Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales PDF Download
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Author: Genesis T. Yengoh
Publisher: Springer
ISBN: 3319241125
Category : Technology & Engineering
Languages : en
Pages : 123
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Book Description
This report examines the scientific basis for the use of remotely sensed data, particularly Normalized Difference Vegetation Index (NDVI), primarily for the assessment of land degradation at different scales and for a range of applications, including resilience of agro-ecosystems. Evidence is drawn from a wide range of investigations, primarily from the scientific peer-reviewed literature but also non-journal sources. The literature review has been corroborated by interviews with leading specialists in the field. The report reviews the use of NDVI for a range of themes related to land degradation, including land cover change, drought monitoring and early warning systems, desertification processes, greening trends, soil erosion and salinization, vegetation burning and recovery after fire, biodiversity loss, and soil carbon. This SpringerBrief also discusses the limits of the use of NDVI for land degradation assessment and potential for future directions of use. A substantial body of peer-reviewed research lends unequivocal support for the use of coarse-resolution time series of NDVI data for studying vegetation dynamics at global, continental and sub-continental levels. There is compelling evidence that these data are highly correlated with biophysically meaningful vegetation characteristics such as photosynthetic capacity and primary production that are closely related to land degradation and to agroecosystem resilience.
Author: Genesis T. Yengoh
Publisher: Springer
ISBN: 3319241125
Category : Technology & Engineering
Languages : en
Pages : 123
Get Book Here
Book Description
This report examines the scientific basis for the use of remotely sensed data, particularly Normalized Difference Vegetation Index (NDVI), primarily for the assessment of land degradation at different scales and for a range of applications, including resilience of agro-ecosystems. Evidence is drawn from a wide range of investigations, primarily from the scientific peer-reviewed literature but also non-journal sources. The literature review has been corroborated by interviews with leading specialists in the field. The report reviews the use of NDVI for a range of themes related to land degradation, including land cover change, drought monitoring and early warning systems, desertification processes, greening trends, soil erosion and salinization, vegetation burning and recovery after fire, biodiversity loss, and soil carbon. This SpringerBrief also discusses the limits of the use of NDVI for land degradation assessment and potential for future directions of use. A substantial body of peer-reviewed research lends unequivocal support for the use of coarse-resolution time series of NDVI data for studying vegetation dynamics at global, continental and sub-continental levels. There is compelling evidence that these data are highly correlated with biophysically meaningful vegetation characteristics such as photosynthetic capacity and primary production that are closely related to land degradation and to agroecosystem resilience.
Author: Nathalie Pettorelli
Publisher: Oxford University Press, USA
ISBN: 0199693161
Category : Nature
Languages : en
Pages : 205
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Book Description
This book provides a coherent review of NDVI including its origin, its availability, its associated advantages and disadvantages, and its possible applications in ecology, environmental monitoring, wildlife management, and conservation.
Author: Nathalie Pettorelli
Publisher: OUP Oxford
ISBN: 0191512729
Category : Science
Languages : en
Pages : 363
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Book Description
There has been a recent surge of interest in remote sensing and its use in ecology and conservation but this is the first book to focus explicitly on the NDVI (Normalised Difference Vegetation Index), a simple numerical indicator and powerful tool that can be used to assess spatio-temporal changes in green vegetation. The NDVI opens the possibility of addressing questions on scales inaccessible to ground-based methods alone; it is mostly freely available with global coverage over several decades. This novel text provides an authoritative overview of the principles and possible applications of the NDVI in ecology, environmental and wildlife management, and conservation. NDVI data can provide valuable information about temporal and spatial changes in vegetation distribution, productivity, and dynamics; allowing monitoring of habitat degradation and fragmentation, or assessment of the ecological effects of climatic disasters such as drought or fire. The NDVI has also provided ecologists with a promising way to couple vegetation with animal distribution, abundance, movement, survival and reproductive parameters. Over the last few decades, numerous studies have highlighted the potential key role of satellite data and the NDVI in macroecology, plant ecology, animal population dynamics, environmental monitoring, habitat selection and habitat use studies, and paleoecology. The chapters are organised around two sections: the first detailing vegetation indices and the NDVI, the principles behind the NDVI, its correlation with climate, the available NDVI datasets, and the possible complications and errors associated with the use of this satellite-based vegetation index. The second section discusses the possible applications of the NDVI in ecology, environmental and wildlife management, and conservation.
Author: Lalit Kumar
Publisher: MDPI
ISBN: 3038978841
Category : Science
Languages : en
Pages : 420
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Book Description
In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.
Author: James B. Campbell
Publisher: CRC Press
ISBN: 9780415282949
Category : Technology & Engineering
Languages : en
Pages : 388
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Book Description
This comprehensive introductory text presents a timely overview of the most widely used forms of remote sensing imagery and their applications in plant sciences, hydrology, earth sciences, and land-use analysis.
Author: Caryl L. Elzinga
Publisher: DIANE Publishing
ISBN: 9780788148378
Category : Science
Languages : en
Pages : 190
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Book Description
This annotated bibliography documents literature addressing the design and implementation of vegetation monitoring. It provides resources managers, ecologists, and scientists access to the great volume of literature addressing many aspects of vegetation monitoring: planning and objective setting, choosing vegetation attributes to measure, sampling design, sampling methods, statistical and graphical analysis, and communication of results. Over half of the 1400 references have been annotated. Keywords pertaining to the type of monitoring or method are included with each bibliographic entry. Keyword index.
Author: Ni-Bin Chang
Publisher: CRC Press
ISBN: 1351650637
Category : Technology & Engineering
Languages : en
Pages : 627
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Book Description
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.
Author: Osvaldo E. Sala
Publisher: Springer Science & Business Media
ISBN: 9780387952499
Category : Nature
Languages : en
Pages : 414
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Book Description
Climatic change, conservation biology
Author: Jay Gao
Publisher: CRC Press
ISBN: 1040154794
Category : Technology & Engineering
Languages : en
Pages : 461
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Book Description
This book provides comprehensive and in-depth explanations of all topics related to quantitative remote sensing and its applications in terrestrial, biospheric, hydrospheric, and atmospheric studies. It elucidates how to retrieve quantitative information on a wide range of environmental parameters from various remote sensing data at the highest accuracy possible and expounds how different aspects of the target of remote sensing can be quantified using diverse analytical methods and level of accuracy. Written in an easy-to-follow language, logically organized, and with step-by-step examples, the book assists readers to deepen their understanding of the theory and cutting-edge research on quantitative remote sensing. Features Explains how to retrieve quantitative information on a wide range of environmental parameters from various tailored remote sensing data at the highest accuracy possible. Manifests the author's decades of teaching and research in quantitative remote sensing and approaches the subject from both theoretical and pragmatic perspectives, informed by the latest research outcomes. Includes practical and real-life examples to illustrate how the quantitative information on a target can be retrieved from a given type of remote sensing data. Focuses on the latest developments in the field of quantitative remote sensing. Introduces sufficient mathematical concepts to reveal how remotely sensed data are converted to quantitative information while providing quality assurance of the retrieved results. This is a suitable textbook for upper-level undergraduate or postgraduate students and serves as a handy and valuable reference for professionals working in monitoring the environment. By reading this book, readers gain a sound understanding of how to retrieve quantitative information on the environment from diverse remote sensing data using the most appropriate cutting-edge methods and software.
Author: Giles M. Foody
Publisher: John Wiley & Sons
ISBN:
Category : Computers
Languages : en
Pages : 340
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Book Description
Remote sensing and geographical information science (GIS) have advanced considerably in recent years. However, the potential of remote sensing and GIS within the environmental sciences is limited by uncertainty, especially in connection with the data sets and methods used. In many studies, the issue of uncertainty has been incompletely addressed. The situation has arisen in part from a lack of appreciation of uncertainty and the problems it can cause as well as of the techniques that may be used to accommodate it. This book provides general overviews on uncertainty in remote sensing and GIS that illustrate the range of uncertainties that may occur, in addition to describing the means of measuring uncertainty and the impacts of uncertainty on analyses and interpretations made. Uncertainty in Remote Sensing and GIS provides readers with comprehensive coverage of this largely undocumented subject: * Relevant to a broad variety of disciplines including geography, environmental science, electrical engineering and statistics * Covers range of material from base overviews to specific applications * Focuses on issues connected with uncertainty at various points along typical data analysis chains used in remote sensing and GIS Written by an international team of researchers drawn from a variety of disciplines, Uncertainty in Remote Sensing and GIS provides focussed discussions on topics of considerable importance to a broad research and user community. The book is invaluable reading for researchers, advanced students and practitioners who want to understand the nature of uncertainty in remote sensing and GIS, its limitations and methods of accommodating it.
