Download Free Remote Sensing Change Analysis Methodology To Support Traffic Monitoring Programs Book in PDF and EPUB Free Download. You can read online Remote Sensing Change Analysis Methodology To Support Traffic Monitoring Programs and write the review.

The Center for Transportation Research and Education (CTRE) issued a report in July 2003, based on a sample study of the application of remote sensed image land use change detection to the methodology of traffic monitoring in Blackhawk County, Iowa. In summary, the results indicated a strong correlation and a statistically significant regression coefficient between the identification of built-up land use change areas from remote sensed data and corresponding changes in traffic patterns, expressed as vehicle miles traveled (VMT). Based on these results, the Iowa Department of Transportation (Iowa DOT) requested that CTRE expand the study area to five counties in the southwest quadrant of the state. These counties are scheduled for traffic counts in 2004, and the Iowa DOT desired the data to 1) evaluate the current methodology used to place the devices; 2) potentially influence the placement of traffic counting devices in areas of high built-up land use change; and 3) determine if opportunities exist to reduce the frequency and/or density of monitoring activity in lower trafficked rural areas of the state. This project is focused on the practical application of built-up land use change data for placement of traffic count data recording devices in five southwest Iowa counties.
Change detection using remotely sensed images has many applications, such as urban monitoring, land-cover change analysis, and disaster management. This work investigates two-dimensional change detection methods. The existing methods in the literature are grouped into four categories: pixel-based, transformation-based, texture analysis-based, and structure-based. In addition to testing existing methods, four new change detection methods are introduced: fuzzy logic-based, shadow detection-based, local feature-based, and bipartite graph matching-based. The latter two methods form the basis for a structural analysis of change detection. Three thresholding algorithms are compared, and their effects on the performance of change detection methods are measured. These tests on existing and novel change detection methods make use of a total of 35 panchromatic and multi-spectral Ikonos image sets. Quantitative test results and their interpretations are provided.
The chapters in this book present state-of-the-art geomatics technologies applied in global environmental studies. This text provides the latest research findings and delivers complete references to related publications. This book will motivate the undergraduate and graduate students, researchers and practitioners to better understand the environmental changes with informed solutions. Global Change studies are increasingly considered a vital source of information to understand the Earth Environment, especially in the framework of human-induced, climate change and land use transformation. Satellite Earth Observing systems and geomatics technologies provide a unique tool to monitor and model the changes, respectively. While the range of applications and innovative techniques are always increasing, this book provides a summary of key study cases where satellite data offers critical information to understand the usefulness of the geomatics technologies and global environmental issues. Geomatics technologies provide powerful tools to model and analyze the effects of those global environmental changes towards minimizing their adverse impacts on human health and the environment.
It gives comprehensive coverage of the fundamentals, the techniques, and the demonstrated results of a variety of projects in a manner accessible to both the novice and the advanced user of remotely sensed data. This is a unique and distinctive work on change detection in landscapes and watersheds and examines topics such as the basis of image analysis, change detection, radar and validation (or accuracy assessment). A number of North American case studies take the reader through real examples - with all the successes and pitfalls. There are advanced topics for the experienced user including advanced classification techniques (eg. pixel mixing). Established data sources such as Landsat MSS and AVHRR data are described as are new sensors such as radar and AVIRIS. Applications include fire, wetlands, forests, environmental change, vegetation, Amazonia, and the savannah. The book concludes with a description of methods for performing a change detection accuracy assessment.
Written by world renowned scientists, this book provides an excellent overview of a wide array of methods and techniques for the processing and analysis of multitemporal remotely sensed images. These methods and techniques include change detection, multitemporal data fusion, coarse-resolution time series processing, and interferometric SAR multitemporal processing, among others. A broad range of multitemporal datasets are used in their methodology demonstrations and application examples, including multispectral, hyperspectral, SAR and passive microwave data. This book features a variety of application examples covering both land and aquatic environments. Land applications include urban, agriculture, habitat disturbance, vegetation dynamics, soil moisture, land surface albedo, land surface temperature, glacier and disaster recovery. Aquatic applications include monitoring water quality, water surface areas and water fluctuation in wetland areas, spatial distribution patterns and temporal fluctuation trends of global land surface water, as well as evaluation of water quality in several coastal and marine environments. This book will help scientists, practitioners, students gain a greater understanding of how multitemporal remote sensing could be effectively used to monitor our changing planet at local, regional, and global scales.
Change detection using remotely sensed images has many applications, such as urban monitoring, land-cover change analysis, and disaster management. This work investigates two-dimensional change detection methods. The existing methods in the literature are grouped into four categories: pixel-based, transformation-based, texture analysis-based, and structure-based. In addition to testing existing methods, four new change detection methods are introduced: fuzzy logic-based, shadow detection-based, local feature-based, and bipartite graph matching-based. The latter two methods form the basis for a structural analysis of change detection. Three thresholding algorithms are compared, and their effects on the performance of change detection methods are measured. These tests on existing and novel change detection methods make use of a total of 35 panchromatic and multi-spectral Ikonos image sets. Quantitative test results and their interpretations are provided.
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
This book thoroughly covers the remote sensing visualization and analysis techniques based on computational imaging and vision in Earth science. Remote sensing is considered a significant information source for monitoring and mapping natural and man-made land through the development of sensor resolutions that committed different Earth observation platforms. The book includes related topics for the different systems, models, and approaches used in the visualization of remote sensing images. It offers flexible and sophisticated solutions for removing uncertainty from the satellite data. It introduces real time big data analytics to derive intelligence systems in enterprise earth science applications. Furthermore, the book integrates statistical concepts with computer-based geographic information systems (GIS). It focuses on image processing techniques for observing data together with uncertainty information raised by spectral, spatial, and positional accuracy of GPS data. The book addresses several advanced improvement models to guide the engineers in developing different remote sensing visualization and analysis schemes. Highlights on the advanced improvement models of the supervised/unsupervised classification algorithms, support vector machines, artificial neural networks, fuzzy logic, decision-making algorithms, and Time Series Model and Forecasting are addressed. This book guides engineers, designers, and researchers to exploit the intrinsic design remote sensing systems. The book gathers remarkable material from an international experts' panel to guide the readers during the development of earth big data analytics and their challenges.
Driven by advances in technology and societal needs, the next frontier in remote sensing is urban areas. With the advent of high-resolution imagery and more capable techniques, the question has become "Now that we have the technology, how do we use it?" The need for a definitive resource that explores the technology of remote sensing and the issues it can resolve in an urban setting has never been more acute. Containing contributions from world renowned experts, Urban Remote Sensing provides a review of basic concepts, methodologies, and case studies. Each chapter demonstrates how to apply up-to-date techniques to the problems identified and how to analyze research results. Organized into five sections, this book: Focuses on data, sensors, and systems considerations as well as algorithms for urban feature extraction Analyzes urban landscapes in terms of composition and structure, especially using sub-pixel analysis techniques Presents methods for monitoring, analyzing, and modeling urban growth Illustrates various approaches to urban planning and socio-economic applications of urban remote sensing Assesses the progress made to date, identifies the existing problems and challenges, and demonstrates new developments and trends in urban remote sensing This book is ideal for upper division undergraduate and graduate students, however it can also serve as a reference for researchers or those individuals interested in the remote sensing of cities in academia, and governmental and commercial sectors. Urban Remote Sensing examines how to apply remote sensing technology to urban and suburban areas.
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.