Title: Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-fooprint lidar data
Author: Kim, Yunsuk; Yang, Zhiqiang; Cohen, Warren B.; Pflugmacher, Dirk; Lauver, Chris L.; Vankat, John L.;
Source: Remote Sensing of Environment. 113: 2499-2510
Publication Series: Scientific Journal (JRNL)
Description: Accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, 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. 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. ln 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 map, we demonstrated the complementary power of these two datasets, revealing that MTBS burn intensity class can be described quantitatively in terms of dead biomass.
Keywords: small footprint, lidar, biomass, dead, intensity, Grand Canyon, North Rim, forest
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Kim, Yunsuk; Yang, Zhiqiang; Cohen, Warren B.; Pflugmacher, Dirk; Lauver, Chris L.; Vankat, John L. 2009. Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-fooprint lidar data. Remote Sensing of Environment. 113: 2499-2510.
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