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 Covers Bayesian approach to sampling design 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 students The 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 sampling designs, tailored to their problems at hand, so that valuable data are collected that can be used to answer the research questions.

Table of Contents:
1 Introduction 2 Introduction to probability sampling 3 Simple random sampling 4 Stratified simple random sampling 5 Systematic random sampling 6 Cluster random sampling 7 Two-stage cluster random sampling 8 Sampling with probabilities proportional to size 9 Balanced and well-spread sampling 10 Model-assisted estimation 11 Two-phase random sampling 12 Computing the required sample size 13 Model-based optimisation of probability sampling designs 14 Sampling for estimating parameters of (small) domains 15 Repeated sample surveys for monitoring population parameters 16 Introduction to sampling for mapping 17 Regular grid and spatial coverage sampling 18 Covariate space coverage sampling 19 Conditioned Latin hypercube sampling 20 Spatial response surface sampling 21 Introduction to kriging 22 Model-based optimisation of the grid spacing 23 Model-based optimisation of the sampling pattern 24 Sampling for estimating the semivariogram 25 Sampling for validation of maps 26 Design-based, model-based, and model-assisted approach for sampling and inference