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Figure 1 Outline of ALS and optical remote sensing data stratification method.
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In the third phase of the study, in which more data stratification methods and different inclusion/exclusion criteria were applied to the analysis, the relative risk of autism for children at three months of Thimerosal exposure dropped to 1.69.
This paper presents a pixel-based forest stratification method that uses both ALS and optical remote sensing data to optimally partition the variability across an ~10,000 ha forest ownership in Mendocino County, CA, USA.
Cover class-specific thresholds were derived for each of the four methods using a previously developed NRB-LC classification (1998 1999) to support data stratification.
This pixel-based stratification method may provide a cost-effective approach to reducing inventory costs over larger areas when the remote sensing data acquisition costs can be kept low on a per acre basis.
The ANN models with randomized data stratification gave better predictions than those with fixed stratification.
Because stratification based on crown length resulted in the biased estimation of crown biomass, the balanced stratification method was applied.
This further confirmed that the models with random data stratification perform better than those with fixed stratification.
This also confirmed that the ANN model with a randomized data stratification (Model 1-2) performed better than the other one with fixed stratification (Model 1-1).
This also agrees with the R 2 results in Fig. 13 that the ANN model with randomized data stratification performed better than that with fixed stratification.
However, for optimal results, the ANN model with randomized data stratification is preferred.
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