Remote Sensing and Digital Image Processing with R

• Main Authors: Alves, Marcelo de Carvalho, Sanches, Luciana
• Format: eBook Book
• Language: English
• Published: Boca Raton, FL CRC Press 2023; Taylor & Francis Group
• Edition: 1
• Subjects: Agriculture & Environmental Sciences; Big Data; Digital images; Earth Sciences; ENVIROnetBASE; Environmental Health; Environmental Remote Sensing; ENVIRONMENTALENGINEERINGnetBASE; ENVIRONMENTALSCIENCEnetBASE; Forestry; Geocomputation; Image Processing; Image processing > Digital techniques; R (Computer program language); R Packages, Software R; Remote Sensing; Remote sensing > Textbooks; Remote Sensing Applications; SCI-TECHnetBASE; Spatial Modeling; STMnetBASE; Water Science; Minas Gerais; Landsat-8 OLI Image; Spatial Modeling; False Color Composition; Altitude Class; Digital Image Processing; UTM Zone 23S; Coffee Leaf Rust; Random Forest; Sentinel-2 Imagery; ESRI Shapefile; Supervised Classification; Geocomputation; Image Processing; MODIS Sensor; Remote Sensing Process; Unsupervised Classification; Vegetation Index; R Packages, Software R; Big Data; Spatial Decision Support Systems; Remote Sensing; NDWI; Brightness Temperature; Landsat-8 OLI; Remote Sensing Applications; DMSP OLS; Environmental Remote Sensing; Brazilian Soil Classification System; Moisture Content; AP Algorithm; Saga GIS
• DOI: 10.1201/9781003329664
• ISBN: 9781032359229; 9781032359823; 103235982X; 1032359226

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.