Author: Christopher M. Swalm
Publisher:
ISBN:
Category : Digital mapping
Languages : en
Pages : 262
Book Description
An Assessment of a General Land Cover Classification Technique
Author: Christopher M. Swalm
Publisher:
ISBN:
Category : Digital mapping
Languages : en
Pages : 262
Book Description
Publisher:
ISBN:
Category : Digital mapping
Languages : en
Pages : 262
Book Description
A Land Use and Land Cover Classification System for Use with Remote Sensor Data
Author: James Richard Anderson
Publisher:
ISBN:
Category : Land cover
Languages : en
Pages : 36
Book Description
Publisher:
ISBN:
Category : Land cover
Languages : en
Pages : 36
Book Description
Register implementation for land cover legends
Author: Food and Agriculture Organization of the United Nations
Publisher: Food & Agriculture Org.
ISBN: 9251345600
Category : Law
Languages : en
Pages : 58
Book Description
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.
Publisher: Food & Agriculture Org.
ISBN: 9251345600
Category : Law
Languages : en
Pages : 58
Book Description
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.
Land Cover Classification of Remotely Sensed Images
Author: S. Jenicka
Publisher: Springer Nature
ISBN: 303066595X
Category : Technology & Engineering
Languages : en
Pages : 176
Book Description
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.
Publisher: Springer Nature
ISBN: 303066595X
Category : Technology & Engineering
Languages : en
Pages : 176
Book Description
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 of Land Use and Land Cover
Author: Chandra P. Giri
Publisher: CRC Press
ISBN: 1420070754
Category : Nature
Languages : en
Pages : 477
Book Description
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
Publisher: CRC Press
ISBN: 1420070754
Category : Nature
Languages : en
Pages : 477
Book Description
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
An Evaluation of High-resolution Land Cover and Land Use Classification Accuracy by Thematic, Spatial, and Algorithm Parameters
Author: Alexander Kirby Smith
Publisher:
ISBN:
Category : Aerial photogrammetry
Languages : en
Pages : 91
Book Description
High resolution land cover and land use classifications have applications in many fields of study such as land use and cover change, carbon storage measurements and environmental impact assessments. The wide range of available imagery at different spatial resolutions, potential thematic classes, and classification methods introduces the problem of understanding how each aspect affects accuracy. This study investigates how these three aspects affect the results of land cover classification. Results show that the maximum likelihood classifier was able to produce the most consistent results with the highest average accuracy (82.9%). Classifiers were able to identify a spatial resolution for each thematic resolution that achieved a distinctly higher overall accuracy. In addition, the effects of different land cover classifications as input to an object-based classification of land use at the parcel scale were evaluated. Results showed that land use classification requires higher resolution imagery to obtain satisfactory results than what is required for land cover classification. Also, the highest accuracy land cover classification did not produce the highest accuracy for land use, where a higher number of thematic classes performs better than fewer thematic classes. The highest accuracy LC classification by MLC with 8 classes occurred at 640 cm and achieved an overall accuracy of 83.3%. The highest accuracy LU classification was produced by the 80 cm LC with 8 classes and achieved an overall accuracy of 88.0%. Aside from the produced land cover and land use classifications, this study produces a lookup table in the form of multiple graphs for future research to reference when selecting imagery and determining thematic classes and classification methods.
Publisher:
ISBN:
Category : Aerial photogrammetry
Languages : en
Pages : 91
Book Description
High resolution land cover and land use classifications have applications in many fields of study such as land use and cover change, carbon storage measurements and environmental impact assessments. The wide range of available imagery at different spatial resolutions, potential thematic classes, and classification methods introduces the problem of understanding how each aspect affects accuracy. This study investigates how these three aspects affect the results of land cover classification. Results show that the maximum likelihood classifier was able to produce the most consistent results with the highest average accuracy (82.9%). Classifiers were able to identify a spatial resolution for each thematic resolution that achieved a distinctly higher overall accuracy. In addition, the effects of different land cover classifications as input to an object-based classification of land use at the parcel scale were evaluated. Results showed that land use classification requires higher resolution imagery to obtain satisfactory results than what is required for land cover classification. Also, the highest accuracy land cover classification did not produce the highest accuracy for land use, where a higher number of thematic classes performs better than fewer thematic classes. The highest accuracy LC classification by MLC with 8 classes occurred at 640 cm and achieved an overall accuracy of 83.3%. The highest accuracy LU classification was produced by the 80 cm LC with 8 classes and achieved an overall accuracy of 88.0%. Aside from the produced land cover and land use classifications, this study produces a lookup table in the form of multiple graphs for future research to reference when selecting imagery and determining thematic classes and classification methods.
Land Cover Assessment and Monitoring
Author:
Publisher: UNEP/Earthprint
ISBN: 9280714899
Category : Forest mapping
Languages : en
Pages : 40
Book Description
Publisher: UNEP/Earthprint
ISBN: 9280714899
Category : Forest mapping
Languages : en
Pages : 40
Book Description
Mixed-Phase Clouds
Author: Constantin Andronache
Publisher: Elsevier
ISBN: 012810550X
Category : Science
Languages : en
Pages : 302
Book Description
Mixed-Phase Clouds: Observations and Modeling presents advanced research topics on mixed-phase clouds. As the societal impacts of extreme weather and its forecasting grow, there is a continuous need to refine atmospheric observations, techniques and numerical models. Understanding the role of clouds in the atmosphere is increasingly vital for current applications, such as prediction and prevention of aircraft icing, weather modification, and the assessment of the effects of cloud phase partition in climate models. This book provides the essential information needed to address these problems with a focus on current observations, simulations and applications. Provides in-depth knowledge and simulation of mixed-phase clouds over many regions of Earth, explaining their role in weather and climate Features current research examples and case studies, including those on advanced research methods from authors with experience in both academia and the industry Discusses the latest advances in this subject area, providing the reader with access to best practices for remote sensing and numerical modeling
Publisher: Elsevier
ISBN: 012810550X
Category : Science
Languages : en
Pages : 302
Book Description
Mixed-Phase Clouds: Observations and Modeling presents advanced research topics on mixed-phase clouds. As the societal impacts of extreme weather and its forecasting grow, there is a continuous need to refine atmospheric observations, techniques and numerical models. Understanding the role of clouds in the atmosphere is increasingly vital for current applications, such as prediction and prevention of aircraft icing, weather modification, and the assessment of the effects of cloud phase partition in climate models. This book provides the essential information needed to address these problems with a focus on current observations, simulations and applications. Provides in-depth knowledge and simulation of mixed-phase clouds over many regions of Earth, explaining their role in weather and climate Features current research examples and case studies, including those on advanced research methods from authors with experience in both academia and the industry Discusses the latest advances in this subject area, providing the reader with access to best practices for remote sensing and numerical modeling
Assessing the Accuracy of Remotely Sensed Data
Author: Russell G. Congalton
Publisher: CRC Press
ISBN: 1420055135
Category : Mathematics
Languages : en
Pages : 210
Book Description
Accuracy assessment of maps derived from remotely sensed data has continued to grow since the first edition of this groundbreaking book. As a result, the much-anticipated new edition is significantly expanded and enhanced to reflect growth in the field. The new edition features three new chapters, including: Fuzzy accuracy assessmentPositional accu
Publisher: CRC Press
ISBN: 1420055135
Category : Mathematics
Languages : en
Pages : 210
Book Description
Accuracy assessment of maps derived from remotely sensed data has continued to grow since the first edition of this groundbreaking book. As a result, the much-anticipated new edition is significantly expanded and enhanced to reflect growth in the field. The new edition features three new chapters, including: Fuzzy accuracy assessmentPositional accu
Assessment of Alternative Methods for Stratifying Landsat TM Data to Improve Land Cover Classification Accuracy Across Areas with Physiographic Variation
Author: Jana S. Stewart
Publisher:
ISBN:
Category :
Languages : en
Pages : 406
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 406
Book Description