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Journal articles on the topic 'Spatio-temporal random fields'
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1
Descombes, X., F. Kruggel, and D. Y. Von Cramon. "Spatio-temporal fMRI analysis using Markov random fields." IEEE Transactions on Medical Imaging 17, no. 6 (1998): 1028–39. http://dx.doi.org/10.1109/42.746636.
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De Iaco, S., and D. Posa. "Predicting spatio-temporal random fields: Some computational aspects." Computers & Geosciences 41 (April 2012): 12–24. http://dx.doi.org/10.1016/j.cageo.2011.11.014.
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Piatkowski, Nico, Sangkyun Lee, and Katharina Morik. "Spatio-temporal random fields: compressible representation and distributed estimation." Machine Learning 93, no. 1 (July 25, 2013): 115–39. http://dx.doi.org/10.1007/s10994-013-5399-7.
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Ip, Ryan H. L., and W. K. Li. "Matérn cross-covariance functions for bivariate spatio-temporal random fields." Spatial Statistics 17 (August 2016): 22–37. http://dx.doi.org/10.1016/j.spasta.2016.04.004.
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Salvaña, Mary Lai O., and Marc G. Genton. "Nonstationary cross-covariance functions for multivariate spatio-temporal random fields." Spatial Statistics 37 (June 2020): 100411. http://dx.doi.org/10.1016/j.spasta.2020.100411.
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Fontanella, L., L. Ippoliti, R. J. Martin, and S. Trivisonno. "Interpolation of spatial and spatio-temporal Gaussian fields using Gaussian Markov random fields." Advances in Data Analysis and Classification 2, no. 1 (April 2008): 63–79. http://dx.doi.org/10.1007/s11634-008-0019-2.
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Das, Sonjoy, Roger Ghanem, and Steven Finette. "Polynomial chaos representation of spatio-temporal random fields from experimental measurements." Journal of Computational Physics 228, no. 23 (December 2009): 8726–51. http://dx.doi.org/10.1016/j.jcp.2009.08.025.
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Western, Luke M., Zhe Sha, Matthew Rigby, Anita L. Ganesan, Alistair J. Manning, Kieran M. Stanley, Simon J. O'Doherty, Dickon Young, and Jonathan Rougier. "Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields." Geoscientific Model Development 13, no. 4 (April 28, 2020): 2095–107. http://dx.doi.org/10.5194/gmd-13-2095-2020.
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Abstract. We present a method to infer spatially and spatio-temporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. The method allows fast computation of spatial emissions using a hierarchical Bayesian framework as an alternative to Markov chain Monte Carlo algorithms. The spatial emissions follow a Gaussian process with a Matérn correlation structure which can be represented by a Gaussian Markov random field through a stochastic partial differential equation approach. The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. Combining an autoregressive temporal correlation and the Matérn field provides a full spatio-temporal correlation structure. We first demonstrate the method on a synthetic data example and follow this using a well-studied test case of inferring UK methane emissions from tall tower measurements of atmospheric mole fraction. Results from these two test cases show that this method can accurately estimate regional greenhouse gas emissions, accounting for spatio-temporal uncertainties that have traditionally been neglected in atmospheric inverse modelling.
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Jadaliha, Mahdi, Jinho Jeong, Yunfei Xu, Jongeun Choi, and Junghoon Kim. "Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields." Sensors 18, no. 9 (August 30, 2018): 2866. http://dx.doi.org/10.3390/s18092866.
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In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements. The spatio-temporal field of interest is modeled by a sum of a time-varying mean function and a Gaussian Markov random field (GMRF) with unknown hyperparameters. We first derive the exact Bayesian solution to the problem of computing the predictive inference of the random field, taking into account observations, uncertain hyperparameters, measurement noise, and uncertain localization in a fully Bayesian point of view. We show that the exact solution for uncertain localization is not scalable as the number of observations increases. To cope with this exponentially increasing complexity and to be usable for mobile sensor networks with limited resources, we propose a scalable approximation with a controllable trade-off between approximation error and complexity to the exact solution. The effectiveness of the proposed algorithms is demonstrated by simulation and experimental results.
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Ghosh, Debraj, and Anup Suryawanshi. "Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition." Communications in Computational Physics 16, no. 1 (July 2014): 75–95. http://dx.doi.org/10.4208/cicp.201112.191113a.
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AbstractA 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-Loève (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.
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Frederiksen, Richard D., Werner J. A. Dahm, and David R. Dowling. "Experimental assessment of fractal scale-similarity in turbulent flows. Part 1. One-dimensional intersections." Journal of Fluid Mechanics 327 (November 25, 1996): 35–72. http://dx.doi.org/10.1017/s0022112096008452.
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Results are presented from an assessment of the applicability of fractal scale-similarity in the spatio–temporal structure of Sc [Gt ] 1 conserved scalar fields ζ(x, t) and scalar energy dissipation rate fields ∇(x, t) in turbulent flows. Over 2 million spatial and temporal intersections were analysed from fully resolved three-dimensional (256) spatial measurements as well as fully resolved four-dimensional spatio–temporal measurements containing up to 3 million points. Statistical criteria were used to assess both deterministic and stochastic fractal scale-similarity and to differentiate between fractal and random sets. Results span the range of spatio–temporal scales from the scalar diffusion scales (ΛD, TD) to the viscous diffusion scales (Λv, Tv) and to the outer scales (δ, Tδ). Over this entire range of scales, slightly over 99.0% of all intersections with the scalar dissipation support geometry showed scale-similarity as fractal as stochastically self-similar fBm sets having the same record length. Dissipation values above the mean were found to have support dimension D = 0.66. The dissipation support dimension decreased sharply with increasing dissipation values. Virtually no intersections showed scaling as random as a random set with the same relative cover. In contrast, intersections with scalar isosurfaces showed scaling only approximately as fractal as a corresponding fBm set and only over the range of spatio–temporal scales between (ΛD, TD) and (Λv, Tv). On these inner scales the isosurface dimension was D = 0.48 and was largely independent of the isoscalar value. At larger scales, scalar isosurfaces showed no fractal scale-similarity. In contrast, isoscalar level crossing sets showed no fractal scale-similarity over any range of scales, even though the scalar dissipation support geometry for the same data is clearly fractal. These results were found to be unaffected by noise.
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Roscher, Ribana, Bernd Uebbing, and Jürgen Kusche. "STAR: Spatio-temporal altimeter waveform retracking using sparse representation and conditional random fields." Remote Sensing of Environment 201 (November 2017): 148–64. http://dx.doi.org/10.1016/j.rse.2017.07.024.
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Bhole, Chetan, and Christopher Pal. "Fully automatic person segmentation in unconstrained video using spatio-temporal conditional random fields." Image and Vision Computing 51 (July 2016): 58–68. http://dx.doi.org/10.1016/j.imavis.2016.04.007.
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Khatoonabadi, S. H., and I. V. Bajic. "Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov Random Fields." IEEE Transactions on Image Processing 22, no. 1 (January 2013): 300–313. http://dx.doi.org/10.1109/tip.2012.2214049.
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Cecconi, Vittorio, Vivek Kumar, Alessia Pasquazi, Juan Sebastian Totero Gongora, and Marco Peccianti. "Nonlinear field-control of terahertz waves in random media for spatiotemporal focusing." Open Research Europe 2 (August 1, 2022): 32. http://dx.doi.org/10.12688/openreseurope.14508.2.
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Controlling the transmission of broadband optical pulses in scattering media is a critical open challenge in photonics. To date, wavefront shaping techniques at optical frequencies have been successfully applied to control the spatial properties of multiple-scattered light. However, a fundamental restriction in achieving an equivalent degree of control over the temporal properties of a broadband pulse is the limited availability of experimental techniques to detect the coherent properties (i.e., the spectral amplitude and absolute phase) of the transmitted field. Terahertz experimental frameworks, on the contrary, enable measuring the field dynamics of broadband pulses at ultrafast (sub-cycle) time scales directly. In this work, we provide a theoretical/numerical demonstration that, within this context, complex scattering can be used to achieve spatio-temporal control of instantaneous fields and manipulate the temporal properties of single-cycle pulses by solely acting on spatial degrees of freedom of the illuminating field. As direct application scenarios, we demonstrate spatio-temporal focusing, chirp compensation, and control of the carrier-envelope-phase (CEP) of a CP-stable, transform-limited THz pulse.
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Cecconi, Vittorio, Vivek Kumar, Alessia Pasquazi, Juan Sebastian Totero Gongora, and Marco Peccianti. "Nonlinear field-control of terahertz waves in random media for spatiotemporal focusing." Open Research Europe 2 (March 8, 2022): 32. http://dx.doi.org/10.12688/openreseurope.14508.1.
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Controlling the transmission of broadband optical pulses in scattering media is a critical open challenge in photonics. To date, wavefront shaping techniques at optical frequencies have been successfully applied to control the spatial properties of multiple-scattered light. However, a fundamental restriction in achieving an equivalent degree of control over the temporal properties of a broadband pulse is the limited availability of experimental techniques to detect the coherent properties (i.e., the spectral amplitude and absolute phase) of the transmitted field. Terahertz experimental frameworks, on the contrary, enable measuring the field dynamics of broadband pulses at ultrafast (sub-cycle) time scales directly. In this work, we provide a theoretical/numerical demonstration that, within this context, complex scattering can be used to achieve spatio-temporal control of instantaneous fields and manipulate the temporal properties of single-cycle pulses by solely acting on spatial degrees of freedom of the illuminating field. As direct application scenarios, we demonstrate spatio-temporal focusing, chirp compensation, and control of the carrier-envelope-offset of a transform-limited THz pulse.
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Cecconi, Vittorio, Vivek Kumar, Alessia Pasquazi, Juan Sebastian Totero Gongora, and Marco Peccianti. "Nonlinear field-control of terahertz waves in random media for spatiotemporal focusing." Open Research Europe 2 (February 13, 2023): 32. http://dx.doi.org/10.12688/openreseurope.14508.3.
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Controlling the transmission of broadband optical pulses in scattering media is a critical open challenge in photonics. To date, wavefront shaping techniques at optical frequencies have been successfully applied to control the spatial properties of multiple-scattered light. However, a fundamental restriction in achieving an equivalent degree of control over the temporal properties of a broadband pulse is the limited availability of experimental techniques to detect the coherent properties (i.e., the spectral amplitude and absolute phase) of the transmitted field. Terahertz experimental frameworks, on the contrary, enable measuring the field dynamics of broadband pulses at ultrafast (sub-cycle) time scales directly. In this work, we provide a theoretical/numerical demonstration that, within this context, complex scattering can be used to achieve spatio-temporal control of instantaneous fields and manipulate the temporal properties of single-cycle pulses by solely acting on spatial degrees of freedom of the illuminating field. As direct application scenarios, we demonstrate spatio-temporal focusing, chirp compensation, and control of the carrier-envelope-phase (CEP) of a CP-stable, transform-limited THz pulse.
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Ip, Ryan H. L., and W. K. Li. "A class of valid Matérn cross-covariance functions for multivariate spatio-temporal random fields." Statistics & Probability Letters 130 (November 2017): 115–19. http://dx.doi.org/10.1016/j.spl.2017.07.019.
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Prevost, Paoline, Kristel Chanard, Luce Fleitout, Eric Calais, Damian Walwer, Tonie van Dam, and Michael Ghil. "Data-adaptive spatio-temporal filtering of GRACE data." Geophysical Journal International 219, no. 3 (September 19, 2019): 2034–55. http://dx.doi.org/10.1093/gji/ggz409.
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SUMMARY Measurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter. In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability. We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements. Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.
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Yuan, Wenjing, Lin Yang, Qing Yang, Yehua Sheng, and Ziyang Wang. "Extracting Spatio-Temporal Information from Chinese Archaeological Site Text." ISPRS International Journal of Geo-Information 11, no. 3 (March 4, 2022): 175. http://dx.doi.org/10.3390/ijgi11030175.
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Archaeological site text is the main carrier of archaeological data at present, which contains rich information. How to efficiently extract useful knowledge from the massive unstructured archaeological site texts is of great significance for the mining and reuse of archaeological information. According to the site information (such as name, location, cultural type, dynasty, etc.) recorded in the Chinese archaeological site text, this paper combines deep learning and natural language processing techniques to study the information extraction method for automatically obtaining the spatio-temporal information of sites. The initial construction of the corpus of Chinese archaeological site text is completed for the first time, and the corpus is input into the Bidirectional Long Short-Term Memory with Conditional Random Fields (BiLSTM-CRF) entity recognition model and Bidirectional Gated Recurrent Units with Dual Attention (BiGRU-Dual Attention) relationship extraction model for training. The F1 values of BiLSTM-CRF model and BiGRU-Dual Attention model on the test set reach 87.87% and 88.05%, respectively. The study demonstrates that the information extraction method proposed in this paper is feasible for the Chinese archaeological site texts, which promotes the establishment of knowledge graphs in archaeology and provides new methods and ideas for the development of information mining technology in archaeology.
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Bulthuis, Kevin, and Eric Larour. "Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19." Geoscientific Model Development 15, no. 3 (February 10, 2022): 1195–217. http://dx.doi.org/10.5194/gmd-15-1195-2022.
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Abstract. Assessing the impact of uncertainties in ice-sheet models is a major and challenging issue that needs to be faced by the ice-sheet community to provide more robust and reliable model-based projections of ice-sheet mass balance. In recent years, uncertainty quantification (UQ) has been increasingly used to characterize and explore uncertainty in ice-sheet models and improve the robustness of their projections. A typical UQ analysis first involves the (probabilistic) characterization of the sources of uncertainty, followed by the propagation and sensitivity analysis of these sources of uncertainty. Previous studies concerned with UQ in ice-sheet models have generally focused on the last two steps but have paid relatively little attention to the preliminary and critical step of the characterization of uncertainty. Sources of uncertainty in ice-sheet models, like uncertainties in ice-sheet geometry or surface mass balance, typically vary in space and potentially in time. For that reason, they are more adequately described as spatio-(temporal) random fields, which account naturally for spatial (and temporal) correlation. As a means of improving the characterization of the sources of uncertainties for forward UQ analysis within the Ice-sheet and Sea-level System Model (ISSM), we present in this paper a stochastic sampler for Gaussian random fields with Matérn covariance function. The class of Matérn covariance functions provides a flexible model able to capture statistical dependence between locations with different degrees of spatial correlation or smoothness properties. The implementation of this stochastic sampler is based on a notable explicit link between Gaussian random fields with Matérn covariance function and a certain stochastic partial differential equation. Discretization of this stochastic partial differential equation by the finite-element method results in a sparse, scalable and computationally efficient representation known as a Gaussian Markov random field. In addition, spatio-temporal samples can be generated by combining an autoregressive temporal model and the Matérn field. The implementation is tested on a set of synthetic experiments to verify that it captures the desired spatial and temporal correlations well. Finally, we illustrate the interest of this stochastic sampler for forward UQ analysis in an application concerned with assessing the impact of various sources of uncertainties on the Pine Island Glacier, West Antarctica. We find that larger spatial and temporal correlations lengths will both likely result in increased uncertainty in the projections.
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Song, Tao, Wei Wei, Fan Meng, Jiarong Wang, Runsheng Han, and Danya Xu. "Inversion of Ocean Subsurface Temperature and Salinity Fields Based on Spatio-Temporal Correlation." Remote Sensing 14, no. 11 (May 27, 2022): 2587. http://dx.doi.org/10.3390/rs14112587.
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Ocean observation is essential for studying ocean dynamics, climate change, and carbon cycles. Due to the difficulty and high cost of in situ observations, existing ocean observations are inadequate, and satellite observations are mostly surface observations. Previous work has not adequately considered the spatio-temporal correlation within the ocean itself. This paper proposes a new method—convolutional long short-term memory network (ConvLSTM)—for the inversion of the ocean subsurface temperature and salinity fields with the sea surface satellite observations (sea surface temperature, sea surface salinity, sea surface height, and sea surface wind) and subsurface Argo reanalyze data. Given the time dependence and spatial correlation of the ocean dynamic parameters, the ConvLSTM model can improve inversion models’ robustness and generalizability by considering ocean variability’s significant spatial and temporal correlation characteristics. Taking the 2018 results as an example, our average inversion results in an overall normalized root mean square error (NRMSE) of 0.0568 °C/0.0027 PSS and a correlation coefficient (R) of 0.9819/0.9997 for subsurface temperature (ST)/subsurface salinity (SS). The results show that SSTA, SSSA SSHA, and SSWA together are valuable parameters for obtaining accurate ST/SS estimates, and the use of multiple channels in shallow seas is effective. This study demonstrates that ConvLSTM is superior in modeling the subsurface temperature and salinity fields, fully taking global ocean data’s spatial and temporal correlation into account, and outperforms the classic random forest and LSTM approaches in predicting subsurface temperature and salinity fields.
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Shi, Zhichao, and Xiaoguang Zhou. "Spatio-Temporal Dynamic Fields Estimating and Modeling of Missing Points in Data Sets Using a Flexible State-Space Model." Applied Sciences 11, no. 19 (September 28, 2021): 9050. http://dx.doi.org/10.3390/app11199050.
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Modelling and estimating spatio-temporal dynamic field are common challenges in much applied research. Most existing spatio-temporal interpolation methods require massive prior calculations and consistent observational data, resulting in low interpolation efficiency. This paper presents a flexible state-space model for iteratively fitting time-series from random missing points in data sets, namely Flexible Universal Kriging state-space model(FUKSS). In this work, a recursive method similar to Kalman filter is used to estimate the time-series, avoiding the problem of increasing data caused by Kriging space-time extension. Based on the statistical characteristics of Kriging, this method introduces a spatial selection matrix to make the different observation data and state vectors identical at different times, which solves the problem of missing data and reduces the calculation complexity. In addition, a dynamic linear autoregressive model is introduced to solve the problem that the universal Kriging state-space model cannot predict. We have demonstrated the superiority of our method by comparing it with different methods through experiments, and verified the effectiveness of this method through practical cases.
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Zhou, Shijie, Robert A. Campbell, and Simon D. Hoyle. "Catch per unit effort standardization using spatio-temporal models for Australia’s Eastern Tuna and Billfish Fishery." ICES Journal of Marine Science 76, no. 6 (March 11, 2019): 1489–504. http://dx.doi.org/10.1093/icesjms/fsz034.
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Abstract The majority of catch per unit effort (cpue) standardizations use generalized linear models (GLMs) or generalized additive models (GAMs). We develop geostatistical models that model catch locations as continuous Gaussian random fields (GRFs) and apply them to standardizing cpue in Australia’s Eastern Tuna and Billfish Fishery (ETBF). The results are compared with the traditional GLMs currently used in ETBF assessments as well as GAMs. Specifically, we compare seven models in three groups: two GLMs, two GAMs, and three GRF models. Within each group, one model treats spatial and temporal variables independently, while the other model(s) treats them together as an interaction term. The two spatio-temporal GRF models differ in treating the spatial–temporal interaction: either as a random process or as an autoregressive process. We simulate catch rate data for five pelagic species based on real fishery catch rates so that the simulated data reflect real fishery patterns while the “true” annual abundance levels are known. The results show that within each group, the model with a spatial–temporal interaction term significantly outperforms the other model treating spatial and temporal variables independently. For spatial–temporal models between the three groups, prediction accuracy tends to improve from GLM to GAM and to the GRF models. Overall, the spatio-temporal GRF autoregressive model reduces mean relative predictive error by 43.4% from the GLM, 33.9% from the GAM, and reduces mean absolute predictive error by 23.5% from the GLM and 3.3% from the GAM, respectively. The comparison suggests a promising direction for further developing the geostatistical models for the ETBF.
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Roberts, O. W., X. Li, and L. Jeska. "Validation of the <i>k</i>-filtering technique for a signal composed of random-phase plane waves and non-random coherent structures." Geoscientific Instrumentation, Methods and Data Systems 3, no. 2 (December 23, 2014): 247–54. http://dx.doi.org/10.5194/gi-3-247-2014.
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Abstract. Recent observations of astrophysical magnetic fields have shown the presence of fluctuations being wave-like (propagating in the plasma frame) and those described as being structure-like (advected by the plasma bulk velocity). Typically with single-spacecraft missions it is impossible to differentiate between these two fluctuations, due to the inherent spatio-temporal ambiguity associated with a single point measurement. However missions such as Cluster which contain multiple spacecraft have allowed for temporal and spatial changes to be resolved, using techniques such as k filtering. While this technique does not assume Taylor's hypothesis it requires both weak stationarity of the time series and that the fluctuations can be described by a superposition of plane waves with random phases. In this paper we test whether the method can cope with a synthetic signal which is composed of a combination of non-random-phase coherent structures with a mean radius d and a mean separation λ, as well as plane waves with random phase.
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Sakuma, Asahi, and Hiroya Yamano. "Satellite Constellation Reveals Crop Growth Patterns and Improves Mapping Accuracy of Cropping Practices for Subtropical Small-Scale Fields in Japan." Remote Sensing 12, no. 15 (July 28, 2020): 2419. http://dx.doi.org/10.3390/rs12152419.
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Mapping of agricultural crop types and practices is important for setting up agricultural production plans and environmental conservation measures. Sugarcane is a major tropical and subtropical crop; in general, it is grown in small fields with large spatio-temporal variations due to various crop management practices, and satellite observations of sugarcane cultivation areas are often obscured by clouds. Surface information with high spatio-temporal resolution obtained through the use of emerging satellite constellation technology can be used to track crop growth patterns with high resolution. In this study, we used Planet Dove imagery to reveal crop growth patterns and to map crop types and practices on subtropical Kumejima Island, Japan (lat. 26°21′01.1″ N, long. 126°46′16.0″ E). We eliminated misregistration between the red-green-blue (RGB) and near-infrared band imagery, and generated a time series of seven vegetation indices to track crop growth patterns. Using the Random Forest algorithm, we classified eight crop types and practices in the sugarcane. All the vegetation indices tested showed high classification accuracy, and the normalized difference vegetation index (NDVI) had an overall accuracy of 0.93 and Kappa of 0.92 range of accuracy for different crop types and practices in the study area. The results for the user’s and producer’s accuracy of each class were good. Analysis of the importance of variables indicated that five image sets are most important for achieving high classification accuracy: Two image sets of the spring and summer sugarcane plantings in each year of a two-year observation period, and one just before harvesting in the second year. We conclude that high-temporal-resolution time series images obtained by a satellite constellation are very effective in small-scale agricultural mapping with large spatio-temporal variations.
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Ahmad, Ola, and Christophe Collet. "Scale-space spatio-temporal random fields: Application to the detection of growing microbial patterns from surface roughness." Pattern Recognition 58 (October 2016): 27–38. http://dx.doi.org/10.1016/j.patcog.2016.03.034.
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Voloshko, V. A., and Yu S. Kharin. "Discrete-valued time series based on the exponential family with the multidimensional parameter and their probabilistic and statistical analysis." Proceedings of the National Academy of Sciences of Belarus. Physics and Mathematics Series 58, no. 3 (October 12, 2022): 280–91. http://dx.doi.org/10.29235/1561-2430-2022-58-3-280-291.
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We propose herein a new parsimonious Markov model for a discrete-valued time series with conditional probability distributions of observations lying in the exponential family with the multidimensional parameter. A family of explicit consistent asymptotically normal statistical estimators is constructed for the parameters of the proposed model for increasing length of observed time series, and asymptotically effective estimator is found within this constructed family. The obtained results can be used for robust statistical analysis of discrete-valued time series,and for statistical analysis of discrete-valued spatio-temporal data and random fields.
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Roberts, O. W., X. Li, and L. Jeska. "Validation of the <i>k</i>-filtering technique for a signal composed of random phase plane waves and non-random coherent structures." Geoscientific Instrumentation, Methods and Data Systems Discussions 4, no. 2 (August 11, 2014): 435–54. http://dx.doi.org/10.5194/gid-4-435-2014.
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Abstract. Recent observations of astrophysical magnetic fields have shown the presence of fluctuations being wave-like (propagating in the plasma frame) and those described as being structure-like (advected by the plasma bulk velocity). Typically with single spacecraft missions it is impossible to differentiate between these two fluctuations, due to the inherent spatio-temporal ambiguity associated with a single point measurement. However missions such as Cluster which contain multiple spacecraft have allowed temporal and spatial changes to be resolved, with techniques such as the k-filtering technique. While this technique does not assume Taylor's hypothesis as is necessary with single spacecraft missions, it does require weak stationarity of the time series, and that the fluctuations can be described by a superposition of plane waves with random phase. In this paper we test whether the method can cope with a synthetic signal which is composed of a combination of non-random phase coherent structures with a mean radius d and a mean separation λ, as well as plane waves with random phase.
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Hengl, Tomislav, Madlene Nussbaum, Marvin N. Wright, Gerard B. M. Heuvelink, and Benedikt Gräler. "Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables." PeerJ 6 (August 29, 2018): e5518. http://dx.doi.org/10.7717/peerj.5518.
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Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as “knowledge engines” in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality—especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.
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Stuhlmeier, R., and M. Stiassnie. "Evolution of statistically inhomogeneous degenerate water wave quartets." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, no. 2111 (December 11, 2017): 20170101. http://dx.doi.org/10.1098/rsta.2017.0101.
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A discretized equation for the evolution of random surface wave fields on deep water is derived from Zakharov's equation, allowing for a general treatment of the stability and long-time behaviour of broad-banded sea states. It is investigated for the simple case of degenerate four-wave interaction, and the instability of statistically homogeneous states to small inhomogeneous disturbances is demonstrated. Furthermore, the long-time evolution is studied for several cases and shown to lead to a complex spatio-temporal energy distribution. The possible impact of this evolution on the statistics of freak wave occurrence is explored. This article is part of the theme issue ‘Nonlinear water waves’.
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Maniyar, Chintan B., Megha Rudresh, Ileana A. Callejas, Katie Osborn, Christine M. Lee, Jennifer Jay, Myles Phillips, et al. "Spatio-Temporal Dynamics of Total Suspended Sediments in the Belize Coastal Lagoon." Remote Sensing 15, no. 23 (December 4, 2023): 5625. http://dx.doi.org/10.3390/rs15235625.
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Increased tourism in Belize over the last decade and the growth of the local population have led to coastal development and infrastructure expansion. Land use alteration and anthropogenic activity may change the sediment and nutrient loads in coastal systems, which can negatively affect ecosystems via mechanisms such as reducing photosynthetically active radiation fields, smothering sessile habitats, and stimulating eutrophication events. Accurate monitoring and prediction of water quality parameters such as Total Suspended Sediments (TSS), are essential in order to understand the influence of land-based changes, climate, and human activities on the coastal systems and devise strategies to mitigate negative impacts. This study implements machine learning algorithms such as Random Forests (RF), Extreme Gradient Boosting (XGB), and Deep Neural Networks (DNN) to estimate TSS using Sentinel-2 reflectance data in the Belize Coastal Lagoon (BCL) and validates the results using TSS data collected in situ. DNN performed the best and estimated TSS with a testing R2 of 0.89. Time-series analysis was also performed on the BCL’s TSS trends using Bayesian Changepoint Detection (BCD) methods to flag anomalously high TSS spatio-temporally, which may be caused by dredging events. Having such a framework can ease the near-real-time monitoring of water quality in Belize, help track the TSS dynamics for anomalies, and aid in meeting and maintaining the sustainable goals for Belize.
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MELGANI, FARID. "CLASSIFICATION OF MULTITEMPORAL REMOTE-SENSING IMAGES BY A FUZZY FUSION OF SPECTRAL AND SPATIO-TEMPORAL CONTEXTUAL INFORMATION." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 02 (March 2004): 143–56. http://dx.doi.org/10.1142/s0218001404003083.
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A fuzzy-logic approach to the classification of multitemporal, multisensor remote-sensing images is proposed. The approach is based on a fuzzy fusion of three basic sources of information: spectral, spatial and temporal contextual information sources. It aims at improving the accuracy over that of single-time noncontextual classification. Single-time class posterior probabilities, which are used to represent spectral information, are estimated by Multilayer Perceptron neural networks trained for each single-time image, thus making the approach applicable to multisensor data. Both the spatial and temporal kinds of contextual information are derived from the single-time classification maps obtained by the neural networks. The expert's knowledge of possible transitions between classes at two different times is exploited to extract temporal contextual information. The three kinds of information are then fuzzified in order to apply a fuzzy reasoning rule for their fusion. Fuzzy reasoning is based on the "MAX" fuzzy operator and on information about class prior probabilities. Finally, the class with the largest fuzzy output value is selected for each pixel in order to provide the final classification map. Experimental results on a multitemporal data set consisting of two multisensor (Landsat TM and ERS-1 SAR) images are reported. The accuracy of the proposed fuzzy spatio-temporal contextual classifier is compared with those obtained by the Multilayer Perceptron neural networks and a reference classification approach based on Markov Random Fields (MRFs). Results show the benefit of adding spatio-temporal contextual information to the classification scheme, and suggest that the proposed approach represents an interesting alternative to the MRF-based approach, in particular, in terms of simplicity.
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Luo, Yilan, Deniz Sezer, David Wood, Mingkuan Wu, and Hamid Zareipour. "Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada." Energies 12, no. 10 (May 24, 2019): 1998. http://dx.doi.org/10.3390/en12101998.
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This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard deviation of the future aggregate wind power generation of Alberta and investigate new wind farm siting on the basis of reducing aggregate variability.
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Verburg, Samuel A., and Efren Fernandez-Grande. "Sensor placement for sound field reconstruction in enclosures." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 1 (August 1, 2021): 5424–32. http://dx.doi.org/10.3397/in-2021-3095.
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Sampling spatio-temporal acoustic fields is a challenging problem since it demands a large number of sensors. Typically, to characterize the pressure field inside an enclosure, the number of measurements required increases linearly with frequency and cubically with volume, becoming an intractable problem for rooms of moderate size even at low and mid frequencies. Sparse representation techniques, such as Compressed Sensing, rely on the sparsity of natural signals in certain representation domain to drastically reduce the number of measurements needed to sample such signals. In this study, we optimize the placement of sensors inside an enclosure in order to reduce the measurements required for a given reconstruction accuracy. The proposed methodology selects a sparse set of sensor positions from predefined grid via the QR factorization of the sensing matrix. Numerical results show an effective reduction in the required number of measurements when their positions are optimized, in contrast to standard random positioning. Unlike the majority of existing approaches, we study the placement problem for wide-band acoustic fields.
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Vannoppen, Astrid, Anne Gobin, Lola Kotova, Sara Top, Lesley De Cruz, Andris Vīksna, Svetlana Aniskevich, et al. "Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia." Remote Sensing 12, no. 14 (July 10, 2020): 2206. http://dx.doi.org/10.3390/rs12142206.
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Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.
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Yamamoto, A., Y. Hasegawa, and N. Kasagi. "Optimal control of dissimilar heat and momentum transfer in a fully developed turbulent channel flow." Journal of Fluid Mechanics 733 (September 23, 2013): 189–220. http://dx.doi.org/10.1017/jfm.2013.436.
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AbstractSustained friction drag reduction and heat transfer augmentation are simultaneously achieved in a fully developed channel flow where the averaged transport equations and wall boundary conditions for momentum and heat have identical form. Zero-net-mass-flux wall blowing and suction is assumed as a control input and its spatio-temporal distribution is determined based on optimal control theory. When the root-mean-square value of the control input is 5 % of the bulk mean velocity, the friction drag is decreased by 24 % from the uncontrolled value, whereas the heat transfer is more than doubled. Optimizations with different amplitudes of the control input and different Reynolds numbers reveal that the optimal control inputs commonly exhibit the property of a downstream travelling wave, whose wavelength is ∼250 in wall units and phase velocity is ∼30 % of the bulk mean velocity. Detailed analyses of the controlled velocity and thermal fields show that the travelling wave input contributes to dissimilar heat transfer enhancement through two distinct mechanisms, i.e. direct modification of the coherent velocity and thermal fields and an indirect effect on the random fields. The present results show that the divergence-free velocity vector and the conservative scalar are essentially different, and this is a key to achieving dissimilar heat transfer enhancement in turbulent shear flows.
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Medina-Cetina, Zenon, Ahran Song, Yichuan Zhu, Alma Rosa Pineda-Contreras, and Amy Rechenmacher. "Global and Local Deformation Effects of Dry Vacuum-Consolidated Triaxial Compression Tests on Sand Specimens: Making a Database Available for the Calibration and Development of Forward Models." Materials 15, no. 4 (February 18, 2022): 1528. http://dx.doi.org/10.3390/ma15041528.
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A comprehensive experimental database containing results of a series of dry vacuum-consolidated triaxial compression tests was populated. The tests were performed on sand specimens and conducted under similar experimental conditions, in which specimens’ boundary deformation was captured using a three-dimensional digital image correlation analysis (3D-DIC). The use of a standard triaxial device along with the 3D-DIC technology allowed the specimens’ global and local boundary displacement fields to be computed from start to end of the compression phase. By repeating each test under the same experimental conditions and building the specimens using the same type of sand, the boundary deformation patterns could be identified, and the statistics associated with both global and local displacement fields could be assessed. Making this experimental database available to others should serve to calibrate as well as develop new forward models to account for effects associated with the specimens’ local displacements and material heterogeneity and include statistics to represent a specimen’s random response. Moreover, this work will serve as a basis for the statistical characterization of spatio-temporal boundary localization effects used to develop stochastic models and machine-learning models, and simulate virtual triaxial tests.
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Zhang, Wenmin, Martin Brandt, Alexander V. Prishchepov, Zhaofu Li, Chunguang Lyu, and Rasmus Fensholt. "Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019)." Remote Sensing 13, no. 6 (March 19, 2021): 1170. http://dx.doi.org/10.3390/rs13061170.
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Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999–2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (β = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999–2019 with overall accuracies ranging from 82% to 99% and the user’s/producer’s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 × 106 ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China.
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Bogaardt, Laurens, Romulo Goncalves, Raul Zurita-Milla, and Emma Izquierdo-Verdiguier. "Dataset Reduction Techniques to Speed Up SVD Analyses on Big Geo-Datasets." ISPRS International Journal of Geo-Information 8, no. 2 (January 26, 2019): 55. http://dx.doi.org/10.3390/ijgi8020055.
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The Singular Value Decomposition (SVD) is a mathematical procedure with multiple applications in the geosciences. For instance, it is used in dimensionality reduction and as a support operator for various analytical tasks applicable to spatio-temporal data. Performing SVD analyses on large datasets, however, can be computationally costly, time consuming, and sometimes practically infeasible. However, techniques exist to arrive at the same output, or at a close approximation, which requires far less effort. This article examines several such techniques in relation to the inherent scale of the structure within the data. When the values of a dataset vary slowly, e.g., in a spatial field of temperature over a country, there is autocorrelation and the field contains large scale structure. Datasets do not need a high resolution to describe such fields and their analysis can benefit from alternative SVD techniques based on rank deficiency, coarsening, or matrix factorization approaches. We use both simulated Gaussian Random Fields with various levels of autocorrelation and real-world geospatial datasets to illustrate our study while examining the accuracy of various SVD techniques. As the main result, this article provides researchers with a decision tree indicating which technique to use when and predicting the resulting level of accuracy based on the dataset’s structure scale.
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Duecker, Daniel Andre, Andreas Rene Geist, Edwin Kreuzer, and Eugen Solowjow. "Learning Environmental Field Exploration with Computationally Constrained Underwater Robots: Gaussian Processes Meet Stochastic Optimal Control." Sensors 19, no. 9 (May 6, 2019): 2094. http://dx.doi.org/10.3390/s19092094.
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Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field.
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Perrard, Stéphane, Adrián Lozano-Durán, Marc Rabaud, Michael Benzaquen, and Frédéric Moisy. "Turbulent windprint on a liquid surface." Journal of Fluid Mechanics 873 (June 28, 2019): 1020–54. http://dx.doi.org/10.1017/jfm.2019.318.
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We investigate the effect of a light turbulent wind on a liquid surface, below the onset of wave generation. In that regime, the liquid surface is populated by small disorganised deformations elongated in the streamwise direction. Formally identified recently by Paquier et al. (Phys. Fluids, vol. 27, 2015, art. 122103), the deformations that occur below the wave onset were named wrinkles. We provide here a theoretical framework for this regime, using the viscous response of a free liquid surface submitted to arbitrary normal and tangential interfacial stresses at its upper boundary. We relate the spatio-temporal spectrum of the surface deformations to that of the applied interfacial pressure and shear stress fluctuations. For that, we evaluate the spatio-temporal statistics of the turbulent forcing using direct numerical simulation of a turbulent channel flow, assuming no coupling between the air and the liquid flows. Combining theory and numerical simulation, we obtain synthetic wrinkles fields that reproduce the experimental observations. We show that the wrinkles are a multi-scale superposition of random wakes generated by the turbulent fluctuations. They result mainly from the nearly isotropic pressure fluctuations generated in the boundary layer, rather than from the elongated shear stress fluctuations. The wrinkle regime described in this paper naturally arises as the viscous-saturated asymptotic of the inviscid growth theory of Phillips (J. Fluid Mech., vol. 2 (05), 1957, pp. 417–445). We finally discuss the possible relation between wrinkles and the onset of regular quasi-monochromatic waves at larger wind velocity. Experiments indicate that the onset of regular waves increases with liquid viscosity. Our theory suggests that regular waves are triggered when the wrinkle amplitude reaches a fraction of the viscous sublayer thickness. This implies that the turbulent fluctuations near the onset may play a key role in the triggering of exponential wave growth.
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Mytnyk, T. H., V. O. Manukalo, O. V. Dubrovina, and Mytnyk O.O. "ON THE ISSUE OF SPECIFYING RELIABILITY CRITERIA USED IN AUTOMATED SPACE-TIME CONTROL OF METEOROLOGICAL OBSERVATION DATA." Hydrology, hydrochemistry and hydroecology, no. 3 (69) (2023): 51–60. http://dx.doi.org/10.17721/2306-5680.2023.3.4.
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In order to ensure a reliability of data on a state of surface layer of atmosphere, data of meteorological observations conducted on the network of the Hydrometeorological Service of Ukraine are subject to regular multi-level technical and critical control. The entire set of errors in data of meteorological measurements, according to a nature of their manifestation, are divided into two groups: 1) random errors associated with instantaneous pulsations of measured meteorological valuables and their distortion during their further collection and processing; 2) systematic errors, which depend on defect in a devices design, their technical condition, as well as errors caused by violation of measurement technique and an influence of local features on measured meteorological valuables. In order to separate measurement errors from the manifestation of natural features of weather processes, all observational data coming from meteorological stations are subject to regular critical control, during which their reliability, that is, suitability for further use, is established. There are two main types of critical control of measurement data – intra-station and spatio-temporal. The basis of intra-station control is an analysis of values of interrelated meteorological values measured on one station. This is done at hydrometeorological stations with a help of special computer programs. Spatial-temporal control consists in comparing averaged over a certain period of time (decade, month) values of spatial fields of meteorological variables around a station, the data of which is checked/controlled. Local distortions of the field of meteorological values under an influence of local natural factors can be partially eliminated if we compare not values of meteorological values themselves, but their deviations from a climatic norm. Carrying out manual spatio-temporal control is a long and routine work, especially when it comes to analyzing a change in time of data observational inconsistencies. This made it necessary to conduct a study on improving the method of automated spatio-temporal data control of meteorological observations, and to present obtained results in a form of a normative document to be implemented in the activitiy of hydrometeorological organizations of the State Emergency Service of Ukraine. Such research was carried out at the Ukrainian Hydrometeorological Institute for 2020-2022. The purpose of the publication is to present scientific and methodological foundations of development and main provisions of the normative document “Clarification of reliability criteria used during automated spatio-temporal control of data from meteorological observations of stations. Methodical recommendations”, as well as the computer program “MeteoControl”, which forms a database of non-connections and calculates statistical characteristics, are needed for analyzing the array of non-connections and making a decision on the feasibility of changing the reliability criteria. The developed regulatory document provides a number of recommendations for specifying reliability criteria of meteorological observation data, in particular: 1) specification of a reliability criteria for all meteorological variables that are determined on a network of stations should be carried out at least once every 5 years; 2) before clarifying reliability criteria, it is necessary to assess an extent to which a current reliability criteria reveal a data, as well as to establish how many stations and how often in recent years have discrepancies that exceed a current reliability criterion; 3) before making a decision to change a reliability criterion for all stations, it is advisable to find out how, in connection with an introduction of new reliability criterion, a number of stations that will require additional analysis will change, or vice versa – whose data will be considered reliable despite to their dubious or erroneous values.
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Prasad, Kumar, Marco Ottinger, Chunzhu Wei, and Patrick Leinenkugel. "Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series." Remote Sensing 11, no. 3 (February 11, 2019): 357. http://dx.doi.org/10.3390/rs11030357.
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Aquaculture is one of the fastest growing primary food production sectors in India and ranks second behind China. Due to its growing economic value and global demand, India’s aquaculture industry experienced exponential growth for more than one decade. In this study, we extract land-based aquaculture at the pond level for the entire coastal zone of India using large-volume time series Sentinel-1 synthetic-aperture radar (SAR) data at 10-m spatial resolution. Elevation and slope from Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) data were used for masking inappropriate areas, whereas a coastline dataset was used to create a land/ocean mask. The pixel-wise temporal median was calculated from all available Sentinel-1 data to significantly reduce the amount of noise in the SAR data and to reduce confusions with temporary inundated rice fields. More than 3000 aquaculture pond vector samples were collected from high-resolution Google Earth imagery and used in an object-based image classification approach to exploit the characteristic shape information of aquaculture ponds. An open-source connected component segmentation algorithm was used for the extraction of the ponds based on the difference in backscatter intensity of inundated surfaces and shape metrics calculated from aquaculture samples as input parameters. This study, for the first time, provides spatial explicit information on aquaculture distribution at the pond level for the entire coastal zone of India. Quantitative spatial analyses were performed to identify the provincial dominance in aquaculture production, such as that revealed in Andhra Pradesh and Gujarat provinces. For accuracy assessment, 2000 random samples were generated based on a stratified random sampling method. The study demonstrates, with an overall accuracy of 0.89, the spatio-temporal transferability of the methodological framework and the high potential for a global-scale application.
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Zhyla, Simeon, Valerii Volosyuk, Vladimir Pavlikov, Nikolay Ruzhentsev, Eduard Tserne, Anatoliy Popov, Oleksandr Shmatko, et al. "Statistical synthesis of aerospace radars structure with optimal spatio-temporal signal processing, extended observation area and high spatial resolution." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (February 23, 2022): 178–94. http://dx.doi.org/10.32620/reks.2022.1.14.
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Using the statistical theory of optimization of radio engineering systems the optimal method of coherent radar imaging of surfaces in airborne synthetic aperture radar with planar antenna arrays is developed. This method summarizes several modes of terrain observation and it is fully consistent with current trends in the development of cognitive radars with the possibilities of radiation pattern restructuring in space and adaptive reception of reflected signals. Possible modifications of the obtained optimal method for the operation of high-precision airborne radars with a wide swath are presented. The idea is to create a theoretical basis and lay the foundations for its practical application in solving a wide range of issues of statistical optimization of methods and algorithms for optimal spatiotemporal signal processing in cognitive radar systems for the formation of both high-precision and global radar images. To implement the idea, the article highlights the concept of statistical optimization of spatio-temporal processing of electromagnetic fields in on-board cognitive radar systems, which will be based on the synthesis and analysis of methods, algorithms and structures of radar devices for coherent imaging, the study of limiting errors in restoring the spatial distribution of the complex scattering coefficient, the synthesis of optimal feedback for receiver and transmitter adaptations in accordance with a priori information about the parameters of the objects of study, the area of observation and the existing sources of interference. Objective is to develop the theory and fundamentals of the technical implementation of airborne radar systems for the formation of high-precision radar images in an extended field of view from aerospace carriers. Tasks. To reach the objective it is necessary to solve following tasks:– formalize mathematical models of spatiotemporal stochastic radio signals and develop likelihood functional for observation equations in which the useful signal, receiver internal noise and interference radiation of anthropogenic objects are random processes;– to synthesize algorithms for optimal processing of spatio-temporal stochastic signals in multi-channel radar systems located on aerospace-based mobile platforms;- in accordance with the synthesized methods, to substantiate the block diagrams of their implementation;– obtain analytical expressions for the potential characteristics of the quality of radar imaging and determine the class of probing signals and space scanning methods necessary to perform various tasks of radar surveillance;‒ to confirm some of the theoretical results by simulation methods, in which to reveal the features of the technical implementation of aerospace remote sensing radar systems.
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Postle, Bradley R., Jeffrey S. Berger, Alexander M. Taich, and Mark D'Esposito. "Activity in Human Frontal Cortex Associated with Spatial Working Memory and Saccadic Behavior." Journal of Cognitive Neuroscience 12, supplement 2 (November 2000): 2–14. http://dx.doi.org/10.1162/089892900564028.
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We examined, with event-related fMRI, two hypotheses about the organization of human working memory function in frontal cortex: (1) that a region immediately anterior to the frontal eye fields (FEF) (superior frontal cortex, SFC) is specialized for spatial working memory (Courtney, et al., 1998); and (2) that dorsolateral prefrontal cortex (PFC) plays a privileged role in the manipulation of spatial stimuli held in working memory (Owen, et al., 1996; Petrides 1994). Our delayed-response task featured 2-D arrays of irregularly arranged squares that were highlighted serially in a random sequence. The Forward Memory condition required maintenance of the spatio-temporal sequence, the Manipulate Memory condition required reordering this sequence into a new spatially defined order, the Guided Saccade condition required saccades to highlighted squares in the array, but no memory, and the Free Saccade condition required self-paced, horizontal saccades. The comparison of fMRI signal intensity associated with 2-D saccade generation (Guided Saccades) versus fMRI signal intensity associated with the delay period of the working memorials condition revealed no evidence for greater working memory-related activity than saccade-related activity in SFC in any individual subject, nor at the level of the group, and greater 2-D saccade than delay-period activity in three of five subjects. These results fail to support the hypothesis that spatial working memory-related activity is represented preferentially in a region of SFC anterior to the FEF (Courtney, et al., 1998). The comparison of maintenance versus manipulation of spatio-temporal information in working memory revealed significantly greater activity associated with the latter in dorsolateral PFC, but not in ventrolateral PFC or in SFC. These results suggest that the delay-related function of SFC is limited to the maintenance of spatial information, and that this region does not support the nonmnemonic executive control functions supported by dorsolateral PFC. These results also indicate that the preferential recruitment of dorsolateral PFC for the manipulation of information held in working memory applies to tasks employing spatial stimuli, as well as to tasks employing verbal stimuli (D'Esposito, et al., 1999); Petrides et al., 1993; Postle et al., 1999).
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Gitis, Valeri, Alexander Derendyaev, and Konstantin Petrov. "Analyzing the Performance of GPS Data for Earthquake Prediction." Remote Sensing 13, no. 9 (May 9, 2021): 1842. http://dx.doi.org/10.3390/rs13091842.
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The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes.
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Brewer, Kiara, Alistair Clulow, Mbulisi Sibanda, Shaeden Gokool, Vivek Naiken, and Tafadzwanashe Mabhaudhi. "Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems." Remote Sensing 14, no. 3 (January 21, 2022): 518. http://dx.doi.org/10.3390/rs14030518.
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Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m−2, 39 µmol/m−2, and 61.6 µmol/m−2, respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m−2 and 69.6 µmol/m−2, respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.
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FUNKE, KLAUS, and ULF T. EYSEL. "Inverse correlation of firing patterns of single topographically matched perigeniculate neurons and cat dorsal lateral geniculate relay cells." Visual Neuroscience 15, no. 4 (April 1998): 711–29. http://dx.doi.org/10.1017/s0952523898154111.
Full textAbstract:
Action potentials of single perigeniculate (PGN) cells and relay cells of the dorsal lateral geniculate nucleus (dLGN) with topographically matched or at least partially overlapping receptive fields (RF) were simultaneously recorded in the anesthetized and paralyzed cat during visual stimulation with moving gratings or flashing light spots of different size. In many cases, PGN cells showed an activity pattern which appeared like a mirror image of distinct periods of dLGN activity. Flashing spots evoked transient volleys of activity in PGN cells which increased in strength and shortened in latency with increasing size of the stimulus. These responses were temporally matched with inhibitory phases in the early part of visual responses in the dLGN. The spatio-temporal properties of the RFs were established by reverse correlation of the spike activity with the spatially random presentation of bright and dark spots within an array of 20 × 20 positions. Anticorrelated firing patterns of such kind could also be elicited as interocular inhibition with stimulation of the perigeniculate RF in the nondominant eye. Inversely correlated changes in spontaneous and visually induced activity were also visible during spontaneous changes in EEG pattern. With increasing synchronization of the EEG (predominance of delta-waves) the strength of geniculate visual responses declined while maintained perigeniculate activity increased. A weakened interocular and monocular inhibition of dLGN relay cells during visual stimulation of PGN RFs could be achieved with local reversible inactivation of PGN areas topographically matched with the dLGN recording sites. The results indicate that the PGN contributes to the state-dependent control of retino-geniculate transmission and to the monocular and interocular inhibitory processes that shape the visual responses in the dLGN.
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Akbari, Elahe, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti, Najmeh Neysani Samany, Saeid Soufizadeh, and Saeid Hamzeh. "Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms." Remote Sensing 15, no. 14 (July 24, 2023): 3690. http://dx.doi.org/10.3390/rs15143690.
Full textAbstract:
Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)–particle swarm optimization (PSO), GPR–genetic algorithm (GA), GPR–tabu search (TS), and GPR–simulated annealing (SA) hyperparameter-optimized algorithms were developed and compared against kernel-based machine learning regression algorithms and artificial neural network (ANN) and random forest (RF) algorithms. The accuracy of the proposed algorithms was assessed using digital hemispherical photography (DHP) data and destructive measurements performed during the growing season of silage maize in agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019. The results on biophysical variables against validation data showed that the developed GPR-PSO algorithm outperformed other algorithms under study in terms of robustness and accuracy (0.917, 0.931, 0.882 using R2 and 0.627, 0.078, and 1.99 using RMSE in LAI, fCover, and biomass of Sentinel-2 20 m, respectively). GPR-PSO also possesses the unique ability to generate pixel-based uncertainty maps (confidence level) for prediction purposes (i.e., estimated uncertainty level <0.7 in LAI, fCover, and biomass, for 96%, 98%, and 71% of the total study area, respectively). Altogether, GPR-PSO appears to be the most suitable option for mapping biophysical variables at the local scale using Sentinel-2 images.