Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/8g84mp52m

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  • Accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, and wildfire behavior, and for forest management. Lidar remote sensing has been used successfully to estimate live biomass, but studies focusing on dead biomass are rare. We used lidar data, in conjunction with field measurements from 58 plots to distinguish between and map standing live and dead tree biomass in the mixed coniferous forest of the North Rim of Grand Canyon National Park, USA. Lidar intensity and canopy volume were key variables for estimating live biomass, whereas for dead biomass, lidar intensity alone was critical for accurate estimation. Regression estimates of both live and dead biomass ranged between zero and 600 Mg ha⁻¹, with means of 195.08 Mg ha⁻¹ and 65.73 Mg ha⁻¹, respectively. Cross validation with field data resulted in correlation coefficients for predicted vs. observed of 0.85 for live biomass (RMSE = 50 Mg ha⁻¹ and %RMSE (RMSE as a percent of the mean) = 26). For dead biomass, correlation was 0.79, RMSE was 42 Mg ha⁻¹, and %RMSE was 63. Biomass maps revealed interesting patterns of live and dead standing tree biomass. Live biomass was highest in the ponderosa pine zone, and decreased from south to north through the mixed conifer and spruce-fir forest zones. Dead biomass exhibited a background range of values in these mature forests from zero to 100 Mg ha⁻¹, with lower values in locations having higher live biomass. In areas with high dead biomass values, live biomass was near zero. These areas were associated with recent wildfires, as indicated by fire maps derived from the Monitoring Trends in Burn Severity Project (MTBS). Combining our dead biomass maps with the MTBS maps, we demonstrated the complementary power of these two datasets, revealing that MTBS burn intensity class can be described quantitatively in terms of dead biomass. Assuming a background range of dead biomass up to 100 Mg ha⁻¹, it is possible to estimate and map the contribution to the standing dead tree biomass pool associated with recent wildfire.
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