Spatial variation in hyperthermia emergency department visits among those with employer-based insurance in the United States - a case-crossover analysis.

PubMed

Saha, Shubhayu; Brock, John W; Vaidyanathan, Ambarish; Easterling, David R; Luber, George

2015-03-04

Predictions of intense heat waves across the United States will lead to localized health impacts, most of which are preventable. There is a need to better understand the spatial variation in the morbidity impacts associated with extreme heat across the country to prevent such adverse health outcomes. Hyperthermia-related emergency department (ED) visits were obtained from the Truven Health MarketScan(®) Research dataset for 2000-2010. Three measures of daily ambient heat were constructed using meteorological observations from the National Climatic Data Center (maximum temperature, heat index) and the Spatial Synoptic Classification. Using a time-stratified case crossover approach, odds ratio of hyperthermia-related ED visit were estimated for the three different heat measures. Random effects meta-analysis was used to combine the odds ratios for 94 Metropolitan Statistical Areas (MSA) to examine the spatial variation by eight latitude categories and nine U.S. climate regions. Examination of lags for all three temperature measures showed that the odds ratio of ED visit was statistically significant and highest on the day of the ED visit. For heat waves lasting two or more days, additional statistically significant association was observed when heat index and synoptic classification was used as the temperature measure. These results were insensitive to the inclusion of air pollution measures. On average, the maximum temperature on the day of an ED visit was 93.4°F in 'South' and 81.9°F in the 'Northwest' climatic regions of United States. The meta-analysis showed higher odds ratios of hyperthermia ED visit in the central and the northern parts of the country compared to the south and southwest. The results showed spatial variation in average temperature on days of ED visit and odds ratio for hyperthermia ED visits associated with extreme heat across United States. This suggests that heat response plans need to be customized for different regions and the potential role of hyperthermia ED visits in syndromic surveillance for extreme heat.

Synoptic Drivers of Precipitation in the Atlantic Sector of the Arctic

NASA Astrophysics Data System (ADS)

Cohen, L.; Hudson, S.; Graham, R.; Renwick, J. A.

2017-12-01

Precipitation in the Arctic has been shown to be increasing in recent decades, from both observational and modelling studies, with largest trends seen in autumn and winter. This trend is attributed to a combination of the warming atmosphere and reduced sea ice extent. The seasonality of precipitation in the Arctic is important as it largely determines whether the precipitation falls as snow or rain. This study assesses the spatial and temporal variability of the synoptic drivers of precipitation in the Atlantic (European) sector of the Arctic. This region of the Arctic is of particular interest as it has the largest inter-annual variability in sea ice extent and is the primary pathway for moisture transport into the Arctic from lower latitudes. This study uses the ECMWF ERA-I reanalysis total precipitation to compare to long-term precipitation observations from Ny Ålesund, Svalbard to show that the reanalysis captures the synoptic variability of precipitation well and that most precipitation in this region is synoptically driven. The annual variability of precipitation in the Atlantic Arctic shows strong regionality. In the Svalbard and Barents Sea region, most of the annual total precipitation occurs during autumn and winter (Oct-Mar) (>60% of annual total), while the high-Arctic (> 80N) and Kara Sea receives most of the annual precipitation ( 60% of annual total) during summer (July-Sept). Using a synoptic classification developed for this region, this study shows that winter precipitation is driven by winter cyclone occurrence, with strong correlations to the AO and NAO indices. High precipitation over Svalbard is also strongly correlated with the Scandinavian blocking pattern, which produces a southerly flow in the Greenland Sea/Svalbard area. An increasing occurrence of these synoptic patterns are seen for winter months (Nov and Jan), which may explain much of the observed winter increase in precipitation.

Towards an Automated Classification of Transient Events in Synoptic Sky Surveys

NASA Technical Reports Server (NTRS)

Djorgovski, S. G.; Donalek, C.; Mahabal, A. A.; Moghaddam, B.; Turmon, M.; Graham, M. J.; Drake, A. J.; Sharma, N.; Chen, Y.

2011-01-01

We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly using Bayesian approaches, due to the sparse nature, heterogeneity, and variable incompleteness of the available data. The classifications are improved iteratively as the new measurements are obtained. One novel featrue is the development of an automated follow-up recommendation engine, that suggest those measruements that would be the most advantageous in terms of resolving classification ambiguities and/or characterization of the astrophysically most interesting objects, given a set of available follow-up assets and their cost funcations. This illustrates the symbiotic relationship of astronomy and applied computer science through the emerging disciplne of AstroInformatics.

What are the most fire-dangerous atmospheric circulations in the Eastern-Mediterranean? Analysis of the synoptic wildfire climatology.

PubMed

Paschalidou, A K; Kassomenos, P A

2016-01-01

Wildfire management is closely linked to robust forecasts of changes in wildfire risk related to meteorological conditions. This link can be bridged either through fire weather indices or through statistical techniques that directly relate atmospheric patterns to wildfire activity. In the present work the COST-733 classification schemes are applied in order to link wildfires in Greece with synoptic circulation patterns. The analysis reveals that the majority of wildfire events can be explained by a small number of specific synoptic circulations, hence reflecting the synoptic climatology of wildfires. All 8 classification schemes used, prove that the most fire-dangerous conditions in Greece are characterized by a combination of high atmospheric pressure systems located N to NW of Greece, coupled with lower pressures located over the very Eastern part of the Mediterranean, an atmospheric pressure pattern closely linked to the local Etesian winds over the Aegean Sea. During these events, the atmospheric pressure has been reported to be anomalously high, while anomalously low 500hPa geopotential heights and negative total water column anomalies were also observed. Among the various classification schemes used, the 2 Principal Component Analysis-based classifications, namely the PCT and the PXE, as well as the Leader Algorithm classification LND proved to be the best options, in terms of being capable to isolate the vast amount of fire events in a small number of classes with increased frequency of occurrence. It is estimated that these 3 schemes, in combination with medium-range to seasonal climate forecasts, could be used by wildfire risk managers to provide increased wildfire prediction accuracy. Copyright © 2015 Elsevier B.V. All rights reserved.

Empirical and modeled synoptic cloud climatology of the Arctic Ocean

NASA Technical Reports Server (NTRS)

Barry, R. G.; Newell, J. P.; Schweiger, A.; Crane, R. G.

1986-01-01

A set of cloud cover data were developed for the Arctic during the climatically important spring/early summer transition months. Parallel with the determination of mean monthly cloud conditions, data for different synoptic pressure patterns were also composited as a means of evaluating the role of synoptic variability on Arctic cloud regimes. In order to carry out this analysis, a synoptic classification scheme was developed for the Arctic using an objective typing procedure. A second major objective was to analyze model output of pressure fields and cloud parameters from a control run of the Goddard Institue for Space Studies climate model for the same area and to intercompare the synoptic climatatology of the model with that based on the observational data.

Atmospheric circulation classification comparison based on wildfires in Portugal

NASA Astrophysics Data System (ADS)

Pereira, M. G.; Trigo, R. M.

2009-04-01

Atmospheric circulation classifications are not a simple description of atmospheric states but a tool to understand and interpret the atmospheric processes and to model the relation between atmospheric circulation and surface climate and other related variables (Radan Huth et al., 2008). Classifications were initially developed with weather forecasting purposes, however with the progress in computer processing capability, new and more robust objective methods were developed and applied to large datasets prompting atmospheric circulation classification methods to one of the most important fields in synoptic and statistical climatology. Classification studies have been extensively used in climate change studies (e.g. reconstructed past climates, recent observed changes and future climates), in bioclimatological research (e.g. relating human mortality to climatic factors) and in a wide variety of synoptic climatological applications (e.g. comparison between datasets, air pollution, snow avalanches, wine quality, fish captures and forest fires). Likewise, atmospheric circulation classifications are important for the study of the role of weather in wildfire occurrence in Portugal because the daily synoptic variability is the most important driver of local weather conditions (Pereira et al., 2005). In particular, the objective classification scheme developed by Trigo and DaCamara (2000) to classify the atmospheric circulation affecting Portugal have proved to be quite useful in discriminating the occurrence and development of wildfires as well as the distribution over Portugal of surface climatic variables with impact in wildfire activity such as maximum and minimum temperature and precipitation. This work aims to present: (i) an overview the existing circulation classification for the Iberian Peninsula, and (ii) the results of a comparison study between these atmospheric circulation classifications based on its relation with wildfires and relevant meteorological variables. To achieve these objectives we consider the main classifications for Iberia developed within the framework of COST action 733 (Radan Huth et al., 2008). This European project aims to provide a wide range of atmospheric circulation classifications for Europe and sub-regions (http://www.cost733.org/) with an ambitious objective of assessing, comparing and classifying all relevant weather situations in Europe. Pereira et al. (2005) "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology,129, 11-25. Radan Huth et al. (2008) "Classifications of Atmospheric circulation patterns. Recent advances and applications". Trends and Directions in Climate Research: Ann. N.Y. Acad. Sci. 1146: 105-152. doi: 10.1196/annals.1446.019. Trigo R.M., DaCamara C. (2000) "Circulation Weather Types and their impact on the precipitation regime in Portugal". Int J of Climatology, 20, 1559-1581.

Analysis of Synoptic Weather Types and Its Influence on Precipitation in the Marmara Region (NW Turkey)

NASA Astrophysics Data System (ADS)

Baltaci, H.; Kindap, T.; Unal, A.; Karaca, M.

2012-04-01

In this study, we investigated the relationship between synoptic weather types and rainfall patterns in the Marmara region, northwestern part of Turkey. For this purpose, the automated Lamb weather type classification method was applied to the NCEP/NCAR reanalysis daily mean sea level pressure data for the period between 2001 and 2010. Ten synoptic weather types were found that represent the 90% of the synoptic patterns that affect the Marmara region. Based on the annual frequency analysis, mainly six synoptic weather types, 24% (NorthEast), 21% (North), 11% (South), 9% (SouthWest), 7% (Anticyclonic), 5% (Cyclonic), were found dominant in the region. Multiple comparison tests suggest that (i.e., Bonferroni test) northerly patterns (i.e., North and NorthEast) have statistically significantly higher percentages as compared to the southerly (i.e., South and SouthWest) and the rest of the patterns (i.e., Anticylonic and Cylonic). During winter months, N- and NE-patterns observed less frequently than the annual frequencies of them, 18% and 13% of the period, respectively. On the other hand, due to the formation of the low pressure center located over the central Mediterranean Sea, S- and SW-patterns were observed more frequently than their annual mean frequencies, 16% and 17%, respectively. During summer months, N- and NE-patterns become dominant in the region, and they constitute about three quarters of the period, 25% and 44%, respectively. The low pressure center located over central Anatolia and Black Sea brings moist and cool air to the region, preventing excessive heating during the summer season. Cyclonic patterns observed less frequent during the winter and fall months, about 3%. They become more frequent during the summer season, 9% as a result of the shifting of the subtropical jet stream to the south, and the seasonal movement of the Basra low pressure toward the inner and northern parts of the Anatolian peninsula. On the other hand, Anticyclonic patterns are more common in the fall season 11% due to the expansion of spatial extent of the anticyclone center located over the Caspian Sea. Daily precipitation records for the period of between 2001 and 2010 belong to 14 meteorological stations in the region were investigated to understand the influence of synoptic weather types on precipitation. Based on daily precipitation records, about one-third of the NE-patterns result in precipitation which is slightly larger than patterns from other directions. The corresponding values for SW-, N- and S-patterns are 29%, 25% and 25%, respectively. Northerly patterns (N and NE) causes more frequent precipitation on the northern and eastern parts of the region. On the other hand, southerly patterns (S and SW) are more influential and cause more frequent precipitation on the south and northwestern parts of the region. Therefore, frequency of synoptic weather types and daily precipitation records suggest that precipitation regimes are of a different nature in northern and southern parts of the Marmara region. Keywords Synoptic weather types; Marmara Region; Lamb classification; Rainfall patterns

Analysis of Numerical Weather Predictions of Reference Evapotranspiration and Precipitation

NASA Astrophysics Data System (ADS)

Bughici, Theodor; Lazarovitch, Naftali; Fredj, Erick; Tas, Eran

2017-04-01

This study attempts to improve the forecast skill of the evapotranspiration (ET0) and Precipitation for the purpose of crop irrigation management over Israel using the Weather Research and Forecasting (WRF) Model. Optimized crop irrigation, in term of timing and quantities, decreases water and agrochemicals demand. Crop water demands depend on evapotranspiration and precipitation. The common method for computing reference evapotranspiration, for agricultural needs, ET0, is according to the FAO Penman-Monteith equation. The weather variables required for ET0 calculation (air temperature, relative humidity, wind speed and solar irradiance) are estimated by the WRF model. The WRF Model with two-way interacting domains at horizontal resolutions of 27, 9 and 3 km is used in the study. The model prediction was performed in an hourly time resolution and a 3 km spatial resolution, with forecast lead-time of up to four days. The WRF prediction of these variables have been compared against measurements from 29 meteorological stations across Israel for the year 2013. The studied area is small but with strong climatic gradient, diverse topography and variety of synoptic conditions. The forecast skill that was used for forecast validation takes into account the prediction bias, mean absolute error and root mean squared error. The forecast skill of the variables was almost robust to lead time, except for precipitation. The forecast skill was tested across stations with respect to topography and geographic location and for all stations with respect to seasonality and synoptic weather system determined by employing a semi-objective synoptic systems classification to the forecasted days. It was noticeable that forecast skill of some of the variables was deteriorated by seasonality and topography. However, larger impacts in the ET0 skill scores on the forecasted day are achieved by a synoptic based forecast. These results set the basis for increasing the robustness of ET0 to synoptic effects and for more precise crop irrigation over Israel.

Effects of synoptic weather on ground-level PM2.5 concentrations in the United States

NASA Astrophysics Data System (ADS)

Liu, Ying; Zhao, Naizhuo; Vanos, Jennifer K.; Cao, Guofeng

2017-01-01

It is known that individual meteorological factors affect the concentrations of fine particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5), yet the specific meteorological effects found in previous studies are largely inconsistent and even conflicting. This study investigates influences of daily and short term changes in synoptic weather on ground-level PM2.5 concentrations in a large geographical area (75 cities across the contiguous United States (U.S.)) by using ten-year (2001-2010) spatial synoptic classification (SSC) data. We find that in the spring, summer, and fall the presence of the tropical weather types (i.e., dry-tropical (DT) and moist-tropical (MT)) is likely to associate with significantly higher levels of PM2.5 as compared to an all-weather-type-day average, and the presence of the polar weather types (i.e., dry-polar (DP) and moist-polar (MP)) is associated with significantly lower PM2.5 concentrations. The short-term (day to day) changes in synoptic weather types in a region are also likely to lead to significant variance in PM2.5 concentrations. For example, the largest increase in PM2.5 concentration occurs with the synoptic weather type changing from DP-to-MT. Conversely, a MT-to-DP weather type change results in the largest decrease in PM2.5 concentrations. Compared to air temperature, the effects of atmospheric moisture on PM2.5 concentration tend to be subtle, demonstrating that in conjunction with moderate temperature, neither the dry nor the moist air (except moist-moderate (MM) in summer) are associated with significantly high or low PM2.5 concentrations. Finally, we find that the effects of the synoptic weather type on PM2.5 concentrations may vary for different seasons and geographical areas. These findings suggest that interactions between atmospheric factors and seasonal and/or geographical factors have considerable impacts on the PM2.5 concentrations, and therefore should be considered in addition to the SSC when conducting environment health assessments.

An assessment of the Jenkinson and Collison synoptic classification to a continental mid-latitude location

NASA Astrophysics Data System (ADS)

Spellman, Greg

2017-05-01

A weather-type catalogue based on the Jenkinson and Collison method was developed for an area in south-west Russia for the period 1961-2010. Gridded sea level pressure data was obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. The resulting catalogue was analysed for frequency of individual types and groups of weather types to characterise long-term atmospheric circulation in this region. Overall, the most frequent type is anticyclonic (A) (23.3 %) followed by cyclonic (C) (11.9 %); however, there are some key seasonal patterns with westerly circulation being significantly more common in winter than summer. The utility of this synoptic classification is evaluated by modelling daily rainfall amounts. A low level of error is found using a simple model based on the prevailing weather type. Finally, characteristics of the circulation classification are compared to those for the original JC British Isles catalogue and a much more equal distribution of flow types is seen in the former classification.

Functional Assessment of Synoptic Pathology Reporting for Ovarian Cancer.

PubMed

Słodkowska, Janina; Cierniak, Szczepan; Patera, Janusz; Kopik, Jarosław; Baranowski, Włodzimierz; Markiewicz, Tomasz; Murawski, Piotr; Buda, Irmina; Kozłowski, Wojciech

2016-01-01

Ovarian cancer has one of the highest death/incidence rates and is commonly diagnosed at an advanced stage. In the recent WHO classification, new histotypes were classified which respond differently to chemotherapy. The e-standardized synoptic cancer pathology reports offer the clinicians essential and reliable information. The aim of our project was to develop an e-template for the standardized synoptic pathology reporting of ovarian carcinoma [based on the checklist of the College of American Pathologists (CAP) and the recent WHO/FIGO classification] to introduce a uniform and improved quality of cancer pathology reports. A functional and qualitative evaluation of the synoptic reporting was performed. An indispensable module for e-synoptic reporting was developed and integrated into the Hospital Information System (HIS). The electronic pathology system used a standardized structure with drop-down lists of defined elements to ensure completeness and consistency of reporting practices with the required guidelines. All ovarian cancer pathology reports (partial and final) with the corresponding glass slides selected from a 1-year current workflow were revised for the standard structured reports, and 42 tumors [13 borderline tumors and 29 carcinomas (mainly serous)] were included in the study. Analysis of the reports for completeness against the CAP checklist standard showed a lack of pTNM staging in 80% of the partial or final unstructured reports; ICD-O coding was missing in 83%. Much less frequently missed or unstated data were: ovarian capsule infiltration, angioinvasion and implant evaluation. The e-records of ovarian tumors were supplemented with digital macro- and micro-images and whole-slide images. The e-module developed for synoptic ovarian cancer pathology reporting was easily incorporated into HIS.CGM CliniNet and facilitated comprehensive reporting; it also provided open access to the database for concerned recipients. The e-synoptic pathology reports appeared more accurate, clear and conclusive than traditional narrative reports. Standardizing structured reporting and electronic tools allows open access and downstream utilization of pathology data for clinicians and tumor registries. © 2016 S. Karger AG, Basel.

Developing and diagnosing climate change indictors of regional aerosol optical properties

NASA Astrophysics Data System (ADS)

Sullivan, Ryan C.; Levy, Robert C.; da Silva, Arlindo M.; Pryor, Sara C.

2017-04-01

The US Global Change Research Program has developed climate indicators (CIs) to track changes in the physical, chemical, biological, and societal components of the climate system. Given the importance of atmospheric aerosol particles to clouds and radiative forcing, human mortality and morbidity, and biogeochemical cycles, we propose new aerosol particle CIs applicable to the US National Climate Assessment (NCA). Here we define these aerosol CIs and use them to quantify temporal trends in each NCA region. Furthermore, we use a synoptic classification (e.g., meteorological variables), and gas and particle emissions inventories to diagnose and attribute causes of observed changes. Our CIs are derived using output from the satellite-constrained Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2) reanalysis. MERRA-2 provides estimates of column-integrated aerosol optical properties at 0.625° by 0.5° resolution, including aerosol optical depth (AOD), Ångström exponent (AE), and single scattering albedo (SSA), which are related to aerosol loading, relative particle size, and chemical composition, respectively. For each NCA region, and for each aerosol variable, we derive statistics that describe mean and extreme values, as well as two metrics (spatial autocorrelation and coherence) that describe the spatial scales of aerosol variability. Consistent with previous analyses of aerosol precursor emissions and near-surface fine aerosol mass concentrations in the US, analyses of our aerosol CIs show that since 2000, both mean and extreme AOD have decreased over most NCA regions. There are significant (α = 0.05, using the non-parametric Kendall's tau) decreases in AOD for the Northeast (NE), Southeast (SE), Midwest (MW), and lower Great Plains (GPl) regions, and notable but not significant decreases in the Southwest (SW). AOD has increased for the Northwest (NW; significant) and upper Great Plains (GPu; not significant). Over all regions, there is a significant positive trend in AE (relative decrease in aerosol size) along with significant negative trend in SSA (relative decrease in scattering versus absorption extinction). Negative trends in AOD and SSA are consistent with documented decreases in sulfur dioxide emissions. Conversely, increased AOD in NW and GPu may reflect a lower impact of emissions standards in more remote regions, and/or that other aerosol and precursor sources (e.g., gas and oil extraction, wildfire frequency, long-range transport) may be increasing. Low AOD days are associated with dry, cool synoptic conditions. Since 2000, the structure of the aerosol field has changed. Using the Moran's I test, all regions exhibit declining spatial autocorrelation, suggesting AOD has become less uniform. At the same time, semivariogram models show that in many regions (NW, GPl, MW, SE) spatial coherence is increasing, and is consistent with an increase in the intensity of certain synoptic conditions. These results suggest that it is the variability in local emissions that accounts for the spatial structure of the AOD fields. However, more intense synoptic features are associated with more intense regional aerosol events.

On the relationship between large-scale climate modes and regional synoptic patterns that drive Victorian rainfall

NASA Astrophysics Data System (ADS)

Verdon-Kidd, D.; Kiem, A. S.

2008-10-01

In this paper regional (synoptic) and large-scale climate drivers of rainfall are investigated for Victoria, Australia. A non-linear classification methodology known as self-organizing maps (SOM) is used to identify 20 key regional synoptic patterns, which are shown to capture a range of significant synoptic features known to influence the climate of the region. Rainfall distributions are assigned to each of the 20 patterns for nine rainfall stations located across Victoria, resulting in a clear distinction between wet and dry synoptic types at each station. The influence of large-scale climate modes on the frequency and timing of the regional synoptic patterns is also investigated. This analysis revealed that phase changes in the El Niño Southern Oscillation (ENSO), the Southern Annular Mode (SAM) and/or Indian Ocean Dipole (IOD) are associated with a shift in the relative frequency of wet and dry synoptic types. Importantly, these results highlight the potential to utilise the link between the regional synoptic patterns derived in this study and large-scale climate modes to improve rainfall forecasting for Victoria, both in the short- (i.e. seasonal) and long-term (i.e. decadal/multi-decadal scale). In addition, the regional and large-scale climate drivers identified in this study provide a benchmark by which the performance of Global Climate Models (GCMs) may be assessed.

Defining Winter and Identifying Synoptic Air Mass Change in the Northeast and Northern Plains U.S. since 1950

NASA Astrophysics Data System (ADS)

Chapman, C. J.; Pennington, D.; Beitscher, M. R.; Godek, M. L.

2017-12-01

Understanding and forecasting the characteristics of winter weather change in the northern U.S. is vital to regional economy, agriculture, tourism and resident life. This is especially true in the Northeast and Northern Plains where substantial changes to the winter season have already been documented in the atmospheric science and biological literature. As there is no single established definition of `winter', this research attempts to identify the winter season in both regions utilizing a synoptic climatological approach with air mass frequencies. The Spatial Synoptic Classification is used to determine the daily air mass/ weather type conditions since 1950 at 40 locations across the two regions. Annual frequencies are first computed as a baseline reference. Then winter air mass frequencies and departures from normal are calculated to define the season along with the statistical significance. Once the synoptic winter is established, long-term regional changes to the season and significance are explored. As evident global changes have occurred after 1975, an Early period of years prior to 1975 and a Late set for all years following this date are compared. Early and Late record synoptic changes are then examined to assess any thermal and moisture condition changes of the regional winter air masses over time. Cold to moderately dry air masses dominate annually in both regions. Northeast winters are also characterized by cold to moderate dry air masses, with coastal locations experiencing more Moist Polar types. The Northern Plains winters are dominated by cold, dry air masses in the east and cold to moderate dry air masses in the west. Prior to 1975, Northeast winters are defined by an increase in cooler and wetter air masses. Dry Tropical air masses only occur in this region after 1975. Northern Plains winters are also characterized by more cold, dry air masses prior to 1975. More Dry Moderate and Moist Moderate air masses have occurred since 1975. These results demonstrate that Northeast winters have air mass conditions that have become warmer and drier in recent decades. Additionally, Northern Plains winters have air mass setups that have become warmer and more moist since the mid 1970s.

Meteorological regimes for the classification of aerospace air quality predictions for NASA-Kennedy Space Center

NASA Technical Reports Server (NTRS)

Stephens, J. B.; Sloan, J. C.

1976-01-01

A method is described for developing a statistical air quality assessment for the launch of an aerospace vehicle from the Kennedy Space Center in terms of existing climatological data sets. The procedure can be refined as developing meteorological conditions are identified for use with the NASA-Marshall Space Flight Center Rocket Exhaust Effluent Diffusion (REED) description. Classical climatological regimes for the long range analysis can be narrowed as the synoptic and mesoscale structure is identified. Only broad synoptic regimes are identified at this stage of analysis. As the statistical data matrix is developed, synoptic regimes will be refined in terms of the resulting eigenvectors as applicable to aerospace air quality predictions.

Weather elements, chemical air pollutants and airborne pollen influencing asthma emergency room visits in Szeged, Hungary: performance of two objective weather classifications

NASA Astrophysics Data System (ADS)

Makra, László; Puskás, János; Matyasovszky, István; Csépe, Zoltán; Lelovics, Enikő; Bálint, Beatrix; Tusnády, Gábor

2015-09-01

Weather classification approaches may be useful tools in modelling the occurrence of respiratory diseases. The aim of the study is to compare the performance of an objectively defined weather classification and the Spatial Synoptic Classification (SSC) in classifying emergency department (ED) visits for acute asthma depending from weather, air pollutants, and airborne pollen variables for Szeged, Hungary, for the 9-year period 1999-2007. The research is performed for three different pollen-related periods of the year and the annual data set. According to age and gender, nine patient categories, eight meteorological variables, seven chemical air pollutants, and two pollen categories were used. In general, partly dry and cold air and partly warm and humid air aggravate substantially the symptoms of asthmatics. Our major findings are consistent with this establishment. Namely, for the objectively defined weather types favourable conditions for asthma ER visits occur when an anticyclonic ridge weather situation happens with near extreme temperature and humidity parameters. Accordingly, the SSC weather types facilitate aggravating asthmatic conditions if warm or cool weather occur with high humidity in both cases. Favourable conditions for asthma attacks are confirmed in the extreme seasons when atmospheric stability contributes to enrichment of air pollutants. The total efficiency of the two classification approaches is similar in spite of the fact that the methodology for derivation of the individual types within the two classification approaches is completely different.

Weather elements, chemical air pollutants and airborne pollen influencing asthma emergency room visits in Szeged, Hungary: performance of two objective weather classifications.

PubMed

Makra, László; Puskás, János; Matyasovszky, István; Csépe, Zoltán; Lelovics, Enikő; Bálint, Beatrix; Tusnády, Gábor

2015-09-01

Weather classification approaches may be useful tools in modelling the occurrence of respiratory diseases. The aim of the study is to compare the performance of an objectively defined weather classification and the Spatial Synoptic Classification (SSC) in classifying emergency department (ED) visits for acute asthma depending from weather, air pollutants, and airborne pollen variables for Szeged, Hungary, for the 9-year period 1999-2007. The research is performed for three different pollen-related periods of the year and the annual data set. According to age and gender, nine patient categories, eight meteorological variables, seven chemical air pollutants, and two pollen categories were used. In general, partly dry and cold air and partly warm and humid air aggravate substantially the symptoms of asthmatics. Our major findings are consistent with this establishment. Namely, for the objectively defined weather types favourable conditions for asthma ER visits occur when an anticyclonic ridge weather situation happens with near extreme temperature and humidity parameters. Accordingly, the SSC weather types facilitate aggravating asthmatic conditions if warm or cool weather occur with high humidity in both cases. Favourable conditions for asthma attacks are confirmed in the extreme seasons when atmospheric stability contributes to enrichment of air pollutants. The total efficiency of the two classification approaches is similar in spite of the fact that the methodology for derivation of the individual types within the two classification approaches is completely different.

On the relationship between large-scale climate modes and regional synoptic patterns that drive Victorian rainfall

NASA Astrophysics Data System (ADS)

Verdon-Kidd, D. C.; Kiem, A. S.

2009-04-01

In this paper regional (synoptic) and large-scale climate drivers of rainfall are investigated for Victoria, Australia. A non-linear classification methodology known as self-organizing maps (SOM) is used to identify 20 key regional synoptic patterns, which are shown to capture a range of significant synoptic features known to influence the climate of the region. Rainfall distributions are assigned to each of the 20 patterns for nine rainfall stations located across Victoria, resulting in a clear distinction between wet and dry synoptic types at each station. The influence of large-scale climate modes on the frequency and timing of the regional synoptic patterns is also investigated. This analysis revealed that phase changes in the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and/or the Southern Annular Mode (SAM) are associated with a shift in the relative frequency of wet and dry synoptic types on an annual to inter-annual timescale. In addition, the relative frequency of synoptic types is shown to vary on a multi-decadal timescale, associated with changes in the Inter-decadal Pacific Oscillation (IPO). Importantly, these results highlight the potential to utilise the link between the regional synoptic patterns derived in this study and large-scale climate modes to improve rainfall forecasting for Victoria, both in the short- (i.e. seasonal) and long-term (i.e. decadal/multi-decadal scale). In addition, the regional and large-scale climate drivers identified in this study provide a benchmark by which the performance of Global Climate Models (GCMs) may be assessed.

Atmospheric circulation types and daily mortality in Athens, Greece.

PubMed Central

Kassomenos, P; Gryparis, A; Samoli, E; Katsouyanni, K; Lykoudis, S; Flocas, H A

2001-01-01

We investigated the short-term effects of synoptic and mesoscale atmospheric circulation types on mortality in Athens, Greece. The synoptic patterns in the lower troposphere were classified in 8 a priori defined categories. The mesoscale weather types were classified into 11 categories, using meteorologic parameters from the Athens area surface monitoring network; the daily number of deaths was available for 1987-1991. We applied generalized additive models (GAM), extending Poisson regression, using a LOESS smoother to control for the confounding effects of seasonal patterns. We adjusted for long-term trends, day of the week, ambient particle concentrations, and additional temperature effects. Both classifications, synoptic and mesoscale, explain the daily variation of mortality to a statistically significant degree. The highest daily mortality was observed on days characterized by southeasterly flow [increase 10%; 95% confidence interval (CI), 6.1-13.9% compared to the high-low pressure system), followed by zonal flow (5.8%; 95% CI, 1.8-10%). The high-low pressure system and the northwesterly flow are associated with the lowest mortality. The seasonal patterns are consistent with the annual pattern. For mesoscale categories, in the cold period the highest mortality is observed during days characterized by the easterly flow category (increase 9.4%; 95% CI, 1.0-18.5% compared to flow without the main component). In the warm period, the highest mortality occurs during the strong southerly flow category (8.5% increase; 95% CI, 2.0-15.4% compared again to flow without the main component). Adjusting for ambient particle levels leaves the estimated associations unchanged for the synoptic categories and slightly increases the effects of mesoscale categories. In conclusion, synoptic and mesoscale weather classification is a useful tool for studying the weather-health associations in a warm Mediterranean climate situation. PMID:11445513

Synoptic and Mesoscale Climatologies of Severe Local Storms for the American Midwest.

NASA Astrophysics Data System (ADS)

Arnold, David Leslie

This study investigates the synoptic and mesoscale environments associated with severe local storms (SELS) in the heart of the American Midwest. This region includes west-central Illinois, most of Indiana, the extreme western counties of Ohio, and a small part of northeastern Kentucky. The primary objectives of this study are to determine the surface and middle-tropospheric synoptic circulation patterns and thermodynamic and kinematic environments associated with SELS event types (tornadoes, hail, severe straight -line winds), and to assess the degree to which the synoptic circulation patterns and meso-beta scale kinematic and thermodynamic climatology of the Midwest differ from that of the Great Plains. A secondary objective is to investigate the possible role that land-surface atmosphere interactions play in the spatial distribution of SELS. A new subjective synoptic typing scheme is developed and applied to determine the synoptic-scale circulation patterns associated with the occurrence of SELS event types. This scheme is based on a combination of surface and middle -tropospheric patterns. Thermodynamic and kinematic parameters are analyzed to determine meso-scale environments favorable for the development of SELS. Results indicate that key synoptic-scale circulation patterns, and specific ranges of thermodynamic and kinematic parameters are related to specific SELS event types. These circulation types and ranges of thermodynamic and kinematic parameters may be used to help improve the medium-range forecasting of severe local storms. Results of the secondary objective reveal that the spatial distribution of SELS events is clustered within the study region, and most occur under a negative climate division-level soil moisture gradient; that is, a drier upwind division than the division in which the event occurs. Moreover, the spatial distribution of SELS events is compared against a map of soil types and vegetation. The resulting distribution depicts a visual correlation between the primary soil and vegetative boundaries and clusters of SELS. This supports the likely role of meso-scale land-surface-atmosphere interactions in severe weather development for humid lowlands of the Midwest United States.

Repeat synoptic sampling reveals drivers of change in carbon and nutrient chemistry of Arctic catchments

NASA Astrophysics Data System (ADS)

Zarnetske, J. P.; Abbott, B. W.; Bowden, W. B.; Iannucci, F.; Griffin, N.; Parker, S.; Pinay, G.; Aanderud, Z.

2017-12-01

Dissolved organic carbon (DOC), nutrients, and other solute concentrations are increasing in rivers across the Arctic. Two hypotheses have been proposed to explain these trends: 1. distributed, top-down permafrost degradation, and 2. discrete, point-source delivery of DOC and nutrients from permafrost collapse features (thermokarst). While long-term monitoring at a single station cannot discriminate between these mechanisms, synoptic sampling of multiple points in the stream network could reveal the spatial structure of solute sources. In this context, we sampled carbon and nutrient chemistry three times over two years in 119 subcatchments of three distinct Arctic catchments (North Slope, Alaska). Subcatchments ranged from 0.1 to 80 km2, and included three distinct types of Arctic landscapes - mountainous, tundra, and glacial-lake catchments. We quantified the stability of spatial patterns in synoptic water chemistry and analyzed high-frequency time series from the catchment outlets across the thaw season to identify source areas for DOC, nutrients, and major ions. We found that variance in solute concentrations between subcatchments collapsed at spatial scales between 1 to 20 km2, indicating a continuum of diffuse- and point-source dynamics, depending on solute and catchment characteristics (e.g. reactivity, topography, vegetation, surficial geology). Spatially-distributed mass balance revealed conservative transport of DOC and nitrogen, and indicates there may be strong in-stream retention of phosphorus, providing a network-scale confirmation of previous reach-scale studies in these Arctic catchments. Overall, we present new approaches to analyzing synoptic data for change detection and quantification of ecohydrological mechanisms in ecosystems in the Arctic and beyond.

Comparing exposure metrics for classifying ‘dangerous heat’ in heat wave and health warning systems

PubMed Central

Zhang, Kai; Rood, Richard B.; Michailidis, George; Oswald, Evan M.; Schwartz, Joel D.; Zanobetti, Antonella; Ebi, Kristie L.; O’Neill, Marie S.

2012-01-01

Heat waves have been linked to excess mortality and morbidity, and are projected to increase in frequency and intensity with a warming climate. This study compares exposure metrics to trigger heat wave and health warning systems (HHWS), and introduces a novel multi-level hybrid clustering method to identify potential dangerously hot days. Two-level and three-level hybrid clustering analysis as well as common indices used to trigger HHWS, including spatial synoptic classification (SSC); and 90th, 95th, and 99th percentiles of minimum and relative minimum temperature (using a 10 day reference period), were calculated using a summertime weather dataset in Detroit from 1976 to 2006. The days classified as ‘hot’ with hybrid clustering analysis, SSC, minimum and relative minimum temperature methods differed by method type. SSC tended to include the days with, on average, 2.6 °C lower daily minimum temperature and 5.3 °C lower dew point than days identified by other methods. These metrics were evaluated by comparing their performance in predicting excess daily mortality. The 99th percentile of minimum temperature was generally the most predictive, followed by the three-level hybrid clustering method, the 95th percentile of minimum temperature, SSC and others. Our proposed clustering framework has more flexibility and requires less substantial meteorological prior information than the synoptic classification methods. Comparison of these metrics in predicting excess daily mortality suggests that metrics thought to better characterize physiological heat stress by considering several weather conditions simultaneously may not be the same metrics that are better at predicting heat-related mortality, which has significant implications in HHWSs. PMID:22673187

Application of a hybrid association rules/decision tree model for drought monitoring

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Molajou, Amir

2017-12-01

The previous researches have shown that the incorporation of the oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST) into hydro-climatic models could provide important predictive information about hydro-climatic variability. In this paper, the hybrid application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between drought of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. Two major steps of the proposed model were the classification of de-trend SST data and selecting the most effective groups and extracting hidden information involved in the data. The techniques of decision tree which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and Heidke Skill Score (HSS) measures were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast drought and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and drought of Tabriz and Kermanshah synoptic stations so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 70 and 80% respectively for Tabriz and Kermanshah synoptic stations.

The effects of synoptic weather on influenza infection incidences: a retrospective study utilizing digital disease surveillance

NASA Astrophysics Data System (ADS)

Zhao, Naizhuo; Cao, Guofeng; Vanos, Jennifer K.; Vecellio, Daniel J.

2018-01-01

The environmental drivers and mechanisms of influenza dynamics remain unclear. The recent development of influenza surveillance-particularly the emergence of digital epidemiology-provides an opportunity to further understand this puzzle as an area within applied human biometeorology. This paper investigates the short-term weather effects on human influenza activity at a synoptic scale during cold seasons. Using 10 years (2005-2014) of municipal level influenza surveillance data (an adjustment of the Google Flu Trends estimation from the Centers for Disease Control's virologic surveillance data) and daily spatial synoptic classification weather types, we explore and compare the effects of weather exposure on the influenza infection incidences in 79 cities across the USA. We find that during the cold seasons the presence of the polar [i.e., dry polar (DP) and moist polar (MP)] weather types is significantly associated with increasing influenza likelihood in 62 and 68% of the studied cities, respectively, while the presence of tropical [i.e., dry tropical (DT) and moist tropical (MT)] weather types is associated with a significantly decreasing occurrence of influenza in 56 and 43% of the cities, respectively. The MP and the DP weather types exhibit similar close positive correlations with influenza infection incidences, indicating that both cold-dry and cold-moist air provide favorable conditions for the occurrence of influenza in the cold seasons. Additionally, when tropical weather types are present, the humid (MT) and the dry (DT) weather types have similar strong impacts to inhibit the occurrence of influenza. These findings suggest that temperature is a more dominating atmospheric factor than moisture that impacts the occurrences of influenza in cold seasons.

Lodgepole pine site index in relation to synoptic measures of climate, soil moisture and soil nutrients.

Treesearch

G. Geoff Wang; Shongming Huang; Robert A. Monserud; Ryan J. Klos

2004-01-01

Lodgepole pine site index was examined in relation to synoptic measures of topography, soil moisture, and soil nutrients in Alberta. Data came from 214 lodgepole pine-dominated stands sampled as a part of the provincial permanent sample plot program. Spatial location (elevation, latitude, and longitude) and natural subregions (NSRs) were topographic variables that...

Assessment of mesoscale convective systems using IR brightness temperature in the southwest of Iran

NASA Astrophysics Data System (ADS)

Rafati, Somayeh; Karimi, Mostafa

2017-07-01

In this research, the spatial and temporal distribution of Mesoscale Convective Systems was assessed in the southwest of Iran using Global merged satellite IR brightness temperature (acquired from Meteosat, GOES, and GMS geostationary satellites) and synoptic station data. Event days were selected using a set of storm reports and precipitation criteria. The following criteria are used to determine the days with occurrence of convective systems: (1) at least one station reported 6-h precipitation exceeding 10 mm and (2) at least three stations reported phenomena related to convection (thunderstorm, lightning, and shower). MCSs were detected based on brightness temperature, maximum areal extent, and duration thresholds (228 K, 10,000 km2, and 3 h, respectively). An MCS occurrence classification system is developed based on mean sea level, 850 and 500 hPa pressure patterns.

Cloud Properties under Different Synoptic Circulations: Comparison of Radiosonde and Ground-Based Active Remote Sensing Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jinqiang; Li, Jun; Xia, Xiangao

In this study, long-term (10 years) radiosonde-based cloud data are compared with the ground-based active remote sensing product under six prevailing large-scale synoptic patterns, i.e., cyclonic center (CC), weak pressure pattern (WP), the southeast bottom of cyclonic center (CB), cold front (CF), anticyclone edge (AE) and anticyclone center (AC) over the Southern Great Plains (SGP) site. The synoptic patterns are generated by applying the self-organizing map weather classification method to the daily National Centers for Environmental Protection mean sea level pressure records from the North American Regional Reanalysis. It reveals that the large-scale synoptic circulations can strongly influence the regionalmore » cloud formation, and thereby have impact on the consistency of cloud retrievals from the radiosonde and ground-based cloud product. The total cloud cover at the SGP site is characterized by the least in AC and the most in CF. The minimum and maximum differences between the two cloud methods are 10.3% for CC and 13.3% for WP. Compared to the synoptic patterns characterized by scattered cloudy and clear skies (AE and AC), the agreement of collocated cloud boundaries between the two cloud approaches tends to be better under the synoptic patterns dominated by overcast and cloudy skies (CC, WP and CB). The rainy and windy weather conditions in CF synoptic pattern influence the consistency of the two cloud retrieval methods associated with the limited capabilities inherent to the instruments. As a result, the cloud thickness distribution from the two cloud datasets compares favorably with each other in all synoptic patterns, with relative discrepancy of ≤0.3 km.« less

Cloud Properties under Different Synoptic Circulations: Comparison of Radiosonde and Ground-Based Active Remote Sensing Measurements

DOE PAGES

Zhang, Jinqiang; Li, Jun; Xia, Xiangao; ...

2016-11-28

In this study, long-term (10 years) radiosonde-based cloud data are compared with the ground-based active remote sensing product under six prevailing large-scale synoptic patterns, i.e., cyclonic center (CC), weak pressure pattern (WP), the southeast bottom of cyclonic center (CB), cold front (CF), anticyclone edge (AE) and anticyclone center (AC) over the Southern Great Plains (SGP) site. The synoptic patterns are generated by applying the self-organizing map weather classification method to the daily National Centers for Environmental Protection mean sea level pressure records from the North American Regional Reanalysis. It reveals that the large-scale synoptic circulations can strongly influence the regionalmore » cloud formation, and thereby have impact on the consistency of cloud retrievals from the radiosonde and ground-based cloud product. The total cloud cover at the SGP site is characterized by the least in AC and the most in CF. The minimum and maximum differences between the two cloud methods are 10.3% for CC and 13.3% for WP. Compared to the synoptic patterns characterized by scattered cloudy and clear skies (AE and AC), the agreement of collocated cloud boundaries between the two cloud approaches tends to be better under the synoptic patterns dominated by overcast and cloudy skies (CC, WP and CB). The rainy and windy weather conditions in CF synoptic pattern influence the consistency of the two cloud retrieval methods associated with the limited capabilities inherent to the instruments. As a result, the cloud thickness distribution from the two cloud datasets compares favorably with each other in all synoptic patterns, with relative discrepancy of ≤0.3 km.« less

An automated cirrus classification

NASA Astrophysics Data System (ADS)

Gryspeerdt, Edward; Quaas, Johannes; Goren, Tom; Klocke, Daniel; Brueck, Matthias

2018-05-01

Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model-observation comparisons and leading to improved parametrisations of cirrus cloud processes.

Geochemical Data for Upper Mineral Creek, Colorado, Under Existing Ambient Conditions and During an Experimental pH Modification, August 2005

USGS Publications Warehouse

Runkel, Robert L.; Kimball, Briant A.; Steiger, Judy I.; Walton-Day, Katherine

2009-01-01

Mineral Creek, an acid mine drainage stream in south-western Colorado, was the subject of a water-quality study that employed a paired synoptic approach. Under the paired synoptic approach, two synoptic sampling campaigns were conducted on the same study reach. The initial synoptic campaign, conducted August 22, 2005, documented stream-water quality under existing ambient conditions. A second synoptic campaign, conducted August 24, 2005, documented stream-water quality during a pH-modification experiment that elevated the pH of Mineral Creek. The experimental pH modification was designed to determine the potential reductions in dissolved constituent concentrations that would result from the implementation of an active treatment system for acid mine drainage. During both synoptic sampling campaigns, a solution containing lithium bromide was injected continuously to allow for the calculation of streamflow using the tracer-dilution method. Synoptic water-quality samples were collected from 30 stream sites and 11 inflow locations along the 2-kilometer study reach. Data from the study provide spatial profiles of pH, concentration, and streamflow under both existing and experimentally-altered conditions. This report presents the data obtained August 21-24, 2005, as well as the methods used for sample collection and data analysis.

Snowfall in the Northwest Iberian Peninsula: Synoptic Circulation Patterns and Their Influence on Snow Day Trends

PubMed Central

Merino, Andrés; Fernández, Sergio; Hermida, Lucía; López, Laura; Sánchez, José Luis; García-Ortega, Eduardo; Gascón, Estíbaliz

2014-01-01

In recent decades, a decrease in snowfall attributed to the effects of global warming (among other causes) has become evident. However, it is reasonable to investigate meteorological causes for such decrease, by analyzing changes in synoptic scale patterns. On the Iberian Peninsula, the Castilla y León region in the northwest consists of a central plateau surrounded by mountain ranges. This creates snowfalls that are considered both an important water resource and a transportation risk. In this work, we develop a classification of synoptic situations that produced important snowfalls at observation stations in the major cities of Castilla y León from 1960 to 2011. We used principal component analysis (PCA) and cluster techniques to define four synoptic patterns conducive to snowfall in the region. Once we confirmed homogeneity of the series and serial correlation of the snowfallday records at the stations from 1960 to 2011, we carried out a Mann-Kendall test. The results show a negative trend at most stations, so there are a decreased number of snowfall days. Finally, variations in these meteorological variables were related to changes in the frequencies of snow events belonging to each synoptic pattern favorable for snowfall production at the observatory locations. PMID:25152912

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

NASA Astrophysics Data System (ADS)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2018-05-01

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image classification of building damages.

Water quality, sediment quality, and stream-channel classification of Rock Creek, Washington, D.C., 1999-2000

USGS Publications Warehouse

Anderson, Anita L.; Miller, Cherie V.; Olsen, Lisa D.; Doheny, Edward J.; Phelan, Daniel J.

2002-01-01

Rock Creek Park is within the National Capital Region in Washington, D.C., and is maintained by the National Park Service. Part of Montgomery County, Maryland, and part of the District of Columbia drain into Rock Creek, which is a tributary of the Potomac River. Water quality in Rock Creek is important to biotic life in and near the creek, and in the Potomac River Basin and the Chesapeake Bay. The water quality of the Rock Creek Basin has been affected by continued urban and agricultural growth and development. The U.S. Geological Survey, in cooperation with the National Park Service, investigated water quality and sediment quality in Rock Creek over a 2-year period (1998?2000), and performed a stream-channel classification to determine the distribution of bottom sediment in Rock Creek. This report presents and evaluates water quality and bottom sediment in Rock Creek for water years 1999 (October 1, 1998 to September 30, 1999) and 2000 (October 1, 1999 to September 30, 2000). A synoptic surface-water assessment was conducted at five stations from June 23 to June 25, 1999, a temporal surface-water assessment was conducted at one station from February 18, 1999 to September 26, 2000, and bed-sediment samples were collected and assessed from three stations from August 17 to August 19, 1999. The synoptic surface-water assessment included pesticides (parent compounds and selected transformation products), field parameters, nutrients, and major ions. The temporal surface-water assessment included pesticides (parent compounds and selected transformation products) and field parameters. The bed-sediment assessment included trace elements and organic compounds (including low- and high-molecular weight polycyclic aromatic hydrocarbons, poly-chlorinated biphenyls, pesticides, and phthalates). Some, but not all, of the pesticides known to be used in the area were included in the synoptic water-quality assessment, the temporal water-quality assessment, and the bed-sediment assessment. In addition to the water-quality and sediment-quality assessments, a Rosgen stream-channel classification was performed on a 900-foot-long segment of Rock Creek. In the synoptic water-quality assessment, two pesticides were found to be above published criteria for the protection of aquatic life. In the temporal water-quality assessment, four pesticides were found to be above published criteria for the protection of aquatic life. In the bed-sediment assessment, 8 trace elements, 14 polycyclic aromatic hydrocarbons, 6 pesticides, and 1 phthalate compound were found to be above published criteria for the protection of aquatic life. In the Rosgen classification, a comparison to a previous classification for this segment showed an increase in sands and other fine-grained sediments in the creek bed.

Quantifying the imprint of mesoscale and synoptic-scale atmospheric transport on total column carbon dioxide measurements

NASA Astrophysics Data System (ADS)

Torres, A. D.; Keppel-Aleks, G.; Doney, S. C.; Feng, S.; Lauvaux, T.; Fendrock, M. A.; Rheuben, J.

2017-12-01

Remote sensing instruments provide an unprecedented density of observations of the atmospheric CO2 column average mole fraction (denoted as XCO2), which can be used to constrain regional scale carbon fluxes. Inferring fluxes from XCO2 observations is challenging, as measurements and inversion methods are sensitive to not only the imprint local and large-scale fluxes, but also mesoscale and synoptic-scale atmospheric transport. Quantifying the fine-scale variability in XCO2 from mesoscale and synoptic-scale atmospheric transport will likely improve overall error estimates from flux inversions by improving estimates of representation errors that occur when XCO2 observations are compared to modeled XCO2 in relatively coarse transport models. Here, we utilize various statistical methods to quantify the imprint of atmospheric transport on XCO2 observations. We compare spatial variations along Orbiting Carbon Observatory (OCO-2) satellite tracks to temporal variations observed by the Total Column Carbon Observing Network (TCCON). We observe a coherent seasonal cycle of both within-day temporal and fine-scale spatial variability (of order 10 km) of XCO2 from these two datasets, suggestive of the imprint of mesoscale systems. To account for other potential sources of error in XCO2 retrieval, we compare observed temporal and spatial variations of XCO2 to high-resolution output from the Weather Research and Forecasting (WRF) model run at 9 km resolution. In both simulations and observations, the Northern hemisphere mid-latitude XCO2 showed peak variability during the growing season when atmospheric gradients are largest. These results are qualitatively consistent with our expectations of seasonal variations of the imprint of synoptic and mesoscale atmospheric transport on XCO2 observations; suggesting that these statistical methods could be sensitive to the imprint of atmospheric transport on XCO2 observations.

Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream.

PubMed

Byrne, Patrick; Runkel, Robert L; Walton-Day, Katherine

2017-07-01

Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on stream water quality; (ii) quantify the spatial pattern of constituent loading; and (iii) identify inflow sources most responsible for observed changes in stream chemistry and constituent loading. Several of the constituents investigated (Al, Cd, Cu, Fe, Mn, Zn) fail to meet chronic aquatic life standards along most of the study reach. The spatial pattern of constituent loading suggests four primary sources of contamination under low flow conditions. Three of these sources are associated with acidic (pH <3.1) seeps that enter along the left bank of Lion Creek. Investigation of inflow water (trace metal and major ion) chemistry using PCA suggests a hydraulic connection between many of the left bank inflows and mine water in the Minnesota Mine shaft located to the north-east of the river channel. In addition, water chemistry data during a rainfall-runoff event suggests the spatial pattern of constituent loading may be modified during rainfall due to dissolution of efflorescent salts or erosion of streamside tailings. These data point to the complexity of contaminant mobilisation processes and constituent loading in mining-affected watersheds but the combined synoptic sampling and PCA approach enables a conceptual model of contaminant dynamics to be developed to inform remediation.

Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream

USGS Publications Warehouse

Byrne, Patrick; Runkel, Robert L.; Walton-Day, Katie

2017-01-01

Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on stream water quality; (ii) quantify the spatial pattern of constituent loading; and (iii) identify inflow sources most responsible for observed changes in stream chemistry and constituent loading. Several of the constituents investigated (Al, Cd, Cu, Fe, Mn, Zn) fail to meet chronic aquatic life standards along most of the study reach. The spatial pattern of constituent loading suggests four primary sources of contamination under low flow conditions. Three of these sources are associated with acidic (pH <3.1) seeps that enter along the left bank of Lion Creek. Investigation of inflow water (trace metal and major ion) chemistry using PCA suggests a hydraulic connection between many of the left bank inflows and mine water in the Minnesota Mine shaft located to the north-east of the river channel. In addition, water chemistry data during a rainfall-runoff event suggests the spatial pattern of constituent loading may be modified during rainfall due to dissolution of efflorescent salts or erosion of streamside tailings. These data point to the complexity of contaminant mobilisation processes and constituent loading in mining-affected watersheds but the combined synoptic sampling and PCA approach enables a conceptual model of contaminant dynamics to be developed to inform remediation.

Comparative analysis of near-present and future synoptic conditions and their contribution to precipitation in central Greece

NASA Astrophysics Data System (ADS)

Karacostas, Theodore S.; Bampzelis, Dimitrios; Karipidou, Symela; Pytharoulis, Ioannis; Tegoulias, Ioannis; Kartsios, Stergios; Kotsopoulos, Stylianos; Pakalidou, Nikoletta

2015-04-01

The objective on this study is to identify and categorize the daily synoptic circulation patterns encountered between the two periods, in near-present (2001-2010) and future (2041-2050), over the greater area of central and northern Greece, under the "DAPHNE" project (www.daphne-meteo.gr). The followed up statistical analyses and comparisons are focus on the demonstration of the differences in the frequency of occurrences of the synoptic situations between the two time periods, aiming at mitigating drought in central Greece by means of Weather Modification. Actually, within the context of the project, the daily synoptic circulation patterns encountered during the near-present ten-year period are identified and classified according to Karacostas et al. (1992) synoptic classification, into ten distinct synoptic conditions, based on the isobaric level of 500hPa. A similar procedure is adopted for the future period 2041-2050, by developing the mid-tropospheric synoptic circulation patterns through the RegCM3 regional climate model, under the IPCC scenario A1B. Results indicate that certain differences exist between near-present and future frequency distribution of occurrences of the synoptic situations over the study area. The northwest (NW) and southwest (SW) synoptic circulation patterns remain the most frequent synoptic conditions observed for both examined periods. The low pressure system activity over the area exhibit significant decrease during the future period, as it is depicted from the inter-comparison of the frequencies of the closed low (L-2) and cut-off low (L-3) systems. On the other hand, the unorganized synoptic conditions, which are mostly identified as high-low patterns (H-L), appear to increase considerably. The frequencies of zonal flow (ZON) and those of synoptic conditions associated with the presence of high-pressure system over the area, that is (H-1) and (H-2), remain almost unchanged between the two periods. The impact of the aforementioned differences in the frequencies of the synoptic conditions during the future period is examined on a yearly and seasonal basis. The contribution of each synoptic condition on the annual precipitation amounts are estimated for the near-present period, which coupled with the altered frequencies of the synoptic conditions for the future period, result to the future projected annual precipitation amounts. Possible decrease in precipitation amounts is indicated during the future period, as a result of the reduction in the frequencies of certain synoptic conditions associated with high amount of precipitation during the near-present conditions. Acknowledgments: This research work is part of DAPHNE project (11SYN_8_1088_TPE) which is co-funded by the European Union (European Regional Development Fund) and Greek National Funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" in the framework of the operational programme "Competitiveness and Enterpreneurship" and Regions in Transition (OPC II, NSRF 2007-2013).

Relative contributions of synoptic and intraseasonal variations to strong cold events over eastern China

NASA Astrophysics Data System (ADS)

Song, Lei; Wu, Renguang; Jiao, Yang

2018-06-01

The present study investigates the relative roles of intraseasonal oscillations (ISOs) and synoptic variations in strong cold events over eastern China during the boreal winter. The ISOs and synoptic variations explain about 55% and 20% of the total area-mean temperature anomaly in eastern China, respectively. The advection of synoptic winds on synoptic temperature gradients has a leading contribution to the temperature decrease before the cold events and thus the synoptic variations are important in determining the time of peak cold anomalies. The ISOs have a larger role in sustaining the cold events. The height anomalies associated with ISOs and synoptic variations are manifested as Rossby wave trains propagating along the polar front jet over the Eurasian continent before the cold events. They both contribute to the deepening of the East Asian trough and the development of cold events. Compared to the ISO wave train, the synoptic wave train has a smaller spatial scale and moves faster. There are obvious intraseasonal signals in the stratosphere about 1 week before the cold events over eastern China. Large negative height anomalies associated with the weakening of the polar vortex are observed over the North Atlantic. These anomalies move eastwards and propagate downwards after reaching the west coast of Europe. The downward moving stratospheric signal triggers height anomalies in the troposphere over the entrance region of the polar front jet. Then the anomalies propagate towards East Asia along the wave train, contributing to the intensification of the Siberian high and the East Asian trough and the occurrence of cold events over eastern China.

Combining dispersion modelling with synoptic patterns to understand the wind-borne transport into the UK of the bluetongue disease vector

NASA Astrophysics Data System (ADS)

Burgin, Laura; Ekström, Marie; Dessai, Suraje

2017-07-01

Bluetongue, an economically important animal disease, can be spread over long distances by carriage of insect vectors ( Culicoides biting midges) on the wind. The weather conditions which influence the midge's flight are controlled by synoptic scale atmospheric circulations. A method is proposed that links wind-borne dispersion of the insects to synoptic circulation through the use of a dispersion model in combination with principal component analysis (PCA) and cluster analysis. We illustrate how to identify the main synoptic situations present during times of midge incursions into the UK from the European continent. A PCA was conducted on high-pass-filtered mean sea-level pressure data for a domain centred over north-west Europe from 2005 to 2007. A clustering algorithm applied to the PCA scores indicated the data should be divided into five classes for which averages were calculated, providing a classification of the main synoptic types present. Midge incursion events were found to mainly occur in two synoptic categories; 64.8% were associated with a pattern displaying a pressure gradient over the North Atlantic leading to moderate south-westerly flow over the UK and 17.9% of the events occurred when high pressure dominated the region leading to south-easterly or easterly winds. The winds indicated by the pressure maps generally compared well against observations from a surface station and analysis charts. This technique could be used to assess frequency and timings of incursions of virus into new areas on seasonal and decadal timescales, currently not possible with other dispersion or biological modelling methods.

Analysis of synoptic patterns in relationship with severe rainfall events in the Ebre Observatory (Catalonia)

NASA Astrophysics Data System (ADS)

Pérez-Zanón, Núria; Casas-Castillo, M. Carmen; Peña, Juan Carlos; Aran, Montserrat; Rodríguez-Solà, Raúl; Redaño, Angel; Solé, German

2018-03-01

The study has obtained a classification of the synoptic patterns associated with a selection of extreme rain episodes registered in the Ebre Observatory between 1905 and 2003, showing a return period of not less than 10 years for any duration from 5 min to 24 h. These episodes had been previously classified in four rainfall intensity groups attending to their meteorological time scale. The synoptic patterns related to every group have been obtained applying a multivariable analysis to three atmospheric levels: sea-level pressure, temperature, and geopotential at 500 hPa. Usually, the synoptic patterns associated with intense rain in southern Catalonia are featured by low-pressure systems advecting warm and wet air from the Mediterranean Sea at the low levels of the troposphere. The configuration in the middle levels of the troposphere is dominated by negative anomalies of geopotential, indicating the presence of a low or a cold front, and temperature anomalies, promoting the destabilization of the atmosphere. These configurations promote the occurrence of severe convective events due to the difference of temperature between the low and medium levels of troposphere and the contribution of humidity in the lowest levels of the atmosphere.

Future Changes in Autumn Flood Type and Frequency in Pacific Northwest North America

NASA Astrophysics Data System (ADS)

Menounos, B.; Cannon, A. J.; Radic, V.; Moore, R. D.; Dery, S. J.; Jackson, P. L.; Anslow, F. S.

2013-12-01

During the 20th and early 21st century, autumn storms in the Pacific Northwest of North America - PNWNA (coastal British Columbia and Washington) caused widespread flooding and landslides. Understanding how these intense storms are likely to change in the future is important given their potential to harm people and cause widespread damage, but assessing these changes using climate models is difficult. Parameterization of precipitation in general circulation and regional climate models (GCM, RCM) is prone to error, especially in the mountainous terrain of the PNWNA. High computational demands of RCMs also limits their use in assessing changes in flood type and frequency for a suite of GCM and emission scenarios. We instead focus our efforts on understanding atmospheric circulation patterns responsible for historical autumn flooding (15 August - 31 December) and examine how these synoptic conditions are likely to change under future emission scenarios. Our analysis includes identification of extreme events (runoff and precipitation) in streamflow and precipitation records from coastal Washington and British Columbia for the period 1948-2010. Our methods to link the instrumental record of extreme autumn events to atmospheric conditions (500 and 850 hPa geopotential height and integrated vapor transport obtained from NCEP and CFSR reanalysis) include: (1) compositing of streamflow and precipitation events (environment-to-circulation); (2) self organizing map synoptic classification (circulation-to-environment); and (3) regression tree synoptic classification (hybrid of environment-to-circulation and circulation-to-environment). We then evaluate changes in flood-generating synoptic types in the CMIP5 ensemble over the period 2010-2100. Our analysis indicates that, as expected, most floods are associated with atmospheric river events that are commonly associated with upper level, quasi stationary low- and high-pressure systems respectively located in the Gulf of Alaska and east of the PNWNA. Based on our initial analysis of the CMIP5 data, we note an increase in autumn flood-producing synoptic weather types for the PNWNA. We discuss the implications of increased autumn flooding to communities and infrastructure.

Water-quality assessment of the eastern Iowa basins- nitrogen, phosphorus, suspended sediment, and organic carbon in surface water, 1996-98

USGS Publications Warehouse

Becher, Kent D.; Kalkhoff, Stephen J.; Schnoebelen, Douglas J.; Barnes, Kimberlee K.; Miller, Von E.

2001-01-01

Synoptic samples collected during low and high base flow had nitrogen, phosphorus, and organic-carbon concentrations that varied spatially and seasonally. Comparisons of water-quality data from six basic-fixed sampling sites and 19 other synoptic sites suggest that the water-quality data from basic-fixed sampling sites were representative of the entire study unit during periods of low and high base flow when most streamflow originates from ground water.

Development of Teaching Materials for Field Identification of Plants and Analysis of Their Effectiveness in Science Education.

ERIC Educational Resources Information Center

Ohkawa, Chizuru

2000-01-01

Introduces teaching materials developed for field identification of plants with synoptical keys, identification tables, cards, and programs. Selects approximately 2000 seed plants and uses visibly identifiable characteristics for classification. Recommends using the methodology of identification in other areas for biological identification. (YDS)

Synoptic and meteorological drivers of extreme ozone concentrations over Europe

NASA Astrophysics Data System (ADS)

Otero, Noelia Felipe; Sillmann, Jana; Schnell, Jordan L.; Rust, Henning W.; Butler, Tim

2016-04-01

The present work assesses the relationship between local and synoptic meteorological conditions and surface ozone concentration over Europe in spring and summer months, during the period 1998-2012 using a new interpolated data set of observed surface ozone concentrations over the European domain. Along with local meteorological conditions, the influence of large-scale atmospheric circulation on surface ozone is addressed through a set of airflow indices computed with a novel implementation of a grid-by-grid weather type classification across Europe. Drivers of surface ozone over the full distribution of maximum daily 8-hour average values are investigated, along with drivers of the extreme high percentiles and exceedances or air quality guideline thresholds. Three different regression techniques are applied: multiple linear regression to assess the drivers of maximum daily ozone, logistic regression to assess the probability of threshold exceedances and quantile regression to estimate the meteorological influence on extreme values, as represented by the 95th percentile. The relative importance of the input parameters (predictors) is assessed by a backward stepwise regression procedure that allows the identification of the most important predictors in each model. Spatial patterns of model performance exhibit distinct variations between regions. The inclusion of the ozone persistence is particularly relevant over Southern Europe. In general, the best model performance is found over Central Europe, where the maximum temperature plays an important role as a driver of maximum daily ozone as well as its extreme values, especially during warmer months.

A Hierarchical and Dynamic Seascape Framework for Scaling and Comparing Ocean Biodiversity Observations

NASA Astrophysics Data System (ADS)

Kavanaugh, M.; Muller-Karger, F. E.; Montes, E.; Santora, J. A.; Chavez, F.; Messié, M.; Doney, S. C.

2016-02-01

The pelagic ocean is a complex system in which physical, chemical and biological processes interact to shape patterns on multiple spatial and temporal scales and levels of ecological organization. Monitoring and management of marine seascapes must consider a hierarchical and dynamic mosaic, where the boundaries, extent, and location of features change with time. As part of a Marine Biodiversity Observing Network demonstration project, we conducted a multiscale classification of dynamic coastal seascapes in the northeastern Pacific and Gulf of Mexico using multivariate satellite and modeled data. Synoptic patterns were validated using mooring and ship-based observations that spanned multiple trophic levels and were collected as part of several long-term monitoring programs, including the Monterey Bay and Florida Keys National Marine Sanctuaries. Seascape extent and habitat diversity varied as a function of both seasonal and interannual forcing. We discuss the patterns of in situ observations in the context of seascape dynamics and the effect on rarefaction, spatial patchiness, and tracking and comparing ecosystems through time. A seascape framework presents an effective means to translate local biodiversity measurements to broader spatiotemporal scales, scales relevant for modeling the effects of global change and enabling whole-ecosystem management in the dynamic ocean.

Coastal Seabed Mapping with Hyperspectral and Lidar data

NASA Astrophysics Data System (ADS)

Taramelli, A.; Valentini, E.; Filipponi, F.; Cappucci, S.

2017-12-01

A synoptic view of the coastal seascape and its dynamics needs a quantitative ability to dissect different components over the complexity of the seafloor where a mixture of geo - biological facies determines geomorphological features and their coverage. The present study uses an analytical approach that takes advantage of a multidimensional model to integrate different data sources from airborne Hyperspectral and LiDAR remote sensing and in situ measurements to detect antropogenic features and ecological `tipping points' in coastal seafloors. The proposed approach has the ability to generate coastal seabed maps using: 1) a multidimensional dataset to account for radiometric and morphological properties of waters and the seafloor; 2) a field spectral library to assimilate the high environmental variability into the multidimensional model; 3) a final classification scheme to represent the spatial gradients in the seafloor. The spatial pattern of the response to anthropogenic forcing may be indistinguishable from patterns of natural variability. It is argued that this novel approach to define tipping points following anthropogenic impacts could be most valuable in the management of natural resources and the economic development of coastal areas worldwide. Examples are reported from different sites of the Mediterranean Sea, both from Marine Protected and un-Protected Areas.

Large-area mapping of biodiversity

USGS Publications Warehouse

Scott, J.M.; Jennings, M.D.

1998-01-01

The age of discovery, description, and classification of biodiversity is entering a new phase. In responding to the conservation imperative, we can now supplement the essential work of systematics with spatially explicit information on species and assemblages of species. This is possible because of recent conceptual, technical, and organizational progress in generating synoptic views of the earth's surface and a great deal of its biological content, at multiple scales of thematic as well as geographic resolution. The development of extensive spatial data on species distributions and vegetation types provides us with a framework for: (a) assessing what we know and where we know it at meso-scales, and (b) stratifying the biological universe so that higher-resolution surveys can be more efficiently implemented, coveting, for example, geographic adequacy of specimen collections, population abundance, reproductive success, and genetic dynamics. The land areas involved are very large, and the questions, such as resolution, scale, classification, and accuracy, are complex. In this paper, we provide examples from the United States Gap Analysis Program on the advantages and limitations of mapping the occurrence of terrestrial vertebrate species and dominant land-cover types over large areas as joint ventures and in multi-organizational partnerships, and how these cooperative efforts can be designed to implement results from data development and analyses as on-the-ground actions. Clearly, new frameworks for thinking about biogeographic information as well as organizational cooperation are needed if we are to have any hope of documenting the full range of species occurrences and ecological processes in ways meaningful to their management. The Gap Analysis experience provides one model for achieving these new frameworks.

Synoptic-scale atmospheric conditions associated with flash flooding in watersheds of the Catskill Mountains, New York, USA

NASA Astrophysics Data System (ADS)

Teale, N. G.; Quiring, S. M.

2015-12-01

Understanding flash flooding is important in unfiltered watersheds, such as portions of the New York City water supply system (NYCWSS), as water quality is degraded by turbidity associated with flooding. To further understand flash flooding in watersheds of the NYCWSS, synoptic-scale atmospheric conditions most frequently associated with flash flooding between 1987 and 2013 were examined. Flash floods were identified during this time period using USGS 15-minute discharge data at the Esopus Creek near Allaben, NY and Neversink River at Claryville, NY gauges. Overall, 25 flash floods were detected, occurring over 17 separate flash flood days. These flash flood days were compared to the days on which flash flood warnings encompassing the study area was issued by the National Weather Service. The success rate for which the flash flood warnings for Ulster County coincided with flash flood in the study watershed was 0.09, demonstrating the highly localized nature of flash flooding in the Catskill Mountain region. The synoptic-scale atmospheric patterns influencing the study area were characterized by a principal component analysis and k-means clustering of NCEP/NCAR 500 mb geopotential height reanalysis data. This procedure was executed in Spatial Synoptic Typer Tools 4.0. While 17 unique synoptic patterns were identified, only 3 types were strongly associated with flash flooding events. A strong southwesterly flow suggesting advection of moisture from the Atlantic Ocean and Gulf of Mexico is shown in composites of these 3 types. This multiscalar study thereby links flash flooding in the NYCWSS with synoptic-scale atmospheric circulation.Understanding flash flooding is important in unfiltered watersheds, such as portions of the New York City water supply system (NYCWSS), as water quality is degraded by turbidity associated with flooding. To further understand flash flooding in watersheds of the NYCWSS, synoptic-scale atmospheric conditions most frequently associated with flash flooding between 1987 and 2013 were examined. Flash floods were identified during this time period using USGS 15-minute discharge data at the Esopus Creek near Allaben, NY and Neversink River at Claryville, NY gauges. Overall, 25 flash floods were detected, occurring over 17 separate flash flood days. These flash flood days were compared to the days on which flash flood warnings encompassing the study area was issued by the National Weather Service. The success rate for which the flash flood warnings for Ulster County coincided with flash flood in the study watershed was 0.09, demonstrating the highly localized nature of flash flooding in the Catskill Mountain region. The synoptic-scale atmospheric patterns influencing the study area were characterized by a principal component analysis and k-means clustering of NCEP/NCAR 500 mb geopotential height reanalysis data. This procedure was executed in Spatial Synoptic Typer Tools 4.0. While 17 unique synoptic patterns were identified, only 3 types were strongly associated with flash flooding events. A strong southwesterly flow suggesting advection of moisture from the Atlantic Ocean and Gulf of Mexico is shown in composites of these 3 types. This multiscalar study thereby links flash flooding in the NYCWSS with synoptic-scale atmospheric circulation.

Improving Prediction of Large-scale Regime Transitions

NASA Astrophysics Data System (ADS)

Gyakum, J. R.; Roebber, P.; Bosart, L. F.; Honor, A.; Bunker, E.; Low, Y.; Hart, J.; Bliankinshtein, N.; Kolly, A.; Atallah, E.; Huang, Y.

2017-12-01

Cool season atmospheric predictability over the CONUS on subseasonal times scales (1-4 weeks) is critically dependent upon the structure, configuration, and evolution of the North Pacific jet stream (NPJ). The NPJ can be perturbed on its tropical side on synoptic time scales by recurving and transitioning tropical cyclones (TCs) and on subseasonal time scales by longitudinally varying convection associated with the Madden-Julian Oscillation (MJO). Likewise, the NPJ can be perturbed on its poleward side on synoptic time scales by midlatitude and polar disturbances that originate over the Asian continent. These midlatitude and polar disturbances can often trigger downstream Rossby wave propagation across the North Pacific, North America, and the North Atlantic. The project team is investigating the following multiscale processes and features: the spatiotemporal distribution of cyclone clustering over the Northern Hemisphere; cyclone clustering as influenced by atmospheric blocking and the phases and amplitudes of the major teleconnection indices, ENSO and the MJO; composite and case study analyses of representative cyclone clustering events to establish the governing dynamics; regime change predictability horizons associated with cyclone clustering events; Arctic air mass generation and modification; life cycles of the MJO; and poleward heat and moisture transports of subtropical air masses. A critical component of the study is weather regime classification. These classifications are defined through: the spatiotemporal clustering of surface cyclogenesis; a general circulation metric combining data at 500-hPa and the dynamic tropopause; Self Organizing Maps (SOM), constructed from dynamic tropopause and 850 hPa equivalent potential temperature data. The resultant lattice of nodes is used to categorize synoptic classes and their predictability, as well as to determine the robustness of the CFSv2 model climate relative to observations. Transition pathways between these synoptic classes, both in the observations and the CFSv2, are investigated. At a future point in the project, the results from these multiscale investigations will be integrated in the form of a prediction tool for important variables (temperatures, precipitation and their extremes) for the 1-4 week timeframe.

Estimating the Spatial Distribution of Groundwater Age Using Synoptic Surveys of Environmental Tracers in Streams

NASA Astrophysics Data System (ADS)

Gardner, W. P.

2017-12-01

A model which simulates tracer concentration in surface water as a function the age distribution of groundwater discharge is used to characterize groundwater flow systems at a variety of spatial scales. We develop the theory behind the model and demonstrate its application in several groundwater systems of local to regional scale. A 1-D stream transport model, which includes: advection, dispersion, gas exchange, first-order decay and groundwater inflow is coupled a lumped parameter model that calculates the concentration of environmental tracers in discharging groundwater as a function of the groundwater residence time distribution. The lumped parameters, which describe the residence time distribution, are allowed to vary spatially, and multiple environmental tracers can be simulated. This model allows us to calculate the longitudinal profile of tracer concentration in streams as a function of the spatially variable groundwater age distribution. By fitting model results to observations of stream chemistry and discharge, we can then estimate the spatial distribution of groundwater age. The volume of groundwater discharge to streams can be estimated using a subset of environmental tracers, applied tracers, synoptic stream gauging or other methods, and the age of groundwater then estimated using the previously calculated groundwater discharge and observed environmental tracer concentrations. Synoptic surveys of SF6, CFC's, 3H and 222Rn, along with measured stream discharge are used to estimate the groundwater inflow distribution and mean age for regional scale surveys of the Berland River in west-central Alberta. We find that groundwater entering the Berland has observable age, and that the age estimated using our stream survey is of similar order to limited samples from groundwater wells in the region. Our results show that the stream can be used as an easily accessible location to constrain the regional scale spatial distribution of groundwater age.

Modern Climate Analogues of Late-Quaternary Paleoclimates for the Western United States.

NASA Astrophysics Data System (ADS)

Mock, Cary Jeffrey

This study examined spatial variations of modern and late-Quaternary climates for the western United States. Synoptic climatological analyses of the modern record identified the predominate climatic controls that normally produce the principal modes of spatial climatic variability. They also provided a modern standard to assess past climates. Maps of the month-to-month changes in 500 mb heights, sea-level pressure, temperature, and precipitation illustrated how different climatic controls govern the annual cycle of climatic response. The patterns of precipitation ratios, precipitation bar graphs, and the seasonal precipitation maximum provided additional insight into how different climatic controls influence spatial climatic variations. Synoptic-scale patterns from general circulation model (GCM) simulations or from analyses of climatic indices were used as the basis for finding modern climate analogues for 18 ka and 9 ka. Composite anomaly maps of atmospheric circulation, precipitation, and temperature were compared with effective moisture maps compiled from proxy data to infer how the patterns, which were evident from the proxy data, were generated. The analyses of the modern synoptic climatology indicate that smaller-scale climatic controls must be considered along with larger-scale ones in order to explain patterns of spatial climate heterogeneity. Climatic extremes indicate that changes in the spatial patterns of precipitation seasonality are the exception rather than the rule, reflecting the strong influence of smaller-scale controls. Modern climate analogues for both 18 ka and 9 ka clearly depict the dry Northwest/wet Southwest contrast that is suggested by GCM simulations and paleoclimatic evidence. 18 ka analogues also show the importance of smaller-scale climatic controls in explaining spatial climatic variation in the Northwest and northern Great Plains. 9 ka analogues provide climatological explanations for patterns of spatial heterogeneity over several mountainous areas as suggested by paleoclimatic evidence. Modern analogues of past climates supplement modeling approaches by providing information below the resolution of model simulations. Analogues can be used to examine the controls of spatial paleoclimatic variation if sufficient instrumental data and paleoclimatic evidence are available, and if one carefully exercises uniformitarianism when extrapolating modern relationships to the past.

Classification of summertime synoptic patterns in Beijing and their associations with boundary layer structure affecting aerosol pollution

NASA Astrophysics Data System (ADS)

Miao, Yucong; Guo, Jianping; Liu, Shuhua; Liu, Huan; Li, Zhanqing; Zhang, Wanchun; Zhai, Panmao

2017-02-01

Meteorological conditions within the planetary boundary layer (PBL) are closely governed by large-scale synoptic patterns and play important roles in air quality by directly and indirectly affecting the emission, transport, formation, and deposition of air pollutants. Partly due to the lack of long-term fine-resolution observations of the PBL, the relationships between synoptic patterns, PBL structure, and aerosol pollution in Beijing have not been well understood. This study applied the obliquely rotated principal component analysis in T-mode to classify the summertime synoptic conditions over Beijing using the National Centers for Environmental Prediction reanalysis from 2011 to 2014, and investigated their relationships with PBL structure and aerosol pollution by combining numerical simulations, measurements of surface meteorological variables, fine-resolution soundings, the concentration of particles with diameters less than or equal to 2.5 µm, total cloud cover (CLD), and reanalysis data. Among the seven identified synoptic patterns, three types accounted for 67 % of the total number of cases studied and were associated with heavy aerosol pollution events. These particular synoptic patterns were characterized by high-pressure systems located to the east or southeast of Beijing at the 925 hPa level, which blocked the air flow seaward, and southerly PBL winds that brought in polluted air from the southern industrial zone. The horizontal transport of pollutants induced by the synoptic forcings may be the most important factor affecting the air quality of Beijing in summer. In the vertical dimension, these three synoptic patterns featured a relatively low boundary layer height (BLH) in the afternoon, accompanied by high CLD and southerly cold advection from the seas within the PBL. The high CLD reduced the solar radiation reaching the surface, and suppressed the thermal turbulence, leading to lower BLH. Besides, the numerical sensitive experiments show that cold advection induced by the large-scale synoptic forcing may have cooled the PBL, leading to an increase in near-surface stability and a decrease in the BLH in the afternoon. Moreover, when warm advection appeared simultaneously above the top level of the PBL, the thermal inversion layer capping the PBL may have been strengthened, resulting in the further suppression of PBL and thus the deterioration of aerosol pollution levels. This study has important implications for understanding the crucial roles that meteorological factors (at both synoptic and local scales) play in modulating and forecasting aerosol pollution in Beijing and its surrounding area.

Synoptic maps of heliospheric Thomson scattering brightness from 1974-1985 as observed by the Helios photometers

NASA Technical Reports Server (NTRS)

Hick, P.; Jackson, B. V.; Schwenn, R.

1992-01-01

We display the electron Thomson scattering intensity of the inner heliosphere as observed by the zodiacal light photometers on board the Helios spacecraft in the form of synoptic maps. The technique extrapolates the brightness information from each photometer sector near the Sun and constructs a latitude/longitude map at a given solar height. These data are unique in that they give a determination of heliospheric structures out of the ecliptic above the primary region of solar wind acceleration. The spatial extent of bright, co-rotating heliospheric structures is readily observed in the data north and south of the ecliptic plane where the Helios photometer coverage is most complete. Because the technique has been used on the complete Helios data set from 1974 to 1985, we observe the change in our synoptic maps with solar cycle. Bright structures are concentrated near the heliospheric equator at solar minimum, while at solar maximum bright structures are found at far higher heliographic latitudes. A comparison of these maps with other forms of synoptic data are shown for two available intervals.

An Automated Solar Synoptic Analysis Software System

NASA Astrophysics Data System (ADS)

Hong, S.; Lee, S.; Oh, S.; Kim, J.; Lee, J.; Kim, Y.; Lee, J.; Moon, Y.; Lee, D.

2012-12-01

We have developed an automated software system of identifying solar active regions, filament channels, and coronal holes, those are three major solar sources causing the space weather. Space weather forecasters of NOAA Space Weather Prediction Center produce the solar synoptic drawings as a daily basis to predict solar activities, i.e., solar flares, filament eruptions, high speed solar wind streams, and co-rotating interaction regions as well as their possible effects to the Earth. As an attempt to emulate this process with a fully automated and consistent way, we developed a software system named ASSA(Automated Solar Synoptic Analysis). When identifying solar active regions, ASSA uses high-resolution SDO HMI intensitygram and magnetogram as inputs and providing McIntosh classification and Mt. Wilson magnetic classification of each active region by applying appropriate image processing techniques such as thresholding, morphology extraction, and region growing. At the same time, it also extracts morphological and physical properties of active regions in a quantitative way for the short-term prediction of flares and CMEs. When identifying filament channels and coronal holes, images of global H-alpha network and SDO AIA 193 are used for morphological identification and also SDO HMI magnetograms for quantitative verification. The output results of ASSA are routinely checked and validated against NOAA's daily SRS(Solar Region Summary) and UCOHO(URSIgram code for coronal hole information). A couple of preliminary scientific results are to be presented using available output results. ASSA will be deployed at the Korean Space Weather Center and serve its customers in an operational status by the end of 2012.

Validation of a Remote Sensing Based Index of Forest Disturbance Using Streamwater Nitrogen Data

NASA Technical Reports Server (NTRS)

Eshleman, Keith N.; McNeil, Brenden E.; Townsend, Philip A.

2008-01-01

Vegetation disturbances are known to alter the functioning of forested ecosystems by contributing to export ('leakage') of dissolved nitrogen (N), typically nitrate-N, from watersheds that can contribute to acidification of acid-sensitive streams, leaching of base cations, and eutrophication of downstream receiving waters. Yet, at a landscape scale, direct evaluation of how disturbance is linked to spatial variability in N leakage is complicated by the fact that disturbances operate at different spatial scales, over different timescales, and at different intensities. In this paper we explore whether data from synoptic streamwater surveys conducted in an Appalachian oak-dominated forested river basin in western MD (USA) can be used to test and validate a scalable, synthetic, and integrative forest disturbance index (FDI) derived from Landsat imagery. In particular, we found support for the hypothesis that the interannual variation in spring baseflow total dissolved nitrogen (TDN) and nitrate-N concentrations measured at 35 randomly selected stream stations varied as a linear function of the change in FDI computed for the corresponding set of subwatersheds. Our results demonstrate that the combined effects of forest disturbances can be detected using synoptic water quality data. It appears that careful timing of the synoptic baseflow sampling under comparable phenological and hydrometeorological conditions increased our ability to identify a forest disturbance signal.

Discrimination of tornadic and non-tornadic severe weather outbreaks

NASA Astrophysics Data System (ADS)

Mercer, Andrew Edward

Outbreaks of severe weather affect the majority of the conterminous United States. An outbreak is characterized by multiple severe weather occurrences within a single synoptic system. Outbreaks can be categorized by whether or not they produce tornadoes. It is hypothesized that the antecedent synoptic signal contains important information about outbreak type. Accordingly, the scope of this research is to determine the extent that the synoptic signal can be utilized to classify outbreak type at various lead times. Outbreak types are classified using the NCEP/NCAR reanalysis data, which are arranged on a global 2.5° latitude-longitude grid, include 17 vertical pressure levels, and span from 1948 to the present (2008). Fifty major tornado outbreak (TO) cases and fifty major non-tornadic severe weather outbreak (NTO) cases are selected for this work. Two types of analyses are performed on these cases to assess discrimination ability. One analysis involves outbreak classification using the Weather Research and Forecasting (WRF) model initialized with the NCEP/NCAR reanalysis dataset. Meteorological covariates are computed from the WRF output and used in training and testing of statistical classification models. The covariate fields are depicted on a 21 X 21 gridpoint field with an 18 km grid spacing centered on the outbreak. Covariates with large discrimination potential are determined using permutation testing. A P-mode principal component analysis (PCA) is used on the subset of covariates determined by permutation testing to reduce data dimensionality, since numerous redundancies exist in the initial covariate set. Three statistical classification models are trained and tested with the resulting PC scores: a support vector machine (SVM), a logistic regression model (LogR), and a multiple linear regression model (LR). Promising results emerge from these methods, as a probability of detection (POD) of 0.89 and a false alarm ratio (FAR) of 0.13 are obtained from the best discriminating statistical technique (SVM) at 24-hours lead time. Results degrade only slightly by 72-hours lead time (maximum POD of 0.833 and minimum FAR of 0.276). Synoptic composites of the outbreak types are the second analysis considered. Composites are used to reveal synoptic features of outbreak types, which can be utilized to diagnose the differences between classes (in this case, TOs and NTOs). The composites are created using PCA. Five raw variables, height, temperature, relative humidity, and u and v wind components, are extracted from the NCEP/NCAR reanalysis data for North America. Converging longitude lines with increasing latitude on the reanalysis grid introduce bias into correlation calculations in higher latitudes; hence, the data are mapped onto both a latitudinal density grid and a Fibonacci grid. The resulting PCA produces two significant principal components (PCs), and a cluster analysis on these PCs for each outbreak type results in two types of TOs and NTOs. TO composites are characterized by a trough of low pressure over the central United States and major quasigeostrophic forcing features such as an upper level jet streak, cyclonic vorticity advection increasing with height, and warm air advection. These dynamics result in a strong surface cyclone in most tornado outbreaks. These features are considerably less pronounced in NTOs. The statistical analyses presented herein were successful in classifying outbreak types at various lead times, using synoptic scale data as input.

Comparison of the Climates of Selected Locations in the United States with the Climate of Beersheba, Israel.

DTIC Science & Technology

1981-01-01

INTRODUCTION .. ...... . . . .. .. .. .... 1 II. THE CLIMATE OF BEERSHEBA .............. 3 A. KZ~ppen Classification. ............. 3 B. Synoptic Features...Local Mean Solar Time. ............. 18 B. Period of Observation .. ........... 20 C. Statistical Calculations. .......... 20 1. Introduction ...157 vi I. INTRODUCTION Battlefield obscuration plays an important role in the performance of Army electro-optical devices. In turn, the type

Classification of weather patterns to study the influence of meteorological characteristics on PM2.5 concentrations in Yunlin County, Taiwan

NASA Astrophysics Data System (ADS)

Hsu, Chia-Hua; Cheng, Fang-Yi

2016-11-01

Yunlin County is located in the central part of western Taiwan with major emissions from the Mailiao industrial park, the Taichung Power Plants and heavy traffic. In order to understand the influence of meteorological conditions on PM2.5 concentrations in Yunlin County, we applied a two-stage cluster analysis method using the daily averaged surface winds from four air quality monitoring stations in Yunlin County to classify the weather pattern. The study period includes 1095 days from Jan 2013 to December 2015. The classification results show that the low PM2.5 concentration occurs when the synoptic weather in Taiwan is affected by the strong southwesterly monsoonal flow. The high PM2.5 concentration occurs when Taiwan is under the influence of weak synoptic weather conditions and continental high-pressure peripheral circulation. A high PM2.5 event was studied and the Weather Research and Forecasting (WRF) meteorological model was performed. The result indicated that due to being blocked by the Central Mountain Range, Yunlin County, which is situated on the leeside of the mountains, exhibits low wind speed and strong subsidence behavior that favors PM2.5 accumulation.

The Synoptic Climatology of Severe Thunderstorms in Manitoba.

NASA Astrophysics Data System (ADS)

Ladochy, Stephen Eugene Gabriel

The thesis presents the climatologies for Manitoba thunderstorms, hailstorms and tornadoes as well as investigates the synoptic weather conditions conducive for their development. The study not only uses standard meteorological information, but also various kinds of proxy data, in the form of damage reports. These damage reports complement the meteorological data by providing a higher resolution of observations, particularly in the sparsely populated regions. The synoptic conditions are relatively similar for all forms of severe thunderstorms, though the upper level jet stream (ULJ) is stronger for tornadoes, in general. Composite charts, drawn for 50 larger, more damaging hail days and 48 tornado days in the 1970's, helped identify important surface and upper air weather parameters and their inter -relationships with each other and the location of the storm. Time sequence composite charts were used to also show the development process in severe weather occurrences. From the composites, a synoptic weather type classification was devised with 10 categories to identify each storm by type. The most common pattern for severe weather has a strong southwesterly ULJ, with the storm occurring ahead of an advancing cold front. The ULJ patterns were drawn for each synoptic type days, showing differences between categories. The average conditions during tornado touchdowns were also seen from composite maps of surface and upper air isobaric charts. While severe thunderstorms are seen to occur under the "ideal" conditions, often described for U.S. severe weather, they can also be produced under other weather patterns and combinations of atmospheric parameters thought less favorable. The ULJ and LLJ (low-level jet stream) models used in U.S. studies do not always fit Manitoba storms, however, less favorable jet positions, at specific levels, can be compensated for by low-level advection of warm, and moist air.

An Air Mass Based Approach to the Establishment of Spring Season Synoptic Characteristics in the Northeast United States

NASA Astrophysics Data System (ADS)

Zander, R.; Messina, A.; Godek, M. L.

2012-12-01

The spring season is indicative of marked meteorological, ecological, and biological changes across the Northeast United States. The onset of spring coincides with distinct meteorological phenomena including an increase in severe weather events and snow meltwaters that can cause localized flooding and other costly damages. Increasing and variable springtime temperatures also influence Northeast tourist operations and agricultural productivity. Even with the vested interest of industry in the season and public awareness of the dynamic characteristics of spring, the definition of spring remains somewhat arbitrary. The primary goal of this research is to obtain a synoptic meteorological definition of the spring season through an assessment of air mass frequency over the past 60 years. A secondary goal examines the validity of recent speculations that the onset and termination of spring has changed in recent decades, particularly since 1975. The Spatial Synoptic Classification is utilized to define daily air masses over the region. Annual and seasonal baseline frequencies are identified and their differences are acquired to characterize the season. Seasonal frequency departures of the early and late segments of the period of record around 1975 are calculated and examined for practical and statistical significance. The daily boundaries of early and late spring are then isolated and frequencies are obtained for these periods. Boundary frequencies are assessed across the period of record to identify important changes in the season's initiation and termination through time. Results indicate that the Northeast spring season is dominated by dry air masses, mainly the Dry Moderate and Dry Polar types. Significant differences in seasonal air mass frequency are also observed through time. Prior to 1975, higher frequencies of polar air mass types are detected while after 1975 there is an increase in the frequencies of both moderate and tropical types. This finding is also identified for the onset of spring. Late spring frequencies are similar but with more variability in all moist variety air mass frequencies. These findings indicate that, from a synoptic perspective, springs in the Northeast can be defined by dry air mass conditions through time but modern springs are also warmer than those of past decades and the initiation of the season is likely arriving earlier. The end of the Northeast spring season may also be represented by more variable day-to-day air mass conditions in modern times than detected in past decades. 1950 - 1975 (black) and 1976 - 2010 (gray) Philadelphia, PA Spring air mass frequency (%).

Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

NASA Technical Reports Server (NTRS)

Crane, Robert G.; Hewitson, B. C.

1991-01-01

A new diagnostic tool is developed for examining relationships between the synoptic scale circulation and regional temperature distributions in GCMs. The 4 x 5 deg GISS GCM is shown to produce accurate simulations of the variance in the synoptic scale sea level pressure distribution over the U.S. An analysis of the observational data set from the National Meteorological Center (NMC) also shows a strong relationship between the synoptic circulation and grid point temperatures. This relationship is demonstrated by deriving transfer functions between a time-series of circulation parameters and temperatures at individual grid points. The circulation parameters are derived using rotated principal components analysis, and the temperature transfer functions are based on multivariate polynomial regression models. The application of these transfer functions to the GCM circulation indicates that there is considerable spatial bias present in the GCM temperature distributions. The transfer functions are also used to indicate the possible changes in U.S. regional temperatures that could result from differences in synoptic scale circulation between a 1XCO2 and a 2xCO2 climate, using a doubled CO2 version of the same GISS GCM.

Non-parametric transient classification using adaptive wavelets

NASA Astrophysics Data System (ADS)

Varughese, Melvin M.; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce A.

2015-11-01

Classifying transients based on multiband light curves is a challenging but crucial problem in the era of GAIA and Large Synoptic Sky Telescope since the sheer volume of transients will make spectroscopic classification unfeasible. We present a non-parametric classifier that predicts the transient's class given training data. It implements two novel components: the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients - as well as the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The classifier is simple to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant. Hence, BAGIDIS does not need the light curves to be aligned to extract features. Further, BAGIDIS is non-parametric so it can be used effectively in blind searches for new objects. We demonstrate the effectiveness of our classifier against the Supernova Photometric Classification Challenge to correctly classify supernova light curves as Type Ia or non-Ia. We train our classifier on the spectroscopically confirmed subsample (which is not representative) and show that it works well for supernova with observed light-curve time spans greater than 100 d (roughly 55 per cent of the data set). For such data, we obtain a Ia efficiency of 80.5 per cent and a purity of 82.4 per cent, yielding a highly competitive challenge score of 0.49. This indicates that our `model-blind' approach may be particularly suitable for the general classification of astronomical transients in the era of large synoptic sky surveys.

Supernova Photometric Lightcurve Classification

NASA Astrophysics Data System (ADS)

Zaidi, Tayeb; Narayan, Gautham

2016-01-01

This is a preliminary report on photometric supernova classification. We first explore the properties of supernova light curves, and attempt to restructure the unevenly sampled and sparse data from assorted datasets to allow for processing and classification. The data was primarily drawn from the Dark Energy Survey (DES) simulated data, created for the Supernova Photometric Classification Challenge. This poster shows a method for producing a non-parametric representation of the light curve data, and applying a Random Forest classifier algorithm to distinguish between supernovae types. We examine the impact of Principal Component Analysis to reduce the dimensionality of the dataset, for future classification work. The classification code will be used in a stage of the ANTARES pipeline, created for use on the Large Synoptic Survey Telescope alert data and other wide-field surveys. The final figure-of-merit for the DES data in the r band was 60% for binary classification (Type I vs II).Zaidi was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program (AST-1262829).

An investigation on thermal patterns in Iran based on spatial autocorrelation

NASA Astrophysics Data System (ADS)

Fallah Ghalhari, Gholamabbas; Dadashi Roudbari, Abbasali

2018-02-01

The present study aimed at investigating temporal-spatial patterns and monthly patterns of temperature in Iran using new spatial statistical methods such as cluster and outlier analysis, and hotspot analysis. To do so, climatic parameters, monthly average temperature of 122 synoptic stations, were assessed. Statistical analysis showed that January with 120.75% had the most fluctuation among the studied months. Global Moran's Index revealed that yearly changes of temperature in Iran followed a strong spatially clustered pattern. Findings showed that the biggest thermal cluster pattern in Iran, 0.975388, occurred in May. Cluster and outlier analyses showed that thermal homogeneity in Iran decreases in cold months, while it increases in warm months. This is due to the radiation angle and synoptic systems which strongly influence thermal order in Iran. The elevations, however, have the most notable part proved by Geographically weighted regression model. Iran's thermal analysis through hotspot showed that hot thermal patterns (very hot, hot, and semi-hot) were dominant in the South, covering an area of 33.5% (about 552,145.3 km2). Regions such as mountain foot and low lands lack any significant spatial autocorrelation, 25.2% covering about 415,345.1 km2. The last is the cold thermal area (very cold, cold, and semi-cold) with about 25.2% covering about 552,145.3 km2 of the whole area of Iran.

A Precipitation Climatology of the Snowy Mountains, Australia

NASA Astrophysics Data System (ADS)

Theobald, Alison; McGowan, Hamish; Speirs, Johanna

2014-05-01

The precipitation that falls in the Snowy Mountains region of southeastern Australia provides critical water resources for hydroelectric power generation. Water storages in this region are also a major source of agricultural irrigation, environmental flows, and offer a degree of flood protection for some of the major river systems in Australia. Despite this importance, there remains a knowledge gap regarding the long-term, historic variability of the synoptic weather systems that deliver precipitation to the region. This research aims to increase the understanding of long-term variations in precipitation-bearing weather systems resulting in runoff into the Snowy Mountains catchments and reservoirs, and the way in which these are influenced by large-scale climate drivers. Here we present initial results on the development of a climatology of precipitation-bearing synoptic weather systems (synoptic typology), spanning a period of over 100 years. The synoptic typology is developed from the numerical weather model re-analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), in conjunction with regional precipitation and temperature data from a network of private gauges. Given the importance of surface, mid- and upper-air patterns on seasonal precipitation, the synoptic typing will be based on a range of meteorological variables throughout the depth of the troposphere, highlighting the importance of different atmospheric levels on the development and steering of synoptic precipitation bearing systems. The temporal and spatial variability of these synoptic systems, their response to teleconnection forcings and their contribution to inflow generation in the headwater catchments of the Snowy Mountains will be investigated. The resulting climatology will provide new understanding of the drivers of regional-scale precipitation variability at inter- and intra-annual timescales. It will enable greater understanding of how variability in synoptic scale atmospheric circulation affects the hydroclimate of alpine environments in southeast Australia - allowing recently observed precipitation declines to be placed in the context of a long-term record spanning at least 100 years. This information will provide further insight into the impacts of predicted anthropogenic climate change and will ultimately lead to more informed water resource management in the Snowy Mountains.

Identifying relationships between baseflow geochemistry and land use with synoptic sampling and R-Mode factor analysis

USGS Publications Warehouse

Wayland, Karen G.; Long, David T.; Hyndman, David W.; Pijanowski, Bryan C.; Woodhams, Sarah M.; Haak, Sheridan K.

2003-01-01

The relationship between land use and stream chemistry is often explored through synoptic sampling rivers at baseflow condition. However, base flow chemistry is likely to vary temporally and spatially with land use. The purpose of our study is to examine the usefulness of the synoptic sampling approach for identifying the relationship between complex land use configurations and stream water quality. This study compares biogeochemical data from three synoptic sampling events representing the temporal variability of baseflow chemistry and land use using R-mode factor analysis. Separate R-mode factor analyses of the data from individual sampling events yielded only two consistent factors. Agricultural activity was associated with elevated levels of Ca2+, Mg2+, alkalinity, and frequently K+, SO42-, and NO3-. Urban areas were associated with higher concentrations of Na+, K+, and Cl-. Other retained factors were not  consistent among sampling events, and some factors were difficult to interpret in the context of biogeochemical sources and processes. When all data were combined, further associations were revealed such as an inverse relationship between the proportion of wetlands and stream nitrate concentrations. We also found that barren lands were associated with elevated sulfate levels. This research suggests that an individual sampling event is unlikely to characterize adequately the complex processes controlling interactions between land uses and stream chemistry. Combining data collected over two years during three synoptic sampling events appears to enhance our ability to understand processes linking stream chemistry and land use.  

Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

NASA Technical Reports Server (NTRS)

Crane, Robert G.; Hewitson, Bruce

1990-01-01

Model simulations of global climate change are seen as an essential component of any program aimed at understanding human impact on the global environment. A major weakness of current general circulation models (GCMs), however, is their inability to predict reliably the regional consequences of a global scale change, and it is these regional scale predictions that are necessary for studies of human/environmental response. This research is directed toward the development of a methodology for the validation of the synoptic scale climatology of GCMs. This is developed with regard to the Goddard Institute for Space Studies (GISS) GCM Model 2, with the specific objective of using the synoptic circulation form a doubles CO2 simulation to estimate regional climate change over North America, south of Hudson Bay. This progress report is specifically concerned with validating the synoptic climatology of the GISS GCM, and developing the transfer function to derive grid-point temperatures from the synoptic circulation. Principal Components Analysis is used to characterize the primary modes of the spatial and temporal variability in the observed and simulated climate, and the model validation is based on correlations between component loadings, and power spectral analysis of the component scores. The results show that the high resolution GISS model does an excellent job of simulating the synoptic circulation over the U.S., and that grid-point temperatures can be predicted with reasonable accuracy from the circulation patterns.

Synoptic Conditions and Moisture Sources Actuating Extreme Precipitation in Nepal

NASA Astrophysics Data System (ADS)

Bohlinger, Patrik; Sorteberg, Asgeir; Sodemann, Harald

2017-12-01

Despite the vast literature on heavy-precipitation events in South Asia, synoptic conditions and moisture sources related to extreme precipitation in Nepal have not been addressed systematically. We investigate two types of synoptic conditions—low-pressure systems and midlevel troughs—and moisture sources related to extreme precipitation events. To account for the high spatial variability in rainfall, we cluster station-based daily precipitation measurements resulting in three well-separated geographic regions: west, central, and east Nepal. For each region, composite analysis of extreme events shows that atmospheric circulation is directed against the Himalayas during an extreme event. The direction of the flow is regulated by midtropospheric troughs and low-pressure systems traveling toward the respective region. Extreme precipitation events feature anomalous high abundance of total column moisture. Quantitative Lagrangian moisture source diagnostic reveals that the largest direct contribution stems from land (approximately 75%), where, in particular, over the Indo-Gangetic Plain moisture uptake was increased. Precipitation events occurring in this region before the extreme event likely provided additional moisture.

Synoptic Storms in the North Atlantic in the Atmospheric Reanalysis and Scatterometer-Based Wind Products

NASA Astrophysics Data System (ADS)

Dukhovskoy, D. S.; Bourassa, M. A.

2016-12-01

The study compares and analyses the characteristics of synoptic storms in the Subpolar North Atlantic over the time period from 2000 through 2009 derived from reanalysis data sets and scatterometer-based gridded wind products. The analysis is performed for ocean 10-m winds derived from the following wind data sets: NCEP/DOE AMIP-II reanalysis (NCEPR2), NCAR/CFSR, Arctic System Reanalysis (ASR) version 1, Cross-Calibrated Multi-Platform (CCMP) wind product versions 1.1 and recently released version 2.0 prepared by the Remote Sensing Systems, and QuikSCAT. A cyclone tracking algorithm employed in this study for storm identification is based on average vorticity fields derived from the wind data. The study discusses storm characteristics such as storm counts, trajectories, intensity, integrated kinetic energy, spatial scale. Interannal variability of these characteristics in the data sets is compared. The analyses demonstrates general agreement among the wind data products on the characteristics of the storms, their spatial distribution and trajectories. On average, the NCEPR2 storms are more energetic mostly due to large spatial scales and stronger winds. There is noticeable interannual variability in the storm characteristics, yet no obvious trend in storms is observed in the data sets.

Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information

NASA Astrophysics Data System (ADS)

Günther, Andreas; Van Den Eeckhaut, Miet; Malet, Jean-Philippe; Reichenbach, Paola; Hervás, Javier

2014-11-01

With the adoption of the EU Thematic Strategy for Soil Protection in 2006, small-scale (1:1 M) assessments of threats affecting soils over Europe received increasing attention. As landslides have been recognized as one of eight threats requiring a Pan-European evaluation, we present an approach for landslide susceptibility evaluation at the continental scale over Europe. Unlike previous continental and global scale landslide susceptibility studies not utilizing spatial information on the events, we collected more than 102,000 landslide locations in 22 European countries. These landslides are heterogeneously distributed over Europe, but are indispensable for the evaluation and classification of Pan-European datasets used as spatial predictors, and the validation of the resulting assessments. For the analysis we subdivided the European territory into seven different climate-physiographical zones by combining morphometric and climatic data for terrain differentiation, and adding a coastal zone defined as a 1 km strip inland from the coastline. Landslide susceptibility modeling was performed for each zone using heuristic spatial multicriteria evaluations supported by analytical hierarchy processes, and validated with the inventory data using the receiver operating characteristics. In contrast to purely data-driven statistical modeling techniques, our semi-quantitative approach is capable to introduce expert knowledge into the analysis, which is indispensable considering quality and resolution of the input data, and incompleteness and bias in the inventory information. The reliability of the resulting susceptibility map ELSUS 1000 Version 1 (1 km resolution) was examined on an administrative terrain unit level in areas with landslide information and through the comparison with available national susceptibility zonations. These evaluations suggest that although the ELSUS 1000 is capable for a correct synoptic prediction of landslide susceptibility in the majority of the area, it needs further improvement in terms of data used.

The use of normalized climatological anomalies to rank synoptic-scale events and their relation to Weather Types

NASA Astrophysics Data System (ADS)

Ramos, A. M.; Lorenzo, M. N.; Gimeno, L.; Nieto, R.; Añel, J. A.

2009-09-01

Several methods have been developed to rank meteorological events in terms of severity, social impact or economic impacts. These classifications are not always objective since they depend of several factors, for instance, the observation network is biased towards the densely populated urban areas against rural or oceanic areas. It is also very important to note that not all rare synoptic-scale meteorological events attract significant media attention. In this work we use a comprehensive method of classifying synoptic-scale events adapted from Hart and Grumm, 2001, to the European region (30N-60N, 30W-15E). The main motivation behind this method is that the more unusual the event (a cold outbreak, a heat wave, or a flood), for a given region, the higher ranked it must be. To do so, we use four basic meteorological variables (Height, Temperature, Wind and Specific Humidity) from NCEP reanalysis dataset over the range of 1000hPa to 200hPa at a daily basis from 1948 to 2004. The climatology used embraces the 1961-1990 period. For each variable, the analysis of raking climatological anomalies was computed taking into account the daily normalized departure from climatology at different levels. For each day (from 1948 to 2004) we have four anomaly measures, one for each variable, and another, a combined where the anomaly (total anomaly) is the average of the anomaly of the four variables. Results will be analyzed on a monthly, seasonal and annual basis. Seasonal trends and variability will also be shown. In addition, and given the extent of the database, the expected return periods associated with the anomalies are revealed. Moreover, we also use an automated version of the Lamb weather type (WT) classification scheme (Jones et al, 1993) adapted for the Galicia area (Northwestern corner of the Iberian Peninsula) by Lorenzo et al (2008) in order to compute the daily local circulation regimes in this area. By combining the corresponding daily WT with the five anomaly measures we can evaluate if there is any preferable WT responsible for high or low values of anomalies. Hart, R.E and R.H. Grumm (2001) Using normalized climatological anomalies to rank synoptic-scale events objectivily. Monthly Weather Review, 129, 2426-2442. Jones, P. D., M. Hulme, K. R. Briffa (1993) A comparison of Lamb circulation types with anobjective classification scheme. International Journal of Climatology, 13: 655- 663. Lorenzo M.N., J.J. Taboada and L.Gimeno (2008). Links between circulation weather types and teleconnection patterns and their influence on precipitation patterns in Galicia (NW Spain). International Journal of Climatology 28(11): 1493:1505 DOI: 10.1002/joc.1646.

Managing the Big Data Avalanche in Astronomy - Data Mining the Galaxy Zoo Classification Database

NASA Astrophysics Data System (ADS)

Borne, Kirk D.

2014-01-01

We will summarize a variety of data mining experiments that have been applied to the Galaxy Zoo database of galaxy classifications, which were provided by the volunteer citizen scientists. The goal of these exercises is to learn new and improved classification rules for diverse populations of galaxies, which can then be applied to much larger sky surveys of the future, such as the LSST (Large Synoptic Sky Survey), which is proposed to obtain detailed photometric data for approximately 20 billion galaxies. The massive Big Data that astronomy projects will generate in the future demand greater application of data mining and data science algorithms, as well as greater training of astronomy students in the skills of data mining and data science. The project described here has involved several graduate and undergraduate research assistants at George Mason University.

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

PubMed

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

Using Advanced Monitoring Tools to Evaluate PM PM2.5 2.5 in San Joaquin Valley

EPA Science Inventory

One of the primary data deficiencies that prevent the advance of policy relevant research on particulate matter, ozone, and associated precursors is the lack of measurement data and knowledge on the true vertical profile and synoptic-scale spatial distributions of the pollutants....

Spatially Explicit Predictors of Indicators of Water Quality: Example from Wadeable Streams in the U.S

EPA Science Inventory

The Environmental Protection Agency (USEPA) in collaboration with the States is assessing and reporting on the condition of surface waters in the United States using synoptic surveys and consistent field collections of water quality indicators (WQI). The survey is a probability-b...

LEAF AREA INDEX CHANGE DETECTION OF UNDERSTORY VEGETATION IN THE ALBEMARLE-PAMLICO BASIN USING IKOMOS AND LANDSAT ETM+ SATELLITE DATA

EPA Science Inventory

The advent of remotely sensed data from satellite platforms has enabled the research community to examine vegetative spatial distributions over regional and global scales. This assessment of ecosystem condition through the synoptic monitoring of terrestrial vegetation extent, bio...

LEAF AREA INDEX (LAI) CHANGES DETECTION OF UNDERSTORY VEGETATION IN THE ALBEMARLE-PAMLICO BASIN IKONOS AND LANDSAT ETM+ SATELLITE DATA

EPA Science Inventory

The advent of remotely sensed data from satellite platforms has enabled the research community to examine vegetative spatial distributions over regional and global scales. This assessment of ecosystem condition through the synoptic monitoring of terrestrial vegetation extent, bio...

Mesoscale and Synoptic Summertime Circulations and Their Impact on Visibility in the Arabian Gulf

NASA Astrophysics Data System (ADS)

Eleuterio, D. P.; Walker, A. L.

2005-12-01

Although frequently characterized as a region of relatively persistent northwesterly winds, often referred to as the 40-day shamal, several researchers have recognized significant temporal and spatial variability in the summer low level winds in the Arabian Gulf. In addition to the synoptically driven gradient between the subtropical high to the north and the monsoon trough across the Gulf of Oman and Northern Arabian Sea, there are complex interactions between the Saudi Arabian and Pakistani heat lows, land-sea breeze circulations, and coastal terrain influence due to the proximity of the Zagros Mountains. These interactions frequently result in several distinct wind regimes within the Arabian Gulf, to include weak thermally and dynamically forced southerlies in the southern Gulf, a diurnally varying region of convergence/ divergence across the central Gulf, and northwesterly shamal type flow in the northern Gulf. The relative orientation and strength of these wind regimes and the strength of the subsidence inversion at the top of the marine boundary layer greatly impact the aerosol loading over water and resulting visibility due to wind-blown sand, dust, and smoke. Several case studies are examined to explore the interaction between mesoscale and synoptic forcing and the resulting spatial and temporal variability in visibility and aerosol optical depth. Conditions range from two to three day periods of rapid and persistent regional clearing with freshening northwesterly winds, to persistent periods of moderate to poor visibility in marine haze under light winds, to large scale events that create a distinct wind and dust front, severely reducing visibility through much of Iraq, Kuwait, and Saudi Arabia, and extending well into the Arabian Gulf. These strong, widespread events may be correlated with synoptically forced conditions farther north. Alternatively, smaller scale regional plumes of mobilized dust are often created by mesoscale events which, in conjunction with oil smoke and industrial pollution, can rapidly reduce visibility in localized regions for periods of 1-2 days and are relatively difficult to forecast because of their mesoscale nature.

Impacts of 2000-2050 Climate Change on Fine Particulate Matter (PM2.5) Air Quality in China Based on Statistical Projections Using an Ensemble of Global Climate Models

NASA Astrophysics Data System (ADS)

Leung, D. M.; Tai, A. P. K.; Shen, L.; Moch, J. M.; van Donkelaar, A.; Mickley, L. J.

2017-12-01

Fine particulate matter (PM2.5) air quality is strongly dependent on not only on emissions but also meteorological conditions. Here we examine the dominant synoptic circulation patterns that control day-to-day PM2.5 variability over China. We perform principal component (PC) analysis on 1998-2016 NCEP/NCAR Reanalysis I daily meteorological fields to diagnose distinct synoptic meteorological modes, and perform PC regression on spatially interpolated 2014-2016 daily mean PM2.5 concentrations in China to identify modes dominantly explaining PM2.5 variability. We find that synoptic systems, e.g., cold-frontal passages, maritime inflow and frontal precipitation, can explain up to 40% of the day-to-day PM2.5 variability in major metropolitan regions in China. We further investigate how annually changing frequencies of synoptic systems, as well as changing local meteorology, drive interannual PM2.5 variability. We apply a spectral analysis on the PC time series to obtain the 1998-2016 annual median synoptic frequency, and use a forward-selection multiple linear regression (MLR) model of satellite-derived 1998-2015 annual mean PM2.5 concentrations on local meteorology and synoptic frequency, selecting predictors that explain the highest fraction of interannual PM2.5 variability while guarding against multicollinearity. To estimate the effect of climate change on future PM2.5 air quality, we project a multimodel ensemble of 15 CMIP5 models under the RCP8.5 scenario on the PM2.5-to-meteorology sensitivities derived for the present-day from the MLR model. Our results show that climate change could be responsible for increases in PM2.5 of more than 25 μg m-3 in northwestern China and 10 mg m-3 in northeastern China by the 2050s. Increases in synoptic frequency of cold-frontal passages cause only a modest 1 μg m-3 decrease in PM2.5 in North China Plain. Our analyses show that climate change imposes a significant penalty on air quality over China and poses serious threat on human health under the RCP8.5 future.

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2010-01-01

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

Linking storm surge activity and circulation variability along the Spanish coast through a synoptic pattern classification

NASA Astrophysics Data System (ADS)

Rasilla Álvarez, Domingo; Garcia Codrón, Juan Carlos

2010-05-01

The potentially negative consequences resulting from the estimations of global sea level rising along the current century are a matter of serious concern in many coastal areas worldwide. Most of the negative consequences of the sea level variability, such as flooding or erosion, are linked to episodic events of strong atmospheric forcing represented by deep atmospheric disturbances, especially if they combine with extreme astronomical high tides. Moreover, the interaction between the prevailing flows during such events and the actual orientation of the coast line might accelerate or mitigate such impacts. This contribution analyses sea surge variations measured at five tide-gauge stations located around the Iberian Peninsula and their relationships with regional scale circulation patterns with local-scale winds. Its aim is to improve the knowledge of surge related-coastal-risks by analysing the relationship between surges and their atmospheric forcing factors at different spatial scales. The oceanographic data set consists of hourly data from 5 tide gauge stations (Santander, Vigo, Bonanza, Málaga, Valencia and Barcelona) disseminated along the Spanish coastline, provided by Puertos del Estado. To explore the atmospheric mechanisms responsible for the sign and magnitude of sea surges, a regional Eulerian approach (a synoptic typing) were combined with a larger-scale Lagrangian method, based on the analysis of storm-tracks over the Atlantic and local information (synop reports) obtained from the closest meteorological stations to the tide gauges. The synoptic catalogue was obtained following a procedure that combines Principal Component Analysis (PCA) for reduction purposes and clustering (Ward plus K-means) to define the circulation types. Sea level pressure, surface 10m U and V wind components gridded data were obtained from NCEP Reanalysis, while storm tracks and cyclone statistics were extracted from the CDC Map Room Climate Products Storm Track Data (http://www.cdc.noaa.gov/map/clim/st_data.html). The second task was to evaluate the performance of each circulation type on the spatial patterns of a daily fire danger risk index (Canadian Fire Weather Index, FWI). Finally, anomaly maps of several surface and low level climate variables, corresponding to the dates of ignition of the very large forest fires within each synoptic pattern, were calculated to provide insight of the specific conditions associated to those extreme events. A principal component analysis upon 6 hourly residuals highlighted the homogeneous behaviour of the tide gauges and provided a regional quantitative index to identify the largest storm surges. The leading PCA displayed a homogeneous spatial pattern, describing the low frequency variability along the entire coast, in spite of different orientations of the coast, accounting for more than 80% of the total variability. The second PCA displayed opposite phases between the Atlantic and the Mediterranean. Furthermore, the results suggest that surges are a regional rather than local phenomenon, probably related to the same single physical forcing. The comparison between extreme surge events and circulation patterns highlighted that single physical mechanism is represented by extratropical cyclonic disturbances located at the north-western corner of the Iberian Peninsula, responsible for an environment characterized by low pressure readings and westerly-southwesterly winds. That wind pattern acquires an onshore component in the Atlantic coast, but becomes offshore in the Mediterranean. So, the main mechanism responsible for those storm surges is the inverse barometer effect, being the wind dragging secondary. The main physical forcing of the storm surges, the extratropical cyclones, have experience a reduction of this frequency and a marked reduction in their strength since 1950, replaced by stable circulations. Both conditions suggest a long term reduction of the frequency and the magnitude of storm surges.

Kinetic energy budgets in areas of convection

NASA Technical Reports Server (NTRS)

Fuelberg, H. E.

1979-01-01

Synoptic scale budgets of kinetic energy are computed using 3 and 6 h data from three of NASA's Atmospheric Variability Experiments (AVE's). Numerous areas of intense convection occurred during the three experiments. Large kinetic energy variability, with periods as short as 6 h, is observed in budgets computed over each entire experiment area and over limited volumes that barely enclose the convection and move with it. Kinetic energy generation and transport processes in the smaller volumes are often a maximum when the enclosed storms are near peak intensity, but the nature of the various energy processes differs between storm cases and seems closely related to the synoptic conditions. A commonly observed energy budget for peak storm intensity indicates that generation of kinetic energy by cross-contour flow is the major energy source while dissipation to subgrid scales is the major sink. Synoptic scale vertical motion transports kinetic energy from lower to upper levels of the atmosphere while low-level horizontal flux convergence and upper-level horizontal divergence also occur. Spatial fields of the energy budget terms show that the storm environment is a major center of energy activity for the entire area.

A transient search using combined human and machine classifications

NASA Astrophysics Data System (ADS)

Wright, Darryl E.; Lintott, Chris J.; Smartt, Stephen J.; Smith, Ken W.; Fortson, Lucy; Trouille, Laura; Allen, Campbell R.; Beck, Melanie; Bouslog, Mark C.; Boyer, Amy; Chambers, K. C.; Flewelling, Heather; Granger, Will; Magnier, Eugene A.; McMaster, Adam; Miller, Grant R. M.; O'Donnell, James E.; Simmons, Brooke; Spiers, Helen; Tonry, John L.; Veldthuis, Marten; Wainscoat, Richard J.; Waters, Chris; Willman, Mark; Wolfenbarger, Zach; Young, Dave R.

2017-12-01

Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys.

A spatially constrained ecological classification: rationale, methodology and implementation

Treesearch

Franz Mora; Louis Iverson; Louis Iverson

2002-01-01

The theory, methodology and implementation for an ecological and spatially constrained classification are presented. Ecological and spatial relationships among several landscape variables are analyzed in order to define a new approach for a landscape classification. Using ecological and geostatistical analyses, several ecological and spatial weights are derived to...

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

PubMed Central

Wang, Guizhou; Liu, Jianbo; He, Guojin

2013-01-01

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy. PMID:24453808

Winter air-mass-based synoptic climatological approach and hospital admissions for myocardial infarction in Florence, Italy.

PubMed

Morabito, Marco; Crisci, Alfonso; Grifoni, Daniele; Orlandini, Simone; Cecchi, Lorenzo; Bacci, Laura; Modesti, Pietro Amedeo; Gensini, Gian Franco; Maracchi, Giampiero

2006-09-01

The aim of this study was to evaluate the relationship between the risk of hospital admission for myocardial infarction (MI) and the daily weather conditions during the winters of 1998-2003, according to an air-mass-based synoptic climatological approach. The effects of time lag and 2-day sequences with specific air mass types were also investigated. Studies concerning the relationship between atmospheric conditions and human health need to take into consideration simultaneous effects of many weather variables. At the moment few studies have surveyed these effects on hospitalizations for MI. Analyses were concentrated on winter, when the maximum peak of hospitalization occurred. An objective daily air mass classification by means of statistical analyses based on ground meteorological data was carried out. A comparison between air mass classification and hospital admissions was made by the calculation of a MI admission index, and to detect significant relationships the Mann-Whitney U test, the analysis of variance, and the Bonferroni test were used. Significant increases in hospital admissions for MI were evident 24h after a day characterized by an anticyclonic continental air mass and 6 days after a day characterized by a cyclonic air mass. Increased risk of hospitalization was found even when specific 2-day air mass sequences occurred. These results represent an important step in identifying reliable linkages between weather and health.

Predicting landscape connectivity for the Asian elephant in its largest remaining subpopulation

Treesearch

J.-P. Puyravaud; Samuel Cushman; P. Davidar; D. Madappa

2016-01-01

Landscape connectivity between protected areas is crucial for the conservation of megafauna. But often, corridor identification relies on expert knowledge that is subjective and not spatially synoptic. Landscape analysis allows generalization of expert knowledge when satellite tracking or genetic data are not available. The Nilgiri Biosphere Reserve in southern India...

Network analysis reveals multiscale controls on streamwater chemistry

Treesearch

Kevin J. McGuire; Christian E. Torgersen; Gene E. Likens; Donald C. Buso; Winsor H. Lowe; Scott W. Bailey

2014-01-01

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in...

Field validation of 1930s aerial photography: What are we missing?

USDA-ARS?s Scientific Manuscript database

Aerial photography from the 1930s serves as the earliest synoptic depiction of vegetation cover. We generated a spatially explicit database of shrub (Prosopis velutina) stand structure within two 1.8 ha field plots established in 1932 to address two questions: (1) What are the detection limits of p...

Extinction coefficients from lidar observations in ice clouds compared to in-situ measurements from the Cloud Integrating Nephelometer during CRYSTAL-FACE

NASA Technical Reports Server (NTRS)

Noel, Vincent; Winker, D. M.; Garrett, T. J.; McGill, M.

2005-01-01

This paper presents a comparison of volume extinction coefficients in tropical ice clouds retrieved from two instruments : the 532-nm Cloud Physics Lidar (CPL), and the in-situ probe Cloud Integrating Nephelometer (CIN). Both instruments were mounted on airborne platforms during the CRYSTAL-FACE campaign and took measurements in ice clouds up to 17km. Coincident observations from three cloud cases are compared : one synoptically-generated cirrus cloud of low optical depth, and two ice clouds located on top of convective systems. Emphasis is put on the vertical variability of the extinction coefficient. Results show small differences on small spatial scales (approx. 100m) in retrievals from both instruments. Lidar retrievals also show higher extinction coefficients in the synoptic cirrus case, while the opposite tendency is observed in convective cloud systems. These differences are generally variations around the average profile given by the CPL though, and general trends on larger spatial scales are usually well reproduced. A good agreement exists between the two instruments, with an average difference of less than 16% on optical depth retrievals.

April 2008 Saharan dust event: Its contribution to PM10 concentrations over the Anatolian Peninsula and relation with synoptic conditions.

PubMed

Kabatas, B; Pierce, R B; Unal, A; Rogal, M J; Lenzen, A

2018-08-15

An online-coupled regional Weather Research and Forecasting model with chemistry (WRF-Chem) is utilized incorporating 0.1°×0.1° spatial resolution HTAP (Hemispheric Transport of Air Pollution) anthropogenic emissions to investigate the spatial and temporal distribution of a Saharan dust outbreak, which contributed to high levels (>50μg/m 3 ) of daily PM 10 concentrations over Turkey in April 2008. Aerosol optical depth and cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board of Aqua satellite are used to better analyze the synoptic conditions that generated the dust outbreak in April 2008. A "Sharav" low pressure system, which transports the dust from Saharan source region over Turkey along the cold front, tends to move faster in WRF-Chem simulations than observed. This causes the predicted dust event to arrive earlier than observed leading to an overestimation of surface PM 10 concentrations in WRF-Chem simulation at the beginning of the event. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Spatial and temporal variations of aerosols around Beijing in summer 2006: Model evaluation and source apportionment

NASA Astrophysics Data System (ADS)

Matsui, H.; Koike, M.; Kondo, Y.; Takegawa, N.; Kita, K.; Miyazaki, Y.; Hu, M.; Chang, S.; Blake, D. R.; Fast, J. D.; Zaveri, R. A.; Streets, D. G.; Zhang, Q.; Zhu, T.

2009-12-01

Regional aerosol model calculations were made using the WRF-CMAQ and WRF-chem models to study spatial and temporal variations of aerosols around Beijing, China, in the summer of 2006, when the CAREBEIJING-2006 intensive campaign was conducted. Model calculations captured temporal variations of primary (such as elemental carbon, EC) and secondary (such as sulfate) aerosols observed in and around Beijing. The spatial distributions of aerosol optical depth observed by the MODIS satellite sensors were also reproduced over northeast China. Model calculations showed distinct differences in spatial distributions between primary and secondary aerosols in association with synoptic-scale meteorology. Secondary aerosols increased in air around Beijing on a scale of about 1000 x 1000 km2 under an anticyclonic pressure system. This airmass was transported northward from the high anthropogenic emission area extending south of Beijing with continuous photochemical production. Subsequent cold front passage brought clean air from the north, and polluted air around Beijing was swept to the south of Beijing. This cycle was repeated about once a week and was found to be responsible for observed enhancements/reductions of aerosols at the intensive measurement sites. In contrast to secondary aerosols, the spatial distributions of primary aerosols (EC) reflected those of emissions, resulting in only slight variability despite the changes in synoptic-scale meteorology. In accordance with these results, source apportionment simulations revealed that primary aerosols around Beijing were controlled by emissions within 100 km around Beijing within the preceding 24 hours, while emissions as far as 500 km and within the preceding 3 days were found to affect secondary aerosols.

Using Conditional Analysis to Investigate Spatial and Temporal patterns in Upland Rainfall

NASA Astrophysics Data System (ADS)

Sakamoto Ferranti, Emma Jayne; Whyatt, James Duncan; Timmis, Roger James

2010-05-01

The seasonality and characteristics of rainfall in the UK are altering under a changing climate. Summer rainfall is generally decreasing whereas winter rainfall is increasing, particularly in northern and western areas (Maraun et al., 2008) and recent research suggests these rainfall increases are amplified in upland areas (Burt and Ferranti, 2010). Conditional analysis has been used to investigate these rainfall patterns in Cumbria, an upland area in northwest England. Cumbria was selected as an example of a topographically diverse mid-latitude region that has a predominately maritime and westerly-defined climate. Moreover it has a dense network of more than 400 rain gauges that have operated for periods between 1900 and present day. Cumbria has experienced unprecedented flooding in the past decade and understanding the spatial and temporal changes in this and other upland regions is important for water resource and ecosystem management. The conditional analysis method examines the spatial and temporal variations in rainfall under different synoptic conditions and in different geographic sub-regions (Ferranti et al., 2009). A daily synoptic typing scheme, the Lamb Weather Catalogue, was applied to classify rainfall into different weather types, for example: south-westerly, westerly, easterly or cyclonic. Topographic descriptors developed using GIS were used to classify rain gauges into 6 directionally-dependant geographic sub-regions: coastal, windward-lowland, windward-upland, leeward-upland, leeward-lowland, secondary upland. Combining these classification methods enabled seasonal rainfall climatologies to be produced for specific weather types and sub-regions. Winter rainfall climatologies were constructed for all 6 sub-regions for 3 weather types - south-westerly (SW), westerly (W), and cyclonic (C); these weather types contribute more than 50% of total winter rainfall. The frequency of wet-days (>0.3mm), the total winter rainfall and the average wet day rainfall amount were analysed for each rainfall sub-region and weather type from 1961-2007 (Ferranti et al., 2010). The conditional analysis showed total rainfall under SW and W weather types to be increasing, with the greatest increases observed in the upland sub-regions. The increase in total SW rainfall is driven by a greater occurrence of SW rain days, and there has been little change to the average wet-day rainfall amount. The increase in total W rainfall is driven in part by an increase in the frequency of wet-days, but more significantly by an increase in the average wet-day rainfall amount. In contrast, total rainfall under C weather types has decreased. Further analysis will investigate how spring, summer and autumn rainfall climatologies have changed for the different weather types and sub-regions. Conditional analysis that combines GIS and synoptic climatology provides greater insights into the processes underlying readily available meteorological data. Dissecting Cumbrian rainfall data under different synoptic and geographic conditions showed the observed changes in winter rainfall are not uniform for the different weather types, nor for the different geographic sub-regions. These intricate details are often lost during coarser resolution analysis, and conditional analysis will provide a detailed synopsis of Cumbrian rainfall processes against which Regional Climate Model (RCM) performance can be tested. Conventionally RCMs try to simulate composite rainfall over many different weather types and sub-regions and by undertaking conditional validation the model performance for individual processes can be tested. This will help to target improvements in model performance, and ultimately lead to better simulation of rainfall in areas of complex topography. BURT, T. P. & FERRANTI, E. J. S. (2010) Changing patterns of heavy rainfall in upland areas: a case study from northern England. Atmospheric Environment, [in review]. FERRANTI, E. J. S., WHYATT, J. D. & TIMMIS, R. J. (2009) Development and application of topographic descriptors for conditional analysis of rainfall. Atmospheric Science Letters, 10, 177-184. FERRANTI, E. J. S., WHYATT, J. D., TIMMIS, R. J. & DAVIES, G. (2010) Using GIS to investigate spatial and temporal variations in upland rainfall. Transactions in GIS, [in press]. MARAUN, D., OSBORN, T. J. & GILLETT, N. P. (2008) United Kingdom daily precipitation intensity: improved early data, error estimates and an update from 2000 to 2006. International Journal of Climatology, 28, 833-842.

Streamflow and water-quality conditions including geologic sources and processes affecting selenium loading in the Toll Gate Creek watershed, Aurora, Arapahoe County, Colorado, 2007

USGS Publications Warehouse

Paschke, Suzanne S.; Runkel, Robert L.; Walton-Day, Katherine; Kimball, Briant A.; Schaffrath, Keelin R.

2013-01-01

Toll Gate Creek is a perennial stream draining a suburban area in Aurora, Colorado, where selenium concentrations have consistently exceeded the State of Colorado aquatic-life standard for selenium of 4.6 micrograms per liter since the early 2000s. In cooperation with the City of Aurora, Colorado, Utilities Department, a synoptic water-quality study was performed along an 18-kilometer reach of Toll Gate Creek extending from downstream from Quincy Reservoir to the confluence with Sand Creek to develop a detailed understanding of streamflow and concentrations and loads of selenium in Toll Gate Creek. Streamflow and surface-water quality were characterized for summer low-flow conditions (July–August 2007) using four spatially overlapping synoptic-sampling subreaches. Mass-balance methods were applied to the synoptic-sampling and tracer-injection results to estimate streamflow and develop spatial profiles of concentration and load for selenium and other chemical constituents in Toll Gate Creek surface water. Concurrent groundwater sampling determined concentrations of selenium and other chemical constituents in groundwater in areas surrounding the Toll Gate Creek study reaches. Multivariate principal-component analysis was used to group samples and to suggest common sources for dissolved selenium and major ions. Hydrogen and oxygen stable-isotope ratios, groundwater-age interpretations, and chemical analysis of water-soluble paste extractions from core samples are presented, and interpretation of the hydrologic and geochemical data support conclusions regarding geologic sources of selenium and the processes affecting selenium loading in the Toll Gate Creek watershed.

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng

2017-10-01

Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Warner, Timothy; Steinmaus, Karen L.

2005-02-01

New high resolution single spectral band imagery offers the capability to conduct image classifications based on spatial patterns in imagery. A classification algorithm based on autocorrelation patterns was developed to automatically extract orchards and vineyards from satellite imagery. The algorithm was tested on IKONOS imagery over Granger, WA, which resulted in a classification accuracy of 95%.

A Survey of Synoptic Waves over West Africa

NASA Astrophysics Data System (ADS)

Cheng, Yuan-Ming; Thorncroft, Chris D.; Kiladis, George N.

2017-04-01

Motivated by the pronounced wave-to-wave variability in African easterly wave (AEW) circulation, the three-dimensional structure of synoptic waves over West Africa is revisited with an Empirical Orthogonal Function (EOF) approach to isolate the dominant wave pattern. In this talk we present results of EOF analyses conducted with brightness temperature (Tb) derived from satellite observation and meridional wind at multiple levels from reanalysis data to examine the characteristics and variability of synoptic waves. The structure of waves is extracted by projecting the wind fields and Tb onto the principle components associated with EOF patterns of appropriately filtered parameters. The Tb EOF shows a confined AEW circulation centered around 7.5°N and a distinct evolution of convection within the wave in line with previous research. However, in striking contrast to the confined flow pattern in the Tb EOF, the EOF of 700-hPa meridional wind is distinguished by a meridionally broad AEW circulation. While the peak in circulation is centered around 10°N, there is marked cross-equatorial flow that is associated with an antisymmetric geopotential signature across the equator. This suggests the presence of a mixed Rossby-gravity wave (MRG) structure consistent with Matsuno's shallow water theory. Granted that the vast majority of studies on MRGs focus on the central and western Pacific region, this "hybrid" between AEWs and MRGs over West Africa and Atlantic sector has received little attention and more work regarding the nature and causes of its wave structure and behavior is needed. In addition, an upper-level synoptic wave is captured by EOFs of 200-hPa meridional wind. The kinematic fields reveal a continental-scale wave straddling the equator that resembles an MRG. This upper-level MRG appears to develop in situ over the Horn of Africa and intensifies as it moves across the continent. The associated lower-level structure shows an AEW-like circulation but with a larger spatial extent. This finding motivates the need for more in-depth investigations of synoptic wave variability over the region including an assessment of the direction of causality between the upper-level MRG and the lower-level AEW. This study highlights the various synoptic wave structures over West Africa and their interaction with AEWs. The results suggest the variability of AEW activity could be modulated by, in addition to the large-scale environment, other synoptic waves in the region. We will pursue the EOF approach to shed light on the characteristics and causes of the variability in synoptic wave activity over West Africa.

Synoptic Aspects of the Climate of Japan: A Preliminary Report

DTIC Science & Technology

1945-08-01

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Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Synoptic Sky Surveys: Lessons Learned and Challenges Ahead

NASA Astrophysics Data System (ADS)

Djorgovski, Stanislav G.; CRTS Team

2014-01-01

A new generation of synoptic sky surveys is now opening the time domain for a systematic exploration, presenting both great new scientific opportunities as well as the challenges. These surveys are touching essentially all subfields of astronomy, producing large statistical samples of the known types of objects and events (e.g., SNe, AGN, variable stars of many kinds), and have already uncovered previously unknown subtypes of these (e.g., rare or peculiar types of SNe). They are generating new science now, and paving the way for even larger surveys to come, e.g., the LSST. Our ability to fully exploit such forthcoming facilities depends critically on the science, methodology, and experience that are being accumulated now. Among the outstanding challenges the foremost is our ability to conduct an effective follow-up of the interesting events discovered by the surveys in any wavelength regime. The follow-up resources, especially spectroscopy, are already be severely limited, and this problem will grow by orders of magnitude. This requires an intelligent down-selection of the most astrophysically interesting events to follow. The first step in that process is an automated, real-time, iterative classification of transient events, that incorporates heterogeneous data from the surveys themselves, archival information (spatial, temporal, and multiwavelength), and the incoming follow-up observations. The second step is an optimal automated event prioritization and allocation of the available follow-up resources that also change in time. Both of these challenges are highly non-trivial, and require a strong cyber-infrastructure based on the Virtual Observatory data grid, and the various astroinformatics efforts now under way. This is inherently an astronomy of telescope-computational systems, that increasingly depends on novel machine learning and artificial intelligence tools. Another arena with a strong potential for discovery is an archival, non-time-critical exploration of the time domain, with the time dimension adding the complexity to an already challenging problem of data mining of highly-dimensional data parameter spaces.

A Synoptic- and Planetary-Scale Analysis of Widespread North American Ice Storms

NASA Astrophysics Data System (ADS)

McCray, C.; Gyakum, J. R.; Atallah, E.

2017-12-01

Freezing rain can have devastating impacts, particularly when it persists for many hours. Predicting the precise temperature stratification necessary for long duration freezing rain events remains an important forecast challenge. To better elucidate the conditions responsible for the most severe events, we concentrate on surface observations of long-duration (6 or more hours) freezing rain events over North America from 1979-2016. Furthermore, we analyze cases in which multiple stations observe long-duration events simultaneously. Following these cases over successive days allows us to generate maps of freezing rain "tracks." We then categorize recurring geographic patterns to examine the meteorological conditions leading to these events. While freezing rain is most frequently observed in the northeastern United States and southeastern Canada, long-duration events have affected areas as far south as the Gulf Coast. Notably, a disproportionately large number of very long duration (18 or more hours) events have occurred in the Southern Plains states relative to the climatological annual frequency of freezing rain there. Classification of individual cases shows that most of these very long duration events are associated with a recurring pattern which produces freezing rain along a southwest-northeast swath from Texas/Oklahoma into the northeastern U.S. and eastern Canada. Storms classified within this pattern include the January 1998 and December 2013 ice storms. While this pattern is the most widespread, additional spatially extensive patterns occur. One of these areas extends from the Southern Plains eastward along the Gulf Coast to Georgia and the Carolinas. A third category of events extends from the Upper Midwest into the northeastern U.S. and southeastern Canada. The expansive areal extent and long duration of these events make them especially problematic. An analysis of the planetary- to synoptic-scale settings responsible for these cases and the differences among individual storms is performed to provide forecasters with additional tools/insight towards the prediction of these damaging weather events.

Development of a Water Clarity Index for the Southeastern U.S. As a Climate Indicator

NASA Astrophysics Data System (ADS)

Sheridan, S. C.; Hu, C.; Lee, C. C.; Barnes, B.; Pirhalla, D.; Ransi, V.; Shein, K. A.

2014-12-01

A common index of water quality is water clarity, which can be estimated by measuring the diffuse attenuation coefficient for downwelling irradiance (Kd). Kd estimates the availability of light to marine organisms at various depths. Marine habitats, including such species as coral and seagrass, can be negatively affected by extreme episodes of sediment suspension, where water clarity is reduced and little light penetrates. Evidence of increased stress on coastal ecosystems exists, partially due to climate change, yet a systematic analysis of extreme events and trends is difficult due to limited data. To address this concern, we have developed as a potential climate indicator a Kd-Index for nine regions along the US coast of the Gulf of Mexico, in which Kd values have been standardized over time and space to allow for a more holistic assessment of climate drivers and their trends. Variability in the Kd-Index is then assessed with regard to occurrences of surface weather types (using the Spatial Synoptic Classification), a synoptic climatology of mean sea-level-pressure patterns across the region, along with heavy precipitation events. Kd can be estimated from MODIS and SeaWiFS observations from 1997 to date; an earlier period of satellite observations from 1978-86 is also available. A non-linear autoregressive neural network model with external input (NARX) is used to develop the historical relationship between Kd-Index and atmospheric conditions, and then this model is used to simulate a full time series from 1948 to 2013. The modeled data set is strongly correlated with observations, with correlations above 0.8 for many regions. Hit rates of extreme Kd-Index values - those which would most likely be associated with a negative environmental impact - exceed 70% in some regions. Across the full data set, long term trends vary slightly across regions but are generally small. Trends in extreme events appear to be more consistently increasing across the domain.

Association of weather and air pollution interactions on daily mortality in 12 Canadian cities.

PubMed

Vanos, J K; Cakmak, S; Kalkstein, L S; Yagouti, Abderrahmane

It has been well established that both meteorological attributes and air pollution concentrations affect human health outcomes. We examined all cause nonaccident mortality relationships for 28 years (1981-2008) in relation to air pollution and synoptic weather type (encompassing air mass) data in 12 Canadian cities. This study first determines the likelihood of summertime extreme air pollution events within weather types using spatial synoptic classification. Second, it examines the modifying effect of weather types on the relative risk of mortality (RR) due to daily concentrations of air pollution (nitrogen dioxide, ozone, sulfur dioxide, and particulate matter <2.5 μm). We assess both single- and two-pollutant interactions to determine dependent and independent pollutant effects using the relatively new time series technique of distributed lag nonlinear modeling (DLNM). Results display dry tropical (DT) and moist tropical plus (MT+) weathers to result in a fourfold and twofold increased likelihood, respectively, of an extreme pollution event (top 5 % of pollution concentrations throughout the 28 years) occurring. We also demonstrate statistically significant effects of single-pollutant exposure on mortality ( p  < 0.05) to be dependent on summer weather type, where stronger results occur in dry moderate (fair weather) and DT or MT+ weather types. The overall average single-effect RR increases due to pollutant exposure within DT and MT+ weather types are 14.9 and 11.9 %, respectively. Adjusted exposures (two-way pollutant effect estimates) generally results in decreased RR estimates, indicating that the pollutants are not independent. Adjusting for ozone significantly lowers 67 % of the single-pollutant RR estimates and reduces model variability, which demonstrates that ozone significantly controls a portion of the mortality signal from the model. Our findings demonstrate the mortality risks of air pollution exposure to differ by weather type, with increased accuracy obtained when accounting for interactive effects through adjustment for dependent pollutants using a DLNM.

Spatial and temporal variations of aerosols around Beijing in summer 2006: Model evaluation and source apportionment

NASA Astrophysics Data System (ADS)

Matsui, H.; Koike, M.; Kondo, Y.; Takegawa, N.; Kita, K.; Miyazaki, Y.; Hu, M.; Chang, S.-Y.; Blake, D. R.; Fast, J. D.; Zaveri, R. A.; Streets, D. G.; Zhang, Q.; Zhu, T.

2009-01-01

Regional aerosol model calculations were made using the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) and WRF-chem models to study spatial and temporal variations of aerosols around Beijing, China, in the summer of 2006, when the Campaigns of Air Quality Research in Beijing and Surrounding Region 2006 (CAREBeijing) intensive campaign was conducted. Model calculations captured temporal variations of primary (such as elemental carbon (EC)) and secondary (such as sulfate) aerosols observed in and around Beijing. The spatial distributions of aerosol optical depth observed by the MODIS satellite sensors were also reproduced over northeast China. Model calculations showed distinct differences in spatial distributions between primary and secondary aerosols in association with synoptic-scale meteorology. Secondary aerosols increased in air around Beijing on a scale of about 1000 × 1000 km2 under an anticyclonic pressure system. This air mass was transported northward from the high anthropogenic emission area extending south of Beijing with continuous photochemical production. Subsequent cold front passage brought clean air from the north, and polluted air around Beijing was swept to the south of Beijing. This cycle was repeated about once a week and was found to be responsible for observed enhancements/reductions of aerosols at the intensive measurement sites. In contrast to secondary aerosols, the spatial distributions of primary aerosols (EC) reflected those of emissions, resulting in only slight variability despite the changes in synoptic-scale meteorology. In accordance with these results, source apportionment simulations revealed that primary aerosols around Beijing were controlled by emissions within 100 km around Beijing within the preceding 24 h, while emissions as far as 500 km and within the preceding 3 days were found to affect secondary aerosols.

Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

NASA Astrophysics Data System (ADS)

Cui, Binge; Ma, Xiudan; Xie, Xiaoyun; Ren, Guangbo; Ma, Yi

2017-03-01

The classification of hyperspectral images with a few labeled samples is a major challenge which is difficult to meet unless some spatial characteristics can be exploited. In this study, we proposed a novel spectral-spatial hyperspectral image classification method that exploited spatial autocorrelation of hyperspectral images. First, image segmentation is performed on the hyperspectral image to assign each pixel to a homogeneous region. Second, the visible and infrared bands of hyperspectral image are partitioned into multiple subsets of adjacent bands, and each subset is merged into one band. Recursive edge-preserving filtering is performed on each merged band which utilizes the spectral information of neighborhood pixels. Third, the resulting spectral and spatial feature band set is classified using the SVM classifier. Finally, bilateral filtering is performed to remove "salt-and-pepper" noise in the classification result. To preserve the spatial structure of hyperspectral image, edge-preserving filtering is applied independently before and after the classification process. Experimental results on different hyperspectral images prove that the proposed spectral-spatial classification approach is robust and offers more classification accuracy than state-of-the-art methods when the number of labeled samples is small.

Automatic Classification of Time-variable X-Ray Sources

NASA Astrophysics Data System (ADS)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat: Tucson

USGS Publications Warehouse

Wallace, Cynthia S.A.; Advised by Marsh, Stuart E.

2002-01-01

The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat. By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data, effective models of habitat preference were created.Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition.Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data. Such relationships, once verified and established, can be used to help indirectly monitor the population.Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations.The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.

Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China

NASA Astrophysics Data System (ADS)

Leung, Danny M.; Tai, Amos P. K.; Mickley, Loretta J.; Moch, Jonathan M.; van Donkelaar, Aaron; Shen, Lu; Martin, Randall V.

2018-05-01

In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ˜ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998-2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing-Tianjin-Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ˜ 0.5 µg m-3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small climate benefit, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

2015-01-01

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

Innovative use of self-organising maps (SOMs) in model validation.

NASA Astrophysics Data System (ADS)

Jolly, Ben; McDonald, Adrian; Coggins, Jack

2016-04-01

We present an innovative combination of techniques for validation of numerical weather prediction (NWP) output against both observations and reanalyses using two classification schemes, demonstrated by a validation of the operational NWP 'AMPS' (the Antarctic Mesoscale Prediction System). Historically, model validation techniques have centred on case studies or statistics at various time scales (yearly/seasonal/monthly). Within the past decade the latter technique has been expanded by the addition of classification schemes in place of time scales, allowing more precise analysis. Classifications are typically generated for either the model or the observations, then used to create composites for both which are compared. Our method creates and trains a single self-organising map (SOM) on both the model output and observations, which is then used to classify both datasets using the same class definitions. In addition to the standard statistics on class composites, we compare the classifications themselves between the model and the observations. To add further context to the area studied, we use the same techniques to compare the SOM classifications with regimes developed for another study to great effect. The AMPS validation study compares model output against surface observations from SNOWWEB and existing University of Wisconsin-Madison Antarctic Automatic Weather Stations (AWS) during two months over the austral summer of 2014-15. Twelve SOM classes were defined in a '4 x 3' pattern, trained on both model output and observations of 2 m wind components, then used to classify both training datasets. Simple statistics (correlation, bias and normalised root-mean-square-difference) computed for SOM class composites showed that AMPS performed well during extreme weather events, but less well during lighter winds and poorly during the more changeable conditions between either extreme. Comparison of the classification time-series showed that, while correlations were lower during lighter wind periods, AMPS actually forecast the existence of those periods well suggesting that the correlations may be unfairly low. Further investigation showed poor temporal alignment during more changeable conditions, highlighting problems AMPS has around the exact timing of events. There was also a tendency for AMPS to over-predict certain wind flow patterns at the expense of others. In order to gain a larger scale perspective, we compared our mesoscale SOM classification time-series with synoptic scale regimes developed by another study using ERA-Interim reanalysis output and k-means clustering. There was good alignment between the regimes and the observations classifications (observations/regimes), highlighting the effect of synoptic scale forcing on the area. However, comparing the alignment between observations/regimes and AMPS/regimes showed that AMPS may have problems accurately resolving the strength and location of cyclones in the Ross Sea to the north of the target area.

Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution. [Sam Houston National Forest, Texas

NASA Technical Reports Server (NTRS)

Nalepka, R. F. (Principal Investigator); Sadowski, F. E.; Sarno, J. E.

1976-01-01

The author has identified the following significant results. A supervised classification within two separate ground areas of the Sam Houston National Forest was carried out for two sq meters spatial resolution MSS data. Data were progressively coarsened to simulate five additional cases of spatial resolution ranging up to 64 sq meters. Similar processing and analysis of all spatial resolutions enabled evaluations of the effect of spatial resolution on classification accuracy for various levels of detail and the effects on area proportion estimation for very general forest features. For very coarse resolutions, a subset of spectral channels which simulated the proposed thematic mapper channels was used to study classification accuracy.

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

PubMed Central

Moran, Emilio Federico.

2010-01-01

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance. PMID:21643433

Architectural Implications for Spatial Object Association Algorithms*

PubMed Central

Kumar, Vijay S.; Kurc, Tahsin; Saltz, Joel; Abdulla, Ghaleb; Kohn, Scott R.; Matarazzo, Celeste

2013-01-01

Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server®, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST). PMID:25692244

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Uav-Based Crops Classification with Joint Features from Orthoimage and Dsm Data

NASA Astrophysics Data System (ADS)

Liu, B.; Shi, Y.; Duan, Y.; Wu, W.

2018-04-01

Accurate crops classification remains a challenging task due to the same crop with different spectra and different crops with same spectrum phenomenon. Recently, UAV-based remote sensing approach gains popularity not only for its high spatial and temporal resolution, but also for its ability to obtain spectraand spatial data at the same time. This paper focus on how to take full advantages of spatial and spectrum features to improve crops classification accuracy, based on an UAV platform equipped with a general digital camera. Texture and spatial features extracted from the RGB orthoimage and the digital surface model of the monitoring area are analysed and integrated within a SVM classification framework. Extensive experiences results indicate that the overall classification accuracy is drastically improved from 72.9 % to 94.5 % when the spatial features are combined together, which verified the feasibility and effectiveness of the proposed method.

The spectrum of progressive derecho formation environments

NASA Astrophysics Data System (ADS)

Guastini, Corey T.

Progressive derechos are severe mesoscale convective systems that often form east of the Rocky Mountains during the warm season (May--August) and cause, by definition, straight-line wind damage along paths upwards of 400 km long. This study develops a subjective, seven-category classification scheme that spans the spectrum of progressive derecho formation environments from those dominated by robust upper-level ridges to those characterized by vigorous upper-level troughs. A climatology of 256 progressive derecho events is created for 1996--2013 and is categorized according to the developed classification scheme. Derecho initiation-relative composites are constructed for each of the seven groups using 0.5° Climate Forecast System Reanalysis data to document the environmental characteristics unique to each group as well as those shared among them. Finally, two in-depth case studies and five cursory case studies provide examples of the seven categories and reveal important nuances in mesoscale dynamic and thermodynamic structure inherent to all derecho cases. Results of the climatology show progressive derecho activity increases from 1 May through 1 July before decreasing again through the end of August and follows a northward trend in latitude from 1 May through 1 August before shifting slightly southward through the end of the warm season. Upslope flow in the vicinity of the Rocky Mountains initiates 28 percent of progressive derechos, upper-level troughs initiate 20 percent, 47 percent form in benign synoptic environments, and 5 percent are unclassifiable. Composite results show all progressive derecho initiation environments are marked by a long axis of instability caused by the overlap of high atmospheric moisture content and steep midlevel lapse rates, but the relative positions and strengths of upper-level troughs and ridges are crucial in determining how the instability axis develops and what its orientation in space will be. Case studies reveal instability axes forming in benign synoptic environments are generally zonally oriented and mainly the result of convergence of low-level moisture, whereas stronger synoptic-scale forcing forms meridionally oriented instability axes through the northward advection of Gulf moisture. The length and magnitude of these instability axes largely determines the duration and severity of a given progressive derecho.

Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Brink, Henrik; Crellin-Quick, Arien

2012-12-01

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.

2012-12-15

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In additionmore » to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.« less

Photometric Supernova Classification with Machine Learning

NASA Astrophysics Data System (ADS)

Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Spectral-spatial hyperspectral image classification using super-pixel-based spatial pyramid representation

NASA Astrophysics Data System (ADS)

Fan, Jiayuan; Tan, Hui Li; Toomik, Maria; Lu, Shijian

2016-10-01

Spatial pyramid matching has demonstrated its power for image recognition task by pooling features from spatially increasingly fine sub-regions. Motivated by the concept of feature pooling at multiple pyramid levels, we propose a novel spectral-spatial hyperspectral image classification approach using superpixel-based spatial pyramid representation. This technique first generates multiple superpixel maps by decreasing the superpixel number gradually along with the increased spatial regions for labelled samples. By using every superpixel map, sparse representation of pixels within every spatial region is then computed through local max pooling. Finally, features learned from training samples are aggregated and trained by a support vector machine (SVM) classifier. The proposed spectral-spatial hyperspectral image classification technique has been evaluated on two public hyperspectral datasets, including the Indian Pines image containing 16 different agricultural scene categories with a 20m resolution acquired by AVIRIS and the University of Pavia image containing 9 land-use categories with a 1.3m spatial resolution acquired by the ROSIS-03 sensor. Experimental results show significantly improved performance compared with the state-of-the-art works. The major contributions of this proposed technique include (1) a new spectral-spatial classification approach to generate feature representation for hyperspectral image, (2) a complementary yet effective feature pooling approach, i.e. the superpixel-based spatial pyramid representation that is used for the spatial correlation study, (3) evaluation on two public hyperspectral image datasets with superior image classification performance.

Regime Behavior in Paleo-Reconstructed Streamflow: Attributions to Atmospheric Dynamics, Synoptic Circulation and Large-Scale Climate Teleconnection Patterns

NASA Astrophysics Data System (ADS)

Ravindranath, A.; Devineni, N.

2017-12-01

Studies have shown that streamflow behavior and dynamics have a significant link with climate and climate variability. Patterns of persistent regime behavior from extended streamflow records in many watersheds justify investigating large-scale climate mechanisms as potential drivers of hydrologic regime behavior and streamflow variability. Understanding such streamflow-climate relationships is crucial to forecasting/simulation systems and the planning and management of water resources. In this study, hidden Markov models are used with reconstructed streamflow to detect regime-like behaviors - the hidden states - and state transition phenomena. Individual extreme events and their spatial variability across the basin are then verified with the identified states. Wavelet analysis is performed to examine the signals over time in the streamflow records. Joint analyses of the climatic data in the 20th century and the identified states are undertaken to better understand the hydroclimatic connections within the basin as well as important teleconnections that influence water supply. Compositing techniques are used to identify atmospheric circulation patterns associated with identified states of streamflow. The grouping of such synoptic patterns and their frequency are then examined. Sliding time-window correlation analysis and cross-wavelet spectral analysis are performed to establish the synchronicity of basin flows to the identified synoptic and teleconnection patterns. The Missouri River Basin (MRB) is examined in this study, both as a means of better understanding the synoptic climate controls in this important watershed and as a case study for the techniques developed here. Initial wavelet analyses of reconstructed streamflow at major gauges in the MRB show multidecadal cycles in regime behavior.

Spectral-spatial classification of hyperspectral imagery with cooperative game

NASA Astrophysics Data System (ADS)

Zhao, Ji; Zhong, Yanfei; Jia, Tianyi; Wang, Xinyu; Xu, Yao; Shu, Hong; Zhang, Liangpei

2018-01-01

Spectral-spatial classification is known to be an effective way to improve classification performance by integrating spectral information and spatial cues for hyperspectral imagery. In this paper, a game-theoretic spectral-spatial classification algorithm (GTA) using a conditional random field (CRF) model is presented, in which CRF is used to model the image considering the spatial contextual information, and a cooperative game is designed to obtain the labels. The algorithm establishes a one-to-one correspondence between image classification and game theory. The pixels of the image are considered as the players, and the labels are considered as the strategies in a game. Similar to the idea of soft classification, the uncertainty is considered to build the expected energy model in the first step. The local expected energy can be quickly calculated, based on a mixed strategy for the pixels, to establish the foundation for a cooperative game. Coalitions can then be formed by the designed merge rule based on the local expected energy, so that a majority game can be performed to make a coalition decision to obtain the label of each pixel. The experimental results on three hyperspectral data sets demonstrate the effectiveness of the proposed classification algorithm.

Comparison of H-alpha synoptic charts with the large-scale solar magnetic field as observed at Stanford

NASA Technical Reports Server (NTRS)

Duvall, T. L., Jr.; Wilcox, J. M.; Svalgaard, L.; Scherrer, P. H.; Mcintosh, P. S.

1977-01-01

Two methods of observing the neutral line of the large-scale photospheric magnetic field are compared: neutral line positions inferred from H-alpha photographs (McIntosh and Nolte, 1975) and observations of the photospheric magnetic field made with low spatial resolution (three minutes) and high sensitivity using the Stanford magnetograph. The comparison is found to be very favorable.

A new perspective on the regional hydrologic cycle over North and South America

NASA Astrophysics Data System (ADS)

Weng, Shu-Ping

The GEOS-1 vertically-integrated 3-hr moisture flux reanalyses and hourly-gridded United States station precipitation plus a satellite-based, 6-hr global precipitation estimate were employed to investigate the impacts of nocturnal low-level jets (LLJs) on the regional hydrological cycle over the central United States (Part I) and the subtropical plains of South America (Part II). Research stressed the influences of upper-level synoptic-scale waves (i.e., synoptic-scale forcings) upon the regional hydrologic processes, which were explored by the impacts associated with the occurrence of LLJ. Besides the conventional budget analysis, the adopted `synoptic-forcing approach' was proven illustrative in describing these impacts through the down-scaling process of LLJs. In Part 1, the major findings include: (1)the seasonal-averaged hydrological cycle over the Great Plains is strongly affected by the occurrence of GPLLJ, (2)the synoptic-scale forcing provided by the upper-level propagating jet (ULJ) streams is essential in generating the large-scale precipitation after the GPLLJ forms from the diurnal boundary layer process, (3)without the dynamic coupling between the ULJ and LLJ, the impact of LLJ on the hydrological cycle is demonstrated to be less important, and (4)the importance of synoptic-scale forcings in preconditioning the setting of wet/dry seasons in the interannual variability of rainfall anomaly is further illustrated by examining the changes of intensity as well as the occurrence frequency between the different types of LLJ. In Part II of this study, it was found that the occurrence of Andean LLJ represents a transient episode that detours the climatic rainfall activity along the South Atlantic Convergent Zone (SACZ) to the subtropical plains (Brazilian Nordeste) in its southwestern (northeastern) flank. The appearance of a seesaw pattern in the rainfall and flux convergence anomalies along the southeastern portion of South America, which is spatially in quadrature with the seasonal mean circulation, reflects the synoptic-scale forcing generated by the upper-level propagating transient-scale waves. In this regard, the function of the Andean LLJ in providing a scale-interaction mechanism that links the synoptic-scale setting with the localized rainfall event is the same as the GPLLJ. Due to the unique geographic background such as the narrow east-west landmass extension and the relative orientation between the Andean LLJ and the ULJ, however, the enhanced rainfall activity over the subtropical plains in response to the perturbed flux convergence is smaller than the case in the GPLLJ.

Synoptical situations and meteorological conditions associated to floods in the mouth of rivers in the European part of Russia

NASA Astrophysics Data System (ADS)

Matveeva, Tatiana; Gushchina, Daria

2013-04-01

The synoptical situations associated to the various type of floods in the mouth of rivers in European part of Russia are described. The storm surges, water flows and ice-jams are considered for Baltic, Barents sea, White sea, Azov sea, Black sea and Caspian sea regions. It is shown that the specific types of flood may be associated to various synoptical situations. Therefore it is unlikely to introduce the classification of synoptical regimes resulting in specific type of floods. However for each zone under consideration and for each specific flood type it is possible to determine the potential predictors of inundation: i.e. meteorological parameters which are characteristics of all cases of specific flood. There are: • for storm surges - long term wind forcing resulting in seiches in the sea, strong wind speed (the threshold varies in dependence on region), the wind direction orthogonal to the flow of river and strong baric gradient; • for water flows - the abundant precipitation, usually associate with the intensive frontal zone, the sudden change of air temperature resulting in snow melting in spring time; • for ice-jams - the strong temperature gradient extended in north-south direction resulting in negative temperature in the river mouth and positive temperature in the other basin. The probability of occurrence of predictors mentioned above was estimated for modern climate and global warming conditions using the outputs of ECHAM5/MPI-OM model. It is shown that the occurrence of intensive frontal zone and rainfall in the South of Russia will increase (decrease) in summer (winter) under warmer climate conditions which may contribute to the increase of water flows in this region. Maximum of floods occurs during the warm period, we can conclude that global warming increases the risk of floods in Black Sea coast.

The family structure of the Mucorales: a synoptic revision based on comprehensive multigene-genealogies.

PubMed

Hoffmann, K; Pawłowska, J; Walther, G; Wrzosek, M; de Hoog, G S; Benny, G L; Kirk, P M; Voigt, K

2013-06-01

The Mucorales (Mucoromycotina) are one of the most ancient groups of fungi comprising ubiquitous, mostly saprotrophic organisms. The first comprehensive molecular studies 11 yr ago revealed the traditional classification scheme, mainly based on morphology, as highly artificial. Since then only single clades have been investigated in detail but a robust classification of the higher levels based on DNA data has not been published yet. Therefore we provide a classification based on a phylogenetic analysis of four molecular markers including the large and the small subunit of the ribosomal DNA, the partial actin gene and the partial gene for the translation elongation factor 1-alpha. The dataset comprises 201 isolates in 103 species and represents about one half of the currently accepted species in this order. Previous family concepts are reviewed and the family structure inferred from the multilocus phylogeny is introduced and discussed. Main differences between the current classification and preceding concepts affects the existing families Lichtheimiaceae and Cunninghamellaceae, as well as the genera Backusella and Lentamyces which recently obtained the status of families along with the Rhizopodaceae comprising Rhizopus, Sporodiniella and Syzygites. Compensatory base change analyses in the Lichtheimiaceae confirmed the lower level classification of Lichtheimia and Rhizomucor while genera such as Circinella or Syncephalastrum completely lacked compensatory base changes.

Geo-spatial distribution of cloud cover and influence of cloud induced attenuation and noise temperature on satellite signal propagation over Nigeria

NASA Astrophysics Data System (ADS)

Ojo, Joseph Sunday

2017-05-01

The study of the influence of cloud cover on satellite propagation links is becoming more demanding due to the requirement of larger bandwidth for different satellite applications. Cloud attenuation is one of the major factors to consider for optimum performance of Ka/V and other higher frequency bands. In this paper, the geo-spatial distribution of cloud coverage over some chosen stations in Nigeria has been considered. The substantial scale spatial dispersion of cloud cover based on synoptic meteorological data and the possible impact on satellite communication links at higher frequency bands was also investigated. The investigation was based on 5 years (2008-2012) achieved cloud cover data collected by the Nigerian Meteorological Agency (NIMET) Federal Ministry of Aviation, Oshodi Lagos over four synoptic hours of the day covering day and night. The performances of satellite signals as they traverse through the cloud and cloud noise temperature at different seasons and over different hours of days at Ku/W-bands frequency are also examined. The overall result shows that the additional total atmospheric noise temperature due to the clear air effect and the noise temperature from the cloud reduces the signal-to-noise ratio of the satellite receiver systems, leading to more signal loss and if not adequately taken care of may lead to significant outage. The present results will be useful for Earth-space link budgeting, especially for the proposed multi-sensors communication satellite systems in Nigeria.

Machine-assisted discovery of relationships in astronomy

NASA Astrophysics Data System (ADS)

Graham, Matthew J.; Djorgovski, S. G.; Mahabal, Ashish A.; Donalek, Ciro; Drake, Andrew J.

2013-05-01

High-volume feature-rich data sets are becoming the bread-and-butter of 21st century astronomy but present significant challenges to scientific discovery. In particular, identifying scientifically significant relationships between sets of parameters is non-trivial. Similar problems in biological and geosciences have led to the development of systems which can explore large parameter spaces and identify potentially interesting sets of associations. In this paper, we describe the application of automated discovery systems of relationships to astronomical data sets, focusing on an evolutionary programming technique and an information-theory technique. We demonstrate their use with classical astronomical relationships - the Hertzsprung-Russell diagram and the Fundamental Plane of elliptical galaxies. We also show how they work with the issue of binary classification which is relevant to the next generation of large synoptic sky surveys, such as the Large Synoptic Survey Telescope (LSST). We find that comparable results to more familiar techniques, such as decision trees, are achievable. Finally, we consider the reality of the relationships discovered and how this can be used for feature selection and extraction.

Documentation of procedures for textural/spatial pattern recognition techniques

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Bryant, W. F.

1976-01-01

A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.

Architectural Implications for Spatial Object Association Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, V S; Kurc, T; Saltz, J

2009-01-29

Spatial object association, also referred to as cross-match of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server R, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation providesmore » insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST).« less

Circulation Type Classifications and their nexus to Van Bebber's storm track Vb

NASA Astrophysics Data System (ADS)

Hofstätter, M.; Chimani, B.

2012-04-01

Circulation Type Classifications (CTCs) are tools to identify repetitive and predominantly stationary patterns of the atmospheric circulation over a certain area, with the purpose to enable the recognition of specific characteristics in surface climate variables. On the other hand storm tracks can be used to identify similar types of synoptic events from a non-stationary, kinematic perspective. Such a storm track classification for Europe has been done in the late 19th century by Van Bebber (1882, 1891), from which the famous type Vb and Vc/d remained up to the present day because of to their association with major flooding events like in August 2002 in Europe. In this work a systematic tracking procedure has been developed, to determine storm track types and their characteristics especially for the Eastern Alpine Region in the period 1961-2002, using ERA40 and ERAinterim reanalysis. The focus thereby is on cyclone tracks of type V as suggested by van Bebber and congeneric types. This new catalogue is used as a reference to verify the hypothesis of a certain coherence of storm track Vb with certain circulation types (e.g. Fricke and Kaminski, 2002). Selected objective and subjective classification schemes from the COST733 action (http://cost733.met.no/, Phillip et al. 2010) are used therefore, as well as the manual classification from ZAMG (Lauscher 1972 and 1985), in which storm track Vb has been classified explicitly on a daily base since 1948. The latter scheme should prove itself as a valuable and unique data source in that issue. Results show that not less than 146 storm tracks are identified as Vb between 1961 and 2002, whereas only three events could be found from literature, pointing to big subjectivity and preconception in the issue of Vb storm tracks. The annual number of Vb storm tracks do not show any significant trend over the last 42 years, but large variations from year to year. Circulation type classification CAP27 (Cluster Analysis of Principal Components) is the best performing, fully objective scheme tested herein, showing the power to discriminate Vb events. Most of the other fully objective schemes do by far not perform as well. Largest skill in that issue can be seen from the subjective/manual CTCs, proving themselves to enhance relevant synoptic phenomena instead of emphasizing mathematic criteria in the classification. The hypothesis of Fricke and Kaminsky can definitely be supported by this work: Vb storm tracks are included in one or the other stationary circulation pattern, but to which extent depends on the specific characteristics of the CTC in question.

Automatic classification of time-variable X-ray sources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, andmore » other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.« less

OLYMPEX Data Workshop: GPM View

NASA Technical Reports Server (NTRS)

Petersen, W.

2017-01-01

OLYMPEX Primary Objectives: Datasets to enable: (1) Direct validation over complex terrain at multiple scales, liquid and frozen precip types, (a) Do we capture terrain and synoptic regime transitions, orographic enhancements/structure, full range of precipitation intensity (e.g., very light to heavy) and types, spatial variability? (b) How well can we estimate space/time-accumulated precipitation over terrain (liquid + frozen)? (2) Physical validation of algorithms in mid-latitude cold season frontal systems over ocean and complex terrain, (a) What are the column properties of frozen, melting, liquid hydrometeors-their relative contributions to estimated surface precipitation, transition under the influence of terrain gradients, and systematic variability as a function of synoptic regime? (3) Integrated hydrologic validation in complex terrain, (a) Can satellite estimates be combined with modeling over complex topography to drive improved products (assimilation, downscaling) [Level IV products] (b) What are capabilities and limitations for use of satellite-based precipitation estimates in stream/river flow forecasting?

A systematic approach to synoptic tornado climatology of Hungary for the recent years (1996 2001) based on official damage reports

NASA Astrophysics Data System (ADS)

Szilárd, Sárközi

2007-02-01

Due to the significant amount of severe storm damage from the mid 1990s, a practical need has arisen for updating risk assessment. For reliable and systematic sampling of events, data acquisition has been arranged through the disaster management official body using a pyramidal national coverage. Post-analysis, including its meteorological part, proceeds in a GIS environment. This paper focuses specifically on damaging tornadoes, since those are the most violent and best-documented phenomena. Different statistics are calculated and explained, such as seasonal, diurnal and magnitude distributions. Spatial occurrence and features are mapped. A complete synoptic climatology is given by typifying the generating conditions and categorizing events into certain classes, while discussing the role of the Carpathian Basin. In the end a conceptual issue in connection with self-similarity is raised for further discussion.

Near real time water quality monitoring of Chivero and Manyame lakes of Zimbabwe

NASA Astrophysics Data System (ADS)

Muchini, Ronald; Gumindoga, Webster; Togarepi, Sydney; Pinias Masarira, Tarirai; Dube, Timothy

2018-05-01

Zimbabwe's water resources are under pressure from both point and non-point sources of pollution hence the need for regular and synoptic assessment. In-situ and laboratory based methods of water quality monitoring are point based and do not provide a synoptic coverage of the lakes. This paper presents novel methods for retrieving water quality parameters in Chivero and Manyame lakes, Zimbabwe, from remotely sensed imagery. Remotely sensed derived water quality parameters are further validated using in-situ data. It also presents an application for automated retrieval of those parameters developed in VB6, as well as a web portal for disseminating the water quality information to relevant stakeholders. The web portal is developed, using Geoserver, open layers and HTML. Results show the spatial variation of water quality and an automated remote sensing and GIS system with a web front end to disseminate water quality information.

Vacillations induced by interference of stationary and traveling planetary waves

NASA Technical Reports Server (NTRS)

Salby, Murry L.; Garcia, Rolando R.

1987-01-01

The interference pattern produced when a traveling planetary wave propagates over a stationary forced wave is explored, examining the interference signature in a variety of diagnostics. The wave field is first restricted to a diatomic spectrum consisting of two components: a single stationary wave and a single monochromatic traveling wave. A simple barotropic normal mode propagating over a simple stationary plane wave is considered, and closed form solutions are obtained. The wave fields are then restricted spatially, providing more realistic structures without sacrificing the advantages of an analytical solution. Both stationary and traveling wave fields are calculated numerically with the linearized Primitive Equations in a realistic basic state. The mean flow reaction to the fluctuating eddy forcing which results from interference is derived. Synoptic geopotential behavior corresponding to the combined wave and mean flow fields is presented, and the synoptic signature in potential vorticity on isentropic surfaces is examined.

Are meteorological conditions favoring hail precipitation change in Southern Europe? Analysis of the period 1948-2015

NASA Astrophysics Data System (ADS)

Sanchez, J. L.; Merino, A.; Melcón, P.; García-Ortega, E.; Fernández-González, S.; Berthet, C.; Dessens, J.

2017-12-01

In the context of a warming climate, one of the variables currently under investigation is related to the detection of possible changes in hail precipitation. In this work, we analyze hail frequencies in one of the most affected areas by this phenomenon in Europe, southern France. Here, an extensive hail detection network has been in operation since 1988. In general, the detection of hailfall is very uncertain. To overcome the constraints of scarcity and poor standardization of hail detection and monitoring systems, some relationships between hailstorm occurrence and synoptic, mesoscale or thermodynamic atmospheric characteristics have been proposed in different areas. Therefore, we analyzed meteorological fields at synoptic scale that are related to the formation of hailstorms in the study area, i.e., geopotential height at 500 hPa, sea level pressure, and lapse-rate between 850 and 500 hPa. These fields describe the state of the atmosphere at low and mid levels, and facilitate the evaluation of thermal and dynamic instability. Using the Mann-Kendall test and Sen estimator, we examined trends in the three fields during the period 1948-2015 and their spatial patterns, revealing an evolution toward synoptic environments that favor hail precipitation in the Mediterranean region.

Detailed Quantitative Classifications of Galaxy Morphology

NASA Astrophysics Data System (ADS)

Nair, Preethi

2018-01-01

Understanding the physical processes responsible for the growth of galaxies is one of the key challenges in extragalactic astronomy. The assembly history of a galaxy is imprinted in a galaxy’s detailed morphology. The bulge-to-total ratio of galaxies, the presence or absence of bars, rings, spiral arms, tidal tails etc, all have implications for the past merger, star formation, and feedback history of a galaxy. However, current quantitative galaxy classification schemes are only useful for broad binning. They cannot classify or exploit the wide variety of galaxy structures seen in nature. Therefore, comparisons of observations with theoretical predictions of secular structure formation have only been conducted on small samples of visually classified galaxies. However large samples are needed to disentangle the complex physical processes of galaxy formation. With the advent of large surveys, like the Sloan Digital Sky Survey (SDSS) and the upcoming Large Synoptic Survey Telescope (LSST) and WFIRST, the problem of statistics will be resolved. However, the need for a robust quantitative classification scheme will still remain. Here I will present early results on promising machine learning algorithms that are providing detailed classifications, identifying bars, rings, multi-armed spiral galaxies, and Hubble type.

The SED Machine: A Robotic Spectrograph for Fast Transient Classification

NASA Astrophysics Data System (ADS)

Blagorodnova, Nadejda; Neill, James D.; Walters, Richard; Kulkarni, Shrinivas R.; Fremling, Christoffer; Ben-Ami, Sagi; Dekany, Richard G.; Fucik, Jason R.; Konidaris, Nick; Nash, Reston; Ngeow, Chow-Choong; Ofek, Eran O.; O’ Sullivan, Donal; Quimby, Robert; Ritter, Andreas; Vyhmeister, Karl E.

2018-03-01

Current time domain facilities are finding several hundreds of transient astronomical events a year. The discovery rate is expected to increase in the future as soon as new surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Sky Survey (LSST) come online. Presently, the rate at which transients are classified is approximately one order or magnitude lower than the discovery rate, leading to an increasing “follow-up drought”. Existing telescopes with moderate aperture can help address this deficit when equipped with spectrographs optimized for spectral classification. Here, we provide an overview of the design, operations and first results of the Spectral Energy Distribution Machine (SEDM), operating on the Palomar 60-inch telescope (P60). The instrument is optimized for classification and high observing efficiency. It combines a low-resolution (R ∼ 100) integral field unit (IFU) spectrograph with “Rainbow Camera” (RC), a multi-band field acquisition camera which also serves as multi-band (ugri) photometer. The SEDM was commissioned during the operation of the intermediate Palomar Transient Factory (iPTF) and has already lived up to its promise. The success of the SEDM demonstrates the value of spectrographs optimized for spectral classification.

Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

NASA Astrophysics Data System (ADS)

Jamshidpour, N.; Homayouni, S.; Safari, A.

2017-09-01

Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

Characterizing multiscale variability of zero intermittency in spatial rainfall

NASA Technical Reports Server (NTRS)

Kumar, Praveen; Foufoula-Georgiou, Efi

1994-01-01

In this paper the authors study how zero intermittency in spatial rainfall, as described by the fraction of area covered by rainfall, changes with spatial scale of rainfall measurement or representation. A statistical measure of intermittency that describes the size distribution of 'voids' (nonrainy areas imbedded inside rainy areas) as a function of scale is also introduced. Morphological algorithms are proposed for reconstructing rainfall intermittency at fine scales given the intermittency at coarser scales. These algorithms are envisioned to be useful in hydroclimatological studies where the rainfall spatial variability at the subgrid scale needs to be reconstructed from the results of synoptic- or mesoscale meteorological numerical models. The developed methodologies are demsonstrated and tested using data from a severe springtime midlatitude squall line and a mild midlatitude winter storm monitored by a meteorological radar in Norman, Oklahoma.

Efficiency of the spectral-spatial classification of hyperspectral imaging data

NASA Astrophysics Data System (ADS)

Borzov, S. M.; Potaturkin, O. I.

2017-01-01

The efficiency of methods of the spectral-spatial classification of similarly looking types of vegetation on the basis of hyperspectral data of remote sensing of the Earth, which take into account local neighborhoods of analyzed image pixels, is experimentally studied. Algorithms that involve spatial pre-processing of the raw data and post-processing of pixel-based spectral classification maps are considered. Results obtained both for a large-size hyperspectral image and for its test fragment with different methods of training set construction are reported. The classification accuracy in all cases is estimated through comparisons of ground-truth data and classification maps formed by using the compared methods. The reasons for the differences in these estimates are discussed.

Classification with spatio-temporal interpixel class dependency contexts

NASA Technical Reports Server (NTRS)

Jeon, Byeungwoo; Landgrebe, David A.

1992-01-01

A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. Experimental results with bitemporal TM data show significant improvement of classification accuracy over noncontextual pixelwise classifiers. This spatiotemporal contextual classifier should find use in many applications of remote sensing, especially when the classification accuracy is important.

Estimating instream constituent loads using replicate synoptic sampling, Peru Creek, Colorado

NASA Astrophysics Data System (ADS)

Runkel, Robert L.; Walton-Day, Katherine; Kimball, Briant A.; Verplanck, Philip L.; Nimick, David A.

2013-05-01

SummaryThe synoptic mass balance approach is often used to evaluate constituent mass loading in streams affected by mine drainage. Spatial profiles of constituent mass load are used to identify sources of contamination and prioritize sites for remedial action. This paper presents a field scale study in which replicate synoptic sampling campaigns are used to quantify the aggregate uncertainty in constituent load that arises from (1) laboratory analyses of constituent and tracer concentrations, (2) field sampling error, and (3) temporal variation in concentration from diel constituent cycles and/or source variation. Consideration of these factors represents an advance in the application of the synoptic mass balance approach by placing error bars on estimates of constituent load and by allowing all sources of uncertainty to be quantified in aggregate; previous applications of the approach have provided only point estimates of constituent load and considered only a subset of the possible errors. Given estimates of aggregate uncertainty, site specific data and expert judgement may be used to qualitatively assess the contributions of individual factors to uncertainty. This assessment can be used to guide the collection of additional data to reduce uncertainty. Further, error bars provided by the replicate approach can aid the investigator in the interpretation of spatial loading profiles and the subsequent identification of constituent source areas within the watershed. The replicate sampling approach is applied to Peru Creek, a stream receiving acidic, metal-rich effluent from the Pennsylvania Mine. Other sources of acidity and metals within the study reach include a wetland area adjacent to the mine and tributary inflow from Cinnamon Gulch. Analysis of data collected under low-flow conditions indicates that concentrations of Al, Cd, Cu, Fe, Mn, Pb, and Zn in Peru Creek exceed aquatic life standards. Constituent loading within the study reach is dominated by effluent from the Pennsylvania Mine, with over 50% of the Cd, Cu, Fe, Mn, and Zn loads attributable to a collapsed adit near the top of the study reach. These estimates of mass load may underestimate the effect of the Pennsylvania Mine as leakage from underground mine workings may contribute to metal loads that are currently attributed to the wetland area. This potential leakage confounds the evaluation of remedial options and additional research is needed to determine the magnitude and location of the leakage.

Estimating instream constituent loads using replicate synoptic sampling, Peru Creek, Colorado

USGS Publications Warehouse

Runkel, Robert L.; Walton-Day, Katherine; Kimball, Briant A.; Verplanck, Philip L.; Nimick, David A.

2013-01-01

The synoptic mass balance approach is often used to evaluate constituent mass loading in streams affected by mine drainage. Spatial profiles of constituent mass load are used to identify sources of contamination and prioritize sites for remedial action. This paper presents a field scale study in which replicate synoptic sampling campaigns are used to quantify the aggregate uncertainty in constituent load that arises from (1) laboratory analyses of constituent and tracer concentrations, (2) field sampling error, and (3) temporal variation in concentration from diel constituent cycles and/or source variation. Consideration of these factors represents an advance in the application of the synoptic mass balance approach by placing error bars on estimates of constituent load and by allowing all sources of uncertainty to be quantified in aggregate; previous applications of the approach have provided only point estimates of constituent load and considered only a subset of the possible errors. Given estimates of aggregate uncertainty, site specific data and expert judgement may be used to qualitatively assess the contributions of individual factors to uncertainty. This assessment can be used to guide the collection of additional data to reduce uncertainty. Further, error bars provided by the replicate approach can aid the investigator in the interpretation of spatial loading profiles and the subsequent identification of constituent source areas within the watershed.The replicate sampling approach is applied to Peru Creek, a stream receiving acidic, metal-rich effluent from the Pennsylvania Mine. Other sources of acidity and metals within the study reach include a wetland area adjacent to the mine and tributary inflow from Cinnamon Gulch. Analysis of data collected under low-flow conditions indicates that concentrations of Al, Cd, Cu, Fe, Mn, Pb, and Zn in Peru Creek exceed aquatic life standards. Constituent loading within the study reach is dominated by effluent from the Pennsylvania Mine, with over 50% of the Cd, Cu, Fe, Mn, and Zn loads attributable to a collapsed adit near the top of the study reach. These estimates of mass load may underestimate the effect of the Pennsylvania Mine as leakage from underground mine workings may contribute to metal loads that are currently attributed to the wetland area. This potential leakage confounds the evaluation of remedial options and additional research is needed to determine the magnitude and location of the leakage.

Submesoscale dispersion in the vicinity of the Deepwater Horizon spill.

PubMed

Poje, Andrew C; Ozgökmen, Tamay M; Lipphardt, Bruce L; Haus, Brian K; Ryan, Edward H; Haza, Angelique C; Jacobs, Gregg A; Reniers, A J H M; Olascoaga, Maria Josefina; Novelli, Guillaume; Griffa, Annalisa; Beron-Vera, Francisco J; Chen, Shuyi S; Coelho, Emanuel; Hogan, Patrick J; Kirwan, Albert D; Huntley, Helga S; Mariano, Arthur J

2014-09-02

Reliable forecasts for the dispersion of oceanic contamination are important for coastal ecosystems, society, and the economy as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010 and the Fukushima nuclear plant incident in the Pacific Ocean in 2011. Accurate prediction of pollutant pathways and concentrations at the ocean surface requires understanding ocean dynamics over a broad range of spatial scales. Fundamental questions concerning the structure of the velocity field at the submesoscales (100 m to tens of kilometers, hours to days) remain unresolved due to a lack of synoptic measurements at these scales. Using high-frequency position data provided by the near-simultaneous release of hundreds of accurately tracked surface drifters, we study the structure of submesoscale surface velocity fluctuations in the Northern Gulf of Mexico. Observed two-point statistics confirm the accuracy of classic turbulence scaling laws at 200-m to 50-km scales and clearly indicate that dispersion at the submesoscales is local, driven predominantly by energetic submesoscale fluctuations. The results demonstrate the feasibility and utility of deploying large clusters of drifting instruments to provide synoptic observations of spatial variability of the ocean surface velocity field. Our findings allow quantification of the submesoscale-driven dispersion missing in current operational circulation models and satellite altimeter-derived velocity fields.

Submesoscale dispersion in the vicinity of the Deepwater Horizon spill

PubMed Central

Poje, Andrew C.; Özgökmen, Tamay M.; Lipphardt, Bruce L.; Haus, Brian K.; Ryan, Edward H.; Haza, Angelique C.; Jacobs, Gregg A.; Reniers, A. J. H. M.; Olascoaga, Maria Josefina; Novelli, Guillaume; Griffa, Annalisa; Beron-Vera, Francisco J.; Chen, Shuyi S.; Coelho, Emanuel; Hogan, Patrick J.; Kirwan, Albert D.; Huntley, Helga S.; Mariano, Arthur J.

2014-01-01

Reliable forecasts for the dispersion of oceanic contamination are important for coastal ecosystems, society, and the economy as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010 and the Fukushima nuclear plant incident in the Pacific Ocean in 2011. Accurate prediction of pollutant pathways and concentrations at the ocean surface requires understanding ocean dynamics over a broad range of spatial scales. Fundamental questions concerning the structure of the velocity field at the submesoscales (100 m to tens of kilometers, hours to days) remain unresolved due to a lack of synoptic measurements at these scales. Using high-frequency position data provided by the near-simultaneous release of hundreds of accurately tracked surface drifters, we study the structure of submesoscale surface velocity fluctuations in the Northern Gulf of Mexico. Observed two-point statistics confirm the accuracy of classic turbulence scaling laws at 200-m to 50-km scales and clearly indicate that dispersion at the submesoscales is local, driven predominantly by energetic submesoscale fluctuations. The results demonstrate the feasibility and utility of deploying large clusters of drifting instruments to provide synoptic observations of spatial variability of the ocean surface velocity field. Our findings allow quantification of the submesoscale-driven dispersion missing in current operational circulation models and satellite altimeter-derived velocity fields. PMID:25136097

Verification of NWP Cloud Properties using A-Train Satellite Observations

NASA Astrophysics Data System (ADS)

Kucera, P. A.; Weeks, C.; Wolff, C.; Bullock, R.; Brown, B.

2011-12-01

Recently, the NCAR Model Evaluation Tools (MET) has been enhanced to incorporate satellite observations for the verification of Numerical Weather Prediction (NWP) cloud products. We have developed tools that match fields spatially (both in the vertical and horizontal dimensions) to compare NWP products with satellite observations. These matched fields provide diagnostic evaluation of cloud macro attributes such as vertical distribution of clouds, cloud top height, and the spatial and seasonal distribution of cloud fields. For this research study, we have focused on using CloudSat, CALIPSO, and MODIS observations to evaluate cloud fields for a variety of NWP fields and derived products. We have selected cases ranging from large, mid-latitude synoptic systems to well-organized tropical cyclones. For each case, we matched the observed cloud field with gridded model and/or derived product fields. CloudSat and CALIPSO observations and model fields were matched and compared in the vertical along the orbit track. MODIS data and model fields were matched and compared in the horizontal. We then use MET to compute the verification statistics to quantify the performance of the models in representing the cloud fields. In this presentation we will give a summary of our comparison and show verification results for both synoptic and tropical cyclone cases.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models tomore » curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.« less

Development of a spatially universal framework for classifying stream assemblages with application to conservation planning for Great Lakes lotic fish communities

USGS Publications Warehouse

McKenna, James E.; Schaeffer, Jeffrey S.; Stewart, Jana S.; Slattery, Michael T.

2015-01-01

Classifications are typically specific to particular issues or areas, leading to patchworks of subjectively defined spatial units. Stream conservation is hindered by the lack of a universal habitat classification system and would benefit from an independent hydrology-guided spatial framework of units encompassing all aquatic habitats at multiple spatial scales within large regions. We present a system that explicitly separates the spatial framework from any particular classification developed from the framework. The framework was constructed from landscape variables that are hydrologically and biologically relevant, covered all space within the study area, and was nested hierarchically and spatially related at scales ranging from the stream reach to the entire region; classifications may be developed from any subset of the 9 basins, 107 watersheds, 459 subwatersheds, or 10,000s of valley segments or stream reaches. To illustrate the advantages of this approach, we developed a fish-guided classification generated from a framework for the Great Lakes region that produced a mosaic of habitat units which, when aggregated, formed larger patches of more general conditions at progressively broader spatial scales. We identified greater than 1,200 distinct fish habitat types at the valley segment scale, most of which were rare. Comparisons of biodiversity and species assemblages are easily examined at any scale. This system can identify and quantify habitat types, evaluate habitat quality for conservation and/or restoration, and assist managers and policymakers with prioritization of protection and restoration efforts. Similar spatial frameworks and habitat classifications can be developed for any organism in any riverine ecosystem.

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Numerical simulations of significant orographic precipitation in Madeira island

NASA Astrophysics Data System (ADS)

Couto, Flavio Tiago; Ducrocq, Véronique; Salgado, Rui; Costa, Maria João

2016-03-01

High-resolution simulations of high precipitation events with the MESO-NH model are presented, and also used to verify that increasing horizontal resolution in zones of complex orography, such as in Madeira island, improve the simulation of the spatial distribution and total precipitation. The simulations succeeded in reproducing the general structure of the cloudy systems over the ocean in the four periods considered of significant accumulated precipitation. The accumulated precipitation over the Madeira was better represented with the 0.5 km horizontal resolution and occurred under four distinct synoptic situations. Different spatial patterns of the rainfall distribution over the Madeira have been identified.

Performance characterization of a cross-flow hydrokinetic turbine in sheared inflow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Forbush, Dominic; Polagye, Brian; Thomson, Jim

2016-12-01

A method for constructing a non-dimensional performance curve for a cross-flow hydrokinetic turbine in sheared flow is developed for a natural river site. The river flow characteristics are quasi-steady, with negligible vertical shear, persistent lateral shear, and synoptic changes dominated by long time scales (days to weeks). Performance curves developed from inflow velocities measured at individual points (randomly sampled) yield inconclusive turbine performance characteristics because of the spatial variation in mean flow. Performance curves using temporally- and spatially-averaged inflow velocities are more conclusive. The implications of sheared inflow are considered in terms of resource assessment and turbine control.

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

NASA Technical Reports Server (NTRS)

Sadowski, F. E.; Sarno, J. E.

1976-01-01

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

Bayesian learning for spatial filtering in an EEG-based brain-computer interface.

PubMed

Zhang, Haihong; Yang, Huijuan; Guan, Cuntai

2013-07-01

Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

The influence of multispectral scanner spatial resolution on forest feature classification

NASA Technical Reports Server (NTRS)

Sadowski, F. G.; Malila, W. A.; Sarno, J. E.; Nalepka, R. F.

1977-01-01

Inappropriate spatial resolution and corresponding data processing techniques may be major causes for non-optimal forest classification results frequently achieved from multispectral scanner (MSS) data. Procedures and results of empirical investigations are studied to determine the influence of MSS spatial resolution on the classification of forest features into levels of detail or hierarchies of information that might be appropriate for nationwide forest surveys and detailed in-place inventories. Two somewhat different, but related studies are presented. The first consisted of establishing classification accuracies for several hierarchies of features as spatial resolution was progressively coarsened from (2 meters) squared to (64 meters) squared. The second investigated the capabilities for specialized processing techniques to improve upon the results of conventional processing procedures for both coarse and fine resolution data.

Collaborative classification of hyperspectral and visible images with convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Mengmeng; Li, Wei; Du, Qian

2017-10-01

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

Macroinvertebrate communities evaluated prior to and following a channel restoration project in Silver Creek, Blaine County, Idaho, 2001-16

USGS Publications Warehouse

MacCoy, Dorene E.; Short, Terry M.

2017-11-22

The U.S. Geological Survey, in cooperation with Blaine County and The Nature Conservancy, evaluated the status of macroinvertebrate communities prior to and following a channel restoration project in Silver Creek, Blaine County, Idaho. The objective of the evaluation was to determine whether 2014 remediation efforts to restore natural channel conditions in an impounded area of Silver Creek caused declines in local macroinvertebrate communities. Starting in 2001 and ending in 2016, macroinvertebrates were sampled every 3 years at two long-term trend sites and sampled seasonally (spring, summer, and autumn) in 2013, 2015, and 2016 at seven synoptic sites. Trend-site communities were collected from natural stream-bottom substrates to represent locally established macroinvertebrate assemblages. Synoptic site communities were sampled using artificial (multi-plate) substrates to represent recently colonized (4–6 weeks) assemblages. Statistical summaries of spatial and temporal patterns in macroinvertebrate taxonomic composition at both trend and synoptic sites were completed.The potential effect of the restoration project on resident macroinvertebrate populations was determined by comparing the following community assemblage metrics:Total taxonomic richness (taxa richness);Total macroinvertebrate abundance (total abundance);Ephemeroptera, Plecoptera, Trichoptera (EPT) richness;EPT abundance;Simpson’s diversity; andSimpson’s evenness for periods prior to and following restoration.A significant decrease in one or more metric values in the period following stream channel restoration was the basis for determining impairment to the macroinvertebrate communities in Silver Creek.Comparison of pre-restoration (2001–13) and post‑restoration (2016) macroinvertebrate community composition at trend sites determined that no significant decreases occurred in any metric parameter for communities sampled in 2016. Taxa and EPT richness of colonized assemblages at synoptic sites increased significantly from pre-restoration in 2013 to post-restoration in 2015 and 2016. Similarly, total and EPT abundances at synoptic sites showed non-significant increases from 2013 to 2015 and 2016. Significant seasonal differences in macroinvertebrate assemblages were apparent at synoptic site locations and likely reflected typical life-history patterns of increased insect emergence and development in the late spring and early summer months. Taxa and EPT richness were each significantly higher in spring and summer than in autumn, and total abundances were significantly higher in spring than in summer and autumn. No significant differences in community diversity or evenness of colonized communities were noted at synoptic site locations between pre- and post-restoration years or among seasons. Select community-metric results from the trend- and synoptic‑site sampling indicated that the Silver Creek restoration effort in 2014 did not result in a significant decline in resident macroinvertebrate communities.

Examples of Level Products Possible from Existing Assets

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.

2012-01-01

How do patterns of human environmental and infectious diseases respond to leading environmental changes, particularly to urban growth and change and the associated impacts of urbanization? We use HyspIRI high spatial resolution, multispectral, and multitemporal TIR data to track energy balance and energy flux characteristics for changing land covers/land uses through time to provide synoptic views of impacts on surface energy fluxes, emissivity and temperature and HyspIRI data in conjunction with spatial growth models to project land cover/land use changes in the future to assess impacts on natural and human ecosystems. We use multispectral thermal IR land cover maps at a high spatial resolution (60m) on a weekly basis for long-term validation of surface energy responses and changes in emissivity and integration of HyspIRI TIR data with spatial modeling to assess changes in land cover/land use through time and subsequent changes in thermal energy responses

Evaluate transport processes in MERRA driven chemical transport models using updated 222Rn emission inventories and global observations

NASA Astrophysics Data System (ADS)

Zhang, B.; Liu, H.; Crawford, J. H.; Fairlie, T. D.; Chen, G.; Chambers, S. D.; Kang, C. H.; Williams, A. G.; Zhang, K.; Considine, D. B.; Payer Sulprizio, M.; Yantosca, R.

2015-12-01

Convective and synoptic processes play a major role in determining the transport and distribution of trace gases and aerosols in the troposphere. The representation of these processes in global models (at ~100-1000 km horizontal resolution) is challenging, because convection is a sub-grid process and needs to be parameterized, while synoptic processes are close to the grid scale. Depending on the parameterization schemes used in climate models, the role of convection in transporting trace gases and aerosols may vary from model to model. 222Rn is a chemically inert and radioactive gas constantly emitted from soil and has a half-life (3.8 days) comparable to synoptic timescale, which makes it an effective tracer for convective and synoptic transport. In this study, we evaluate the convective and synoptic transport in two chemical transport models (GMI and GEOS-Chem), both driven by the NASA's MERRA reanalysis. Considering the uncertainties in 222Rn emissions, we incorporate two more recent scenarios with regionally varying 222Rn emissions into GEOS-Chem/MERRA and compare the simulation results with those using the relatively uniform 222Rn emissions in the standard model. We evaluate the global distribution and seasonality of 222Rn concentrations simulated by the two models against an extended collection of 222Rn observations from 1970s to 2010s. The intercomparison will improve our understanding of the spatial variability in global 222Rn emissions, including the suspected excessive 222Rn emissions in East Asia, and provide useful feedbacks on 222Rn emission models. We will assess 222Rn vertical distributions at different latitudes in the models using observations at surface sites and in the upper troposphere and lower stratosphere. Results will be compared with previous models driven by other meteorological fields (e.g., fvGCM and GEOS4). Since the decay of 222Rn is the source of 210Pb, a useful radionuclide tracer attached to submicron aerosols, improved understanding of emissions and transport of 222Rn will provide insights into the transport, distribution, and wet deposition of 210Pb aerosols.

Changes in Pacific Northwest Heat Waves and Associated Synoptic/Mesoscale Drivers Under Anthropogenic Global Warming

NASA Astrophysics Data System (ADS)

Brewer, M.; Mass, C.

2014-12-01

Though western Oregon and Washington summers are typically mild due to the influence of the nearby Pacific Ocean, this region occasionally experiences heat waves with temperatures in excess of 35ºC. These heat waves can have a substantial impact on this highly populated region, particularly since the population is unaccustomed to and generally unprepared for such conditions. A comprehensive evaluation is needed of past and future heat wave trends in frequency, intensity, and duration. Furthermore, it is important to understand the physical mechanisms of Northwest heat waves and how such mechanisms might change under anthropogenic global warming. Lower-tropospheric heat waves over the west coast of North America are the result of both synoptic and mesoscale factors, the latter requiring high-resolution models (roughly 12-15 km grid spacing) to simulate. Synoptic factors include large-scale warming due to horizontal advection and subsidence, as well as reductions in large-scale cloudiness. An important mesoscale factor is the occurrence of offshore (easterly) flow, resulting in an adiabatically warmed continental air mass spreading over the western lowlands rather than the more usual cool, marine air influence. To fully understand how heat waves will change under AGW, it is necessary to determine the combined impacts of both synoptic and mesoscale effects in a warming world. General Circulation Models (GCM) are generally are too coarse to simulate mesoscale effects realistically and thus may provide unreliable estimates of the frequency and magnitudes of West Coast heat waves. Therefore, to determine the regional implications of global warming, this work made use of long-term, high-resolution WRF simulations, at 36- and 12-km resolution, produced by dynamically downscaling GCM grids. This talk will examine the predicted trends in Pacific Northwest heat wave intensity, duration, and frequency during the 21st century (through 2100). The spatial distribution in the trends in heat waves, and the variability of these trends at different resolutions and among different models will also be described. Finally, changes in the synoptic and mesoscale configurations that drive Pacific Northwest heat waves and the modulating effects of local terrain and land/water contrast will be discussed.

Comparisons of neural networks to standard techniques for image classification and correlation

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1994-01-01

Neural network techniques for multispectral image classification and spatial pattern detection are compared to the standard techniques of maximum-likelihood classification and spatial correlation. The neural network produced a more accurate classification than maximum-likelihood of a Landsat scene of Tucson, Arizona. Some of the errors in the maximum-likelihood classification are illustrated using decision region and class probability density plots. As expected, the main drawback to the neural network method is the long time required for the training stage. The network was trained using several different hidden layer sizes to optimize both the classification accuracy and training speed, and it was found that one node per class was optimal. The performance improved when 3x3 local windows of image data were entered into the net. This modification introduces texture into the classification without explicit calculation of a texture measure. Larger windows were successfully used for the detection of spatial features in Landsat and Magellan synthetic aperture radar imagery.

Preserving a Unique Archive for Long-Term Solar Variability Studies

NASA Astrophysics Data System (ADS)

Webb, David F.; Hewins, Ian; McFadden, Robert; Emery, Barbara; Gibson, Sarah; Denig, William

2016-05-01

In 1964 (solar cycle 20) Patrick McIntosh began creating hand-drawn synoptic maps of solar activity, based on Hydrogen alpha (Hα) imaging measurements. These synoptic maps were unique because they traced the polarity inversion lines (PILs), connecting widely separated filaments, fibril patterns and plage corridors to reveal the large-scale organization of the solar magnetic field. He and his assistants later included coronal hole (CH) boundaries to the maps, usually from ground-based He-I 10830 images. They continued making these maps until 2010 (the start of solar cycle 24), yielding more than 40 years (~ 540 Carrington rotations) or nearly four complete solar cycles (SCs) of synoptic maps. The McIntosh collection of maps forms a unique and consistent set of global solar magnetic field data, and are unique tools for studying the structure and evolution of the large-scale solar fields and polarity boundaries, because: 1) they have excellent spatial resolution for defining polarity boundaries, 2) the organization of the fields into long-lived, coherent features is clear, and 3) the data are relatively homogeneous over four solar cycles. After digitization and archiving, these maps -- along with computer codes permitting efficient searches of the map arrays -- will be made publicly available at NOAA’s National Centers for Environmental Information (NCEI) in their final, searchable form. This poster is a progress report of the project so far and some suggested scientific applications.

Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.

PubMed

Yuan, Yuan; Lin, Jianzhe; Wang, Qi

2016-12-01

Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.

Palaeocirculation across New Zealand during the last glacial maximum at ˜21 ka

NASA Astrophysics Data System (ADS)

Lorrey, Andrew M.; Vandergoes, Marcus; Almond, Peter; Renwick, James; Stephens, Tom; Bostock, Helen; Mackintosh, Andrew; Newnham, Rewi; Williams, Paul W.; Ackerley, Duncan; Neil, Helen; Fowler, Anthony M.

2012-03-01

What circulation pattern drove Southern Alps glacial advances at ˜21 ka? Late 20th century glacial advances in New Zealand are commonly attributed to a dual precipitation increase and cooler than normal temperatures associated with enhanced westerly flow that occur under synoptic pressure patterns termed 'zonal' regimes (Kidson, 2000). But was the circulation pattern that supported major Southern Alps glacial advances during the global LGM similar to the modern analog? Here, a Regional Climate Regime Classification (RCRC) time slice was used to infer past circulation for New Zealand during the LGM at ˜21 ka. Palaeoclimate information that supported the construction of the ˜21 ka time slice was derived from the NZ-INTIMATE Climate Event Stratigraphy (CES), one new Auckland maar proxy record, and additional low-resolution data sourced from the literature. The terrestrial evidence at ˜21 ka implicates several possibilities for past circulation, depending on how interpretations for some proxies are made. The interpretation considered most tenable for the LGM, based on the agreement between terrestrial evidence, marine reconstructions and palaeoclimate model results is an 'anticyclonic/zonal' circulation regime characterized by increased influences from blocking 'highs' over the South Island during winter and an increase in zonal and trough synoptic types (with southerly to westerly quarter wind flow) during summer. These seasonal circulation traits would have generated lower mean annual temperatures, cooler than normal summer temperatures, and overall lower mean annual precipitation for New Zealand (particularly in the western South Island) at ˜21 ka. The anticyclonic/zonal time slice reconstruction presented in this study has different spatial traits than the late 20th Century and the early Little Ice Age signatures, suggesting more than one type of regional circulation pattern can drive Southern Alps glacial activity. This finding lends support to the hypothesis that temperature over precipitation change is more important as the primary modulator of Southern Alps ice advances. The RCRC approach also demonstrates some subtle advantages of integrating multi-proxy data within a palaeocirculation context for New Zealand, notably because this reconstruction technique enables direct comparisons to coarsely resolved palaeoclimate model outputs that do not have downscaled information.

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Cao, Xiangyong; Zhou, Feng; Xu, Lin; Meng, Deyu; Xu, Zongben; Paisley, John

2018-05-01

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

USDA-ARS?s Scientific Manuscript database

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified ...

Impact of large-scale dynamics on the microphysical properties of midlatitude cirrus

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muhlbauer, Andreas; Ackerman, Thomas P.; Comstock, Jennifer M.

2014-04-16

In situ microphysical observations 3 of mid-latitude cirrus collected during the Department of Energy Small Particles in Cirrus (SPAR-TICUS) field campaign are combined with an atmospheric state classification for the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site to understand statistical relationships between cirrus microphysics and the large-scale meteorology. The atmospheric state classification is informed about the large-scale meteorology and state of cloudiness at the ARM SGP site by combining ECMWF ERA-Interim reanalysis data with 14 years of continuous observations from the millimeter-wavelength cloud radar. Almost half of the cirrus cloud occurrences in the vicinity of the ARM SGPmore » site during SPARTICUS can be explained by three distinct synoptic condi- tions, namely upper-level ridges, mid-latitude cyclones with frontal systems and subtropical flows. Probability density functions (PDFs) of cirrus micro- physical properties such as particle size distributions (PSDs), ice number con- centrations and ice water content (IWC) are examined and exhibit striking differences among the different synoptic regimes. Generally, narrower PSDs with lower IWC but higher ice number concentrations are found in cirrus sam- pled in upper-level ridges whereas cirrus sampled in subtropical flows, fronts and aged anvils show broader PSDs with considerably lower ice number con- centrations but higher IWC. Despite striking contrasts in the cirrus micro- physics for different large-scale environments, the PDFs of vertical velocity are not different, suggesting that vertical velocity PDFs are a poor predic-tor for explaining the microphysical variability in cirrus. Instead, cirrus mi- crophysical contrasts may be driven by differences in ice supersaturations or aerosols.« less

Dimensionality-varied deep convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun

2018-01-01

Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.

Effects of spatial variations of soil moisture and vegetation on the evolution of a prestorm environment - A numerical case study

NASA Technical Reports Server (NTRS)

Chang, Jy-Tai; Wetzel, Peter J.

1991-01-01

To examine the effects of spatial variations of soil moisture and vegetation coverage on the evolution of a prestorm environment, the Goddard mesoscale model is modified to incorporate a simple evapotranspiration model that requires these two parameters. The case study of 3-4 June 1980 is of special interest due to the development of a tornado producing convective complex near Grand Island, Nebraska during a period of comparatively weak synoptic-scale forcing. It is shown that the observed stationary front was strongly enhanced by differential heating created by observed gradients of soil moisture, as acted upon by the vegetation cover.

Spatial and temporal variations in lagoon and coastal processes of the southern Brazilian coast

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Herz, R.

1980-01-01

From a collection of information gathered during a long period, through the orbital platforms SKYLAB and LANDSAT, it was possible to establish a method for the systematic study of the dynamical regime of lagoon and marine surface waters, on coastal plain of Rio Grande do Sul. The series of multispectral images analyzed by visual and automatic techniques put in evidence spatial and temporal variations reflected in the optical properties of waters, which carry different loads of materials in suspension. The identified patterns offer a synoptic picture of phenomena of great amplitude, from which trends of circulation can be inferred, correlating the atmospheric and hydrologic variables simultaneously to the overflight of orbital vehicles.

Urbanization-induced urban heat island and aerosol effects on climate extremes in the Yangtze River Delta region of China

NASA Astrophysics Data System (ADS)

Zhong, Shi; Qian, Yun; Zhao, Chun; Leung, Ruby; Wang, Hailong; Yang, Ben; Fan, Jiwen; Yan, Huiping; Yang, Xiu-Qun; Liu, Dongqing

2017-04-01

The WRF-Chem model coupled with a single-layer urban canopy model (UCM) is integrated for 5 years at convection-permitting scale to investigate the individual and combined impacts of urbanization-induced changes in land cover and pollutant emissions on regional climate in the Yangtze River Delta (YRD) region in eastern China. Simulations with the urbanization effects reasonably reproduced the observed features of temperature and precipitation in the YRD region. Urbanization over the YRD induces an urban heat island (UHI) effect, which increases the surface temperature by 0.53 °C in summer and increases the annual heat wave days at a rate of 3.7 d yr-1 in the major megacities in the YRD, accompanied by intensified heat stress. In winter, the near-surface air temperature increases by approximately 0.7 °C over commercial areas in the cities but decreases in the surrounding areas. Radiative effects of aerosols tend to cool the surface air by reducing net shortwave radiation at the surface. Compared to the more localized UHI effect, aerosol effects on solar radiation and temperature influence a much larger area, especially downwind of the city cluster in the YRD. Results also show that the UHI increases the frequency of extreme summer precipitation by strengthening the convergence and updrafts over urbanized areas in the afternoon, which favor the development of deep convection. In contrast, the radiative forcing of aerosols results in a surface cooling and upper-atmospheric heating, which enhances atmospheric stability and suppresses convection. The combined effects of the UHI and aerosols on precipitation depend on synoptic conditions. Two rainfall events under two typical but different synoptic weather patterns are further analyzed. It is shown that the impact of urban land cover and aerosols on precipitation is not only determined by their influence on local convergence but also modulated by large-scale weather systems. For the case with a strong synoptic forcing associated with stronger winds and larger spatial convergence, the UHI and aerosol effects are relatively weak. When the synoptic forcing is weak, however, the UHI and aerosol effects on local convergence dominate. This suggests that synoptic forcing plays a significant role in modulating the urbanization-induced land-cover and aerosol effects on individual rainfall event. Hence precipitation changes due to urbanization effects may offset each other under different synoptic conditions, resulting in little changes in mean precipitation at longer timescales.

Urbanization-induced urban heat island and aerosol effects on climate extremes in the Yangtze River Delta region of China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhong, Shi; Qian, Yun; Zhao, Chun

The WRF-Chem model coupled with a single-layer urban canopy model (UCM) is integrated for 5 years at convection-permitting scale to investigate the individual and combined impacts of urbanization-induced changes in land cover and pollutant emissions on regional climate in the Yangtze River Delta (YRD) region in eastern China. Simulations with the urbanization effects reasonably reproduced the observed features of temperature and precipitation in the YRD region. Urbanization over the YRD induces an urban heat island (UHI) effect, which increases the surface temperature by 0.53 °C in summer and increases the annual heat wave days at a rate of 3.7 d yr −1 in themore » major megacities in the YRD, accompanied by intensified heat stress. In winter, the near-surface air temperature increases by approximately 0.7 °C over commercial areas in the cities but decreases in the surrounding areas. Radiative effects of aerosols tend to cool the surface air by reducing net shortwave radiation at the surface. Compared to the more localized UHI effect, aerosol effects on solar radiation and temperature influence a much larger area, especially downwind of the city cluster in the YRD. Results also show that the UHI increases the frequency of extreme summer precipitation by strengthening the convergence and updrafts over urbanized areas in the afternoon, which favor the development of deep convection. In contrast, the radiative forcing of aerosols results in a surface cooling and upper-atmospheric heating, which enhances atmospheric stability and suppresses convection. The combined effects of the UHI and aerosols on precipitation depend on synoptic conditions. Two rainfall events under two typical but different synoptic weather patterns are further analyzed. It is shown that the impact of urban land cover and aerosols on precipitation is not only determined by their influence on local convergence but also modulated by large-scale weather systems. For the case with a strong synoptic forcing associated with stronger winds and larger spatial convergence, the UHI and aerosol effects are relatively weak. When the synoptic forcing is weak, however, the UHI and aerosol effects on local convergence dominate. This suggests that synoptic forcing plays a significant role in modulating the urbanization-induced land-cover and aerosol effects on individual rainfall event. Hence precipitation changes due to urbanization effects may offset each other under different synoptic conditions, resulting in little changes in mean precipitation at longer timescales.« less

Urbanization-induced urban heat island and aerosol effects on climate extremes in the Yangtze River Delta region of China

DOE PAGES

Zhong, Shi; Qian, Yun; Zhao, Chun; ...

2017-04-27

The WRF-Chem model coupled with a single-layer urban canopy model (UCM) is integrated for 5 years at convection-permitting scale to investigate the individual and combined impacts of urbanization-induced changes in land cover and pollutant emissions on regional climate in the Yangtze River Delta (YRD) region in eastern China. Simulations with the urbanization effects reasonably reproduced the observed features of temperature and precipitation in the YRD region. Urbanization over the YRD induces an urban heat island (UHI) effect, which increases the surface temperature by 0.53 °C in summer and increases the annual heat wave days at a rate of 3.7 d yr −1 in themore » major megacities in the YRD, accompanied by intensified heat stress. In winter, the near-surface air temperature increases by approximately 0.7 °C over commercial areas in the cities but decreases in the surrounding areas. Radiative effects of aerosols tend to cool the surface air by reducing net shortwave radiation at the surface. Compared to the more localized UHI effect, aerosol effects on solar radiation and temperature influence a much larger area, especially downwind of the city cluster in the YRD. Results also show that the UHI increases the frequency of extreme summer precipitation by strengthening the convergence and updrafts over urbanized areas in the afternoon, which favor the development of deep convection. In contrast, the radiative forcing of aerosols results in a surface cooling and upper-atmospheric heating, which enhances atmospheric stability and suppresses convection. The combined effects of the UHI and aerosols on precipitation depend on synoptic conditions. Two rainfall events under two typical but different synoptic weather patterns are further analyzed. It is shown that the impact of urban land cover and aerosols on precipitation is not only determined by their influence on local convergence but also modulated by large-scale weather systems. For the case with a strong synoptic forcing associated with stronger winds and larger spatial convergence, the UHI and aerosol effects are relatively weak. When the synoptic forcing is weak, however, the UHI and aerosol effects on local convergence dominate. This suggests that synoptic forcing plays a significant role in modulating the urbanization-induced land-cover and aerosol effects on individual rainfall event. Hence precipitation changes due to urbanization effects may offset each other under different synoptic conditions, resulting in little changes in mean precipitation at longer timescales.« less

Considering the Spatial Layout Information of Bag of Features (BoF) Framework for Image Classification.

PubMed

Mu, Guangyu; Liu, Ying; Wang, Limin

2015-01-01

The spatial pooling method such as spatial pyramid matching (SPM) is very crucial in the bag of features model used in image classification. SPM partitions the image into a set of regular grids and assumes that the spatial layout of all visual words obey the uniform distribution over these regular grids. However, in practice, we consider that different visual words should obey different spatial layout distributions. To improve SPM, we develop a novel spatial pooling method, namely spatial distribution pooling (SDP). The proposed SDP method uses an extension model of Gauss mixture model to estimate the spatial layout distributions of the visual vocabulary. For each visual word type, SDP can generate a set of flexible grids rather than the regular grids from the traditional SPM. Furthermore, we can compute the grid weights for visual word tokens according to their spatial coordinates. The experimental results demonstrate that SDP outperforms the traditional spatial pooling methods, and is competitive with the state-of-the-art classification accuracy on several challenging image datasets.

A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees

Treesearch

Suzanne M. Joy; R. M. Reich; Richard T. Reynolds

2003-01-01

Traditional land classification techniques for large areas that use Landsat Thematic Mapper (TM) imagery are typically limited to the fixed spatial resolution of the sensors (30m). However, the study of some ecological processes requires land cover classifications at finer spatial resolutions. We model forest vegetation types on the Kaibab National Forest (KNF) in...

An investigation of the observability of ocean-surface parameters using GEOS-3 backscatter data

NASA Technical Reports Server (NTRS)

Miller, L. S.; Priester, R. W.

1978-01-01

The degree to which ocean surface roughness can be synoptically observed through use of the information extracted from the GEOS-3 backscattered waveform data was evaluated. Algorithms are given for use in estimating the radar sensed waveheight distribution or ocean-surface impulse response. Other factors discussed include comparisons between theoretical and experimental radar cross section values, sea state bias effects, spatial variability of significant waveheight data, and sensor-related considerations.

Westerly Wind Bursts: a Synoptic-Dynamic Study

NASA Astrophysics Data System (ADS)

Hartten, Leslie Marie

This research examines the synoptic and climatological settings of westerly wind bursts (WWBs) during the 1980s and the dynamical processes active during them. Probabilities of strong westerly and easterly 1000 mb winds over the western equatorial Pacific are presented. Westerlies exhibit a clear annual cycle, appearing in the north in July, moving southeastward as the year progresses, and disappearing by June. Conditional probabilities, dependent on the value of the SOI, show that strong westerlies are more likely and more geographically extensive when the SOI is low, especially from July through January. A newly developed two-dimensional classification scheme qualitatively describes the near-surface synoptic flow of almost 90% of the 131 WWBs identified during the decade. Only 8% of the WWBs are described by the pattern involving twin cyclonic circulations straddling the equator. The trades, tropical cyclones, and the southeast Asian monsoon are all at times linked to WWBs, and the synoptic patterns often contain a significant barotropic component. Breaks in WWB activity are well correlated with a cooler than normal western Pacific warm pool. However, near-equatorial WWBs do not show a good correlation with the Madden-Julian Oscillation. Four near-equatorial WWBs are examined in detail. All are associated with broad cross-equatorial flow; two also have a cyclonic circulation poleward of the westerlies. Anticyclonic relative vorticity equatorward of the burst displaces the zero line of absolute vorticity, eta, into the burst hemisphere. In the three Southern Hemisphere cases, horizontal advection in a region extending from north of New Guinea east-southeast toward the dateline is crucial to the generation and maintenance of the eta pattern. Vorticity stretching associated with convection helps maintain a tight gradient of eta near and poleward of the burst, but also drives the eta = 0 line back towards the equator as the burst ends. In the Northern Hemisphere case, advection is less efficient because the trades slow and turn further away from the equator. This research indicates that Gill's (1980) solution to the linear shallow -water equations forced by near-equatorial heating is not a good model for WWBs.

Assessment of the relationship between satellite AOD and ground PM₁₀ measurement data considering synoptic meteorological patterns and Lidar data.

PubMed

Zeeshan, Muhammad; Kim Oanh, N T

2014-03-01

Correlation between satellite aerosol optical depth (AOD) and ground monitoring particulate matter (PM) depends on the meteorology that determines PM optical properties, its dispersion, accumulation and vertical distribution. This study presents a novel approach to analyze PM-AOD relationship considering the totality of meteorological factors expressed as synoptic patterns. Meteorological observations at 07:00 Bangkok time from 9 regional meteorological stations, in dry seasons (November-April) of 11 years (2000-2010), were used to categorize governing meteorology over Central Thailand into four categories representing the typical observed synoptic patterns. The MANOVA analysis showed that these patterns were statistically different. PM10 recorded at 22 air quality stations in Bangkok Metropolitan Region were examined which showed the highest levels for the days belonging to pattern 1, followed by pattern 4, both with presence of a high pressure ridge, while the minimum for pattern 2 when thermal lows dominated. Lidar aerosol backscatter profiles recorded at Pimai station were used as indicator of PM vertical distribution that showed similarity within each pattern. R(2) between MODIS and Sun photometer AODs at Pimai was above 0.8. Correlation coefficients (R) between MODIS AOD and corresponding 1h PM10 for clear sky days (cloudiness ≤ 3/10) were examined for each pattern in comparison with lump case. Significant improvements were observed for pattern 1, average R across 22 stations was 0.46 for Terra and 0.38 for Aqua AOD compared to lump case with R of 0.34 and 0.31, respectively. Comparable improvement was also observed for pattern 4. For pattern 2, R values were significantly reduced which may be caused by the deeper mixing layers and varying vertical profiles with overall low values of Lidar backscatter coefficients. Improved R values in pattern 1 and 4, which had highest PM10 in BMR, suggested a better potential of using MODIS AOD for PM10 monitoring with synoptic pattern classification. Copyright © 2013 Elsevier B.V. All rights reserved.

Evaluation of operational numerical weather predictions in relation to the prevailing synoptic conditions

NASA Astrophysics Data System (ADS)

Pytharoulis, Ioannis; Tegoulias, Ioannis; Karacostas, Theodore; Kotsopoulos, Stylianos; Kartsios, Stergios; Bampzelis, Dimitrios

2015-04-01

The Thessaly plain, which is located in central Greece, has a vital role in the financial life of the country, because of its significant agricultural production. The aim of DAPHNE project (http://www.daphne-meteo.gr) is to tackle the problem of drought in this area by means of Weather Modification in convective clouds. This problem is reinforced by the increase of population and the water demand for irrigation, especially during the warm period of the year. The nonhydrostatic Weather Research and Forecasting model (WRF), is utilized for research and operational purposes of DAPHNE project. The WRF output fields are employed by the partners in order to provide high-resolution meteorological guidance and plan the project's operations. The model domains cover: i) Europe, the Mediterranean sea and northern Africa, ii) Greece and iii) the wider region of Thessaly (at selected periods), at horizontal grid-spacings of 15km, 5km and 1km, respectively, using 2-way telescoping nesting. The aim of this research work is to investigate the model performance in relation to the prevailing upper-air synoptic circulation. The statistical evaluation of the high-resolution operational forecasts of near-surface and upper air fields is performed at a selected period of the operational phase of the project using surface observations, gridded fields and weather radar data. The verification is based on gridded, point and object oriented techniques. The 10 upper-air circulation types, which describe the prevailing conditions over Greece, are employed in the synoptic classification. This methodology allows the identification of model errors that occur and/or are maximized at specific synoptic conditions and may otherwise be obscured in aggregate statistics. Preliminary analysis indicates that the largest errors are associated with cyclonic conditions. Acknowledgments This research work of Daphne project (11SYN_8_1088) is co-funded by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" in the framework of the Operational Programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-2013).

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

NASA Technical Reports Server (NTRS)

Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

2013-01-01

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas.

Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new method for spectral-spatial classification of hyperspectral images is proposed. The method is based on the integration of probabilistic classification within the hierarchical best merge region growing algorithm. For this purpose, preliminary probabilistic support vector machines classification is performed. Then, hierarchical step-wise optimization algorithm is applied, by iteratively merging regions with the smallest Dissimilarity Criterion (DC). The main novelty of this method consists in defining a DC between regions as a function of region statistical and geometrical features along with classification probabilities. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana s vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

NASA Astrophysics Data System (ADS)

Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai

2010-08-01

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

Evolution of the Planetary Boundary Layer on the northern coast of Brazil during the CHUVA campaign

NASA Astrophysics Data System (ADS)

Ramos, Diogo Nunes da Silva; Fernandez, Julio Pablo Reyes; Fisch, Gilberto

2018-05-01

This study aims to characterize the wind and thermodynamic structure of the Planetary Boundary Layer (PBL) on the northern coast of Brazil (NCB) via the CHUVA datasets. Three synoptic conditions were present in the NCB region between March 1 and 25, 2010: a dry period, the Upper Tropospheric Cyclonic Vortex (UTCV) and the Intertropical Convergence Zone (ITCZ). Nighttime precipitation accounted for 78% of the total precipitation observed in the month, mainly during the ITCZ. In general, the surface meteorological fields were few changed by intense weather events due to proximity to the ocean and the predominant contribution of the northeasterly trade winds. There was also a weak sea breeze signal that maintained the horizontal moisture flow in the studied area. On dry days, the PBL depth was higher, drier, and warmer, resulting in stronger winds below 500 m. Moreover, trends throughout the period suggest that PBLs are near-neutral below 500 m. However, the wind variability was intensified by up to 20% due to downdrafts and higher wind shears during the deep convection mechanisms derived by UTCV. Furthermore, ITCZ mixed rainfall cooled the PBL at approximately 2 K, making it very stable according to the Richardson number classification adopted. The observed temporal and spatial scale represent challenges to the physical parameterizations used to improve numerical weather prediction models over tropical coastal areas.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

Analysis of mesoscale factors at the onset of deep convection on hailstorm days in Southern France and their relation to the synoptic patterns

NASA Astrophysics Data System (ADS)

Sanchez, Jose Luis; Wu, Xueke; Gascón, Estibaliz; López, Laura; Melcón, Pablo; García-Ortega, Eduardo; Berthet, Claude; Dessens, Jean; Merino, Andrés

2013-04-01

Storms and the weather phenomena associated to intense precipitation, lightning, strong winds or hail, are among the most common and dangerous weather risks in many European countries. To get a reliable forecast of their occurrence is remaining an open problem. The question is: how is possible to improve the reliability of forecast? Southwestern France is frequently affected by hailstorms, producing severe damages on crops and properties. Considerable efforts were made to improve the forecast of hailfall in this area. First of all, if we want to improve this type of forecast, it is necessary to have a good "ground truth" of the hail days and zones affected by hailfall. Fortunately, ANELFA has deployed thousands of hailpad stations in Southern France. The ANELFA processed the point hailfall data recorded during each hail season at these stations. The focus of this paper presents a methodology to improve the forecast of the occurrence of hailfall according to the synoptic environment and mesoscale factors in the study area. One hundred of hail days were selected, following spatial and severity criteria, occurred in the period 2000-2010. The mesoscale model WRF was applied for all cases to study the synoptic environment of mean geopotential and temperature fields at 500 hPa. Three nested domains have been defined following a two-way nesting strategy, with a horizontal spatial resolution of 36, 12 and 4 km, and 30 vertical terrains— following σ-levels. Then, using the Principal Component Analysis in T-Mode, 4 mesoscale configurations were defined for the fields of convective instability (CI), water vapor flux divergence and wind flow and humidity at low layer (850hPa), and several clusters were classified followed by using the K-means Clustering. Finally, we calculated several characteristic values of four hail forecast parameters: Convective Available Potential Energy (CAPE), Storm Relative Helicity between 0 and 3 km (SRH0-3), Energy-Helicity Index (EHI) and Showalter Index (SI) provided by WRF simulations for each hail grid point, which is very conducive to predicting the occurrence of hail in each one of the mesoscale configurations. This mesoscale analysis and its relation to the synoptic anomalies is discussed and the contribution to improve the numerical model applied to the forecast of hailfall in this area. Acknowledgments: This study was supported by the Plan Nacional de I+D of Spain, through the grants CGL2010-15930, Micrometeo IPT-310000-2010-022 and the Junta de Castilla y León through the grant LE220A11-2

Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Liu, Wanjun; Liang, Xuejian; Qu, Haicheng

2017-11-01

Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

Global Autocorrelation Scales of the Partial Pressure of Oceanic CO2

NASA Technical Reports Server (NTRS)

Li, Zhen; Adamec, David; Takahashi, Taro; Sutherland, Stewart C.

2004-01-01

A global database of approximately 1.7 million observations of the partial pressure of carbon dioxide in surface ocean waters (pCO2) collected between 1970 and 2003 is used to estimate its spatial autocorrelation structure. The patterns of the lag distance where the autocorrelation exceeds 0.8 is similar to patterns in the spatial distribution of the first baroclinic Rossby radius of deformation indicating that ocean circulation processes play a significant role in determining the spatial variability of pCO2. For example, the global maximum of the distance at which autocorrelations exceed 0.8 averages about 140 km in the equatorial Pacific. Also, the lag distance at which the autocorrelation exceed 0.8 is greater in the vicinity of the Gulf Stream than it is near the Kuroshio, approximately 50 km near the Gulf Stream as opposed to 20 km near the Kuroshio. Separate calculations for times when the sun is north and south of the equator revealed no obvious seasonal dependence of the spatial autocorrelation scales. The pCO2 measurements at Ocean Weather Station (OWS) 'P', in the eastern subarctic Pacific (50 N, 145 W) is the only fixed location where an uninterrupted time series of sufficient length exists to calculate a meaningful temporal autocorrelation function for lags greater than a few days. The estimated temporal autocorrelation function at OWS 'P', is highly variable. A spectral analysis of the longest four pCO2 time series indicates a high level of variability occurring over periods from the atmospheric synoptic to the maximum length of the time series, in this case 42 days. It is likely that a relative peak in variability with a period of 3-6 days is related to atmospheric synoptic period variability and ocean mixing events due to wind stirring. However, the short length of available time series makes identifying temporal relationships between pCO2 and atmospheric or ocean processes problematic.

Optimal design of a bank of spatio-temporal filters for EEG signal classification.

PubMed

Higashi, Hiroshi; Tanaka, Toshihisa

2011-01-01

The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interfaces (MI-BCI). To achieve accurate classification in CSP, the frequency filter should be properly designed. To this end, several methods for designing the filter have been proposed. However, the existing methods cannot consider plural brain activities described with different frequency bands and different spatial patterns such as activities of mu and beta rhythms. In order to efficiently extract these brain activities, we propose a method to design plural filters and spatial weights which extract desired brain activity. The proposed method designs finite impulse response (FIR) filters and the associated spatial weights by optimization of an objective function which is a natural extension of CSP. Moreover, we show by a classification experiment that the bank of FIR filters which are designed by introducing an orthogonality into the objective function can extract good discriminative features. Moreover, the experiment result suggests that the proposed method can automatically detect and extract brain activities related to motor imagery.

Estimates of tropical analysis differences in daily values produced by two operational centers

NASA Technical Reports Server (NTRS)

Kasahara, Akira; Mizzi, Arthur P.

1992-01-01

To assess the uncertainty of daily synoptic analyses for the atmospheric state, the intercomparison of three First GARP Global Experiment level IIIb datasets is performed. Daily values of divergence, vorticity, temperature, static stability, vertical motion, mixing ratio, and diagnosed diabatic heating rate are compared for the period of 26 January-11 February 1979. The spatial variance and mean, temporal mean and variance, 2D wavenumber power spectrum, anomaly correlation, and normalized square difference are employed for comparison.

Recent changes in the spatial distribution of annual precipitation in Israel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steinberger, E.H.; Gazit-Yaari, N.

1996-12-01

Analysis of rainfall series in Israel during the period 1960-1990 for 99 stations has revealed that precipitation amounts have decreased in the northern and central coastal areas and in the northern mountain area. In the southern coastal area and the western slopes of the central mountains precipitation increased. There are indications that the observed trends may be the outcome of changes in the synoptic climate during the winter in the Eastern Mediterranean region. 8 refs., 12 figs., 1 tab.

Special section introduction on MicroMars to MegaMars

USGS Publications Warehouse

Bridges, Nathan T.; Dundas, Colin M.; Edgar, Lauren

2016-01-01

The study of Earth's surface and atmosphere evolved from local investigations to the incorporation of remote sensing on a global scale. The study of Mars has followed the opposite progression, beginning with telescopic observations, followed by flyby and orbital missions, landers, and finally rover missions in the last ∼20 years. This varied fleet of spacecraft (seven of which are currently operating as of this writing) provides a rich variety of datasets at spatial scales ranging from microscopic images to synoptic orbital remote sensing.

D Object Classification Based on Thermal and Visible Imagery in Urban Area

NASA Astrophysics Data System (ADS)

Hasani, H.; Samadzadegan, F.

2015-12-01

The spatial distribution of land cover in the urban area especially 3D objects (buildings and trees) is a fundamental dataset for urban planning, ecological research, disaster management, etc. According to recent advances in sensor technologies, several types of remotely sensed data are available from the same area. Data fusion has been widely investigated for integrating different source of data in classification of urban area. Thermal infrared imagery (TIR) contains information on emitted radiation and has unique radiometric properties. However, due to coarse spatial resolution of thermal data, its application has been restricted in urban areas. On the other hand, visible image (VIS) has high spatial resolution and information in visible spectrum. Consequently, there is a complementary relation between thermal and visible imagery in classification of urban area. This paper evaluates the potential of aerial thermal hyperspectral and visible imagery fusion in classification of urban area. In the pre-processing step, thermal imagery is resampled to the spatial resolution of visible image. Then feature level fusion is applied to construct hybrid feature space include visible bands, thermal hyperspectral bands, spatial and texture features and moreover Principle Component Analysis (PCA) transformation is applied to extract PCs. Due to high dimensionality of feature space, dimension reduction method is performed. Finally, Support Vector Machines (SVMs) classify the reduced hybrid feature space. The obtained results show using thermal imagery along with visible imagery, improved the classification accuracy up to 8% respect to visible image classification.

Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach.

PubMed

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Changsen; Liu, Feixiang

2017-02-15

Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naïve Bayesian Classifier (WNBC) for classification. Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. The proposed approach is a promising candidate for future BCI systems. Copyright © 2016 Elsevier B.V. All rights reserved.

Edited Synoptic Cloud Reports from Ships and Land Stations Over the Globe, 1982-1991 (NDP-026B)

DOE Data Explorer

Hahn, Carole J. [University of Arizona; Warren, Stephen G. [University of Washington; London, Julius [University of Colorado

1996-01-01

Surface synoptic weather reports for the entire globe for the 10-year period from December 1981 through November 1991 have been processed, edited, and rewritten to provide a data set designed for use in cloud analyses. The information in these reports relating to clouds, including the present weather information, was extracted and put through a series of quality control checks. Reports not meeting certain quality control standards were rejected, as were reports from buoys and automatic weather stations. Correctable inconsistencies within reports were edited for consistency, so that the "edited cloud report" can be used for cloud analysis without further quality checking. Cases of "sky obscured" were interpreted by reference to the present weather code as to whether they indicated fog, rain or snow and were given appropriate cloud type designations. Nimbostratus clouds, which are not specifically coded for in the standard synoptic code, were also given a special designation. Changes made to an original report are indicated in the edited report so that the original report can be reconstructed if desired. While low cloud amount is normally given directly in the synoptic report, the edited cloud report also includes the amounts, either directly reported or inferred, of middle and high clouds, both the non-overlapped amounts and the "actual" amounts (which may be overlapped). Since illumination from the moon is important for the adequate detection of clouds at night, both the relative lunar illuminance and the solar altitude are given, as well as a parameter that indicates whether our recommended illuminance criterion was satisfied. This data set contains 124 million reports from land stations and 15 million reports from ships. Each report is 56 characters in length. The archive consists of 240 files, one file for each month of data for land and ocean separately. With this data set a user can develop a climatology for any particular cloud type or group of types, for any geographical region and any spatial and temporal resolution desired.

Ecosystem classifications based on summer and winter conditions.

PubMed

Andrew, Margaret E; Nelson, Trisalyn A; Wulder, Michael A; Hobart, George W; Coops, Nicholas C; Farmer, Carson J Q

2013-04-01

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

NASA Astrophysics Data System (ADS)

Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

2017-10-01

Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

Development and implementation of a synoptic MRI report for preoperative staging of rectal cancer on a population-based level.

PubMed

Kennedy, Erin D; Milot, Laurent; Fruitman, Mark; Al-Sukhni, Eisar; Heine, Gabrielle; Schmocker, Selina; Brown, Gina; McLeod, Robin S

2014-06-01

Colorectal cancer physician champions across the province of Ontario, Canada, reported significant concern about appropriate selection of patients for preoperative chemoradiotherapy because of perceived variation in the completeness and consistency of MRI reports. The purpose of this work was to develop, pilot test, and implement a synoptic MRI report for preoperative staging of rectal cancer. This was an integrated knowledge translation project. This study was conducted in Ontario, Canada. Surgeons, radiologists, radiation oncologists, medical oncologists, and pathologists treating patients with rectal cancer were included in this study. A multifaceted knowledge translation strategy was used to develop, pilot test, and implement a synoptic MRI report. This strategy included physician champions, audit and feedback, assessment of barriers, and tailoring to the local context. A radiology webinar was conducted to pilot test the synoptic MRI report. Seventy-three (66%) of 111 Ontario radiologists participated in the radiology webinar and evaluated the synoptic MRI report. A total of 78% and 90% radiologists expressed that the synoptic MRI report was easy to use and included all of the appropriate items; 82% noted that the synoptic MRI report improved the overall quality of their information, and 83% indicated they would consider using this report in their clinical practice. An MRI report audit after implementation of the synoptic MRI report showed a 39% improvement in the completeness of MRI reports and a 37% uptake of the synoptic MRI report format across the province. Radiologists evaluating the synoptic MRI report and participating in the radiology webinar may not be representative of gastroenterologic radiologists in other geographic jurisdictions. The evaluation of completeness and uptake of the synoptic MRI reports is limited because of unmeasured differences that may occur before and after the MRI. A synoptic MRI report for preoperative staging of rectal cancer was successfully developed and pilot tested in the province of Ontario, Canada.

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

NASA Astrophysics Data System (ADS)

Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

PubMed Central

Pham, Tuan D.

2014-01-01

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction. PMID:25157744

Analysis of the impact of spatial resolution on land/water classifications using high-resolution aerial imagery

USGS Publications Warehouse

Enwright, Nicholas M.; Jones, William R.; Garber, Adrienne L.; Keller, Matthew J.

2014-01-01

Long-term monitoring efforts often use remote sensing to track trends in habitat or landscape conditions over time. To most appropriately compare observations over time, long-term monitoring efforts strive for consistency in methods. Thus, advances and changes in technology over time can present a challenge. For instance, modern camera technology has led to an increasing availability of very high-resolution imagery (i.e. submetre and metre) and a shift from analogue to digital photography. While numerous studies have shown that image resolution can impact the accuracy of classifications, most of these studies have focused on the impacts of comparing spatial resolution changes greater than 2 m. Thus, a knowledge gap exists on the impacts of minor changes in spatial resolution (i.e. submetre to about 1.5 m) in very high-resolution aerial imagery (i.e. 2 m resolution or less). This study compared the impact of spatial resolution on land/water classifications of an area dominated by coastal marsh vegetation in Louisiana, USA, using 1:12,000 scale colour-infrared analogue aerial photography (AAP) scanned at four different dot-per-inch resolutions simulating ground sample distances (GSDs) of 0.33, 0.54, 1, and 2 m. Analysis of the impact of spatial resolution on land/water classifications was conducted by exploring various spatial aspects of the classifications including density of waterbodies and frequency distributions in waterbody sizes. This study found that a small-magnitude change (1–1.5 m) in spatial resolution had little to no impact on the amount of water classified (i.e. percentage mapped was less than 1.5%), but had a significant impact on the mapping of very small waterbodies (i.e. waterbodies ≤ 250 m2). These findings should interest those using temporal image classifications derived from very high-resolution aerial photography as a component of long-term monitoring programs.

a Data Field Method for Urban Remotely Sensed Imagery Classification Considering Spatial Correlation

NASA Astrophysics Data System (ADS)

Zhang, Y.; Qin, K.; Zeng, C.; Zhang, E. B.; Yue, M. X.; Tong, X.

2016-06-01

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary's C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

Classification of spatially unresolved objects

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Horwitz, H. M.; Hyde, P. D.; Morgenstern, J. P.

1972-01-01

A proportion estimation technique for classification of multispectral scanner images is reported that uses data point averaging to extract and compute estimated proportions for a single average data point to classify spatial unresolved areas. Example extraction calculations of spectral signatures for bare soil, weeds, alfalfa, and barley prove quite accurate.

The effects of implementing synoptic pathology reporting in cancer diagnosis: a systematic review.

PubMed

Sluijter, Caro E; van Lonkhuijzen, Luc R C W; van Slooten, Henk-Jan; Nagtegaal, Iris D; Overbeek, Lucy I H

2016-06-01

Pathology reporting is evolving from a traditional narrative report to a more structured synoptic report. Narrative reporting can cause misinterpretation due to lack of information and structure. In this systematic review, we evaluate the impact of synoptic reporting on completeness of pathology reports and quality of pathology evaluation for solid tumours. Pubmed, Embase and Cochrane databases were systematically searched to identify studies describing the effect of synoptic reporting implementation on completeness of reporting and quality of pathology evaluation of solid malignant tumours. Thirty-three studies met the inclusion criteria. All studies, except one, reported an increased overall completeness of pathology reports after introduction of synoptic reporting (SR). Most frequently studied cancers were breast (n = 9) and colorectal cancer (n = 16). For breast cancer, narrative reports adequately described 'tumour type' and 'nodal status'. Synoptic reporting resulted in improved description of 'resection margins', 'DCIS size', 'location' and 'presence of calcifications'. For colorectal cancer, narrative reports adequately reported 'tumour type', 'invasion depth', 'lymph node counts' and 'nodal status'. Synoptic reporting resulted in increased reporting of 'circumferential margin', 'resection margin', 'perineural invasion' and 'lymphovascular invasion'. In addition, increased numbers of reported lymph nodes were found in synoptic reports. Narrative reports of other cancer types described the traditional parameters adequately, whereas for 'resection margins' and '(lympho)vascular/perineural invasion', implementation of synoptic reporting was necessary. Synoptic reporting results in improved reporting of clinical relevant data. Demonstration of clinical impact of this improved method of pathology reporting is required for successful introduction and implementation in daily pathology practice.

Probability-based classifications for spatially characterizing the water temperatures and discharge rates of hot springs in the Tatun Volcanic Region, Taiwan.

PubMed

Jang, Cheng-Shin

2015-05-01

Accurately classifying the spatial features of the water temperatures and discharge rates of hot springs is crucial for environmental resources use and management. This study spatially characterized classifications of the water temperatures and discharge rates of hot springs in the Tatun Volcanic Region of Northern Taiwan by using indicator kriging (IK). The water temperatures and discharge rates of the springs were first assigned to high, moderate, and low categories according to the two thresholds of the proposed spring classification criteria. IK was then used to model the occurrence probabilities of the water temperatures and discharge rates of the springs and probabilistically determine their categories. Finally, nine combinations were acquired from the probability-based classifications for the spatial features of the water temperatures and discharge rates of the springs. Moreover, various combinations of spring water features were examined according to seven subzones of spring use in the study region. The research results reveal that probability-based classifications using IK provide practicable insights related to propagating the uncertainty of classifications according to the spatial features of the water temperatures and discharge rates of the springs. The springs in the Beitou (BT), Xingyi Road (XYR), Zhongshanlou (ZSL), and Lengshuikeng (LSK) subzones are suitable for supplying tourism hotels with a sufficient quantity of spring water because they have high or moderate discharge rates. Furthermore, natural hot springs in riverbeds and valleys should be developed in the Dingbeitou (DBT), ZSL, Xiayoukeng (XYK), and Macao (MC) subzones because of low discharge rates and low or moderate water temperatures.

The Analysis of Burrows Recognition Accuracy in XINJIANG'S Pasture Area Based on Uav Visible Images with Different Spatial Resolution

NASA Astrophysics Data System (ADS)

Sun, D.; Zheng, J. H.; Ma, T.; Chen, J. J.; Li, X.

2018-04-01

The rodent disaster is one of the main biological disasters in grassland in northern Xinjiang. The eating and digging behaviors will cause the destruction of ground vegetation, which seriously affected the development of animal husbandry and grassland ecological security. UAV low altitude remote sensing, as an emerging technique with high spatial resolution, can effectively recognize the burrows. However, how to select the appropriate spatial resolution to monitor the calamity of the rodent disaster is the first problem we need to pay attention to. The purpose of this study is to explore the optimal spatial scale on identification of the burrows by evaluating the impact of different spatial resolution for the burrows identification accuracy. In this study, we shoot burrows from different flight heights to obtain visible images of different spatial resolution. Then an object-oriented method is used to identify the caves, and we also evaluate the accuracy of the classification. We found that the highest classification accuracy of holes, the average has reached more than 80 %. At the altitude of 24 m and the spatial resolution of 1cm, the accuracy of the classification is the highest We have created a unique and effective way to identify burrows by using UAVs visible images. We draw the following conclusion: the best spatial resolution of burrows recognition is 1 cm using DJI PHANTOM-3 UAV, and the improvement of spatial resolution does not necessarily lead to the improvement of classification accuracy. This study lays the foundation for future research and can be extended to similar studies elsewhere.

Multi-Sensor Investigation of a Regional High-Arctic Cloudy Event

NASA Astrophysics Data System (ADS)

Ivanescu, L.; O'Neill, N. T.; Blanchet, J. P.; Baibakov, K.; Chaubey, J. P.; Perro, C. W.; Duck, T. J.

2014-12-01

A regional high-Arctic cloud event observed in March, 2011 at the PEARL Observatory, near the Eureka Weather Station (80°N, 86°W), was investigated with a view to better understanding cloud formation mechanisms during the Polar night. We analysed the temporal cloud evolution with a suite of nighttime, ground-based remote sensing (RS) instruments, supplemented by radiosonde profiles and surface weather measurements. The RS suite included Raman lidar, cloud radar, a star-photometer and microwave-radiometers. In order to estimate the spatial extent and vertical variability of the cloud mass, we employed satellite-based lidar (CALIPSO) and radar (CloudSat) profiles in the regional neighbourhood of Eureka (at a latitude of 80°N, Eureka benefits from a high frequency of CALIPSO and CloudSat overpasses). The ground-based and satellite-based observations provide quantitative measurements of extensive (bulk) properties (cloud and aerosol optical depths), and intensive (per particle properties) such as aerosol and cloud particle size as well as shape, density and aggregation phase of the cloud particulates. All observations were then compared with the upper atmosphere NCEP/NCAR reanalyses in order to understand better the synoptic context of the cloud mass dynamics as a function of key meteorological parameters such as upper air temperature and water vapor circulation. Preliminary results indicated the presence of a particular type of thin ice cloud (TIC-2) associated with a deep and stable atmospheric low. A classification into small and large ice crystal size (< 40 μm and > 40 μm, respectively), identifies the clouds as TIC-1 or TIC-2. This classification is hypothesized to be associated with the nature of the aerosols (non-anthropogenic versus anthropogenic) serving as ice nuclei in their formation. Such a distinction has important implications on the initiation of precipitation, removal rate of the cloud particles and, in consequence, the radiative forcing properties on a regional basis.

Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces.

PubMed

Dai, Shengfa; Wei, Qingguo

2017-01-01

Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern. Each individual in the population is a N-dimensional vector, with each component representing one channel. A population of binary codes generate randomly in the beginning, and then channels are selected according to the evolution of these codes. The number and positions of 1's in the code denote the number and positions of chosen channels. The objective function of backtracking search optimization algorithm is defined as the combination of classification error rate and relative number of channels. Experimental results suggest that higher classification accuracy can be achieved with much fewer channels compared to standard common spatial pattern with whole channels.

Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-11-30

Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Interpolation of Water Quality Along Stream Networks from Synoptic Data

NASA Astrophysics Data System (ADS)

Lyon, S. W.; Seibert, J.; Lembo, A. J.; Walter, M. T.; Gburek, W. J.; Thongs, D.; Schneiderman, E.; Steenhuis, T. S.

2005-12-01

Effective catchment management requires water quality monitoring that identifies major pollutant sources and transport and transformation processes. While traditional monitoring schemes involve regular sampling at fixed locations in the stream, there is an interest synoptic or `snapshot' sampling to quantify water quality throughout a catchment. This type of sampling enables insights to biogeochemical behavior throughout a stream network at low flow conditions. Since baseflow concentrations are temporally persistence, they are indicative of the health of the ecosystems. A major problem with snapshot sampling is the lack of analytical techniques to represent the spatially distributed data in a manner that is 1) easily understood, 2) representative of the stream network, and 3) capable of being used to develop land management scenarios. This study presents a kriging application using the landscape composition of the contributing area along a stream network to define a new distance metric. This allows for locations that are more `similar' to stay spatially close together while less similar locations `move' further apart. We analyze a snapshot sampling campaign consisting of 125 manually collected grab samples during a summer recession flow period in the Townbrook Research Watershed. The watershed is located in the Catskill region of New York State and represents the mixed forest-agriculture land uses of the region. Our initial analysis indicated that stream nutrients (nitrogen and phosphorus) and chemical (major cations and anions) concentrations are controlled by the composition of landscape characteristics (landuse classes and soil types) surrounding the stream. Based on these relationships, an intuitively defined distance metric is developed by combining the traditional distance between observations and the relative difference in composition of contributing area. This metric is used to interpolate between the sampling locations with traditional geostatistic techniques (semivariograms and ordinary kriging). The resulting interpolations provide continuous stream nutrient and chemical concentrations with reduced kriging RMSE (i.e., the interpolation fits the actual data better) performed without path restriction to the stream channel (i.e., the current default for most geostatistical packages) or performed with an in-channel, Euclidean distance metric (i.e., `as the fish swims' distance). In addition to being quantifiably better, the new metric also produces maps of stream concentrations that match expected continuous stream concentrations based on expert knowledge of the watershed. This analysis and its resulting stream concentration maps provide a representation of spatially distributed synoptic data that can be used to quantify water quality for more effective catchment management that focuses on pollutant sources and transport and transformation processes.

Development of advanced acreage estimation methods

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr. (Principal Investigator)

1980-01-01

The use of the AMOEBA clustering/classification algorithm was investigated as a basis for both a color display generation technique and maximum likelihood proportion estimation procedure. An approach to analyzing large data reduction systems was formulated and an exploratory empirical study of spatial correlation in LANDSAT data was also carried out. Topics addressed include: (1) development of multiimage color images; (2) spectral spatial classification algorithm development; (3) spatial correlation studies; and (4) evaluation of data systems.

Contextual classification of multispectral image data: Approximate algorithm

NASA Technical Reports Server (NTRS)

Tilton, J. C. (Principal Investigator)

1980-01-01

An approximation to a classification algorithm incorporating spatial context information in a general, statistical manner is presented which is computationally less intensive. Classifications that are nearly as accurate are produced.

Heavy Thunderstorm Synoptic Climatology and Forcing Mechanisms in Saudi Arabia.

NASA Astrophysics Data System (ADS)

Ghulam, Ayman S.

2010-05-01

Meteorologists are required to provide accurate and comprehensive weather information for planning and operational aviation, agricultural, water projects and also for the public. In general, weather phenomena such as thunderstorms over the area between the tropics and the middle latitudes are not fully understood, particularly in the Middle East area, for many reasons such as: 1) the complexity of the nature of the climate due to the wide-ranging diversity in the topography and landscape in the area; 2) the lack of meteorological data in the area; and 3) the lack of studies on local weather situations. In arid regions such as Saudi Arabia, the spatial and temporal variation of thunderstorms and associated rainfall are essential in determining their effects on social and economic conditions. Thunderstorms form rapidly, due to the fact that the significant heating of the air from the surface and the ensuing rainfall usually occurs within a short period of time. Thus, understanding thunderstorms and rainfall distribution in time and space would be useful for hydrologists, meteorologists and for environmental studies. Research all over the world has shown, however, that consideration of local factors like Low Level Jets (LLJ), moisture flux, sea breezes, and the Red Sea Convergence Zone (RSCZ) would be valuable in thunderstorm prediction. The combined effects of enhanced low-level moisture convergence and layer destabilization due to upslope flow over mountainous terrain has been shown to be responsible for thunderstorm development in otherwise non-favourable conditions. However, there might be other synoptic features associated with heavy thunderstorms or cause them, but these features have not been investigated in any research in Saudi Arabia. Thus, relating the local weather and synoptic situations with those over the middle latitudes will provide a valuable background for the forecasters to issue the medium-range forecasts which are important for many projects. These forecasts become possible when the movement and the development of the mid-latitude disturbances are known very well. To further increase our understanding of the inter-annual variability of thunderstorms in semi-arid areas such as Saudi Arabia, it is necessary to consider the relationship between this variability and the large-scale atmospheric parameters in addition to the geographical features. Moreover, better insight into the monthly variations of the synoptic situations in Saudi Arabia is considered to be important for understanding the broad mechanisms responsible for thunderstorm occurrences in this geographical area. This information is highly important for aviation and other sectors in Saudi Arabia - both public and private. This paper aims to investigate the favourable synoptic environments for heavy thunderstorm initiation and development in Saudi Arabia. The importance of the monthly synoptic analysis of all days (1998-2003), heavy thunderstorm days, and dry days was intended to be demonstrated. Therefore, the monthly mean charts and deviations from the mean (anomalies) of specific meteorological parameters for heavy thunderstorm days and dry days for the months of January-December for the period 1998-2003, was illustrated to examine the synoptic conditions leading to heavy thunderstorm events in Saudi Arabia.

The effect of spatial, spectral and radiometric factors on classification accuracy using thematic mapper data

NASA Technical Reports Server (NTRS)

Wrigley, R. C.; Acevedo, W.; Alexander, D.; Buis, J.; Card, D.

1984-01-01

An experiment of a factorial design was conducted to test the effects on classification accuracy of land cover types due to the improved spatial, spectral and radiometric characteristics of the Thematic Mapper (TM) in comparison to the Multispectral Scanner (MSS). High altitude aircraft scanner data from the Airborne Thematic Mapper instrument was acquired over central California in August, 1983 and used to simulate Thematic Mapper data as well as all combinations of the three characteristics for eight data sets in all. Results for the training sites (field center pixels) showed better classification accuracies for MSS spatial resolution, TM spectral bands and TM radiometry in order of importance.

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

NASA Astrophysics Data System (ADS)

Wutsqa, D. U.; Marwah, M.

2017-06-01

In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

Spatial Mutual Information Based Hyperspectral Band Selection for Classification

PubMed Central

2015-01-01

The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results. PMID:25918742

Spectral-Spatial Classification of Hyperspectral Images Using Hierarchical Optimization

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new spectral-spatial method for hyperspectral data classification is proposed. For a given hyperspectral image, probabilistic pixelwise classification is first applied. Then, hierarchical step-wise optimization algorithm is performed, by iteratively merging neighboring regions with the smallest Dissimilarity Criterion (DC) and recomputing class labels for new regions. The DC is computed by comparing region mean vectors, class labels and a number of pixels in the two regions under consideration. The algorithm is converged when all the pixels get involved in the region merging procedure. Experimental results are presented on two remote sensing hyperspectral images acquired by the AVIRIS and ROSIS sensors. The proposed approach improves classification accuracies and provides maps with more homogeneous regions, when compared to previously proposed classification techniques.

Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification

PubMed Central

Wu, Lin

2017-01-01

With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images. Our approach achieves better performance than BoVW as a tool for geographical scene classification, respectively, in three datasets which contain a variety of scene categories. PMID:28706534

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

[Land cover classification of Four Lakes Region in Hubei Province based on MODIS and ENVISAT data].

PubMed

Xue, Lian; Jin, Wei-Bin; Xiong, Qin-Xue; Liu, Zhang-Yong

2010-03-01

Based on the differences of back scattering coefficient in ENVISAT ASAR data, a classification was made on the towns, waters, and vegetation-covered areas in the Four Lakes Region of Hubei Province. According to the local cropping systems and phenological characteristics in the region, and by using the discrepancies of the MODIS-NDVI index from late April to early May, the vegetation-covered areas were classified into croplands and non-croplands. The classification results based on the above-mentioned procedure was verified by the classification results based on the ETM data with high spatial resolution. Based on the DEM data, the non-croplands were categorized into forest land and bottomland; and based on the discrepancies of mean NDVI index per month, the crops were identified as mid rice, late rice, and cotton, and the croplands were identified as paddy field and upland field. The land cover classification based on the MODIS data with low spatial resolution was basically consistent with that based on the ETM data with high spatial resolution, and the total error rate was about 13.15% when the classification results based on ETM data were taken as the standard. The utilization of the above-mentioned procedures for large scale land cover classification and mapping could make the fast tracking of regional land cover classification.

Incorporating spatial context into statistical classification of multidimensional image data

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Tilton, J. C.; Swain, P. H.

1981-01-01

Compound decision theory is employed to develop a general statistical model for classifying image data using spatial context. The classification algorithm developed from this model exploits the tendency of certain ground-cover classes to occur more frequently in some spatial contexts than in others. A key input to this contextural classifier is a quantitative characterization of this tendency: the context function. Several methods for estimating the context function are explored, and two complementary methods are recommended. The contextural classifier is shown to produce substantial improvements in classification accuracy compared to the accuracy produced by a non-contextural uniform-priors maximum likelihood classifier when these methods of estimating the context function are used. An approximate algorithm, which cuts computational requirements by over one-half, is presented. The search for an optimal implementation is furthered by an exploration of the relative merits of using spectral classes or information classes for classification and/or context function estimation.

A Comparison of Local Variance, Fractal Dimension, and Moran's I as Aids to Multispectral Image Classification

NASA Technical Reports Server (NTRS)

Emerson, Charles W.; Sig-NganLam, Nina; Quattrochi, Dale A.

2004-01-01

The accuracy of traditional multispectral maximum-likelihood image classification is limited by the skewed statistical distributions of reflectances from the complex heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze Landsat 7 imagery of Atlanta, Georgia. Although segmentation of panchromatic images is possible using indicators of spatial complexity, different land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification. The addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per-pixel spectral classification techniques.

Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA (Final)

EPA Science Inventory

EPA announced the availability of the final report,. This report and associated land use/land cover (LULC) coverage is the result o...

Single-trial classification of auditory event-related potentials elicited by stimuli from different spatial directions.

PubMed

Cabrera, Alvaro Fuentes; Hoffmann, Pablo Faundez

2010-01-01

This study is focused on the single-trial classification of auditory event-related potentials elicited by sound stimuli from different spatial directions. Five naϊve subjects were asked to localize a sound stimulus reproduced over one of 8 loudspeakers placed in a circular array, equally spaced by 45°. The subject was seating in the center of the circular array. Due to the complexity of an eight classes classification, our approach consisted on feeding our classifier with two classes, or spatial directions, at the time. The seven chosen pairs were 0°, which was the loudspeaker directly in front of the subject, with all the other seven directions. The discrete wavelet transform was used to extract features in the time-frequency domain and a support vector machine performed the classification procedure. The average accuracy over all subjects and all pair of spatial directions was 76.5%, σ = 3.6. The results of this study provide evidence that the direction of a sound is encoded in single-trial auditory event-related potentials.

The imprint of surface fluxes and transport on variations in total column carbon dioxide

NASA Astrophysics Data System (ADS)

Keppel-Aleks, G.; Wennberg, P. O.; Washenfelder, R. A.; Wunch, D.; Schneider, T.; Toon, G. C.; Andres, R. J.; Blavier, J.-F.; Connor, B.; Davis, K. J.; Desai, A. R.; Messerschmidt, J.; Notholt, J.; Roehl, C. M.; Sherlock, V.; Stephens, B. B.; Vay, S. A.; Wofsy, S. C.

2011-07-01

New observations of the vertically integrated CO2 mixing ratio, ⟨CO2⟩, from ground-based remote sensing show that variations in ⟨CO2⟩ are primarily determined by large-scale flux patterns. They therefore provide fundamentally different information than observations made within the boundary layer, which reflect the combined influence of large scale and local fluxes. Observations of both ⟨CO2⟩ and CO2 concentrations in the free troposphere show that large-scale spatial gradients induce synoptic-scale temporal variations in ⟨CO2⟩ in the Northern Hemisphere midlatitudes through horizontal advection. Rather than obscure the signature of surface fluxes on atmospheric CO2, these synoptic-scale variations provide useful information that can be used to reveal the meridional flux distribution. We estimate the meridional gradient in ⟨CO2⟩ from covariations in ⟨CO2⟩ and potential temperature, θ, a dynamical tracer, on synoptic timescales to evaluate surface flux estimates commonly used in carbon cycle models. We find that Carnegie Ames Stanford Approach (CASA) biospheric fluxes underestimate both the ⟨CO2⟩ seasonal cycle amplitude throughout the Northern Hemisphere midlatitudes as well as the meridional gradient during the growing season. Simulations using CASA net ecosystem exchange (NEE) with increased and phase-shifted boreal fluxes better reflect the observations. Our simulations suggest that boreal growing season NEE (between 45-65° N) is underestimated by ~40 % in CASA. We describe the implications for this large seasonal exchange on inference of the net Northern Hemisphere terrestrial carbon sink.

The imprint of surface fluxes and transport on variations in total column carbon dioxide

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keppel-Aleks, G; Wennberg, PO; Washenfelder, RA

2012-01-01

New observations of the vertically integrated CO{sub 2} mixing ratio, , from ground-based remote sensing show that variations in are primarily determined by large-scale flux patterns. They therefore provide fundamentally different information than observations made within the boundary layer, which reflect the combined influence of large-scale and local fluxes. Observations of both and CO{sub 2} concentrations in the free troposphere show that large-scale spatial gradients induce synoptic-scale temporal variations in in the Northern Hemisphere midlatitudes through horizontal advection. Rather than obscure the signature of surface fluxes on atmospheric CO{sub 2}, these synoptic-scale variationsmore » provide useful information that can be used to reveal the meridional flux distribution. We estimate the meridional gradient in from covariations in and potential temperature, {theta}, a dynamical tracer, on synoptic timescales to evaluate surface flux estimates commonly used in carbon cycle models. We find that simulations using Carnegie Ames Stanford Approach (CASA) biospheric fluxes underestimate both the seasonal cycle amplitude throughout the Northern Hemisphere midlatitudes and the meridional gradient during the growing season. Simulations using CASA net ecosystem exchange (NEE) with increased and phase-shifted boreal fluxes better fit the observations. Our simulations suggest that climatological mean CASA fluxes underestimate boreal growing season NEE (between 45-65{sup o} N) by {approx}40%. We describe the implications for this large seasonal exchange on inference of the net Northern Hemisphere terrestrial carbon sink.« less

The imprint of surface fluxes and transport on variations in total column carbon dioxide

NASA Astrophysics Data System (ADS)

Keppel-Aleks, G.; Wennberg, P. O.; Washenfelder, R. A.; Wunch, D.; Schneider, T.; Toon, G. C.; Andres, R. J.; Blavier, J.-F.; Connor, B.; Davis, K. J.; Desai, A. R.; Messerschmidt, J.; Notholt, J.; Roehl, C. M.; Sherlock, V.; Stephens, B. B.; Vay, S. A.; Wofsy, S. C.

2012-03-01

New observations of the vertically integrated CO2 mixing ratio, ⟨CO2⟩, from ground-based remote sensing show that variations in CO2⟩ are primarily determined by large-scale flux patterns. They therefore provide fundamentally different information than observations made within the boundary layer, which reflect the combined influence of large-scale and local fluxes. Observations of both ⟨CO2⟩ and CO2 concentrations in the free troposphere show that large-scale spatial gradients induce synoptic-scale temporal variations in ⟨CO2⟩ in the Northern Hemisphere midlatitudes through horizontal advection. Rather than obscure the signature of surface fluxes on atmospheric CO2, these synoptic-scale variations provide useful information that can be used to reveal the meridional flux distribution. We estimate the meridional gradient in ⟨CO2⟩ from covariations in ⟨CO2⟩ and potential temperature, θ, a dynamical tracer, on synoptic timescales to evaluate surface flux estimates commonly used in carbon cycle models. We find that simulations using Carnegie Ames Stanford Approach (CASA) biospheric fluxes underestimate both the ⟨CO2⟩ seasonal cycle amplitude throughout the Northern Hemisphere midlatitudes and the meridional gradient during the growing season. Simulations using CASA net ecosystem exchange (NEE) with increased and phase-shifted boreal fluxes better fit the observations. Our simulations suggest that climatological mean CASA fluxes underestimate boreal growing season NEE (between 45-65° N) by ~40%. We describe the implications for this large seasonal exchange on inference of the net Northern Hemisphere terrestrial carbon sink.

a Hyperspectral Image Classification Method Using Isomap and Rvm

NASA Astrophysics Data System (ADS)

Chang, H.; Wang, T.; Fang, H.; Su, Y.

2018-04-01

Classification is one of the most significant applications of hyperspectral image processing and even remote sensing. Though various algorithms have been proposed to implement and improve this application, there are still drawbacks in traditional classification methods. Thus further investigations on some aspects, such as dimension reduction, data mining, and rational use of spatial information, should be developed. In this paper, we used a widely utilized global manifold learning approach, isometric feature mapping (ISOMAP), to address the intrinsic nonlinearities of hyperspectral image for dimension reduction. Considering the impropriety of Euclidean distance in spectral measurement, we applied spectral angle (SA) for substitute when constructed the neighbourhood graph. Then, relevance vector machines (RVM) was introduced to implement classification instead of support vector machines (SVM) for simplicity, generalization and sparsity. Therefore, a probability result could be obtained rather than a less convincing binary result. Moreover, taking into account the spatial information of the hyperspectral image, we employ a spatial vector formed by different classes' ratios around the pixel. At last, we combined the probability results and spatial factors with a criterion to decide the final classification result. To verify the proposed method, we have implemented multiple experiments with standard hyperspectral images compared with some other methods. The results and different evaluation indexes illustrated the effectiveness of our method.

Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach

NASA Astrophysics Data System (ADS)

Paul, Subir; Nagesh Kumar, D.

2018-04-01

Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.

Bone Marrow Synoptic Reporting for Hematologic Neoplasms: Guideline From the College of American Pathologists Pathology and Laboratory Quality Center.

PubMed

Sever, Cordelia; Abbott, Charles L; de Baca, Monica E; Khoury, Joseph D; Perkins, Sherrie L; Reichard, Kaaren Kemp; Taylor, Ann; Terebelo, Howard R; Colasacco, Carol; Rumble, R Bryan; Thomas, Nicole E

2016-09-01

-There is ample evidence from the solid tumor literature that synoptic reporting improves accuracy and completeness of relevant data. No evidence-based guidelines currently exist for synoptic reporting for bone marrow samples. -To develop evidence-based recommendations to standardize the basic components of a synoptic report template for bone marrow samples. -The College of American Pathologists Pathology and Laboratory Quality Center convened a panel of experts in hematopathology to develop recommendations. A systematic evidence review was conducted to address 5 key questions. Recommendations were derived from strength of evidence, open comment feedback, and expert panel consensus. -Nine guideline statements were established to provide pathology laboratories with a framework by which to develop synoptic reporting templates for bone marrow samples. The guideline calls for specific data groups in the synoptic section of the pathology report; provides a list of evidence-based parameters for key, pertinent elements; and addresses ancillary testing. -A framework for bone marrow synoptic reporting will improve completeness of the final report in a manner that is clear, succinct, and consistent among institutions.

On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements.

PubMed

Li, Zhan; Schaefer, Michael; Strahler, Alan; Schaaf, Crystal; Jupp, David

2018-04-06

The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL's novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens doors to study biophysical and biochemical properties of forested and agricultural ecosystems at more detailed scales.

CENTER FOR CLIMATIC RESEARCH, UNIVERSITY OF DELAWARE

EPA Science Inventory

The synoptic climatology group performs research into a variety of applied climatological issues that affect humans and other organisms around the world. Synoptic climatology is essentially an holistic approach to weather and climate. Synoptic climatologists attempt to characteri...

A preliminary look at AVE-SESAME 5 conducted on 20-21 May 1979

NASA Technical Reports Server (NTRS)

July, M.; Turner, R. E.

1981-01-01

Information on data collected, synoptic conditions, and severe and unusual weather reported during the period are presented. Records of the synoptic conditions include synoptic charts, radar charts, satellite photographs, and rainfall observations.

An investigation of relationships between meso- and synoptic-scale phenomena

NASA Technical Reports Server (NTRS)

Scoggins, J. R.; Wood, J. E.; Fuelberg, H. E.; Read, W. L.

1972-01-01

Methods based on the vorticity equation, the adiabatic method, the curvature of the vertical wind profile, and the structure of synoptic waves are used to determine areas of positive vertical motion in the mid-troposphere for a period in each season. Parameters indicative of low-level moisture and conditional instability are areas in which mesoscale systems may be present. The best association between mesoscale and synoptic-scale phenomena was found for a period during December when synoptic-scale systems were well developed. A good association between meso- and synoptic-scale events also was found for a period during March, while the poorest association was found for a June period. Daytime surface heating apparently is an important factor in the formation of mesoscale systems during the summer. It is concluded that the formation of mesoscale phenomena may be determined essentially from synoptic-scale conditions during winter, late fall, and early spring.

Evaluation of normalization methods for cDNA microarray data by k-NN classification

PubMed Central

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, I Saira; Bissell, Mina J

2005-01-01

Background Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Results Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Conclusion Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics. PMID:16045803

Object-based vegetation classification with high resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions such as (1) whether the sample characteristics affect the classification accuracy and how significant if it does; (2) how much variance of classification uncertainty can be explained by above factors. This research is carried out on a hilly peninsular area in Mediterranean climate, Point Reyes National Seashore (PRNS) in Northern California. The area mainly consists of a heterogeneous, semi-natural broadleaf and conifer woodland, shrub land, and annual grassland. A detailed list of vegetation alliances is used in this study. Research results from the first phase indicates that vegetation spatial variation as reflected by the average local variance (ALV) keeps a high level of magnitude between 1 m and 4 m resolution. (Abstract shortened by UMI.)

Evaluation of normalization methods for cDNA microarray data by k-NN classification.

PubMed

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, I Saira; Bissell, Mina J

2005-07-26

Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics.

Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream

NASA Astrophysics Data System (ADS)

Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika D.; Wang, Zhe; Lochner, Michelle; Matheson, Thomas; Saha, Abhijit; Yang, Shuo; Zhao, Zhenge; Kececioglu, John; Scheidegger, Carlos; Snodgrass, Richard T.; Axelrod, Tim; Jenness, Tim; Maier, Robert S.; Ridgway, Stephen T.; Seaman, Robert L.; Evans, Eric Michael; Singh, Navdeep; Taylor, Clark; Toeniskoetter, Jackson; Welch, Eric; Zhu, Songzhe; The ANTARES Collaboration

2018-05-01

The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. Alert-brokers—automated software system to sift through, characterize, annotate, and prioritize events for follow-up—will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate, and retrospective classification of alerts. The first takes the form of variable versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

"Relative CIR": an image enhancement and visualization technique

USGS Publications Warehouse

Fleming, Michael D.

1993-01-01

Many techniques exist to spectrally and spatially enhance digital multispectral scanner data. One technique enhances an image while keeping the colors as they would appear in a color-infrared (CIR) image. This "relative CIR" technique generates an image that is both spectrally and spatially enhanced, while displaying a maximum range of colors. The technique enables an interpreter to visualize either spectral or land cover classes by their relative CIR characteristics. A relative CIR image is generated by developed spectral statistics for each class in the classifications and then, using a nonparametric approach for spectral enhancement, the means of the classes for each band are ranked. A 3 by 3 pixel smoothing filter is applied to the classification for spatial enhancement and the classes are mapped to the representative rank for each band. Practical applications of the technique include displaying an image classification product as a CIR image that was not derived directly from a spectral image, visualizing how a land cover classification would look as a CIR image, and displaying a spectral classification or intermediate product that will be used to label spectral classes.

A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhao, Bei; Zhong, Yanfei; Zhang, Liangpei

2016-06-01

Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes.

Inter-Annual Variability of the Acoustic Propagation in the Mediterranean Sea Identified from a Synoptic Monthly Gridded Database as Compared with GDEM

DTIC Science & Technology

2016-12-01

VARIABILITY OF THE ACOUSTIC PROPAGATION IN THE MEDITERRANEAN SEA IDENTIFIED FROM A SYNOPTIC MONTHLY GRIDDED DATABASE AS COMPARED WITH GDEM by...ANNUAL VARIABILITY OF THE ACOUSTIC PROPAGATION IN THE MEDITERRANEAN SEA IDENTIFIED FROM A SYNOPTIC MONTHLY GRIDDED DATABASE AS COMPARED WITH GDEM 5...profiles obtained from the synoptic monthly gridded World Ocean Database (SMD-WOD) and Generalized Digital Environmental Model (GDEM) temperature (T

LANDSAT applications to wetlands classification in the upper Mississippi River Valley. Ph.D. Thesis. Final Report

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Werth, L. F. (Principal Investigator)

1980-01-01

A 25% improvement in average classification accuracy was realized by processing double-date vs. single-date data. Under the spectrally and spatially complex site conditions characterizing the geographical area used, further improvement in wetland classification accuracy is apparently precluded by the spectral and spatial resolution restrictions of the LANDSAT MSS. Full scene analysis of scanning densitometer data extracted from scale infrared photography failed to permit discrimination of many wetland and nonwetland cover types. When classification of photographic data was limited to wetland areas only, much more detailed and accurate classification could be made. The integration of conventional image interpretation (to simply delineate wetland boundaries) and machine assisted classification (to discriminate among cover types present within the wetland areas) appears to warrant further research to study the feasibility and cost of extending this methodology over a large area using LANDSAT and/or small scale photography.

Photometric classification and redshift estimation of LSST Supernovae

NASA Astrophysics Data System (ADS)

Dai, Mi; Kuhlmann, Steve; Wang, Yun; Kovacs, Eve

2018-07-01

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of an SN classifier that uses SN colours to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an area-under-the-curve of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99 per cent SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias (⟨zphot - zspec⟩) of 0.012 with σ (z_phot-z_spec/1+z_spec) = 0.0294 without using a host-galaxy photo-z prior, and a mean bias (⟨zphot - zspec⟩) of 0.0017 with σ (z_phot-z_spec/1+z_spec) = 0.0116 using a host-galaxy photo-z prior. Assuming a flat ΛCDM model with Ωm = 0.3, we obtain Ωm of 0.305 ± 0.008 (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter σint = 0.11) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.

The dynamics of human-induced land cover change in miombo ecosystems of southern Africa

NASA Astrophysics Data System (ADS)

Jaiteh, Malanding Sambou

Understanding human-induced land cover change in the miombo require the consistent, geographically-referenced, data on temporal land cover characteristics as well as biophysical and socioeconomic drivers of land use, the major cause of land cover change. The overall goal of this research to examine the applications of high-resolution satellite remote sensing data in studying the dynamics of human-induced land cover change in the miombo. Specific objectives are to: (1) evaluate the applications of computer-assisted classification of Landsat Thematic Mapper (TM) data for land cover mapping in the miombo and (2) analyze spatial and temporal patterns of landscape change locations in the miombo. Stepwise Thematic Classification, STC (a hybrid supervised-unsupervised classification) procedure for classifying Landsat TM data was developed and tested using Landsat TM data. Classification accuracy results were compared to those from supervised and unsupervised classification. The STC provided the highest classification accuracy i.e., 83.9% correspondence between classified and referenced data compared to 44.2% and 34.5% for unsupervised and supervised classification respectively. Improvements in the classification process can be attributed to thematic stratification of the image data into spectrally homogenous (thematic) groups and step-by-step classification of the groups using supervised or unsupervised classification techniques. Supervised classification failed to classify 18% of the scene evidence that training data used did not adequately represent all of the variability in the data. Application of the procedure in drier miombo produced overall classification accuracy of 63%. This is much lower than that of wetter miombo. The results clearly demonstrate that digital classification of Landsat TM can be successfully implemented in the miombo without intensive fieldwork. Spatial characteristics of land cover change in agricultural and forested landscapes in central Malawi were analyzed for the period 1984 to 1995 spatial pattern analysis methods. Shifting cultivation areas, Agriculture in forested landscape, experienced highest rate of woodland cover fragmentation with mean patch size of closed woodland cover decreasing from 20ha to 7.5ha. Permanent bare (cropland and settlement) in intensive agricultural matrix landscapes increased 52% largely through the conversion of fallow areas. Protected National Park area remained fairly unchanged although closed woodland area increased by 4%, mainly from regeneration of open woodland. This study provided evidence that changes in spatial characteristics in the miombo differ with landscape. Land use change (i.e. conversion to cropland) is the primary driving force behind changes in landscape spatial patterns. Also, results revealed that exclusion of intense human use (i.e. cultivation and woodcutting) through regulations and/or fencing increased both closed woodland area (through regeneration of open woodland) and overall connectivity in the landscape. Spatial characteristics of land cover change were analyzed at locations in Malawi (wetter miombo) and Zimbabwe (drier miombo). Results indicate land cover dynamics differ both between and within case study sites. In communal areas in the Kasungu scene, land cover change is dominated by woodland fragmentation to open vegetation. Change in private commercial lands was dominantly expansion of bare (settlement and cropland) areas primarily at the expense of open vegetation (fallow land).

Vulnerable land ecosystems classification using spatial context and spectral indices

NASA Astrophysics Data System (ADS)

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier

2017-10-01

Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Jung, Jinha; Pasolli, Edoardo; Prasad, Saurabh; Tilton, James C.; Crawford, Melba M.

2014-01-01

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

Aquifer configuration and geostructural links control the groundwater quality in thin-bedded carbonate-siliciclastic alternations of the Hainich CZE, central Germany

NASA Astrophysics Data System (ADS)

Kohlhepp, Bernd; Lehmann, Robert; Seeber, Paul; Küsel, Kirsten; Trumbore, Susan E.; Totsche, Kai U.

2017-12-01

The quality of near-surface groundwater reservoirs is controlled, but also threatened, by manifold surface-subsurface interactions. Vulnerability studies typically evaluate the variable interplay of surface factors (land management, infiltration patterns) and subsurface factors (hydrostratigraphy, flow properties) in a thorough way, but disregard the resulting groundwater quality. Conversely, hydrogeochemical case studies that address the chemical evolution of groundwater often lack a comprehensive analysis of the structural buildup. In this study, we aim to reconstruct the actual spatial groundwater quality pattern from a synoptic analysis of the hydrostratigraphy, lithostratigraphy, pedology and land use in the Hainich Critical Zone Exploratory (Hainich CZE). This CZE represents a widely distributed yet scarcely described setting of thin-bedded mixed carbonate-siliciclastic strata in hillslope terrains. At the eastern Hainich low-mountain hillslope, bedrock is mainly formed by alternated marine sedimentary rocks of the Upper Muschelkalk (Middle Triassic) that partly host productive groundwater resources. Spatial patterns of the groundwater quality of a 5.4 km long well transect are derived by principal component analysis and hierarchical cluster analysis. Aquifer stratigraphy and geostructural links were deduced from lithological drill core analysis, mineralogical analysis, geophysical borehole logs and mapping data. Maps of preferential recharge zones and recharge potential were deduced from digital (soil) mapping, soil survey data and field measurements of soil hydraulic conductivities (Ks). By attributing spatially variable surface and subsurface conditions, we were able to reconstruct groundwater quality clusters that reflect the type of land management in their preferential recharge areas, aquifer hydraulic conditions and cross-formational exchange via caprock sinkholes or ascending flow. Generally, the aquifer configuration (spatial arrangement of strata, valley incision/outcrops) and related geostructural links (enhanced recharge areas, karst phenomena) control the role of surface factors (input quality and locations) vs. subsurface factors (water-rock interaction, cross-formational flow) for groundwater quality in the multi-layered aquifer system. Our investigation reveals general properties of alternating sequences in hillslope terrains that are prone to forming multi-layered aquifer systems. This synoptic analysis is fundamental and indispensable for a mechanistic understanding of ecological functioning, sustainable resource management and protection.

Texture characterization for joint compression and classification based on human perception in the wavelet domain.

PubMed

Fahmy, Gamal; Black, John; Panchanathan, Sethuraman

2006-06-01

Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are: 1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented.

Impact of seasonal synoptic weather types on local PM10 concentrations in Bavaria/Germany: recent conditions and future projections

NASA Astrophysics Data System (ADS)

Weitnauer, Claudia; Beck, Christoph; Jacobeit, Jucundus

2015-04-01

It is a matter of common knowledge that local concentrations of PM10 (fine particles in the air with a medium diameter less than 10 μm) vary with the seasons in Europe. These concentrations are influenced on the one hand by the amount of natural and anthropogenic emissions and on the other hand by large-scale and local meteorological conditions. In Bavaria (part of southern Germany) as the target region of the present study, the PM10 concentrations are particularly high in winter time. One reason for this are increased particle emissions due to domestic heating and traffic load in December, January and February. As several studies in other European regions indicated, a distinct effect of the large-scale synoptic weather situation in winter on local PM10 concentrations should be considered as another reason. The main task of this study is to use seasonal synoptic weather types, which are optimized with respect to daily mean PM10 data at 16 Bavarian cities, and therefore are classified by using daily gridded NCEP/NCAR reanalysis data (2.5° x 2.5° horizontal resolution) for the recent period 1980 - 2011 over a Central European spatial domain, to describe the impact of the large-scale meteorological conditions on the local particle concentrations. The weather types are related to monthly PM10 indices by using different transfer techniques like direct synoptic downscaling, multiple regression and generalized linear models as well as random forests. The PM10 indices are determined by averaging daily to monthly data (PMmean) or by counting the daily exceedances of a particular threshold (> 50 μg/m3, PMe50). The generated transfer models are evaluated in calibration and validation periods using several forecast skills, for example the mean squared skill score (MSSS) or the Heidke Skill Score (HSS). The sufficiently performing models are then applied to weather types derived from future climate change scenarios of the global climate model ECHAM 6 for the IPCC scenarios RCP 4.5 and 8.5 in order to estimate future climate-change induced modifications of local PM10 concentrations in Bavaria.

Resolution dependence of cross-tropopause ozone transport over east Asia

NASA Astrophysics Data System (ADS)

Büker, M. L.; Hitchman, Matthew H.; Tripoli, Gregory J.; Pierce, R. B.; Browell, E. V.; Avery, M. A.

2005-02-01

Detailed analysis of mesoscale transport of ozone across the tropopause over east Asia during the spring of 2001 is conducted using regional simulations with the University of Wisconsin Nonhydrostatic Modeling System (UWNMS), in situ flight data, and a new two-scale approach to diagnosing this ozone flux. From late February to early April, synoptic activity regularly deformed the tropopause, leading to observations of ozone-rich (concentration exceeding 80 ppbv) stratospheric intrusions and filaments at tropospheric altitudes. Since model resolution is generally not sufficient to capture detailed small-scale mixing processes, an upper bound on the flux is proposed by assuming that there exists a dynamical division by spatial scale, above which the wind conservatively advects large-scale structures, while below it the wind leads to irreversible transport through nonconservative random strain. A formulation for this diagnosis is given and applied to ozone flux across the dynamical tropopause. Simulations were chosen to correspond with DC-8 flight 15 on 26-27 March over east Asia during the Transport and Chemical Evolution Over the Pacific (TRACE-P) campaign. Local and domain-averaged flux values using this method agree with other numerical and observational studies in similar synoptic environments. Sensitivity to numerical resolution, prescribed divisional spatial scale, and potential vorticity (PV) level is investigated. Divergent residual flow in regions of high ozone, and PV gradients tended to maximize flux magnitudes. We estimated the domain-integrated flow of ozone out of the lowermost stratosphere to be about 0.127 Tg/day. Spectral analysis of the wind field lends support for utilization of this dynamical division in this methodology.

Atmospheric conditions associated with extreme fire activity in the Western Mediterranean region.

PubMed

Amraoui, Malik; Pereira, Mário G; DaCamara, Carlos C; Calado, Teresa J

2015-08-15

Active fire information provided by TERRA and AQUA instruments on-board sun-synchronous polar MODIS platform is used to describe fire activity in the Western Mediterranean and to identify and characterize the synoptic patterns of several meteorological fields associated with the occurrence of extreme fire activity episodes (EEs). The spatial distribution of the fire pixels during the period of 2003-2012 leads to the identification of two most affected sub-regions, namely the Northern and Western parts of the Iberian Peninsula (NWIP) and Northern Africa (NAFR). The temporal distribution of the fire pixels in these two sub-regions is characterized by: (i) high and non-concurrent inter- and intra-annual variability with maximum values during the summer of 2003 and 2005 in NWIP and 2007 and 2012 in NAFR; and, (ii) high intra-annual variability dominated by a prominent annual cycle with a main peak centred in August in both sub-regions and a less pronounced secondary peak in March only evident in NWIP region. The 34 EEs identified were grouped according to the location, period of occurrence and spatial configuration of the associated synoptic patterns into 3 clusters (NWIP-summer, NWIP-winter and NAFR-summer). Results from the composite analysis reveal similar fire weather conditions (statistically significant positive anomalies of air temperature and negative anomalies of air relative humidity) but associated with different circulation patterns at lower and mid-levels of the atmosphere associated with the occurrence of EEs in each cluster of the Western Mediterranean region. Copyright © 2015 Elsevier B.V. All rights reserved.

Modelling extreme dry spells in the Mediterranean region in connection with atmospheric circulation

NASA Astrophysics Data System (ADS)

Tramblay, Yves; Hertig, Elke

2018-04-01

Long droughts periods can affect the Mediterranean region during the winter season, when most of annual precipitation occurs, and consequently have strong impacts on agriculture, groundwater levels and water resources. The goal of this study is to model annual maximum dry spells lengths (AMDSL) that occur during the extended winter season (October to April). The spatial patterns of extreme dry spells and their relationships with large-scale atmospheric circulation were first investigated. Then, AMDSL were modelled using Generalized Extreme Value (GEV) distributions incorporating climatic covariates, to evaluate the dependences of extreme dry spells to synoptic patterns using an analogue approach. The data from a network of 160 rain gauges having daily precipitation measurements between 1960 and 2009 are considered together with the ERA-20C reanalysis of the 20th century to provide atmospheric variables (geopotential heights, humidity, winds). A regional classification of both the occurrence and the duration of AMDSL helped to distinguish three spatially contiguous regions in which the regional distributions were found homogeneous. From composite analysis, significant positive anomalies in geopotential height (Z500) and negative anomalies in zonal wind (U850) and relative and specific humidity (S850, R850) were found to be associated with AMDSL in the three regions and provided the reference to build analogue days. Finally, non-stationary GEV models have been compared, in which the location and scale parameters are related to different atmospheric indices. Results indicates, at the whole Mediterranean scale, that positives anomalies of the North Atlantic Oscillation index and to a lesser extent the Mediterranean Oscillation index are linked to longer extreme dry spells in the majority of stations. For the three regions identified, the frequency of U850 negative anomalies over North Africa is significantly associated with the magnitude of AMDSL. AMDL are also associated with the frequency of S850 negative anomalies for the southeastern region, and with positive Z500 anomalies for the Western and North-eastern Mediterranean regions.

Contrasting environments associated with storm prediction center tornado outbreak forecasts using synoptic-scale composite analysis

NASA Astrophysics Data System (ADS)

Bates, Alyssa Victoria

Tornado outbreaks have significant human impact, so it is imperative forecasts of these phenomena are accurate. As a synoptic setup lays the foundation for a forecast, synoptic-scale aspects of Storm Prediction Center (SPC) outbreak forecasts of varying accuracy were assessed. The percentages of the number of tornado outbreaks within SPC 10% tornado probability polygons were calculated. False alarm events were separately considered. The outbreaks were separated into quartiles using a point-in-polygon algorithm. Statistical composite fields were created to represent the synoptic conditions of these groups and facilitate comparison. Overall, temperature advection had the greatest differences between the groups. Additionally, there were significant differences in the jet streak strengths and amounts of vertical wind shear. The events forecasted with low accuracy consisted of the weakest synoptic-scale setups. These results suggest it is possible that events with weak synoptic setups should be regarded as areas of concern by tornado outbreak forecasters.

Spatio-Temporal Evolution and Scaling Properties of Human Settlements (Invited)

NASA Astrophysics Data System (ADS)

Small, C.; Milesi, C.; Elvidge, C.; Baugh, K.; Henebry, G. M.; Nghiem, S. V.

2013-12-01

Growth and evolution of cities and smaller settlements is usually studied in the context of population and other socioeconomic variables. While this is logical in the sense that settlements are groups of humans engaged in socioeconomic processes, our means of collecting information about spatio-temporal distributions of population and socioeconomic variables often lack the spatial and temporal resolution to represent the processes at scales which they are known to occur. Furthermore, metrics and definitions often vary with country and through time. However, remote sensing provides globally consistent, synoptic observations of several proxies for human settlement at spatial and temporal resolutions sufficient to represent the evolution of settlements over the past 40 years. We use several independent but complementary proxies for anthropogenic land cover to quantify spatio-temporal (ST) evolution and scaling properties of human settlements globally. In this study we begin by comparing land cover and night lights in 8 diverse settings - each spanning gradients of population density and degree of land surface modification. Stable anthropogenic night light is derived from multi-temporal composites of emitted luminance measured by the VIIRS and DMSP-OLS sensors. Land cover is represented as mixtures of sub-pixel fractions of rock, soil and impervious Substrates, Vegetation and Dark surfaces (shadow, water and absorptive materials) estimated from Landsat imagery with > 94% accuracy. Multi-season stability and variability of land cover fractions effectively distinguishes between spectrally similar land covers that corrupt thematic classifications based on single images. We find that temporal stability of impervious substrates combined with persistent shadow cast between buildings results in temporally stable aggregate reflectance across seasons at the 30 m scale of a Landsat pixel. Comparison of night light brightness with land cover composition, stability and variability yields several consistent relationships that persist across a variety of settlement types and physical environments. We use the multiple threshold method of Small et al (2011) to represent a continuum of settlement density by segmenting both night light brightness and multi-season land cover characteristics. Rank-size distributions of spatially contiguous segments quantify scaling and connectivity of land cover. Spatial and temporal evolution of rank-size distributions is consistent with power laws as suggested by Zipf's Law for city size based on population. However, unlike Zipf's Law, the observed distributions persist to global scales in which the larger agglomerations are much larger than individual cities. The scaling relations observed extend from the scale of cities and smaller settlements up to vast spatial networks of interconnected settlements.

Characterizing relative humidity with respect to ice in midlatitude cirrus clouds as a function of atmospheric state

NASA Astrophysics Data System (ADS)

Dzambo, Andrew M.; Turner, David D.

2016-10-01

Midlatitude cirrus cloud macrophysical and microphysical properties have been shown in previous studies to vary seasonally and in various large-scale dynamical regimes, but relative humidity with respect to ice (RHI) within cirrus clouds has not been studied extensively in this context. Using a combination of radiosonde and millimeter-wavelength cloud radar data, we identify 1076 cirrus clouds spanning a 7 year period from 2004 to 2011. These data are separated into five classes using a previously published algorithm that is based largely on synoptic conditions. Using these data and classification scheme, we find that RHI in cirrus clouds varies seasonally. Variations in cirrus cloud RHI exist within the prescribed classifications; however, most of the variations are within the measurement uncertainty. Additionally, with the exception of nonsummer class cirrus, these variations are not statistically significant. We also find that cirrus cloud occurrence is not necessarily correlated with higher observed values of RHI. The structure of RHI in cirrus clouds varies more in thicker clouds, which follows previous studies showing that macrophysical and microphysical variability increases in thicker cirrus clouds.

a Rough Set Decision Tree Based Mlp-Cnn for Very High Resolution Remotely Sensed Image Classification

NASA Astrophysics Data System (ADS)

Zhang, C.; Pan, X.; Zhang, S. Q.; Li, H. P.; Atkinson, P. M.

2017-09-01

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

Landcover Classification Using Deep Fully Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Li, X.; Zhou, S.; Tang, J.

2017-12-01

Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.

West Coast atmospheric river climatology in CMIP5 climate models

NASA Astrophysics Data System (ADS)

Warner, M.; Mass, C.; Salathe, E. P.

2015-12-01

In recent years, there has been a flurry of research on how atmospheric river events (ARs) will respond to anthropogenic global warming. This study uses 10 CMIP5 RCP 8.5 climate models to focus on changes in AR frequency, seasonality, and synoptic conditions along the west coast of the United States and is a follow-up to previous work by the same authors (Warner et al. 2015) which investigated expected changes in AR intensity in the same region. There are only very slight changes in annual AR climatology from the end of the last century to the end of this century when considering the most extreme integrated water vapor transport (IVT) events (99th percentile). However, when evaluating by the number of future days exceeding a historical threshold, there are significant increases in extreme IVT events in all months, especially during months when the majority of events take place. The peaks in historical and future frequency occur in similar months given the amount of model variability. Extreme IVT events appear to be occurring slightly earlier in the season, particularly along the northern US coast, and these results are similar to other studies. Spatially, 10-model mean historical composites of IVT reveal canonical AR conditions. At locations farther south, there is less model agreement on the spatial extent and intensity of AR events; whereas farther north, the various models are in agreement. Composites of future events indicate very little spatial change from historical events. The location and orientation of AR events in the historical and future time periods are similar, and the upper-level winds change little over that time period (Warner et al. 2015). This suggests little change in synoptic conditions for approaching ARs. The future-historical difference plots highlight the largest changes expected in the future, namely increases in IVT intensity which are primarily associated with thermodynamic changes related to future integrated water vapor increases due to a warming atmosphere.

Dissipation of Energy in a Concentric ER Clutch and its Refined Quasi-Static Model

NASA Astrophysics Data System (ADS)

Oravský, Vladimír

A concentric electrorheological clutch (ERC) constituting the central part of a broader system: electro-hydro-aggregate (EHA) with an electrodrive (ED) on one side and a loading machine (brake B) on the other side is considered. The corresponding quasi-static model (at constant load and speed) is investigated and refined by insertion of power absorbed by electrorheological fluid (ERF). This increases the number of nondimensional parameters of the model from 8 to 12. Classification of several kinds of dissipation of energy in ERC is presented. Description and analysis of dissipation of the first kind are given more in detail and illustrated by synoptical diagrams. Also two definitions of efficiency of ERC are introduced and discussed.

Classification of cloud fields based on textural characteristics

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Chen, D. W.

1987-01-01

The present study reexamines the applicability of texture-based features for automatic cloud classification using very high spatial resolution (57 m) Landsat multispectral scanner digital data. It is concluded that cloud classification can be accomplished using only a single visible channel.

Object-Based Land Use Classification of Agricultural Land by Coupling Multi-Temporal Spectral Characteristics and Phenological Events in Germany

NASA Astrophysics Data System (ADS)

Knoefel, Patrick; Loew, Fabian; Conrad, Christopher

2015-04-01

Crop maps based on classification of remotely sensed data are of increased attendance in agricultural management. This induces a more detailed knowledge about the reliability of such spatial information. However, classification of agricultural land use is often limited by high spectral similarities of the studied crop types. More, spatially and temporally varying agro-ecological conditions can introduce confusion in crop mapping. Classification errors in crop maps in turn may have influence on model outputs, like agricultural production monitoring. One major goal of the PhenoS project ("Phenological structuring to determine optimal acquisition dates for Sentinel-2 data for field crop classification"), is the detection of optimal phenological time windows for land cover classification purposes. Since many crop species are spectrally highly similar, accurate classification requires the right selection of satellite images for a certain classification task. In the course of one growing season, phenological phases exist where crops are separable with higher accuracies. For this purpose, coupling of multi-temporal spectral characteristics and phenological events is promising. The focus of this study is set on the separation of spectrally similar cereal crops like winter wheat, barley, and rye of two test sites in Germany called "Harz/Central German Lowland" and "Demmin". However, this study uses object based random forest (RF) classification to investigate the impact of image acquisition frequency and timing on crop classification uncertainty by permuting all possible combinations of available RapidEye time series recorded on the test sites between 2010 and 2014. The permutations were applied to different segmentation parameters. Then, classification uncertainty was assessed and analysed, based on the probabilistic soft-output from the RF algorithm at the per-field basis. From this soft output, entropy was calculated as a spatial measure of classification uncertainty. The results indicate that uncertainty estimates provide a valuable addition to traditional accuracy assessments and helps the user to allocate error in crop maps.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps

DOE PAGES

Kennedy, Aaron D.; Dong, Xiquan; Xi, Baike

2015-02-15

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997–2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrumentmore » record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. Lastly, this result may be due to a manifestation of seasonally dependent biases in NARR.« less

Evaluation criteria for software classification inventories, accuracies, and maps

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1976-01-01

Statistical criteria are presented for modifying the contingency table used to evaluate tabular classification results obtained from remote sensing and ground truth maps. This classification technique contains information on the spatial complexity of the test site, on the relative location of classification errors, on agreement of the classification maps with ground truth maps, and reduces back to the original information normally found in a contingency table.

Classification of High Spatial Resolution, Hyperspectral ...

EPA Pesticide Factsheets

EPA announced the availability of the final report,

Applicability of Various Interpolation Approaches for High Resolution Spatial Mapping of Climate Data in Korea

NASA Astrophysics Data System (ADS)

Jo, A.; Ryu, J.; Chung, H.; Choi, Y.; Jeon, S.

2018-04-01

The purpose of this study is to create a new dataset of spatially interpolated monthly climate data for South Korea at high spatial resolution (approximately 30m) by performing various spatio-statistical interpolation and comparing with forecast LDAPS gridded climate data provided from Korea Meterological Administration (KMA). Automatic Weather System (AWS) and Automated Synoptic Observing System (ASOS) data in 2017 obtained from KMA were included for the spatial mapping of temperature and rainfall; instantaneous temperature and 1-hour accumulated precipitation at 09:00 am on 31th March, 21th June, 23th September, and 24th December. Among observation data, 80 percent of the total point (478) and remaining 120 points were used for interpolations and for quantification, respectively. With the training data and digital elevation model (DEM) with 30 m resolution, inverse distance weighting (IDW), co-kriging, and kriging were performed by using ArcGIS10.3.1 software and Python 3.6.4. Bias and root mean square were computed to compare prediction performance quantitatively. When statistical analysis was performed for each cluster using 20 % validation data, co kriging was more suitable for spatialization of instantaneous temperature than other interpolation method. On the other hand, IDW technique was appropriate for spatialization of precipitation.

SRNL PARTICIPATION IN THE MULTI-SCALE ENSEMBLE EXERCISES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buckley, R

2007-10-29

Consequence assessment during emergency response often requires atmospheric transport and dispersion modeling to guide decision making. A statistical analysis of the ensemble of results from several models is a useful way of estimating the uncertainty for a given forecast. ENSEMBLE is a European Union program that utilizes an internet-based system to ingest transport results from numerous modeling agencies. A recent set of exercises required output on three distinct spatial and temporal scales. The Savannah River National Laboratory (SRNL) uses a regional prognostic model nested within a larger-scale synoptic model to generate the meteorological conditions which are in turn used inmore » a Lagrangian particle dispersion model. A discussion of SRNL participation in these exercises is given, with particular emphasis on requirements for provision of results in a timely manner with regard to the various spatial scales.« less

A synoptic climatology of derecho producing mesoscale convective systems in the North-Central Plains

NASA Astrophysics Data System (ADS)

Bentley, Mace L.; Mote, Thomas L.; Byrd, Stephen F.

2000-09-01

Synoptic-scale environments favourable for producing derechos, or widespread convectively induced windstorms, in the North-Central Plains are examined with the goal of providing pattern-recognition/diagnosis techniques. Fifteen derechos were identified across the North-Central Plains region during 1986-1995. The synoptic environment at the initiation, mid-point and decay of each derecho was then evaluated using surface, upper-air and National Center for Atmospheric Research (NCAR)/National Center for Environmental Prediction (NCEP) reanalysis datasets.Results suggest that the synoptic environment is critical in maintaining derecho producing mesoscale convective systems (DMCSs). The synoptic environment in place downstream of the MCS initiation region determines the movement and potential strength of the system. Circulation around surface low pressure increased the instability gradient and maximized leading edge convergence in the initiation region of nearly all events regardless of DMCS location or movement. Other commonalities in the environments of these events include the presence of a weak thermal boundary, high convective instability and a layer of dry low-to-mid-tropospheric air. Of the two corridors sampled, northeastward moving derechos tend to initiate east of synoptic-scale troughs, while southeastward moving derechos form on the northeast periphery of a synoptic-scale ridge. Other differences between these two DMCS events are also discussed.

Use of hydrologic landscape classification to diagnose streamflow predictability in Oregon

EPA Science Inventory

We implement a spatially lumped rainfall-runoff model to predict daily streamflow at 88 catchments within Oregon, USA and analyze its performance within the context of Oregon Hydrologic Landscapes (OHL) classification. OHL classification is used to characterize the physio-climat...

A review of supervised object-based land-cover image classification

NASA Astrophysics Data System (ADS)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial vehicle) or agricultural sites where it also correlates with the number of targeted classes. More than 95.6% of studies involve an area less than 300 ha, and the spatial resolution of images is predominantly between 0 and 2 m. Furthermore, we identify some methods that may advance supervised object-based image classification. For example, deep learning and type-2 fuzzy techniques may further improve classification accuracy. Lastly, scientists are strongly encouraged to report results of uncertainty studies to further explore the effects of varied factors on supervised object-based image classification.

Definition of a temporal distribution index for high temporal resolution precipitation data over Peninsular Spain and the Balearic Islands: the fractal dimension; and its synoptic implications

NASA Astrophysics Data System (ADS)

Meseguer-Ruiz, Oliver; Osborn, Timothy J.; Sarricolea, Pablo; Jones, Philip D.; Cantos, Jorge Olcina; Serrano-Notivoli, Roberto; Martin-Vide, Javier

2018-03-01

Precipitation on the Spanish mainland and in the Balearic archipelago exhibits a high degree of spatial and temporal variability, regardless of the temporal resolution of the data considered. The fractal dimension indicates the property of self-similarity, and in the case of this study, wherein it is applied to the temporal behaviour of rainfall at a fine (10-min) resolution from a total of 48 observatories, it provides insights into its more or less convective nature. The methodology of Jenkinson & Collison which automatically classifies synoptic situations at the surface, as well as an adaptation of this methodology at 500 hPa, was applied in order to gain insights into the synoptic implications of extreme values of the fractal dimension. The highest fractal dimension values in the study area were observed in places with precipitation that has a more random behaviour over time with generally high totals. Four different regions in which the atmospheric mechanisms giving rise to precipitation at the surface differ from the corresponding above-ground mechanisms have been identified in the study area based on the fractal dimension. In the north of the Iberian Peninsula, high fractal dimension values are linked to a lower frequency of anticyclonic situations, whereas the opposite occurs in the central region. In the Mediterranean, higher fractal dimension values are associated with a higher frequency of the anticyclonic type and a lower frequency of the advective type from the east. In the south, lower fractal dimension values indicate higher frequency with respect to the anticyclonic type from the east and lower frequency with respect to the cyclonic type.

Edited synoptic cloud reports from ships and land stations over the globe, 1982--1991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hahn, C.J.; Warren, S.G.; London, J.

1996-02-01

Surface synoptic weather reports for the entire globe for the 10-year period from December 1981 through November 1991 have been processed, edited, and rewritten to provide a data set designed for use in cloud analyses. The information in these reports relating to clouds, including the present weather information, was extracted and put through a series of quality control checks. Correctable inconsistencies within reports were edited for consistency, so that the ``edited cloud report`` can be used for cloud analysis. Cases of ``sky obscured`` were interpreted by reference to the present weather code as to whether they indicated fog, rain ormore » snow and were given appropriate cloud type designations. Nimbostratus clouds were also given a special designation. Changes made to an original report are indicated in the edited report so that the original report can be reconstructed if desired. While low cloud amount is normally given directly in the synoptic report, the edited cloud report also includes the amounts, either directly reported or inferred, of middle and high clouds, both the non-overlapped amounts and the ``actual`` amounts. Since illumination from the moon is important for the adequate detection of clouds at night, both the relative lunar illuminance and the solar altitude are given; well as a parameter that indicates whether our recommended illuminance criterion was satisfied. This data set contains 124 million reports from land stations and 15 million reports from ships. Each report is 56 characters in length. The archive consists of 240 files, one file for each month of data for land and ocean separately. With this data set a user can develop a climatology for any particular cloud type or group of types, for any geographical region and any spatial and temporal resolution desired.« less

Characterization of synoptic patterns causing dust outbreaks that affect the Arabian Peninsula

NASA Astrophysics Data System (ADS)

Hermida, L.; Merino, A.; Sánchez, J. L.; Fernández-González, S.; García-Ortega, E.; López, L.

2018-01-01

Dust storms pose serious weather hazards in arid and semiarid regions of the earth. Understanding the main synoptic conditions that give rise to dust outbreaks is important for issuing forecasts and warnings to the public in cases of severe storms. The aim of the present study is to determine synoptic patterns that are associated with or even favor dust outbreaks over the Arabian Peninsula. In this respect, red-green-blue dust composite images from the Meteosat Second Generation (MSG) satellite are used to detect dust outbreaks affecting the Arabian Peninsula, with possible influences in southwestern Asia and northeastern Africa, between 2005 and 2013. The Meteosat imagery yielded a sample of 95 dust storm days. Meteorological fields from NCEP/NCAR reanalysis data of wind fields at 10 m and 250 hPa, mean sea level pressure, and geopotential heights at 850 and 500 hPa were obtained for the dust storm days. Using principal component analysis in T-mode and non-hierarchical k-means clustering, we obtained four major atmospheric circulation patterns associated with dust outbreaks during the study days. Cluster 4 had the largest number of days with dust events, which were constrained to summer, and cluster 3 had the fewest. In clusters 1, 2 and 3, the jet stream favored the entry of a low-pressure area or trough that varied in location between the three clusters. Their most northerly location was found in cluster 4, along with an extensive low-pressure area supporting strong winds over the Arabian Peninsula. The spatial distribution of aerosol optical depth for each cluster obtained was characterized using the Moderate Resolution Imaging Spectroradiometer data. Then, using METAR stations, clusters were also characterized in terms of frequency and visibility.

Synoptic channel morphodynamics with topo-bathymetric airborne lidar: promises, pitfalls and research needs

NASA Astrophysics Data System (ADS)

Lague, D.; Launeau, P.; Gouraud, E.

2017-12-01

Topo-bathymetric airborne lidar sensors using a green laser penetrating water and suitable for hydrography are now sold by major manufacturers. In the context of channel morphodynamics, repeat surveys could offer synoptic high resolution measurement of topo-bathymetric change, a key data that is currently missing. Yet, beyond the technological promise, what can we really achieve with these sensors in terms of depth penetration and bathymetric accuracy ? Can all rivers be surveyed ? How easy it is to process this new type of data to get the data needed by geomorphologists ? Here we report on the use of the Optech Titan dual wavelength (1064 nm & 532 nm) operated by the universities of Rennes and Nantes (France) and deployed over several rivers and lakes in France, including repeat surveys. We will illustrate cases where the topo-bathymetric survey is complete, reaching up to 6 m in rivers and offers unprecedented data for channel morphology analysis over tens of kilometres. We will also present challenging cases for which the technology will never work, or for which new algorithms to process full waveform are required. We will illustrate new developments for automated processing of large datasets, including the critical step of water surface detection and refraction correction. In suitable rivers, airborne topo-bathymetric surveys offer unprecedented synoptic 3D data at very high resolution (> 15 pts/m² in bathy) and precision (better than 10 cm for the bathy) down to 5-6 meters depth, with a perfectly continuous topography to bathymetry transition. This presentation will illustrate how this new type of data, when combined with 2D hydraulics modelling offers news insights into the spatial variations of friction in relation to channel bedforms, and the connectivity between rivers and floodplains.

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

PubMed Central

Fan, Yong; Batmanghelich, Nematollah; Clark, Chris M.; Davatzikos, Christos

2010-01-01

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. PMID:18053747

Spatial-temporal discriminant analysis for ERP-based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Zhao, Qibin; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2013-03-01

Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries to maximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.

A New Tool for Climatic Analysis Using the Koppen Climate Classification

ERIC Educational Resources Information Center

Larson, Paul R.; Lohrengel, C. Frederick, II

2011-01-01

The purpose of climate classification is to help make order of the seemingly endless spatial distribution of climates. The Koppen classification system in a modified format is the most widely applied system in use today. This system may not be the best nor most complete climate classification that can be conceived, but it has gained widespread…

Analysis of Solar Coronal Holes with Synoptic Magnetogram Data

NASA Astrophysics Data System (ADS)

Canner, A.; Kim, T. K.; Pogorelov, N.; Yalim, M. S.

2017-12-01

Coronal holes are regions in which the magnetic field of the Sun is open with high magnetic flux and low plasma density. Because of the low plasma beta in these regions, the open field lines transport plasma from the Sun throughout the heliosphere. Coronal hole area is closely related to the expansion factor of the magnetic flux tube, as demonstrated by Tokumaru et al. (2017). Following the approach of Tokumaru et al. (2017), we employ a potential field source surface model to identify the open field regions on the photosphere and estimate the area and expansion factor for each coronal hole. While Tokumaru et al. (2017) analyzed synoptic maps from Kitt Peak National Observatory for the period 1995-2011, we use different magnetograph observations with higher spatial resolution (e.g., SOHO-MDI) for the same time period. We compare the coronal hole area - expansion factor relationship with the original results of Tokumaru et al (2017). This work was supported by the NSF-funded Research Experience for Undergraduates program "Solar and Heliospheric Physics at UAH and MSFC" run by the University of Alabama in Huntsville in partnership with the Marshall Space Flight Center through grant AGS-1460767.

Seismic Forecasting of Solar Activity

NASA Technical Reports Server (NTRS)

Braun, Douglas; Lindsey, Charles

2001-01-01

We have developed and improved helioseismic imaging techniques of the far-side of the Sun as part of a synoptic monitor of solar activity. In collaboration with the MIDI team at Stanford University we are routinely applying our analysis to images within 24 hours of their acquisition by SOHO. For the first time, real-time seismic maps of large active regions on the Sun's far surface are publicly available. The synoptic images show examples of active regions persisting for one or more solar rotations, as well as those initially detected forming on the solar far side. Until recently, imaging the far surface of the Sun has been essentially blind to active regions more than about 50 degrees from the antipode of disk center. In a paper recently accepted for publication, we have demonstrated how acoustic travel-time perturbations may be mapped over the entire hemisphere of the Sun facing away from the Earth, including the polar regions. In addition to offering significant improvements to ongoing space weather forecasting efforts, the procedure offers the possibility of local seismic monitoring of both the temporal and spatial variations in the acoustic properties of the Sun over the entire far surface.

Spatial methods for deriving crop rotation history

NASA Astrophysics Data System (ADS)

Mueller-Warrant, George W.; Trippe, Kristin M.; Whittaker, Gerald W.; Anderson, Nicole P.; Sullivan, Clare S.

2017-08-01

Benefits of converting 11 years of remote sensing classification data into cropping history of agricultural fields included measuring lengths of rotation cycles and identifying specific sequences of intervening crops grown between final years of old grass seed stands and establishment of new ones. Spatial and non-spatial methods were complementary. Individual-year classification errors were often correctable in spreadsheet-based non-spatial analysis, whereas their presence in spatial data generally led to exclusion of fields from further analysis. Markov-model testing of non-spatial data revealed that year-to-year cropping sequences did not match average frequencies for transitions among crops grown in western Oregon, implying that rotations into new grass seed stands were influenced by growers' desires to achieve specific objectives. Moran's I spatial analysis of length of time between consecutive grass seed stands revealed that clustering of fields was relatively uncommon, with high and low value clusters only accounting for 7.1 and 6.2% of fields.

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Evaluation of spatial filtering on the accuracy of wheat area estimate

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Moreira, M. A.; Chen, S. C.; Delima, A. M.

1982-01-01

A 3 x 3 pixel spatial filter for postclassification was used for wheat classification to evaluate the effects of this procedure on the accuracy of area estimation using LANDSAT digital data obtained from a single pass. Quantitative analyses were carried out in five test sites (approx 40 sq km each) and t tests showed that filtering with threshold values significantly decreased errors of commission and omission. In area estimation filtering improved the overestimate of 4.5% to 2.7% and the root-mean-square error decreased from 126.18 ha to 107.02 ha. Extrapolating the same procedure of automatic classification using spatial filtering for postclassification to the whole study area, the accuracy in area estimate was improved from the overestimate of 10.9% to 9.7%. It is concluded that when single pass LANDSAT data is used for crop identification and area estimation the postclassification procedure using a spatial filter provides a more accurate area estimate by reducing classification errors.

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA

USGS Publications Warehouse

Howard, Daniel M.; Wylie, Bruce K.; Tieszen, Larry L.

2012-01-01

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R 2 = 0.90).

Baroclinic wave configurations evolution at European scale in the period 1948-2013

NASA Astrophysics Data System (ADS)

Carbunaru, Daniel; Burcea, Sorin; Carbunaru, Felicia

2016-04-01

The main aim of the study was to investigate the dynamic characteristics of synoptic configurations at European scale and especially in south-eastern part of Europe for the period 1948-2013. Using the empirical orthogonal functions analysis, simultaneously applied to daily average geopotential field at different pressure levels (200 hPa, 300 hPa, 500 hPa and 850 hPa) during warm (April-September) and cold (October-March) seasons, on a synoptic spatial domain centered on Europe (-27.5o lon V to 45o lon E and 32.5o lat N to 72.5o lat N), the main mode of oscillation characteristic to vertical shift of mean baroclinic waves was obtained. The analysis independently applied on 66 years showed that the first eigenvectors in warms periods describe about 60% of the data and in cold season 40% of the data for each year. In comparison secondary eigenvectors describe up to 20% and 10% of the data. Thus, the analysis was focused on the complex evolution of the first eigenvector in 66 years, during the summer period. On average, this eigenvector describes a small vertical phase shift in the west part of the domain and a large one in the eastern part. Because the spatial extent of the considered synoptic domain incorporates in the west part AMO (Atlantic Multidecadal Oscillation) and NAO (North Atlantic Oscillation) oscillations, and in the north part being sensitive to AO (Arctic Oscillation) oscillation, these three oscillations were considered as modulating dynamic factors at hemispherical scale. The preliminary results show that in the summer seasons AMO and NAO oscillations modulated vertical phase shift of baroclinic wave in the west of the area (Northwestern Europe), and the relationship between AO and NAO oscillations modulated vertical phase shift in the southeast area (Southeast Europe). Second, it was shown the way in which this vertical phase shift modulates the overall behavior of cyclonic activity, particularly in Southeastern Europe. This work has been developed within the research project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX), code PN II-ID-2011-2-0073, financed by the Romanian Executive Agency for Higher Education Research, Development and Innovation Funding (UEFISCDI).

a Novel Framework for Remote Sensing Image Scene Classification

NASA Astrophysics Data System (ADS)

Jiang, S.; Zhao, H.; Wu, W.; Tan, Q.

2018-04-01

High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57 % and 83.35 % respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

Regional climates in the GISS global circulation model - Synoptic-scale circulation

NASA Technical Reports Server (NTRS)

Hewitson, B.; Crane, R. G.

1992-01-01

A major weakness of current general circulation models (GCMs) is their perceived inability to predict reliably the regional consequences of a global-scale change, and it is these regional-scale predictions that are necessary for studies of human-environmental response. For large areas of the extratropics, the local climate is controlled by the synoptic-scale atmospheric circulation, and it is the purpose of this paper to evaluate the synoptic-scale circulation of the Goddard Institute for Space Studies (GISS) GCM. A methodology for validating the daily synoptic circulation using Principal Component Analysis is described, and the methodology is then applied to the GCM simulation of sea level pressure over the continental United States (excluding Alaska). The analysis demonstrates that the GISS 4 x 5 deg GCM Model II effectively simulates the synoptic-scale atmospheric circulation over the United States. The modes of variance describing the atmospheric circulation of the model are comparable to those found in the observed data, and these modes explain similar amounts of variance in their respective datasets. The temporal behavior of these circulation modes in the synoptic time frame are also comparable.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

Analysis of the changes in the tarcrete layer on the desert surface of Kuwait using satellite imagery and cell-based modeling

NASA Astrophysics Data System (ADS)

Al-Doasari, Ahmad E.

The 1991 Gulf War caused massive environmental damage in Kuwait. Deposition of oil and soot droplets from hundreds of burning oil-wells created a layer of tarcrete on the desert surface covering over 900 km2. This research investigates the spatial change in the tarcrete extent from 1991 to 1998 using Landsat Thematic Mapper (TM) imagery and statistical modeling techniques. The pixel structure of TM data allows the spatial analysis of the change in tarcrete extent to be conducted at the pixel (cell) level within a geographical information system (GIS). There are two components to this research. The first is a comparison of three remote sensing classification techniques used to map the tarcrete layer. The second is a spatial-temporal analysis and simulation of tarcrete changes through time. The analysis focuses on an area of 389 km2 located south of the Al-Burgan oil field. Five TM images acquired in 1991, 1993, 1994, 1995, and 1998 were geometrically and atmospherically corrected. These images were classified into six classes: oil lakes; heavy, intermediate, light, and traces of tarcrete; and sand. The classification methods tested were unsupervised, supervised, and neural network supervised (fuzzy ARTMAP). Field data of tarcrete characteristics were collected to support the classification process and to evaluate the classification accuracies. Overall, the neural network method is more accurate (60 percent) than the other two methods; both the unsupervised and the supervised classification accuracy assessments resulted in 46 percent accuracy. The five classifications were used in a lagged autologistic model to analyze the spatial changes of the tarcrete through time. The autologistic model correctly identified overall tarcrete contraction between 1991--1993 and 1995--1998. However, tarcrete contraction between 1993--1994 and 1994--1995 was less well marked, in part because of classification errors in the maps from these time periods. Initial simulations of tarcrete contraction with a cellular automaton model were not very successful. However, more accurate classifications could improve the simulations. This study illustrates how an empirical investigation using satellite images, field data, GIS, and spatial statistics can simulate dynamic land-cover change through the use of a discrete statistical and cellular automaton model.

Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales

NASA Astrophysics Data System (ADS)

Caras, Tamir; Hedley, John; Karnieli, Arnon

2017-12-01

Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size < 0.1 m) may compensate for some low spectral resolution drawbacks. In this regard, it is shown that the post-classification majority filtering substantially improves the accuracy of all pixel sizes up to the point where the kernel size reaches the average unit size (pixel < 0.25 m). However, careful investigation as to the effect of band distribution and choice could improve the sensor suitability for the marine environment task. This in mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.

A spatial classification and database for management, research, and policy making: The Great Lakes aquatic habitat framework

USGS Publications Warehouse

Wang, Lizhu; Riseng, Catherine M.; Mason, Lacey; Werhrly, Kevin; Rutherford, Edward; McKenna, James E.; Castiglione, Chris; Johnson, Lucinda B.; Infante, Dana M.; Sowa, Scott P.; Robertson, Mike; Schaeffer, Jeff; Khoury, Mary; Gaiot, John; Hollenhurst, Tom; Brooks, Colin N.; Coscarelli, Mark

2015-01-01

Managing the world's largest and most complex freshwater ecosystem, the Laurentian Great Lakes, requires a spatially hierarchical basin-wide database of ecological and socioeconomic information that is comparable across the region. To meet such a need, we developed a spatial classification framework and database — Great Lakes Aquatic Habitat Framework (GLAHF). GLAHF consists of catchments, coastal terrestrial, coastal margin, nearshore, and offshore zones that encompass the entire Great Lakes Basin. The catchments captured in the database as river pour points or coastline segments are attributed with data known to influence physicochemical and biological characteristics of the lakes from the catchments. The coastal terrestrial zone consists of 30-m grid cells attributed with data from the terrestrial region that has direct connection with the lakes. The coastal margin and nearshore zones consist of 30-m grid cells attributed with data describing the coastline conditions, coastal human disturbances, and moderately to highly variable physicochemical and biological characteristics. The offshore zone consists of 1.8-km grid cells attributed with data that are spatially less variable compared with the other aquatic zones. These spatial classification zones and their associated data are nested within lake sub-basins and political boundaries and allow the synthesis of information from grid cells to classification zones, within and among political boundaries, lake sub-basins, Great Lakes, or within the entire Great Lakes Basin. This spatially structured database could help the development of basin-wide management plans, prioritize locations for funding and specific management actions, track protection and restoration progress, and conduct research for science-based decision making.

Energy transformations associated with the synoptic and planetary scales during the evolution of a blocking anticyclone and an upstream explosively-developing cyclone

NASA Technical Reports Server (NTRS)

Smith, Phillip J.; Tsou, Chih-Hua

1992-01-01

The eddy kinetic energy (KE), release of eddy potential energy, generation of eddy kinetic energy, and exchange between eddy and zonal kinetic energy are investigated for a blocking anticyclone over the North Atlantic Ocean and an extratropical cyclone that developed during January 17-21, 1979. The results indicate that KE was maintained by baroclinic conversion of potential to kinetic. As released potential energy was being used to generate KE, a portion of the KE was barotropically converted to zonal KE. These transformations were dominated by the synoptic-scale component. While changes in the mass field depended not only on the synoptic scale but also on the interactions between the synoptic and planetary scales, the corresponding changes in the eddy motion fields responded largely to synoptic-scale processes.

Neuroimaging classification of progression patterns in glioblastoma: a systematic review.

PubMed

Piper, Rory J; Senthil, Keerthi K; Yan, Jiun-Lin; Price, Stephen J

2018-03-30

Our primary objective was to report the current neuroimaging classification systems of spatial patterns of progression in glioblastoma. In addition, we aimed to report the terminology used to describe 'progression' and to assess the compliance with the Response Assessment in Neuro-Oncology (RANO) Criteria. We conducted a systematic review to identify all neuroimaging studies of glioblastoma that have employed a categorical classification system of spatial progression patterns. Our review was registered with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) registry. From the included 157 results, we identified 129 studies that used labels of spatial progression patterns that were not based on radiation volumes (Group 1) and 50 studies that used labels that were based on radiation volumes (Group 2). In Group 1, we found 113 individual labels and the most frequent were: local/localised (58%), distant/distal (51%), diffuse (20%), multifocal (15%) and subependymal/subventricular zone (15%). We identified 13 different labels used to refer to 'progression', of which the most frequent were 'recurrence' (99%) and 'progression' (92%). We identified that 37% (n = 33/90) of the studies published following the release of the RANO classification were adherent compliant with the RANO criteria. Our review reports significant heterogeneity in the published systems used to classify glioblastoma spatial progression patterns. Standardization of terminology and classification systems used in studying progression would increase the efficiency of our research in our attempts to more successfully treat glioblastoma.

On the nature of rainfall in dry climate: Space-time patterns of convective rain cells over the Dead Sea region and their relations with synoptic state and flash flood generation

NASA Astrophysics Data System (ADS)

Belachsen, Idit; Marra, Francesco; Peleg, Nadav; Morin, Efrat

2017-04-01

Space-time patterns of rainfall are important climatic characteristics that influence runoff generation and flash flood magnitude. Their derivation requires high-resolution measurements to adequately represent the rainfall distribution, and is best provided by remote sensing tools. This need is further emphasized in dry climate regions, where rainfall is scarce and, often, local and highly variable. Our research is focused on understanding the nature of rainfall events in the dry Dead Sea region (Eastern Mediterranean) by identifying and characterizing the spatial structure and the dynamics of convective storm cores (known as rain cells). To do so, we take advantage of 25 years of corrected and gauge-adjusted weather radar data. A statistical analysis of convective rain-cells spatial and temporal characteristics was performed with respect to synoptic pattern, geographical location, and flash flood generation. Rain cells were extracted from radar data using a cell segmentation method and a tracking algorithm and were divided into rain events. A total of 10,500 rain cells, 2650 cell tracks and 424 rain events were elicited. Rain cell properties, such as mean areal and maximal rain intensity, area, life span, direction and speed, were derived. Rain events were clustered, according to several ERA-Interim atmospheric parameters, and associated with three main synoptic patterns: Cyprus Low, Low to the East of the study region and Active Red Sea Trough. The first two originate from the Mediterranean Sea, while the third is an extension of the African monsoon. On average, the convective rain cells in the region are 90 km2 in size, moving from West to East in 13 ms-1 and living 18 minutes. Several significant differences between rain cells of the various synoptic types were observed. In particular, Active Red Sea Trough rain cells are characterized by higher rain intensities and lower speeds, suggesting a higher flooding potential for small catchments. The north-south negative gradient of mean annual rainfall in the study region was found to be negatively correlated with rain cells intensity and positively correlated with rain cells area. Additional analysis was done for convective rain cells over two nearby catchments located in the central part of the study region, by ascribing some of the rain events to observed flash-flood events. It was found that rain events associated with flash-floods have higher maximal rain cell intensity and lower minimal cell speed than rain events that did not lead to a flash-flood in the watersheds. This information contributes to our understanding of rain patterns over the dry area of the Dead Sea and their connection to flash-floods. The statistical distributions of rain cells properties can be used for high space-time resolution stochastic simulations of rain storms that can serve as an input to hydrological models.

Classification and spatial mapping of riparian habitat with applications toward management of streams impacted by nonpoint source pollution

NASA Astrophysics Data System (ADS)

Delong, Michael D.; Brusven, Merlyn A.

1991-07-01

Management of riparian habitats has been recognized for its importance in reducing instream effects of agricultural nonpoint source pollution. By serving as a buffer, well structured riparian habitats can reduce nonpoint source impacts by filtering surface runoff from field to stream. A system has been developed where key characteristics of riparian habitat, vegetation type, height, width, riparian and shoreline bank slope, and land use are classified as discrete categorical units. This classification system recognizes seven riparian vegetation types, which are determined by dominant plant type. Riparian and shoreline bank slope, in addition to riparian width and height, each consist of five categories. Classification by discrete units allows for ready digitizing of information for production of spatial maps using a geographic information system (GIS). The classification system was tested for field efficiency on Tom Beall Creek watershed, an agriculturally impacted third-order stream in the Clearwater River drainage, Nez Perce County, Idaho, USA. The classification system was simple to use during field applications and provided a good inventory of riparian habitat. After successful field tests, spatial maps were produced for each component using the Professional Map Analysis Package (pMAP), a GIS program. With pMAP, a map describing general riparian habitat condition was produced by combining the maps of components of riparian habitat, and the condition map was integrated with a map of soil erosion potential in order to determine areas along the stream that are susceptible to nonpoint source pollution inputs. Integration of spatial maps of riparian classification and watershed characteristics has great potential as a tool for aiding in making management decisions for mitigating off-site impacts of agricultural nonpoint source pollution.

Ways of Viewing Pictorial Plasticity

PubMed Central

2017-01-01

The plastic effect is historically used to denote various forms of stereopsis. The vivid impression of depth often associated with binocular stereopsis can also be achieved in other ways, for example, using a synopter. Accounts of this go back over a hundred years. These ways of viewing all aim to diminish sensorial evidence that the picture is physically flat. Although various viewing modes have been proposed in the literature, their effects have never been compared. In the current study, we compared three viewing modes: monocular blur, synoptic viewing, and free viewing (using a placebo synopter). By designing a physical embodiment that was indistinguishable for the three experimental conditions, we kept observers naïve with respect to the differences between them; 197 observers participated in an experiment where the three viewing modes were compared by performing a rating task. Results indicate that synoptic viewing causes the largest plastic effect. Monocular blur scores lower than synoptic viewing but is still rated significantly higher than the baseline conditions. The results strengthen the idea that synoptic viewing is not due to a placebo effect. Furthermore, monocular blur has been verified for the first time as a way of experiencing the plastic effect, although the effect is smaller than synoptic viewing. We discuss the results with respect to the theoretical basis for the plastic effect. We show that current theories are not described with sufficient details to explain the differences we found. PMID:28491270

Development of a synoptic MRI report for primary rectal cancer.

PubMed

Spiegle, Gillian; Leon-Carlyle, Marisa; Schmocker, Selina; Fruitman, Mark; Milot, Laurent; Gagliardi, Anna R; Smith, Andy J; McLeod, Robin S; Kennedy, Erin D

2009-12-02

Although magnetic resonance imaging (MRI) is an important imaging modality for pre-operative staging and surgical planning of rectal cancer, to date there has been little investigation on the completeness and overall quality of MRI reports. This is important because optimal patient care depends on the quality of the MRI report and clear communication of these reports to treating physicians. Previous work has shown that the use of synoptic pathology reports improves the quality of pathology reports and communication between physicians. The aims of this project are to develop a synoptic MRI report for rectal cancer and determine the enablers and barriers toward the implementation of a synoptic MRI report for rectal cancer in the clinical setting. A three-step Delphi process with an expert panel will extract the key criteria for the MRI report to guide pre-operative chemoradiation and surgical planning following a review of the literature, and a synoptic template will be developed. Furthermore, standardized qualitative research methods will be used to conduct interviews with radiologists to determine the enablers and barriers to the implementation and sustainability of the synoptic MRI report in the clinic setting. Synoptic MRI reports for rectal cancer are currently not used in North America and may improve the overall quality of MRI report and communication between physicians. This may, in turn, lead to improved patient care and outcomes for rectal cancer patients.

Objectively classifying Southern Hemisphere extratropical cyclones

NASA Astrophysics Data System (ADS)

Catto, Jennifer

2016-04-01

There has been a long tradition in attempting to separate extratropical cyclones into different classes depending on their cloud signatures, airflows, synoptic precursors, or upper-level flow features. Depending on these features, the cyclones may have different impacts, for example in their precipitation intensity. It is important, therefore, to understand how the distribution of different cyclone classes may change in the future. Many of the previous classifications have been performed manually. In order to be able to evaluate climate models and understand how extratropical cyclones might change in the future, we need to be able to use an automated method to classify cyclones. Extratropical cyclones have been identified in the Southern Hemisphere from the ERA-Interim reanalysis dataset with a commonly used identification and tracking algorithm that employs 850 hPa relative vorticity. A clustering method applied to large-scale fields from ERA-Interim at the time of cyclone genesis (when the cyclone is first detected), has been used to objectively classify identified cyclones. The results are compared to the manual classification of Sinclair and Revell (2000) and the four objectively identified classes shown in this presentation are found to match well. The relative importance of diabatic heating in the clusters is investigated, as well as the differing precipitation characteristics. The success of the objective classification shows its utility in climate model evaluation and climate change studies.

Test of spectral/spatial classifier

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator); Kast, J. L.; Davis, B. J.

1977-01-01

The author has identified the following significant results. The supervised ECHO processor (which utilizes class statistics for object identification) successfully exploits the redundancy of states characteristic of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The nonsupervised ECHO processor (which identifies objects without the benefit of class statistics) successfully reduces the number of classifications required and the variability of the classification results.

Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan A.; Gloaguen, Richard

2017-09-01

Lithological mapping in mountainous regions is often impeded by limited accessibility due to relief. This study aims to evaluate (1) the performance of different supervised classification approaches using remote sensing data and (2) the use of additional information such as geomorphology. We exemplify the methodology in the Bardi-Zard area in NE Iraq, a part of the Zagros Fold - Thrust Belt, known for its chromite deposits. We highlighted the improvement of remote sensing geological classification by integrating geomorphic features and spatial information in the classification scheme. We performed a Maximum Likelihood (ML) classification method besides two Machine Learning Algorithms (MLA): Support Vector Machine (SVM) and Random Forest (RF) to allow the joint use of geomorphic features, Band Ratio (BR), Principal Component Analysis (PCA), spatial information (spatial coordinates) and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The RF algorithm showed reliable results and discriminated serpentinite, talus and terrace deposits, red argillites with conglomerates and limestone, limy conglomerates and limestone conglomerates, tuffites interbedded with basic lavas, limestone and Metamorphosed limestone and reddish green shales. The best overall accuracy (∼80%) was achieved by Random Forest (RF) algorithms in the majority of the sixteen tested combination datasets.

Where can pixel counting area estimates meet user-defined accuracy requirements?

NASA Astrophysics Data System (ADS)

Waldner, François; Defourny, Pierre

2017-08-01

Pixel counting is probably the most popular way to estimate class areas from satellite-derived maps. It involves determining the number of pixels allocated to a specific thematic class and multiplying it by the pixel area. In the presence of asymmetric classification errors, the pixel counting estimator is biased. The overarching objective of this article is to define the applicability conditions of pixel counting so that the estimates are below a user-defined accuracy target. By reasoning in terms of landscape fragmentation and spatial resolution, the proposed framework decouples the resolution bias and the classifier bias from the overall classification bias. The consequence is that prior to any classification, part of the tolerated bias is already committed due to the choice of the spatial resolution of the imagery. How much classification bias is affordable depends on the joint interaction of spatial resolution and fragmentation. The method was implemented over South Africa for cropland mapping, demonstrating its operational applicability. Particular attention was paid to modeling a realistic sensor's spatial response by explicitly accounting for the effect of its point spread function. The diagnostic capabilities offered by this framework have multiple potential domains of application such as guiding users in their choice of imagery and providing guidelines for space agencies to elaborate the design specifications of future instruments.

Spatial Patterns of NLCD Land Cover Change Thematic Accuracy (2001 - 2011)

EPA Science Inventory

Research on spatial non-stationarity of land cover classification accuracy has been ongoing for over two decades. We extend the understanding of thematic map accuracy spatial patterns by: 1) quantifying spatial patterns of map-reference agreement for class-specific land cover c...

Forecast skill of synoptic conditions associated with Santa Ana winds in Southern California

Treesearch

Charles Jones; Francis Fujioka; Leila M.V. Carvalho

2010-01-01

Santa Ana winds (SAW) are synoptically driven mesoscale winds observed in Southern California usually during late fall and winter. Because of the complex topography of the region, SAW episodes can sometimes be extremely intense and pose significant environmental hazards, especially during wildfire incidents. A simple set of criteria was used to identify synoptic-scale...

A Model Based Analysis of the Role of an Upper-Level Front and Stratospheric Intrusion in the Mack Lake Fire

Treesearch

Tarisa K. Zimet; Jonathan E. Martin

2003-01-01

Meteorological assessment of wildfire risk has traditionally involved identification of several synoptic types empirically determined to influence wildfire spread. Such weather types are characterized by identifiable synoptic-scale structures and processes. Schroeder et. al. (1964) identified four recognizable synoptic-scale patterns that contribute most frequently to...

Photometric classification and redshift estimation of LSST Supernovae

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Mi; Kuhlmann, Steve; Wang, Yun

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of an SN classifier that uses SN colours to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an area-under-the-curve of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99 percent SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z)more » of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias (⟨zphot - zspec⟩) of 0.012 with σ(z phot -z spec 1+z spec )=0.0294 σ(zphot-zspec1+zspec)=0.0294 without using a host-galaxy photo-z prior, and a mean bias (⟨zphot - zspec⟩) of 0.0017 with σ(z phot -z spec 1+z spec )=0.0116 σ(zphot-zspec1+zspec)=0.0116 using a host-galaxy photo-z prior. Assuming a flat ΛCDM model with Ωm = 0.3, we obtain Ωm of 0.305 ± 0.008 (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter σint = 0.11) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.« less

Evaluation of AMOEBA: a spectral-spatial classification method

USGS Publications Warehouse

Jenson, Susan K.; Loveland, Thomas R.; Bryant, J.

1982-01-01

Muitispectral remotely sensed images have been treated as arbitrary multivariate spectral data for purposes of clustering and classifying. However, the spatial properties of image data can also be exploited. AMOEBA is a clustering and classification method that is based on a spatially derived model for image data. In an evaluation test, Landsat data were classified with both AMOEBA and a widely used spectral classifier. The test showed that irrigated crop types can be classified as accurately with the AMOEBA method as with the generally used spectral method ISOCLS; the AMOEBA method, however, requires less computer time.

Local neighborhood transition probability estimation and its use in contextual classification

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

The problem of incorporating spatial or contextual information into classifications is considered. A simple model that describes the spatial dependencies between the neighboring pixels with a single parameter, Theta, is presented. Expressions are derived for updating the posteriori probabilities of the states of nature of the pattern under consideration using information from the neighboring patterns, both for spatially uniform context and for Markov dependencies in terms of Theta. Techniques for obtaining the optimal value of the parameter Theta as a maximum likelihood estimate from the local neighborhood of the pattern under consideration are developed.

Data Mining Algorithms for Classification of Complex Biomedical Data

ERIC Educational Resources Information Center

Lan, Liang

2012-01-01

In my dissertation, I will present my research which contributes to solve the following three open problems from biomedical informatics: (1) Multi-task approaches for microarray classification; (2) Multi-label classification of gene and protein prediction from multi-source biological data; (3) Spatial scan for movement data. In microarray…

Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan; Gloaguen, Richard

2015-04-01

Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

Evaluation of the synoptic and mesoscale predictive capabilities of a mesoscale atmospheric simulation system

NASA Technical Reports Server (NTRS)

Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K.; Keyser, D. A.; Mccumber, M. C.

1983-01-01

The overall performance characteristics of a limited area, hydrostatic, fine (52 km) mesh, primitive equation, numerical weather prediction model are determined in anticipation of satellite data assimilations with the model. The synoptic and mesoscale predictive capabilities of version 2.0 of this model, the Mesoscale Atmospheric Simulation System (MASS 2.0), were evaluated. The two part study is based on a sample of approximately thirty 12h and 24h forecasts of atmospheric flow patterns during spring and early summer. The synoptic scale evaluation results benchmark the performance of MASS 2.0 against that of an operational, synoptic scale weather prediction model, the Limited area Fine Mesh (LFM). The large sample allows for the calculation of statistically significant measures of forecast accuracy and the determination of systematic model errors. The synoptic scale benchmark is required before unsmoothed mesoscale forecast fields can be seriously considered.

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

Unbiased classification of spatial strategies in the Barnes maze.

PubMed

Illouz, Tomer; Madar, Ravit; Clague, Charlotte; Griffioen, Kathleen J; Louzoun, Yoram; Okun, Eitan

2016-11-01

Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze. Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis. Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application. eitan.okun@biu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images

NASA Astrophysics Data System (ADS)

Bhardwaj, Kaushal; Patra, Swarnajyoti

2018-04-01

Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.

Surface Currents and Winds at the Delaware Bay Mouth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muscarella, P A; Barton, N P; Lipphardt, B L

2011-04-06

Knowledge of the circulation of estuaries and adjacent shelf waters has relied on hydrographic measurements, moorings, and local wind observations usually removed from the region of interest. Although these observations are certainly sufficient to identify major characteristics, they lack both spatial resolution and temporal coverage. High resolution synoptic observations are required to identify important coastal processes at smaller scales. Long observation periods are needed to properly sample low-frequency processes that may also be important. The introduction of high-frequency (HF) radar measurements and regional wind models for coastal studies is changing this situation. Here we analyze synoptic, high-resolution surface winds andmore » currents in the Delaware Bay mouth over an eight-month period (October 2007 through May 2008). The surface currents were measured by two high-frequency radars while the surface winds were extracted from a data-assimilating regional wind model. To illustrate the utility of these monitoring tools we focus on two 45-day periods which previously were shown to present contrasting pictures of the circulation. One, the low-outflow period is from 1 October through 14 November 2007; the other is the high-outflow period from 3 March through 16 April 2008. The large-scale characteristics noted by previous workers are clearly corroborated. Specifically the M2 tide dominates the surface currents, and the Delaware Bay outflow plume is clearly evident in the low frequency currents. Several new aspects of the surface circulation were also identified. These include a map of the spatial variability of the M2 tide (validating an earlier model study), persistent low-frequency cross-mouth flow, and a rapid response of the surface currents to a changing wind field. However, strong wind episodes did not persist long enough to set up a sustained Ekman response.« less

Synoptic estimates of diffuse groundwater seepage to a spring-fed karst river at high spatial resolution using an automated radon measurement technique

NASA Astrophysics Data System (ADS)

Khadka, Mitra B.; Martin, Jonathan B.; Kurz, Marie J.

2017-01-01

Groundwater (GW) seepage can provide a major source of water, solutes, and contaminants to rivers, but identifying magnitudes, directions and locations of seepage is complicated by its diffuse and heterogeneous distributions. However, such information is necessary to develop programs and policies for protecting ecosystems and managing water resources. Here, we assess GW seepage to the Ichetucknee River, a spring-fed, low gradient, gaining stream in north-central Florida, through automated longitudinal surveys of radon (222Rn) activities at three different flow conditions. A 222Rn mass balance model, which integrates groundwater and spring water end member 222Rn activities and longitudinal 222Rn distributions in river water, shows that diffuse groundwater seepage represents about 16% of the total river baseflow, consistent with previous results obtained from ion (Ca2+, Cl-, SRP and Fe) mass balances and dye tracer methods. During high river stage, the contribution from seepage increases to 18-23% of the river flow. The spatial distribution of GW seepage is more variable in the upper 2.2-km reach of the river than the lower 2.8-km reach, regardless of river flow conditions. The upper reach has a narrower flood plain than the lower reach, which limits evapotranspiration and increases hydraulic gradients toward the river following storm events. Seepage in the lower reach is also limited by hydrologic damming by the receiving river, which inundates the floodplain during high flow conditions, and reduces the hydraulic head gradient. These results demonstrate the variable nature of seepage to a gaining river in both time and space and indicate that multiple synoptic analyses of GW seepage are required to assess seepage rates, determine time-averaged solute fluxes, and develop optimal management policies for riverine ecosystems.

Effect of climate change on wind waves generated by anticyclonic cold front intrusions in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Appendini, Christian M.; Hernández-Lasheras, Jaime; Meza-Padilla, Rafael; Kurczyn, Jorge A.

2018-01-01

Anticyclonic cold surges entering the Gulf of Mexico (Nortes) generate ocean waves that disrupt maritime activities. Norte derived waves are less energetic than the devastating waves from tropical cyclones, but more frequent ( 22 events/year) and with larger spatial influence. Despite their importance, few studies characterize Nortes derived waves and assess the effects of climate change on their occurrence. This study presents a method to identify and characterize Nortes with relation to their derived waves in the Gulf of Mexico. We based the identification of Nortes on synoptic measurements of pressure differences between Yucatan and Texas and wind speed at different buoy locations in the Gulf of Mexico. Subsequently, we identified the events in the CFSR reanalysis (present climate) and the CNRM-M5 model for the present climate and the RCP 8.5 scenario. We then forced a wave model to characterize the wave power generated by each event, followed by a principal component analysis and classification by k-means clustering analysis. Five different Nortes types were identified, each one representing a characteristic intensity and area of influence of the Norte driven waves. Finally, we estimated the occurrence of each Norte type for the present and future climates, where the CNRM-M5 results indicate that the high-intensity events will be less frequent in a warming climate, while mild events will become more frequent. The consequences of such changes may provide relief for maritime and coastal operations because of reduced downtimes. This result is particularly relevant for the operational design of coastal and marine facilities.

A Seasonal Air Transport Climatology for Kenya

NASA Technical Reports Server (NTRS)

Gatebe, C. K.; Tyson, P. D.; Annegarn, H.; Piketh, S.; Helas, G.

1998-01-01

A climatology of air transport to and from Kenya has been developed using kinematic trajectory modeling. Significant months for trajectory analysis have been determined from a classification of synoptic circulation fields. Five-point back and forward trajectory clusters to and from Kenya reveal that the transport corridors to Kenya are clearly bounded and well defined. Air reaching the country originates mainly from the Saharan region and northwestern Indian Ocean of the Arabian Sea in the northern hemisphere and from the Madagascan region of the Indian Ocean in the southern hemisphere. Transport from each of these source regions show distinctive annual cycles related to the northeasterly Asian monsoon and the southeasterly trade wind maximum over Kenya in May. The Saharan transport in the lower troposphere is at a maximum when the subtropical high over northern Africa is strongly developed in the boreal winter. Air reaching Kenya between 700 and 500 hPa is mainly from Sahara and northwest India Ocean flows in the months of January and March, which gives way to southwest Indian Ocean flow in May and November. In contrast, air reaching Kenya at 400 hPa is mainly from southwest Indian Ocean in January and March, which is replaced by Saharan transport in May and November. Transport of air from Kenya is invariant, both spatially and temporally, in the tropical easterlies to the Congo Basin and Atlantic Ocean in comparison to the transport to the country. Recirculation of air has also been observed, but on a limited and often local scale and not to the extent reported in southern Africa.

Assessing variability in the impacts of heat on health outcomes in New York City over time, season, and heat-wave duration.

PubMed

Sheridan, Scott C; Lin, Shao

2014-12-01

While the impacts of heat upon mortality and morbidity have been frequently studied, few studies have examined the relationship between heat, morbidity, and mortality across the same events. This research assesses the relationship between heat events and morbidity and mortality in New York City for the period 1991-2004. Heat events are defined based on oppressive weather types as determined by the Spatial Synoptic Classification. Morbidity data include hospitalizations for heat-related, respiratory, and cardiovascular causes; mortality data include these subsets as well as all-cause totals. Distributed-lag models assess the relationship between heat and health outcome for a cumulative 15-day period following exposure. To further refine analysis, subset analyses assess the differences between early- and late-season events, shorter and longer events, and earlier and later years. The strongest heat-health relationships occur with all-cause mortality, cardiovascular mortality, and heat-related hospital admissions. The impacts of heat are greater during longer heat events and during the middle of summer, when increased mortality is still statistically significant after accounting for mortality displacement. Early-season heat waves have increases in mortality that appear to be largely short-term displacement. The impacts of heat on mortality have decreased over time. Heat-related hospital admissions have increased during this time, especially during the earlier days of heat events. Given the trends observed, it suggests that a greater awareness of heat hazards may have led to increased short-term hospitalizations with a commensurate decrease in mortality.

Mesoscale Dynamical Regimes in the Midlatitudes

NASA Astrophysics Data System (ADS)

Craig, G. C.; Selz, T.

2018-01-01

The atmospheric mesoscales are characterized by a complex variety of meteorological phenomena that defy simple classification. Here a full space-time spectral analysis is carried out, based on a 7 day convection-permitting simulation of springtime midlatitude weather on a large domain. The kinetic energy is largest at synoptic scales, and on the mesoscale it is largely confined to an "advective band" where space and time scales are related by a constant of proportionality which corresponds to a velocity scale of about 10 m s-1. Computing the relative magnitude of different terms in the governing equations allows the identification of five dynamical regimes. These are tentatively identified as quasi-geostrophic flow, propagating gravity waves, stationary gravity waves related to orography, acoustic modes, and a weak temperature gradient regime, where vertical motions are forced by diabatic heating.

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

PubMed

Cho, Youngsang; Seong, Joon-Kyung; Jeong, Yong; Shin, Sung Yong

2012-02-01

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

Changes in the Mechanisms Causing Rapid Drought Cessation in the Southeastern United States

NASA Astrophysics Data System (ADS)

Maxwell, Justin T.; Knapp, Paul A.; Ortegren, Jason T.; Ficklin, Darren L.; Soulé, Peter T.

2017-12-01

The synoptic processes that end droughts are poorly understood, yet have significant climatological implications. Here we examined the spatiotemporal patterns of rapid drought cessation (RDC) in the southeastern United States during the1979-2013 warm season (April-November) for three storm types: Frontal, Tropical, and Air mass. We defined RDC as a 1 month shift in soil moisture sufficient to alleviate an existing drought. We found that 73% of all warm-season droughts were ended by RDC events and the three storm-type groups ended droughts over similar spatial areas. Frontal storms were the most frequent mechanism for RDC events, yet their occurrences significantly decreased and were negatively related to increases in Northern Hemisphere air temperatures. Projected future warming in the Northern Hemisphere suggests a continued decline in the frequency and relative contribution of Frontal storms as RDC events, potentially influencing the timing and spatial scale of drought cessation in the southeastern U.S.

Spatial variability of the Black Sea surface temperature from high resolution modeling and satellite measurements

NASA Astrophysics Data System (ADS)

Mizyuk, Artem; Senderov, Maxim; Korotaev, Gennady

2016-04-01

Large number of numerical ocean models were implemented for the Black Sea basin during last two decades. They reproduce rather similar structure of synoptical variability of the circulation. Since 00-s numerical studies of the mesoscale structure are carried out using high performance computing (HPC). With the growing capacity of computing resources it is now possible to reconstruct the Black Sea currents with spatial resolution of several hundreds meters. However, how realistic these results can be? In the proposed study an attempt is made to understand which spatial scales are reproduced by ocean model in the Black Sea. Simulations are made using parallel version of NEMO (Nucleus for European Modelling of the Ocean). A two regional configurations with spatial resolutions 5 km and 2.5 km are described. Comparison of the SST from simulations with two spatial resolutions shows rather qualitative difference of the spatial structures. Results of high resolution simulation are compared also with satellite observations and observation-based products from Copernicus using spatial correlation and spectral analysis. Spatial scales of correlations functions for simulated and observed SST are rather close and differs much from satellite SST reanalysis. Evolution of spectral density for modelled SST and reanalysis showed agreed time periods of small scales intensification. Using of the spectral analysis for satellite measurements is complicated due to gaps. The research leading to this results has received funding from Russian Science Foundation (project № 15-17-20020)

Modeling of Subsurface Lagrangian Sensor Swarms for Spatially Distributed Current Measurements in High Energy Coastal Environments

NASA Astrophysics Data System (ADS)

Harrison, T. W.; Polagye, B. L.

2016-02-01

Coastal ecosystems are characterized by spatially and temporally varying hydrodynamics. In marine renewable energy applications, these variations strongly influence project economics and in oceanographic studies, they impact accuracy of biological transport and pollutant dispersion models. While stationary point or profile measurements are relatively straight forward, spatial representativeness of point measurements can be poor due to strong gradients. Moving platforms, such as AUVs or surface vessels, offer better coverage, but suffer from energetic constraints (AUVs) and resolvable scales (vessels). A system of sub-surface, drifting sensor packages is being developed to provide spatially distributed, synoptic data sets of coastal hydrodynamics with meter-scale resolution over a regional extent of a kilometer. Computational investigation has informed system parameters such as drifter size and shape, necessary position accuracy, number of drifters, and deployment methods. A hydrodynamic domain with complex flow features was created using a computational fluid dynamics code. A simple model of drifter dynamics propagate the drifters through the domain in post-processing. System parameters are evaluated relative to their ability to accurately recreate domain hydrodynamics. Implications of these results for an inexpensive, depth-controlled Lagrangian drifter system is presented.

Methods for Monitoring the Detection of Multi-Temporal Land Use Change Through the Classification of Urban Areas

NASA Astrophysics Data System (ADS)

Alhaddad, B. I.; Burns, M. C.; Roca, J.

2011-08-01

Urban areas are complicated due to the mix of man-made features and natural features. A higher level of structural information plays an important role in land cover/use classification of urban areas. Additional spatial indicators have to be extracted based on structural analysis in order to understand and identify spatial patterns or the spatial organization of features, especially for man-made feature. It's very difficult to extract such spatial patterns by using only classification approaches. Clusters of urban patterns which are integral parts of other uses may be difficult to identify. A lot of public resources have been directed towards seeking to develop a standardized classification system and to provide as much compatibility as possible to ensure the widespread use of such categorized data obtained from remote sensor sources. In this paper different methods applied to understand the change in the land use areas by understanding and monitoring the change in urban areas and as its hard to apply those methods to classification results for high elements quantities, dusts and scratches (Roca and Alhaddad, 2005). This paper focuses on a methodology developed based relation between urban elements and how to join this elements in zones or clusters have commune behaviours such as form, pattern, size. The main objective is to convert urban class category in to various structure densities depend on conjunction of pixel and shortest distance between them, Delaunay triangulation has been widely used in spatial analysis and spatial modelling. To identify these different zones, a spatial density-based clustering technique was adopted. In highly urban zones, the spatial density of the pixels is high, while in sparsely areas the density of points is much lower. Once the groups of pixels are identified, the calculation of the boundaries of the areas containing each group of pixels defines the new regions indicate the different contains inside such as high or low urban areas. Multi-temporal datasets from 1986, 1995 and 2004 used to urban region centroid to be our reference in this study which allow us to follow the urban movement, increase and decrease by the time. Kernel Density function used to Calculates urban magnitude, Voronoi algorithm is proposed for deriving explicit boundaries between objects units. To test the approach, we selected a site in a suburban area in Barcelona Municipality, the Spain.

Synoptic variability of extreme snowfall in the St. Elias Mountains, Yukon, Canada

NASA Astrophysics Data System (ADS)

Andin, Caroline; Zdanowicz, Christian; Copland, Luke

2015-04-01

Glaciers in the Wrangell and St. Elias Mountains (Alaska and Yukon) are presently experiencing some of the highest regional wastage rates worldwide. While the effect of regional temperatures on glacier melt rates in this region has been investigated, comparatively little is known about how synoptic climate variations, for example in the position and strength of the Aleutian Low, modulate snow accumulation on these glaciers. Such information is needed to accurately forecast future wastage rates, glacier-water resource availability, and contributions to sea-level rise. Starting in 2000, automated weather stations (AWS) were established in the central St-Elias Mountains (Yukon) at altitudes ranging from 1190 to 5400 m asl, to collect climatological data in support of glaciological research. These data are the longest continuous year-round observations of surface climate ever obtained from this vast glaciated region. Here we present an analysis of snowfall events in the icefields of the St-Elias Mountains based on a decade-long series of AWS observations of snow accumulation. Specifically, we investigated the synoptic patterns and air mass trajectories associated with the largest snowfall events (> 25 cm/12 hours) that occurred between 2002 and 2012. Nearly 80% of these events occurred during the cold season (October-March), and in 74 % of cases the precipitating air masses originated from the North Pacific south of 50°N. Zonal air mass advection over Alaska, or from the Bering Sea or the Arctic Ocean, was comparatively rare (20%). Somewhat counter-intuitively, dominant surface winds in the St. Elias Mountains during high snowfall events were predominantly easterly, probably due to boundary-layer frictional drag and topographic funneling effects. Composite maps of sea-level pressure and 700 mb winds reveal that intense snowfall events between 2002 and 2012 were associated with synoptic situations characterized by a split, eastwardly-shifted or longitudinally-stretched Aleutian Low (AL) having an easternmost node near the Kenai Peninsula, conditions that drove a strong southwesterly upper airstream across the Gulf of Alaska towards the coast. Situations with a single-node, westerly-shifted AL were comparatively rare. The spatial configuration of the synoptic AL pressure pattern appears to play a greater role in determining snowfall amount in the central St. Elias Mountains than do pressure anomalies within the AL. The estimated snowfall gradient from coastal Alaska to the central St. Elias Mountains during intense snowfall events averaged +2.0 ± 0.7 mm/km (SWE), while the continental-side gradient from the mountains towards the Yukon plateau averaged -3.3 ± 0.9 mm/km (SWE). The findings presented here can better constrain the climatic interpretation of long proxy records of snow accumulation variations developed from glacier cores drilled in the St. Elias Mountains or nearby regions.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

PubMed

Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo

2017-01-01

Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR. An advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.

Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data.

PubMed

Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R

2015-09-11

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite's Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds.

Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data

PubMed Central

Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R.

2015-01-01

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite’s Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds. PMID:26378538

Sunspot Pattern Classification using PCA and Neural Networks (Poster)

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Thompson, D. E.; Slater, G. L.

2005-01-01

The sunspot classification scheme presented in this paper is considered as a 2-D classification problem on archived datasets, and is not a real-time system. As a first step, it mirrors the Zuerich/McIntosh historical classification system and reproduces classification of sunspot patterns based on preprocessing and neural net training datasets. Ultimately, the project intends to move from more rudimentary schemes, to develop spatial-temporal-spectral classes derived by correlating spatial and temporal variations in various wavelengths to the brightness fluctuation spectrum of the sun in those wavelengths. Once the approach is generalized, then the focus will naturally move from a 2-D to an n-D classification, where "n" includes time and frequency. Here, the 2-D perspective refers both to the actual SOH0 Michelson Doppler Imager (MDI) images that are processed, but also refers to the fact that a 2-D matrix is created from each image during preprocessing. The 2-D matrix is the result of running Principal Component Analysis (PCA) over the selected dataset images, and the resulting matrices and their eigenvalues are the objects that are stored in a database, classified, and compared. These matrices are indexed according to the standard McIntosh classification scheme.

The Potential Observation Network Design with Mesoscale Ensemble Sensitivities in Complex Terrain

DTIC Science & Technology

2012-03-01

in synoptic storms , extratropical transition and developing hurricanes. Because they rely on lagged covariances from a finite-sized ensemble, they...diagnose predictors of forecast error in synoptic storms , extratropical transition and developing hurricanes. Because they rely on lagged covariances...sensitivities can be used successfully to diagnose predictors of forecast error in synoptic storms (Torn and Hakim 2008), extratropical transition (Torn and

Synoptic-scale and mesoscale environments conducive to forest fires during the October 2003 extreme fire event in Southern California

Treesearch

Chenjie Huang; Y.L. Lin; M.L. Kaplan; Joseph J.J. Charney

2009-01-01

This study has employed both observational data and numerical simulation results to diagnose the synoptic-scale and mesoscale environments conducive to forest fires during the October 2003 extreme fire event in southern California. A three-stage process is proposed to illustrate the coupling of the synoptic-scale forcing that is evident from the observations,...

Light extinction by fine atmospheric particles in the White Mountains region of New Hampshire and its relationship to air mass transport.

PubMed

Slater, John F; Dibb, Jack E; Keim, Barry D; Talbot, Robert W

2002-03-27

Chemical, optical, and physical measurements of fine aerosols (aerodynamic diameter < or = 2.5 microm) have been performed at a mountaintop location adjacent to the White Mountain National Forest in northern NH, USA. A 1-month long sampling campaign was conducted at Cranmore Mountain during spring 2000. We report on the apportionment of light extinction by fine aerosols into its major chemical components, and relationships between variations in aerosol parameters and changes in air mass origin. Filter-based, 24-h integrated samples were collected and analyzed for major inorganic ions, as well as organic (OC), elemental (EC), and total carbon. Light scattering and light absorption coefficients were measured at 5-min intervals using an integrating nephelometer and a light absorption photometer. Fine particle number density was measured with a condensation particle counter. Air mass origins and transport patterns were investigated through the use of 3-day backward trajectories and a synoptic climate classification system. Two distinct transport regimes were observed: (1) flow from the north/northeast (N/NE) occurred during 9 out of 18 sample-days; and (2) flow from the west/southwest (W/SW) occurred 8 out of 18 sample-days. All measured and derived aerosol and meteorological parameters were separated into two categories based on these different flow scenarios. During W/SW flow, higher values of aerosol chemical concentration, absorption and scattering coefficients, number density, and haziness were observed compared to N/NE flow. The highest level of haziness was associated with the climate classification Frontal Atlantic Return, which brought polluted air into the region from the mid-Atlantic corridor. Fine particle mass scattering efficiencies of (NH4)2SO4 and OC were 5.35 +/- 0.42 m2 g(-1) and 1.56 +/- 0.40 m2 g(-1), respectively, when transport was out of the N/NE. When transport was from the W/SW the values were 4.94 +/- 0.68 m2 g(-1) for (NH4)2SO4 and 2.18 +/- 0.91 m2 g(-1) for OC. EC mass absorption efficiency when transport was from the N/NE was 9.66 +/- 1.06 m2 g(-1) and 10.80 +/- 1.76 m2 g(-1) when transport was from the W/SW. Results from this work can be used to predict visual air quality in the White Mountain National Forest based on a forecasted synoptic climate classification and its associated visibility.

Cloud fraction at the ARM SGP site: reducing uncertainty with self-organizing maps

NASA Astrophysics Data System (ADS)

Kennedy, Aaron D.; Dong, Xiquan; Xi, Baike

2016-04-01

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997-2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91-105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. This result may be due to a manifestation of seasonally dependent biases in NARR. With use of the SOMs, the average uncertainty in monthly CF is reduced in half from the values calculated in Kennedy et al. (Theor Appl Climatol 115:91-105, 2014).

Comparing Features for Classification of MEG Responses to Motor Imagery.

PubMed

Halme, Hanna-Leena; Parkkonen, Lauri

2016-01-01

Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Deep convection over Northern Italy: synoptic and thermodynamic analysis

NASA Astrophysics Data System (ADS)

Costa, S.; Mezzasalma, P.; Levizzani, V.; Alberoni, P. P.; Nanni, S.

Synoptic and thermodynamic characteristics of severe storm outbreaks, including supercells, over northern Italy's Po valley are examined over a 3-year period. Storms are divided into three main categories according to the most relevant associated ground phenomenon: tornado-like, hailfall and heavy rain. For each category, the most common synoptic characteristics are investigated. Sounding data are used to calculate stability indices that help define the storm's environment. Results indicate that the interaction between the synoptic flow and the steep Alpine orography is the key factor responsible for building up the mesoscale circulation that leads to different kinds of severe storms. Some of the stability indices can be regarded as predictors of intense convection.

High resolution remote sensing of densely urbanised regions: a case study of Hong Kong.

PubMed

Nichol, Janet E; Wong, Man Sing

2009-01-01

Data on the urban environment such as climate or air quality is usually collected at a few point monitoring stations distributed over a city. However, the synoptic viewpoint of satellites where a whole city is visible on a single image permits the collection of spatially comprehensive data at city-wide scale. In spite of rapid developments in remote sensing systems, deficiencies in image resolution and algorithm development still exist for applications such as air quality monitoring and urban heat island analysis. This paper describes state-of-the-art techniques for enhancing and maximising the spatial detail available from satellite images, and demonstrates their applications to the densely urbanised environment of Hong Kong. An Emissivity Modulation technique for spatial enhancement of thermal satellite images permits modelling of urban microclimate in combination with other urban structural parameters at local scale. For air quality monitoring, a Minimum Reflectance Technique (MRT) has been developed for MODIS 500 m images. The techniques described can promote the routine utilization of remotely sensed images for environmental monitoring in cities of the 21(st) century.

Tidal and atmospheric forcing of the upper ocean in the Gulf of California. I - Sea surface temperature variability

NASA Technical Reports Server (NTRS)

Paden, Cynthia A.; Winant, Clinton D.; Abbott, Mark R.

1991-01-01

SST variability in the northern Gulf of California is examined on the basis of findings of two years of satellite infrared imagery (1984-1986). Empirical orthogonal functions of the temporal and spatial SST variance for 20 monthly mean images show that the dominant SST patterns are generated by spatially varying tidal mixing in the presence of seasonal heating and cooling. Atmospheric forcing of the northern gulf appears to occur over large spatial scales. Area-averaged SSTs for the Guaymas Basin, island region, and northern basin exhibit significant fluctuations which are highly correlated. These fluctuations in SST correspond to similar fluctuations in the air temperature which are related to synoptic weather events over the gulf. A regression analysis of the SST relative to the fortnightly tidal range shows that tidal mixing occurs over the sills in the island region as well as on the shallow northern shelf. Mixing over the sills occurs as a result of large breaking internal waves of internal hydraulic jumps which mix over water in the upper 300-500 m.

The Use of Convolutional Neural Network in Relating Precipitation to Circulation

NASA Astrophysics Data System (ADS)

Pan, B.; Hsu, K. L.; AghaKouchak, A.; Sorooshian, S.

2017-12-01

Precipitation prediction in dynamical weather and climate models depends on 1) the predictability of pressure or geopotential height for the forecasting period and 2) the successive work of interpreting the pressure field in terms of precipitation events. The later task is represented as parameterization schemes in numerical models, where detailed computing inevitably blurs the hidden cause-and-effect relationship in precipitation generation. The "big data" provided by numerical simulation, reanalysis and observation networks requires better causation analysis for people to digest and realize their use. While classic synoptical analysis methods are very-often insufficient for spatially distributed high dimensional data, a Convolutional Neural Network(CNN) is developed here to directly relate precipitation with circulation. Case study carried over west coast United States during boreal winter showed that CNN can locate and capture key pressure zones of different structures to project precipitation spatial distribution with high accuracy across hourly to monthly scales. This direct connection between atmospheric circulation and precipitation offers a probe for attributing precipitation to the coverage, location, intensity and spatial structure of characteristic pressure zones, which can be used for model diagnosis and improvement.

High Resolution Remote Sensing of Densely Urbanised Regions: a Case Study of Hong Kong

PubMed Central

Nichol, Janet E.; Wong, Man Sing

2009-01-01

Data on the urban environment such as climate or air quality is usually collected at a few point monitoring stations distributed over a city. However, the synoptic viewpoint of satellites where a whole city is visible on a single image permits the collection of spatially comprehensive data at city-wide scale. In spite of rapid developments in remote sensing systems, deficiencies in image resolution and algorithm development still exist for applications such as air quality monitoring and urban heat island analysis. This paper describes state-of-the-art techniques for enhancing and maximising the spatial detail available from satellite images, and demonstrates their applications to the densely urbanised environment of Hong Kong. An Emissivity Modulation technique for spatial enhancement of thermal satellite images permits modelling of urban microclimate in combination with other urban structural parameters at local scale. For air quality monitoring, a Minimum Reflectance Technique (MRT) has been developed for MODIS 500 m images. The techniques described can promote the routine utilization of remotely sensed images for environmental monitoring in cities of the 21st century. PMID:22408549

Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013

USGS Publications Warehouse

Hartley, Stephen B.; Couvillion, Brady R.; Enwright, Nicholas M.

2017-05-30

The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.

An interdisciplinary study of the estuarine and coastal oceanography of Block Island Sound and adjacent New York coastal waters

NASA Technical Reports Server (NTRS)

Yost, E. (Principal Investigator)

1972-01-01

The author has identified the following significant results. The synoptic repetitive coverage of the multispectral imagery from the ERTS-1 satellite, when photographically reprocessed using the state-of-the-art techniques, has given indication of spectral differences in Block Island and adjacent New England waters which were heretofore unknown. Of particular interest was the possible detection of relatively small amounts of phytoplankton prior to the occurrence of the red tide in Massachusetts waters. Preparation of spatial and temporal hydrographic charts using ERTS-1 imagery and ground truth analysis will hopefully determine the environmental impact on New York coastal waters.

Integrating Windblown Dust Forecasts with Public Safety and Health Systems

NASA Astrophysics Data System (ADS)

Sprigg, W. A.

2014-12-01

Experiments in real-time prediction of desert dust emissions and downstream plume concentrations (~ 3.5 km near-surface spatial resolution) succeed to the point of challenging public safety and public health services to beta test a dust storm warning and advisory system in lowering risks of highway and airline accidents and illnesses such as asthma and valley fever. Key beta test components are: high-resolution models of dust emission, entrainment and diffusion, integrated with synoptic weather observations and forecasts; satellite-based detection and monitoring of soil properties on the ground and elevated above; high space and time resolution for health surveillance and transportation advisories.

A Multi-modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling.

PubMed

Asif, Umar; Bennamoun, Mohammed; Sohel, Ferdous

2017-08-30

While deep convolutional neural networks have shown a remarkable success in image classification, the problems of inter-class similarities, intra-class variances, the effective combination of multimodal data, and the spatial variability in images of objects remain to be major challenges. To address these problems, this paper proposes a novel framework to learn a discriminative and spatially invariant classification model for object and indoor scene recognition using multimodal RGB-D imagery. This is achieved through three postulates: 1) spatial invariance - this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations, rotations, and scale changes, 2) high discriminative capability - this is achieved by introducing Fisher encoding within the CNN architecture to learn features which have small inter-class similarities and large intra-class compactness, and 3) multimodal hierarchical fusion - this is achieved through the regularization of semantic segmentation to a multi-modal CNN architecture, where class probabilities are estimated at different hierarchical levels (i.e., imageand pixel-levels), and fused into a Conditional Random Field (CRF)- based inference hypothesis, the optimization of which produces consistent class labels in RGB-D images. Extensive experimental evaluations on RGB-D object and scene datasets, and live video streams (acquired from Kinect) show that our framework produces superior object and scene classification results compared to the state-of-the-art methods.

Synoptic Sun during the first Whole Sun Month Campaign: August 10 to September 8, 1996

NASA Astrophysics Data System (ADS)

Biesecker, D. A.; Thompson, B. J.; Gibson, S. E.; Alexander, D.; Fludra, A.; Gopalswamy, N.; Hoeksema, J. T.; Lecinski, A.; Strachan, L.

1999-05-01

A large number of synoptic maps from a variety of instruments are used to show the general morphology of the Sun at the time of the First Whole Sun Month Campaign. The campaign was conducted from August 10 to September 8, 1996. The synoptic maps cover the period from Carrington rotation 1912/253° to Carrington rotation 1913/45°. The synoptic maps encompass both on-disk data and limb data from several heights in the solar atmosphere. The maps are used to illustrate which wavelengths and data sets show particular features, such as active regions and coronal holes. Of particular interest is the equatorial coronal hole known as the ``elephant's trunk,'' which is clearly evident in the synoptic maps of on-disk data. The elephant's trunk is similar in appearance to the Skylab-era, ``Boot of Italy,'' equatorial coronal hole. The general appearance of the limb maps is explained as well. The limb maps also show evidence for equatorial coronal holes.

Synoptic maps for the heliospheric Thomson scattering brightness as observed by the Helios photometers

NASA Technical Reports Server (NTRS)

Hick, P.; Jackson, B. V.; Schwenn, R.

1991-01-01

A method for displaying the electron Thomson scattering intensity in the inner heliosphere as observed by the zodiacal light photometers on board the Helios spacecraft in the form of synoptic maps is presented. The method is based on the assumption that the bulk of the scattering electrons along the line of sight is located near the point closest to the sun. Inner-heliospheric structures will generally be represented properly in these synoptic maps only if they are sufficiently long-lived (that is, a significant fraction of a solar rotation period). The examples of Helios synoptic maps discussed (from data in April 1976 and November 1978), indicate that it is possible to identify large-scale, long-lived density enhancements in the inner heliosphere. It is expected that the Helios synoptic maps will be particularly useful in the study of corotating structures (e.g., streamers), and the maps will be most reliable during periods when few transient featurs are present in the corona, i.e., during solar minimum.

Blob-level active-passive data fusion for Benthic classification

NASA Astrophysics Data System (ADS)

Park, Joong Yong; Kalluri, Hemanth; Mathur, Abhinav; Ramnath, Vinod; Kim, Minsu; Aitken, Jennifer; Tuell, Grady

2012-06-01

We extend the data fusion pixel level to the more semantically meaningful blob level, using the mean-shift algorithm to form labeled blobs having high similarity in the feature domain, and connectivity in the spatial domain. We have also developed Bhattacharyya Distance (BD) and rule-based classifiers, and have implemented these higher-level data fusion algorithms into the CZMIL Data Processing System. Applying these new algorithms to recent SHOALS and CASI data at Plymouth Harbor, Massachusetts, we achieved improved benthic classification accuracies over those produced with either single sensor, or pixel-level fusion strategies. These results appear to validate the hypothesis that classification accuracy may be generally improved by adopting higher spatial and semantic levels of fusion.

Complex land use and cover trajectories in the northern Choco bioregion of Colombia

NASA Astrophysics Data System (ADS)

Santos, Carolina

The Choco bioregion in Northwestern Colombia is a lowland rain forest and hotspot of biodiversity. Significant land use and cover change (LUCC) is occurring throughout the region driven by global markets, illicit drug production, and civil unrest. The dominant land cover conversion is from primary forest to African Palm plantations, mediated and modified by complex combinations of social and biophysical drivers. This research combined a remote sensing based methodology to monitor LUCC in the region with an analytical approach for evaluating the possible trajectories of LUCC in a complex biological, socio-economical, and political environment. Synoptic LUCC models were developed using textural classification derived from Synthetic Aperture Radar (SAR) images for the period 1995 to 2010. LUCC models along with empirical social and spatial biophysical drivers were used to project historical land use trajectories. DINAMICA EGO a complex systems based spatial analytical framework was adopted as the platform to model land use change. The RADAR backscatter was able to capture areas were forest has been converted to African Oil Palm Plantations. However, an in depth characterization of the LUC dynamics was problematic given the spectral and spatial limitations of the sensor combined with the lack of ground data. The results of the LUC model suggest that under the current socio-political conditions African oil palm plantations will continue to expand toward forested areas into the territories traditionally inhabited by Afro-Colombians and Indigenous populations. Insecure land tenure appears as a main driver of the transformation in close association with the conditions created by the armed conflict, and the drug traffic. The rate of the transformation appears to slow down in the period after 2007. However, according to the model by 2020 most of the area inhabited by ethnic groups will be transform to AOP. This study contributes towards the understanding of land use change in the context of social conflict. Although, it is recognized that conflicts impact the land--use and land-cover, few studies have address the linkages between particular circumstances and events in the conflict and the consequences for the land. As resource conflicts spread around the globe a better understanding of how it impacts the land and the people is paramount.

Determination of instream metal loads using tracer-injection and synoptic-sampling techniques, Wightman Fork, southwestern Colorado, July 1999

USGS Publications Warehouse

Ortiz, Roderick F.

2001-01-01

In July 1999, a tracer-injection study was conducted concurrently with synoptic sampling to generate mass-load profiles in Wightman Fork near the Summitville Mine site. The mine site is located in the San Juan Mountains of southwestern Colorado at an elevation of about 3,500 meters above sea level. Metal loads increased substantially along the 2,815-meter study reach along the boundary of the mine site. Spatial determinations of dissolved aluminum, copper, iron, manganese, and zinc loads were used to identify potential source areas to the stream. Overall, four source areas appeared to contribute most of the specific load at the end of the study reach. One source area was along a 60-meter reach downgradient from the toe of the North Waste Dump that generally corresponded to a region of radial faults. Another source area was a short reach that included inputs from the Summitville Water Treatment Facility and the Pump House Fault. In July 1999, seepage from the Summitville Dam Impoundment was a substantial contributor of metal load at the end of the study reach. Finally, the metal load contributed along a 60-meter reach that included Cropsy Creek is considered a substantial source of metal load to Wightman Fork.

A comparative study of measurements from radiosondes, rocketsondes, and satellites

NASA Technical Reports Server (NTRS)

Nestler, M. S.

1983-01-01

Direct comparisons of operational products derived from measurements of radiance by satellites to measurements from conventional in situ sensors are important for the evaluation of satellite systems. However, errors in the in situ measurements themselves complicate such comparisons. Atmospheric temporal and spatial variability are also influential. These issues are investigated by means of a special field program composed of flights of dual radiosondes and multiple radiosondes launched near the time of NOAA-6 overpasses. Satellite derived mean layer temperatures, geopotential heights, and winds are compared with the same quantities determined from the in situ sensors. Of particular interest is the impact of in situ errors on these comparisons. It is shown that the radiosonde provides a precise pressure height relationship and therefore precise data for synoptic type use. Radar tracking of the radiosondes reveals, however, an imprecise pressure measurement which causes large differences between the actual altitude of the radiosonde and the altitude at which it is calculated to be. Radiosondes should be radar tracked and pressures calculated if the data are to be used for purposes other than synoptic use. Evaluation of rocketsonde data reveals a temperature precision of 1 to 2 K below about 55 km. Above 55 km, the precision decreases rapidly; rms differences of up to 11 K are obtained.

Network analysis reveals multiscale controls on streamwater chemistry

USGS Publications Warehouse

McGuire, Kevin J.; Torgersen, Christian E.; Likens, Gene E.; Buso, Donald C.; Lowe, Winsor H.; Bailey, Scott W.

2014-01-01

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.

Network analysis reveals multiscale controls on streamwater chemistry

PubMed Central

McGuire, Kevin J.; Torgersen, Christian E.; Likens, Gene E.; Buso, Donald C.; Lowe, Winsor H.; Bailey, Scott W.

2014-01-01

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks. PMID:24753575

Network analysis reveals multiscale controls on streamwater chemistry.

PubMed

McGuire, Kevin J; Torgersen, Christian E; Likens, Gene E; Buso, Donald C; Lowe, Winsor H; Bailey, Scott W

2014-05-13

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.

The synergy between complex channel-specific FIR filter and spatial filter for single-trial EEG classification.

PubMed

Yu, Ke; Wang, Yue; Shen, Kaiquan; Li, Xiaoping

2013-01-01

The common spatial pattern analysis (CSP), a frequently utilized feature extraction method in brain-computer-interface applications, is believed to be time-invariant and sensitive to noises, mainly due to an inherent shortcoming of purely relying on spatial filtering. Therefore, temporal/spectral filtering which can be very effective to counteract the unfavorable influence of noises is usually used as a supplement. This work integrates the CSP spatial filters with complex channel-specific finite impulse response (FIR) filters in a natural and intuitive manner. Each hybrid spatial-FIR filter is of high-order, data-driven and is unique to its corresponding channel. They are derived by introducing multiple time delays and regularization into conventional CSP. The general framework of the method follows that of CSP but performs better, as proven in single-trial classification tasks like event-related potential detection and motor imagery.

Antecedent Synoptic Environments Conducive to North American Polar/Subtropical Jet Superpositions

NASA Astrophysics Data System (ADS)

Winters, A. C.; Keyser, D.; Bosart, L. F.

2017-12-01

The atmosphere often exhibits a three-step pole-to-equator tropopause structure, with each break in the tropopause associated with a jet stream. The polar jet stream (PJ) typically resides in the break between the polar and subtropical tropopause and is positioned atop the strongly baroclinic, tropospheric-deep polar front around 50°N. The subtropical jet stream (STJ) resides in the break between the subtropical and the tropical tropopause and is situated on the poleward edge of the Hadley cell around 30°N. On occasion, the latitudinal separation between the PJ and the STJ can vanish, resulting in a vertical jet superposition. Prior case study work indicates that jet superpositions are often attended by a vigorous transverse vertical circulation that can directly impact the production of extreme weather over North America. Furthermore, this work suggests that there is considerable variability among antecedent environments conducive to the production of jet superpositions. These considerations motivate a comprehensive study to examine the synoptic-dynamic mechanisms that operate within the double-jet environment to produce North American jet superpositions. This study focuses on the identification of North American jet superposition events in the CFSR dataset during November-March 1979-2010. Superposition events will be classified into three characteristic types: "Polar Dominant" events will consist of events during which only the PJ is characterized by a substantial excursion from its climatological latitude band; "Subtropical Dominant" events will consist of events during which only the STJ is characterized by a substantial excursion from its climatological latitude band; and "Hybrid" events will consist of those events characterized by an excursion of both the PJ and STJ from their climatological latitude bands. Following their classification, frequency distributions of jet superpositions will be constructed to highlight the geographical locations most often associated with jet superpositions for each event type. PV inversion and composite analysis will also be performed on each event type in an effort to illustrate the antecedent environments and the dominant synoptic-dynamic mechanisms that favor the production of North American jet superpositions for each event type.

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Aquifer Hydrogeologic Layer Zonation at the Hanford Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Savelieva-Trofimova, Elena A.; Kanevski, Mikhail; timonin, v.

2003-09-10

Sedimentary aquifer layers are characterized by spatial variability of hydraulic properties. Nevertheless, zones with similar values of hydraulic parameters (parameter zones) can be distinguished. This parameter zonation approach is an alternative to the analysis of spatial variation of the continuous hydraulic parameters. The parameter zonation approach is primarily motivated by the lack of measurements that would be needed for direct spatial modeling of the hydraulic properties. The current work is devoted to the problem of zonation of the Hanford formation, the uppermost sedimentary aquifer unit (U1) included in hydrogeologic models at the Hanford site. U1 is characterized by 5 zonesmore » with different hydraulic properties. Each sampled location is ascribed to a parameter zone by an expert. This initial classification is accompanied by a measure of quality (also indicated by an expert) that addresses the level of classification confidence. In the current study, the coneptual zonation map developed by an expert geologist was used as an a priori model. The parameter zonation problem was formulated as a multiclass classification task. Different geostatistical and machine learning algorithms were adapted and applied to solve this problem, including: indicator kriging, conditional simulations, neural networks of different architectures, and support vector machines. All methods were trained using additional soft information based on expert estimates. Regularization methods were used to overcome possible overfitting. The zonation problem was complicated because there were few samples for some zones (classes) and by the spatial non-stationarity of the data. Special approaches were developed to overcome these complications. The comparison of different methods was performed using qualitative and quantitative statistical methods and image analysis. We examined the correspondence of the results with the geologically based interpretation, including the reproduction of the spatial orientation of the different classes and the spatial correlation structure of the classes. The uncertainty of the classification task was examined using both probabilistic interpretation of the estimators and by examining the results of a set of stochastic realizations. Characterization of the classification uncertainty is the main advantage of the proposed methods.« less

Creating a three level building classification using topographic and address-based data for Manchester

NASA Astrophysics Data System (ADS)

Hussain, M.; Chen, D.

2014-11-01

Buildings, the basic unit of an urban landscape, host most of its socio-economic activities and play an important role in the creation of urban land-use patterns. The spatial arrangement of different building types creates varied urban land-use clusters which can provide an insight to understand the relationships between social, economic, and living spaces. The classification of such urban clusters can help in policy-making and resource management. In many countries including the UK no national-level cadastral database containing information on individual building types exists in public domain. In this paper, we present a framework for inferring functional types of buildings based on the analysis of their form (e.g. geometrical properties, such as area and perimeter, layout) and spatial relationship from large topographic and address-based GIS database. Machine learning algorithms along with exploratory spatial analysis techniques are used to create the classification rules. The classification is extended to two further levels based on the functions (use) of buildings derived from address-based data. The developed methodology was applied to the Manchester metropolitan area using the Ordnance Survey's MasterMap®, a large-scale topographic and address-based data available for the UK.

Purification of Training Samples Based on Spectral Feature and Superpixel Segmentation

NASA Astrophysics Data System (ADS)

Guan, X.; Qi, W.; He, J.; Wen, Q.; Chen, T.; Wang, Z.

2018-04-01

Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.

Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Wang, Yanchun; Liu, Wu

2017-11-01

This paper proposes a novel classification paradigm for hyperspectral image (HSI) using feature-level fusion and deep learning-based methodologies. Operation is carried out in three main steps. First, during a pre-processing stage, wave atoms are introduced into bilateral filter to smooth HSI, and this strategy can effectively attenuate noise and restore texture information. Meanwhile, high quality spectral-spatial features can be extracted from HSI by taking geometric closeness and photometric similarity among pixels into consideration simultaneously. Second, higher order statistics techniques are firstly introduced into hyperspectral data classification to characterize the phase correlations of spectral curves. Third, multifractal spectrum features are extracted to characterize the singularities and self-similarities of spectra shapes. To this end, a feature-level fusion is applied to the extracted spectral-spatial features along with higher order statistics and multifractal spectrum features. Finally, stacked sparse autoencoder is utilized to learn more abstract and invariant high-level features from the multiple feature sets, and then random forest classifier is employed to perform supervised fine-tuning and classification. Experimental results on two real hyperspectral data sets demonstrate that the proposed method outperforms some traditional alternatives.

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Rule-guided human classification of Volunteered Geographic Information

NASA Astrophysics Data System (ADS)

Ali, Ahmed Loai; Falomir, Zoe; Schmid, Falko; Freksa, Christian

2017-05-01

During the last decade, web technologies and location sensing devices have evolved generating a form of crowdsourcing known as Volunteered Geographic Information (VGI). VGI acted as a platform of spatial data collection, in particular, when a group of public participants are involved in collaborative mapping activities: they work together to collect, share, and use information about geographic features. VGI exploits participants' local knowledge to produce rich data sources. However, the resulting data inherits problematic data classification. In VGI projects, the challenges of data classification are due to the following: (i) data is likely prone to subjective classification, (ii) remote contributions and flexible contribution mechanisms in most projects, and (iii) the uncertainty of spatial data and non-strict definitions of geographic features. These factors lead to various forms of problematic classification: inconsistent, incomplete, and imprecise data classification. This research addresses classification appropriateness. Whether the classification of an entity is appropriate or inappropriate is related to quantitative and/or qualitative observations. Small differences between observations may be not recognizable particularly for non-expert participants. Hence, in this paper, the problem is tackled by developing a rule-guided classification approach. This approach exploits data mining techniques of Association Classification (AC) to extract descriptive (qualitative) rules of specific geographic features. The rules are extracted based on the investigation of qualitative topological relations between target features and their context. Afterwards, the extracted rules are used to develop a recommendation system able to guide participants to the most appropriate classification. The approach proposes two scenarios to guide participants towards enhancing the quality of data classification. An empirical study is conducted to investigate the classification of grass-related features like forest, garden, park, and meadow. The findings of this study indicate the feasibility of the proposed approach.

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Annual crop type classification of the U.S. Great Plains for 2000 to 2011

USGS Publications Warehouse

Howard, Daniel M.; Wylie, Bruce K.

2014-01-01

The purpose of this study was to increase the spatial and temporal availability of crop classification data. In this study, nearly 16.2 million crop observation points were used in the training of the US Great Plains classification tree crop type model (CTM). Each observation point was further defined by weekly Normalized Difference Vegetation Index, annual climate, and a number of other biogeophysical environmental characteristics. This study accounted for the most prevalent crop types in the region, including, corn, soybeans, winter wheat, spring wheat, cotton, sorghum, and alfalfa. Annual CTM crop maps of the US Great Plains were created for 2000 to 2011 at a spatial resolution of 250 meters. The CTM achieved an 87 percent classification success rate on 1.8 million observation points that were withheld from model training. Product validation was performed on greater than 15,000 county records with a coefficient of determination of R2 = 0.76.

Combining High Spatial Resolution Optical and LIDAR Data for Object-Based Image Classification

NASA Astrophysics Data System (ADS)

Li, R.; Zhang, T.; Geng, R.; Wang, L.

2018-04-01

In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based object-based classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging (LiDAR) data. The eCognition software is employed to conduct the whole process. In detail, firstly, the FBSP optimizer (Fuzzy-based Segmentation Parameter) is used to obtain the optimal scale parameters for different land cover types. Then, using the segmented regions as basic units, the classification rules for various land cover types are established according to the spectral, morphological and texture features extracted from the optical images, and the height feature from LiDAR respectively. Thirdly, the object classification results are evaluated by using the confusion matrix, overall accuracy and Kappa coefficients. As a result, a method using the combination of an aerial image and the airborne Lidar data shows higher accuracy.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

Spatial Characteristics of Geothermal Spring Temperatures and Discharge Rates in the Tatun Volcanic Area, Taiwan

NASA Astrophysics Data System (ADS)

Jang, C. S.; Liu, C. W.

2014-12-01

The Tatun volcanic area is the only potential volcanic geothermal region in the Taiwan island, and abundant in hot spring resources owing to stream water mixing with fumarolic gases. According to the Meinzer's classification, spring temperatures and discharge rates are the most important properties for characterizing spring classifications. This study attempted to spatially characterize spring temperatures and discharge rates in the Tatun volcanic area, Taiwanusing indicator kriging (IK). First, data on spring temperatures and discharge rates, which were collected from surveyed data of the Taipei City Government, were divided into high, moderate and low categories according to spring classification criteria, and the various categories were regarded as estimation thresholds. Then, IK was adopted to model occurrence probabilities of specified temperatures and discharge rates in springs, and to determine their classifications based on estimated probabilities. Finally, nine combinations were obtained from the classifications of temperatures and discharge rates in springs. Moreover, the combinations and features of spring water were spatially quantified according to seven sub-zones of spring utilization. A suitable and sustainable development strategy of the spring area was proposed in each sub-zone based on probability-based combinations and features of spring water.The research results reveal that the probability-based classifications using IK provide an excellent insight in exploring the uncertainty of spatial features in springs, and can provide Taiwanese government administrators with detailed information on sustainable spring utilization and conservation in the overexploited spring tourism areas. The sub-zones BT (Beitou), RXY (Rd. Xingyi), ZSL (Zhongshanlou) and LSK (Lengshuikeng) with high or moderate discharge rates are suitable to supply spring water for tourism hotels.Local natural hot springs should be planned in the sub-zones DBT (Dingbeitou), ZSL, XYK (Xiayoukeng), and MC (Macao) with low discharge rates, and low or moderate temperatures, particularly in riverbeds or valleys.Keywords: Spring; Temperature; Discharge rate; Indicator kriging; Uncertainty

The effect of water temperature and synoptic winds on the development of surface flows over narrow, elongated water bodies

NASA Technical Reports Server (NTRS)

Segal, M.; Pielke, R. A.

1985-01-01

Simulations of the thermally induced breeze involved with a relatively narrow, elongated water body is presented in conjunction with evaluations of sensible heat fluxes in a stable marine atmospheric surface layer. The effect of the water surface temperature and of the large-scale synoptic winds on the development of surface flows over the water is examined. As implied by the sensible heat flux patterns, the simulation results reveal the following trends: (1) when the synoptic flow is absent or light, the induced surface breeze is not affected noticeably by a reduction of the water surface temperature; and (2) for stronger synoptic flow, the resultant surface flow may be significantly affected by the water surface temperature.

Storm Identification and Tracking for Hydrologic Modeling Using Hourly Accumulated NEXRAD Precipitation Data

NASA Astrophysics Data System (ADS)

Olivera, F.; Choi, J.; Socolofsky, S.

2006-12-01

Watershed responses to storm events are strongly affected by the spatial and temporal patterns of rainfall; that is, the spatial distribution of the precipitation intensity and its evolution over time. Although real storms are moving entities with non-uniform intensities in both space and time, hydrological applications often synthesize these attributes by assuming storms that are uniformly distributed and have variable intensity according to a pre-defined hyetograph shape. As one considers watersheds of greater size, the non-uniformity of rainfall becomes more important, because a storm may not cover the watershed's entire area and may not stay in the watershed for its full duration. In order to incorporate parameters such as storm area, propagation velocity and direction, and intensity distribution in the definition of synthetic storms, it is necessary to determine these storm characteristics from spatially distributed precipitation data. To date, most algorithms for identifying and tracking storms have been applied to short time-step radar reflectivity data (i.e., 15 minutes or less), where storm features are captured in an effectively synoptic manner. For the entire United States, however, the most reliable distributed precipitation data are the one-hour accumulated 4 km × 4 km gridded NEXRAD data of the U.S. National Weather Service (NWS) (NWS 2005. The one-hour aggregation level of the data, though, makes it more difficult to identify and track storms than when using sequences of synoptic radar reflectivity data, because storms can traverse over a number of NEXRAD cells and change size and shape appreciably between consecutive data maps. In this paper, we present a methodology to overcome the identification and tracking difficulties and to extract the characteristics of moving storms (e.g. size, propagation velocity and direction, and intensity distribution) from one-hour accumulated distributed rainfall data. The algorithm uses Gaussian Mixture Models (GMM) for storm identification and image processing for storm tracking. The method has been successfully applied to Brazos County in Texas using the 2003 Multi-sensor Precipitation Estimator (MPE) NEXRAD rainfall data.

GPS Tomography: Water Vapour Monitoring for Germany

NASA Astrophysics Data System (ADS)

Bender, Michael; Dick, Galina; Wickert, Jens; Raabe, Armin

2010-05-01

Ground based GPS atmosphere sounding provides numerous atmospheric quantities with a high temporal resolution for all weather conditions. The spatial resolution of the GPS observations is mainly given by the number of GNSS satellites and GPS ground stations. The latter could considerably be increased in the last few years leading to more reliable and better resolved GPS products. New techniques such as the GPS water vapour tomography gain increased significance as data from large and dense GPS networks become available. The GPS tomography has the potential to provide spatially resolved fields of different quantities operationally, i. e. the humidity or wet refractivity as required for meteorological applications or the refraction index which is important for several space based observations or for precise positioning. The number of German GPS stations operationally processed by the GFZ in Potsdam was recently enlarged to more than 300. About 28000 IWV observations and more than 1.4 millions of slant total delay data are now available per day with a temporal resolution of 15 min and 2.5 min, respectively. The extended network leads not only to a higher spatial resolution of the tomographically reconstructed 3D fields but also to a much higher stability of the inversion process and with that to an increased quality of the results. Under these improved conditions the GPS tomography can operate continuously over several days or weeks without applying too tight constraints. Time series of tomographically reconstructed humidity fields will be shown and different initialisation strategies will be discussed: Initialisation with a simple exponential profile, with a 3D humidity field extrapolated from synoptic observations and with the result of the preceeding reconstruction. The results are compared to tomographic reconstructions initialised with COSMO-DE analyses and to the corresponding model fields. The inversion can be further stabilised by making use of independent adequately weighted observations, such as synoptic observations or IWV data. The impact of such observations on the quality of the tomographic reconstruction will be discussed together with different alternatives for weighting different types of observations.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

Effects of Digitization and JPEG Compression on Land Cover Classification Using Astronaut-Acquired Orbital Photographs

NASA Technical Reports Server (NTRS)

Robinson, Julie A.; Webb, Edward L.; Evangelista, Arlene

2000-01-01

Studies that utilize astronaut-acquired orbital photographs for visual or digital classification require high-quality data to ensure accuracy. The majority of images available must be digitized from film and electronically transferred to scientific users. This study examined the effect of scanning spatial resolution (1200, 2400 pixels per inch [21.2 and 10.6 microns/pixel]), scanning density range option (Auto, Full) and compression ratio (non-lossy [TIFF], and lossy JPEG 10:1, 46:1, 83:1) on digital classification results of an orbital photograph from the NASA - Johnson Space Center archive. Qualitative results suggested that 1200 ppi was acceptable for visual interpretive uses for major land cover types. Moreover, Auto scanning density range was superior to Full density range. Quantitative assessment of the processing steps indicated that, while 2400 ppi scanning spatial resolution resulted in more classified polygons as well as a substantially greater proportion of polygons < 0.2 ha, overall agreement between 1200 ppi and 2400 ppi was quite high. JPEG compression up to approximately 46:1 also did not appear to have a major impact on quantitative classification characteristics. We conclude that both 1200 and 2400 ppi scanning resolutions are acceptable options for this level of land cover classification, as well as a compression ratio at or below approximately 46:1. Auto range density should always be used during scanning because it acquires more of the information from the film. The particular combination of scanning spatial resolution and compression level will require a case-by-case decision and will depend upon memory capabilities, analytical objectives and the spatial properties of the objects in the image.

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

PubMed

Kumar, Shiu; Mamun, Kabir; Sharma, Alok

2017-12-01

Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mining the Galaxy Zoo Database: Machine Learning Applications

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Wallin, J.; Vedachalam, A.; Baehr, S.; Lintott, C.; Darg, D.; Smith, A.; Fortson, L.

2010-01-01

The new Zooniverse initiative is addressing the data flood in the sciences through a transformative partnership between professional scientists, volunteer citizen scientists, and machines. As part of this project, we are exploring the application of machine learning techniques to data mining problems associated with the large and growing database of volunteer science results gathered by the Galaxy Zoo citizen science project. We will describe the basic challenge, some machine learning approaches, and early results. One of the motivators for this study is the acquisition (through the Galaxy Zoo results database) of approximately 100 million classification labels for roughly one million galaxies, yielding a tremendously large and rich set of training examples for improving automated galaxy morphological classification algorithms. In our first case study, the goal is to learn which morphological and photometric features in the Sloan Digital Sky Survey (SDSS) database correlate most strongly with user-selected galaxy morphological class. As a corollary to this study, we are also aiming to identify which galaxy parameters in the SDSS database correspond to galaxies that have been the most difficult to classify (based upon large dispersion in their volunter-provided classifications). Our second case study will focus on similar data mining analyses and machine leaning algorithms applied to the Galaxy Zoo catalog of merging and interacting galaxies. The outcomes of this project will have applications in future large sky surveys, such as the LSST (Large Synoptic Survey Telescope) project, which will generate a catalog of 20 billion galaxies and will produce an additional astronomical alert database of approximately 100 thousand events each night for 10 years -- the capabilities and algorithms that we are exploring will assist in the rapid characterization and classification of such massive data streams. This research has been supported in part through NSF award #0941610.

Model of Numerical Spatial Classification for Sustainable Agriculture in Badung Regency and Denpasar City, Indonesia

NASA Astrophysics Data System (ADS)

Trigunasih, N. M.; Lanya, I.; Subadiyasa, N. N.; Hutauruk, J.

2018-02-01

Increasing number and activity of the population to meet the needs of their lives greatly affect the utilization of land resources. Land needs for activities of the population continue to grow, while the availability of land is limited. Therefore, there will be changes in land use. As a result, the problems faced by land degradation and conversion of agricultural land become non-agricultural. The objectives of this research are: (1) to determine parameter of spatial numerical classification of sustainable food agriculture in Badung Regency and Denpasar City (2) to know the projection of food balance in Badung Regency and Denpasar City in 2020, 2030, 2040, and 2050 (3) to specify of function of spatial numerical classification in the making of zonation model of sustainable agricultural land area in Badung regency and Denpasar city (4) to determine the appropriate model of the area to protect sustainable agricultural land in spatial and time scale in Badung and Denpasar regencies. The method used in this research was quantitative method include: survey, soil analysis, spatial data development, geoprocessing analysis (spatial analysis of overlay and proximity analysis), interpolation of raster digital elevation model data, and visualization (cartography). Qualitative methods consisted of literature studies, and interviews. The parameters observed for a total of 11 parameters Badung regency and Denpasar as much as 9 parameters. Numerical classification parameter analysis results used the standard deviation and the mean of the population data and projections relationship rice field in the food balance sheet by modelling. The result of the research showed that, the number of different numerical classification parameters in rural areas (Badung) and urban areas (Denpasar), in urban areas the number of parameters is less than the rural areas. The based on numerical classification weighting and scores generate population distribution parameter analysis results of a standard deviation and average value. Numerical classification produced 5 models, which was divided into three zones are sustainable neighbourhood, buffer and converted in Denpasar and Badung. The results of Population curve parameter analysis in Denpasar showed normal curve, in contrast to the Badung regency showed abnormal curve, therefore Denpasar modeling carried out throughout the region, while in the Badung regency modeling done in each district. Relationship modelling and projections lands role in food balance in Badung views of sustainable land area whereas in Denpasar seen from any connection to the green open spaces in the spatial plan Denpasar 2011-2031. Modelling in Badung (rural) is different in Denpasar (urban), as well as population curve parameter analysis results in Badung showed abnormal curve while in Denpasar showed normal curve. Relationship modelling and projections lands role in food balance in the Badung regency sustainable in terms of land area, while in Denpasar in terms of linkages with urban green space in Denpasar City’s regional landuse plan of 2011-2031.

Analysis of spatial distribution of land cover maps accuracy

NASA Astrophysics Data System (ADS)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain yielded similar AUC; iv) for the larger sample size (i.e., very dense spatial sample) and per-class predictions, the spatial domain yielded larger AUC; v) increasing the sample size improved accuracy predictions with a greater benefit accruing to the spatial domain; and vi) the function used for interpolation had the smallest effect on AUC.

[Galaxy/quasar classification based on nearest neighbor method].

PubMed

Li, Xiang-Ru; Lu, Yu; Zhou, Jian-Ming; Wang, Yong-Jun

2011-09-01

With the wide application of high-quality CCD in celestial spectrum imagery and the implementation of many large sky survey programs (e. g., Sloan Digital Sky Survey (SDSS), Two-degree-Field Galaxy Redshift Survey (2dF), Spectroscopic Survey Telescope (SST), Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) program and Large Synoptic Survey Telescope (LSST) program, etc.), celestial observational data are coming into the world like torrential rain. Therefore, to utilize them effectively and fully, research on automated processing methods for celestial data is imperative. In the present work, we investigated how to recognizing galaxies and quasars from spectra based on nearest neighbor method. Galaxies and quasars are extragalactic objects, they are far away from earth, and their spectra are usually contaminated by various noise. Therefore, it is a typical problem to recognize these two types of spectra in automatic spectra classification. Furthermore, the utilized method, nearest neighbor, is one of the most typical, classic, mature algorithms in pattern recognition and data mining, and often is used as a benchmark in developing novel algorithm. For applicability in practice, it is shown that the recognition ratio of nearest neighbor method (NN) is comparable to the best results reported in the literature based on more complicated methods, and the superiority of NN is that this method does not need to be trained, which is useful in incremental learning and parallel computation in mass spectral data processing. In conclusion, the results in this work are helpful for studying galaxies and quasars spectra classification.

Remote sensing in Iowa agriculture. [land use, crop identification, and soil mapping

NASA Technical Reports Server (NTRS)

Mahlstede, J. P. (Principal Investigator); Carlson, R. E.; Fenton, T. E.

1974-01-01

The author has identified the following significant results. Analysis of 1972 single-date coverage indicated that a complete crop classification was not attainable at the test sites. Good multi-date coverage during 1973 indicates that many of the problems encountered in 1972 will be minimized. In addition, the compilation of springtime imagery covering the entire state of Iowa has added a new dimension to interpretation of Iowa's natural resources. ERTS-1 has provided data necessary to achieve the broad synoptic view not attainable through other means. This should provide soils and crop researchers and land use planners a base map of Iowa. Granted and due to the resolution of ERTS-1, not all details are observable for many land use planning needs, but this gives a general and current view of Iowa.

Wavelet-based multicomponent denoising on GPU to improve the classification of hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco; Mouriño, J. C.

2017-10-01

Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Object Based Image Analysis Combining High Spatial Resolution Imagery and Laser Point Clouds for Urban Land Cover

NASA Astrophysics Data System (ADS)

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

2016-06-01

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

Synoptic volumetric variations and flushing of the Tampa Bay estuary

NASA Astrophysics Data System (ADS)

Wilson, M.; Meyers, S. D.; Luther, M. E.

2014-03-01

Two types of analyses are used to investigate the synoptic wind-driven flushing of Tampa Bay in response to the El Niño-Southern Oscillation (ENSO) cycle from 1950 to 2007. Hourly sea level elevations from the St. Petersburg tide gauge, and wind speed and direction from three different sites around Tampa Bay are used for the study. The zonal (u) and meridional (v) wind components are rotated clockwise by 40° to obtain axial and co-axial components according to the layout of the bay. First, we use the subtidal observed water level as a proxy for mean tidal height to estimate the rate of volumetric bay outflow. Second, we use wavelet analysis to bandpass sea level and wind data in the time-frequency domain to isolate the synoptic sea level and surface wind variance. For both analyses the long-term monthly climatology is removed and we focus on the volumetric and wavelet variance anomalies. The overall correlation between the Oceanic Niño Index and volumetric analysis is small due to the seasonal dependence of the ENSO response. The mean monthly climatology between the synoptic wavelet variance of elevation and axial winds are in close agreement. During the winter, El Niño (La Niña) increases (decreases) the synoptic variability, but decreases (increases) it during the summer. The difference in winter El Niño/La Niña wavelet variances is about 20 % of the climatological value, meaning that ENSO can swing the synoptic flushing of the bay by 0.22 bay volumes per month. These changes in circulation associated with synoptic variability have the potential to impact mixing and transport within the bay.

A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

PubMed Central

Mei, Xiaoguang; Ma, Yong; Li, Chang; Fan, Fan; Huang, Jun; Ma, Jiayi

2015-01-01

The state-of-the-art ultra-spectral sensor technology brings new hope for high precision applications due to its high spectral resolution. However, it also comes with new challenges, such as the high data dimension and noise problems. In this paper, we propose a real-time method for infrared ultra-spectral signature classification via spatial pyramid matching (SPM), which includes two aspects. First, we introduce an infrared ultra-spectral signature similarity measure method via SPM, which is the foundation of the matching-based classification method. Second, we propose the classification method with reference spectral libraries, which utilizes the SPM-based similarity for the real-time infrared ultra-spectral signature classification with robustness performance. Specifically, instead of matching with each spectrum in the spectral library, our method is based on feature matching, which includes a feature library-generating phase. We calculate the SPM-based similarity between the feature of the spectrum and that of each spectrum of the reference feature library, then take the class index of the corresponding spectrum having the maximum similarity as the final result. Experimental comparisons on two publicly-available datasets demonstrate that the proposed method effectively improves the real-time classification performance and robustness to noise. PMID:26205263

Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

Role of the Angola Low in modulating southern African austral summer rainfall and relationships with synoptic and interannual modes of variability

NASA Astrophysics Data System (ADS)

Crétat, Julien; Pohl, Benjamin; Dieppois, Bastien

2017-04-01

The Angola Low has been suggested in many previous studies to be an important regional feature governing southern African rainfall variability during austral summer, which is, in particular, expressed through modulations of El Niño Southern Oscillation (ENSO) impacts on rainfall at the interannual timescale. Here, we analyse a variety of state-of-the-art reanalyses (NCEP2, ERA-Interim and MERRA2) and rainfall data (in situ rain-gauges and satellite-derived products) for: i) identifying the recurrent regimes of the Angola Low (position and intensity) at the daily timescale; ii) diagnosing how they modulate the spatio-temporal variability of austral summer rainfall; and iii) examining their relationships with synoptic convective regimes and ENSO, both at the interannual timescale. The recurrent regimes of the Angola Low are identified over the 1980-2015 period by applying a cluster analysis to daily 700-hPa wind vorticity anomalies over the Angola sector from November to March. The exact number and morphological properties of vorticity regimes vary significantly among the reanalyses, in particular when using the lowest spatial resolution reanalysis (i.e., NCEP2) that leads to detect less diversity, smoothest patterns and weakest intensity across the recurrent regimes. Despite such uncertainties, the regimes describing active Angola Low are quite robust among the reanalyses. Three preferential locations (locked over eastern Angola, shifted few degrees eastward or south-westward), which significantly impact on the rainfall spatial distribution over tropical and subtropical southern Africa, are identified. Independently from its location, Angola Low favours moisture advection from the southwest Indian Ocean and reduces moisture export towards the southeast Atlantic, hence contributing to increase moisture convergence over the subcontinent. Lead/lag correlations with synoptic convective regimes suggest that Angola Low may be a local precursor of tropical-temperate troughs, but this relationship is far from being systematic and quite sensitive to the reanalyses. Finally, the influence of ENSO on the seasonal occurrence of active Angola Low appears to be highly dependent on the choice of the reanalyses. For instance, active Angola Low tends to be independent from ENSO in NCEP2, while it is clearly driven by ENSO, through increasing occurrence during La Niña conditions, in ERA-Interim and MERRA2. Our results point thus toward strong uncertainties in state-of-the-art reanalyses for studying regional circulation features, and their connection with large-scale climate dynamics at the interannual timescale.

Use of thermal infrared remote sensing data for fisheries, environmental monitoring, oil and gas exploration, and ship routing.

NASA Astrophysics Data System (ADS)

Roffer, M. A.; Gawlikowski, G.; Muller-Karger, F.; Schaudt, K.; Upton, M.; Wall, C.; Westhaver, D.

2006-12-01

Thermal infrared (TIR) and ocean color remote sensing data (1.1 - 4.0 km) are being used as the primary data source in decision making systems for fisheries management, commercial and recreational fishing advisory services, fisheries research, environmental monitoring, oil and gas operations, and ship routing. Experience over the last 30 years suggests that while ocean color and other remote sensing data (e.g. altimetry) are important data sources, TIR presently yields the most useful data for studying ocean surface circulation synoptically on a daily basis. This is due primarily to the greater temporal resolution, but also due to one's better understanding of the dynamics of sea surface temperature compared with variations in ocean color and the spatial limitations of altimeter data. Information derived from commercial operations and research is being used to improve the operational efficiency of fishing vessels (e.g. reduce search time and increase catch rate) and to improve our understanding of the variations in catch distribution and rate needed to properly manage fisheries. This information is also being used by the oil and gas industry to minimize transit time and thus, save costs (e.g., tug charter, insurance), to increase production and revenue up to 500K dollars a day. The data are also be used to reduce the risk of equipment loss, loss of time and revenue to sudden and unexpected currents such as eddies. Sequential image analysis integrating TIR and ocean color provided near-real time, synoptic visualization of the rapid and wide dispersal of coastal waters from the northern Gulf of Mexico following Hurricanes Katrina and Rita in September 2005. The satellite data and analysis techniques have also been used to monitor the effects and movement of other potential environmentally damaging substances, such as dispersing nutrient enriched waste water offshore. A review of our experience in several commercial applications and research efforts will reinforce the importance and benefits of TIR compared to other remote sensing data. Examples of sequential image analysis and side by side image comparisons will demonstrate the utility of TIR for oceanographic applications. This will emphasize that TIR research and development be continued, as well as, implemented on all new research sensor packages. Sea surface temperature, derived from TIR, has the longest history and reliability for synoptic observations of ocean circulation. Thus, any new sensor packages should be fitted with TIR at the same temporal and spatial resolution to facilitate an objective comparison of the utility of the new sensors compared with the TIR.

Unraveling cognitive traits using the Morris water maze unbiased strategy classification (MUST-C) algorithm.

PubMed

Illouz, Tomer; Madar, Ravit; Louzon, Yoram; Griffioen, Kathleen J; Okun, Eitan

2016-02-01

The assessment of spatial cognitive learning in rodents is a central approach in neuroscience, as it enables one to assess and quantify the effects of treatments and genetic manipulations from a broad perspective. Although the Morris water maze (MWM) is a well-validated paradigm for testing spatial learning abilities, manual categorization of performance in the MWM into behavioral strategies is subject to individual interpretation, and thus to biases. Here we offer a support vector machine (SVM) - based, automated, MWM unbiased strategy classification (MUST-C) algorithm, as well as a cognitive score scale. This model was examined and validated by analyzing data obtained from five MWM experiments with changing platform sizes, revealing a limitation in the spatial capacity of the hippocampus. We have further employed this algorithm to extract novel mechanistic insights on the impact of members of the Toll-like receptor pathway on cognitive spatial learning and memory. The MUST-C algorithm can greatly benefit MWM users as it provides a standardized method of strategy classification as well as a cognitive scoring scale, which cannot be derived from typical analysis of MWM data. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

The impact of land and sea surface variations on the Delaware sea breeze at local scales

NASA Astrophysics Data System (ADS)

Hughes, Christopher P.

The summertime climate of coastal Delaware is greatly influenced by the intensity, frequency, and location of the local sea breeze circulation. Sea breeze induced changes in temperature, humidity, wind speed, and precipitation influence many aspects of Delaware's economy by affecting tourism, farming, air pollution density, energy usage, and the strength, and persistence of Delaware's wind resource. The sea breeze front can develop offshore or along the coastline and often creates a near surface thermal gradient in excess of 5°C. The purpose of this dissertation is to investigate the dynamics of the Delaware sea breeze with a focus on the immediate coastline using observed and modeled components, both at high resolutions (~200m). The Weather Research and Forecasting model (version 3.5) was employed over southern Delaware with 5 domains (4 levels of nesting), with resolutions ranging from 18km to 222m, for June 2013 to investigate the sensitivity of the sea breeze to land and sea surface variations. The land surface was modified in the model to improve the resolution, which led to the addition of land surface along the coastline and accounted for recent urban development. Nine-day composites of satellite sea surface temperatures were ingested into the model and an in-house SST forcing dataset was developed to account for spatial SST variation within the inland bays. Simulations, which include the modified land surface, introduce a distinct secondary atmospheric circulation across the coastline of Rehoboth Bay when synoptic offshore wind flow is weak. Model runs using high spatial- and temporal-resolution satellite sea surface temperatures over the ocean indicate that the sea breeze landfall time is sensitive to the SST when the circulation develops offshore. During the summer of 2013 a field campaign was conducted in the coastal locations of Rehoboth Beach, DE and Cape Henlopen, DE. At each location, a series of eleven small, autonomous thermo-sensors (i-buttons) were placed along 1-km transects oriented perpendicular to the coastline where each sensor recorded temperatures at five-minute intervals. This novel approach allows for detailed characterization of the sea breeze front development over the immediate coastline not seen in previous studies. These observations provide evidence of significant variability in frontal propagation (advancing, stalling, and retrograding) within the first kilometer of the coast. Results from this observational study indicate that the land surface has the largest effect on the frontal location when the synoptic winds have a strong offshore component, which forces the sea breeze front to move slowly through the region. When this happens, the frequency of occurrence and sea breeze frontal speed decreases consistently across the first 500 m of Rehoboth Beach, after which, the differences become insignificant. At Cape Henlopen the decrease in intensity across the transect is much less evident and the reduction in frequency does not occur until after the front is 500 m from the coast. Under these conditions at Rehoboth Beach, the near surface air behind the front warms due to the land surface which, along with the large surface friction component of the urbanized land surface, causes the front to slow as it traverses the region. Observation and modeling results suggest that the influence of variations in the land and sea surface on the sea breeze circulation is complex and highly dependent on the regional synoptic wind regime. This result inspired the development of a sea breeze prediction algorithm using a generalized linear regression model which, incorporated real-time synoptic conditions to forecast the likelihood of a sea breeze front passing through a coastal station. The forecast skill increases through the morning hours after sunrise. The inland synoptic wind direction is the most influential variable utilized by the algorithm. Such a model could be enhanced to forecast local temperature with coonfidence, which could be useful in an economic or energy usage model.

Synoptic weather types and aeroallergens modify the effect of air pollution on hospitalisations for asthma hospitalisations in Canadian cities.

PubMed

Hebbern, Christopher; Cakmak, Sabit

2015-09-01

Pollution levels and the effect of air pollution on human health can be modified by synoptic weather type and aeroallergens. We investigated the effect modification of aeroallergens on the association between CO, O3, NO2, SO2, PM10, PM2.5 and asthma hospitalisation rates in seven synoptic weather types. We developed single air pollutant models, adjusted for the effect of aeroallergens and stratified by synoptic weather type, and pooled relative risk estimates for asthma hospitalisation in ten Canadian cities. Aeroallergens significantly modified the relative risk in 19 pollutant-weather type combinations, reducing the size and variance for each single pollutant model. However, aeroallergens did not significantly modify relative risk for any pollutant in the DT or MT weather types, or for PM10 in any weather type. Thus, there is a modifying effect of aeroallergens on the association between CO, O3, NO2, SO2, PM2.5 and asthma hospitalisations that differs under specific synoptic weather types. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

PubMed Central

Tsai, Yu Hsin; Stow, Douglas; Weeks, John

2013-01-01

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. PMID:24415810

Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model

NASA Astrophysics Data System (ADS)

Ghotbi, Saba; Sotoudeheian, Saeed; Arhami, Mohammad

2016-09-01

Satellite remote sensing products of AOD from MODIS along with appropriate meteorological parameters were used to develop statistical models and estimate ground-level PM10. Most of previous studies obtained meteorological data from synoptic weather stations, with rather sparse spatial distribution, and used it along with 10 km AOD product to develop statistical models, applicable for PM variations in regional scale (resolution of ≥10 km). In the current study, meteorological parameters were simulated with 3 km resolution using WRF model and used along with the rather new 3 km AOD product (launched in 2014). The resulting PM statistical models were assessed for a polluted and largely variable urban area, Tehran, Iran. Despite the critical particulate pollution problem, very few PM studies were conducted in this area. The issue of rather poor direct PM-AOD associations existed, due to different factors such as variations in particles optical properties, in addition to bright background issue for satellite data, as the studied area located in the semi-arid areas of Middle East. Statistical approach of linear mixed effect (LME) was used, and three types of statistical models including single variable LME model (using AOD as independent variable) and multiple variables LME model by using meteorological data from two sources, WRF model and synoptic stations, were examined. Meteorological simulations were performed using a multiscale approach and creating an appropriate physic for the studied region, and the results showed rather good agreements with recordings of the synoptic stations. The single variable LME model was able to explain about 61%-73% of daily PM10 variations, reflecting a rather acceptable performance. Statistical models performance improved through using multivariable LME and incorporating meteorological data as auxiliary variables, particularly by using fine resolution outputs from WRF (R2 = 0.73-0.81). In addition, rather fine resolution for PM estimates was mapped for the studied city, and resulting concentration maps were consistent with PM recordings at the existing stations.

Geochemistry of Red Mountain Creek, Colorado, under low-flow conditions, August 2002

USGS Publications Warehouse

Runkel, Robert L.; Kimball, Briant A.; Walton-Day, Katherine; Verplanck, Philip L.

2005-01-01

Red Mountain Creek, an acid mine drainage stream in southwestern Colorado, was the subject of a synoptic study conducted in August 2002. During the synoptic study, a solution containing lithium chloride was injected continuously to allow for the calculation of streamflow using the tracer-dilution method. Synoptic water-quality samples were collected from 48 stream sites and 29 inflow locations along a 5.4-kilometer study reach. Data from the study provide profiles of pH, concentration, and mass load with a high degree of spatial resolution. Despite the presence of 10 circumneutral inflows, pH remained below 3.4 at all stream sites. Concentration profiles indicate that dissolved concentrations of aluminum, cadmium, copper, lead, and zinc exceed chronic aquatic-life standards established by the State of Colorado along the entire study reach. Comparison of total recoverable and dissolved concentrations suggests that most constituents were transported conservatively. Exceptions to this pattern include arsenic, iron, molybdenum, and vanadium, four constituents that were subject to precipitation and(or) sorption reactions as the addition of a circumneutral tributary resulted in a slight increase in instream pH. Evaluation of data from the 29 inflow locations indicates a sharp contrast between the east and west sides of the watershed; inflows from the east side have high constituent concentrations and acidic pH, whereas inflows from the west side have lower concentrations and generally higher pH. Loading profiles, the product of streamflow and concentration, are used to rank potential sources of metals and acidity within the watershed. Four sources account for 83, 72, 70, 69, 64, and 61 percent of the aluminum, iron, arsenic, zinc, copper, and cadmium loading within the study reach, respectively. All four sources appear to be the result of surface inflows that have been affected by mining activities. The relatively small number of major sources and the fact that they are attributable to surface inflows are two factors that may facilitate effective remediation.

Analysis of southeast Australian zooplankton observations of 1938-42 using synoptic oceanographic conditions

NASA Astrophysics Data System (ADS)

Baird, Mark E.; Everett, Jason D.; Suthers, Iain M.

2011-03-01

The research vessel Warreen obtained 1742 planktonic samples along the continental shelf and slope of southeast Australia from 1938-42, representing the earliest spatially and temporally resolved zooplankton data from Australian marine waters. In this paper, Warreen observations along the southeast Australian seaboard from 28°S to 38°S are interpreted based on synoptic meteorological and oceanographic conditions and ocean climatologies. Meteorological conditions are based on the NOAA-CIRES 20th Century Reanalysis Project; oceanographic conditions use Warreen hydrological observations, and the ocean climatology is the CSIRO Atlas of Regional Seas. The Warreen observations were undertaken in waters on average 0.45 °C cooler than the climatological average, and included the longest duration El Niño of the 20th century. In northern New South Wales (NSW), week time-scale events dominate zooplankton response. In August 1940 an unusual winter upwelling event occurred in northern NSW driven by a stronger than average East Australian Current (EAC) and anomalous northerly winds that resulted in high salp and larvacean abundance. In January 1941 a strong upwelling event between 28° and 33°S resulted in a filament of upwelled water being advected south and alongshore, which was low in zooplankton biovolume. In southern NSW a seasonal cycle in physical and planktonic characteristics is observed. In January 1941 the poleward extension of the EAC was strong, advecting more tropical tunicate species southward. Zooplankton abundance and distribution on the continental shelf and slope are more dependent on weekly to monthly timescales on local oceanographic and meteorological conditions than continental-scale interannual trends. The interpretation of historical zooplankton observations of the waters off southeast Australia for the purpose of quantifying anthropogenic impacts will be improved with the use of regional hindcasts of synoptic ocean and atmospheric weather that can explain some of the physically forced natural variability.

The South ``West'' Pacific Convergence Zone: Large-scale feedback on atmospheric subsidence to the east

NASA Astrophysics Data System (ADS)

Widlansky, M. J.; Webster, P. J.; Hoyos, C.

2010-12-01

Three semi-permanent convective cloud bands exist in the Southern Hemisphere extending southeastward from the equator, through the tropics, and into the subtropics. The most prominent of these features occurs in the South Pacific during summer and is referred to as the South Pacific Convergence Zone (SPCZ). Similar cloud bands, with less intensity, exist in the South Indian and Atlantic basins. To the east of each convective zone is a large-scale region of atmospheric subsidence. We attempt to explain the physical mechanisms that promote the diagonal orientation of the SPCZ and also teleconnections that may exist with stratocumulus cloud cover in the southeastern Pacific. It is argued that slowly varying sea surface temperature patterns produce upper tropospheric wind fields that vary substantially in longitude (∂U/∂x). Regions where 200 hPa zonal winds decrease with longitude (i.e., negative zonal stretching deformation, or ∂U/∂x<0) reduce the group speed of the eastward propagating synoptic (3-6 day period) Rossby waves and locally increase the wave energy density. Such a region of wave accumulation occurs in the vicinity of the SPCZ (see Figure), thus providing a hypothesis for the diagonal orientation and a physical basis for earlier observations that the zone traps eastward propagating synoptic disturbances. Controlled numerical experiments and composites of observed life cycles of synoptic waves confirm that disturbances slow in the SPCZ. From the hypothesis comes a more general theory accounting for the SPCZ’s spatial orientation and the lack of disturbances to the east. December-February climatology of 200 hPa zonal winds (shading) and negative zonal stretching deformation (red contours). Large black box located at 20°S-35°S, 165°W-135°W encloses the diagonal region of the SPCZ. 240 W m-2 OLR contour outlined by blue lines.

Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data

PubMed Central

Puttonen, Eetu; Jaakkola, Anttoni; Litkey, Paula; Hyyppä, Juha

2011-01-01

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin. PMID:22163894

Tree classification with fused mobile laser scanning and hyperspectral data.

PubMed

Puttonen, Eetu; Jaakkola, Anttoni; Litkey, Paula; Hyyppä, Juha

2011-01-01

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin.

Synoptic backgrounds of the widest wildfire in Mazandaran Province of Iran during December 11-13, 2010

NASA Astrophysics Data System (ADS)

Ghavidel, Yousef; Farajzadeh, Manuchehr; Khaleghi Babaei, Meysam

2016-12-01

In this paper, atmospheric origins of the widest wildfire in Mazandaran province on 11-13th of December, 2010 have been investigated. Data sets of this research include maximum daily temperature (MDT), minimum relative humidity (MRH) of terrestrial stations, dynamic and thermodynamic features of the atmosphere, Gridded data sets of Self-Calibrated Palmer drought severity index (SCPDSI) and global drought dataset standardized precipitation-evapotranspiration index (SPEI) and data related to the time and the extent of the wildfire. The ``environmental to circulation'' approach to synoptic classification has been used to investigate relationships between local-scale surface environment (wildfire) and the synoptic-scale atmospheric circulation conditions. Results of study show that during the 3-day wide wildfire, the average of MDT and the MRH was significantly different from the long-term average. During the aforementioned wildfire, the average of MDT in Mazandaran province was 26 °C and the average of MRH was reported 35 %. The long-term average of MDT and the MRH in Mazandaran province during 3 days of wildfire was 12.3 °C and 68 %, respectively. Therefore, the MDT has a positive abnormality of 13.7 °C and the MRH has a negative abnormality of 33 %. In addition, monthly SCPDSI and SPEI indicated severe drought conditions at December 2010 in Mazandaran. Analysis of SLP maps shows that during the 3-day fire, a pressure center of 1110 hPa on Persian Gulf and a very low-pressure center on Turkey and Asia Minor were created. Normally, this event has caused the pressure gradient and warm and dry air advection from Arabian Peninsula to higher longitudes, particularly Mazandaran province. Consequently, the MDT increased and the wildfire of Mazandaran forest took place in an area of 220 ha. Zonal wind maps signify the weakness of Zonal wind and meridional wind maps show the southern direction of meridional wind flow during the wide wildfire. Moreover, Omega maps prove that during the aforementioned wildfire, the Omega flow has been positive and the warm air flow has subsidence. This made the infusion of warm air and increased the MDT substantially and consequently the wildfire occurrence was facilitated. Temperature advection maps showed that in the level of 1000 hPa, the warm air blowing source is originated from Arabian Peninsula, in the level of 850 hPa from the Arabian Peninsula and Iraq and in the level of 700 and 500 hPa from Ethiopia, Arabian Peninsula and Iraq. The Hybrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model outputs confirm the synoptic mechanisms for the discussed wildfire.

Hyperspectral classification of grassland species: towards a UAS application for semi-automatic field surveys

NASA Astrophysics Data System (ADS)

Lopatin, Javier; Fassnacht, Fabian E.; Kattenborn, Teja; Schmidtlein, Sebastian

2017-04-01

Grasslands are one of the ecosystems that have been strongly intervened during the past decades due to anthropogenic impacts, affecting their structural and functional composition. To monitor the spatial and/or temporal changes of these environments, a reliable field survey is first needed. As quality relevés are usually expensive and time consuming, the amount of information available is usually poor or not well spatially distributed at the regional scale. In the present study, we investigate the possibility of a semi-automated method used for repeated surveys of monitoring sites. We analyze the applicability of very high spatial resolution hyperspectral data to classify grassland species at the level of individuals. The AISA+ imaging spectrometer mounted on a scaffold was applied to scan 1 m2 grassland plots and assess the impact of four sources of variation on the predicted species cover: (1) the spatial resolution of the scans, (2) the species number and structural diversity, (3) the species cover, and (4) the species functional types (bryophytes, forbs and graminoids). We found that the spatial resolution and the diversity level (mainly structural diversity) were the most important source of variation for the proposed approach. A spatial resolution below 1 cm produced relatively high model performances, while predictions with pixel sizes over that threshold produced non adequate results. Areas with low interspecies overlap reached classification median values of 0.8 (kappa). On the contrary, results were not satisfactory in plots with frequent interspecies overlap in multiple layers. By means of a bootstrapping procedure, we found that areas with shadows and mixed pixels introduce uncertainties into the classification. We conclude that the application of very high resolution hyperspectral remote sensing as a robust alternative or supplement to field surveys is possible for environments with low structural heterogeneity. This study presents the first try of a full classification of grassland species at the individuum level using spectral data.

Variations in synoptic-scale eddy activity during the life cycles of persistent flow anomalies

NASA Technical Reports Server (NTRS)

Dole, Randall M.; Neilley, Peter P.

1991-01-01

The objective of the study was to identify how synoptic-scale eddy activity varies throughout the life cycles of major scale flow anomalies. In particular, composite analyses of various measures of synoptic-scale eddy activity are constructed, with the composites obtained relative to the onset and termination times of cases typically associated with either blocking or abnormally intense zonal flows. The potential mechanisms that are likely to contribute to the observed changes in eddy behavior are discussed.

Synoptic scale forecast skill and systematic errors in the MASS 2.0 model. [Mesoscale Atmospheric Simulation System

NASA Technical Reports Server (NTRS)

Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K. F.

1985-01-01

The synoptic scale performance characteristics of MASS 2.0 are determined by comparing filtered 12-24 hr model forecasts to same-case forecasts made by the National Meteorological Center's synoptic-scale Limited-area Fine Mesh model. Characteristics of the two systems are contrasted, and the analysis methodology used to determine statistical skill scores and systematic errors is described. The overall relative performance of the two models in the sample is documented, and important systematic errors uncovered are presented.

The effect of lossy image compression on image classification

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1995-01-01

We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network.

Spatial Differentiation of Arable Land and Permanent Grasslands to Improve a Regional Land Management Model for Nutrient Balancing

NASA Astrophysics Data System (ADS)

Gómez Giménez, M.; Della Peruta, R.; de Jong, R.; Keller, A.; Schaepman, M. E.

2015-12-01

Agroecosystems play an important role providing economic and ecosystem services, which directly impact society. Inappropriate land use and unsustainable agricultural management with associated nutrient cycles can jeopardize important soil functions such as food production, livestock feeding and conservation of biodiversity. The objective of this study was to integrate remotely sensed land cover information into a regional Land Management Model (LMM) to improve the assessment of spatial explicit nutrient balances for agroecosystems. Remotely sensed data as well as an optimized parameter set contributed to feed the LMM providing a better spatial allocation of agricultural data aggregated at farm level. The integration of land use information in the land allocation process relied predominantly on three factors: i) spatial resolution, ii) classification accuracy and iii) parcels definition. The best-input parameter combination resulted in two different land cover classifications with overall accuracies of 98%, improving the LMM performance by 16% as compared to using non-spatially explicit input. Firstly, the use of spatial explicit information improved the spatial allocation output resulting in a pattern that better followed parcel boundaries (Figure 1). Second, the high classification accuracies ensured consistency between the datasets used. Third, the use of a suitable spatial unit to define the parcels boundaries influenced the model in terms of computational time and the amount of farmland allocated. We conclude that the combined use of remote sensing (RS) data with the LMM has the potential to provide highly accurate information of spatial explicit nutrient balances that are crucial for policy options concerning sustainable management of agricultural soils. Figure 1. Details of the spatial pattern obtained: a) Using only the farm census data, b) using also land use information. Framed in black in the left image (a), examples of artifacts that disappeared when using land use information (right image, b). Colors represent different ownership.

Plant species classification using flower images—A comparative study of local feature representations

PubMed Central

Seeland, Marco; Rzanny, Michael; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick

2017-01-01

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification. PMID:28234999

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Treesearch

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

Comparing Features for Classification of MEG Responses to Motor Imagery

PubMed Central

Halme, Hanna-Leena; Parkkonen, Lauri

2016-01-01

Background Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. Methods MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio—spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. Results The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. Conclusions We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system. PMID:27992574

Climatology of winter transition days for the contiguous USA, 1951-2007

NASA Astrophysics Data System (ADS)

Hondula, David M.; Davis, Robert E.

2011-01-01

In middle and high latitudes, climate change could impact the frequency and characteristics of frontal passages. Although transitions between air masses are significant features of the general circulation that influence human activities and other surface processes, they are much more difficult to objectively identify than single variables like temperature or even extreme events like fires, droughts, and floods. The recently developed Spatial Synoptic Classification (SSC) provides a fairly objective means of identifying frontal passages. In this research, we determine the specific meteorological patterns represented by the SSC's Transition category, a "catch-all" group that attempts to identify those days that cannot be characterized as a single, homogeneous air mass type. The result is a detailed transition climatology for the continental USA. We identify four subtypes of the Transition category based on intra-day sea level pressure change and dew point temperature change. Across the contiguous USA, most transition days are identified as cold fronts and warm fronts during the winter season. Among the two less common subtypes, transition days in which the dew point temperature and pressure both rise are more frequently observed across the western states, and days in which both variables fall are more frequently observed in coastal regions. The relative frequencies of wintertime warm and cold fronts have changed over the period 1951-2007. Relative cold front frequency has significantly increased in the Northeast and Midwest regions, and warm front frequencies have declined in the Midwest, Rocky Mountain, and Pacific Northwest regions. The overall shift toward cold fronts and away from warm fronts across the northern USA arises from a combination of an enhanced ridge over western North America and a northward shift of storm tracks throughout the mid-latitudes. These results are consistent with projections of climate change associated with elevated greenhouse gas concentrations.

Temperature extremes in Alaska: temporal variability and circulation background

NASA Astrophysics Data System (ADS)

Sulikowska, Agnieszka; Walawender, Jakub P.; Walawender, Ewelina

2018-06-01

The aims of this study are to characterize the spatial and temporal variability of extremely warm days (WDs) and warm spells (WSs) in summer as well as extremely cold days (CDs) and cold spells (CSs) in winter in Alaska in the years 1951-2015 and to determine the role of atmospheric circulation in their occurrence. The analysis is performed using daily temperature maxima (T MAX) and minima (T MIN) measured at 10 weather stations in Alaska as well as mean daily values of sea level pressure and wind direction at the 850 hPa isobaric level. WD (CD) is defined as a day with T MAX above the 95th (T MIN below the 5th) percentile of a probability density function calculated from observations, and WS (CS) equals at least three consecutive WDs (CDs). Frequency of the occurrence and severity of warm and cold extremes as well as duration of WSs and CSs is analyzed. In order to characterize synoptic conditions during temperature extremes, the objective classification scheme of advection types considering jointly the direction of the air influx and type of pressure system is employed. The results show that the general trend is towards the warmer temperatures, and the warming is greater in the winter than summer and for T MAX as opposed to T MIN. This is reflected in changes in the frequency of occurrence and intensity of temperature extremes which are much more pronounced in the case of winter cold extremes (decreasing tendencies) than summer warm extremes (increasing tendencies). The occurrence of temperature extremes is generally favored by anticyclonic weather with advection direction indicating air mass flows from the interior of the North American continent as well as the south (warm extremes in summer) and north (cold extremes in winter).

A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi

USGS Publications Warehouse

Smith, Kathryn E.L.; Nayegandhi, Amar; Brock, John C.

2010-01-01

INTRODUCTION Land-use and land-cover (LULC) data provide important information for environmental management. Data pertaining to land-cover and land-management activities are a common requirement for spatial analyses, such as watershed modeling, climate change, and hazard assessment. In coastal areas, land development, storms, and shoreline modification amplify the need for frequent and detailed land-cover datasets. The northern Gulf of Mexico coastal area is no exception. The impact of severe storms, increases in urban area, dramatic changes in land cover, and loss of coastal-wetland habitat all indicate a vital need for reliable and comparable land-cover data. Four main attributes define a land-cover dataset: the date/time of data collection, the spatial resolution, the type of classification, and the source data. The source data are the foundation dataset used to generate LULC classification and are typically remotely sensed data, such as aerial photography or satellite imagery. These source data have a large influence on the final LULC data product, so much so that one can classify LULC datasets into two general groups: LULC data derived from aerial photography and LULC data derived from satellite imagery. The final LULC data can be converted from one format to another (for instance, vector LULC data can be converted into raster data for analysis purposes, and vice versa), but each subsequent dataset maintains the imprint of the source medium within its spatial accuracy and data features. The source data will also influence the spatial and temporal resolution, as well as the type of classification. The intended application of the LULC data typically defines the type of source data and methodology, with satellite imagery being selected for large landscapes (state-wide, national data products) and repeatability (environmental monitoring and change analysis). The coarse spatial scale and lack of refined land-use categories are typical drawbacks to satellite-based land-use classifications. Aerial photography is typically selected for smaller landscapes (watershed-basin scale), for greater definition of the land-use categories, and for increased spatial resolution. Disadvantages of using photography include time-consuming digitization, high costs for imagery collection, and lack of seasonal data. Recently, the availability of high-resolution satellite imagery has generated a new category of LULC data product. These new datasets have similar strengths to the aerial-photo-based LULC in that they possess the potential for refined definition of land-use categories and increased spatial resolution but also have the benefit of satellite-based classifications, such as repeatability for change analysis. LULC classification based on high-resolution satellite imagery is still in the early stages of development but merits greater attention because environmental-monitoring and landscape-modeling programs rely heavily on LULC data. This publication summarizes land-use and land-cover mapping activities for Alabama and Mississippi coastal areas within the U.S. Geological Survey (USGS) Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project boundaries. Existing LULC datasets will be described, as well as imagery data sources and ancillary data that may provide ground-truth or satellite training data for a forthcoming land-cover classification. Finally, potential areas for a high-resolution land-cover classification in the Alabama-Mississippi region will be identified.

Terrain classification in navigation of an autonomous mobile robot

NASA Astrophysics Data System (ADS)

Dodds, David R.

1991-03-01

In this paper we describe a method of path planning that integrates terrain classification (by means of fractals) the certainty grid method of spatial representation Kehtarnavaz Griswold collision-zones Dubois Prade fuzzy temporal and spatial knowledge and non-point sized qualitative navigational planning. An initially planned (" end-to-end" ) path is piece-wise modified to accommodate known and inferred moving obstacles and includes attention to time-varying multiple subgoals which may influence a section of path at a time after the robot has begun traversing that planned path.

Mixing-height measurement by lidar, particle counter, and rawinsonde in the Williamette Valley, Oregon

NASA Technical Reports Server (NTRS)

Mccormick, M. P.; Melfi, S. H.; Olsson, L. E.; Tuft, W. L.; Elliott, W. P.; Egami, R.

1972-01-01

The feasibility of using laser radar (lidar) to measure the spatial distribution of aerosols and water vapor in the earth's mixing or boundary layer is shown. From these data the important parameter of actual mixing height was determined, that is, the maximum height to which particulate pollutants actually mix. Data are shown for simultaneous lidar, rawinsonde, and aircraft-mounted condensation nuclei counter and temperature measurements. The synoptic meteorology is also presented. The Williamette Valley, Oregon, was chosen for the measurements because of its unique combination of meteorology, terrain, and pollutant source, along with an ongoing Oregon State University study of the natural ventilation of this valley.

Are there interactions of iodine and sulfur species in marine air photochemistry?

NASA Technical Reports Server (NTRS)

Chatfield, Robert B.; Crutzen, Paul J.

1990-01-01

The effects of dimethyl sulfide (DMS) and methyl iodide (MI) emissions are analyzed using a two-dimensional photochemical model of a marine tropical tropospheric synoptic system. The model traces the atmospheric transformation cycles of the emissions to aerosols. The study is focused on remote tropical ocean regions and includes simulations of the spatial and diurnal variations of various iodine and sulfur species, and the species OH, HO2, and H2O2. One line of analysis leads to the conclusion that the reaction IO + DMS yields DMSO + I may play a significant role in destroying DMS if it proceeds at the published fast rate. Alternative lines of analyses are presented.

Addressing the mischaracterization of extreme rainfall in regional climate model simulations - A synoptic pattern based bias correction approach

NASA Astrophysics Data System (ADS)

Li, Jingwan; Sharma, Ashish; Evans, Jason; Johnson, Fiona

2018-01-01

Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non-convective) in a warmer climate is of little relevance in the estimation of overall change, an assumption that is not supported by empirical or physical evidence. This study proposes an alternative approach to account for the potential change of alternate rainfall types, characterized here by synoptic weather patterns (SPs) using self-organizing maps classification. The objective of this study is to evaluate the added influence of SPs on the bias correction, which is achieved by comparing the corrected distribution of future extreme rainfall with that using conventional quantile mapping. A comprehensive synthetic experiment is first defined to investigate the conditions under which the additional information of SPs makes a significant difference to the bias correction. Using over 600,000 synthetic cases, statistically significant differences are found to be present in 46% cases. This is followed by a case study over the Sydney region using a high-resolution run of the Weather Research and Forecasting (WRF) regional climate model, which indicates a small change in the proportions of the SPs and a statistically significant change in the extreme rainfall over the region, although the differences between the changes obtained from the two bias correction methods are not statistically significant.

Unsupervised classification of operator workload from brain signals.

PubMed

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects' error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Object-Based Classification as an Alternative Approach to the Traditional Pixel-Based Classification to Identify Potential Habitat of the Grasshopper Sparrow

NASA Astrophysics Data System (ADS)

Jobin, Benoît; Labrecque, Sandra; Grenier, Marcelle; Falardeau, Gilles

2008-01-01

The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. Object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Québec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

NASA Astrophysics Data System (ADS)

Ghorpade, Vijaya K.; Checchin, Paul; Malaterre, Laurent; Trassoudaine, Laurent

2017-12-01

The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a result, determining the similarity between 3D shapes has become consequential and is a fundamental task in shape-based recognition, retrieval, clustering, and classification. Several decades of research in Content-Based Information Retrieval (CBIR) has resulted in diverse techniques for 2D and 3D shape or object classification/retrieval and many benchmark data sets. In this article, a novel technique for 3D shape representation and object classification has been proposed based on analyses of spatial, geometric distributions of 3D keypoints. These distributions capture the intrinsic geometric structure of 3D objects. The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes. Each class/instance of an object can be uniquely represented by a PDF. This shape representation is robust yet with a simple idea, easy to implement but fast enough to compute. Both Euclidean and topological space on object's surface are considered to build the PDFs. Topology-based geodesic distances between keypoints exploit the non-planar surface properties of the object. The performance of the novel shape signature is tested with object classification accuracy. The classification efficacy of the new shape analysis method is evaluated on a new dataset acquired with a Time-of-Flight camera, and also, a comparative evaluation on a standard benchmark dataset with state-of-the-art methods is performed. Experimental results demonstrate superior classification performance of the new approach on RGB-D dataset and depth data.

Unsupervised classification of operator workload from brain signals

NASA Astrophysics Data System (ADS)

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Large-area settlement pattern recognition from Landsat-8 data

NASA Astrophysics Data System (ADS)

Wieland, Marc; Pittore, Massimiliano

2016-09-01

The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties.

Standardized synoptic cancer pathology reports - so what and who cares? A population-based satisfaction survey of 970 pathologists, surgeons, and oncologists.

PubMed

Lankshear, Sara; Srigley, John; McGowan, Thomas; Yurcan, Marta; Sawka, Carol

2013-11-01

Cancer Care Ontario implemented synoptic pathology reporting across Ontario, impacting the practice of pathologists, surgeons, and medical and radiation oncologists. The benefits of standardized synoptic pathology reporting include enhanced completeness and improved consistency in comparison with narrative reports, with reported challenges including increased workload and report turnaround time. To determine the impact of synoptic pathology reporting on physician satisfaction specific to practice and process. A descriptive, cross-sectional design was utilized involving 970 clinicians across 27 hospitals. An 11-item survey was developed to obtain information regarding timeliness, completeness, clarity, and usability. Open-ended questions were also employed to obtain qualitative comments. A 51% response rate was obtained, with descriptive statistics reporting that physicians perceive synoptic reports as significantly better than narrative reports. Correlation analysis revealed a moderately strong, positive relationship between respondents' perceptions of overall satisfaction with the level of information provided and perceptions of completeness for clinical decision making (r = 0.750, P < .001) and ease of finding information for clinical decision making (r = 0.663, P < .001). Dependent t tests showed a statistically significant difference in the satisfaction scores of pathologists and oncologists (t169 = 3.044, P = .003). Qualitative comments revealed technology-related issues as the most frequently cited factor impacting timeliness of report completion. This study provides evidence of strong physician satisfaction with synoptic cancer pathology reporting as a clinical decision support tool in the diagnosis, prognosis, and treatment of cancer patients.

A parametric multiclass Bayes error estimator for the multispectral scanner spatial model performance evaluation

NASA Technical Reports Server (NTRS)

Mobasseri, B. G.; Mcgillem, C. D.; Anuta, P. E. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed.

Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2014-03-01

As an emerging technology, hyperspectral imaging (HSI) combines both the chemical specificity of spectroscopy and the spatial resolution of imaging, which may provide a non-invasive tool for cancer detection and diagnosis. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. In this paper, we introduce a tensor-based computation and modeling framework for the analysis of hyperspectral images to detect head and neck cancer. The proposed classification method can distinguish between malignant tissue and healthy tissue with an average sensitivity of 96.97% and an average specificity of 91.42% in tumor-bearing mice. The hyperspectral imaging and classification technology has been demonstrated in animal models and can have many potential applications in cancer research and management.

Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Lu, Guolan; Halig, Luma; Wang, Dongsheng; Qin, Xulei; Chen, Zhuo Georgia; Fei, Baowei

2014-10-01

Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.

Extracting alpha band modulation during visual spatial attention without flickering stimuli using common spatial pattern.

PubMed

Fujisawa, Junya; Touyama, Hideaki; Hirose, Michitaka

2008-01-01

In this paper, alpha band modulation during visual spatial attention without visual stimuli was focused. Visual spatial attention has been expected to provide a new channel of non-invasive independent brain computer interface (BCI), but little work has been done on the new interfacing method. The flickering stimuli used in previous work cause a decline of independency and have difficulties in a practical use. Therefore we investigated whether visual spatial attention could be detected without such stimuli. Further, the common spatial patterns (CSP) were for the first time applied to the brain states during visual spatial attention. The performance evaluation was based on three brain states of left, right and center direction attention. The 30-channel scalp electroencephalographic (EEG) signals over occipital cortex were recorded for five subjects. Without CSP, the analyses made 66.44 (range 55.42 to 72.27) % of average classification performance in discriminating left and right attention classes. With CSP, the averaged classification accuracy was 75.39 (range 63.75 to 86.13) %. It is suggested that CSP is useful in the context of visual spatial attention, and the alpha band modulation during visual spatial attention without flickering stimuli has the possibility of a new channel for independent BCI as well as motor imagery.

Evaluation of LANDSAT-4 Thematic Mapper Data as Applied to Geologic Exploration: Summary of Results. [Death Valley, California, Cement-Velma, Oklahoma; Big Horn and Wind River Basins, Wyoming; Spanish Peaks, Colorado; and the Four Corners area (Paradox Basin of Utah and Colorado)

NASA Technical Reports Server (NTRS)

Dykstra, J. D.; Sheffield, C. A.; Everett, J. R.

1984-01-01

As with any tool applied to geologic exploration, maximum value results from the innovative integration of optimally processed LANDSAT-4 data with existing pertinent information and perceptive geologic thinking. The synoptic view of the satellite images and the relatively high resolution of the data permits recognization of regional tectonic patterns and their detailed mapping. The refined spatial and spectral characteristics and digital nature surface alterations associated with hydrothermal activity and microseepage of hydrocarbons. In general, as vegetation and soil cover increase, the value of spectral components of TM data decreases with respect to the value of the spatial component of the data. This observation reinforces the experience from working with MSS data that digital processing must be optimized both for the area and for the application.

Study of the tornado event in Greece on March 25, 2009: Synoptic analysis and numerical modeling using modified topography

NASA Astrophysics Data System (ADS)

Matsangouras, I. T.; Nastos, P. T.; Pytharoulis, I.

2016-03-01

Recent research revealed that western Greece and NW Peloponnese are regions that favor prefrontal tornadic incidence. On March 25, 2009 a tornado developed approximately at 10:30 UTC near Varda village (NW Peloponnese). Tornado intensity was T4-T5 (TORRO scale) and consequently caused an economic impact of 350,000 € over the local society. The goals of this study are: (i) to analyze synoptic and remote sensing features regarding the tornado event over NW Peloponnese and (ii) to investigate the role of topography in tornadogenesis triggered under strong synoptic scale forcing over that area. Synoptic analysis was based on the European Centre for Medium-Range Weather Forecasts (ECMWF) data sets. The analysis of daily anomaly of synoptic conditions with respect to 30 years' climatology (1981-2010), was based on the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis data sets. In addition, numerous remote sensing data sets were derived by the Hellenic National Meteorological Service (HNMS) weather station network in order to better interpret the examined tornado event. Finally, numerical modeling was performed using the non-hydrostatic Weather Research and Forecasting model (WRF), initialized by ECMWF gridded analyses, with telescoping nested grids that allow the representation of atmospheric circulations ranging from the synoptic scale down to the meso-scale. The two numerical experiments were performed on the basis of: (a) the presence and (b) the absence of topography (landscape), so as to determine whether the occurrence of a tornado - identified by diagnostic instability indices - could be indicated by modifying topography. The energy helicity index (EHI), the bulk Richardson number (BRN) shear, the storm-relative environmental helicity (SRH), and the maximum convective available potential energy (MCAPE, for parcels with maximum θe) were considered as principal diagnostic instability variables and employed in both numerical experiments. Furthermore, model verification was conducted, accompanied by analysis of the absolute vorticity budget. Synoptic analysis revealed that the synoptic weather conditions on March 25, 2009 are in agreement with the composite synoptic climatology for tornado days over western Greece. In addition, maximum daily anomalies at the barometric levels of 500, 700, 850 and 925 hPa were found, compared to the climatology of composite mean anomalies for tornado days over western Greece. Numerical simulations revealed that the topography of NW Peloponnese did not constitute an important factor during the tornado event on March 25, 2009, based on EHI, SRH, BRN, and MCAPE analyses.

GENIE: a hybrid genetic algorithm for feature classification in multispectral images

NASA Astrophysics Data System (ADS)

Perkins, Simon J.; Theiler, James P.; Brumby, Steven P.; Harvey, Neal R.; Porter, Reid B.; Szymanski, John J.; Bloch, Jeffrey J.

2000-10-01

We consider the problem of pixel-by-pixel classification of a multi- spectral image using supervised learning. Conventional spuervised classification techniques such as maximum likelihood classification and less conventional ones s uch as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why: the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or maybe the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. In this paper we show that the use of a hybrid GA provides significant advantages over using either a GA alone or more conventional classification methods alone. We present results using high-resolution IKONOS data, looking for regions of burned forest and for roads.

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2009-10-01

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

Monitoring tropical vegetation succession with LANDSAT data

NASA Technical Reports Server (NTRS)

Robinson, V. B. (Principal Investigator)

1983-01-01

The shadowing problem, which is endemic to the use of LANDSAT in tropical areas, and the ability to model changes over space and through time are problems to be addressed when monitoring tropical vegetation succession. Application of a trend surface analysis model to major land cover classes in a mountainous region of the Phillipines shows that the spatial modeling of radiance values can provide a useful approach to tropical rain forest succession monitoring. Results indicate shadowing effects may be due primarily to local variations in the spectral responses. These variations can be compensated for through the decomposition of the spatial variation in both elevation and MSS data. Using the model to estimate both elevation and spectral terrain surface as a posteriori inputs in the classification process leads to improved classification accuracy for vegetation of cover of this type. Spatial patterns depicted by the MSS data reflect the measurement of responses to spatial processes acting at several scales.

Multisensor multiresolution data fusion for improvement in classification

NASA Astrophysics Data System (ADS)

Rubeena, V.; Tiwari, K. C.

2016-04-01

The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

Breast density characterization using texton distributions.

PubMed

Petroudi, Styliani; Brady, Michael

2011-01-01

Breast density has been shown to be one of the most significant risks for developing breast cancer, with women with dense breasts at four to six times higher risk. The Breast Imaging Reporting and Data System (BI-RADS) has a four class classification scheme that describes the different breast densities. However, there is great inter and intra observer variability among clinicians in reporting a mammogram's density class. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. The new method represents the mammogram using textons, which can be thought of as the building blocks of texture under the operational definition of Leung and Malik as clustered filter responses. The new proposed method characterizes the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) in the breast region's corresponding texton map. The TSDM is a texture model that captures both statistical and structural texture characteristics. The normalized TSDM matrices are evaluated for mammograms from the different density classes and corresponding texture models are established. Classification is achieved using a chi-square distance measure. The fully automated TSDM breast density classification method is quantitatively evaluated on mammograms from all density classes from the Oxford Mammogram Database. The incorporation of texton spatial dependencies allows for classification accuracy reaching over 82%. The breast density classification accuracy is better using texton TSDM compared to simple texton histograms.

A multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan

An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the affected area in the case of a nuclear event. This imagery served as a second source of data to augment results from the time series approach. The classifications from the two approaches were integrated using an a posteriori probability-based fusion approach. This was done by establishing a relationship between the classes, obtained after classification of the two data sources. Despite the coarse spatial resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion-based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. This fusion thus contributed to classification accuracy refinement, with a few additional advantages, such as correction for cloud cover and providing for an approach that is robust against point-in-time seasonal anomalies, due to the inclusion of multi-temporal data. We concluded that this approach is capable of generating land cover maps of acceptable accuracy and rapid turnaround, which in turn can yield reliable estimates of crop acreage of a region. The final algorithm is part of an automated software tool, which can be used by emergency response personnel to generate a nuclear ingestion pathway information product within a few hours of data collection.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

Spatial and thematic assessment of object-based forest stand delineation using an OFA-matrix

NASA Astrophysics Data System (ADS)

Hernando, A.; Tiede, D.; Albrecht, F.; Lang, S.

2012-10-01

The delineation and classification of forest stands is a crucial aspect of forest management. Object-based image analysis (OBIA) can be used to produce detailed maps of forest stands from either orthophotos or very high resolution satellite imagery. However, measures are then required for evaluating and quantifying both the spatial and thematic accuracy of the OBIA output. In this paper we present an approach for delineating forest stands and a new Object Fate Analysis (OFA) matrix for accuracy assessment. A two-level object-based orthophoto analysis was first carried out to delineate stands on the Dehesa Boyal public land in central Spain (Avila Province). Two structural features were first created for use in class modelling, enabling good differentiation between stands: a relational tree cover cluster feature, and an arithmetic ratio shadow/tree feature. We then extended the OFA comparison approach with an OFA-matrix to enable concurrent validation of thematic and spatial accuracies. Its diagonal shows the proportion of spatial and thematic coincidence between a reference data and the corresponding classification. New parameters for Spatial Thematic Loyalty (STL), Spatial Thematic Loyalty Overall (STLOVERALL) and Maximal Interfering Object (MIO) are introduced to summarise the OFA-matrix accuracy assessment. A stands map generated by OBIA (classification data) was compared with a map of the same area produced from photo interpretation and field data (reference data). In our example the OFA-matrix results indicate good spatial and thematic accuracies (>65%) for all stand classes except for the shrub stands (31.8%), and a good STLOVERALL (69.8%). The OFA-matrix has therefore been shown to be a valid tool for OBIA accuracy assessment.

Decorrelation scales for Arctic Ocean hydrography - Part I: Amerasian Basin

NASA Astrophysics Data System (ADS)

Sumata, Hiroshi; Kauker, Frank; Karcher, Michael; Rabe, Benjamin; Timmermans, Mary-Louise; Behrendt, Axel; Gerdes, Rüdiger; Schauer, Ursula; Shimada, Koji; Cho, Kyoung-Ho; Kikuchi, Takashi

2018-03-01

Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Amerasian Basin of the Arctic Ocean. For this purpose, we compile historical measurements from 1980 to 2015. Assuming spatial and temporal homogeneity of the decorrelation scale in the basin interior (abyssal plain area), we calculate autocorrelations as a function of spatial distance and temporal lag. The examination of the functional form of autocorrelation in each depth range reveals that the autocorrelation is well described by a Gaussian function in space and time. We derive decorrelation scales of 150-200 km in space and 100-300 days in time. These scales are directly applicable to quantify the representation error, which is essential for use of ocean in situ measurements in data assimilation. We also describe how the estimated autocorrelation function and decorrelation scale should be applied for cost function calculation in a data assimilation system.

Lava Morphology Classification of a Fast-Spreading Ridge Using Deep-Towed Sonar Data: East Pacific Rise

NASA Astrophysics Data System (ADS)

Meyer, J.; White, S.

2005-05-01

Classification of lava morphology on a regional scale contributes to the understanding of the distribution and extent of lava flows at a mid-ocean ridge. Seafloor classification is essential to understand the regional undersea environment at midocean ridges. In this study, the development of a classification scheme is found to identify and extract textural patterns of different lava morphologies along the East Pacific Rise using DSL-120 side-scan and ARGO camera imagery. Application of an accurate image classification technique to side-scan sonar allows us to expand upon the locally available visual ground reference data to make the first comprehensive regional maps of small-scale lava morphology present at a mid-ocean ridge. The submarine lava morphologies focused upon in this study; sheet flows, lobate flows, and pillow flows; have unique textures. Several algorithms were applied to the sonar backscatter intensity images to produce multiple textural image layers useful in distinguishing the different lava morphologies. The intensity and spatially enhanced images were then combined and applied to a hybrid classification technique. The hybrid classification involves two integrated classifiers, a rule-based expert system classifier and a machine learning classifier. The complementary capabilities of the two integrated classifiers provided a higher accuracy of regional seafloor classification compared to using either classifier alone. Once trained, the hybrid classifier can then be applied to classify neighboring images with relative ease. This classification technique has been used to map the lava morphology distribution and infer spatial variability of lava effusion rates along two segments of the East Pacific Rise, 17 deg S and 9 deg N. Future use of this technique may also be useful for attaining temporal information. Repeated documentation of morphology classification in this dynamic environment can be compared to detect regional seafloor change.

SOHO EIT Carrington maps from synoptic full-disk data

NASA Technical Reports Server (NTRS)

Thompson, B. J.; Newmark, J. S.; Gurman, J. B.; Delaboudiniere, J. P.; Clette, F.; Gibson, S. E.

1997-01-01

The solar synoptic maps, obtained from observations carried out since May 1996 by the extreme-ultraviolet imaging telescope (EIT) onboard the Solar and Heliospheric Observatory (SOHO), are presented. The maps were constructed for each Carrington rotation with the calibrated data. The off-limb maps at 1.05 and 1.10 solar radii were generated for three coronal lines using the standard applied to coronagraph synoptic maps. The maps reveal several aspects of the solar structure over the entire rotation and are used in the whole sun month modeling campaign. @txt extreme-ultraviolet imaging telescope

Development of specifications for surface and subsurface oceanic environmental data

NASA Technical Reports Server (NTRS)

Wolff, P. M.

1976-01-01

The existing need for synoptic subsurface observations was demonstrated giving special attention to the requirements of meteorology. The current state of synoptic oceanographic observations was assessed; a preliminary design for the Basic Observational Network needed to fulfill the minimum needs of synoptic meteorology and oceanography was presented. There is an existing critical need for such a network in the support of atmospheric modeling and operational meteorological prediction, and through utilization of the regional water mass concept an adequate observational system can be designed which is realistic in terms of cost and effort.

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification.

PubMed

Spinnato, J; Roubaud, M-C; Burle, B; Torrésani, B

2015-06-01

The main goal of this work is to develop a model for multisensor signals, such as magnetoencephalography or electroencephalography (EEG) signals that account for inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI-type experiments. The method involves the linear mixed effects statistical model, wavelet transform, and spatial filtering, and aims at the characterization of localized discriminant features in multisensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e., discriminant) and background noise, using a very simple Gaussian linear mixed model. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. The combination of the linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves upon earlier results on similar problems, and the three main ingredients all play an important role.

Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach.

PubMed

Ecker, Christine; Marquand, Andre; Mourão-Miranda, Janaina; Johnston, Patrick; Daly, Eileen M; Brammer, Michael J; Maltezos, Stefanos; Murphy, Clodagh M; Robertson, Dene; Williams, Steven C; Murphy, Declan G M

2010-08-11

Autism spectrum disorder (ASD) is a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. Here, we demonstrate how a multiparameter classification approach can be used to characterize the complex and subtle structural pattern of gray matter anatomy implicated in adults with ASD, and to reveal spatially distributed patterns of discriminating regions for a variety of parameters describing brain anatomy. A set of five morphological parameters including volumetric and geometric features at each spatial location on the cortical surface was used to discriminate between people with ASD and controls using a support vector machine (SVM) analytic approach, and to find a spatially distributed pattern of regions with maximal classification weights. On the basis of these patterns, SVM was able to identify individuals with ASD at a sensitivity and specificity of up to 90% and 80%, respectively. However, the ability of individual cortical features to discriminate between groups was highly variable, and the discriminating patterns of regions varied across parameters. The classification was specific to ASD rather than neurodevelopmental conditions in general (e.g., attention deficit hyperactivity disorder). Our results confirm the hypothesis that the neuroanatomy of autism is truly multidimensional, and affects multiple and most likely independent cortical features. The spatial patterns detected using SVM may help further exploration of the specific genetic and neuropathological underpinnings of ASD, and provide new insights into the most likely multifactorial etiology of the condition.

A Land System representation for global assessments and land-use modeling.

PubMed

van Asselen, Sanneke; Verburg, Peter H

2012-10-01

Current global scale land-change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land-use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human-environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi-)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land-use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land-change models that include the human drivers of land change are best parameterized at sub-global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions. © 2012 Blackwell Publishing Ltd.

Case study modeling of turbulent and mesoscale fluxes over the BOREAS region

USGS Publications Warehouse

Vidale, P.L.; Pielke, R.A.; Steyaert, L.T.; Barr, A.

1997-01-01

Results from aircraft and surface observations provided evidence for the existence of mesoscale circulations over the Boreal Ecosystem-Atmosphere Study (BOREAS) domain. Using an integrated approach that included the use of analytical modeling, numerical modeling, and data analysis, we have found that there are substantial contributions to the total budgets of heat over the BOREAS domain generated by mesoscale circulations. This effect is largest when the synoptic flow is relatively weak, yet it is present under less favorable conditions, as shown by the case study presented here. While further analysis is warranted to document this effect, the existence of mesoscale flow is not surprising, since it is related to the presence of landscape patches, including lakes, which are of a size on the order of the local Rossby radius and which have spatial differences in maximum sensible heat flux of about 300 W m-2. We have also analyzed the vertical temperature profile simulated in our case study as well as high-resolution soundings and we have found vertical profiles of temperature change above the boundary layer height, which we attribute in part to mesoscale contributions. Our conclusion is that in regions with organized landscapes, such as BOREAS, even with relatively strong synoptic winds, dynamical scaling criteria should be used to assess whether mesoscale effects should be parameterized or explicitly resolved in numerical models of the atmosphere.

Landscape Characteristics and Variations in Longitudinal Stream Flow Contribution in two Headwater Semi-Arid Mountain Watersheds

NASA Astrophysics Data System (ADS)

Shakespeare, B.; Gooseff, M. N.

2005-12-01

Understanding what role particular catchment attributes (slope, aspect, landcover, and contributing area) play in the contribution of stream flow is important for land management decisions, especially in the semi-arid western areas of the United States. Our study site is paired small catchments (approximately 9 and 11 km2) in the headwaters of the Weber drainage basin in Northern Utah. These catchments are surrounded by Wasatch formation with loamy textured soils. One catchment is predominantly underlain by quartzite while the other catchment is mostly underlain by limestone. We measured lateral flow gains every 200 to 400 meters using salt dilution gauging techniques throughout the ~5 km long streams. These measurements were taken synoptically 3 times during the seasonal discharge recession (summer 2005). The flows ranged spatially from 4 L s-1 to 55 L s-1 and varied temporally by as much as 50% when comparing the same reaches. Using GIS software, landscape analysis of slope, aspect, contributing area, topographic convergence, riparian and hillslope area, and landcover was performed for each of the delineated stream reach contributing areas. The results were tested for correlations between lateral flow gains measured in the field and different landscape characteristics. Each of the synoptic events was compared with each other to explore effects of seasonal recession on the relationships between flow gain and landscape characteristics.

Solar system exploration from the Moon: Synoptic and comparative study of bodies in our Planetary system

NASA Technical Reports Server (NTRS)

Bruston, P.; Mumma, M. J.

1994-01-01

An observational approach to Planetary Sciences and exploration from Earth applies to a quite limited number of targets, but most of these are spatially complex, and exhibit variability and evolution on a number of temporal scales which lie within the scope of possible observations. Advancing our understanding of the underlying physics requires the study of interactions between the various elements of such systems, and also requires study of the comparative response of both a given object to various conditions and of comparable objects to similar conditions. These studies are best conducted in 'campaigns', i.e. comprehensive programs combining simultaneous coherent observations of every interacting piece of the puzzle. The requirements include both imaging and spectroscopy over a wide spectral range, from UV to IR. While temporal simultaneity of operation in various modes is a key feature, these observations are also conducted over extended periods of time. The moon is a prime site offering long unbroken observation times and high positional stability, observations at small angular separation from the sun, comparative studies of planet Earth, and valuable technical advantages. A lunar observatory should become a central piece of any coherent set of planetary missions, supplying in-situ explorations with the synoptic and comparative data necessary for proper advance planning, correlative observations during the active exploratory phase, and follow-up studies of the target body or of related objects.

Object-based methods for individual tree identification and tree species classification from high-spatial resolution imagery

NASA Astrophysics Data System (ADS)

Wang, Le

2003-10-01

Modern forest management poses an increasing need for detailed knowledge of forest information at different spatial scales. At the forest level, the information for tree species assemblage is desired whereas at or below the stand level, individual tree related information is preferred. Remote Sensing provides an effective tool to extract the above information at multiple spatial scales in the continuous time domain. To date, the increasing volume and readily availability of high-spatial-resolution data have lead to a much wider application of remotely sensed products. Nevertheless, to make effective use of the improving spatial resolution, conventional pixel-based classification methods are far from satisfactory. Correspondingly, developing object-based methods becomes a central challenge for researchers in the field of Remote Sensing. This thesis focuses on the development of methods for accurate individual tree identification and tree species classification. We develop a method in which individual tree crown boundaries and treetop locations are derived under a unified framework. We apply a two-stage approach with edge detection followed by marker-controlled watershed segmentation. Treetops are modeled from radiometry and geometry aspects. Specifically, treetops are assumed to be represented by local radiation maxima and to be located near the center of the tree-crown. As a result, a marker image was created from the derived treetop to guide a watershed segmentation to further differentiate overlapping trees and to produce a segmented image comprised of individual tree crowns. The image segmentation method developed achieves a promising result for a 256 x 256 CASI image. Then further effort is made to extend our methods to the multiscales which are constructed from a wavelet decomposition. A scale consistency and geometric consistency are designed to examine the gradients along the scale-space for the purpose of separating true crown boundary from unwanted textures occurring due to branches and twigs. As a result from the inverse wavelet transform, the tree crown boundary is enhanced while the unwanted textures are suppressed. Based on the enhanced image, an improvement is achieved when applying the two-stage methods to a high resolution aerial photograph. To improve tree species classification, we develop a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. The optimal scale parameter is then fed in the process of a region-growing-based segmentation as a break-off value. Our object classification achieves a better accuracy in separating tree species when compared to the conventional Maximum Likelihood Classification (MLC). In summary, we develop two object-based methods for identifying individual trees and classifying tree species from high-spatial resolution imagery. Both methods achieve promising results and will promote integration of Remote Sensing and GIS in forest applications.

Agricultural Land Use mapping by multi-sensor approach for hydrological water quality monitoring

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Kodes, Vit

2010-05-01

The main objective of this study is to demonstrate potential of operational use of the high and medium resolution remote sensing data for hydrological water quality monitoring by mapping agriculture intensity and crop structures. In particular use of remote sensing mapping for optimization of pesticide monitoring. The agricultural mapping task is tackled by means of medium spatial and high temporal resolution ESA Envisat MERIS FR images together with single high spatial resolution IRS AWiFS image covering the whole area of interest (the Czech Republic). High resolution data (e.g. SPOT, ALOS, Landsat) are often used for agricultural land use classification, but usually only at regional or local level due to data availability and financial constraints. AWiFS data (nominal spatial resolution 56 m) due to the wide satellite swath seems to be more suitable for use at national level. Nevertheless, one of the critical issues for such a classification is to have sufficient image acquisitions over the whole vegetation period to describe crop development in appropriate way. ESA MERIS middle-resolution data were used in several studies for crop classification. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. However, spatial resolution of 300 m results in mixture signal in a single pixel. AWiFS-MERIS data synergy brings new perspectives in agricultural Land Use mapping. Also, the developed methodology procedure is fully compatible with future use of ESA (GMES) Sentinel satellite images. The applied methodology of hybrid multi-sensor approach consists of these main stages: a/ parcel segmentation and spectral pre-classification of high resolution image (AWiFS); b/ ingestion of middle resolution (MERIS) vegetation spectro-temporal features; c/ vegetation signatures unmixing; and d/ semantic object-oriented classification of vegetation classes into final classification scheme. These crop groups were selected to be classified: winter crops, spring crops, oilseed rape, legumes, summer and other crops. This study highlights operational potentials of high temporal full resolution MERIS images in agricultural land use monitoring. Practical application of this methodology is foreseen, among others, in the water quality monitoring. Effective pesticide monitoring relies also on spatial distribution of applied pesticides, which can be derived from crop - plant protection product relationship. Knowledge of areas with predominant occurrence of specific crop based on remote sensing data described above can be used for a forecast of probable plant protection product application, thus cost-effective pesticide monitoring. The remote sensing data used on a continuous basis can be used in other long-term water management issues and provide valuable data for decision makers. Acknowledgement: Authors acknowledge the financial support of the Ministry of Education, Youth and Sports of the Czech Republic (grants No. 2B06095 and No. MSM 6046070901). The study was also supported by ESA CAT-1 (ref. 4358) and SOSI projects (Spatial Observation Services and Infrastructure; ref. GSTP-RTDA-EOPG-SW-08-0004).

Multi-proxy monitoring approaches at Kangaroo Island, South Australia

NASA Astrophysics Data System (ADS)

Dixon, Bronwyn; Drysdale, Russell; Tyler, Jonathan; Goodwin, Ian

2017-04-01

Interpretations of geochemical signals preserved in young speleothems are greatly enhanced by comprehensive cave-site monitoring. In the light of this, a cave monitoring project is being conducted concurrently with the development of a new palaeoclimate record from Kelly Hill Cave (Kangaroo Island, South Australia). The site is strategically located because it is situated between longer-lived monitoring sites in southeastern and southwestern Australia, as well as being climatically 'upstream' from major population and agricultural centres. This study aims to understand possible controls on speleothem δ18O in Kelly Hill Cave through i. identification of local and regional δ18O drivers in precipitation; and ii. preservation and modification of climatic signals within the epikarst as indicated by dripwater δ18O. These aims are achieved through analysis of a five-year daily rainfall (amount and δ18O) dataset in conjunction with in-cave drip monitoring. Drivers of precipitation δ18O were identified through linear regression between δ18O values and local meteorological variables, air-parcel back trajectories, and synoptic-typing. Synoptically driven moisture sources were identified through the use of NCEP/NCAR climate reanalysis sea-level pressure, precipitable moisture, and outgoing longwave radiation data in order to trace moisture sources and travel mechanisms from surrounding ocean basins. Local controls on δ18O at Kelly Hill Cave are consistent with published interpretations of southern Australia sites, with oxygen isotopes primarily controlled by rainfall amount on both daily and monthly time scales. Back-trajectory analysis also supports previous observations that the Southern Ocean is the major source for moisture-bearing cold-front systems. However, synoptic typing of daily rainfall δ18O and amount extremes reveals a previously unreported tropical connection and moisture source. This tropical connection appears to be strongest in summer and autumn, but exists throughout the year. This indicates that a wider range of precipitation data sources can be combined to present a more comprehensive understanding of moisture dynamics and interaction of synoptic conditions to drive rainfall geochemistry. Within the cave environment at Kelly Hill Cave there is high spatial variability in drip characteristics, both in terms of drip frequency and drip water δ18O. Ongoing analyses are aimed at determining if monthly and/or seasonal rainfall δ18O drivers are also reflected in dripwater values. Overall, Kangaroo Island presents a new location to investigate the interplay between tropical and temperate influences in southern Australia, as well as a location for east - west comparisons between monitoring sites across southern Australia.

Analysis of land surface and synoptic conditions during dust storm events in the Middle East via a new high resolution inventory of mineral dust derived from SEVIRI.

NASA Astrophysics Data System (ADS)

Hennen, Mark

2017-04-01

This paper provides the most up-to-date dust climatology for the Middle East, presenting a new high resolution inventory of over 27,000 dust emission events observed over the Middle East in 2006 - 2013. The inventory was derived from the dust RGB product of the Spinning Enhanced Visual and InfraRed Imager (SEVIRI) on-board Meteosat's second generation satellite (MSG). Mineral dust emissions were derived from visual inspection of the SEVIRI scenes which have 4-5 km2 spatial and 15-minute temporal resolution. The location of every emission event was recorded in a database, along with time and trajectory of dust movement. This is an improvement on previous studies, which derive dust source areas from the daily observations of Aerosol Optical Depth whose maxima do not necessarily coincide with sources of emissions and produces more accurate information on the location of the key dust sources in the region. Results showed that dust sources are constrained to relatively small areas, with 21% of dust emission generated from just 0.9% of total surface area of the Middle East, mainly from eight source regions including the Tigris-Euphrates flood plains of Iraq and Syria, Western and Northern Saudi Arabia and the Sistan Basin in Eastern Iran. The Tigris-Euphrates flood plain was the most active dust region, producing 41% of all dust events with a peak activity in 2009. The southern areas of the Arabian Peninsula recorded very few dust emission observations, in contrast to many previous studies which do not use such high temporal resolution data. The activation and frequency of dust emissions are characterised by strong seasonality developing in response to specific synoptic conditions. To characterise synoptic conditions conducive to the development of dust storms, dust days' emission thresholds, based on number of dust emission events per day / per region and specific to each of the eight main dust emitting regions, were determined. ERA Interim reanalysis data were used to characterise synoptic conditions on the identified dust days. With vegetation cover dictating the ability for surface areas to deflate, Normalised Difference Vegetation Index (NDVI) data was acquired from the Moderate Resolution Imaging Spectrodiometer (MODIS) (MOD13A2) 1km database and correlated with dust emission frequency data in the region of greatest dust activity, the Tigris and Euphrates flood plain in Iraq and Syria.

Identification of mosquito larval habitats in high resolution satellite data

NASA Astrophysics Data System (ADS)

Kiang, Richard K.; Hulina, Stephanie M.; Masuoka, Penny M.; Claborn, David M.

2003-09-01

Mosquito-born infectious diseases are a serious public health concern, not only for the less developed countries, but also for developed countries like the U.S. Larviciding is an effective method for vector control and adverse effects to non-target species are minimized when mosquito larval habitats are properly surveyed and treated. Remote sensing has proven to be a useful technique for large-area ground cover mapping, and hence, is an ideal tool for identifying potential larval habitats. Locating small larval habitats, however, requires data with very high spatial resolution. Textural and contextual characteristics become increasingly evident at higher spatial resolution. Per-pixel classification often leads to suboptimal results. In this study, we use pan-sharpened Ikonos data, with a spatial resolution approaching 1 meter, to classify potential mosquito larval habitats for a test site in South Korea. The test site is in a predominantly agricultural region. When spatial characteristics were used in conjunction with spectral data, reasonably good classification accuracy was obtained for the test site. In particular, irrigation and drainage ditches are important larval habitats but their footprints are too small to be detected with the original spectral data at 4-meter resolution. We show that the ditches are detectable using automated classification on pan-sharpened data.

Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface.

PubMed

Ng, Kian B; Bradley, Andrew P; Cunnington, Ross

2012-06-01

The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.

Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface

NASA Astrophysics Data System (ADS)

Ng, Kian B.; Bradley, Andrew P.; Cunnington, Ross

2012-06-01

The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.

Lithologic discrimination of volcanic and sedimentary rocks by spectral examination of Landsat TM data from the Puma, Central Andes Mountains

NASA Technical Reports Server (NTRS)

Fielding, E. J.

1986-01-01

The Central Andes are widely used as a modern example of noncollisional mountain-building processes. The Puna is a high plateau in the Chilean and Argentine Central Andes extending southward from the altiplano of Bolivia and Peru. Young tectonic and volcanic features are well exposed on the surface of the arid Puna, making them prime targets for the application of high-resolution space imagery such as Shuttle Imaging Radar B and Landsat Thematic Mapper (TM). Two TM scene quadrants from this area are analyzed using interactive color image processing, examination, and automated classification algorithms. The large volumes of these high-resolution datasets require significantly different techniques than have been used previously for the interpretation of Landsat MSS data. Preliminary results include the determination of the radiance spectra of several volcanic and sedimentary rock units and the use of the spectra for automated classification. Structural interpretations have revealed several previously unknown folds in late Tertiary strata, and key zones have been targeted to be investigated in the field. The synoptic view of space imagery is already filling a critical gap between low-resolution geophysical data and traditional geologic field mapping in the reconnaissance study of poorly mapped mountain frontiers such as the Puna.

Simulated life cycles of persistent anticyclonic anomalies over the North Pacific: Role of synoptic-scale eddies

NASA Technical Reports Server (NTRS)

Higgins, R. W.; Schubert, S. D.

1994-01-01

This study examines the role of synoptic-scale eddies during the development of persistent anticyclonic height anomalies over the central North Pacific in a general circulation model under perpetual January conditions. The General Circulation Model (GCM) replicates the basic characteristics of the evolution of the anomaly patterns found in observations. The life cycle is characterized by the rapid establishment of the major anomaly center and considerably longer maintenance and decay phases, which include the development of downstream anomaly centers. The simulation also shows a realistic evolution of synoptic-scale activity beginning with enhanced activity off the east coast of Asia prior to onset, followed by a northward shift of the Pacific storm track, which lasts throughout the maintenance phase. The initial enhancement of synoptic-scale eddy activity is associated with a large-scale cyclonic anomaly that developes over Siberia several days prior to the onset of the main anticyclonic anomaly over the central North Pacific. The observations, however, show considerable interdecadel variability in the details of the composite onset behavior; it is unclear whether this variability is real or whether it reflects differences in the data assimilation systems. The role of the time mean flow and synoptic-scale eddies in the development of the persistent Pacific anomalies is studied within the context of a kinetic energy budget in which the flow is decomposed into the time-mean, low-frequency (timescales longer than 10 days), and synoptic (timescales less than 6 days) components. The budget, which is carried out for the simulation at 500 mb, shows that the initial growth of the persistent anticyclonic anomalies is associated with barotropic conversions of energy, with approximately equal contributions coming from the mean flow and the synoptic-scale eddies. After onset the barotropic conversion from the mean flow dominates, whereas the decay phase is associated with baroclinic processes within the low-frequency flow.

Cluster analysis of Southeastern U.S. climate stations

NASA Astrophysics Data System (ADS)

Stooksbury, D. E.; Michaels, P. J.

1991-09-01

A two-step cluster analysis of 449 Southeastern climate stations is used to objectively determine general climate clusters (groups of climate stations) for eight southeastern states. The purpose is objectively to define regions of climatic homogeneity that should perform more robustly in subsequent climatic impact models. This type of analysis has been successfully used in many related climate research problems including the determination of corn/climate districts in Iowa (Ortiz-Valdez, 1985) and the classification of synoptic climate types (Davis, 1988). These general climate clusters may be more appropriate for climate research than the standard climate divisions (CD) groupings of climate stations, which are modifications of the agro-economic United States Department of Agriculture crop reporting districts. Unlike the CD's, these objectively determined climate clusters are not restricted by state borders and thus have reduced multicollinearity which makes them more appropriate for the study of the impact of climate and climatic change.

Development of computer software to analyze entire LANDSAT scenes and to summarize classification results of variable-size polygons

NASA Technical Reports Server (NTRS)

Turner, B. J. (Principal Investigator); Baumer, G. M.; Myers, W. L.; Sykes, S. G.

1981-01-01

The Forest Pest Management Division (FPMD) of the Pennsylvania Bureau of Forestry has the responsibility for conducting annual surveys of the State's forest lands to accurately detect, map, and appraise forest insect infestations. A standardized, timely, and cost-effective method of accurately surveying forests and their condition should enhance the probability of suppressing infestations. The repetitive and synoptic coverage provided by LANDSAT (formerly ERTS) makes such satellite-derived data potentially attractive as a survey medium for monitoring forest insect damage over large areas. Forest Pest Management Division personnel have expressed keen interest in LANDSAT data and have informally cooperated with NASA/Goddard Space Flight Center (GSFC) since 1976 in the development of techniques to facilitate their use. The results of this work indicate that it may be feasible to use LANDSAT digital data to conduct annual surveys of insect defoliation of hardwood forests.

Summary of an integrated ERTS-1 project and its results at the Missouri Geological Survey

NASA Technical Reports Server (NTRS)

Martin, J. A.; Allen, W. H.; Rath, D. L.; Rueff, A.

1974-01-01

Use of the ERTS imagery involved the recognition and interpretation of various ground patterns. Analysis and application are tied to ongoing programs. Specific studies utilizing the imagery and NASA aircraft photography are: a statewide lake and dam inventory; assessment of flooding and floodprone areas along the Missouri portion of the Mississippi and Missouri Rivers; land-use classification for several counties; structural features in selected areas; and Pleistocene features in northern Missouri. Though it has been suggested that repetitive coverage is not necessary for geologic studies, it is this specific feature along with the synoptic view of large portions of the State that provided the potential for the utilization of the ERTS imagery in Missouri. Other State agencies, Departments of Conservation, Agriculture, and Community Affairs, have expressed interest in the potential application of ERTS data in their respective fields.

Projected Near-Earth Object Discovery Performance of the Large Synoptic Survey Telescope

NASA Technical Reports Server (NTRS)

Chesley, Steven R.; Veres, Peter

2017-01-01

This report describes the methodology and results of an assessment study of the performance of the Large Synoptic Survey Telescope (LSST) in its planned efforts to detect and catalog near-Earth objects (NEOs).

Neighbourhood-Scale Urban Forest Ecosystem Classification

Treesearch

James W.N. Steenberg; Andrew A. Millward; Peter N. Duinker; David J. Nowak; Pamela J. Robinson

2015-01-01

Urban forests are now recognized as essential components of sustainable cities, but there remains uncertainty concerning how to stratify and classify urban landscapes into units of ecological significance at spatial scales appropriate for management. Ecosystem classification is an approach that entails quantifying the social and ecological processes that shape...

INVENTORY AND CLASSIFICATION OF GREAT LAKES COASTAL WETLANDS FOR MONITORING AND ASSESSMENT AT LARGE SPATIAL SCALES

EPA Science Inventory

Monitoring aquatic resources for regional assessments requires an accurate and comprehensive inventory of the resource and useful classification of exosystem similarities. Our research effort to create an electronic database and work with various ways to classify coastal wetlands...

Automated feature extraction and classification from image sources

USGS Publications Warehouse

,

1995-01-01

The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

On unravelling mechanism of interplay between cloud and large scale circulation: a grey area in climate science

NASA Astrophysics Data System (ADS)

De, S.; Agarwal, N. K.; Hazra, Anupam; Chaudhari, Hemantkumar S.; Sahai, A. K.

2018-04-01

The interaction between cloud and large scale circulation is much less explored area in climate science. Unfolding the mechanism of coupling between these two parameters is imperative for improved simulation of Indian summer monsoon (ISM) and to reduce imprecision in climate sensitivity of global climate model. This work has made an effort to explore this mechanism with CFSv2 climate model experiments whose cloud has been modified by changing the critical relative humidity (CRH) profile of model during ISM. Study reveals that the variable CRH in CFSv2 has improved the nonlinear interactions between high and low frequency oscillations in wind field (revealed as internal dynamics of monsoon) and modulates realistically the spatial distribution of interactions over Indian landmass during the contrasting monsoon season compared to the existing CRH profile of CFSv2. The lower tropospheric wind error energy in the variable CRH simulation of CFSv2 appears to be minimum due to the reduced nonlinear convergence of error to the planetary scale range from long and synoptic scales (another facet of internal dynamics) compared to as observed from other CRH experiments in normal and deficient monsoons. Hence, the interplay between cloud and large scale circulation through CRH may be manifested as a change in internal dynamics of ISM revealed from scale interactive quasi-linear and nonlinear kinetic energy exchanges in frequency as well as in wavenumber domain during the monsoon period that eventually modify the internal variance of CFSv2 model. Conversely, the reduced wind bias and proper modulation of spatial distribution of scale interaction between the synoptic and low frequency oscillations improve the eastward and northward extent of water vapour flux over Indian landmass that in turn give feedback to the realistic simulation of cloud condensates attributing improved ISM rainfall in CFSv2.

Sea Surface Temperature Influence on Terrestrial Gross Primary Production along the Southern California Current.

PubMed

Reimer, Janet J; Vargas, Rodrigo; Rivas, David; Gaxiola-Castro, Gilberto; Hernandez-Ayon, J Martin; Lara-Lara, Ruben

2015-01-01

Some land and ocean processes are related through connections (and synoptic-scale teleconnections) to the atmosphere. Synoptic-scale atmospheric (El Niño/Southern Oscillation [ENSO], Pacific Decadal Oscillation [PDO], and North Atlantic Oscillation [NAO]) decadal cycles are known to influence the global terrestrial carbon cycle. Potentially, smaller scale land-ocean connections influenced by coastal upwelling (changes in sea surface temperature) may be important for local-to-regional water-limited ecosystems where plants may benefit from air moisture transported from the ocean to terrestrial ecosystems. Here we use satellite-derived observations to test potential connections between changes in sea surface temperature (SST) in regions with strong coastal upwelling and terrestrial gross primary production (GPP) across the Baja California Peninsula. This region is characterized by an arid/semiarid climate along the southern California Current. We found that SST was correlated with the fraction of photosynthetic active radiation (fPAR; as a proxy for GPP) with lags ranging from 0 to 5 months. In contrast ENSO was not as strongly related with fPAR as SST in these coastal ecosystems. Our results show the importance of local-scale changes in SST during upwelling events, to explain the variability in GPP in coastal, water-limited ecosystems. The response of GPP to SST was spatially-dependent: colder SST in the northern areas increased GPP (likely by influencing fog formation), while warmer SST at the southern areas was associated to higher GPP (as SST is in phase with precipitation patterns). Interannual trends in fPAR are also spatially variable along the Baja California Peninsula with increasing secular trends in subtropical regions, decreasing trends in the most arid region, and no trend in the semi-arid regions. These findings suggest that studies and ecosystem process based models should consider the lateral influence of local-scale ocean processes that could influence coastal ecosystem productivity.

Sea Surface Temperature Influence on Terrestrial Gross Primary Production along the Southern California Current

PubMed Central

Reimer, Janet J.; Vargas, Rodrigo; Rivas, David; Gaxiola-Castro, Gilberto; Hernandez-Ayon, J. Martin; Lara-Lara, Ruben

2015-01-01

Some land and ocean processes are related through connections (and synoptic-scale teleconnections) to the atmosphere. Synoptic-scale atmospheric (El Niño/Southern Oscillation [ENSO], Pacific Decadal Oscillation [PDO], and North Atlantic Oscillation [NAO]) decadal cycles are known to influence the global terrestrial carbon cycle. Potentially, smaller scale land-ocean connections influenced by coastal upwelling (changes in sea surface temperature) may be important for local-to-regional water-limited ecosystems where plants may benefit from air moisture transported from the ocean to terrestrial ecosystems. Here we use satellite-derived observations to test potential connections between changes in sea surface temperature (SST) in regions with strong coastal upwelling and terrestrial gross primary production (GPP) across the Baja California Peninsula. This region is characterized by an arid/semiarid climate along the southern California Current. We found that SST was correlated with the fraction of photosynthetic active radiation (fPAR; as a proxy for GPP) with lags ranging from 0 to 5 months. In contrast ENSO was not as strongly related with fPAR as SST in these coastal ecosystems. Our results show the importance of local-scale changes in SST during upwelling events, to explain the variability in GPP in coastal, water-limited ecosystems. The response of GPP to SST was spatially-dependent: colder SST in the northern areas increased GPP (likely by influencing fog formation), while warmer SST at the southern areas was associated to higher GPP (as SST is in phase with precipitation patterns). Interannual trends in fPAR are also spatially variable along the Baja California Peninsula with increasing secular trends in subtropical regions, decreasing trends in the most arid region, and no trend in the semi-arid regions. These findings suggest that studies and ecosystem process based models should consider the lateral influence of local-scale ocean processes that could influence coastal ecosystem productivity. PMID:25923109

Modeling Spatial Dependencies and Semantic Concepts in Data Mining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju

Data mining is the process of discovering new patterns and relationships in large datasets. However, several studies have shown that general data mining techniques often fail to extract meaningful patterns and relationships from the spatial data owing to the violation of fundamental geospatial principles. In this tutorial, we introduce basic principles behind explicit modeling of spatial and semantic concepts in data mining. In particular, we focus on modeling these concepts in the widely used classification, clustering, and prediction algorithms. Classification is the process of learning a structure or model (from user given inputs) and applying the known model to themore » new data. Clustering is the process of discovering groups and structures in the data that are ``similar,'' without applying any known structures in the data. Prediction is the process of finding a function that models (explains) the data with least error. One common assumption among all these methods is that the data is independent and identically distributed. Such assumptions do not hold well in spatial data, where spatial dependency and spatial heterogeneity are a norm. In addition, spatial semantics are often ignored by the data mining algorithms. In this tutorial we cover recent advances in explicitly modeling of spatial dependencies and semantic concepts in data mining.« less

Climatic Redistribution of Canada's Water Resources (CROCWR): An analysis of spatial and temporal hydrological trends and patterns in western Canada

NASA Astrophysics Data System (ADS)

Bawden, A. J.; Burn, D. H.; Prowse, T. D.

2012-12-01

Climate variability and change can have profound impacts on the hydrologic regime of a watershed. These effects are likely to be especially severe in regions particularly sensitive to changes in climate, such as the Canadian north, or when there are other stresses on the hydrologic regime, such as may occur when there are large withdrawals from, or land-use changes within, a watershed. A recent report of the Intergovernmental Panel on Climate Change (IPCC) stressed that future climate is likely to accelerate the hydrologic cycle and hence may affect water security in certain locations. For some regions, this will mean enhanced access to water resources, but because the effects will not be spatially uniform, other regions will experience reduced access. Understanding these patterns is critical for water managers and government agencies in western Canada - an area of highly contrasting hydroclimatic regimes and overlapping water-use and jurisdictional borders - as adapting to climate change may require reconsideration of inter-regional transfers and revised allocation of water resources to competing industrial sectors, including agriculture, hydroelectric production, and oil and gas. This research involves the detection and examination of spatial and temporal streamflow trends in western Canadian rivers as a response to changing climatic factors, including temperature, precipitation, snowmelt, and the synoptic patterns controlling these drivers. The study area, known as the CROCWR region, extends from the Pacific coast of British Columbia as far east as the Saskatchewan-Manitoba border and from the Canada-United States international border through a large portion of the Northwest Territories. This analysis examines hydrologic trends in monthly and annual streamflow for a collection of 34 hydrometric gauging stations believed to adequately represent the overall effects of climate variability and change on flows in western Canada by means of the Mann-Kendall non-parametric trend test. Large-scale spatial patterns are determined through examination of trends and contrasts between upper and lower reaches of individual sub-basins, as well as via analysis of streamflow redistributions within the CROCWR region as an entirety (i.e. north, south, east and/or west-moving patterns). Results are used to predict future implications of hydroclimatic variability and change on western Canada's water resources and recommend measures to be taken by water managers in response to these changes. This research is part of a larger hydroclimatic study that includes an analysis of the climatic drivers contributing to shifting flow regimes in western Canada as well as a study of the controlling synoptic patterns and teleconnections associated with changes in these driving forces.

Synoptic Control of Contrail Cirrus Life Cycles and Their Modification Due to Reduced Soot Number Emissions

NASA Astrophysics Data System (ADS)

Bier, A.; Burkhardt, U.; Bock, L.

2017-11-01

The atmospheric state, aircraft emissions, and engine properties determine formation and initial properties of contrails. The synoptic situation controls microphysical and dynamical processes and causes a wide variability of contrail cirrus life cycles. A reduction of soot particle number emissions, resulting, for example, from the use of alternative fuels, strongly impacts initial ice crystal numbers and microphysical process rates of contrail cirrus. We use the European Centre/Hamburg (ECHAM) climate model version 5 including a contrail cirrus modul, studying process rates, properties, and life cycles of contrail cirrus clusters within different synoptic situations. The impact of reduced soot number emissions is approximated by a reduction in the initial ice crystal number, exemplarily studied for 80%. Contrail cirrus microphysical and macrophysical properties can depend much more strongly on the synoptic situation than on the initial ice crystal number. They can attain a large cover, optical depth, and ice water content in long-lived and large-scale ice-supersaturated areas, making them particularly climate-relevant. In those synoptic situations, the accumulated ice crystal loss due to sedimentation is increased by around 15% and the volume of contrail cirrus, exceeding an optical depth of 0.02, and their short-wave radiative impact are strongly decreased due to reduced soot emissions. These reductions are of little consequence in short-lived and small-scale ice-supersaturated areas, where contrail cirrus stay optically very thin and attain a low cover. The synoptic situations in which long-lived and climate-relevant contrail cirrus clusters can be found over the eastern U.S. occur in around 25% of cases.

High Altitude Bird Migration at Temperate Latitudes: A Synoptic Perspective on Wind Assistance

PubMed Central

Dokter, Adriaan M.; Shamoun-Baranes, Judy; Kemp, Michael U.; Tijm, Sander; Holleman, Iwan

2013-01-01

At temperate latitudes the synoptic patterns of bird migration are strongly structured by the presence of cyclones and anticyclones, both in the horizontal and altitudinal dimensions. In certain synoptic conditions, birds may efficiently cross regions with opposing surface wind by choosing a higher flight altitude with more favourable wind. We observed migratory passerines at mid-latitudes that selected high altitude wind optima on particular nights, leading to the formation of structured migration layers at varying altitude up to 3 km. Using long-term vertical profiling of bird migration by C-band Doppler radar in the Netherlands, we find that such migration layers occur nearly exclusively during spring migration in the presence of a high-pressure system. A conceptual analytic framework providing insight into the synoptic patterns of wind assistance for migrants that includes the altitudinal dimension has so far been lacking. We present a simple model for a baroclinic atmosphere that relates vertical profiles of wind assistance to the pressure and temperature patterns occurring at temperate latitudes. We show how the magnitude and direction of the large scale horizontal temperature gradient affects the relative gain in wind assistance that migrants obtain through ascending. Temperature gradients typical for northerly high-pressure systems in spring are shown to cause high altitude wind optima in the easterly sectors of anticyclones, thereby explaining the frequent observations of high altitude migration in these synoptic conditions. Given the recurring synoptic arrangements of pressure systems across temperate continents, the opportunities for exploiting high altitude wind will differ between flyways, for example between easterly and westerly oceanic coasts. PMID:23300969

High altitude bird migration at temperate latitudes: a synoptic perspective on wind assistance.

PubMed

Dokter, Adriaan M; Shamoun-Baranes, Judy; Kemp, Michael U; Tijm, Sander; Holleman, Iwan

2013-01-01

At temperate latitudes the synoptic patterns of bird migration are strongly structured by the presence of cyclones and anticyclones, both in the horizontal and altitudinal dimensions. In certain synoptic conditions, birds may efficiently cross regions with opposing surface wind by choosing a higher flight altitude with more favourable wind. We observed migratory passerines at mid-latitudes that selected high altitude wind optima on particular nights, leading to the formation of structured migration layers at varying altitude up to 3 km. Using long-term vertical profiling of bird migration by C-band Doppler radar in the Netherlands, we find that such migration layers occur nearly exclusively during spring migration in the presence of a high-pressure system. A conceptual analytic framework providing insight into the synoptic patterns of wind assistance for migrants that includes the altitudinal dimension has so far been lacking. We present a simple model for a baroclinic atmosphere that relates vertical profiles of wind assistance to the pressure and temperature patterns occurring at temperate latitudes. We show how the magnitude and direction of the large scale horizontal temperature gradient affects the relative gain in wind assistance that migrants obtain through ascending. Temperature gradients typical for northerly high-pressure systems in spring are shown to cause high altitude wind optima in the easterly sectors of anticyclones, thereby explaining the frequent observations of high altitude migration in these synoptic conditions. Given the recurring synoptic arrangements of pressure systems across temperate continents, the opportunities for exploiting high altitude wind will differ between flyways, for example between easterly and westerly oceanic coasts.

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

A higher order conditional random field model for simultaneous classification of land cover and land use

NASA Astrophysics Data System (ADS)

Albert, Lena; Rottensteiner, Franz; Heipke, Christian

2017-08-01

We propose a new approach for the simultaneous classification of land cover and land use considering spatial as well as semantic context. We apply a Conditional Random Fields (CRF) consisting of a land cover and a land use layer. In the land cover layer of the CRF, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Intra-layer edges of the CRF model spatial dependencies between neighbouring image sites. All spatially overlapping sites in both layers are connected by inter-layer edges, which leads to higher order cliques modelling the semantic relation between all land cover and land use sites in the clique. A generic formulation of the higher order potential is proposed. In order to enable efficient inference in the two-layer higher order CRF, we propose an iterative inference procedure in which the two classification tasks mutually influence each other. We integrate contextual relations between land cover and land use in the classification process by using contextual features describing the complex dependencies of all nodes in a higher order clique. These features are incorporated in a discriminative classifier, which approximates the higher order potentials during the inference procedure. The approach is designed for input data based on aerial images. Experiments are carried out on two test sites to evaluate the performance of the proposed method. The experiments show that the classification results are improved compared to the results of a non-contextual classifier. For land cover classification, the result is much more homogeneous and the delineation of land cover segments is improved. For the land use classification, an improvement is mainly achieved for land use objects showing non-typical characteristics or similarities to other land use classes. Furthermore, we have shown that the size of the super-pixels has an influence on the level of detail of the classification result, but also on the degree of smoothing induced by the segmentation method, which is especially beneficial for land cover classes covering large, homogeneous areas.

Trends in winter circulation over the British Isles and central Europe in twenty-first century projections by 25 CMIP5 GCMs

NASA Astrophysics Data System (ADS)

Stryhal, Jan; Huth, Radan

2018-03-01

Winter midlatitude atmospheric circulation has been extensively studied for its tight link to surface weather, and automated circulation classifications have often been used to this end. Here, eight such classifications are applied to daily sea level pressure patterns simulated by an ensemble of CMIP5 GCMs twenty-first century projections for the British Isles and central Europe in order to robustly estimate future changes in frequency, persistence, and strength of synoptic-scale circulation there. All methods are able to identify present-day biases of models reported before, such as an overestimated occurrence of zonal flow and underestimation of anticyclonic conditions and easterly advection, although the strength of these biases varies among the methods. In future, models show that the zonal flow will become more frequent while the strength of the mean flow is not projected to change. Over the British Isles, the models that better simulate the latitude of zonal flow over the historical period indicate a slight equatorward shift of westerlies in their projections, while the poleward expansion of circulation—expected in future at global scale—is apparent in those models that have large errors. Over central Europe, some classifications indicate an increase in persistence and especially in frequency of anticyclonic types, which is, however, shown to be rather an artifact of some methods than a real feature. On the other hand, the easterly flow is robustly projected to become markedly weaker in central Europe, which we hypothesize might be an important factor contributing to the projected decrease of cold extremes there.

Classification of Farmland Landscape Structure in Multiple Scales

NASA Astrophysics Data System (ADS)

Jiang, P.; Cheng, Q.; Li, M.

2017-12-01

Farmland is one of the basic terrestrial resources that support the development and survival of human beings and thus plays a crucial role in the national security of every country. Pattern change is the intuitively spatial representation of the scale and quality variation of farmland. Through the characteristic development of spatial shapes as well as through changes in system structures, functions and so on, farmland landscape patterns may indicate the landscape health level. Currently, it is still difficult to perform positioning analyses of landscape pattern changes that reflect the landscape structure variations of farmland with an index model. Depending on a number of spatial properties such as locations and adjacency relations, distance decay, fringe effect, and on the model of patch-corridor-matrix that is applied, this study defines a type system of farmland landscape structure on the national, provincial, and city levels. According to such a definition, the classification model of farmland landscape-structure type at the pixel scale is developed and validated based on mathematical-morphology concepts and on spatial-analysis methods. Then, the laws that govern farmland landscape-pattern change in multiple scales are analyzed from the perspectives of spatial heterogeneity, spatio-temporal evolution, and function transformation. The result shows that the classification model of farmland landscape-structure type can reflect farmland landscape-pattern change and its effects on farmland production function. Moreover, farmland landscape change in different scales displayed significant disparity in zonality, both within specific regions and in urban-rural areas.

Exploring the interpersonal-, organization-, and system-level factors that influence the implementation and use of an innovation-synoptic reporting-in cancer care.

PubMed

Urquhart, Robin; Porter, Geoffrey A; Grunfeld, Eva; Sargeant, Joan

2012-03-01

The dominant method of reporting findings from diagnostic and surgical procedures is the narrative report. In cancer care, this report inconsistently provides the information required to understand the cancer and make informed patient care decisions. Another method of reporting, the synoptic report, captures specific data items in a structured manner and contains only items critical for patient care. Research demonstrates that synoptic reports vastly improve the quality of reporting. However, synoptic reporting represents a complex innovation in cancer care, with implementation and use requiring fundamental shifts in physician behaviour and practice, and support from the organization and larger system. The objective of this study is to examine the key interpersonal, organizational, and system-level factors that influence the implementation and use of synoptic reporting in cancer care. This study involves three initiatives in Nova Scotia, Canada, that have implemented synoptic reporting within their departments/programs. Case study methodology will be used to study these initiatives (the cases) in-depth, explore which factors were barriers or facilitators of implementation and use, examine relationships amongst factors, and uncover which factors appear to be similar and distinct across cases. The cases were selected as they converge and differ with respect to factors that are likely to influence the implementation and use of an innovation in practice. Data will be collected through in-depth interviews, document analysis, observation of training sessions, and examination/use of the synoptic reporting tools. An audit will be performed to determine/quantify use. Analysis will involve production of a case record/history for each case, in-depth analysis of each case, and cross-case analysis, where findings will be compared and contrasted across cases to develop theoretically informed, generalisable knowledge that can be applied to other settings/contexts. Ethical approval was granted for this study. This study will contribute to our knowledge base on the multi-level factors, and the relationships amongst factors in specific contexts, that influence implementation and use of innovations such as synoptic reporting in healthcare. Such knowledge is critical to improving our understanding of implementation processes in clinical settings, and to helping researchers, clinicians, and managers/administrators develop and implement ways to more effectively integrate innovations into routine clinical care.