Marcelo de Carvalho Alves: Laboratory Manual for Environmental Geocomputation with R, Gebunden

Klappentext

ThisLaboratory 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.