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. Features 1. 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 with Remote Sensing Course and Geoprocessing Course, civil and environmental engineering, geosciences, and environmental sciences, electrical engineering, biology, hydrology, agriculture Engineering. Professionals in different areas who use remote sensing and image processing. 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. Features 1. 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. Undergraduate and graduate level students will benefit from the exercises in this lab manual, as 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.

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 nowadays geospatial big data technologies Explains data standards, equipment used, possible analyzes and the importance of error evaluation for scientific surveying Discusses different scales of landscapes and bring 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, applications of sensors and satellite systems using remote sensing data collection resources with emphasis on 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 1. Explains the theory of passive and active remote sensing and its applications in water, soil, vegetation, and atmosphere. 2. Covers data analysis in free and open-source (FOSS) R platform, which makes remote sensing accessible to anyone with a computer. 3. Includes case studies from different environments with free software algorithm and R toolset for active learning and learn-by-doing. 4. 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. 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 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.