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Although the development of remote sensing techniques focuses greatly on construction of new sensors with higher spatial and spectral resolution, it is advisable to also use data of older sensors (especially, the LANDSAT-mission) when the historical mapping of land use/land cover and monitoring of their dynamics are needed. Using data from LANDSAT missions as well as from Terra (ASTER) Sensors, the authors shows in his book maps of historical land cover changes with a focus on agricultural irrigation projects. The kernel of this study was whether, how and to what extent applying the various remotely sensed data that were used here, would be an effective approach to classify the historical and current land use/land cover, to monitor the dynamics of land use/land cover during the last four decades, to map the development of the irrigation areas, and to classify the major strategic winter- and summer-irrigated agricultural crops in the study area of the Euphrates River Basin.
Filling the need for a comprehensive book that covers both theory and application, Remote Sensing of Land Use and Land Cover: Principles and Applications provides a synopsis of how remote sensing can be used for land-cover characterization, mapping, and monitoring from the local to the global scale. With contributions by leading scientists from aro
The book introduces two domains namely Remote Sensing and Digital Image Processing. It discusses remote sensing, texture, classifiers, and procedures for performing the texture-based segmentation and land cover classification. The first chapter discusses the important terminologies in remote sensing, basics of land cover classification, types of remotely sensed images and their characteristics. The second chapter introduces the texture and a detailed literature survey citing papers related to texture analysis and image processing. The third chapter describes basic texture models for gray level images and multivariate texture models for color or remotely sensed images with relevant Matlab source codes. The fourth chapter focuses on texture-based classification and texture-based segmentation. The Matlab source codes for performing supervised texture based segmentation using basic texture models and minimum distance classifier are listed. The fifth chapter describes supervised and unsupervised classifiers. The experimental results obtained using a basic texture model (Uniform Local Binary Pattern) with the classifiers described earlier are discussed through the relevant Matlab source codes. The sixth chapter describes land cover classification procedure using multivariate (statistical and spectral) texture models and minimum distance classifier with Matlab source codes. A few performance metrics are also explained. The seventh chapter explains how texture based segmentation and land cover classification are performed using the hidden Markov model with relevant Matlab source codes. The eighth chapter gives an overview of spatial data analysis and other existing land cover classification methods. The ninth chapter addresses the research issues and challenges associated with land cover classification using textural approaches. This book is useful for undergraduates in Computer Science and Civil Engineering and postgraduates who plan to do research or project work in digital image processing. The book can serve as a guide to those who narrow down their research to processing remotely sensed images. It addresses a wide range of texture models and classifiers. The book not only guides but aids the reader in implementing the concepts through the Matlab source codes listed. In short, the book will be a valuable resource for growing academicians to gain expertise in their area of specialization and students who aim at gaining in-depth knowledge through practical implementations. The exercises given under texture based segmentation (excluding land cover classification exercises) can serve as lab exercises for the undergraduate students who learn texture based image processing.
Remote sensing is an integral part of geography, GIS and cartography, used by academics in the field and professionals in all sorts of occupations. The 1990s saw the development of a range of new methods of classifying remote sensing images and data, both optical imaging and microwave imaging. This comprehensive survey of the various techniques pul
"Its range is far broader than the majority of methods texts, being concerned with both human and physical geography... Given the seriousness with which Key Methods in Geography approaches all aspects of research, it will continue to find wide favour among undergraduate geographers." - Times Higher Education Textbook Guide "All geographers, whatever their interest, need to do research. This book will help them get started in the best possible way, with thoughtful advice on everything from project design, through choice of methods, to data analysis and presentation. The editors have assembled an impressive array of authors, all experts in their chosen field." - Tim Burt, University of Durham "Excellent book. Valuable teaching aid. Well written and covers a wide range of methods thoroughly." - Sue Rodway-Dyer, Exeter University "This is an excellent book and deals with a number of topics (which I teach) outside of the tutorial module where it is a recommended text for geographers. A very useful textbook throughout a 3 year Geography programme." - Ian Harris, Bangor University Key Methods in Geography is an introduction to the principal methodological issues involved in the collection, analysis and presentation of geographical information. It is unique in the reference literature for providing an overview of qualitative and quantitative methods for human and physical geography. An accessible primer, it will be used by students as a reference throughout their degree, on all issues from research design to presentation. This second edition has been fully revised and updated and includes new chapters on internet mediated research, diaries as a research method, making observations and measurements in the field, and the analysis of natural systems. Organized into four sections: Getting Started in Geographical Research; Generating and Working with Data in Human Geography; Generating and Working with Data in Physical Geography; Representing and Interpreting Geographical Data; each chapter comprises: A short definition A summary of the principal arguments A substantive 5,000-word discussion Use of real-life examples Annotated notes for further reading. The teaching of research methods is integral to all geography courses: Key Methods in Geography, 2nd Edition explains all of the key methods with which geography undergraduates must be conversant.
This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also provides a concise and practical reference tutorial, which equips readers to immediately start using the software platform and R packages for image processing and classification. This book is divided into five chapters. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing. Chapter 3 focuses on image transformation, and chapter 4 addresses image classification. Lastly, chapter 5 deals with improving image classification. R is advantageous in that it is open source software, available free of charge and includes several useful features that are not available in commercial software packages. This book benefits all undergraduate and graduate students, researchers, university teachers and other remote- sensing practitioners interested in the practical implementation of remote sensing in R.
Land cover assessment and monitoring of its dynamics are essential requirements for the sustainable management of natural resources, environmental protection, food security, humanitarian programmes as well as core data for monitoring and modelling. Land Cover (LC) data are therefore fundamental in fulfilling the mandates of many United Nations (UN), international and national institutions and programmes. Despite the recognition of such importance, current users of LC data still lack access to sufficient reliable or comparable baseline LC data. These data are essential to tackle the increasing concerns in regard to food security, environmental degradation, and climate change. Critically, maintaining and restoring land resources plays a vital task in tackling climate change, securing biodiversity, and maintaining crucial ecosystem services, while ensuring resilient livelihoods and food security.
Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition introduces techniques used in the processing of remote sensing digital imagery. It emphasizes the development and implementation of statistically motivated, data-driven techniques. The author achieves this by tightly interweaving theory, algorithms, and computer codes. See What’s New in the Third Edition: Inclusion of extensive code in Python, with a cloud computing example New material on synthetic aperture radar (SAR) data analysis New illustrations in all chapters Extended theoretical development The material is self-contained and illustrated with many programming examples in IDL. The illustrations and applications in the text can be plugged in to the ENVI system in a completely transparent fashion and used immediately both for study and for processing of real imagery. The inclusion of Python-coded versions of the main image analysis algorithms discussed make it accessible to students and teachers without expensive ENVI/IDL licenses. Furthermore, Python platforms can take advantage of new cloud services that essentially provide unlimited computational power. The book covers both multispectral and polarimetric radar image analysis techniques in a way that makes both the differences and parallels clear and emphasizes the importance of choosing appropriate statistical methods. Each chapter concludes with exercises, some of which are small programming projects, intended to illustrate or justify the foregoing development, making this self-contained text ideal for self-study or classroom use.