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Application of Pattern Recognition and Adaptive DSP Methods for Spatio-temporal Analysis of Satellite Based Hydrological Datasets

2010-06-17
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Release : 2010-06-17
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Book Synopsis Application of Pattern Recognition and Adaptive DSP Methods for Spatio-temporal Analysis of Satellite Based Hydrological Datasets by : Anish Chand Turlapaty

Download or read book Application of Pattern Recognition and Adaptive DSP Methods for Spatio-temporal Analysis of Satellite Based Hydrological Datasets written by Anish Chand Turlapaty. This book was released on 2010-06-17. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation of satellite-based observations of hydrological variables with full numerical physics models can be used to downscale these observations from coarse to high resolution to improve microwave sensor-based soil moisture observations. Moreover, assimilation can also be used to predict related hydrological variables, e.g., precipitation products can be assimilated in a land information system to estimate soil moisture. High quality spatio-temporal observations of these processes are vital for a successful assimilation which in turn needs a detailed analysis and improvement. In this research, pattern recognition and adaptive signal processing methods are developed for the spatio-temporal analysis and enhancement of soil moisture and precipitation datasets. These methods are applied to accomplish the following tasks: (i) a consistency analysis of level-3 soil moisture data from the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) against in-situ soil moisture measurements from the USDA Soil Climate Analysis Network (SCAN). This method performs a consistency assessment of the entire time series in relation to others and provides a spatial distribution of consistency levels. The methodology is based on a combination of wavelet-based feature extraction and oneclass support vector machines (SVM) classifier. Spatial distribution of consistency levels are presented as consistency maps for a region, including the states of Mississippi, Arkansas, and Louisiana. These results are well correlated with the spatial distributions of average soil moisture, and the cumulative counts of dense vegetation; (ii) a modified singular spectral analysis based interpolation scheme is developed and validated on a few geophysical data products including GODAE's high resolution sea surface temperature (GHRSST). This method is later employed to fill the systematic gaps in level-3 AMSR-E soil moisture dataset; (iii) a combination of artificial neural networks and vector space transformation function is used to fuse several high resolution precipitation products (HRPP). The final merged product is statistically superior to any of the individual datasets over a seasonal period. The results have been tested against ground based measurements of rainfall over our study area and average accuracies obtained are 85% in the summer and 55% in the winter 2007.

Spatiotemporal Data Analytics and Modeling

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Book Rating : 511/5 ( reviews)

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Book Synopsis Spatiotemporal Data Analytics and Modeling by : John A

Download or read book Spatiotemporal Data Analytics and Modeling written by John A. This book was released on . Available in PDF, EPUB and Kindle. Book excerpt:

Change Detection and Image Time Series Analysis 2

2021-12-01 Computers
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Release : 2021-12-01
Genre : Computers
Kind : eBook
Book Rating : 281/5 ( reviews)

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Book Synopsis Change Detection and Image Time Series Analysis 2 by : Abdourrahmane M. Atto

Download or read book Change Detection and Image Time Series Analysis 2 written by Abdourrahmane M. Atto. This book was released on 2021-12-01. Available in PDF, EPUB and Kindle. Book excerpt: Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.

Analysis and Modeling Techniques for Geo-spatial and Spatio-temporal Datasets

2017 Big data
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Release : 2017
Genre : Big data
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Book Synopsis Analysis and Modeling Techniques for Geo-spatial and Spatio-temporal Datasets by : Kulsawasd Jitkajornwanich

Download or read book Analysis and Modeling Techniques for Geo-spatial and Spatio-temporal Datasets written by Kulsawasd Jitkajornwanich. This book was released on 2017. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, spatio-temporal data has received a lot of attention and increasingly plays an important role in our everyday lives as we can witness from the fast-growing mobile technologies and its location-based application development. By spatio-temporal data, we mean data that is associated with specific spatial locations that change over time. For example, a cellphone or car with GPS will generate the object location at regular time intervals. Another example would be the track of a storm center as it moves. Spatio-temporal data could be thought of as a huge data warehouse, which contains hidden and meaningful information. However, to analyze the available spatiotemporal data directly from its original formats and locations is not easy because the data is often in a format that is difficult to analyze and is usually 'big'. Our research goals focus on spatio-temporal datasets and how to summarize, model, and conceptualize them for analysis and mining. Five main parts of this dissertation include: 1) spatio-temporal knowledge representation, 2) identifying meaningful concepts from raw data, 3) converting raw data to conceptual data, 4) analysis and mining of conceptual data, and 5) a general framework for big data analysis and mining. In the first part of the dissertation, we look at the spatio-temporal datasets in general by considering spatio-temporal data semantics using techniques similar to those utilized in the “Semantic Web”. We work towards creating a spatio-temporal ontology framework, which can be used to represent and reason about spatio-temporal data. In the next three parts, we focus on the spatio-temporal datasets in a specific domain, which is rainfall precipitation data in the hydrology domain. However, the techniques and methodology that we use can be adapted to different types of hydrological data such as soil moisture, water level, etc., as well as other types of big spatio-temporal data. Therefore, in the final part, we propose a generalized framework for analyzing and mining big data in any given domain. The framework allows big data in a particular domain to be conceptually analyzed and mined by using ontologies and EER.

Spatiotemporal Analysis of Extreme Hydrological Events

2018-11-20 Science
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Release : 2018-11-20
Genre : Science
Kind : eBook
Book Rating : 311/5 ( reviews)

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Book Synopsis Spatiotemporal Analysis of Extreme Hydrological Events by : Gerald Corzo

Download or read book Spatiotemporal Analysis of Extreme Hydrological Events written by Gerald Corzo. This book was released on 2018-11-20. Available in PDF, EPUB and Kindle. Book excerpt: Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest developments and methodologies for analyzing and understanding extreme environmental and hydrological events. The book addresses the topic using spatio-temporal methods, such as space-time geostatistics, machine learning, statistical theory, hydrological modelling, neural network and evolutionary algorithms. This important resource for both hydrologists and statisticians interested in the framework of spatial and temporal analysis of hydrological events will provide users with an enhanced understanding of the relationship between magnitude, dynamics and the probability of extreme hydrological events. Presents spatio-temporal processes, including multivariate dynamic modelling Provides varying methodological approaches, giving the readers multiple hydrological modelling information to use in their work Includes a variety of case studies making the context of the book relatable to everyday working situations

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