Modeling of evolving textures using granulometries

This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975

Tropical cyclone rainbands can trigger meteotsunamis

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Shi, L., Olabarrieta, M., Nolan, D. S., & Warner, J. C. Tropical cyclone rainbands can trigger meteotsunamis. Nature Communications, 11(1), (2020): 678, doi:10.1038/s41467-020-14423-9.Tropical cyclones are one of the most destructive natural hazards and much of the damage and casualties they cause are flood-related. Accurate characterization and prediction of total water levels during extreme storms is necessary to minimize coastal impacts. While meteotsunamis are known to influence water levels and to produce severe consequences, their impacts during tropical cyclones are underappreciated. This study demonstrates that meteotsunami waves commonly occur during tropical cyclones, and that they can contribute significantly to total water levels. We use an idealized coupled ocean–atmosphere–wave numerical model to analyze tropical cyclone-induced meteotsunami generation and propagation mechanisms. We show that the most extreme meteotsunami events are triggered by inherent features of the structure of tropical cyclones: inner and outer spiral rainbands. While outer distant spiral rainbands produce single-peak meteotsunami waves, inner spiral rainbands trigger longer lasting wave trains on the front side of the tropical cyclones.We thank all the developers of COAWST, ROMS, WRF, and SWAN models. D.N. was supported by NSF grant AGS-1654831. We would like to thank Dr. K. Bagamian for her editorial and writing suggestions. We would like to thank Dr. A. Aretxabaleta for the internal US Geological Survey internal revision and suggestions

Model for Estimation of Bounds in Digital Coding of Seabed Images

This paper proposes the novel model for estimation of bounds in digital coding of images. Entropy coding of images is exploited to measure the useful information content of the data. The bit rate achieved by reversible compression using the rate-distortion theory approach takes into account the contribution of the observation noise and the intrinsic information of hypothetical noise-free image. Assuming the Laplacian probability density function of the quantizer input signal, SQNR gains are calculated for image predictive coding system with non-adaptive quantizer for white and correlated noise, respectively. The proposed model is evaluated on seabed images. However, model presented in this paper can be applied to any signal with Laplacian distribution

Integrated Remote Sensing and Forecasting of Regional Terrestrial Precipitation with Global Nonlinear and Nonstationary Teleconnection Signals Using Wavelet Analysis

Global sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in estimates by utilizing simple linear analyses to examine chaotic teleconnection relationships. Still, non-stationary signals exist, making teleconnection identification difficult at the local scale. Part 1 of this research establishes short-term (10-year), linear and non-stationary teleconnection signals between SST at the North Atlantic and North Pacific oceans and terrestrial responses of greenness and precipitation along multiple pristine sites in the northeastern U.S., including (1) White Mountain National Forest - Pemigewasset Wilderness, (2) Green Mountain National Forest - Lye Brook Wilderness and (3) Adirondack State Park - Siamese Ponds Wilderness. Each site was selected to avoid anthropogenic influences that may otherwise mask climate teleconnection signals. Lagged pixel-wise linear teleconnection patterns across anomalous datasets found significant correlation regions between SST and the terrestrial sites. Non-stationary signals also exhibit salient co-variations at biennial and triennial frequencies between terrestrial responses and SST anomalies across oceanic regions in agreement with the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) signals. Multiple regression analysis of the combined ocean indices explained up to 50% of the greenness and 42% of the precipitation in the study sites. The identified short-term teleconnection signals improve the understanding and projection of climate change impacts at local scales, as well as harness the interannual periodicity information for future climate projections. Part 2 of this research paper builds upon the earlier short-term study by exploring a long-term (30-year) teleconnection signal investigation between SST at the North Atlantic and Pacific oceans and the precipitation within Adirondack State Park in upstate New York. Non-traditional teleconnection signals are identified using wavelet decomposition and teleconnection mapping specific to the Adirondack region. Unique SST indices are extracted and used as input variables in an artificial neural network (ANN) prediction model. The results show the importance of considering non-leading teleconnection patterns as well as the known teleconnection patterns. Additionally, the effects of the Pacific Ocean SST or the Atlantic Ocean SST on terrestrial precipitation in the study region were compared with each other to deepen the insight of sea-land interactions. Results demonstrate reasonable prediction skill at forecasting precipitation trends with a lead time of one month, with r values of 0.6. The results are compared against a statistical downscaling approach using the HadCM3 global circulation model output data and the SDSM statistical downscaling software, which demonstrate less predictive skill at forecasting precipitation within the Adirondacks

Subtidal water level variation controlled by river flow and tides

Subtidal water level dynamics in the Berau river, East Kalimantan, Indonesia, feature a pronounced fortnightly variation. The daily mean water levels at a station about 60 km from the sea are 0.2–0.6 m higher during spring tide than during neap tide. To explain the underlying mechanisms, a local subtidal momentum balance is set up from field data, using continuous discharge estimates inferred from measurements taken with a horizontal acoustic Doppler current profiler. It is demonstrated that terms accounting for friction and variation in the water surface gradient are dominant in the subtidal momentum balance. To further investigate the sources of subtidal water level variation, a generic method of analysis is proposed to decompose the subtidal friction term into contributions caused by river flow, by interaction between tidal motions and river flow, and by the tidal motions alone. At the station under study, mainly the river-tide interaction term is responsible for generating fortnightly variation of the subtidal water level. The contribution from interaction between diurnal, semidiurnal, and quarterdiurnal tides to subtidal friction is significantly smaller. Provided that the reduction of tidal velocity amplitudes with increasing discharges can be predicted from a regression model, the results presented herein can be used to predict changes in subtidal water levels as a result of increased river discharges

Relationship between Ocean-Atmospheric Climate Variables and Regional Streamflow of the Conterminous United States

Understanding the interconnections between oceanic-atmospheric climate variables and regional streamflow of the conterminous United States may aid in improving regional long lead-time streamflow forecasting. The current research evaluates the time-lagged relationship between streamflow of six geographical regions defined from National Climate Assessment and sea surface temperature (SST), 500-mbar geopotential height (Z500), 500-mbar specific humidity (SH500), and 500-mbar east-west wind (U500) of the Pacific and the Atlantic Ocean using singular value decomposition (SVD). The spatio-temporal correlation between streamflow and SST was developed first from SVD and thus obtained correlation was later associated with Z500, SH500, and U500 separately to evaluate the coupled interconnections between the climate variables. Furthermore, the associations between regional streamflow and the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation, and Atlantic Multidecadal Oscillation were evaluated using the derivatives of continuous wavelet transform. Regional SVD analysis revealed significant teleconnection between several regions and climate variables. The warm phase of equatorial SST had shown a stronger correlation with the majority of streamflow. Both SVD and wavelet analyses concluded that the streamflow variability of the regions in close proximity to the Pacific Ocean was strongly associated with the ENSO. Improved knowledge of teleconnection of climate variables with regional streamflow variability may help in regional water management and streamflow prediction studies

Soil moisture and hydrological drought in the Colorado River basin

This dissertation investigates the interannual variability of soil moisture as related to large-scale climate variability, and oceanic-atmospheric patterns. Firstly, a three-layer hydrological model VIC-3L (Variable Infiltration Capacity Model - 3 layers) was used in the Upper Colorado River basin at a daily time step and a 1/8 spatial resolution over a 50-year (1950 to 2000) period. Using wavelet analysis, deep soil moisture was compared to the Palmer Drought Severity Index (PDSI), precipitation, and streamflow to determine whether deep soil moisture is an indicator of climate extremes; Secondly, this research evaluates the spatial and temporal variability of soil moisture by using map analysis and t-test statistical method. The soil moisture in drought years was significantly different from the soil moisture in normal and wet years. An extended temporal soil moisture evaluation was performed in pre-drought, drought, and post-drought periods. The results show that soil moisture may be a potential drought indicator, which could improve drought predictability. Finally, the correlation between soil moisture and oceanic-atmospheric patterns, such as Sea Surface Temperatures (SSTs), El Nino-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO) were evaluated. Singular Variable Decomposition (SVD) was used in evaluating the relation between soil moisture and Pacific Ocean SSTs; The current research resulted in several significant contributions: The main contributions of this research are: (a) the development of a 1/8 spatial resolution and a temporal daily time step soil moisture dataset for the Upper Colorado River basin, (b) the evaluation of the soil moisture as a drought indicator, (c) improving the comprehensive understanding of how spatial and temporal variability of soil moisture varies during drought periods, and (d) the coupling of oceanic-atmospheric/patterns with soil moisture to improve long-term drought forecasts