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Among these new techniques, high resolution (small footprint) airborne Lidar data can provide the most accurate measurements of forest height and vertical structure, allowing estimation of forest biomass with reasonably low uncertainty compared to ground estimates [12, 21, 22].
The US national forest inventory (ground) estimates are based on 119,414 ground plots.
ALS AGB estimates were reported by land cover class and compared to the NFI ground estimates.
The lidar-augmented survey results from these two sampling strategies were compared to NFI ground estimates for the County.
Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements.
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.
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Theft below ground estimated using the EOL adj* method was not significantly correlated with theft above ground.
See Homer et al. (2012) Percent bare ground Estimated percent of each pixel comprising the raster surface that is bare ground.
At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon, − 40.8% to North Dakota, 128.6%).
For CONUS at the national level, the GLAS LNI model estimate (23.95 ± 0.45 Gt AG B, agreed most closely with the US national forest inventory ground estimate, 24.17 ± 0.06 Gt, i.e., within 1%.
Liu et al. (2016) surveyed the worst-case GPS scintillations on the ground estimated from radio occultation observations of F3/C during 2007 2014 and are ready to develop an empirical model for the ionospheric S4 scintillation.
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