Scientific research often starts with data collection. However, many researchers pay insufficient attention to this first step in their research. The author, researcher at Wageningen University and Research, often had to conclude that the data collected by fellow researchers were suboptimal, or in some cases even unsuitable for their aim. One reason is that sampling is frequently overlooked in statistics courses. Another reason is the lack of practical textbooks on sampling. Numerous books have been published on the statistical analysis and modelling of data using R, but to date no book has been published in this series on how these data can best be collected. This book fills this gap. Spatial Sampling with R presents an overview of sampling designs for spatial sample survey and monitoring. It shows how to implement the sampling designs and how to estimate (sub)population- and space-time parameters in R.Key features

Describes classical, basic sampling designs for spatial survey, as well as recently developed, advanced sampling designs and estimators

Presents probability sampling designs for estimating parameters for a (sub)population, as well as non-probability sampling designs for mapping

Gives comprehensive overview of model-assisted estimators

Illustrates sampling designs with surveys of soil organic carbon, above-ground biomass, air temperature, opium poppy

Explains integration of wall-to-wall data sets (e.g. remote sensing images) and sample data

Data and R code available on github

Exercises added making the book suitable as a textbook for studentsThe target group of this book are researchers and practitioners of sample surveys, as well as students in environmental, ecological, agricultural science or any other science in which knowledge about a population of interest is collected through spatial sampling. This book helps to implement proper sam

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