Lidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observations
Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. 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. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 ± 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 ± 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 ± 0.49 Gt) overestimated the national ground-based estimate by 7.5%.
| Main Authors: | , , , , , , , , , , , |
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| Format: | Texto biblioteca |
| Language: | eng |
| Subjects: | Biomasa forestal, Inventarios forestales, Monitoreo forestal, |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0034425716304175 |
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