Ronald E. McRoberts

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This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where(More)
Almost all relevant data in forestry databases arise from either field measurement or model prediction. In either case, these values have some amount of uncertainty that is often overlooked when doing analyses. In this study, the uncertainty associated with both measured and predicted data was quantified for upper-stem diameter at 5.27 m. This uncertainty(More)
The effects on large-area volume estimates of uncertainty in individual tree volume model predictions were negligible when using simple random sampling estimators for large-area estimation, but non-negligible when using stratified estimators which reduced the effects of sampling variability. Forest inventory estimates of tree volume for large areas are(More)
When areas of interest experience little change, remote sensing-based maps whose dates deviate from ground data can still substantially enhance precision. However, when change is substantial, deviations in dates reduce the utility of such maps for this purpose. Remote sensing-based maps are well-established as means of increasing the precision of estimates(More)
The relationship between number of species and area observed has been described using numerous approaches and has been discussed for more than a century. The general objectives of our study were fourfold: (1) to evaluate the behaviour of species–area curves across geographic scales, (2) to determine sample sizes necessary to produce acceptably precise(More)
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