Cities and towns are the original producers of many of the global environmental problems related to waste disposal, and air and water pollution. There is a rapidly growing need for technologies that will enable monitoring of the world's natural resources and urban assets, and managing exposure to natural and man-made risks. The Group on Earth Observation (GEO) calls for strengthening the cooperation and coordination among global observing systems and research programs. Global Urban Monitoring and Assessment through Earth Observation introduces this important international collaborative effort, reviews the current state of global urban remote sensing, and expands on future directions in the field. The book reviews the current state of global urban monitoring, assessment, modeling, and prediction through Earth observation and related technologies. It then introduces GEO's important international collaborative effort-Global Urban Observation and Information Task-and the current state of global urban remote sensing and future directions. It explores groundbreaking work in urban remote sensing and examines how it could contribute to the development of innovative concepts and techniques for sustainable urban development. Despite significant progress in recent years, there remain substantial gaps in ongoing national, regional, and global efforts to address environmental challenges. Edited by a well-known expert in the field of remote sensing, GIS, and other geospatial technologies, this book addresses the gaps in an effective and long-term manner, highlighting the importance of increased coordination and networking among major stakeholders and of working together with other key international mechanisms. Drawing on the expertise of pioneers in the field from across the globe, the book details emerging research in the theory, methods, and techniques of urban remote sensing that provide insight into how to solve the major issues of sustainable devel

Remote sensing of impervious surfaces has matured using advances in geospatial technology so recent that its applications have received only sporadic coverage in remote sensing literature. Remote Sensing of Impervious Surfaces is the first to focus entirely on this developing field. It provides detailed coverage of mapping, data extraction, and modeling techniques specific to analyzing impervious surfaces, such as roads and buildings. Written by renowned experts in the field, this book reviews the major approaches that apply to this emerging field as well as current challenges, developments, and trends. The authors introduce remote sensing digital image processing techniques for estimating and mapping impervious surfaces in urban and rural areas. Presenting the latest modeling tools and algorithms for data extraction and analysis, the book explains how to differentiate roads, roofs, and other manmade structures from remotely sensed images for individual analysis. The final chapters examine how to use impervious surface data for predicting the flow of storm- or floodwater and studying trends in population, land use, resource distribution, and other real-world applications in environmental, urban, and regional planning. Each chapter offers a consistent format including a concise review of basic concepts and methodologies, timely case studies, and guidance for solving problems and analyzing data using the techniques presented.

Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for evaluation of natural resources, including sampling design, land use and land cover classification, natural landscape and ecosystem assessment, forestry, agriculture, biomass and carbon-cycle modeling, wetland classification and dynamics monitoring, and soils and minerals mapping. The book combines review articles with case studies that demonstrate recent advances and developments of methods, techniques, and applications of remote sensing, with each chapter on a specific area of natural resources. Through a comprehensive examination of the wide range of applications of remote sensing technologies to natural resources, the book provides insight into advanced remote sensing systems, technologies, and algorithms for researchers, scientists, engineers, and decision makers.

Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas offers a complete and thorough system for using optical and synthetic aperture radar (SAR) remote sensing data for improving impervious surface estimation (ISE). Highlighting tropical and subtropical areas where there is significant cloud occurrence and varying phenology, the book addresses the challenges impacting impervious surfaces in tropical and subtropical zones. It examines the potential for estimating urban impervious surfaces in a rainy and cloudy environment, considers the difficulties encountered when using optical remote sensing in this type of climate, and assesses existing methods employing remote sensing data for accurate ISE in tropical and subtropical regions. Using the results of comparative studies conducted during the four seasons and in six different cities (Guangzhou, Shenzhen, Hong Kong, Mumbai, Sao Paulo, and Cape Town), the authors develop a framework for ISE using optical and SAR image data. They address the advantages and disadvantages of optical and SAR data, consider fusion strategies for combining optical and SAR data, and examine different feature extractions for optical and SAR data. They also detail the limitations of the research, suggest possible topics for future analysis, and cover previous findings on the synergistic use of optical and SAR data. Concentrates on the effect a tropical and subtropical urban climate can have on impervious surface estimation (ISE) Reviews literature on the significance of ISE and the phonological and climatic characteristics of tropical and subtropical regions Describes datasets including satellite data, digital orthophoto data, in situ data, and more Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas investigates the state of the art in creating new algorithms for digital imag

Driven by the societal needs and improvement in sensor technology and image processing techniques, remote sensing has become an essential geospatial tool for understanding the Earth and managing Human-Earth interactions. Remote Sensing for Sustainability introduces the current state of the art remote sensing knowledge integral for monitoring the world's natural resources and environments, managing exposure to natural disasters and man-made risks, and helping understand the sustainability and productivity of natural ecosystems. Bridging the gap between remote sensing and sustainability science this book examines theories and methods as well as practical applications of sustainable development for cities using remote sensing; focuses on remote sensing methods and techniques for sustainable natural resources with emphasize on forests; answers questions on how and what the remote sensing methods and techniques can do for the sustainability of environmental systems; and examines the issues of energy use and sustainable energy sources using remote sensing technology in countries such as Germany, China, the U.S, drawing on case studies to demonstrate the applicability of remote sensing techniques. This comprehensive guide, which can serve to professors, researchers, and students alike, takes in consideration the United Nations set of sustainable development goals and intends to contribute to the GEO's Strategic Plan by addressing and exemplifying a number of societal benefit areas of remote sensing data sets, methods, and techniques for sustainable development.

Cities and towns are the original producers of many of the global environmental problems related to waste disposal, and air and water pollution. There is a rapidly growing need for technologies that will enable monitoring of the world's natural resources and urban assets, and managing exposure to natural and man-made risks. The Group on Earth Observation (GEO) calls for strengthening the cooperation and coordination among global observing systems and research programs. Global Urban Monitoring and Assessment through Earth Observation introduces this important international collaborative effort, reviews the current state of global urban remote sensing, and expands on future directions in the field. The book reviews the current state of global urban monitoring, assessment, modeling, and prediction through Earth observation and related technologies. It then introduces GEO's important international collaborative effort-Global Urban Observation and Information Task-and the current state of global urban remote sensing and future directions. It explores groundbreaking work in urban remote sensing and examines how it could contribute to the development of innovative concepts and techniques for sustainable urban development. Despite significant progress in recent years, there remain substantial gaps in ongoing national, regional, and global efforts to address environmental challenges. Edited by a well-known expert in the field of remote sensing, GIS, and other geospatial technologies, this book addresses the gaps in an effective and long-term manner, highlighting the importance of increased coordination and networking among major stakeholders and of working together with other key international mechanisms. Drawing on the expertise of pioneers in the field from across the globe, the book details emerging research in the theory, methods, and techniques of urban remote sensing that provide insight into how to solve the major issues of sustainable development-one of the most important issues facing society in the future.

Generating a satisfactory classification image from remote sensing data is not a straightforward task. Many factors contribute to this difficulty including the characteristics of a study area, availability of suitable remote sensing data, ancillary and ground reference data, proper use of variables and classification algorithms, and the analyst's experience. An authoritative text, Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications compiles comprehensive review articles to examine the developments in concepts, methods, techniques, and applications as well as focused articles and case studies on the latest on a particular topic. Divided into four sections, the first deals with various sensors, systems, or sensing operations using different regions of wavelengths. Drawing on the data and lessons learned from the U.S. Landsat remote sensing programs, it reviews key concepts, methods, and practical uses of particular sensors/sensing systems. Section II presents new developments in algorithms and techniques, specifically in image preprocessing, thematic information extraction, and digital change detection. It gives correction algorithms for hyperspectral, thermal, and multispectral sensors, discusses the combined method for performing topographic and atmospheric corrections, and provides examples of correcting non-standard atmospheric conditions, including haze, cirrus, and cloud shadow. Section III focuses on remote sensing of vegetation and related features of the Earth's surface. It reviews advancements in the remote sensing of ecosystem structure, process, and function, and notes important trade-offs and compromises in characterizing ecosystems from space related to spatial, spectral, and temporal resolutions of the imaging sensors. It discusses the mismatch between leaf-level and species-level ecological variables and satellite spatial resolutions and the resulting difficulties in validating satellite-derived products. Finally, Section IV examines developments in the remote sensing of air, water, and other terrestrial features, reviews MODIS algorithms for aerosol retrieval at both global and local scales, and demonstrates the retrieval of aerosol optical thickness (AOT). This section rounds out coverage with a look at remote sensing approaches to measure the urban environment and examines the most important concepts and recent research.

Urban Remote Sensing, Second Edition assembles a team of professional experts to provide a much-needed update on the applications of remote sensing technology to urban and suburban areas. This book reflects new developments in spaceborne and airborne sensors, image processing methods and techniques, and wider applications of urban remote sensing to meet societal and economic challenges. In various sections of the book the authors address methods for upscaling urban feature extraction to the global scale, new methods in mapping and detecting urban landscape features and structures, and mapping and monitoring urbanization in developing countries. Additionally, readers are provided with valuable case studies such as the HEAT (Heat Energy Assessment Technologies) project in Calgary, Canada and the use of VHR (very high resolution) satellite monitoring in Salzburg, Austria to tackle challenges of urban green planning. Features Explores the most up-to-date developments in the field of urban remote sensing Integrates both technical and practical aspects covering all different topics of global urban growth issues Provides new and updated contributions addressing data mining of remotely sensed big data, recent urban studies on a global scale, accuracy assessment and validation, and new technical challenges Examines various applications of urban remote sensing in support of urban planning, environmental management, and sustainable urban development Authors are renowned figures in the field of remote sensing

Today, remote sensing technology is an essential tool for understanding the Earth and managing human-Earth interactions. There is a rapidly growing need for remote sensing and Earth observation technology that enables monitoring of world's natural resources and environments, managing exposure to natural and man-made risks and more frequently occurring disasters, and helping the sustainability and productivity of natural and human ecosystems. The improvement in temporal resolution/revisit allows for the large accumulation of images for a specific location, creating a possibility for time series image analysis and eventual real-time assessments of scene dynamics. As an authoritative text, Remote Sensing Time Series Image Processing brings together active and recognized authors in the field of time series image analysis and presents to the readers the current state of knowledge and its future directions. Divided into three parts, the first addresses methods and techniques for generating time series image datasets. In particular, it provides guidance on the selection of cloud and cloud shadow detection algorithms for various applications. Part II examines feature development and information extraction methods for time series imagery. It presents some key remote sensing-based metrics, and their major applications in ecosystems and climate change studies. Part III illustrates various applications of time series image processing in land cover change, disturbance attribution, vegetation dynamics, and urbanization. This book is intended for researchers, practitioners, and students in both remote sensing and imaging science. It can be used as a textbook by undergraduate and graduate students majoring in remote sensing, imaging science, civil and electrical engineering, geography, geosciences, planning, environmental science, land use, energy, and GIS, and as a reference book by practitioners and professionals in the government, commercial, and industrial sectors.

Today, remote sensing technology is an essential tool for understanding the Earth and managing human-Earth interactions. There is a rapidly growing need for remote sensing and Earth observation technology that enables monitoring of world's natural resources and environments, managing exposure to natural and man-made risks and more frequently occurring disasters, and helping the sustainability and productivity of natural and human ecosystems. The improvement in temporal resolution/revisit allows for the large accumulation of images for a specific location, creating a possibility for time series image analysis and eventual real-time assessments of scene dynamics. As an authoritative text, Remote Sensing Time Series Image Processing brings together active and recognized authors in the field of time series image analysis and presents to the readers the current state of knowledge and its future directions. Divided into three parts, the first addresses methods and techniques for generating time series image datasets. In particular, it provides guidance on the selection of cloud and cloud shadow detection algorithms for various applications. Part II examines feature development and information extraction methods for time series imagery. It presents some key remote sensing-based metrics, and their major applications in ecosystems and climate change studies. Part III illustrates various applications of time series image processing in land cover change, disturbance attribution, vegetation dynamics, and urbanization. This book is intended for researchers, practitioners, and students in both remote sensing and imaging science. It can be used as a textbook by undergraduate and graduate students majoring in remote sensing, imaging science, civil and electrical engineering, geography, geosciences, planning, environmental science, land use, energy, and GIS, and as a reference book by practitioners and professionals in the government, commercial, and industrial sectors.

Urban Remote Sensing, Second Edition assembles a team of professional experts to provide a much-needed update on the applications of remote sensing technology to urban and suburban areas. This book reflects new developments in spaceborne and airborne sensors, image processing methods and techniques, and wider applications of urban remote sensing to meet societal and economic challenges. In various sections of the book the authors address methods for upscaling urban feature extraction to the global scale, new methods in mapping and detecting urban landscape features and structures, and mapping and monitoring urbanization in developing countries. Additionally, readers are provided with valuable case studies such as the HEAT (Heat Energy Assessment Technologies) project in Calgary, Canada and the use of VHR (very high resolution) satellite monitoring in Salzburg, Austria to tackle challenges of urban green planning. Features Explores the most up-to-date developments in the field of urban remote sensing Integrates both technical and practical aspects covering all different topics of global urban growth issues Provides new and updated contributions addressing data mining of remotely sensed big data, recent urban studies on a global scale, accuracy assessment and validation, and new technical challenges Examines various applications of urban remote sensing in support of urban planning, environmental management, and sustainable urban development Authors are renowned figures in the field of remote sensing

High spatial resolution remote sensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more details through high and very high resolution images (10 to 100 cm/pixel). This unprecedented level of detail offers the potential extraction of a range of multi-resource management information, such as precision farming, invasive and endangered vegetative species delineation, forest gap sizes and distribution, locations of highly valued habitats, or sub-canopy topographic information. Information extracted in high spatial remote sensing data right after a devastating earthquake can help assess the damage to roads and buildings and aid in emergency planning for contact and evacuation. To effectively utilize information contained in high spatial resolution imagery, High Spatial Resolution Remote Sensing: Data, Analysis, and Applications addresses some key questions: What are the challenges of using new sensors and new platforms? What are the cutting-edge methods for fine-level information extraction from high spatial resolution images? How can high spatial resolution data improve the quantification and characterization of physical-environmental or human patterns and processes? The answers are built in three separate parts: (1) data acquisition and preprocessing, (2) algorithms and techniques, and (3) case studies and applications. They discuss the opportunities and challenges of using new sensors and platforms and high spatial resolution remote sensing data and recent developments with a focus on UAVs. This work addresses the issues related to high spatial image processing and introduces cutting-edge methods, summarizes state-of-the-art high spatial resolution applications, and demonstrates how high spatial resolution remote sensing can support the extraction of detailed information needed in different systems. Using various high spatial resolution data, the third part of this book covers a range of unique applications, from grasslands to wetlands, karst areas, and cherry orchard trees.

Generating a satisfactory classification image from remote sensing data is not a straightforward task. Many factors contribute to this difficulty including the characteristics of a study area, availability of suitable remote sensing data, ancillary and ground reference data, proper use of variables and classification algorithms, and the analyst's experience. An authoritative text, Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications compiles comprehensive review articles to examine the developments in concepts, methods, techniques, and applications as well as focused articles and case studies on the latest on a particular topic.

Divided into four sections, the first deals with various sensors, systems, or sensing operations using different regions of wavelengths. Drawing on the data and lessons learned from the U.S. Landsat remote sensing programs, it reviews key concepts, methods, and practical uses of particular sensors/sensing systems. Section II presents new developments in algorithms and techniques, specifically in image preprocessing, thematic information extraction, and digital change detection. It gives correction algorithms for hyperspectral, thermal, and multispectral sensors, discusses the combined method for performing topographic and atmospheric corrections, and provides examples of correcting non-standard atmospheric conditions, including haze, cirrus, and cloud shadow.

Section III focuses on remote sensing of vegetation and related features of the Earth's surface. It reviews advancements in the remote sensing of ecosystem structure, process, and function, and notes important trade-offs and compromises in characterizing ecosystems from space related to spatial, spectral, and temporal resolutions of the imaging sensors. It discusses the mismatch between leaf-level and species-level ecological variables and satellite spatial resolutions and the resulting difficulties in validating satellite-derived products.

Finally, Section IV examines developments in the remote sensing of air, water, and other terrestrial features, reviews MODIS algorithms for aerosol retrieval at both global and local scales, and demonstrates the retrieval of aerosol optical thickness (AOT). This section rounds out coverage with a look at remote sensing approaches to measure the urban environment and examines the most important concepts and recent research.

Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for evaluation of natural resources, including sampling design, land use and land cover classification, natural landscape and ecosystem assessment, forestry, agriculture, biomass and carbon-cycle modeling, wetland classification and dynamics monitoring, and soils and minerals mapping. The book combines review articles with case studies that demonstrate recent advances and developments of methods, techniques, and applications of remote sensing, with each chapter on a specific area of natural resources. Through a comprehensive examination of the wide range of applications of remote sensing technologies to natural resources, the book provides insight into advanced remote sensing systems, technologies, and algorithms for researchers, scientists, engineers, and decision makers.

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. A PRACTICAL, FULL-COLOR GUIDE TO REMOTE SENSINGWritten by an internationally renowned expert in the field, An Introduction to Contemporary Remote Sensing introduces the latest developments in remote sensing and imaging science. This book covers basic principles and discusses essential technologies, such as aerial photography, radar, Lidar (light detection and ranging), photogrammetry, satellites, thermal radiation, and much more. Full-color images illustrate the concepts presented, and review questions at the end of each chapter help reinforce learning. This detailed resource provides the solid foundation in remote sensing required for developing diverse geospatial applications. Coverage includes: Geographic information system (GIS) Global positioning system (GPS), mobile mapping, and Google Earth Electromagnetic radiation principles Characteristics of remotely sensed data Spatial, spectral, radiometric, and temporal resolution Aerial photo interpretation Photogrammetry Remote sensors Earth observation satellites Digital image analysis Thermal remote sensing Active remote sensing

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. Maximize a geographical information tool by incorporating it with up-to-date remotely sensed data GIS is predominantly a data-handling technology, while remote sensing is a data retrieval and analysis technology. This book addresses the need to combine remotely sensed data with cartographic, socioeconomic, and environmental data and GIS functionalities. Remote Sensing and GIS Integration begins with theoretical discussions, followed by a series of application areas in urban and environmental studies that employ the integration of remote sensing and GIS. Each application area is examined through analysis of state-of-the-art methods and detailed presentations of one or more case studies.

Driven by the societal needs and improvement in sensor technology and image processing techniques, remote sensing has become an essential geospatial tool for understanding the Earth and managing Human-Earth interactions. Remote Sensing for Sustainability introduces the current state of the art remote sensing knowledge integral for monitoring the world's natural resources and environments, managing exposure to natural disasters and man-made risks, and helping understand the sustainability and productivity of natural ecosystems. Bridging the gap between remote sensing and sustainability science this book examines theories and methods as well as practical applications of sustainable development for cities using remote sensing; focuses on remote sensing methods and techniques for sustainable natural resources with emphasize on forests; answers questions on how and what the remote sensing methods and techniques can do for the sustainability of environmental systems; and examines the issues of energy use and sustainable energy sources using remote sensing technology in countries such as Germany, China, the U.S, drawing on case studies to demonstrate the applicability of remote sensing techniques. This comprehensive guide, which can serve to professors, researchers, and students alike, takes in consideration the United Nations set of sustainable development goals and intends to contribute to the GEO's Strategic Plan by addressing and exemplifying a number of societal benefit areas of remote sensing data sets, methods, and techniques for sustainable development.

Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas offers a complete and thorough system for using optical and synthetic aperture radar (SAR) remote sensing data for improving impervious surface estimation (ISE). Highlighting tropical and subtropical areas where there is significant cloud occurrence and varying phenology, the book addresses the challenges impacting impervious surfaces in tropical and subtropical zones. It examines the potential for estimating urban impervious surfaces in a rainy and cloudy environment, considers the difficulties encountered when using optical remote sensing in this type of climate, and assesses existing methods employing remote sensing data for accurate ISE in tropical and subtropical regions. Using the results of comparative studies conducted during the four seasons and in six different cities (Guangzhou, Shenzhen, Hong Kong, Mumbai, Sao Paulo, and Cape Town), the authors develop a framework for ISE using optical and SAR image data. They address the advantages and disadvantages of optical and SAR data, consider fusion strategies for combining optical and SAR data, and examine different feature extractions for optical and SAR data. They also detail the limitations of the research, suggest possible topics for future analysis, and cover previous findings on the synergistic use of optical and SAR data. Concentrates on the effect a tropical and subtropical urban climate can have on impervious surface estimation (ISE) Reviews literature on the significance of ISE and the phonological and climatic characteristics of tropical and subtropical regions Describes datasets including satellite data, digital orthophoto data, in situ data, and more Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas investigates the state of the art in creating new algorithms for digital images processing and remotely sensed images classification, as well as in developing the meteorological modeling of urban heat islands, and the hydrological modeling of surface run-off and urban floods.