Multiscale Hydrologic Remote Sensing: Perspectives and Applications integrates advances in hydrologic science and innovative remote sensing technologies. Raising the visibility of interdisciplinary research on water resources, it offers a suite of tools and platforms for investigating spatially and temporally continuous hydrological variables and processes. Illustrated in color, this book examines components in the hydrologic cycle with a range of space and time scales. Organized into five parts, it explores hydrologic remote sensing at the local, urban, watershed, and regional scales, as well as the continental and global scale. Contributors address questions such as What are the local, watershed, and regional differences in soil moisture and evapotranspiration when using different measurement methods and models? How can we fit the scenarios of global warming potential and the remote sensing products of snow water equivalent into hydrologic modeling to address the changing flood and drought conditions in a watershed? How can we fuse the images collected by different satellites to improve the accuracy of predictions at the global scale? Tackling these and many other topics, the book presents new techniques and methods for spaceborne, airborne, and ground-based measurements and mathematical modeling. It also discusses remote sensing image processing tools and features a wealth of real-world applications and case studies. This book is a useful reference for students, professionals, scientists, and policy makers involved in the study of global change, hydrologic science, meteorology, climatology, biology, ecology, and the agricultural and forest sciences. It shows how hydrologic remote sensing technologies can be used more effectively to explore global change impacts and improve the design of hydrologic observatories.

Multiscale Hydrologic Remote Sensing: Perspectives and Applications integrates advances in hydrologic science and innovative remote sensing technologies. Raising the visibility of interdisciplinary research on water resources, it offers a suite of tools and platforms for investigating spatially and temporally continuous hydrological variables and processes.

Illustrated in color, this book examines components in the hydrologic cycle with a range of space and time scales. Organized into five parts, it explores hydrologic remote sensing at the local, urban, watershed, and regional scales, as well as the continental and global scale. Contributors address questions such as

• What are the local, watershed, and regional differences in soil moisture and evapotranspiration when using different measurement methods and models?
• How can we fit the scenarios of global warming potential and the remote sensing products of snow water equivalent into hydrologic modeling to address the changing flood and drought conditions in a watershed?
• How can we fuse the images collected by different satellites to improve the accuracy of predictions at the global scale?

Tackling these and many other topics, the book presents new techniques and methods for spaceborne, airborne, and ground-based measurements and mathematical modeling. It also discusses remote sensing image processing tools and features a wealth of real-world applications and case studies.

This book is a useful reference for students, professionals, scientists, and policy makers involved in the study of global change, hydrologic science, meteorology, climatology, biology, ecology, and the agricultural and forest sciences. It shows how hydrologic remote sensing technologies can be used more effectively to explore global change impacts and improve the design of hydrologic observatories.

In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.

Using a systems analysis approach and extensive case studies, Environmental Remote Sensing and Systems Analysis shows how remote sensing can be used to support environmental decision making. It presents a multidisciplinary framework and the latest remote sensing tools to understand environmental impacts, management complexity, and policy implications. Organized into three parts, this full-color book provides systematic coverage of water quality monitoring, air quality monitoring, and monitoring of land use patterns and degradation. Chapters elaborate on the interactions between human and natural systems, addressing questions such as "what are the regional impacts of an oil spill in coastal environments?" and "how does urbanization affect the rate of infiltration of water at urban-rural interfaces?" Throughout, contributors discuss new techniques and methods for measurements, mathematical modeling, and image processing. Key features: Compares data mining, machine learning, and image processing methods to help you select the best one for your needs Incorporates ground-based monitoring networks as part of air and water quality management Emphasizes environmental information management, with proper integration of GPS, GIS, and existing environmental databases Shows how modeling can be used for decision making Examines how remote sensing can be used to understand environmental health implications and to shape environmental management policies Demonstrates how systems analysis can be used to aid in environmental policy analysis Discusses opportunities for integrated field and laboratory studies in remote sensing education A useful reference for students, professionals, scientists, and policy makers in environmental management and informatics as well as environmental, agricultural, forest, and sustainability sciences, this book shows readers how to m

Using a systems analysis approach and extensive case studies, Environmental Remote Sensing and Systems Analysis shows how remote sensing can be used to support environmental decision making. It presents a multidisciplinary framework and the latest remote sensing tools to understand environmental impacts, management complexity, and policy implications.

Organized into three parts, this full-color book provides systematic coverage of water quality monitoring, air quality monitoring, and monitoring of land use patterns and degradation. Chapters elaborate on the interactions between human and natural systems, addressing questions such as "what are the regional impacts of an oil spill in coastal environments?" and "how does urbanization affect the rate of infiltration of water at urban rural interfaces?" Throughout, contributors discuss new techniques and methods for measurements, mathematical modeling, and image processing.

Key features:

• Compares data mining, machine learning, and image processing methods to help you select the best one for your needs
• Incorporates ground-based monitoring networks as part of air and water quality management
• Emphasizes environmental information management, with proper integration of GPS, GIS, and existing environmental databases
• Shows how modeling can be used for decision making
• Examines how remote sensing can be used to understand environmental health implications and to shape environmental management policies
• Demonstrates how systems analysis can be used to aid in environmental policy analysis
• Discusses opportunities for integrated field and laboratory studies in remote sensing education

A useful reference for students, professionals, scientists, and policy makers in environmental management and informatics as well as environmental, agricultural, forest, and sustainability sciences, this book shows readers how to monitor air, soil, and water quality using state-of-the-art remote sensing tools."

In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.

Ecological modelling has developed rapidly in recent decades, with the focus primarily on the restoration of lakes and wetlands." Ecological Modelling and Engineeringin Lakes and Wetlands" presents the progress being made in modelling for a wealth of applications. It covers the older biogeochemical models still in use today, structurally dynamic models, 3D models, biophysical models, entire watershed models, and ecotoxicological models, as well as the expansion of modeling to the Arctic and Antarctic climate-zones.

The book also addresses modelling the effect of climate change, including the development of ecological models for addressing storm water pond issues, which are increasingly important in urban regions where more concentrated rainfalls are a consequence of climate change. The ecological engineering topics covered in the book also emphasize the advancements being made in applying ecological engineering regimes for better environmental management of lakes and wetlands.
Examines recent progress towards a better understanding of these two important ecosystemsPresents new results and approaches that can be used to develop better modelsDiscusses how to increase the synergistic effect between ecosystems engineering and modelling
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Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. IMPLEMENT SYSTEMS ANALYSIS TOOLS IN SUSTAINABLE ENGINEERINGFeaturing a multidisciplinary approach, Systems Analysis for Sustainable Engineering: Theory and Applications provides a proven framework for applying systems analysis tools to account for environmental impacts, energy efficiency, cost-effectiveness, socioeconomic implications, and ecosystem health in engineering solutions. This pioneering work addresses the increased levels of sophistication embedded in many complex large-scale infrastructure systems and their interactions with the natural environment. After a detailed overview of sustainable systems engineering, the book covers mathematical theories of systems analysis, environmental resources management, industrial ecology, and sustainable design. Real-world examples highlight the methodologies presented in this authoritative resource. COVERAGE INCLUDES: Structured systems analysis for sustainable design Systems analysis and sustainable management strategies Ecomomic valuation, instruments, and project selection Statistical forecasting models Linear, nonlinear, integer, and dynamic programming models Multicriteria decision analyses System dynamics models and simulation analyses Water resources and quality management Air quality management Solid waste management Soil and groundwater remediation planning Industrial ecology and sustainability Green building and green infrastructure systems Energy resources management and energy systems engineering Land resources management and agricultural sustainability