Scientists and consultants need to estimate and map properties
of the terrestrial environment. These include plant nutrients and
parasites in soil, gaseous emissions from soil, pollutant metals
and xenobiotics in waste and contaminated land, salt in groundwater
and species abundances above ground. The scale varies from small
experimental plots to catchments, and the land may be enclosed in
fields or be open grassland, forest or desert. Those who sample the
variables to obtain the necessary data need guidance on the design
and analysis of sampling methods for their conclusions and
recommendations to be valid.
This book provides that guidance, backed by sound rationale and
statistical theory. It concentrates on design-based sampling for
estimates of mean values of environmental properties, emphasizing
replication and randomization. It starts with simple random
sampling and then progresses to more efficient designs, such as
spatially stratified random sampling, stratification by classes and
cluster sampling. It includes a section on purposive sampling in
classical soil survey, which is relevant to other environmental
properties such as vegetation. It also describes the effects of
bulking on errors and the use of ancillary information and
regression to improve estimates. The authors draw the important
distinction between design-based sampling for estimating means and
model-based methods (geostatistics) for local spatial prediction
and mapping, and focus on the latter. They describe designs
suitable for computing variograms and prediction by kriging, as
well as a staged approach, so that sampling is neither inadequate
nor excessive, and designs adapt as knowledge is accumulated.
Including numerous worked case studies of sampling in agriculture,
ecology and environmental science, the book will be of immediate
practical value.
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