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Furthermore, the mapping accuracy produced using this method can be quantitatively related to the number of additional samples needed which opens a new horizon for digital soil mapping.
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RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%.
We showed that imagery was not necessarily the most important data source for mapping where a large number of classes are used, and also showed that even data sources that produce low accuracy scores when used for mapping by themselves do improve the accuracy of maps produced using this integrative approach.
This was followed by sequencing of each library on a single lane of Illumina Hi-Seq to produce 100 bp paired end reads, leading to increased mapping accuracy compared to a previous study using single reads [ 12].
Given that expression analyses produce a large number of reads, we evaluated not only the mapping accuracy, but also in the mapping speed of the programs used.
Moreover, figure 4 shows that simple areal weighting of level 3 (division) data produced improved accuracy over APD and Landscan, indicating that the modelling approaches used were detrimental to mapping accuracy.
Landscape context also affects the mapping accuracy.
Overall, we found that the mapping accuracy was high.
They may provide additional benefits to further improve mapping accuracy.
Third, it increases the mapping accuracy.
We found this dramatically increase the mapping accuracy.
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