting). In tests of forecasting accuracy, dummy regression method and monthly mean method exhibited smallest average…

  14. The Prediction of Teacher Turnover Employing Time Series Analysis.

    ERIC Educational Resources Information Center

    Costa, Crist H.

    The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

  15. Measurements of spatial population synchrony: influence of time series transformations.

    PubMed

    Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

    2015-09-01

    Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies. PMID:25953116

  16. Earthshots: Satellite images of environmental change - Phoenix, Arizona, USA

    USGS Publications Warehouse

    Adamson, Thomas

    2013-01-01

    Phoenix doesn’t have many cloudy days, so it’s perfect for studying urban growth with satellite images. Scientists and city planners study population growth and urban expansion in fast-growing cities like Phoenix to determine the changes that have occurred over time and to see how those changes impact the surrounding environment, affect the availability of natural resources such as water, and alter the landscape and how it’s used. That information can help people plan for future changes as cities continue to grow.

  17. The scaling of time series size towards detrended fluctuation analysis

    NASA Astrophysics Data System (ADS)

    Gao, Xiaolei; Ren, Liwei; Shang, Pengjian; Feng, Guochen

    2016-06-01

    In this paper, we introduce a modification of detrended fluctuation analysis (DFA), called multivariate DFA (MNDFA) method, based on the scaling of time series size N. In traditional DFA method, we obtained the influence of the sequence segmentation interval s, and it inspires us to propose a new model MNDFA to discuss the scaling of time series size towards DFA. The effectiveness of the procedure is verified by numerical experiments with both artificial and stock returns series. Results show that the proposed MNDFA method contains more significant information of series compared to traditional DFA method. The scaling of time series size has an influence on the auto-correlation (AC) in time series. For certain series, we obtain an exponential relationship, and also calculate the slope through the fitting function. Our analysis and finite-size effect test demonstrate that an appropriate choice of the time series size can avoid unnecessary influences, and also make the testing results more accurate.

  18. Complex Network from Pseudoperiodic Time Series: Topology versus Dynamics

    NASA Astrophysics Data System (ADS)

    Zhang, J.; Small, M.

    2006-06-01

    We construct complex networks from pseudoperiodic time series, with each cycle represented by a single node in the network. We investigate the statistical properties of these networks for various time series and find that time series with different dynamics exhibit distinct topological structures. Specifically, noisy periodic signals correspond to random networks, and chaotic time series generate networks that exhibit small world and scale free features. We show that this distinction in topological structure results from the hierarchy of unstable periodic orbits embedded in the chaotic attractor. Standard measures of structure in complex networks can therefore be applied to distinguish different dynamic regimes in time series. Application to human electrocardiograms shows that such statistical properties are able to differentiate between the sinus rhythm cardiograms of healthy volunteers and those of coronary care patients.

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

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

  20. 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

  1. 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.

  2. 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.

  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. 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.

  5. Instrument calibration architecture of Radar Imaging Satellite (RISAT-1)

    NASA Astrophysics Data System (ADS)

    Misra, T.; Bhan, R.; Putrevu, D.; Mehrotra, P.; Nandy, P. S.; Shukla, S. D.; Rao, C. V. N.; Dave, D. B.; Desai, N. M.

    2016-05-01

    Radar Imaging Satellite (RISAT-1) payload system is configured to perform self-calibration of transmit and receive paths before and after imaging sessions through a special instrument calibration technique. Instrument calibration architecture of RISAT-1 supported ground verification and validation of payload including active array antenna. During on-ground validation of 126 beams of active array antenna which needed precise calibration of boresight pointing, a unique method called "collimation coefficient error estimation" was utilized. This method of antenna calibration was supported by special hardware and software calibration architecture of RISAT-1. This paper concentrates on RISAT-1 hardware and software architecture which supports in-orbit and on-ground instrument calibration. Efforts are also put here to highlight use of special calibration scheme of RISAT-1 instrument to evaluate system response during ground verification and validation.

  6. 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.

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

    NASA Astrophysics Data System (ADS)

    Coluzzi, R.; Didonna, I.

    2009-04-01

    The size distribution and the dynamic expansion of urban areas is a key issue for the management of city growth and mitigation of negative impacts on environment and ecosystems. Even if urban growth is perceived as necessary for a sustainable economy, uncontrolled or sprawling urban growth can cause various problems such as loss of open space, landscape alteration, environmental pollution, traffic congestion, infrastructure pressure, and other social and economical issues. To face these drawbacks, a continuous monitoring of the urban growth evolution in terms of type and extent of changes over time is essential for supporting planners and decision makers in future urban planning. The analysis of the city size distribution deals with different disciplines such as geography, economy, demography, ecology, physics, statistics because the evolution of a city is a dynamic process involving a number of different factors. The main issue of great importance in modelling urban growth includes spatial and temporal dynamics, scale dynamics, man-induced land use change. The understanding and the monitoring of urban expansion processes are a challenging issue concerning the availability of both (i) time-series data set and (ii) updated information relating to current urban spatial structure and city edges in order to define and locate the evolution trends. In such a context, an effective contribution can be offered by satellite remote sensing technologies, which are able to provide both historical data archive and up-to-date imagery. Satellite technologies represent a cost-effective mean for obtaining useful data that can be easily and systematically updated for the whole globe. The use of satellite imagery along with spatial analysis techniques can be used for the monitoring and planning purposes as these enable the reporting of ongoing trends of urban growth at a detailed level. This paper analyses the spatial characterization of urban expansion by using multidate

  8. 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.

  9. 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.

  10. 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

  11. Model-free quantification of time-series predictability

    NASA Astrophysics Data System (ADS)

    Garland, Joshua; James, Ryan; Bradley, Elizabeth

    2014-11-01

    This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-world time-series data, which results from the dimension, nonlinearity, and nonstationarity of the generating process, as well as from measurement issues such as noise, aggregation, and finite data length, is both empirically quantifiable and directly correlated with predictability. In particular, we argue that redundancy is an effective way to measure complexity and predictive structure in an experimental time series and that weighted permutation entropy is an effective way to estimate that redundancy. To validate these conjectures, we study 120 different time-series data sets. For each time series, we construct predictions using a wide variety of forecast models, then compare the accuracy of the predictions with the permutation entropy of that time series. We use the results to develop a model-free heuristic that can help practitioners recognize when a particular prediction method is not well matched to the task at hand: that is, when the time series has more predictive structure than that method can capture and exploit.

  12. 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.

  13. Model-free quantification of time-series predictability.

    PubMed

    Garland, Joshua; James, Ryan; Bradley, Elizabeth

    2014-11-01

    This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-world time-series data, which results from the dimension, nonlinearity, and nonstationarity of the generating process, as well as from measurement issues such as noise, aggregation, and finite data length, is both empirically quantifiable and directly correlated with predictability. In particular, we argue that redundancy is an effective way to measure complexity and predictive structure in an experimental time series and that weighted permutation entropy is an effective way to estimate that redundancy. To validate these conjectures, we study 120 different time-series data sets. For each time series, we construct predictions using a wide variety of forecast models, then compare the accuracy of the predictions with the permutation entropy of that time series. We use the results to develop a model-free heuristic that can help practitioners recognize when a particular prediction method is not well matched to the task at hand: that is, when the time series has more predictive structure than that method can capture and exploit.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. Laser Guidestar Satellite for Ground-based Adaptive Optics Imaging of Geosynchronous Satellites and Astronomical Targets

    NASA Astrophysics Data System (ADS)

    Marlow, W. A.; Cahoy, K.; Males, J.; Carlton, A.; Yoon, H.

    2015-12-01

    Real-time observation and monitoring of geostationary (GEO) satellites with ground-based imaging systems would be an attractive alternative to fielding high cost, long lead, space-based imagers, but ground-based observations are inherently limited by atmospheric turbulence. Adaptive optics (AO) systems are used to help ground telescopes achieve diffraction-limited seeing. AO systems have historically relied on the use of bright natural guide stars or laser guide stars projected on a layer of the upper atmosphere by ground laser systems. There are several challenges with this approach such as the sidereal motion of GEO objects relative to natural guide stars and limitations of ground-based laser guide stars; they cannot be used to correct tip-tilt, they are not point sources, and have finite angular sizes when detected at the receiver. There is a difference between the wavefront error measured using the guide star compared with the target due to cone effect, which also makes it difficult to use a distributed aperture system with a larger baseline to improve resolution. Inspired by previous concepts proposed by A.H. Greenaway, we present using a space-based laser guide starprojected from a satellite orbiting the Earth. We show that a nanosatellite-based guide star system meets the needs for imaging GEO objects using a low power laser even from 36,000 km altitude. Satellite guide star (SGS) systemswould be well above atmospheric turbulence and could provide a small angular size reference source. CubeSatsoffer inexpensive, frequent access to space at a fraction of the cost of traditional systems, and are now being deployed to geostationary orbits and on interplanetary trajectories. The fundamental CubeSat bus unit of 10 cm cubed can be combined in multiple units and offers a common form factor allowing for easy integration as secondary payloads on traditional launches and rapid testing of new technologies on-orbit. We describe a 6U CubeSat SGS measuring 10 cm x 20 cm x

  19. Cloud Detection of Optical Satellite Images Using Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Lee, Kuan-Yi; Lin, Chao-Hung

    2016-06-01

    Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection

  20. Detecting unstable periodic orbits from transient chaotic time series

    PubMed

    Dhamala; Lai; Kostelich

    2000-06-01

    We address the detection of unstable periodic orbits from experimentally measured transient chaotic time series. In particular, we examine recurrence times of trajectories in the vector space reconstructed from an ensemble of such time series. Numerical experiments demonstrate that this strategy can yield periodic orbits of low periods even when noise is present. We analyze the probability of finding periodic orbits from transient chaotic time series and derive a scaling law for this probability. The scaling law implies that unstable periodic orbits of high periods are practically undetectable from transient chaos.

  1. Wavelet analysis and scaling properties of time series

    NASA Astrophysics Data System (ADS)

    Manimaran, P.; Panigrahi, Prasanta K.; Parikh, Jitendra C.

    2005-10-01

    We propose a wavelet based method for the characterization of the scaling behavior of nonstationary time series. It makes use of the built-in ability of the wavelets for capturing the trends in a data set, in variable window sizes. Discrete wavelets from the Daubechies family are used to illustrate the efficacy of this procedure. After studying binomial multifractal time series with the present and earlier approaches of detrending for comparison, we analyze the time series of averaged spin density in the 2D Ising model at the critical temperature, along with several experimental data sets possessing multifractal behavior.

  2. 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.

  3. High Performance Biomedical Time Series Indexes Using Salient Segmentation

    PubMed Central

    Woodbridge, Jonathan; Mortazavi, Bobak; Bui, Alex A.T.; Sarrafzadeh, Majid

    2016-01-01

    The advent of remote and wearable medical sensing has created a dire need for efficient medical time series databases. Wearable medical sensing devices provide continuous patient monitoring by various types of sensors and have the potential to create massive amounts of data. Therefore, time series databases must utilize highly optimized indexes in order to efficiently search and analyze stored data. This paper presents a highly efficient technique for indexing medical time series signals using Locality Sensitive Hashing (LSH). Unlike previous work, only salient (or interesting) segments are inserted into the index. This technique reduces search times by up to 95% while yielding near identical search results. PMID:23367072

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

    NASA Astrophysics Data System (ADS)

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

    2014-02-01

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

  5. Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

    PubMed

    Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

    2014-01-01

    Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

  6. [A coarse-to-fine registration method for satellite infrared image and visual image].

    PubMed

    Hu, Yong-Li; Wang, Liang; Liu, Rong; Zhang, Li; Duan, Fu-Qing

    2013-11-01

    In the present paper, in order to resolve the registration of the multi-mode satellite images with different signal properties and features, a two-phase coarse-to-fine registration method is presented and is applied to the registration of satellite infrared images and visual images. In the coarse registration phase of this method, the edge of infrared and visual images is firstly detected. Then the Fourier-Mellin transform is adopted to process the edge images. Finally, the affine transformation parameters of the registration are computed rapidly by the transformation relation between the registering images in frequency domain. In the fine registration phase of the proposed method, the feature points of infrared and visual images are firstly detected by Harris operator. Then the matched feature points of infrared and visual images are determined by the cross-correlation similarity of their local neighborhoods. The fine registration is finally realized according to the spatial correspondent relation of the matched feature points in infrared and visual images. The proposed coarse-to-fine registration method derives both the advantages of two methods, the high efficiency of Fourier-Mellin transform based registration method and the accuracy of Harris operator based registration method, which is considered the novelty and merit of the proposed method. To evaluate the performance of the proposed registration method, the coarse-to-fine registration method is implemented on the infrared and visual images captured by the FY-2D meteorological satellite. The experimental results show that the presented registration method is robust and has acceptable registration accuracy.

  7. 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.

  8. Practical overview of ARIMA models for time-series forecasting

    SciTech Connect

    Pack, D.J.

    1980-01-01

    Single series analysis methodology is illustrated. The commentary summarizes the Box-Jenkins philosophy and the ARIMA model structure, with particular emphasis on practical aspects of application, forecast interpretation, strengths weaknesses, and comparison to other time series forecasting approaches. (GHT)

  9. Vehicle extraction from high-resolution satellite image using template matching

    NASA Astrophysics Data System (ADS)

    Natt, Dehchaiwong; Cao, Xiaoguang

    2015-12-01

    The process of vehicle examination by using satellite images is complicated and cumbersome process. At the present, the high definition satellite images are being used, however, the images of the vehicles can be seen as just a small point which is difficult to separate it out from the background that the image details are not sufficient to identify small objects. In this research, the techniques for the process of vehicle examination by using satellite images were applied by using image data from Pléiades which is the satellite image with high resolution of 0.40 m. The objective of this research is to study and develop the device for data extracting from satellite images, and the received data would be organized and created as Geospatial information by the concept of the picture matching with a pattern matching or Template Matching developed with Matlab program and Sum of Absolute Difference method collaborated with Neural Network technique in order to help evaluating pattern matching between template images of cars and cars' images which were used to examine from satellite images. The result obtained from the comparison with template data shows that data extraction accuracy is greater than 90%, and the extracted data can be imported into Geospatial information database. Moreover, the data can be displayed in Geospatial information Software, and it also can be searched by quantity condition and satellite image position.

  10. 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.

  11. The use of synthetic input sequences in time series modeling

    NASA Astrophysics Data System (ADS)

    de Oliveira, Dair José; Letellier, Christophe; Gomes, Murilo E. D.; Aguirre, Luis A.

    2008-08-01

    In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure.

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

    SciTech Connect

    Kashgarian, M; Guilderson, T P

    2001-02-23

    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.

  13. 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.

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

    PubMed

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

    2008-09-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1994-01-01

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

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

    EPA Science Inventory

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

  17. Dsm Based Orientation of Large Stereo Satellite Image Blocks

    NASA Astrophysics Data System (ADS)

    d'Angelo, P.; Reinartz, P.

    2012-07-01

    High resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). A system for highly automated and operational DSM and orthoimage generation based on CARTOSAT-1 imagery is presented, with emphasis on fully automated georeferencing. The proposed system processes level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. The RPC are derived from orbit and attitude information and have a much lower accuracy than the ground resolution of approximately 2.5 m. In order to use the images for orthorectification or DSM generation, an affine RPC correction is required. In this paper, GCP are automatically derived from lower resolution reference datasets (Landsat ETM+ Geocover and SRTM DSM). The traditional method of collecting the lateral position from a reference image and interpolating the corresponding height from the DEM ignores the higher lateral accuracy of the SRTM dataset. Our method avoids this drawback by using a RPC correction based on DSM alignment, resulting in improved geolocation of both DSM and ortho images. Scene based method and a bundle block adjustment based correction are developed and evaluated for a test site covering the nothern part of Italy, for which 405 Cartosat-1 Stereopairs are available. Both methods are tested against independent ground truth. Checks against this ground truth indicate a lateral error of 10 meters.

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

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

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

  19. Advanced satellite sensors: Low Energy Neutral Atom (LENA) imager

    SciTech Connect

    Funsten, H.O.; McComas, D.J.

    1996-09-01

    This is the final report of a three-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). Imaging of low energy neutral atoms (LENDs) created by electron capture by magnetospheric plasma ions from interactions with cold geocoronal neutrals promises to be a revolutionary technique for providing unprecedented information about the global structure and dynamics of the terrestrial magnetosphere. This has significant implications in space weather forecasting, weather-induced satellite upset diagnostics, and revolutionary insights into global magnetospheric physics. The Los Alamos Space and Atmospheric Sciences Group has completed extensive neutral atom simulations and detailed instrument definition, and we designed a proof-of-concept demonstration prototype and have obtained externally- funded programs for full instrument development

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

    USGS Publications Warehouse

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

    1996-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

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

    SciTech Connect

    Cai, D Michael

    2011-01-18

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

  3. Comparison of time series using entropy and mutual correlation

    NASA Astrophysics Data System (ADS)

    Madonna, Fabio; Rosoldi, Marco

    2015-04-01

    The potential for redundant time series to reduce uncertainty in atmospheric variables has not been investigated comprehensively for climate observations. Moreover, comparison among time series of in situ and ground based remote sensing measurements have been performed using several methods, but quite often relying on linear models. In this work, the concepts of entropy (H) and mutual correlation (MC), defined in the frame of the information theory, are applied to the study of essential climate variables with the aim of characterizing the uncertainty of a time series and the redundancy of collocated measurements provided by different surface-based techniques. In particular, integrated water vapor (IWV) and water vapour mixing ratio times series obtained at five highly instrumented GRUAN (GCOS, Global Climate Observing System, Reference Upper-Air Network) stations with several sensors (e.g radiosondes, GPS, microwave and infrared radiometers, Raman lidar), in the period from 2010-2012, are analyzed in terms of H and MC. The comparison between the probability density functions of the time series shows that caution in using linear assumptions is needed and the use of statistics, like entropy, that are robust to outliers, is recommended to investigate measurements time series. Results reveals that the random uncertainties on the IWV measured with radiosondes, global positioning system, microwave and infrared radiometers, and Raman lidar measurements differed by less than 8 % over the considered time period. Comparisons of the time series of IWV content from ground-based remote sensing instruments with in situ soundings showed that microwave radiometers have the highest redundancy with the IWV time series measured by radiosondes and therefore the highest potential to reduce the random uncertainty of the radiosondes time series. Moreover, the random uncertainty of a time series from one instrument can be reduced by 60% by constraining the measurements with those from

  4. Ultrasound-guided characterization of interstitial ablated tissue using RF time series: feasibility study.

    PubMed

    Imani, Farhad; Abolmaesumi, Purang; Wu, Mark Z; Lasso, Andras; Burdette, Everett C; Ghoshal, Goutam; Heffter, Tamas; Williams, Emery; Neubauer, Paul; Fichtinger, Gabor; Mousavi, Parvin

    2013-06-01

    This paper presents the results of a feasibility study to demonstrate the application of ultrasound RF time series imaging to accurately differentiate ablated and nonablated tissue. For 12 ex vivo and two in situ tissue samples, RF ultrasound signals are acquired prior to, and following, high-intensity ultrasound ablation. Spatial and temporal features of these signals are used to characterize ablated and nonablated tissue in a supervised-learning framework. In cross-validation evaluation, a subset of four features extracted from RF time series produce a classification accuracy of 84.5%, an area under ROC curve of 0.91 for ex vivo data, and an accuracy of 85% for in situ data. Ultrasound RF time series is a promising approach for characterizing ablated tissue.

  5. Time Series Analysis and Prediction of AE and Dst Data

    NASA Astrophysics Data System (ADS)

    Takalo, J.; Lohikiski, R.; Timonen, J.; Lehtokangas, M.; Kaski, K.

    1996-12-01

    A new method to analyse the structure function has been constructed and used in the analysis of the AE time series for the years 1978-85 and Dst time series for 1957-84. The structure function (SF) was defined by S(l) = <|x(ti + lDt) - x(ti)|>, where Dt is the sampling time, l is an integer, and <|.|> denotes the average of absolute values. If a time series is self-affine its SF should scale for small values of l as S(l) is proportional to lH, where 0 < H < 1 is called the scaling exponent. It is known that for power-law (coloured) noise, which has P ~ f-a, a ~ 2H + 1 for 1 < a < 3. In this work the scaling exponent H was analysed by considering the local slopes dlog(S(l))/dlog(l) between two adjacent points as a function of l. For self-affine time series the local slopes should stay constant, at least for small values of l. The AE time series was found to be affine such that the scaling exponent changes at a time scale of 113 (+/-9) minutes. On the other hand, in the SF function analysis, the Dst data were dominated by the 24-hour and 27-day periods. The 27-day period was further modulated by the annual variation. These differences between the two time series arise from the difference in their periodicities in relation to their respective characteristic time scales. In the AE data the dominating periods are longer than that related to the characteristic time scale, i.e. they appear in the flatter part of the power spectrum. This is why the affinity is the dominating feature of the AE time series. In contrast with this the dominating periods of the Dst data are shorter than the characteristic time scale, and appear in the steeper part of the spectrum. Consequently periodicity is the dominating feature of the Dst data. Because of their different dynamic characteristics, prediction of Dst and AE time series appear to presuppose rather different approaches. In principle it is easier to produce the gross features of the Dst time series correctly as it is periodicity

  6. Adaptive Optics for Satellite Imaging and Space Debris Ranging

    NASA Astrophysics Data System (ADS)

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

    Earth's space environment is becoming crowded and at risk of a Kessler syndrome, and will require careful management for the future. Modern low noise high speed detectors allow for wavefront sensing and adaptive optics (AO) in extreme circumstances such as imaging small orbiting bodies in Low Earth Orbit (LEO). The Research School of Astronomy and Astrophysics (RSAA) at the Australian National University have been developing AO systems for telescopes between 1 and 2.5m diameter to image and range orbiting satellites and space debris. Strehl ratios in excess of 30% can be achieved for targets in LEO with an AO loop running at 2kHz, allowing the resolution of small features (<30cm) and the capability to determine object shape and spin characteristics. The AO system developed at RSAA consists of a high speed EMCCD Shack-Hartmann wavefront sensor, a deformable mirror (DM), and realtime computer (RTC), and an imaging camera. The system works best as a laser guide star system but will also function as a natural guide star AO system, with the target itself being the guide star. In both circumstances tip-tilt is provided by the target on the imaging camera. The fast tip-tilt modes are not corrected optically, and are instead removed by taking images at a moderate speed (>30Hz) and using a shift and add algorithm. This algorithm can also incorporate lucky imaging to further improve the final