orchestrate http://www.elevatorpartssource.com/86314-prescription-prilosec.html In a study published in Remote Sensing of Environment an airborne profiling lidar was used to estimate total aboveground dry biomass, a surrogate for aboveground carbon stocks, for all land cover types in Hedmark County, Norway. One-hundred-five parallel profiling flight lines systematically spaced three km apart, totaling 8309 km, were acquired to measure forest canopy height and density across the 27,390 km2 County. Two sampling/estimation strategies were investigated; both use profiling lidar data to augment field data collected as part of a systematic network of Norwegian National Forest Inventory (NFI) ground plots. The two strategies include (1) a model-dependent (MD), two-phase approach which permits calculation of sampling and model error, and (2) a model-assisted (MA), probability-based, two-stage approach which utilizes asymptotically design-unbiased estimators. The lidar-augmented survey results from these two sampling strategies were compared to NFI ground estimates for the County. The MD estimate (38.88 ± 1.07 Mg ha− 1, one σ) differs from the NFI ground estimate (37.64 ± 0.94 Mg ha− 1) by + 1.24 Mg ha− 1 (+ 3.3%) and the MA (36.09 ± 1.56 Mg ha− 1) versus NFI difference is − 1.55 Mg ha− 1 (− 4.1%). As expected, as smaller geographical areas are considered, lidar–NFI differences increase, and at the individual cover class level, MD lidar–ground absolute differences average 3.1 Mg ha− 1 for the eight individual classes (8.2% of the County mean). The comparable MA lidar–ground average absolute difference for the individual cover classes is 4.3 Mg ha− 1 (11.4% of the County mean). For all of Hedmark, in general, the MD approach agreed most closely with NFI estimates. The standard errors (SE) generated using the MA approach were, in general, 2–3 times larger than the MD SEs for productive forest classes at the County level, though neither lidar sample was consistently more precise than the NFI ground estimates alone. For Hedmark County, the results indicate that profiling lidar surveys can provide estimates comparable to ground surveys but, at this particular juncture, the MD and MA estimators did not improve the precision of adequately designed (in terms of sample size), probability-based ground samples. However when smaller political units are considered, i.e., as estimates are made on smaller and smaller subunits within Hedmark County, in general, MD-SE < NFI-SE ≪ MA-SE. Of the two designs considered in this study, the MD approach is most amenable to satellite lidar surveys since it does not require that profiling lidar satellite pulses intercept ground plots already established for a regional or national forest inventory. Direct comparisons of lidar and ground SEs are complicated by two factors: (1) the influence of treating both lidar and ground surveys as random samples when, in fact, they’re systematic, and (2) by the two stage/phase structure of the lidar surveys versus the single stage structure of the NFI. These results should be viewed as preliminary, subject to validation using a Monte Carlo simulator.