Evaluation of aerial photography for predicting trends in structural attributes of Australian woodland including comparison with ground-based monitoring data

The accurate assessment of trends in the woody structure of savannas has important implications for greenhouse accounting and land-use industries such as pastoralism. Two recent assessments of live woody biomass change from north-east Australian eucalypt woodland between the 1980s and 1990s present divergent results. The first estimate is derived from a network of permanent monitoring plots and the second from woody cover assessments from aerial photography. The differences between the studies are reviewed and include sample density, spatial scale and design. Further analyses targeting potential biases in the indirect aerial photography technique are conducted including a comparison of basal area estimates derived from 28 permanent monitoring sites with basal area estimates derived by the aerial photography technique. It is concluded that the effect of photo-scale; or the failure to include appropriate back-transformation of biomass estimates in the aerial photography study are not likely to have contributed significantly to the discrepancy. However, temporal changes in the structure of woodlands, for example, woodlands maturing from many smaller trees to fewer larger trees or seasonal changes, which affect the relationship between cover and basal area could impact on the detection of trends using the aerial photography technique. It is also possible that issues concerning photo-quality may bias assessments through time, and that the limited sample of the permanent monitoring network may inadequately represent change at regional scales

Short-window spectral analysis using AMVAR and multitaper methods: a comparison

We compare two popular methods for estimating the power spectrum from short data windows, namely the adaptive multivariate autoregressive (AMVAR) method and the multitaper method. By analyzing a simulated signal (embedded in a background Ornstein-Uhlenbeck noise process) we demonstrate that the AMVAR method performs better at detecting short bursts of oscillations compared to the multitaper method. However, both methods are immune to jitter in the temporal location of the signal. We also show that coherence can still be detected in noisy bivariate time series data by the AMVAR method even if the individual power spectra fail to show any peaks. Finally, using data from two monkeys performing a visuomotor pattern discrimination task, we demonstrate that the AMVAR method is better able to determine the termination of the beta oscillations when compared to the multitaper method.

Comparison and integration of MEG and fMRI

MEG directly measures the neuronal events and has greater temporal resolution than fMRI, which has limited temporal resolution mainly due to the larger timescale of the hemodynamic response. On the other hand fMRI has advantages in spatial resolution, while the localization results with MEG can be ambiguous due to the non-uniqueness of the electromagnetic inverse problem. Thus, these methods could provide complementary information and could be used to create both spatially and temporally accurate models of brain function. We investigated the degree of overlap, revealed by the two imaging methods, in areas involved in sensory or motor processing in healthy subjects and neurosurgical patients. Furthermore, we used the spatial information from fMRI to construct a spatiotemporal model of the MEG data in order to investigate the sensorimotor system and to create a spatiotemporal model of its function. We compared the localization results from the MEG and fMRI with invasive electrophysiological cortical mapping. We used a recently introduced method, contextual clustering, for hypothesis testing of fMRI data and assessed the the effect of neighbourhood information use on the reproducibility of fMRI results. Using MEG, we identified the ipsilateral primary sensorimotor cortex (SMI) as a novel source area contributing to the somatosensory evoked fields (SEF) to median nerve stimulation. Using combined MEG and fMRI measurements we found that two separate areas in the lateral fissure may be the generators for the SEF responses from the secondary somatosensory cortex region. The two imaging methods indicated activation in corresponding locations. By using complementary information from MEG and fMRI we established a spatiotemporal model of somatosensory cortical processing. This spatiotemporal model of cerebral activity was in good agreement with results from several studies using invasive electrophysiological measurements and with anatomical studies in monkey and man concerning the connections between somatosensory areas. In neurosurgical patients, the MEG dipole model turned out to be more reliable than fMRI in the identification of the central sulcus. This was due to prominent activation in non-primary areas in fMRI, which in some cases led to erroneous or ambiguous localization of the central sulcus.

Comparison of ANN models for predicting water quality in distribution systems

Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and ``noisy'' data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.

Comparison of 10 Multi-Sensor Image Fusion Paradigms for IKONOS Images

Fusion of multi-sensor imaging data enables a synergetic interpretation of complementary information obtained by sensors of different spectral ranges. Multi-sensor data of diverse spectral, spatial and temporal resolutions require advanced numerical techniques for analysis and interpretation. This paper reviews ten advanced pixel based image fusion techniques – Component substitution (COS), Local mean and variance matching, Modified IHS (Intensity Hue Saturation), Fast Fourier Transformed-enhanced IHS, Laplacian Pyramid, Local regression, Smoothing filter (SF), Sparkle, SVHC and Synthetic Variable Ratio. The above techniques were tested on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution). Evaluation of the fused results through various accuracy measures, revealed that SF and COS methods produce images closest to corresponding multi-sensor would observe at the highest resolution level (1 m).

Premixed flame response to equivalence ratio fluctuations: Comparison between reduced order modeling and detailed computations

Combustion instability events in lean premixed combustion systems can cause spatio-temporal variations in unburnt mixture fuel/air ratio. This provides a driving mechanism for heat-release oscillations when they interact with the flame. Several Reduced Order Modelling (ROM) approaches to predict the characteristics of these oscillations have been developed in the past. The present paper compares results for flame describing function characteristics determined from a ROM approach based on the level-set method, with corresponding results from detailed, fully compressible reacting flow computations for the same two dimensional slot flame configuration. The comparison between these results is seen to be sensitive to small geometric differences in the shape of the nominally steady flame used in the two computations. When the results are corrected to account for these differences, describing function magnitudes are well predicted for frequencies lesser than and greater than a lower and upper cutoff respectively due to amplification of flame surface wrinkling by the convective Darrieus-Landau (DL) instability. However, good agreement in describing function phase predictions is seen as the ROM captures the transit time of wrinkles through the flame correctly. Also, good agreement is seen for both magnitude and phase of the flame response, for large forcing amplitudes, at frequencies where the DL instability has a minimal influence. Thus, the present ROM can predict flame response as long as the DL instability, caused by gas expansion at the flame front, does not significantly alter flame front perturbation amplitudes as they traverse the flame. (C) 2012 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Feasibility of laser-induced breakdown spectroscopy for the study of the temporal distribution of trace elements trapped in snow collected from greater himalayan range

The potential merit of laser-induced breakdown spectroscopy (LIBS) has been demonstrated for detection and quantification of trace pollutants trapped in snow/ice samples. In this technique, a high-power pulsed laser beam from Nd:YAG Laser (Model no. Surelite III-10, Continuum, Santa Clara, CA, USA) is focused on the surface of the target to generate plasma. The characteristic emissions from laser-generated plasma are collected and recorded by a fiber-coupled LIBS 2000+ (Ocean Optics, Santa Clara, CA, USA) spectrometer. The fingerprint of the constituents present in the sample is obtained by analyzing the spectral lines by using OOI LIBS software. Reliable detection of several elements like Zn, Al, Mg, Fe, Ca, C, N, H, and O in snow/ice samples collected from different locations (elevation) of Manali and several snow samples collected from the Greater Himalayan region (from a cold lab in Manali, India) in different months has been demonstrated. The calibration curve approach has been adopted for the quantitative analysis of these elements like Zn, Al, Fe, and Mg. Our results clearly demonstrate that the level of contamination is higher in those samples that were collected in the month of January in comparison to those collected in February and March.

Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition

A new representation of spatio-temporal random processes is proposed in this work. In practical applications, such processes are used to model velocity fields, temperature distributions, response of vibrating systems, to name a few. Finding an efficient representation for any random process leads to encapsulation of information which makes it more convenient for a practical implementations, for instance, in a computational mechanics problem. For a single-parameter process such as spatial or temporal process, the eigenvalue decomposition of the covariance matrix leads to the well-known Karhunen-Loeve (KL) decomposition. However, for multiparameter processes such as a spatio-temporal process, the covariance function itself can be defined in multiple ways. Here the process is assumed to be measured at a finite set of spatial locations and a finite number of time instants. Then the spatial covariance matrix at different time instants are considered to define the covariance of the process. This set of square, symmetric, positive semi-definite matrices is then represented as a third-order tensor. A suitable decomposition of this tensor can identify the dominant components of the process, and these components are then used to define a closed-form representation of the process. The procedure is analogous to the KL decomposition for a single-parameter process, however, the decompositions and interpretations vary significantly. The tensor decompositions are successfully applied on (i) a heat conduction problem, (ii) a vibration problem, and (iii) a covariance function taken from the literature that was fitted to model a measured wind velocity data. It is observed that the proposed representation provides an efficient approximation to some processes. Furthermore, a comparison with KL decomposition showed that the proposed method is computationally cheaper than the KL, both in terms of computer memory and execution time.

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m(3)/m(3) for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m(3)/m(3) and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

Report of the 2005 Workshop on Ocean Ecodynamics Comparison in the Subarctic Pacific

I. Scientific Issues Posed by OECOS II. Participant Contributions to the OECOS Workshop A. ASPECTS OF PHYTOPLANKTON ECOLOGY IN THE SUBARCTIC PACIFIC Microbial community compositions by Karen E. Selph Subarctic Pacific lower trophic interactions: Production-based grazing rates and grazing-corrected production rates by Nicholas Welschmeyer Phytoplankton bloom dynamics and their physiological status in the western subarctic Pacific by Ken Furuya Temporal and spatial variability of phytoplankton biomass and productivity in the northwestern Pacific by Sei-ichi Saitoh, Suguru Okamoto, Hiroki Takemura and Kosei Sasaoka The use of molecular indicators of phytoplankton iron limitation by Deana Erdner B. IRON CONCENTRATION AND CHEMICAL SPECIATION Iron measurements during OECOS by Zanna Chase and Jay Cullen 25 The measurement of iron, nutrients and other chemical components in the northwestern North Pacific Ocean by Kenshi Kuma The measurement of iron, nutrients and other chemical components in the northwestern North Pacific Ocean by Kenshi Kuma C. PHYSICAL OCEANOGRAPHY, FINE-SCALE DISTRIBUTION PATTERNS AND AUTONOMOUS DRIFTERS The use of drifters in Lagrangian experiments: Positives, negatives and what can really be measured by Peter Strutton The interaction between plankton distribution patterns and vertical and horizontal physical processes in the eastern subarctic North Pacific by Timothy J. Cowles D. MICROZOOPLANKTON Microzooplankton processes in oceanic waters of the eastern subarctic Pacific: Project OECOS by Suzanne Strom Functional role of microzooplankton in the pelagic marine ecosystem during phytoplankton blooms in the western subarctic Pacific by Takashi Ota and Akiyoshi Shinada E. MESOZOOPLANKTON Vertical zonation of mesozooplankton, and its variability in response to food availability, density stratification, and turbulence by David L. Mackas and Moira Galbraith Marine ecosystem characteristics and seasonal abundance of dominant calanoid copepods in the Oyashio region by Atsushi Yamaguchi, Tsutomu Ikeda and Naonobu Shiga OECOS: Proposed mesozooplankton research in the Oyashio region, western subarctic Pacific by Tsutomu Ikeda Some background on Neocalanus feeding by Michael Dagg Size and growth of interzonally migrating copepods by Charles B. Miller Growth of large interzonal migrating copepods by Toru Kobari F. MODELING Ecosystem and population dynamics modeling by Harold P. Batchelder III. Reports from Workshop Breakout Groups A. PHYSICAL AND CHEMICAL ASPECTS WITH EMPHASIS ON IRON AND IRON SPECIATION B. PHYTOPLANKTON/MICROZOOPLANKTON STUDIES C. MESOZOOPLANKTON STUDIES IV. Issues arising during the workshop A. PHYTOPLANKTON STOCK VARIATIONS IN HNLC SYSTEMS AND TROPHIC CASCADES IN THE NANO AND MICRO REGIMES B. DIFFERENCES BETWEEN EAST AND WEST IN SITE SELECTION FOR OECOS TIME SERIES C. TIMING OF OECOS EXPEDITIONS D. CHARACTERIZATION OF PHYSICAL OCEANOGRAPHY V. Concluding Remarks VI. References (109 page document)

Tendência temporal do índice de massa corporal em alistados do Brasil (1980-2005)

Apesar da crescente prevalência da obesidade em países desenvolvidos e em desenvolvimento, há pouca evidência da associação com fatores ambientais. Objetivos: Investigar a evolução temporal do IMC em jovens alistados do sexo masculino de 18 anos no Brasil entre 1980 e 2005; identificar pontos específicos de maior variância na série temporal e comparar pontos específicos no tempo, a evolução temporal do IMC com as mudanças socioeconômicas no Brasil. Métodos: O presente estudo explorou uma série temporal de 26 anos em homens brasileiros que se alistaram no período de 1980 a 2005. A amostra compreendeu cerca de 35-40% de todos os jovens brasileiros de 18 anos de idade. O peso corporal e a estatura foram obtidos no momento do exame médico durante o alistamento militar. Todas as mensurações antropométricas foram realizadas por pessoal especializado e treinado. As prevalências do sobrepeso e da obesidade foram calculadas com intervalos de confiança de 95%. Com a finalidade de testar a presença de heterocedasticidade na série do IMC, realizou-se o teste de Multiplicador de Lagrange (LM). Para os pontos no tempo, com oscilações acima da média do IMC, variáveis dummies foram testadas utilizando-se o modelo ARCH (Autoregressivo de Heterocedasticidade Condicionada), com um nível de significância de p <0,05. Para aqueles pontos no tempo com oscilações acima da média do IMC (anos de 1985, 1994 e 2000), variáveis dummy foram incluídos sob a hipótese foi de que a taxa de crescimento do IMC não fosse a mesma ao longo da série temporal. Para as possíveis explicações para os aumentos bruscos na curva do IMC, foram consideradas as alterações nos principais indicadores econômicos do Brasil (Instituto Brasileiro de Geografia e Estatística e Instituto de Pesquisa Econômica Aplicada). Os fatores econômicos analisados foram: taxa de inflação anual, produção de alimentos, pobreza (%), o consumo de refrigerantes e o rendimento médio anual. Resultados: A prevalência de sobrepeso também passou de 4,5%, em 1980, para 12,5%, em 2005, um aumento de 2,6 vezes, enquanto a prevalência de obesidade aumentou de 0,5%, em 1980, para 1,9%, em 2005, um aumento de quase 300%, mas por comparação internacional estão abaixo da média. Particularmente em 1985-6 e 1994-5, houve um aumento acentuado e significativo do IMC. Em 1985-6, a média do IMC aumentou de 21,4 kg/m2 para 21,5 kg/m2 e, em 1994-5, a média do IMC médio aumentou de 21,7 kg/m2 para 21,9 kg/m2. Nesses dois pontos (1985-1986 e 1994-1995) ocorreram logo após duas grandes mudanças políticas econômicas que aumentaram o poder de compra da população. Em 1985-6, as mudanças foram principalmente relacionadas a fatores econômicos, tais como: a redução do nível de desigualdade social; aumento da renda familiar; redução da pobreza; o controle da inflação; aumento do tempo assistindo televisão e aumento do consumo de alimentos. Em 1994-5, além das mudanças no poder de compra, houve uma modificação na atividade física obrigatória nas escolas. Conclusão: O presente estudo mostrou um aumento abrupto da obesidade na população de homens jovens no Brasil em duas ocasiões durante esta série temporal (anos de 1985-6 e 1994-5), quando uma possível redução no gasto calórico e aumento do consumo de alimentos da população foram observados.

Assessing trends in the density of Atlantic croaker (Micropogonias undulatus): a comparison of passive acoustic and trawl methods

Using data collected simultaneously from a trawl and a hydrophone, we found that temporal and spatial trends in densities of juvenile Atlantic croaker (Micropogonias undulatus) in the Neuse River estuary in North Carolina can be identified by monitoring their sound production. Multivariate analysis of covariance (MA NCOVA) revealed that catch per unit of effort (CPUE) of Atlantic croaker had a significant relationship with the dependent variables of sound level and peak frequency of Atlantic croaker calls. Tests of between-subject correspondence failed to detect relationships between CPUE and either of the call parameters, but statistical power was low. Williamson’s index of spatial overlap indicated that call detection rate (expressed by a 0–3 calling index) was correlated in time and space with Atlantic croaker CPUE. The correspondence between acoustic parameters and trawl catch rates varied by month and by habitat. In general, the calling index had a higher degree of overlap with this species’ density than did the received sound level of their calls. Classification and regression tree analysis identified calling index as the strongest correlate of CPUE. Passive acoustics has the potential to be an inexpensive means of identifying spatial and temporal trends in abundance for soniferous fish species.

Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.

Temporal and spatial variability in nitrogen uptake kinetics during harmful dinoflagellate blooms in the East China Sea

Temporal and spatial variability in the kinetic parameters of uptake of nitrate (NO3-), ammonium (NH4+), urea, and glycine was measured during dinoflagellate blooms in Changjiang River estuary and East China Sea coast, 2005. Karenia mikimotoi was the dominant species in the early stage of the blooms and was succeeded by Prorocentrum donghaiense. The uptake of nitrogen (N) was determined using N-15 tracer techniques. The results of comparison kinetic parameters with ambient nutrients confirmed that different N forms were preferentially taken up during different stages of the bloom. NO3- (V-max 0.044 h(-1); K-s 60.8 mu M-N) was an important N source before it was depleted. NH4+ (V-max 0.049 h(-1); K-s 2.15 mu M-N) was generally the preferred N. Between the 2 organic N sources, urea was more preferred when K. mikimotoi dominated the bloom (V-max 0.020 h(-1); K-s 1.35 mu M-N) and glycine, considered as a dominant amino acid, was more preferred when P. donghaiense dominated the bloom (V-max 0.025 h(-1); K-s 1.76 mu M-N). The change of N uptake preference by the bloom-forming algae was also related to the variation in ambient N concentrations. Published by Elsevier B.V.

Spatial and temporal relationships between precipitation and ANPP of four types of grasslands in northern China

Precipitation is considered to be the primary resource limiting terrestrial biological activity in water-limited regions. Its overriding effect on the production of grassland is complex. In this paper, field data of 48 sites (including temperate meadow steppe,temperate steppe, temperate desert steppe and alpine meadow) were gathered from 31 published papers and monographs to analyze the relationship between above-ground net primary productivity (ANPP) and precipitation by the method of regression analysis. The results indicated that there was a great difference between spatial pattern and temporal pattern by which precipitation influenced grassland ANPP. Mean annual precipitation (MAP) was the main factor determining spatial distribution of grassland ANPP (r~2 = 0.61,P ＜ 0.01); while temporally, no significant relationship was found between the variance of AN PP and inter-annual precipitation for the four types of grassland. However, after dividing annual precipitation into monthly value and taking time lag effect into account, the study found significant relationships between ANPP and precipitation. For the temperate meadow steppe, the key variable determining inter-annual change of ANPP was last August-May precipitation (r~2= 0.47, P = 0.01); for the temperate steppe, the key variable was July precipitation (r~2 = 0.36, P = 0.02); for the temperate desert steppe, the key variable was April-June precipitation (r~2 = 0.51, P ＜0.01); for the alpine meadow, the key variable was last September-May precipitation (r~2 = 0.29, P ＜ 0.05). In comparison with analogous research, the study demonstrated that the key factor determining inter-annual changes of grassland ANPP was the cumulative precipitation in certain periods of that year or the previous year.