Marcelo de Carvalho Alves – författare

Surveying with Geomatics and R

This book explains basic concepts of surveying science and techniques with geomatics using R software and R packages. It engages students in learning about surveying through real field examples and using differing degrees of complexity while exploring surveying problems based on field observations and advanced geospatial technology. It includes a wide range of case studies as hands-on and self-paced tutorials along with detailed computer programming routines that are linked to the theories and applications explained in each chapter. This innovative textbook also teaches how to explore other possibilities of using geomatics in geocomputation, remote sensing, geography and cartography courses focused on surveying tasks.

Features include:

Provides modern surveying practices with free software algorithm and R toolset for active learning

Includes case studies from different geographical areas using arbitrary and international cartographic reference systems

Enables and demonstrates the integration of traditional geomatics with modern geospatial big data technologies

Explains data standards, equipment used, possible analyses and the importance of error evaluation for scientific surveying

Discusses different scales of landscapes and brings together the experiences of leading experts in the field

Surveying with Geomatics and R

This book explains basic concepts of surveying science and techniques with geomatics using R software and R packages. It engages students in learning about surveying through real field examples and using differing degrees of complexity while exploring surveying problems based on field observations and advanced geospatial technology. It includes a wide range of case studies as hands-on and self-paced tutorials along with detailed computer programming routines that are linked to the theories and applications explained in each chapter. This innovative textbook also teaches how to explore other possibilities of using geomatics in geocomputation, remote sensing, geography and cartography courses focused on surveying tasks.

Features include:

Provides modern surveying practices with free software algorithm and R toolset for active learning

Includes case studies from different geographical areas using arbitrary and international cartographic reference systems

Enables and demonstrates the integration of traditional geomatics with modern geospatial big data technologies

Explains data standards, equipment used, possible analyses and the importance of error evaluation for scientific surveying

Discusses different scales of landscapes and brings together the experiences of leading experts in the field

This new textbook on remote sensing and digital image processing of natural resources includes numerous, practical problem-solving exercises and applications of sensors and satellite systems using remote sensing data collection resources, and emphasizes the free and open-source platform R. It explains basic concepts of remote sensing and multidisciplinary applications using R language and R packages, by engaging students in learning theory through hands-on, real-life projects. All chapters are structured with learning objectives, computation, questions, solved exercises, resources, and research suggestions.

Features

Explains the theory of passive and active remote sensing and its applications in water, soil, vegetation, and atmosphere. Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer. Includes case studies from different environments with free software algorithms and an R toolset for active learning and a learn-by-doing approach. Provides hands-on exercises at the end of each chapter and encourages readers to understand the potential and the limitations of the environments, remote sensing targets, and process. Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution data sources for target recognition with image processing techniques.

While the focus of the book is on environmental and agriculture engineering, it can be applied widely to a variety of subjects such as physical, natural, and social sciences. Students in upper-level undergraduate or graduate programs, taking courses in remote sensing, geoprocessing, civil and environmental engineering, geosciences, environmental sciences, electrical engineering, biology, and hydrology will also benefit from the learning objectives in the book. Professionals who use remote sensing and digital processing will also find this text enlightening.

This Lab Manual is a companion to the textbook Remote Sensing and Digital Image Processing with R. It covers examples of natural resource data analysis applications including numerous, practical problem-solving exercises, and case studies that use the free and open-source platform R. The intuitive, structural workflow helps students better understand a scientific approach to each case study in the book and learn how to replicate, transplant, and expand the workflow for further exploration with new data, models, and areas of interest.

Features

Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages. Engages students in learning theory through hands-on real-life projects. All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments. Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer. Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution information.

Undergraduate- and graduate-level students will benefit from the exercises in this Lab Manual, because they are applicable to a variety of subjects including environmental science, agriculture engineering, as well as natural and social sciences. Students will gain a deeper understanding and first-hand experience with remote sensing and digital processing, with a learn-by-doing methodology using applicable examples in natural resources.

This new textbook on remote sensing and digital image processing of natural resources includes numerous, practical problem-solving exercises and applications of sensors and satellite systems using remote sensing data collection resources, and emphasizes the free and open-source platform R. It explains basic concepts of remote sensing and multidisciplinary applications using R language and R packages, by engaging students in learning theory through hands-on, real-life projects. All chapters are structured with learning objectives, computation, questions, solved exercises, resources, and research suggestions.

Features

Explains the theory of passive and active remote sensing and its applications in water, soil, vegetation, and atmosphere. Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer. Includes case studies from different environments with free software algorithms and an R toolset for active learning and a learn-by-doing approach. Provides hands-on exercises at the end of each chapter and encourages readers to understand the potential and the limitations of the environments, remote sensing targets, and process. Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution data sources for target recognition with image processing techniques.

While the focus of the book is on environmental and agriculture engineering, it can be applied widely to a variety of subjects such as physical, natural, and social sciences. Students in upper-level undergraduate or graduate programs, taking courses in remote sensing, geoprocessing, civil and environmental engineering, geosciences, environmental sciences, electrical engineering, biology, and hydrology will also benefit from the learning objectives in the book. Professionals who use remote sensing and digital processing will also find this text enlightening.

This Lab Manual is a companion to the textbook Remote Sensing and Digital Image Processing with R. It covers examples of natural resource data analysis applications including numerous, practical problem-solving exercises, and case studies that use the free and open-source platform R. The intuitive, structural workflow helps students better understand a scientific approach to each case study in the book and learn how to replicate, transplant, and expand the workflow for further exploration with new data, models, and areas of interest.

Features

Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages. Engages students in learning theory through hands-on real-life projects. All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments. Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer. Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution information.

Undergraduate- and graduate-level students will benefit from the exercises in this Lab Manual, because they are applicable to a variety of subjects including environmental science, agriculture engineering, as well as natural and social sciences. Students will gain a deeper understanding and first-hand experience with remote sensing and digital processing, with a learn-by-doing methodology using applicable examples in natural resources.

Surveying with Geomatics and RThis book explains basic concepts of surveying science and techniques with geomatics using R software and R packages. It engages students in learning about surveying through real field examples and using differing degrees of complexity while exploring surveying problems based on field observations and advanced geospatial technology. It includes a wide range of case studies as hands-on and self-paced tutorials along with detailed computer programming routines that are linked to the theories and applications explained in each chapter. This innovative textbook also teaches how to explore other possibilities of using geomatics in geocomputation, remote sensing, geography and cartography courses focused on surveying tasks.Features include: Provides modern surveying practices with free software algorithm and R toolset for active learning Includes case studies from different geographical areas using arbitrary and international cartographic reference systems Enables and demonstrates the integration of traditional geomatics with modern geospatial big data technologies Explains data standards, equipment used, possible analyses and the importance of error evaluation for scientific surveying Discusses different scales of landscapes and brings together the experiences of leading experts in the field

Surveying with Geomatics and RThis book explains basic concepts of surveying science and techniques with geomatics using R software and R packages. It engages students in learning about surveying through real field examples and using differing degrees of complexity while exploring surveying problems based on field observations and advanced geospatial technology. It includes a wide range of case studies as hands-on and self-paced tutorials along with detailed computer programming routines that are linked to the theories and applications explained in each chapter. This innovative textbook also teaches how to explore other possibilities of using geomatics in geocomputation, remote sensing, geography and cartography courses focused on surveying tasks.Features include: Provides modern surveying practices with free software algorithm and R toolset for active learning Includes case studies from different geographical areas using arbitrary and international cartographic reference systems Enables and demonstrates the integration of traditional geomatics with modern geospatial big data technologies Explains data standards, equipment used, possible analyses and the importance of error evaluation for scientific surveying Discusses different scales of landscapes and brings together the experiences of leading experts in the field

This new textbook on remote sensing and digital image processing of natural resources includes numerous, practical problem-solving exercises and applications of sensors and satellite systems using remote sensing data collection resources, and emphasizes the free and open-source platform R. It explains basic concepts of remote sensing and multidisciplinary applications using R language and R packages, by engaging students in learning theory through hands-on, real-life projects. All chapters are structured with learning objectives, computation, questions, solved exercises, resources, and research suggestions.FeaturesExplains the theory of passive and active remote sensing and its applications in water, soil, vegetation, and atmosphere.Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer.Includes case studies from different environments with free software algorithms and an R toolset for active learning and a learn-by-doing approach.Provides hands-on exercises at the end of each chapter and encourages readers to understand the potential and the limitations of the environments, remote sensing targets, and process.Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution data sources for target recognition with image processing techniques.While the focus of the book is on environmental and agriculture engineering, it can be applied widely to a variety of subjects such as physical, natural, and social sciences. Students in upper-level undergraduate or graduate programs, taking courses in remote sensing, geoprocessing, civil and environmental engineering, geosciences, environmental sciences, electrical engineering, biology, and hydrology will also benefit from the learning objectives in the book. Professionals who use remote sensing and digital processing will also find this text enlightening.

This new textbook on remote sensing and digital image processing of natural resources includes numerous, practical problem-solving exercises and applications of sensors and satellite systems using remote sensing data collection resources, and emphasizes the free and open-source platform R. It explains basic concepts of remote sensing and multidisciplinary applications using R language and R packages, by engaging students in learning theory through hands-on, real-life projects. All chapters are structured with learning objectives, computation, questions, solved exercises, resources, and research suggestions.FeaturesExplains the theory of passive and active remote sensing and its applications in water, soil, vegetation, and atmosphere.Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer.Includes case studies from different environments with free software algorithms and an R toolset for active learning and a learn-by-doing approach.Provides hands-on exercises at the end of each chapter and encourages readers to understand the potential and the limitations of the environments, remote sensing targets, and process.Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution data sources for target recognition with image processing techniques.While the focus of the book is on environmental and agriculture engineering, it can be applied widely to a variety of subjects such as physical, natural, and social sciences. Students in upper-level undergraduate or graduate programs, taking courses in remote sensing, geoprocessing, civil and environmental engineering, geosciences, environmental sciences, electrical engineering, biology, and hydrology will also benefit from the learning objectives in the book. Professionals who use remote sensing and digital processing will also find this text enlightening.

This new textbook and lab manual on remote sensing and digital image processing of natural resources includes numerous practical, problem-solving exercises, and case studies that use the free and open-source platform R. It explains the basic concepts of remote sensing and its multidisciplinary applications using R language and R packages, and engages students in learning theory through hands-on real-life projects.Features1. Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages.2. Engages students in learning theory through hands-on real-life projects.3. All chapters are structured with solved exercises and homework and encourages readers to understand the potential and the limitations of the environments.4. Covers data analysis in free and open-source (FOSS) R platform, which makes remote sensing accessible to anyone with a computer.5. Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution information.Students in upper-level undergraduate or graduate programs taking courses in Remote Sensing and Geoprocessing, civil and environmental engineering, geosciences, and environmental sciences, electrical engineering, biology, hydrology, agricultural engineering, as well as professionals in different areas who use remote sensing and image processing, will gain a deeper understanding and first-hand experience with remote sensing and digital processing, with a learn-by-doing methodology using applicable examples in natural resources..

This Lab Manual is a companion to the textbook Remote Sensing and Digital Image Processing with R. It covers examples of natural resource data analysis applications including numerous, practical problem-solving exercises, and case studies that use the free and open-source platform R. The intuitive, structural workflow helps students better understand a scientific approach to each case study in the book and learn how to replicate, transplant, and expand the workflow for further exploration with new data, models, and areas of interest. FeaturesAims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages.Engages students in learning theory through hands-on real-life projects.All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments.Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer.Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution information.Undergraduate- and graduate-level students will benefit from the exercises in this Lab Manual, because they are applicable to a variety of subjects including environmental science, agriculture engineering, as well as natural and social sciences. Students will gain a deeper understanding and first-hand experience with remote sensing and digital processing, with a learn-by-doing methodology using applicable examples in natural resources.

Environmental Geocomputation with R presents theoretical and practical principles for geospatial data analysis, visualization, and modeling, covering both the individual object approach and the continuous field approach. The book addresses spatial data uncertainties and error management while integrating spatial context into analytical results for better understanding of the environment. It progressively builds complexity enabling readers to develop geocomputational projects in the real world using algorithms and big data with R and its packages. The applications are focused on environmental and agricultural analyses, helping to incorporate spatial-temporal dimensions into decision-making processes. Features Explains the theory and applications of geocomputational methods for solving environmental problems involving spatial data and analysis. Covers big data, machine learning, methods for pattern recognition in spatial data, and tools and data analytics in the free and open-source (FOSS) R platform, making them accessible to everyone. Includes case studies from different environments and explains the diversity of big data and its applications. Provides hands-on exercises at the end of each chapter and encourages readers to understand both the potential and limitations of environmental systems. Supplemented by a comprehensive laboratory manual with solved exercises, homework assignments, and research suggestions in geocomputation within an environmental science context. This textbook is intended for students in upper-level undergraduate or graduate programs using GIS, geocomputation, geoprocessing in environmental sciences and engineering, surveying, geosciences, and agriculture engineering. It is also an excellent resource for professionals in different areas who use geocomputation and image processing and wish to develop their skillset in these areas.

This Laboratory Manual complements the textbook Environmental Geocomputation with R, featuring practical applications for natural resource data analysis through hands-on exercises and case studies using the open-source R platform. Its structured workflow enhances students' understanding of scientific approaches to each case study, teaching them to replicate, adapt, and expand methodologies with new data sets, models, and research areas. Designed for both undergraduate and graduate students in environmental, agricultural, and social sciences, Laboratory Manual for Environmental Geocomputation with R provides a comprehensive foundation in computational environmental analysis while developing transferable skills in data manipulation and visualization.Features Aims to expand theoretical approaches of environmental geocomputation through multidisciplinary applications using R and R packages. Engages students in learning theory through hands-on real-life projects. All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments. Covers data analysis in free and open-source (FOSS) R platform, which makes geocomputation accessible to anyone with a computer. Explores current trends and developments in geocomputation in homework assignments with CBERS-4A, GADM, WorldClim, ESA WorldCover, SRTM, GEE, climate change CMIP6 models, Black Marble, AppEEARS, GPW UN-adjusted Population, MODIS, OpenStreetMap, and COVID-19 data to further explore the use of free applications and geoprocessing concerns. The Labratory Manual is intended for students in upper-level undergraduate or graduate programs using GIS, geocomputation, geoprocessing in environmental sciences and engineering, surveying, geosciences, and agriculture engineering. It is also an excellent resource for professionals in different areas who use geocomputation and image processing and wish to develop their skillset in these areas.

Environmental Geocomputation with R presents theoretical and practical principles for geospatial data analysis, visualization, and modeling, covering both the individual object approach and the continuous field approach. The book addresses spatial data uncertainties and error management while integrating spatial context into analytical results for better understanding of the environment. It progressively builds complexity enabling readers to develop geocomputational projects in the real world using algorithms and big data with R and its packages. The applications are focused on environmental and agricultural analyses, helping to incorporate spatial-temporal dimensions into decision-making processes. Features Explains the theory and applications of geocomputational methods for solving environmental problems involving spatial data and analysis. Covers big data, machine learning, methods for pattern recognition in spatial data, and tools and data analytics in the free and open-source (FOSS) R platform, making them accessible to everyone. Includes case studies from different environments and explains the diversity of big data and its applications. Provides hands-on exercises at the end of each chapter and encourages readers to understand both the potential and limitations of environmental systems. Supplemented by a comprehensive laboratory manual with solved exercises, homework assignments, and research suggestions in geocomputation within an environmental science context. This textbook is intended for students in upper-level undergraduate or graduate programs using GIS, geocomputation, geoprocessing in environmental sciences and engineering, surveying, geosciences, and agriculture engineering. It is also an excellent resource for professionals in different areas who use geocomputation and image processing and wish to develop their skillset in these areas.

This Laboratory Manual complements the textbook Environmental Geocomputation with R, featuring practical applications for natural resource data analysis through hands-on exercises and case studies using the open-source R platform. Its structured workflow enhances students' understanding of scientific approaches to each case study, teaching them to replicate, adapt, and expand methodologies with new data sets, models, and research areas. Designed for both undergraduate and graduate students in environmental, agricultural, and social sciences, Laboratory Manual for Environmental Geocomputation with R provides a comprehensive foundation in computational environmental analysis while developing transferable skills in data manipulation and visualization.Features Aims to expand theoretical approaches of environmental geocomputation through multidisciplinary applications using R and R packages. Engages students in learning theory through hands-on real-life projects. All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments. Covers data analysis in free and open-source (FOSS) R platform, which makes geocomputation accessible to anyone with a computer. Explores current trends and developments in geocomputation in homework assignments with CBERS-4A, GADM, WorldClim, ESA WorldCover, SRTM, GEE, climate change CMIP6 models, Black Marble, AppEEARS, GPW UN-adjusted Population, MODIS, OpenStreetMap, and COVID-19 data to further explore the use of free applications and geoprocessing concerns. The Labratory Manual is intended for students in upper-level undergraduate or graduate programs using GIS, geocomputation, geoprocessing in environmental sciences and engineering, surveying, geosciences, and agriculture engineering. It is also an excellent resource for professionals in different areas who use geocomputation and image processing and wish to develop their skillset in these areas.

Environmental Geocomputation with R presents theoretical and practical principles for geospatial data analysis, visualization, and modeling, covering both the individual object approach and the continuous field approach. The book addresses spatial data uncertainties and error management while integrating spatial context into analytical results for better understanding of the environment. It progressively builds complexity enabling readers to develop geocomputational projects in the real world using algorithms and big data with R and its packages. The applications are focused on environmental and agricultural analyses, helping to incorporate spatial-temporal dimensions into decision-making processes. FeaturesExplains the theory and applications of geocomputational methods for solving environmental problems involving spatial data and analysis.Covers big data, machine learning, methods for pattern recognition in spatial data, and tools and data analytics in the free and open-source (FOSS) R platform, making them accessible to everyone.Includes case studies from different environments and explains the diversity of big data and its applications.Provides hands-on exercises at the end of each chapter and encourages readers to understand both the potential and limitations of environmental systems.Supplemented by a comprehensive laboratory manual with solved exercises, homework assignments, and research suggestions in geocomputation within an environmental science context.This textbook is intended for students in upper-level undergraduate or graduate programs using GIS, geocomputation, geoprocessing in environmental sciences and engineering, surveying, geosciences, and agriculture engineering. It is also an excellent resource for professionals in different areas who use geocomputation and image processing and wish to develop their skillset in these areas.

This Laboratory Manual complements the textbook Environmental Geocomputation with R, featuring practical applications for natural resource data analysis through hands-on exercises and case studies using the open-source R platform. Its structured workflow enhances students' understanding of scientific approaches to each case study, teaching them to replicate, adapt, and expand methodologies with new data sets, models, and research areas. Designed for both undergraduate and graduate students in environmental, agricultural, and social sciences, Laboratory Manual for Environmental Geocomputation with R provides a comprehensive foundation in computational environmental analysis while developing transferable skills in data manipulation and visualization.FeaturesAims to expand theoretical approaches of environmental geocomputation through multidisciplinary applications using R and R packages.Engages students in learning theory through hands-on real-life projects.All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments.Covers data analysis in free and open-source (FOSS) R platform, which makes geocomputation accessible to anyone with a computer.Explores current trends and developments in geocomputation in homework assignments with CBERS-4A, GADM, WorldClim, ESA WorldCover, SRTM, GEE, climate change CMIP6 models, Black Marble, AppEEARS, GPW UN-adjusted Population, MODIS, OpenStreetMap, and COVID-19 data to further explore the use of free applications and geoprocessing concerns.The Labratory Manual is intended for students in upper-level undergraduate or graduate programs using GIS, geocomputation, geoprocessing in environmental sciences and engineering, surveying, geosciences, and agriculture engineering. It is also an excellent resource for professionals in different areas who use geocomputation and image processing and wish to develop their skillset in these areas.