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Methods for constructing a sampling design for large area forest inventories are presented. The methods, data sets
used, and the procedures are demonstrated in a real setting: constructing a sampling design for the first national forest inventory
for Tanzania. The approach of the paper constructs a spatial model of forests, landscape, and land use. Sampling errors of the key
parameters as well as the field measurement costs of the inventory were estimated using sampling simulation on data. Forests
and land use often vary within a country or an area of interest, implying that stratified sampling is an efficient inventory design.
Double sampling for stratification was taken for the statistical framework. The work was motivated by the approach used by The
Food and Agriculture Organization of the United Nations (FAO) in supporting nations to establish forest inventories. The
approach taken deviates significantly from the traditional FAO approaches, making it possible to calculate forest resource
estimates at the subnational level without increasing the costs. |
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